Question: Generating open worlds with constraints through locally annealed reversible jump MCMC.

In the realm of urban planning and architectural design, the concept of a pattern language serves as a guide for creating towns, buildings, and construction projects that are not only functional but also aesthetically and culturally rich. This pattern language is a collection of design patterns that address various aspects of town planning, building design, and construction techniques. Unlike a prescriptive set of rules, it offers a flexible framework for designers and planners to interpret and apply in diverse contexts.

The essence of a pattern language lies in its ability to capture the essence of what makes a place truly livable and appealing. It acknowledges the complexity and interconnectedness of urban environments, recognizing that the success of a town or building is not solely determined by its individual components but also by how they interact with each other and the surrounding landscape. This holistic approach encourages designers to think beyond the immediate needs of a project and consider the broader social, economic, and environmental implications of their work.

Each pattern within the language is a response to a specific challenge or need that has been identified through the collective experience of urban planners, architects, and communities. These patterns are not static or rigid; instead, they are meant to be adapted and evolved as new insights and technologies emerge. This dynamic nature of a pattern language ensures that it remains relevant and effective in the face of changing circumstances.

In applying a pattern language to the design of towns, buildings, and construction projects, designers are encouraged to engage in a process of creative synthesis. They are invited to draw from the patterns that resonate most with the unique context of their project, combining and modifying them as necessary to create a solution that is both innovative and practical. This approach fosters a sense of ownership and investment among all stakeholders, from the designers and builders to the residents and visitors who will ultimately inhabit and interact with the spaces.

Moreover, a pattern language recognizes the importance of constraints in the design process. These constraints, whether they be financial, regulatory, or physical, are not seen as limitations but rather as opportunities for creative problem-solving. By embracing constraints, designers can develop solutions that are not only functional but also sustainable, resilient, and adaptable to future changes.

In essence, a pattern language is a powerful tool for creating open worlds with constraints. It offers a framework for synthesizing diverse elements into a cohesive whole, enabling designers to navigate the complexities of urban planning and architectural design with confidence and creativity. Through its use, towns, buildings, and construction projects can be transformed into vibrant, livable spaces that enhance the quality of life for all who interact with them.

Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.

In this paper, we propose an automatic method for generating roads based on a weighted anisotropic shortest path algorithm. Given an input scene, we automatically create a path connecting an initial and a final point. The trajectory of the road minimizes a cost function that takes into account the different parameters of the scene including the slope of the terrain, natural obstacles such as rivers, lakes, mountains and forests. The road is generated by excavating the terrain along the path and instantiating generic parameterized models.

Computer vision can be understood as the ability to perform inference on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques, as even the model design is often dictated by the complexity of inference in them. This thesis proposes learning based inference schemes and demonstrates applications in computer vision. We propose techniques for inference in both generative and discriminative computer vision models. Despite their intuitive appeal, the use of generative models in vision is hampered by the difficulty of posterior inference, which is often too complex or too slow to be practical. We propose techniques for improving inference in two widely used techniques: Markov Chain Monte Carlo (MCMC) sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative vision models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge in a principled way. For better inference in discriminative models, we propose techniques that modify the original model itself, as inference is simple evaluation of the model. We concentrate on convolutional neural network (CNN) models and propose a generalization of standard spatial convolutions, which are the basic building blocks of CNN architectures, to bilateral convolutions. First, we generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call ‘Bilateral Neural Networks’. We show how the bilateral filtering modules can be used for modifying existing CNN architectures for better image segmentation and propose a neural network approach for temporal information propagation in videos. Experiments demonstrate the potential of the proposed bilateral networks on a wide range of vision tasks and datasets. In summary, we propose learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks. In generative vision models, our inference techniques alleviate some of the crucial hurdles in Bayesian posterior inference, paving new ways for the use of model based machine learning in vision. In discriminative CNN models, the proposed filter generalizations aid in the design of new neural network architectures that can handle sparse high-dimensional data as well as provide a way for incorporating prior knowledge into CNNs.

We introduce a new class of upper bounds on the log partition function of a Markov random field (MRF). This quantity plays an important role in various contexts, including approximating marginal distributions, parameter estimation, combinatorial enumeration, statistical decision theory, and large-deviations bounds. Our derivation is based on concepts from convex duality and information geometry: in particular, it exploits mixtures of distributions in the exponential domain, and the Legendre mapping between exponential and mean parameters. In the special case of convex combinations of tree-structured distributions, we obtain a family of variational problems, similar to the Bethe variational problem, but distinguished by the following desirable properties: i) they are convex, and have a unique global optimum; and ii) the optimum gives an upper bound on the log partition function. This optimum is defined by stationary conditions very similar to those defining fixed points of the sum-product algorithm, or more generally, any local optimum of the Bethe variational problem. As with sum-product fixed points, the elements of the optimizing argument can be used as approximations to the marginals of the original model. The analysis extends naturally to convex combinations of hypertree-structured distributions, thereby establishing links to Kikuchi approximations and variants.

This talk proposes a very simple “baseline architecture” for a learning agent that can handle stochastic, partially observable environments. The architecture uses reinforcement learning together with a method for representing temporal processes as graphical models. I will discuss methods for leaming the parameters and structure of such representations from sensory inputs, and for computing posterior probabilities. Some open problems remain before we can try out the complete agent; more arise when we consider scaling up. A second theme of the talk will be whether reinforcement learning can provide a good model of animal and human learning. To answer this question, we must do inverse reinforcement learning: given the observed behaviour, what reward signal, if any, is being optimized? This seems to be a very interesting problem for the COLT, UAI, and ML communities, and has been addressed in econometrics under the heading of structural estimation of Markov decision processes. 1 Learning in uncertain environments AI is about the construction of intelligent agents, i.e., systems that perceive and act effectively (according to some performance measure) in an environment. I have argued elsewhere Russell and Norvig (1995) that most AI research has focused on environments that are static, deterministic, discrete, and fully observable. What is to be done when, as in the real world, the environment is dynamic, stochastic, continuous, and partially observable? ‘This paper draws on a variety of research efforts supported by NSF @I-9634215), ONR (N00014-97-l-0941), and AR0 (DAAH04-96-1-0341). Permission to make digital or hard copies of all or p.art of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prolit or commercial adwantage and that copies bear this notice and the full citation on the first page. To copy otherwise. to republish, to post on servers or to redistribute to lists, requires prior specific pemlission and/or a fee. COLT 98 Madison WI IJSA Copyright ACM 1998 1-5X1 13-057--0/9X/ 7...$5.00 In recent years, reinforcement learning (also called neurodynamic programming) has made rapid progress as an approachfor building agents automatically (Sutton, 1988; Kaelbling et al., 1996; Bertsekas & Tsitsiklis, 1996). The basic idea is that the performance measure is made available to the agent in the form of a rewardfunction specifying the reward for each state that the agent passes through. The performance measure is then the sum of the rewards obtained. For example, when a bumble bee forages, the reward function at each time step might be some combination of the distance flown (weighted negatively) and the nectar ingested. Reinforcement learning (RL) methods are essentially online algorithmd for solving Markovdecisionprocesses (MDPs). An MDP is defined by the reward function and a model, that is, the state transition probabilities conditioned on each possible action. RL algorithms can be model-based, where the agent learns a model, or model-free-e.g., Q-learning citeWatkins: 1989, which learns just a function Q(s, a) specifying the long-term value of taking action a in state s and acting optimally thereafter. Despite their successes, RL methods have been restricted largely tofully observable MDPs, in which the sensory input at each state is sufficient to identify the state. Obviously, in the real world, we must often deal with partially observable MDPs (POMDPs). Astrom (1965) proved that optimal decisions in POMDPs depend on the belief state b at each point in time, i.e., the posterior probability distribution over all possible actual states, given all evidence to date. The functions V and Q then become functions of b instead of s. Parr and Russell (1995) describes a very simple POMDP RL algorithm using an explicit representation of b as a vector of probabilities, and McCallum (1993) shows a way to approximate the belief state using recent percept sequences. Neither approach is likely to scale up to situations with large numbers of state variables and long-term temporal dependencies. What is needed is a way of representing the model compactly and updating the belief state efficiently given the model and each new observation. Dynamic Bayesian networks (Dean & Kanazawa, 1989) seem to have some of the required properties; in particular, they have significant advantages over other approaches such as Kalman filters and hidden Markov models. Our baseline architecture, shown in Figure 1, uses DBNs to represent and update the belief state as new sensor information arrives. Given a representation for b, the reward signal is used to learn a Q-function represented by some “black-box” function approximator such as a neural network. Provided we can handle hybrid (dis-

We present Project Malmo – an AI experimentation platform built on top of the popular computer game Minecraft, and designed to support fundamental research in artificial intelligence. As the AI research community pushes for artificial general intelligence (AGI), experimentation platforms are needed that support the development of flexible agents that learn to solve diverse tasks in complex environments. Minecraft is an ideal foundation for such a platform, as it exposes agents to complex 3D worlds, coupled with infinitely varied game-play. Project Malmo provides a sophisticated abstraction layer on top of Minecraft that supports a wide range of experimentation scenarios, ranging from navigation and survival to collaboration and problem solving tasks. In this demo we present the Malmo platform and its capabilities. The platform is publicly released as open source software at IJCAI, to support openness and collaboration in AI research.

Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches proposed so far have only been applicable to a few simple network architectures. This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks. Along the way it revisits several common regularisers from a variational perspective. It also provides a simple pruning heuristic that can both drastically reduce the number of network weights and lead to improved generalisation. Experimental results are provided for a hierarchical multidimensional recurrent neural network applied to the TIMIT speech corpus.

Decision-making is challenging in continuous settings where complex sequences of events determine rewards, even when these event sequences are largely observable. In particular, traditional trial-and-error learning strategies may have a hard time associating continuous actions with their reward because of the size of the state space and the complexity of the reward function. Given a model of the world, a different strategy is to use imagination to exploit the knowledge embedded in that model. In this regime, the system directly optimizes the decision for each episode based on predictions from the model. We extend deep learning methods that have been previously used for model-free learning and apply them towards a model-based approach in which an expert is consulted multiple times in the agents’ imagination before it takes an action in the world. We show preliminary results on a difficult physical reasoning task where our model-based approach outperforms a model-free baseline, even when using an inaccurate expert.

We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literature, its emphasis is on the presentation and explanation of applications to important models that are studied in econometrics. We include a discussion of some implementation issues, the use of the methods in connection with the EM algorithm, and how the methods can be helpful in model specification questions. Many of the applications of these methods are of particular interest to Bayesians, but we also point out ways in which frequentist statisticians may find the techniques useful.

In this manuscript we review new ideas and first results on application of the Graphical Models approach, originated from Statistical Physics, Information Theory, Computer Science and Machine Learning, to optimization problems of network flow type with additional constraints related to the physics of the flow. We illustrate the general concepts on a number of enabling examples from power system and natural gas transmission (continental scale) and distribution (district scale) systems. 1.1 Introductory remarks In this chapter we discuss optimization problems which appears naturally in the classical settings describing flows over networks constrained by the physical nature Michael Chertkov Theoretical Division, T-4 & CNLS, Los Alamos National Laboratory Los Alamos, NM 87545, USA and Energy System Center, Skoltech, Moscow, 143026, Russia, e-mail: chertkov@lanl. gov Sidhant Misra Theoretical Division, T-5, Los Alamos National Laboratory Los Alamos, NM 87545, USA, e-mail: sidhant@lanl.gov Marc Vuffray Theoretical Division, T-4, Los Alamos National Laboratory Los Alamos, NM 87545, USA, e-mail: sidhant@lanl.gov Krishnamurthy Dvijotham Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA e-mail: krishnamurthy.dvijotham@pnnl.gov Pascal Van Hentenryck University of Michigan, Department of Industrial & Operations Engineering Ann Arbor, MI 48109, USA, e-mail: pvanhent@umich.edu 1 ar X iv :1 70 2. 01 89 0v 1 [ cs .S Y ] 7 F eb 2 01 7 2 M. Chertkov, S. Misra, M. Vuffray, D. Krishnamurthy, and P. Van Hentenryck of the flows which appear in the context of electric power systems, see e.g. [27, 44], and natural gas application, see e.g. [13] and references there in. Other examples of physical flows where similar optimization problem arise include pipe-flow systems, such as district heating [75, 1] and water [54], as well as traffic systems [40]. We aim to show that the network flow optimization problem can be stated naturally in terms of the so-called Graphical Models (GM). In general, GMs for optimization and inference are wide spread in statistical disciplines such as Applied Probability, Machine Learning and Artificial Intelligence [53, 29, 16, 12, 32, 50], Information Theory [55] and Statistical Physics [47]. Main benefit of adopting GM methodology to the physics-constrained network flows is in modularity and flexibility of the approach – any new constraints, set of new variables, and any modification of the optimization objective can be incorporated in the GM formulation with an ease. Besides, if all (or at least majority of) constraints and modifications are factorized, i.e. can be stated in terms of a small subset of variables, underlying GM optimization or GM statistical inference problems can be solved exactly or approximately with the help of an emerging set of techniques, algorithms and computational approaches coined collectively Belief Propagation (BP), see e.g. an important original paper [74] and recent reviews [47, 55, 66]. It is also important to emphasize that an additional benefit of the GM formulation is in its principal readiness for generalization. Even though we limit our discussion to application of the GM and BP framework to deterministic optimizations, many probabilistic and/or mixed generalizations (largely not discussed in this paper) fit very naturally in this universal framework as well. We will focus on optimization problems associated with Physics-Constrained Newtork Flow (PCNF) problems. Structure of the networks will obviously be inherited in the GM formulation, however indirectly through graphand variabletransformations and modifications. Specifically, next Section 1.2 is devoted solely to stating a number of exemplary energy system formulations in GM terms. Thus, in Section 1.2.1 and Section 1.2.2 we consider dissipation optimal and respectively general physics-constrained network flow problems. In particular, Section 1.2.2 includes discussion of power flow problems in both power-voltage, Section 1.2.2.1, and current-voltage, Section 1.2.2.2, formats, as well as discussion of the gas flow formulation in Section 1.2.2.3 and general k-component physics-constrained network flow problem in Section 1.2.2.4. Section 1.2.3 describes problems of the next level of complexity – these including optimization over resources. In particular, general optimal physics-controlled network flow problem is discussed in Section 1.2.3.1 and more specific cases of optimal flows, involving optimal power flow (in both power-flow and current-voltage formulations) and gas flows are discussed in Sections 1.2.3.2,1.2.3.3,1.2.2.3, respectively. Section 1.2.4 introduces a number of feasibility problems, all stated as special kinds of optimizations. Here we discuss the so-called instanton, Section 1.2.4.1, containment Section 1.2.4.2, and state estimation, Section 1.2.4.3, formulation. The long introductory section concludes with a discussion in Section 1.2.5 of an exemplary (and even more) complex optimization involving split of resources between participants/aggregators. 1 Graphical Models for Optimal Flows 3 In Section 1.3 we describe how any of the aforementioned PCNF and optimal PCNF problems can be re-stated in the universal Graphical Model format. Then, in Section 1.4, we take advantage of the factorized form of the PCNF GM and illustrate how BP methodology can be used to solve the optimization problems exactly and/or approximately. Specifically, in Section 1.4.1 we restate the optimization (Maximum Likelihood) GM problem as a Linear Programming (LP) in the space of beliefs (proxies for probabilities). The resulting LP is generally difficult as working with all variables in a combination. We take advantage of the GM factorization and introduce in Section 1.4.2 the so-called Linear Programming Belief Propagation (LP-BP) relaxation, providing a provable lower bound for the optimal. Finally, in Section 1.4.3 we construct a tractable relaxation of LP-BP based on an interval partitioning of the underlying space. Section 1.5 discuss hierarchies which allow to generalize, and thus improve LPBP. The so-called LP-BP hierarchies, related to earlier papers on the subject [65, 30, 63] are discussed in Section 1.5.1. Then, relation between the LP-BP hierarchies and classic LP-based Sherali-Adams [59] and Semi-Definite-Programming based Lasserre hierarchies [41, 36, 52, 37] are discussed in Section 1.5.2. Section 1.6 discuss the special case of a GM defined over a tree (graph without loops). In this case LP-BP is exac, equivalent to the so-called Dynamic Programming approach, and as such it provides a distributed alternative to the global optimization through a sequence of graph-element-local optimizations. However, even in the tree case the exact LP-BP and/or DP are not tractable for GM stated in terms of physical variables, such as flows, voltages and/or pressures, drawn from a continuous set. Following, [18] we discuss here how the problem can be resolved with a proper interval-partitioning (discretization). We conclude the manuscript presenting summary and discussing path forward in Section 1.7. 1.2 Problems of Interest: Formulations In this Section we formulate a number of physics-constrained network flow problems which we will then attempt to analyze and solve with the help of Graphical Model (GM)/Belief Propagation (BP) approaches/techniques in the following Sections. 1.2.1 Dissipation-Optimal Network Flow We start introducing/discussing Network Flows constrained by a minimum dissipation principle, i.e. one which can be expressed as an unconstrained optimization/minimization of an energy function (potential). 4 M. Chertkov, S. Misra, M. Vuffray, D. Krishnamurthy, and P. Van Hentenryck Consider a static flow of a commodity over an undirected graph, G = (V ,E ) described through the following network flow equations i ∈ V : qi = ∑ j:(i, j)∈E φi j, (1.1) where qi stand for injection, qi > 0, or consumption, qi < 0, of the flow at the node i and φi j =−φ ji stands for the value of the flow through the directed edge (i, j) – in the direction from i to j 1. We consider a balanced network, ∑i∈V qi = 0. We constraint the flow requiring that the minimum dissipation principle is obeyed min φ ∑ {i, j}∈E Ei j(φi j) ∣∣∣∣ Eq. (1.1) , (1.2) where φ . = (φi j = −φ ji|{i, j} ∈ E ), and Ei j(x) are local (energy) functions of their arguments for all {i, j} ∈ E . The local energy functions Ei j(x) are required to be convex at least on a restricted domain. We call the sum of local energy functions E(φ) = ∑{i, j}∈E Ei j(φi j) the global energy function or simply the energy function. Versions of this problem appear in the context of the feasibility analysis of the dissipative network flows, that is flows whose redistribution over the network is constrained by potentials, e.g. voltages or pressures in the context of resistive electric networks and gas flow networks, respectively [22, 48, 64]. Note, that the formulation (1.2) can also be supplemented by additional flow or potential constraints. Requiring Karush-Kuhn-Tucker (KKT) stationary point conditions on the optimization problem stated in Eq. (1.2) leads to the following set of equations ∀{i, j} ∈ E : E ′ i j(φi j) = λi−λ j, (1.3) where λi is a Lagrangian multiplier corresponding to the i’s equation (1.1). The problem becomes fully defined by the pair of Eqs. (1.1,1.3), which can also be restated solely in terms of the λ -variables i ∈ V : qi = ∑ j:{i, j}∈E ( E ′ i j )−1 (λi−λ j). (1.4) 1.2.2 General Physics-Constrained Network Flows We call “unconstrained” a network flow for which only conservation of flow(s), described by Eq. (1.1), is enforced. Contrariwise we call “Physics-constrained” a 1 In the following we will use notation {i, j} for the undirected graph and (i, j) for the respective directed graph. When the meaning is clear we slightly abuse notations denoting by E both the set of undirected and directed edges. 1 Graphical Models for Optimal Flows 5 network flow t


Question: Leveraging reactive power in deep neural models for non-intrusive load monitoring applications.

This paper delves into additive factorial hidden Markov models (AFHMMs), an advanced variant of HMMs where the state decomposes into several independent chains, and the output is a sum of all the hidden states' functions. Despite the model's robustness, performing accurate inference is challenging due to computational infeasibility of exact inference and the high susceptibility of existing approximation methods to local optima. Here, we present a novel inference method for AFHMMs that capitalizes on their additive structure. Our approach involves three key steps: 1) analyzing the difference signal of the observation, 2) incorporating a "robust" mixture component to handle unmodeled observations, and 3) constraining the posterior to permit alterations in only one hidden state at a given time. By integrating these components, we formulate an approximate inference method that is both computationally efficient and devoid of local optima issues, significantly outperforming current techniques in practice. The development of this method is driven by the energy disaggregation problem, which involves decomposing a household's electricity signal into its individual appliance components. When applied to this task, our algorithm excels, achieving state-of-the-art performance and nearly flawless separation of numerous appliances using solely the aggregate signal.

**Question**: Evaluating the Influence of Non-Linear Dynamics on Deep Learning Techniques for Power System Transient Stability Assessment

**Documents**: Deep Learning Based Fault Location Estimation for Power Grids
This paper presents a novel method for estimating fault location in power grids based on deep learning. The method uses Convolutional Neural Networks (CNN) to extract features from the current and voltage signals recorded by Phasor Measurement Units (PMUs) during a fault event. The CNN is trained on a large dataset of simulated fault events, covering a wide range of fault types, locations, and conditions. The proposed method is evaluated on a real-world power grid and compared to traditional methods such as the Distance Relay and the Traveling Wave Fault Location (TWFL) method. The results show that the proposed method can estimate the fault location with high accuracy, even under challenging conditions such as high fault resistance and noisy signals. The method also has the advantage of being able to estimate the fault location in real-time, making it suitable for online monitoring and control applications.

**Similar Documents**: This article introduces an innovative approach for estimating the location of faults in power grids utilizing deep learning techniques. The proposed methodology employs Convolutional Neural Networks (CNNs) to analyze current and voltage signals from Phasor Measurement Units

With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.

This paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.

DC-bus voltage control is an important task in the operation of a dc or a hybrid ac/dc microgrid system. To improve the dc-bus voltage control dynamics, traditional approaches attempt to measure and feedforward the load or source power in the dc-bus control scheme. However, in a microgrid system with distributed dc sources and loads, the traditional feedforward-based methods need remote measurement with communications. In this paper, a nonlinear disturbance observer (NDO) based dc-bus voltage control is proposed, which does not need the remote measurement and enables the important “plug-and-play” feature. Based on this observer, a novel dc-bus voltage control scheme is developed to suppress the transient fluctuations of dc-bus voltage and improve the power quality in such a microgrid system. Details on the design of the observer, the dc-bus controller and the pulsewidth-modulation (PWM) dead-time compensation are provided in this paper. The effects of possible dc-bus capacitance variation are also considered. The performance of the proposed control strategy has been successfully verified in a 30 kVA hybrid microgrid including ac/dc buses, battery energy storage system, and photovoltaic (PV) power generation system.

Studies related to Smart Energy technologies have been highlighted in the last years, specially by significantly contributing to the saving of energy consumption, to the reduction of CO2 emissions and to the diminishing of the paid values in electricity bills. The electrical-thermal analogy can be used in order to simulate heating behavior in real situations and it also may be considered a contributor to Smart Energy technologies. This is the reason why several simulation tools are used. Taking this into account, this paper presents a framework using NGSpice simulator to identify both, the thermal capacitance and the thermal resistance parameters from a hypothetical monitored room which in a Smart Building context. The results from the proposed framework are compared to OpenModelica software and to the contributions from literature.

Now-a-days, decadence of power quality is foremost issue of electrical society. In practice, utility of switching device is increased in industrial as well as in domestic applications. Non-linearity causes adverse effects on system efficiency, utility of the power supply, power factor, etc. As reduction in power factor increases reactive power which does not have any contribution in energy transfer so its compensation is needed. So efforts are made to upgrade power quality, concept of Filter is in demand. In contrast to passive filter, active filters are popular due to its smaller size and weight. In this paper, work is done on shunt active power filter (SAPF) using P-Q theory for current harmonic mitigation and reactive power compensation. Here the simulation as well as its parameters, with or without active filter is well presented. We commence the paper with power quality issues then better understanding of PQ theory, simulation circuits, results, comparison and finally ended with conclusion. An effort is made to achieve THD value of source current below 5% to meet the required IEEE standards.

Deep learning has been given a great deal of attention with its success story in many areas such as image analysis and speech recognition. In particular, deep learning is good at dealing with high-dimensional data exhibiting non-linearity. Our preliminary study reveals a very high degree of non-linearity from network traffic data, which explains why it is hard to improve the detection accuracy by using conventional machine learning techniques (e.g., SVM, Random Forest, Ad-aboosting). In this study, we empirically evaluate deep learning to see its feasibility for network anomaly detection. We examine a set of deep learning models constructed based on the Fully Connected Network (FCN), Variational AutoEncoder (VAE), and Long Short-Term Memory with Sequence to Sequence (LSTM Seq2Seq) structures, with two public traffic data sets that have distinctive properties with respect to the distribution of normal and attack populations. Our experimental results confirm the potential of deep learning models for network anomaly detection, and the model based on the LSTM Seq2Seq structure shows a highly promising performance, yielding 99% of binary classification accuracy on the public data sets.

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the “coding criterion” to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than “shallow” methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.

We conduct the first detailed measurement study of the properties of a class of WiFi active power/energy consumption models based on parameters readily available to smartphone app developers. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. Using a large dataset and an 802.11n-equipped smartphone, we build four versions of a previously proposed linear power-throughput model, which allow us to explore the fundamental trade off between accuracy and simplicity. We study the properties of the model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios which have not been considered in previous studies. Our study shows that the model works well in a number of scenarios but its accuracy drops with high throughput values or when tested on different hardware. We further show that a non-linear model can greatly improve the accuracy in these two cases.

We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation tasks using this approach. Keywords—Machine Learning, Software Radio, Protocol Recognition, Recurrent Neural Networks, LSTM, Protocol Learning, Traffic Classification, Cognitive Radio, Deep Learning

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.


Question: Leveraging reactive power within deep neural models for non-intrusive load monitoring applications.

This paper delves into the critical role of initialization and momentum in the training of deep and recurrent neural networks (DNNs and RNNs). Historically, these complex models were deemed nearly untrainable through stochastic gradient descent (SGD) with momentum due to the challenges posed by their depth and the presence of long-term dependencies in RNNs. However, we present evidence that when SGD with momentum is paired with a meticulously designed random initialization and a specific incremental schedule for the momentum parameter, it can achieve performance levels on par with those obtained using advanced optimization techniques like Hessian-Free optimization. Our findings underscore the dual importance of both the initialization phase and the momentum parameter, as we observe that networks initialized poorly are untrainable with momentum, while well-initialized networks exhibit significantly reduced performance in its absence or when it is not finely tuned. This success in training such models suggests that previous failures in training deep and recurrent neural networks from random initializations may have been due to inadequate initialization strategies. Moreover, our results indicate that carefully calibrated momentum methods are sufficient to address the curvature challenges inherent in the training objectives of deep and recurrent networks, negating the need for more complex second-order optimization methods.

**Question**: A Comparative Study of Hybrid Algorithms for Solving Multi-Objective Optimization Problems in Engineering Design

**Documents**: A comparative study of swarm intelligence algorithms for solving multi-objective optimization problems
This paper presents a comparative study of various swarm intelligence algorithms for solving multi-objective optimization problems. The algorithms considered in this study include Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) algorithms. The performance of these algorithms is evaluated on a set of benchmark problems from the ZDT and DTLZ test suites. The results of the study show that all three algorithms are capable of finding good quality Pareto front solutions for the problems considered. However, PSO and ABC algorithms are found to be more efficient in terms of computational effort required to converge to the Pareto front solutions. ACO algorithm, on the other hand, is found to be more robust in terms of solution quality and consistency across multiple runs. The study also highlights the importance of parameter tuning in achieving good performance with these algorithms. The results of this study can be used as a reference for selecting the most suitable algorithm for a given multi-objective optimization problem in engineering design.

**Similar Documents**: This paper offers a comprehensive comparison of different swarm intelligence algorithms for tackling multi-objective optimization problems. Among the algorithms scrutinized are Particle Swarm Optimization (PSO),

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).

Demand response is a critical part of renewable integration and energy cost reduction goals across the world. Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand. Our work is inspired by a novel connection we make to crowdsourcing mechanisms. The proposed mechanism incorporates realistic features of the demand response problem including time varying and quadratic cost function. The mechanism marries auctions, that allow users to report their preferences, with online algorithms, that allow distribution companies to learn user-specific parameters. We show that MAB-MDR is dominant strategy incentive compatible, individually rational, and achieves sublinear regret. Such mechanisms can be effectively deployed in smart grids using new information and control architecture innovations and lead to welcome savings in energy costs.

This paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.

This paper presents an improved dc-link voltage fast control strategy based on energy balance for a pulse width modulation (PWM) rectifier-inverter system to reduce the fluctuation of dc-link voltage. A conclusion is drawn that the energy in dc-link capacitor cannot be kept constant in dynamic process when the system operates in rectifier mode, even in ideal case. Meanwhile, the minimum dc-link voltage deviation is analyzed. Accordingly, a predictive dc-link voltage control scheme based on energy balance is proposed, while the grid current is regulated by deadbeat predictive control. A prediction method for output power is also introduced. Furthermore, a small-signal model with the control delay is adopted to analyze the stability and robustness of the control strategy. The simulation and experimental results demonstrate both good dynamic and steady-state performances of the rectifier-inverter system with the proposed control scheme.

To improve the efficiency in power generation and to reduce the greenhouse gas emission, both Demand Response (DR) strategy and intermittent renewable energy have been proposed or applied in electric power systems. However, the uncertainty and the generation pattern in wind farms and the complexity of demand side management pose huge challenges in power system operations. In this paper, we analytically investigate how to integrate DR and wind energy with fossil fuel generators to (i) minimize power generation cost; (2) fully take advantage wind energy with managed demand to reduce greenhouse emission. We first build a two-stage robust unit commitment model to obtain day-ahead generator schedules where wind uncertainty is captured by a polyhedron. Then, we extend our model to include DR strategy such that both price levels and generator schedule will be derived for the next day. For these two NP-hard problems, we derive their mathematical properties and develop a novel and analytical solution method. Our computational study on a IEEE 118 system with 36 units shows that (i) the robust unit commitment model can significantly reduce total cost and fully make use of wind energy; (ii) the cutting plane method is computationally superior to known algorithms.

The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel approach to inspect the insulators with Deep Convolutional Neural Networks (CNN). A CNN model with multi-patch feature extraction method is applied to represent the status of insulators and a Support Vector Machine (SVM) is trained based on these features. A thorough evaluation is conducted on our insulator status dataset of six classes from real inspection videos. The experimental results show that a pre-trained model for classification is more accurate than the shallow features by hand-crafted. Our approach achieves 98.7095% mean Average Precision (mAP) in status classification. We also study the behavior of the neural activations of the convolutional layers. Different results vary with different fully connected layers, and interesting findings are discussed.

With the recent advance of wearable devices and Internet of Things (IoTs), it becomes attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and portable systems. Currently, executing the software-based DCNNs requires high-performance servers, restricting the widespread deployment on embedded and mobile IoT devices. To overcome this obstacle, considerable research efforts have been made to develop highly-parallel and specialized DCNN accelerators using GPGPUs, FPGAs or ASICs.
 Stochastic Computing (SC), which uses a bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has high potential for implementing DCNNs with high scalability and ultra-low hardware footprint. Since multiplications and additions can be calculated using AND gates and multiplexers in SC, significant reductions in power (energy) and hardware footprint can be achieved compared to the conventional binary arithmetic implementations. The tremendous savings in power (energy) and hardware resources allow immense design space for enhancing scalability and robustness for hardware DCNNs.
 This paper presents SC-DCNN, the first comprehensive design and optimization framework of SC-based DCNNs, using a bottom-up approach. We first present the designs of function blocks that perform the basic operations in DCNN, including inner product, pooling, and activation function. Then we propose four designs of feature extraction blocks, which are in charge of extracting features from input feature maps, by connecting different basic function blocks with joint optimization. Moreover, the efficient weight storage methods are proposed to reduce the area and power (energy) consumption. Putting all together, with feature extraction blocks carefully selected, SC-DCNN is holistically optimized to minimize area and power (energy) consumption while maintaining high network accuracy. Experimental results demonstrate that the LeNet5 implemented in SC-DCNN consumes only 17 mm2 area and 1.53 W power, achieves throughput of 781250 images/s, area efficiency of 45946 images/s/mm2, and energy efficiency of 510734 images/J.

Multi-converter power electronic systems exist in land, sea, air, and space vehicles. In these systems, load converters exhibit constant power load (CPL) behavior for the feeder converters and tend to destabilize the system. In this paper, the implementation of novel active-damping techniques on dc/dc converters has been shown. Moreover, the proposed active-damping method is used to overcome the negative impedance instability problem caused by the CPLs. The effectiveness of the new proposed approach has been verified by PSpice simulations and experimental results.

With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to detect illegitimate use is through monitoring unusual user activity. Methods of intrusion detection based on hand-coded rule sets or predicting commands on-line are laborous to build or not very reliable. This paper proposes a new way of applying neural networks to detect intrusions. We believe that a user leaves a ’print’ when using the system; a neural network can be used to learn this print and identify each user much like detectives use thumbprints to place people at crime scenes. If a user’s behavior does not match his/her print, the system administrator can be alerted of a possible security breech. A backpropagation neural network called NNID (Neural Network Intrusion Detector) was trained in the identification task and tested experimentally on a system of 10 users. The system was 96% accurate in detecting unusual activity, with 7% false alarm rate. These results suggest that learning user profiles is an effective way for detecting intrusions.

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Improving the performance of neural decoding algorithms allows us to better understand the information contained in a neural population, and can help advance engineering applications such as brain machine interfaces. Here, we apply modern machine learning techniques, including neural networks and gradient boosting, to decode from spiking activity in 1) motor cortex, 2) somatosensory cortex, and 3) hippocampus. We compare the predictive ability of these modern methods with traditional decoding methods such as Wiener and Kalman filters. Modern methods, in particular neural networks and ensembles, significantly outperformed the traditional approaches. For instance, for all of the three brain areas, an LSTM decoder explained over 40% of the unexplained variance from a Wiener filter. These results suggest that modern machine learning techniques should become the standard methodology for neural decoding. We provide a tutorial and code to facilitate wider implementation of these methods. Introduction: Neural decoding uses activity recorded from the brain to make predictions about variables in the outside world. For example, researchers predict movements based on activity in motor cortex [1, 2], predict decisions based on activity in prefrontal and parietal cortices [3, 4], and predict locations based on activity in the hippocampus [5, 6]. There are two primary purposes of decoding. First, it is an increasingly critical tool for understanding how neural signals relate to the outside world. It can be used to determine how much information the brain contains about an external variable (e.g., sensation or movement) [7-9], and how this information differs across brain areas [10-12], experimental conditions [13, 14], disease states [15], and more. Second, it is useful in engineering contexts, such as for brain machine interfaces (BMIs), where signals from motor cortex are used to control computer cursors [1], robotic arms [16], and muscles [2]. Decoding is a central tool for neural data analysis. When predicting a continuous variable, decoding is simply a regression problem and when predicting a discrete variable, decoding is simply a classification problem. Thus, there are many methods that can be used for neural decoding. However, despite the recent advances in machine learning techniques, it is still common to use traditional methods such as linear regression. Using modern machine learning tools for neural decoding would likely significantly boost performance, and might allow deeper insights into neural function. Here, we first give a brief tutorial so that readers can get started with using standard machine learning methods for decoding. We provide companion code so that readers can easily use a variety of decoding methods. Next, we compare the performance of many different machine learning methods to decode information from neural spiking activity. We predict movement velocities from macaque motor cortex and sensorimotor cortex, and locations in space from rat hippocampus. In all brain regions, modern methods, in particular neural networks and ensembles, led to the highest accuracy decoding, even for limited amounts of data. Tutorial for getting started with using machine learning for decoding: Code We have made Python code available at https://github.com/KordingLab/Neural_Decoding, which accompanies the tutorial below. This includes code that will correctly format the neural and output data for decoding, a tutorial for hyperparameter optimization, and examples of using many different decoders. We go into more detail on these topics below. General framework for decoding The decoding problem we are considering can be summarized as follows. We have N neurons whose spiking activity is recorded for a period of time, T (Fig. 1a). While we focus here on spiking neurons, the same methods could be used with other forms of neural data, such as the BOLD activity of N voxels, or the power in particular frequency bands of N LFP or EEG signals. We have also recorded outputs that we are trying to predict over that same time period (Fig. 1a). Here, we focus on output variables that are continuous (e.g., velocity, position), rather than discrete (e.g., choice). However, the general framework is very similar for discrete output variables. The first choice we need to make is to decide the temporal resolution, R, for decoding. That is, do we want to make a prediction every 50ms, 100ms, etc? We need to put the input and output into bins of length R (Fig. 1a). It is common (although not necessary) to use the same bin size for the neural data and output data, and we do so here. Thus, we will have approximately T/R total bins of neural activity and outputs. Within each bin, we compute the average activity of all neurons and the average value of the output. Next, we need to choose the time period of neural activity used to predict a given output. In the simplest case, the activity from all neurons in a given time bin would be used to predict the output in that same time bin. However, it is often the case that we want the neural data to precede the output (e.g., in the case of movements) or follow the decoder output (e.g., in the case of sensation). Plus, we often want to use neural data from more than one bin (e.g., using 500 ms of preceding neural data to predict a movement in the current 50 ms bin). In the following, we use the nomenclature that B time bins of neural activity are being used to predict a given output. For example, if we use one bin preceding the output, one concurrent bin, and one following bin, then B=3 (Fig. 1a). Note that when multiple bins of neural data are used to predict an output (B>1), then overlapping neural data will be used to predict different output times (Fig. 1a). When multiple bins of neural data are used to predict an output, then we will need to exclude some output bins. For instance, if we are using one bin of neural data preceding the output, then we cannot predict the first output bin, and if we are using one bin of neural data following the output, then we cannot predict the final output bin (Fig. 1a). Thus, we will be predicting K total output bins, where K is less than the total number of bins (T/R). To summarize, our decoders will be predicting each of these K outputs using B surrounding bins of activity from N neurons. Below, we describe how to format the neural data and output variables for use in different types of decoders. Non-recurrent decoders: For many “non-recurrent” decoders, we are just solving a standard machine learning regression problem. We have N x B features (the firing rates of each neuron in each relevant time bin) that are used to predict each output (Fig. 1b). If there is a single output that is being predicted, it can be put in a vector, Y, of length K. Note that for many decoders, if there are multiple outputs, each is independently decoded. If multiple outputs are being simultaneously predicted, which can occur with neural network decoders, the outputs can be put in a matrix Y, that has K rows and d columns, where d is the number of outputs being predicted. The input covariate matrix, X, has N x B columns (one for each feature) and K rows (corresponding to each output being predicted). This is now the format of a standard regression problem. Linear regression simply finds a linear combination of these features that predicts the output. More sophisticated forms of regression use nonlinear combinations of features for predictions. In general, this format is beneficial because there are many machine learning regression techniques that can easily be substituted for one another. We provide code for a Wiener filter (linear regression), a Wiener cascade (a linear-nonlinear model), support vector regression, XGBoost (gradient boosted trees), and feedforward neural networks (see Methods). We test the performance of these decoders in Results. Recurrent neural network decoders: When using recurrent neural networks (RNNs) for decoding, we need to put the inputs in a different format. Recurrent neural networks explicitly model temporal transitions across time (Fig. 1c). In the non-recurrent decoders, there were N x B features that were equivalently used for prediction, regardless of the time bin they came from. However, with a recurrent decoder, at each time bin, N features (the firing rates of all neurons in that time bin) are used for predicting the hidden state of the system at that time. Along with being a function of the N features, the hidden state at a time bin is also a function of the hidden state at the previous time bin (Fig. 1c). After transitioning through all B bins, the hidden state in this final bin is used to predict the output. This architecture allows the decoder to take advantage of temporal structure in the data, and allowing it (via its hidden state) to integrate the effect of neural inputs over an extended period of time. For use in this type of decoder, the input can be formatted as a 3-dimensional matrix of size K x N x B (Fig. 1c). That is, for each row (corresponding to the output that is predicted), there will be N features (2nd matrix dimension) over B bins (3rd matrix dimension) used for prediction. Within this format, different types of RNNs, including those more sophisticated than the standard RNN shown in Fig. 1c, can be easily switched for one another. We provide code for a standard recurrent network, a gated recurrent unit (GRU) network, and a long short-term memory (LSTM) network. In Results, we test the performance of these decoders. Decoders with additional information: While the focus of this tutorial is on decoders that fit into standard machine learning frameworks, we want to briefly mention two other commonly used decoders. The Kalman filter and its variants have frequently been used in the brain computer interface field fo


Question: Leveraging reactive power in deep neural models for non-intrusive load monitoring applications.

This paper offers an overview of contemporary non-intrusive load monitoring (NILM) methodologies in residential environments, highlighting their significance in the context of Smart Grids. Smart Grids emphasize energy efficiency and load management, yet quantifying the impact of conservation measures in homes demands precise end-use energy data. To address this, NILM techniques have emerged as a viable solution due to their user-friendly installation and economical benefits. Unlike traditional monitoring systems that necessitate individual sensors for each appliance, NILM relies on a single sensor placed at the household's main power entry. By analyzing fluctuations in the aggregated power consumption, these systems can accurately infer the operational status of various appliances, thereby negating the need for multiple plug-meters. The paper critically evaluates recent advancements in NILM algorithms, presenting a comprehensive review of the state-of-the-art approaches. It concludes by outlining a foundational roadmap and the overarching objectives for future research in this field, emphasizing the potential for innovative solutions that enhance energy monitoring and management in residential settings.

We propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function. We first pre-train the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then, we perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-GP), can learn much more meaningful representation of the data by the finite-dimensional but deep-layered feature-mapping function. Unlike standard Gaussian processes, our model scales well with the size of the training set due to the avoidance of kernel matrix inversion. Moreover, we present a mixture of DNN-GPs to further improve the regression performance. For the experiments on three representative large datasets, our proposed models significantly outperform the state-of-the-art algorithms of Gaussian process regression.

The accurate description of chemical processes often requires the use of computationally demanding methods like density-functional theory (DFT), making long simulations of large systems unfeasible. In this Letter we introduce a new kind of neural-network representation of DFT potential-energy surfaces, which provides the energy and forces as a function of all atomic positions in systems of arbitrary size and is several orders of magnitude faster than DFT. The high accuracy of the method is demonstrated for bulk silicon and compared with empirical potentials and DFT. The method is general and can be applied to all types of periodic and nonperiodic systems.

The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel approach to inspect the insulators with Deep Convolutional Neural Networks (CNN). A CNN model with multi-patch feature extraction method is applied to represent the status of insulators and a Support Vector Machine (SVM) is trained based on these features. A thorough evaluation is conducted on our insulator status dataset of six classes from real inspection videos. The experimental results show that a pre-trained model for classification is more accurate than the shallow features by hand-crafted. Our approach achieves 98.7095% mean Average Precision (mAP) in status classification. We also study the behavior of the neural activations of the convolutional layers. Different results vary with different fully connected layers, and interesting findings are discussed.

This paper presents estimates of the customer-level demand for electricity by industrial and commercial customers purchasing electricity according to the half-hourly energy prices from the England and Wales (E&W) electricity market. These customers also face the possibility of a demand charge on their electricity consumption during the three half-hour periods that are coincident with E&W system peaks. Although energy charges are largely known by 4:00 P.M. the day prior to consumption, a fraction of the energy charge and the identity of the half-hour periods when demand charges occur are only known with certainty ex post of consumption. Four years of data from a Regional Electricity Company (REC) in the United Kingdom is used to quantify the half-hourly customer-level demands under this real-time pricing program. The econometric model developed and estimated here quantifies the extent of intertemporal substitution in electricity consumption across pricing periods within the day due to changes in all of the components of the day-ahead E&W electricity prices, the level of the demand charge and the probability that a demand charge will be imposed. The results of this modeling framework can be used by retail companies supplying consumers purchasing electricity according to real-time market prices to construct demand-side bids into an electricity market open to competition. The paper closes with several examples of how this might be done.

At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.

In this paper we present a use case related to the intelligent processing of events coming from the conventional ("cheap") sensors in order to support better energy consumption in commercial buildings. The approach has been implemented using our iCEP framework and deployed in the office space of a real working environment. This research is a kind of the proof of the concept for a new technology that the industry partner would like to exploit.
 The results are very encouraging: smart decisions for the efficient usage of energy can be made by the intelligent processing of "cheap" sensor events.

Indoor localization is one of the key enablers for various application and service areas that rely on precise locations of people, goods, and assets, ranging from home automation and assisted living to increased automation of production and logistic processes and wireless network optimization. Existing solutions provide various levels of precision, which also depends on the complexity of the indoor radio environment. In this paper, we propose two methods for reducing the localization error in indoor non-line-of-sight (NLoS) conditions using raw channel impulse response (CIR) information obtained from ultra-wide band radios requiring no prior knowledge about the radio environment. The methods are based on NLoS channel classification and ranging error regression models, both using convolutional neural networks (CNNs) and implemented in the TensorFlow computational framework. We first show that NLoS channel classification using raw CIR data outperforms existing approaches that are based on derived input signal features. We further demonstrate that the predicted NLoS channel state and predicted ranging error information, used in combination with least squares (LS) and weighted LS location estimation algorithms, significantly improve indoor localization performance. We also evaluate the computational performance and suitability of the proposed CNN-based algorithms on various computing platforms with a wide range of different capabilities and show that in a distributed localization system, they can also be used on computationally restricted devices.

In recent years, deep neural network exhibits its powerful superiority on information discrimination in many computer vision applications. However, the capacity of deep neural network architecture is still a mystery to the researchers. Intuitively, larger capacity of neural network can always deposit more information to improve the discrimination ability of the model. But, the learnable parameter scale is not feasible to estimate the capacity of deep neural network. Due to the overfitting, directly increasing hidden nodes number and hidden layer number are already demonstrated not necessary to effectively increase the network discrimination ability. In this paper, we propose a novel measurement, named “total valid bits”, to evaluate the capacity of deep neural networks for exploring how to quantitatively understand the deep learning and the insights behind its super performance. Specifically, our scheme to retrieve the total valid bits incorporates the skilled techniques in both training phase and inference phase. In the network training, we design decimal weight regularization and 8-bit forward quantization to obtain the integer-oriented network representations. Moreover, we develop adaptive-bitwidth and non-uniform quantization strategy in the inference phase to find the neural network capacity, total valid bits. By allowing zero bitwidth, our adaptive-bitwidth quantization can execute the model reduction and valid bits finding simultaneously. In our extensive experiments, we first demonstrate that our total valid bits is a good indicator of neural network capacity. We also analyze the impact on network capacity from the network architecture and advanced training

This research has been motivated by industrial demand for single phase PWM rectifier in traction application. This paper presents an advanced control structure design for a single phase PWM rectifier. The PWM rectifier is controlled to consume nearly sinusoidal current with power factor nearing to unity. The control structure consists of a DC-link voltage controller and a current controller implemented by a proportionalresonant controller using a fast phase angle and frequency estimator. The estimation algorithm is derived from the weighted least-squares estimation method. The feasibility of the proposed control structure is confirmed by experimental tests performed on designed laboratory prototype.

Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time-series. Detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies. We propose a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct ‘normal’ time-series behavior, and thereafter uses reconstruction error to detect anomalies. We experiment with three publicly available quasi predictable time-series datasets: power demand, space shuttle, and ECG, and two realworld engine datasets with both predictive and unpredictable behavior. We show that EncDecAD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi-periodic time-series. Further, we show that EncDec-AD is able to detect anomalies from short time-series (length as small as 30) as well as long time-series (length as large as 500).


Question: Leveraging reactive power in deep neural models for enhanced non-intrusive load monitoring.

This paper delves into the application of multi-label classification algorithms for the purpose of non-intrusive load monitoring in the context of demand-side management. As an integral component of the smart grid, this technology aims to optimize the use of generation assets by utilities through reducing consumer energy demand during peak times. Given that many modern appliances can react to price signals from utilities, a significant number of older appliances lack this capability. Consequently, utilities must deduce which appliances are in operation within a household based solely on the power signals from the main feeder to the home—essentially disaggregating the home's power consumption into individual appliance usage. We present an extensive exploration of multi-label classification algorithms for the disaggregation of appliances within power signals. A comprehensive review of relevant literature reveals that these algorithms have not been extensively studied for this particular application, despite their potential suitability for addressing the disaggregation challenge more effectively than traditional single-label classifiers. Our investigation includes the evaluation of a multi-label meta-classification framework known as RAkEL, alongside a custom multi-label classification algorithm, MLkNN. Both time-domain and wavelet-domain feature sets are employed in this study. The efficacy of these classifiers is tested on data from two real-world houses sourced from the Reference Energy Disaggregation Dataset. Our findings indicate that multi-label algorithms demonstrate impressive performance and are on par with previously published results on the analyzed datasets.

Two important issues when constructing a neural network (NN) for time series prediction: proper selection of (1) the input dimension and (2) the time delay between the inputs. These two parameters determine the structure, computing complexity and accuracy of the NN. This paper is to formulate an autonomous data-driven approach to identify a parsimonious structure for the NN so as to reduce the prediction error and enhance the modeling accuracy. The reinforcement learning based dimension and delay estimator (RLDDE) is proposed. It involves a trial-error learning process to formulate a selection policy for designating the above-mentioned two parameters. The proposed method is evaluated by the prediction of the benchmark sunspot time series.

This paper presents a closed-loop control scheme for the three-level three-phase neutral-point-clamped dc-ac converter using the optimized nearest three virtual-space-vector pulsewidth modulation, which is a modulation that produces low output-voltage distortion with a significant reduction of the dc-link capacitance. A new specific loop modifying the modulating waveforms is proposed to rapidly control possible perturbations in the neutral-point voltage balance. An online estimation of the load displacement angle and load linear/nonlinear nature is introduced at no extra cost. The remaining part of the control is analogous to the control for a two-level converter with an appropriate interfacing to the selected modulation. The closed-loop control is designed for the case of a renewable-energy source connected to the ac mains, and its performance is analyzed through simulation and experiments.

Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability. Moreover, it has traditionally been difficult to reason about the behavior of these models beyond a certain level of input complexity. In order to address these issues, we propose augmenting neural architectures with a key abstraction: recursion. As an application, we implement recursion in the Neural Programmer-Interpreter framework on four tasks: grade-school addition, bubble sort, topological sort, and quicksort. We demonstrate superior generalizability and interpretability with small amounts of training data. Recursion divides the problem into smaller pieces and drastically reduces the domain of each neural network component, making it tractable to prove guarantees about the overall system’s behavior. Our experience suggests that in order for neural architectures to robustly learn program semantics, it is necessary to incorporate a concept like recursion.

To provide insurance on the resistance of a system against side-channel analysis, several national or private schemes are today promoting an evaluation strategy, common in classical cryptography, which is focussing on the most powerful adversary who may train to learn about the dependency between the device behaviour and the sensitive data values. Several works have shown that this kind of analysis, known as Template Attacks in the side-channel domain, can be rephrased as a classical Machine Learning classification problem with learning phase. Following the current trend in the latter area, recent works have demonstrated that deep learning algorithms were very efficient to conduct security evaluations of embedded systems and had many advantages compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in Machine Learning and (2) it does not allow for the reproducibility of the presented results. This paper aims to address these limitations in several ways. First, completing recent works, we propose a comprehensive study of deep learning algorithms when applied in the context of side-channel analysis and we discuss the links with the classical template attacks. Secondly, we address the question of the choice of the hyper-parameters for the class of multi-layer perceptron networks and convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a masked implementation of the AES algorithm. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign of electromagnetic measurements exploited in our benchmarks. This open database, named ASCAD, has been specified to serve as a common basis for further works on this subject. Our work confirms the conclusions made by Cagli et al. at CHES 2017 about the high potential of convolutional neural networks. Interestingly, it shows that the approach followed to design the algorithm VGG-16 used for image recognition seems also to be sound when it comes to fix an architecture for side-channel analysis.

In this study, a new method is proposed for the suppression of voltage amplitude fluctuations on the load terminals. Moreover, the voltage harmonics on the load terminals are effectively reduced below the defined limits in the IEEE-519 standard. In the studied topology, a twelve-switch three-phase voltage source converter is used for effective control on the zero-sequence components due to the unbalanced voltages or single phase voltage sags. In order to obtain a fast dynamic response, sliding mode control strategy is used to control the system. The existence condition of the sliding mode is presented. The performance of the closed-loop system is verified with cases of voltage sag and utility voltage harmonics. The overall system is explained in detail and verified by using MATLAB/Simulink.

A data-driven approach to the performance analysis of wind turbines is presented. Turbine performance is captured with a power curve. The power curves are constructed using historical wind turbine data. Three power curve models are developed, one by the least squares method and the other by the maximum likelihood estimation method. The models are solved by an evolutionary strategy algorithm. The power curve model constructed by the least squares method outperforms the one built by the maximum likelihood approach. The third model is non-parametric and is built with the k-nearest neighbor (k-NN) algorithm. The least squares (parametric) model and the non-parametric model are used for on-line monitoring of the power curve and their performance is analyzed. 2008 Elsevier Ltd. All rights reserved.

With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.

Neutral-point (NP) voltage drift is the main technical drawback of NP-clamped (NPC) three-level inverters. Traditional space vector pulsewidth modulation (SVPWM) is incapable of controlling the NP voltage for high modulation indexes and low power factors. Virtual SVPWM (VSVPWM) is capable of controlling the NP voltage under full modulation indexes and full power factors. However, this modulation strategy is more complex than SVPWM, increases the switching frequency, and deteriorates the output waveforms of the inverter. A novel PWM concept that includes NP voltage-balancing conditions is proposed. Based on this concept, a hybrid modulation scheme that uses both SVPWM and VSVPWM is presented for complete control of the NP voltage in NPC three-level inverters. The performance of this new modulation approach and its benefits over SVPWM and VSVPWM are verified by simulation and experiments.

The present paper considers some technical issues related to the “energy blockchain” paradigm applied to microgrids. In particular, what appears from the study is that the superposition of energy transactions in a microgrid creates a variation of the power losses in all the branches of the microgrid. Traditional power losses allocation in distribution systems takes into account only generators while, in this paper, a real-time attribution of power losses to each transaction involving one generator and one load node is done by defining some suitable indices. Besides, the presence of P–V nodes increases the level of reactive flows and provides a more complex technical perspective. For this reason, reactive power generation for voltage support at P–V nodes poses a further problem of reactive power flow exchange, which is worth of investigation in future works in order to define a possible way of remuneration. The experimental section of the paper considers a medium voltage microgrid and two different operational scenarios.

The double-layer capacitor (DLC) for power applications is a new device. A simple resistive capacitive equivalent circuit is insufficient to characterize its terminal behaviour. Based on physical reasoning, an equivalent circuit is proposed to provide the power electronics engineer with a model for the terminal behaviour of the DLC. The equivalent model consists of three Re branches, one of them with a voltage dependent capacitor. A method to identify the circuit parameters is presented. Measurements of carbon-based DLCs for power applications are presented, analysed, and the equivalent circuit response is compared with experimental results.


Question: Leveraging reactive power in deep neural models for enhanced non-intrusive load monitoring.

Introducing ElectriSense: A Pioneering EMI-Based System for Single-Point Sensing and Classification of Electrical Device Usage in Homes

This paper introduces ElectriSense, a novel system designed to automatically detect and classify the use of electronic devices in homes using a single sensing point. The system leverages the presence of switch mode power supplies (SMPS) in modern consumer electronics and fluorescent lighting, which generate high-frequency electromagnetic interference (EMI) during operation. This interference is consistently emitted and propagates through the home's power wiring, creating a unique and stable signature for each device based on its switching frequency characteristics. Unlike previous solutions that rely on transient noise, ElectriSense utilizes these EMI signatures to distinguish between devices across different homes, even when devices are similar in nature. To validate the effectiveness of ElectriSense, we conducted comprehensive evaluations in seven homes, including a long-term, six-month deployment. The results demonstrated that ElectriSense is capable of accurately identifying and categorizing individual device usage with a mean accuracy of 93.82%, showcasing its potential for non-intrusive load monitoring applications.

Now-a-days, decadence of power quality is foremost issue of electrical society. In practice, utility of switching device is increased in industrial as well as in domestic applications. Non-linearity causes adverse effects on system efficiency, utility of the power supply, power factor, etc. As reduction in power factor increases reactive power which does not have any contribution in energy transfer so its compensation is needed. So efforts are made to upgrade power quality, concept of Filter is in demand. In contrast to passive filter, active filters are popular due to its smaller size and weight. In this paper, work is done on shunt active power filter (SAPF) using P-Q theory for current harmonic mitigation and reactive power compensation. Here the simulation as well as its parameters, with or without active filter is well presented. We commence the paper with power quality issues then better understanding of PQ theory, simulation circuits, results, comparison and finally ended with conclusion. An effort is made to achieve THD value of source current below 5% to meet the required IEEE standards.

Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training framework with a graph-regularised objective, namely Neural Graph Machines, that can combine the power of neural networks and label propagation. This work generalises previous literature on graphaugmented training of neural networks, enabling it to be applied to multiple neural architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a) allowing the network to train using labeled data as in the supervised setting, (b) biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs, with a runtime that is linear in the number of edges. The proposed joint training approach convincingly outperforms many existing methods on a wide range of tasks (multi-label classification on social graphs, news categorization, document classification and semantic intent classification), with multiple forms of graph inputs (including graphs with and without node-level features) and using different types of neural networks. Work done during an internship at Google.

Sub-8-bit representation of DNNs incur some noticeable loss of accuracy despite rigorous (re)training at low-precision. Such loss of accuracy essentially makes them equivalent to a much shallower counterpart, diminishing the power of being deep networks. To address this problem of accuracy drop we introduce the notion of residual networks where we add more low-precision edges to sensitive branches of the sub-8-bit network to compensate for the lost accuracy. Further, we present a perturbation theory to identify such sensitive edges. Aided by such an elegant trade-off between accuracy and model size, the 8-2 architecture (8-bit activations, ternary weights), enhanced by residual ternary edges, turns out to be sophisticated enough to achieve similar accuracy as 8-8 representation (∼ 1% drop from our FP-32 baseline), despite ∼ 1.6× reduction in model size, ∼ 26× reduction in number of multiplications , and potentially ∼ 2× inference speed up comparing to 8-8 representation, on the state-of-the-art deep network ResNet-101 pre-trained on ImageNet dataset. Moreover, depending on the varying accuracy requirements in a dynamic environment, the deployed low-precision model can be upgraded/downgraded on-the-fly by partially enabling/disabling residual connections. For example, disabling the least important residual connections in the above enhanced network, the accuracy drop is ∼ 2% (from our FP-32 baseline), despite ∼ 1.9× reduction in model size, ∼ 32× reduction in number of multiplications, and potentially ∼ 2.3× inference speed up comparing to 8-8 representation. Finally, all the ternary connections are sparse in nature, and the residual ternary conversion can be done in a resource-constraint setting without any low-precision (re)training and without accessing the data.

Energy and environmental sustainability can benefit a lot from advances in data mining and machine learning techniques. However, those advances rely on the availability of relevant datasets required to develop, improve and validate new techniques. Only recently the first datasets were made publicly available for the energy and sustainability research community. In this paper we present a freely available dataset containing power usage and related information from 50 homes. Here we describe our dataset, the hardware and software setups used when collecting the data and how others can access it. We then discuss potential uses of this data in the future of energy ecofeedback and demand side management research.

This paper presents a generic and comprehensive distribution optimal power flow (DOPF) model that can be used by local distribution companies (LDCs) to integrate their distribution system feeders into a Smart Grid. The proposed three-phase DOPF framework incorporates detailed modeling of distribution system components and considers various operating objectives. Phase specific and voltage dependent modeling of customer loads in the three-phase DOPF model allows LDC operators to determine realistic operating strategies that can improve the overall feeder efficiency. The proposed distribution system operation objective is based on the minimization of the energy drawn from the substation while seeking to minimize the number of switching operations of load tap changers and capacitors. A novel method for solving the three-phase DOPF model by transforming the mixed-integer nonlinear programming problem to a nonlinear programming problem is proposed which reduces the computational burden and facilitates its practical implementation and application. Two practical case studies, including a real distribution feeder test case, are presented to demonstrate the features of the proposed methodology. The results illustrate the benefits of the proposed DOPF in terms of reducing energy losses while limiting the number of switching operations.

Deep generative models can perform dramatically better than traditional graphical models in a number of machine learning tasks. However, training such models remains challenging because their latent variables typically do not have an analytical posterior distribution, largely due to the nonlinear activation nodes. We present a deep convolutional factor analyser (DCFA) for multivariate time series modeling. Our network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency. Our model can thus give a time series different representations at different depths. DCFA only consists of linear Gaussian nodes. Therefore, the posterior distributions of latent variables are also Gaussian and can be estimated easily using standard variational Bayes algorithm. We show that even without nonlinearity the proposed deep model can achieve state-of-the-art results in anomaly detection, classification and clustering using both synthetic and real-world datasets.

A new neutral-point-clamped pulsewidth modulation (PWM) inverter composed of main switching devices which operate as switches for PWM and auxiliary switching devices to clamp the output terminal potential to the neutral point potential has been developed. This inverter output contains less harmonic content as compared with that of a conventional type. Two inverters are compared analytically and experimentally. In addition, a new PWM technique suitable for an ac drive system is applied to this inverter. The neutral-point-clamped PWM inverter adopting the new PWM technique shows an excellent drive system efficiency, including motor efficiency, and is appropriate for a wide-range variable-speed drive system.

The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, Uchida et al. (2017) proposed to watermark convolutional neural networks by embedding information into their weights. While this is a clear progress towards model protection, this technique solely allows for extracting the watermark from a network that one accesses locally and entirely. This is a clear impediment, as leaked models can be re-used privately, and thus not released publicly for ownership inspection. Instead, we aim at allowing the extraction of the watermark from a neural network (or any other machine learning model) that is operated remotely, and available through a service API. To this end, we propose to operate on the model’s action itself, tweaking slightly its decision frontiers so that a set of specific queries convey the desired information. In present paper, we formally introduce the problem and propose a novel zerobit watermarking algorithm that makes use of adversarial model examples (called adversaries for short). While limiting the loss of performance of the protected model, this algorithm allows subsequent extraction of the watermark using only few remote queries. We experiment this approach on the MNIST dataset with three types of neural networks, demonstrating that e.g., watermarking with 100 images incurs a slight accuracy degradation, while being resilient to most removal attacks.

We present an approach for the verification of feed-forward neural networks in which all nodes have a piece-wise linear activation function. Such networks are often used in deep learning and have been shown to be hard to verify for modern satisfiability modulo theory (SMT) and integer linear programming (ILP) solvers. The starting point of our approach is the addition of a global linear approximation of the overall network behavior to the verification problem that helps with SMT-like reasoning over the network behavior. We present a specialized verification algorithm that employs this approximation in a search process in which it infers additional node phases for the non-linear nodes in the network from partial node phase assignments, similar to unit propagation in classical SAT solving. We also show how to infer additional conflict clauses and safe node fixtures from the results of the analysis steps performed during the search. The resulting approach is evaluated on collision avoidance and handwritten digit recognition case studies.

This paper proposes a novel control for voltage-source inverters with the capability to flexibly operate in grid-connected and islanded modes. The control scheme is based on the droop method, which uses some estimated grid parameters such as the voltage and frequency and the magnitude and angle of the grid impedance. Hence, the inverter is able to inject independently active and reactive power to the grid. The controller provides a proper dynamics decoupled from the grid-impedance magnitude and phase. The system is also able to control active and reactive power flows independently for a large range of impedance grid values. Simulation and experimental results are provided in order to show the feasibility of the control proposed.