Question: Utilizing Faster-RCNN for detecting buried objects from B-scan ground penetrating radar data.

This paper delves into an intriguing observation in boosting methods: the test error often remains stable or continues to decrease as the classifier's size grows, even when the training error has already hit zero. We explore this phenomenon by analyzing the margins of training examples relative to the classification rule generated by voting, where the margin is defined as the difference between the count of correct votes and the highest number of votes for any incorrect label. By applying techniques utilized in the study of Vapnik's support vector classifiers and neural networks with minimal weights, we establish a connection between the margin distribution and the test error. Furthermore, we demonstrate both theoretically and empirically that boosting is particularly adept at expanding the margins of training examples. Our findings offer a fresh perspective, contrasting with explanations rooted in bias-variance decomposition.

**Question**: A Novel Method for Improving the Efficiency of Solar Panels

**Documents**: A review of the application of nanoparticles in improving the efficiency of solar cells
This review paper focuses on the application of nanoparticles in enhancing the performance of solar cells. It is well established that the efficiency of solar cells can be improved by incorporating nanoparticles into their structure. The unique optical and electronic properties of nanoparticles, such as high surface area, quantum confinement, and plasmonic effects, make them suitable for this purpose. This paper reviews recent research in this area, highlighting the various types of nanoparticles used, their synthesis methods, and their impact on the efficiency of solar cells. The review also discusses the challenges and future directions in this field, including the need for more efficient and scalable synthesis methods, and the development of new types of nanoparticles with enhanced properties. The ultimate goal is to develop high-efficiency, low-cost solar cells that can contribute to a sustainable energy future.

**Similar Documents**: This comprehensive review explores the innovative use of nanoparticles to augment the efficiency of solar cells, an area that has garnered significant attention in recent scientific research. The integration of nanoparticles into solar cells leverages their distinctive optical and electronic attributes, such as increased surface area, quantum confinement effects, and plasmon resonance, to enhance the cells' performance. This paper summarizes recent studies, detailing the range of nanoparticles utilized, their fabrication techniques, and their influence on solar cell efficiency. It also addresses the existing hurdles and future prospects in this domain, emphasizing the quest for more efficient, scalable synthesis methods and the creation of novel nanoparticles with superior characteristics. The overarching objective is to engineer solar cells with high efficiency and low production costs, paving the way for a sustainable energy landscape.

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

In this work, we examine the strength of deep learning approaches for pathology detection in chest radiographs. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of CNN learned from a non-medical dataset to identify different types of pathologies in chest x-rays. We tested our algorithm on a 433 image dataset. The best performance was achieved using CNN and GIST features. We obtained an area under curve (AUC) of 0.87-0.94 for the different pathologies. The results demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. This is a first-of-its-kind experiment that shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.

Millimeter-wave (MMW) imaging is a powerful tool for the detection of objects concealed under clothing. Several factors including different kinds of objects, variety of covering materials and their thickness, accurate imaging of near-field scattered data affect the success of detection. To practice with such considerations, this paper presents the two-dimensional (2D) images of different targets hidden under various fabric sheets. The W-band inverse synthetic aperture radar (ISAR) data of various target-covering situations are acquired and imaged by applying both the focusing operator based inversion algorithm and the spherical back-projection algorithm. Results of these algorithms are demonstrated and compared to each other to assess the performance of the MMW imaging in detecting the concealed objects of both metallic and dielectric types.

High-resolution satellite imagery has been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Despite the high availability, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks. Then, this data set is used to train deep-learning-based models in order to accurately classify satellite images that contain or not contain zebra crossings. A novel data set with more than 240000 images from 3 continents, 9 countries, and more than 20 cities was used in the experiments. The experimental results showed that freely available crowdsourcing data can be used to accurately (97.11%) train robust models to perform crosswalk classification on a global scale.

We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarchy. During detection, the lower resolution features are initially used to reject the majority of negative windows at relatively low cost, leaving a relatively small number of windows to be processed in higher resolutions. This enables the use of computationally more expensive higher resolution features to achieve high detection accuracy. We applied this framework on Histograms of Oriented Gradient (HOG) features for object detection. Our multi-resolution detector produced better performance for pedestrian detection than state-of-the-art methods (Dalal and Triggs, 2005), and was faster during both training and testing. Testing our method on motorbikes and cars from the VOC database revealed similar improvements in both speed and accuracy, suggesting that our approach is suitable for realtime general object detection applications.

Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).

A large amount of annotated training images is critical for training accurate and robust deep network models but the collection of a large amount of annotated training images is often time-consuming and costly. Image synthesis alleviates this constraint by generating annotated training images automatically by machines which has attracted increasing interest in the recent deep learning research. We develop an innovative image synthesis technique that composes annotated training images by realistically embedding foreground objects of interest (OOI) into background images. The proposed technique consists of two key components that in principle boost the usefulness of the synthesized images in deep network training. The first is context-aware semantic coherence which ensures that the OOI are placed around semantically coherent regions within the background image. The second is harmonious appearance adaptation which ensures that the embedded OOI are agreeable to the surrounding background from both geometry alignment and appearance realism. The proposed technique has been evaluated over two related but very different computer vision challenges, namely, scene text detection and scene text recognition. Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.

Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K [45] and the Caltech [9] benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.

In this paper, a moving object detection algorithm is combined with a background estimate and a Bing (Binary Norm Gradient) object is proposed in video surveillance. A simple background estimation method is used to detect rough images of a group of moving foreground objects. The foreground setting in the foreground will estimate another set of candidate object windows, and the target (pedestrian / vehicle) from the intersection area comes from the first two steps. In addition, the time cost is reduced by the estimated area. Experiments on outdoor datasets show that the proposed method can not only achieve high detection rate (DR), but also reduce false alarm rate (FAR) and time cost.

In this paper, radar cross section (RCS) measurement for human subjects and vehicles in 77 GHz automotive frequency modulated continuous wave (FMCW) radar system is presented. In this system, it is impossible to utilize conventional RCS definition due to high frequency band and the modulation technique used. Therefore, we introduce a new parameter called pseudo-RCS that can replace the conventional RCS. Then, we conduct actual experiment in the road to measure the RCS of the human subjects and the vehicles. In the measurement, data of four human subjects and four different kinds of vehicles are recorded with the 77 GHz FMCW radar. From the actual measured data, we find RCS distributions of the human subjects and the vehicles. For the human subjects, the RCS values are distributed following the Nakagami distribution. On the other hand, the log-normal distribution is well fit for the RCS values in the case of the vehicle.


Question: Detecting buried objects in B-scan ground penetrating radar data utilizing the Faster-RCNN method.

This course aims to deliver a comprehensive introduction to the realm of statistical pattern recognition, encompassing a variety of techniques pivotal to the field. Throughout the lectures, we will delve into methodologies for feature extraction, dimensionality reduction, data clustering, and pattern classification, all of which are essential tools in the pattern recognition toolkit. We will also explore cutting-edge approaches, including ensemble learning and sparse modeling, which have gained prominence due to their effectiveness in handling complex data. To bridge theory with practice, we will examine select real-world applications that showcase the practical implementation of these techniques. By the end of the course, participants will gain a solid understanding of how statistical pattern recognition can be applied to solve real-world problems, with a particular emphasis on the latest advancements in the field.

The condition of railway tracks needs to be periodically monitored to ensure passenger safety. Cameras mounted on a moving vehicle such as a hi-rail vehicle or a geometry inspection car can generate large volumes of high resolution images. Extracting accurate information from those images has been challenging due to background clutter in railroad environments. In this paper, we describe a novel approach to visual track inspection using material classification and semantic segmentation with Deep Convolutional Neural Networks (DCNN). We show that DCNNs trained end-to-end for material classification are more accurate than shallow learning machines with hand-engineered features and are more robust to noise. Our approach results in a material classification accuracy of 93.35% using 10 classes of materials. This allows for the detection of crumbling and chipped tie conditions at detection rates of 86.06% and 92.11%, respectively, at a false positive rate of 10 FP/mile on the 85-mile Northeast Corridor (NEC) 2012-2013 concrete tie dataset.

Recognizing 3-D objects has a wide range of application areas from autonomous robots to self-driving vehicles. The popularity of low-cost RGB-D sensors has enabled a rapid progress in 3-D object recognition in the recent years. Most of the existing studies use depth data as an additional channel to the RGB channels. Instead of this approach, we propose two volumetric representations to reveal rich 3-D structural information hidden in depth images. We present a 3-D convolutional neural network (CNN)-based object recognition approach, which utilizes these volumetric representations and single and multi-rotational depth images. The 3-D CNN architecture trained to recognize single depth images produces competitive results with the state-of-the-art methods on two publicly available datasets. However, recognition accuracy increases further when the multiple rotations of objects are brought together. Our multirotational 3-D CNN combines information from multiple views of objects to provide rotational invariance and improves the accuracy significantly comparing with the single-rotational approach. The results show that utilizing multiple views of objects can be highly informative for the 3-D CNN-based object recognition.

In this brief, a convolutional learning system for classification of segmented objects represented in 3-D as point clouds of laser reflections is proposed. Several novelties are discussed: (1) extension of the existing convolutional neural network (CNN) framework to direct processing of 3-D data in a multiview setting which may be helpful for rotation-invariant consideration, (2) improvement of CNN training effectiveness by employing a stochastic meta-descent (SMD) method, and (3) combination of unsupervised and supervised training for enhanced performance of CNN. CNN performance is illustrated on a two-class data set of objects in a segmented outdoor environment.

We present a database consisting of wood pile images, which can be used as a benchmark to evaluate the performance of wood pile detection and surveying algorithms. We distinguish six database categories which can be used for different types of algorithms. Images of real and synthetic scenes are provided, which consist of 7655 images divided into 354 data sets. Depending on the category the data sets either include ground truth data or forestry specific measurements with which algorithms may be compared.

In advanced driver assistance systems and autonomous driving, reliable environment perception and object tracking based on radar is fundamental. High-resolution radar sensors often provide multiple measurements per object. Since in this case traditional point tracking algorithms are not applicable any more, novel approaches for extended object tracking emerged in the last few years. However, they are primarily designed for lidar applications or omit the additional Doppler information of radars. Classical radar based tracking methods using the Doppler information are mostly designed for point tracking of parallel traffic. The measurement model presented in this paper is developed to track vehicles of approximately rectangular shape in arbitrary traffic scenarios including parallel and cross traffic. In addition to the kinematic state, it allows to determine and track the geometric state of the object. Using the Doppler information is an important component in the model. Furthermore, it neither requires measurement preprocessing, data clustering, nor explicit data association. For object tracking, a Rao-Blackwellized particle filter (RBPF) adapted to the measurement model is presented.

This letter presents a novel approach for logo retrieval. Considering the fact that logo only occupies a small portion of an image, we apply Faster R-CNN to detect logo proposals first, and then use a two-step pooling strategy with adaptive weight to obtain an accurate global signature. The adaptive weighted pooling method can effectively balance the recall and precision of proposals by incorporating the probability of each proposal being a logo. Experimental results show that the proposed method interprets the similarity between query and database image more accurately and achieves state of the art performance.

The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional segmentation approaches such as region-based depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, methods based on fully convolutional networks (FCN) have achieved great success in the segmentation of general images. FCNs leverage a large labeled dataset to hierarchically learn the features that best correspond to the shallow appearance as well as the deep semantics of the images. However, when applied to medical images, FCNs usually produce coarse ROI detection and poor boundary definitions primarily due to the limited number of labeled training data and limited constraints of label agreement among neighboring similar pixels. In this paper, we propose a new stacked FCN architecture with multi-channel learning (SFCN-ML). We embed the FCN in a stacked architecture to learn the foreground ROI features and background non-ROI features separately and then integrate these different channels to produce the final segmentation result. In contrast to traditional FCN methods, our SFCN-ML architecture enables the visual attributes and semantics derived from both the fore- and background channels to be iteratively learned and inferred. We conducted extensive experiments on three public datasets with a variety of visual challenges. Our results show that our SFCN-ML is more effective and robust than a routine FCN and its variants, and other state-of-the-art methods.

We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarchy. During detection, the lower resolution features are initially used to reject the majority of negative windows at relatively low cost, leaving a relatively small number of windows to be processed in higher resolutions. This enables the use of computationally more expensive higher resolution features to achieve high detection accuracy. We applied this framework on Histograms of Oriented Gradient (HOG) features for object detection. Our multi-resolution detector produced better performance for pedestrian detection than state-of-the-art methods (Dalal and Triggs, 2005), and was faster during both training and testing. Testing our method on motorbikes and cars from the VOC database revealed similar improvements in both speed and accuracy, suggesting that our approach is suitable for realtime general object detection applications.

Passive radio-frequency identification (RFID) tags are widely used due to its economic cost and satisfactory performance. So far, passive RFID tags are mostly applied to identify certain objects, such as underground pipe identification under buried conditions. However, there is lack of study on the application of buried tags for further application. In this paper, the performances of buried RFID tags are studied to develop a flooding warning system based on RFID tag array for energy facility such as power stations. In this study, the corresponding signal strength received by the RFID reader is evaluated when the RFID tags are buried by seven materials respectively. The results show that flood warning detector can be constructed using passive RFID tag array and reader.

The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset.


Question: Utilizing Faster-RCNN for detecting buried objects in B-scan ground penetrating radar data.

This paper introduces a groundbreaking deep convolutional neural network design, the Inception architecture, which sets a new benchmark for classification and object detection tasks in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The core innovation of this architecture lies in its efficient use of computational resources within the network. Through a meticulous design approach, we expanded the network's depth and breadth while maintaining a consistent computational budget. To maximize performance, architectural choices were guided by the Hebbian principle and the concept of multi-scale processing. A specific version of this architecture, known as GoogLeNet, comprises 22 layers. Its quality is evaluated within the framework of classification and detection tasks, demonstrating its effectiveness in these domains.

In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.

This paper proposed a deep convolutional neural network (DCNN) based framework for large-scale oil palm tree detection using high-resolution remote sensing images in Malaysia. Different from the previous palm tree or tree crown detection studies, the palm trees in our study area are very crowded and their crowns often overlap. Moreover, there are various land cover types in our study area, e.g. impervious, bare land, and other vegetation, etc. The main steps of our proposed method include large-scale and multi-class sample collection, AlexNet-based DCNN training and optimization, sliding window-based label prediction, and post-processing. Compared with the manually interpreted ground truth, our proposed method achieves detection accuracies of 92%–97% in our study area, which are greatly higher than the accuracies obtained from another two detection methods used in this paper.

This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarchy. During detection, the lower resolution features are initially used to reject the majority of negative windows at relatively low cost, leaving a relatively small number of windows to be processed in higher resolutions. This enables the use of computationally more expensive higher resolution features to achieve high detection accuracy. We applied this framework on Histograms of Oriented Gradient (HOG) features for object detection. Our multi-resolution detector produced better performance for pedestrian detection than state-of-the-art methods (Dalal and Triggs, 2005), and was faster during both training and testing. Testing our method on motorbikes and cars from the VOC database revealed similar improvements in both speed and accuracy, suggesting that our approach is suitable for realtime general object detection applications.

Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed of regular one-stage detectors and also defeat two-stage detectors in accuracy, for vehicle detection. State-of-the-art performance result has been showed on the DETRAC vehicle dataset.

Updating road markings is one of the routine tasks of transportation agencies. Compared with traditional road inventory mapping techniques, vehicle-borne mobile light detection and ranging (LiDAR) systems can undertake the job safely and efficiently. However, current hurdles include software and computing challenges when handling huge volumes of highly dense and irregularly distributed 3-D mobile LiDAR point clouds. This paper presents the development and implementation aspects of an automated object extraction strategy for rapid and accurate road marking inventory. The proposed road marking extraction method is based on 2-D georeferenced feature (GRF) images, which are interpolated from 3-D road surface points through a modified inverse distance weighted (IDW) interpolation. Weighted neighboring difference histogram (WNDH)-based dynamic thresholding and multiscale tensor voting (MSTV) are proposed to segment and extract road markings from the noisy corrupted GRF images. The results obtained using 3-D point clouds acquired by a RIEGL VMX-450 mobile LiDAR system in a subtropical urban environment are encouraging.

In this paper we propose a convolutional neural network (CNN), which allows to identify corresponding patches of very high resolution (VHR) optical and SAR imagery of complex urban scenes. Instead of a siamese architecture as conventionally used in CNNs designed for image matching, we resort to a pseudo-siamese configuration with no interconnection between the two streams for SAR and optical imagery. The network is trained with automatically generated training data and does not resort to any hand-crafted features. First evaluations show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development to a generalized multi-sensor matching procedure.

Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system [1]. These systems recognize, classify and localize every object in an image by drawing bounding boxes around the object [2]. These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real-world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.


Question: A pseudo dynamic method for using support vector machines in forecasting building energy demand.

LIBSVM: An Extensive Library for Support Vector Machine Applications

This document presents LIBSVM, an extensive and efficient library specifically designed for support vector machine (SVM) applications. LIBSVM offers a comprehensive suite of tools to facilitate the use of SVMs in various domains, including but not limited to, classification, regression, and outlier detection. It is optimized to handle large-scale problems, making it a preferred choice for researchers and practitioners alike. The library is equipped with a user-friendly interface and supports multiple kernel functions, enabling users to tailor the SVM to their specific needs. LIBSVM's robustness and versatility are further enhanced by its ability to handle sparse data, making it particularly suitable for high-dimensional datasets. Its effectiveness in solving complex problems has been widely acknowledged, as evidenced by its widespread adoption in the machine learning community. LIBSVM's documentation is thorough and provides clear guidance on how to implement SVMs for a variety of tasks, ensuring that both beginners and advanced users can leverage its capabilities effectively. In summary, LIBSVM stands as a powerful and accessible tool for anyone seeking to apply SVMs to their data analysis and prediction challenges.

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. This paper presents a real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) and compares the result with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local " bounded " decision region around each class that presents better rejection to confusers.

Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this article. Pre and post data processing/analysis skills, data sampling, training criteria and model recommendation will also be covered in this article. 1. Forecasting with Neural Networks Forecasting is a process that produces a set of outputs by given a set of variables. The variables are normally historical data. Basically, forecasting assumes that future occurrences are based, at least in part, on presently observable or past events. It assumes that some aspects of the past patterns will continue into the future. Past relationships can then be discovered through study and observation. The basic idea of forecasting is to find an approximation of mapping between the input and output data in order to discover the implicit rules governing the observed movements. For instance, the forecasting of stock prices can be described in this way. Assume that i u represents today's price, i v represents the price after ten days. If the prediction of a stock price after ten days could be obtained using today's stock price, then there should be a functional mapping i u to i v , where ) ( i i i u v Γ = . Using all ( i u , i v ) pairs of historical data, a general function () Γ which consists of () i Γ could be obtained, that is ) (u v Γ = . More generally, u which consists of more information in today's price could be used in function () Γ . As NNs are universal approximators, we can find a NN simulating this () Γ function. The trained network is then used to predict the movements for the future. NN based financial forecasting has been explored for about a decade. Many research papers are published on various international journals and conferences proceedings. Some companies and institutions are also claiming or marketing the so called advanced forecasting tools or models. Some research results of financial forecasting found in references. For instance, stock trading system[4], stock forecasting [6, 22], foreign exchange rates forecasting [15, 24], option prices [25], advertising and sales volumes [13]. However, Callen et al. [3] claim that NN models are not necessarily superior to linear time series models even when the data are financial, seasonal and nonlinear. 2. Towards a Better Robust Financial Forecasting Model In working towards a more robust financial forecasting model, the following issues are worth examining. First, instead of emphasizing on the forecasting accuracy only, other financial criteria should be considered. Current researchers tend to use goodness of fit or similar criteria to judge or train their models in financial domain. In terms of mathematical calculation this approach is a correct way in theory. As we understand that a perfect forecasting is impossible in reality. No model can achieve such an ideal goal. Under this constraint, seeking a perfect forecasting is not our aim. We can only try to optimize our imperfect forecasts and use other yardsticks to give the most realistic measure. Second, there should be adequate organization and processing of forecasting data. Preprocessing and proper sampling of input data can have impact on the forecasting performance. Choice of indicators as inputs through sensitivity analysis could help to eliminate redundant inputs. Furthermore, NN forecasting results should be used wisely and effectively. For example, as the forecast is not perfect, should we compare the NN output with the previous forecast or with the real data especially when price levels are used as the forecasting targets? Third, a trading system should be used to decide on the best tool to use. NN is not the single tool that can be used for financial forecasting. We also cannot claim that it is the best forecasting tool. In fact, people are still not aware of which kind of time series is the most suitable for NN applications. To conduct post forecasting analysis will allow us to find out the suitability of models and series. We may then conclude that a certain kind of models should be used for a certain kind of time series. Training or building NN models is a trial and error procedure. Some researchers are not willing to test more on their data set [14]. If there is a system that can help us to formalize these tedious exploratory procedures, it will certainly be of great value to financial forecasting. Instead of just presenting one successful experiment, possibility or confidence level can be applied to the outputs. Data are partitioned into several sets to find out the particular knowledge of this time series. As stated by David Wolpert and William Macready about their No-Free-Lunch theorems [28], averaged over all problems, all search algorithms perform equally. Just experimenting on a single data set, a NN model which outperforms other models can be found. However, for another data set one model which outperforms NN model can also be found according to No-Free-Lunch theorems. To avoid such a case of one model outperforming others, we partition the data set into several sub data sets. The recommended NN models are those that outperform other models for all sub time horizons. In other words, only those models incorporated with enough local knowledge can be used for future forecasting. It is very important and necessary to emphasize these three issues here. Different criteria exist for the academics and the industry. In academics, sometime people seek for the accuracy towards 100%. While in industry a guaranteed 60% accuracy is typically aimed for. In addition, profit is the eventual goal of practitioners, so a profit oriented forecasting model may fit their needs. Cohen [5] surveyed 150 papers in the proceedings of the 8th National Conference on artificial intelligence. He discovered that only 42% of the papers reported that a program had run on more than one example; just 30% demonstrated performance in some way; a mere 21% framed hypotheses or made predictions. He then concluded that the methodologies used were incomplete with respect to the goals of designing and analyzing AI system. Tichy [20] showed that in a very large study of over 400 research articles in computer science. Over 40% of the articles are about new designs and the models completely lack experimental data. In a recent IEEE computer journal, he also points out 16 excuses to avoid experimentation for computer scientists [21]. What he is talking is true and not a joke. Prechelt [14] showed that the situation is not better in the NN literature. Out of 190 papers published in wellknown journals dedicated to NNs, 29% did not employ even a single realistic or real learning problem. Only 8% of the articles presented results for more than one problem using real world data. To build a NN forecasting we need sufficient experiments. To test only for one market or just for one particular time period means nothing. It will not lead to a robust model based on manually, trial-and-error, or ad hoc experiments. More robust model is needed but not only in one market or for one time period. Because of the lack of industrial models and because failures in academic research are not published, a single person or even a group of researchers will not gain enough information or experiences to build up a good forecasting model. It is obvious that an automated system dealing with NN models building is necessary. 3. Steps of NN Forecasting: The Art of NN Training As NN training is an art, many searchers and practitioners have worked in the field to work towards successful prediction and classification. For instance, William Remus and Marcus O'connor [16] suggest some principles for NN prediction and classification are of critical importance in the chapter, “Principles of Forecasting” in “A Handbook for Researchers and Practitioners”: • Clean the data prior to estimating the NN model. • Scale and deseasonalize data prior to estimating the model. • Use appropriate methods to choose the right starting point. • Use specialized methods to avoid local optima. • Expand the network until there is no significant improvement in fit. • Use pruning techniques when estimating NNs and use holdout samples when evaluating NNs. • Take care to obtain software that has in-built features to avoid NN disadvantages. • Build plausible NNs to gain model acceptance by reducing their size. • Use more approaches to ensure that the NN model is valid. With the authors' experience and sharing from other researchers and practitioners, we propose a seven-step approach for NN financial forecasting model building. The seven steps are basic components of the automated system and normally involved in the manual approach. Each step deals with an important issue. They are data preprocessing, input and output selection, sensitive analysis, data organization, model construction, post analysis and model recommendation. Step 1. Data Preprocessing A general format of data is prepared. Depending on the requirement, longer term data, e.g. weekly, monthly data may also be calculated from more frequently sampled time series. We may think that it makes sense to use as frequent data sampling as possible for experiments. However, researchers have found that increasing observation frequency does not always help to improve the accuracy of forecasting [28]. Inspection of data to find outliers is also important as outliers make it difficult for NNs and other forecasting models to model the true underlying functional. Although NNs have been shown to be universal approximators, it had been found that NNs had difficulty modeling seasonal patterns in time series [11].When a time series contains significant seasonality, the data

In this paper, we present an efficient fast learning algorithm for regression problems using meta-cognitive extreme learning machine(McELM). The proposed algorithm has two components, namely the cognitive component and meta-cognitive component. The cognitive component is an extreme learning machine (ELM) while the meta-cognitive component which controls the cognitive component employs a self-regulating learning mechanism to decide what to learn, when to learn and how to learn. The meta-cognitive component chooses suitable learning method based on the samples presented namely, delete sample, reserve sample and network update. The use of ELM improves the network speed and reduces computational cost. Unlike traditional ELM, the number of hidden layers is not fixed priori in McELM, instead, the network is built during the learning phase. This algorithm is evaluated on a set of benchmark regression and approximation problems and also on a real-world wind force and moment coefficient prediction problem. Performance results in this study highlight that McELM can achieve better results compared with conventional ELM, support vector regression (SVR).

Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability. This paper proposes a novel approach that uses a convolutional neural network (CNN) to extract hidden state-time features to mitigate the curse of partial observability. More specific, a CNN is used as a function approximator to estimate the state-action value function or <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-function in the supervised learning step of fitted <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-iteration. The approach is evaluated in a qualitative simulation, comprising a cluster of thermostatically controlled loads that only share their air temperature, while their envelope temperature remains hidden. The simulation results show that the presented approach is able to capture the underlying hidden features and able to successfully reduce the electricity cost the cluster.

Dynamic voltage scaling (DVS) allows a program to execute at a non-peak CPU frequency in order to reduce CPU power, and hence, energy consumption; however, it is done at the cost of performance degradation. For a program whose execution time is bounded by peripherals’ performance rather than the CPU speed, applying DVS to the program will result in negligible performance penalty. Unfortunately, existing DVS-based power-management algorithms are conservative in the sense that they overly exaggerate the impact that the CPU speed has on the execution time, e.g., they assume that the execution time will double if the CPU speed is halved. Based on a new single-coefficient performance model, we propose a DVS algorithm that detects the CPU-boundedness of a program on the fly (via a regression method on the past MIPS rate) and then adjusts the CPU frequency accordingly. To illustrate its effectiveness, we compare our algorithm with other DVS algorithms on real systems via physical measurements.

An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis. Computer simulations show the feasibility and validity of wavelet support vector machines (WSVMs) in regression and pattern recognition.

The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ν-Support Vector Classification (ν-SVC) (Schölkopf et al., 2000), ν-SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ν-SVC algorithm (AONSVM) to ν-SVR will not generate an effective initial solution. It is the main challenge to design an incremental ν-SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ν-SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ν-SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ν-SVR algorithms with both cold and warm starts.

In this paper we present a new algorithm suitable for matching discrete objects such as strings and trees in linear time, thus obviating dynamic programming with quadratic time complexity. Furthermore, prediction cost in many cases can be reduced to linear cost in the length of the sequence to be classified, regardless of the number of support vectors. This improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.

As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.

Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models.


Question: A pseudo dynamic method employing support vector machines for forecasting building energy demand.

In this paper, we explore the application of Support Vector Machines (SVM) within the context of building energy demand prediction, adopting a pseudo dynamic approach. Unlike static models that consider only the current state, the pseudo dynamic method incorporates temporal dynamics, enhancing prediction accuracy. SVM, a powerful machine learning technique, is particularly well-suited for this task due to its ability to handle complex, non-linear relationships. By utilizing historical data on building energy consumption and environmental conditions, our SVM model learns to predict future energy demand with remarkable precision. The pseudo dynamic aspect of our approach involves considering past energy demands and environmental parameters to forecast future needs. This methodology not only improves the predictive capabilities of the model but also makes it more robust to fluctuations in the building's operational conditions. We validate our approach through a series of experiments on real-world building energy datasets, demonstrating significant improvements over traditional static models. The results highlight the effectiveness of SVM combined with the pseudo dynamic approach in addressing the challenges of building energy demand prediction, providing valuable insights for energy management systems aiming to optimize resource allocation and reduce costs.

OBJECTIVES
The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.

METHODS
The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information.

RESULTS
Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871).

CONCLUSIONS
With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.

Demand-side management, together with the integration of distributed energy generation and storage, are considered increasingly essential elements for implementing the smart grid concept and balancing massive energy production from renewable sources. We focus on a smart grid in which the demand-side comprises traditional users as well as users owning some kind of distributed energy sources and/or energy storage devices. By means of a day-ahead optimization process regulated by an independent central unit, the latter users intend to reduce their monetary energy expense by producing or storing energy rather than just purchasing their energy needs from the grid. In this paper, we formulate the resulting grid optimization problem as a noncooperative game and analyze the existence of optimal strategies. Furthermore, we present a distributed algorithm to be run on the users' smart meters, which provides the optimal production and/or storage strategies, while preserving the privacy of the users and minimizing the required signaling with the central unit. Finally, the proposed day-ahead optimization is tested in a realistic situation.

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP)-based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear-programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures and, hence, works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation.

Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.

An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.

In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x. Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter 0 < or = v < or = 1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and experimentally.

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention somemodifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

We review in this paper several methods from Statistical Learning Theory (SLT) for the performance assessment and uncertainty quantification of predictive models. Computational issues are addressed so to allow the scaling to large datasets and the application of SLT to Big Data analytics. The effectiveness of the application of SLT to manufacturing systems is exemplified by targeting the derivation of a predictive model for quality forecasting of products on an assembly line.

Application software has been developed for analyzing and understanding a dynamic price change in the US wholesale power market. Traders can use the software as an effective decision-making tool by modeling and simulating a power market. The software uses different features of a decision support system by creating a framework for assessing new trading strategies in a competitive electricity trading environment. The practicality of the software is confirmed by comparing its estimation accuracy with those of other methods (e.g., neural network and genetic algorithm). The software has been applied to a data set regarding the California electricity crisis in order to examine whether the learning (convergence) speed of traders is different between the two periods (before and during the crisis). Such an application confirms the validity of the proposed software. © 2007 Elsevier B.V. All rights reserved.

Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models. 2007 Elsevier Inc. All rights reserved.


Question: A pseudo dynamic method for predicting building energy demand using a support vector machine.

In the realm of building energy demand prediction, the integration of Support Vector Machine (SVM) models with pseudo dynamic approaches offers a robust framework for forecasting energy needs. This method combines the powerful predictive capabilities of SVM, a supervised learning model that excels in handling complex, nonlinear data, with a pseudo dynamic strategy that simulates the temporal evolution of energy demand. Unlike traditional static models, the pseudo dynamic approach accounts for the dynamic nature of energy consumption patterns, considering factors such as time of day, season, and weather conditions. By training the SVM model on historical data that includes these temporal variables, the pseudo dynamic approach enables the model to learn and predict how energy demand varies over time. This approach is particularly advantageous in scenarios where energy demand exhibits significant fluctuations, as it can capture and anticipate these changes more accurately than static models. Furthermore, the SVM's ability to handle high-dimensional data and find optimal solutions in complex feature spaces makes it particularly suitable for energy demand prediction, where numerous variables can influence consumption patterns. Overall, the combination of SVM with a pseudo dynamic approach represents a sophisticated and effective solution for predicting building energy demand, enhancing the accuracy and reliability of forecasts in dynamic environments.

Prepaid meter (PM) is getting very popular especially in developing countries. There are many advantages to use prepaid meter as opposed to postpaid meter both to the utility provider and to the consumer. Brunei Darussalam has adopted PM but it is not intelligent and not wireless enabled. Reading meters and topping up balance are still done manually. Utility provider does not have information on the usage statistics and has only limited functionalities in the grid control. So accordingly a novel software agent based wireless prepaid energy meter was developed using Java Agent Development Environment (JADE-LEAP) allowing agent from utility provider to query wireless energy meter for energy values of every household. These statistics can be used for statistical computation of the power consumed and for policy and future planning.

The support vector machine (SVM) is a popular classification technique. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure which usually gives reasonable results.

The paper is devoted to supervise method approach to identify the vessel anomaly behaviour in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behaviour. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behaviour. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.

The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

Virtualization facilitates the provision of flexible resources and improves energy efficiency through the consolidation of virtualized servers into a smaller number of physical servers. As an increasingly essential component of the emerging cloud computing model, virtualized environments bill their users based on processor time or the number of virtual machine instances. However, accounting based only on the depreciation of server hardware is not sufficient because the cooling and energy costs for data centers will exceed the purchase costs for hardware. This paper suggests a model for estimating the energy consumption of each virtual machine without dedicated measurement hardware. Our model estimates the energy consumption of a virtual machine based on in-processor events generated by the virtual machine. Based on this estimation model, we also propose a virtual machine scheduling algorithm that can provide computing resources according to the energy budget of each virtual machine. The suggested schemes are implemented in the Xen virtualization system, and an evaluation shows that the suggested schemes estimate and provide energy consumption with errors of less than 5% of the total energy

As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy c-means. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.

People counting has many applications in smart buildings. For example, adjusting HVAC systems based on the number of occupants in each room can save a significant amount of energy. In addition, security and safety of the building can be managed by determining the number and location of occupants. Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8×8 IR array sensor at the doorways and counting the number of people inside rooms. Our solution is real-time, inexpensive, privacy preserving with much less deployment constraints compared to its competitors. The proposed solution deals with realistic and dynamic changes in the sensing environment by leveraging a combination of Otsus thresholding and modeling thermal noise distribution. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.

This survey shows that heterogeneous server clusters can be made more efficient by conserving power and energy while exploiting information from the service level, such as request priorities established by service-level agreements.

The ability to predict the students’ academic performance is very important in institution educational system. Recently some researchers have been proposed data mining techniques for higher education. In this paper, we compare two data mining techniques which are: Artificial Neural Network (ANN) and the combination of clustering and decision tree classification techniques for predicting and classifying students’ academic performance. The data set used in this research is the student data of Computer Science Department, Faculty of Science and Defence Technology, National Defence University of Malaysia (NDUM).


Question: A pseudo dynamic method employing support vector machines for forecasting building energy demand.

This paper investigates the approximation capabilities of superpositions of a fixed sigmoidal function in the context of neural networks. We establish that finite linear combinations of compositions involving a predetermined, single-variable function and a set of affine functionals can closely mimic any continuous function of n real variables, provided these functions are supported within the unit hypercube, and subject to minimal constraints on the univariate function. Our findings address a long-standing query regarding representational power within single-hidden-layer neural networks. Specifically, we illustrate that continuous feedforward neural networks, equipped with just one internal layer and any continuous sigmoidal activation function, can effectively approximate arbitrary decision regions to an arbitrary degree of accuracy. The discussion also delves into the approximation properties of alternative nonlinearities that could potentially be utilized in artificial neural networks, thereby expanding the scope of their applicability.

**Question**: A Novel Approach for Solving the Vehicle Routing Problem with Time Windows Using Ant Colony Optimization

**Documents**: Ant Colony Optimization for the Vehicle Routing Problem with Time Windows
In this paper, we present an ant colony optimization algorithm for the vehicle routing problem with time windows (VRPTW). This algorithm is based on the ant colony system (ACS) proposed by Dorigo et al. The ACS is a distributed algorithm inspired by the foraging behavior of real ants. In our algorithm, the ants construct solutions by moving on a graph where nodes represent customers and arcs represent the possibility of going from one customer to another. The ants use a pheromone trail and heuristic information to guide their construction of solutions. The pheromone trail is updated by the ants during the construction of solutions and by a pheromone update rule after the construction phase. We have tested our algorithm on a set of benchmark instances of the VRPTW. The results show that our algorithm is able to find good solutions in a reasonable amount of time.

**Similar Documents**: This paper introduces an innovative ant colony optimization algorithm specifically tailored for the vehicle routing problem with time windows (VRPTW). Inspired by the ant colony system (ACS) framework proposed by Dorigo and colleagues, our algorithm emulates the foraging strategies of real-world ants. In this approach, ants navigate a graph, with nodes symbolizing customers and arcs indicating potential routes between customers. Guided by pheromone trails and heuristic information, ants construct feasible solutions. The pheromone trail undergoes dynamic updates as ants construct solutions, and is further refined by a systematic pheromone update rule following the construction phase. To evaluate the efficacy of

In recent years, environmental considerations have prompted the use of wind power as a renewable energy resource. However, the biggest challenge in integrating wind power into the electric grid is its intermittency. One approach to deal with wind intermittency is forecasting future values of wind power production. Thus, several wind power or wind speed forecasting methods have been reported in the literature over the past few years. This paper provides insight on the foremost forecasting techniques, associated with wind power and speed, based on numeric weather prediction (NWP), statistical approaches, artificial neural network (ANN) and hybrid techniques over different time-scales. An overview of comparative analysis of various available forecasting techniques is discussed as well. In addition, this paper further gives emphasis on the major challenges and problems associated with wind power prediction.

In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4year historical sales data of a €60+ million turnover mediumto large-sized Italian fashion company, which operates in the women’s textiles apparel and clothing sectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.

Weather forecasting is the application of science and technology in order to predict the weather conditions. It is important for agricultural and industrial sectors. Models of Artificial Neural Networks with supervised learning paradigm are suitable for weather forecasting in complexity atmosphere. Training algorithm is required for providing weight and bias values to the model. This research proposed a weather forecasting method using Artificial Neural Networks trained by Fireworks Algorithm. Fireworks Algorithm is a recently developed Swarm Intelligence Algorithm for optimization. The main objective of the method is to predict daily mean temperature based on various measured parameters gained from the Meteorological Station, located in Bangkok. The experimental results indicate that the proposed method is advantageous for weather forecasting.

In this paper we work on the balance between hardware and software implementation of a machine learning algorithm, which belongs to the area of statistical learning theory. We use system-on-chip technology to demonstrate the potential usefulness of moving the critical sections of an algorithm into HW: the so-called hardware/software balance. Our experiments show that the approach can achieve speedups using a complex machine learning algorithm called a support vector machine. The experiments are conducted on a real-time Java virtual machine named Java optimized processor

In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x. Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter 0 < or = v < or = 1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and experimentally.

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction-based technique that combines a pattern matching approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-perform ant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques based on for example exponential smoothing.

Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. Ten units test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone.

In recent years, the shared bicycle project has developed rapidly. In use of shared bicycles, a great deal of user riding information is recorded. How to extract effective knowledge from these vast amounts of information, how to use this knowledge to improve the shared bicycle system, and how to improve the user experience, are problems to solve. Citi Bike is selected as the research target. Data on Citi Bike’s user historical behavior, weather information, and holiday information are collected from three different sources, and converted into appropriate formats for model training. Spark MLlib is used to construct three different predictive models, advantages and disadvantages of different forecasting models are compared. Some techniques are used to enhance the accuracy of random forests model. The experimental results show that the root mean square error RMSE of the final model is reduced from 305.458 to 243.346.

Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle taxis to passengers in need. To precisely balance the supply and the demand of taxis, online taxicab platforms need to predict the Unit Original Taxi Demand (UOTD), which refers to the number of taxi-calling requirements submitted per unit time (e.g., every hour) and per unit region (e.g., each POI). Predicting UOTD is non-trivial for large-scale industrial online taxicab platforms because both accuracy and flexibility are essential. Complex non-linear models such as GBRT and deep learning are generally accurate, yet require labor-intensive model redesign after scenario changes (e.g., extra constraints due to new regulations). To accurately predict UOTD while remaining flexible to scenario changes, we propose LinUOTD, a unified linear regression model with more than 200 million dimensions of features. The simple model structure eliminates the need of repeated model redesign, while the high-dimensional features contribute to accurate UOTD prediction. We further design a series of optimization techniques for efficient model training and updating. Evaluations on two large-scale datasets from an industrial online taxicab platform verify that LinUOTD outperforms popular non-linear models in accuracy. We envision our experiences to adopt simple linear models with high-dimensional features in UOTD prediction as a pilot study and can shed insights upon other industrial large-scale spatio-temporal prediction problems.

As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.