Question: A comprehensive challenge corpus for sentence comprehension via inference.

This paper presents a pioneering visualization method that elucidates the workings of Convolutional Network models, which have recently achieved remarkable classification accuracy on the ImageNet benchmark (Krizhevsky et al., 2012). Despite their success, the underlying reasons for their superior performance and potential avenues for enhancement remain obscure. Our study tackles these questions by introducing a visualization technique that offers insights into the intermediate feature layers and the classifier's operation. By leveraging these visualizations in a diagnostic capacity, we identify model architectures that surpass those of Krizhevsky et al. in ImageNet classification benchmarks. Additionally, we conduct an ablation analysis to quantify the performance impact of various model layers. Our findings reveal that the ImageNet model exhibits robust generalization capabilities, extending its superiority to other datasets. Specifically, when the softmax classifier is retrained, our model outperforms existing state-of-the-art results on the Caltech-101 and Caltech-256 datasets convincingly.

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.

We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FBNYT dataset.

We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account. We present a novel structured vector space model that addresses these issues by incorporating the selectional preferences for words’ argument positions. This makes it possible to integrate syntax into the computation of word meaning in context. In addition, the model performs at and above the state of the art for modeling the contextual adequacy of paraphrases.

A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system. We show that trainable sentence planning can produce output comparable to that of MATCH’s template-based generator even for quite complex information presentations.

Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.1

Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the existing methods encode two sequences with separate encoders, in which a sentence is encoded with little or no information from the other sentence. In this paper, we propose a deep architecture to model the strong interaction of sentence pair with two coupled-LSTMs. Specifically, we introduce two coupled ways to model the interdependences of two LSTMs, coupling the local contextualized interactions of two sentences. We then aggregate these interactions and use a dynamic pooling to select the most informative features. Experiments on two very large datasets demonstrate the efficacy of our proposed architecture and its superiority to state-ofthe-art methods.

This paper describes how language is grounded by a comprehension system called Lucia within a robotic agent called Rosie that can manipulate objects and navigate indoors. The whole system is built within the Soar cognitive architecture and uses Embodied Construction Grammar (ECG) as a formalism for describing linguistic knowledge. Grounding is performed using knowledge from the grammar itself, from the linguistic context, from the agent's perception, and from an ontology of long-term knowledge about object categories and properties and actions the agent can perform. The paper also describes a benchmark corpus of 200 sentences in this domain, along with test versions of the world model and ontology, and gold-standard meanings for each of the sentences. The benchmark is contained in the supplemental materials.

Supervised word sense disambiguation (WSD) systems are usually the best performing systems when evaluated on standard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and annotate a large number of samples and release them for public use, and also to evaluate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD system trained on our dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines.

An interactive Question Answering (QA) system frequently encounters non-sentential (incomplete) questions. These non-sentential questions may not make sense to the system when a user asks them without the context of conversation. The system thus needs to take into account the conversation context to process the incomplete question. In this work, we present a recurrent neural network (RNN) based encoder decoder network that can generate a complete (intended) question, given an incomplete question and conversation context. RNN encoder decoder networks have been show to work well when trained on a parallel corpus with millions of sentences, however it is extremely hard to obtain conversation data of this magnitude. We therefore propose to decompose the original problem into two separate simplified problems where each problem focuses on an abstraction. Specifically, we train a semantic sequence model to learn semantic patterns, and a syntactic sequence model to learn linguistic patterns. We further combine syntactic and semantic sequence models to generate an ensemble model. Our model achieves a BLEU score of 30.15 as compared to 18.54 using a standard RNN encoder decoder model.

In this paper, we propose a sequential neural encoder with latent structured description SNELSD for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory LSTM units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.


Question: A comprehensive test set for sentence comprehension via inference in a broad-coverage challenge corpus.

This paper explores the versatility of features derived from the activation layers of a deep convolutional neural network (DCNN) trained on a broad spectrum of object recognition tasks, in tackling a variety of generic visual recognition challenges. These generic tasks, which may substantially diverge from the original training objectives, often suffer from a scarcity of labeled or unlabeled data, rendering conventional adaptation or retraining of the DCNN impractical. We conduct an in-depth analysis and visualization of the semantic clustering properties of these deep convolutional features when applied to diverse tasks, encompassing scene classification, domain adaptation, and fine-grained recognition. We scrutinize the effectiveness of utilizing features from different network layers to establish a fixed representation, and present novel outcomes that markedly surpass state-of-the-art performance on a number of significant visual recognition benchmarks. To foster further research and experimentation with deep representations across a multitude of visual concept learning paradigms, we are making available DeCAF, an open-source implementation of these deep convolutional activation features, together with the associated network parameters.

We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FBNYT dataset.

This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation should lead to the most accurate assessment of sentiment in text. We call this automated process Human Reading for Sentiment (HRS). Previous research in sentiment analysis has produced many frameworks that can fit one or more of the HRS aspects; however, none of these methods has addressed them all in one approach. HRS provides a meta-framework for developing new sentiment analysis methods or improving existing ones. The proposed framework provides a theoretical lens for zooming in and evaluating aspects of any sentiment analysis method to identify gaps for improvements towards matching the human reading process. Key steps in HRS include the automation of humans low-level and high-level cognitive text processing. This methodology paves the way towards the integration of psychology with computational linguistics and machine learning to employ models of pragmatics and discourse analysis for sentiment analysis. HRS is tested with two state-of-the-art methods; one is based on feature engineering, and the other is based on deep learning. HRS highlighted the gaps in both methods and showed improvements for both.

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this thesis work, we explore how can we teach machines to ask clarification questions when faced with uncertainty, a goal of increasing importance in today’s automated society. We do a preliminary study using data from StackExchange, a plentiful online resource where people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We build neural network models inspired by the idea of the expected value of perfect information: a good question is one whose expected answer is going to be most useful. To build generalizable systems, we propose two future research directions: a template-based model and a sequence-to-sequence based neural generative model.

Language generation from purely semantic representations is a challenging task. This paper addresses generating English from the Abstract Meaning Representation (AMR), consisting of re-entrant graphs whose nodes are concepts and edges are relations. The new method is trained statistically from AMRannotated English and consists of two major steps: (i) generating an appropriate spanning tree for the AMR, and (ii) applying tree-tostring transducers to generate English. The method relies on discriminative learning and an argument realization model to overcome data sparsity. Initial tests on held-out data show good promise despite the complexity of the task. The system is available open-source as part of JAMR at: http://github.com/jflanigan/jamr

The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.

Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

Evaluating the output of NLG systems is notoriously difficult, and performin g assessments of text quality even more so. A range of automated and subject-based approaches to the evaluation of text quality h ave been taken, including comparison with a putative gold standard text, analysis of specific linguistic features of the output, expert review and task-based evaluation. In this paper we present the results of a variety of such approaches in the context of a case study application. We discuss the problems encountered in the implementation of each approach in the context of the literature, and propo se that a test based on the Turing test for machine intelligence offers a way forward in the evaluation of the subjective notion of text qua lity.

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.

This paper introduces a task of identifying and semantically classifying lexical expressions in running text. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether singleor multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1.


Question: A comprehensive challenge corpus for sentence comprehension via inference.

An Improved LSTM Model for Natural Language Inference
(1) Custom-engineered attributes [BAPM15] 78.2 (2) LSTM [BGR+16] 80.6 (3) GRU [VKFU15] 81.4 (4) Hierarchical CNN [MML+16] 82.1 (5) SPINN-PI [BGR+16] 83.2 (6) BiLSTM with Intralayer Attention [LSLW16] 84.2 (7) Neural Semantic Encoder [MY16a] 84.6 (8) Attention-augmented LSTM [RGH+15] 83.5 (9) Memory-augmented LSTM [WJ16] 86.1 (10) Nested LSTM [CDL16] 86.3 (11) Decomposable Attention [PTDU16] 86.3 (12) Intralayer Attention combined with (11) [PTDU16] 86.8 (13) Neural Tree-based Inference - Structured LSTM - LSTM [MY16b] 87.3 (14) Revisiting LSTM [SCSL16] 87.5 (15) Binary Tree LSTM [PAD+16] 87.6

This document outlines an advanced Long Short-Term Memory (LSTM) model for Natural Language Inference (NLI), a critical task in natural language understanding. NLI involves determining the relationship between a pair of sentences: whether one entails the other, contradicts it, or neither. The table above compares various models designed for NLI, each employing distinct techniques to process and analyze sentences. These models range from those utilizing handcrafted features, such as those developed by [BAPM15], which achieved an accuracy of 78.2%, to more sophisticated models like the Nested LSTM [CDL16] and Decomposable Attention [PTDU16], both achieving 86.3% accuracy. Notably, the Binary Tree LSTM [PAD+16] outperforms these models with an accuracy of 87.6%. The Neural Tree-based Inference combined with Structured LSTM and LSTM [MY16b] also performs exceptionally well, reaching 87.3% accuracy. This highlights the effectiveness

Extracting semantic relationships between entities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.

In this thesis, we present a novel approach for modeling textual entailment using lexicalsemantic information on the level of predicate-argument structure. To this end, we adopt information provided by the Berkeley FrameNet repository and embed it into an implemented end-to-end system. The two main goals of this thesis are the following: (i) to provide an analysis of the potential contribution of frame semantic information to the recognition textual entailment and (ii) to present a robust system architecture that can serve as basis for future experiments, research, and improvement. Our work was carried out in the context of the textual entailment initiative, which since 2005 has set the stage for the broad investigation of inference in natural-language processing tasks, including empirical evaluation of its coverage and reliability. In short, textual entailment describes inferential relations between (entailing) texts and (entailed) hypotheses as interpreted by typical language users. This pre-theoretic notion captures a natural range of inferences as compared to logical entailment, which has traditionally been used within theoretical approaches to natural language semantics. Various methods for modeling textual entailment have been proposed in the literature, ranging from shallow techniques like lexical overlap to shallow syntactic parsing and the exploitation of WordNet relations. Recently, there has been a move towards more structured meaning representations. In particular, the level of predicate-argument structure has gained much attention, which seems to be a natural and straightforward choice. Predicate-argument structure allows annotating sentences or texts with nuclear meaning representations (“who did what to whom”), which are of obvious relevance for this task. For example, it can account for paraphrases like “Ghosts scare John” vs. “John is scared by ghosts”. In this thesis, we present an approach to textual entailment that is centered around the analysis of predicate-argument structure. It combines LFG grammatical analysis, predicate-argument structure in the FrameNet paradigm, and taxonomic information from WordNet into tripartite graph structures. By way of a declarative graph matching algorithm, the “structural and semantic” similarity of hypotheses and texts is computed and the result is represented as feature vectors. A supervised machine learning architecture trained on entailment corpora is used to check textual entailment for new text/hypothesis pairs. The approach is implemented in the SALSA RTE system, which successfully participated in the second and third RTE challenge. While system performance is on a par with that of comparable systems, the intuitively expected strong positive effect of using FrameNet information has not yet been confirmed. In order to evaluate different system components and to assess the potential contribution of FrameNet information for checking textual entailment, we conducted a number of experiments. For example, with the help of a gold-standard corpus, we

This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.

Natural language processing systems have the ability to analyse not only the sentiment of human language, but also the stance of the speaker. Representing this information visually from unevenly distributed and potentially sparse datasets is challenging, in particular when trying to facilitate exploration and knowledge discovery. We present work on a novel visualisation approach for scalable visualisation of sentiment and stance and provide a language resource of e-government public engagement of 9,278 user comments with stance explicitly declared by the author.

Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.

We present a method for generating English sentences from Abstract Meaning Representation (AMR) graphs, exploiting a parallel corpus of AMRs and English sentences. We treat AMR-to-English generation as phrase-based machine translation (PBMT). We introduce a method that learns to linearize tokens of AMR graphs into an English-like order. Our linearization reduces the amount of distortion in PBMT and increases generation quality. We report a Bleu score of 26.8 on the standard AMR/English test set.

In this paper, we present a system that automatically generates questions from natural language text using discourse connectives. We explore the usefulness of the discourse connectives for Question Generation (QG) that looks at the problem beyond sentence level. Our work divides the QG task into content selection and question formation. Content selection consists of finding the relevant part in text to frame question from while question formation involves sense disambiguation of the discourse connectives, identification of question type and applying syntactic transformations on the content. The system is evaluated manually for syntactic and semantic correctness.

Several recent approaches use distributional similarity for making symbolic reasoning more flexible. While an important step in the right direction, the use of similarity has a number of inherent limitations. We argue that similarity-based reasoning should be complemented with commonsense reasoning patterns such as interpolation and a fortiori inference. We show how the required background knowledge for these inference patterns can be obtained from distributional models.

Cloze-style queries are representative problems in reading comprehension. Over the past few months, we have seen much progress that utilizing neural network approach to solve Cloze-style questions. In this paper, we present a novel model called attention-over-attention reader for the Cloze-style reading comprehension task. Our model aims to place another attention mechanism over the document-level attention, and induces “attended attention” for final predictions. Unlike the previous works, our neural network model requires less pre-defined hyper-parameters and uses an elegant architecture for modeling. Experimental results show that the proposed attentionover-attention model significantly outperforms various state-of-the-art systems by a large margin in public datasets, such as CNN and Children’s Book Test datasets.

The two current approaches to language generation, Template-based and rule-based (linguistic) NLG, have limitations when applied to spoken dialogue systems, in part because they were developed for text generation. In this paper, we propose a new corpus-based approach to natural language generation, specifically designed for spoken dialogue systems.


Question: A comprehensive test set for sentence comprehension via inference, designed to address broad-coverage challenges.

This paper introduces the Dynamic Convolutional Neural Network (DCNN), a novel convolutional architecture designed for the semantic analysis of sentences, a critical component in language comprehension. The DCNN employs Dynamic k-Max Pooling, a technique that performs a global pooling operation over linear sequences, enabling it to process sentences of varying lengths. This architecture generates a feature graph over the sentence that can explicitly identify both short and long-range relationships, without the need for a parse tree, making it versatile for any language. The effectiveness of the DCNN is evaluated through four distinct experiments: binary and multi-class sentiment prediction on a small scale, six-way question classification, and sentiment prediction on Twitter data through distant supervision. In the first three tasks, the network demonstrates outstanding performance, and in the Twitter sentiment prediction task, it achieves a significant improvement of more than 25% in error reduction compared to the best-performing baseline.

The mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches—meaning distributed representations that exploit co-occurrence statistics of large corpora—have proved popular and successful across a number of tasks. However, natural language usually comes in structures beyond the word level, with meaning arising not only from the individual words but also the structure they are contained in at the phrasal or sentential level. Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is an equally fundamental task of NLP. This dissertation explores methods for learning distributed semantic representations and models for composing these into representations for larger linguistic units. Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks in natural language processing. The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP. Part I focuses on distributed representations and their application. In particular, in Chapter 3 we explore the semantic usefulness of distributed representations by evaluating their use in the task of semantic frame identification. Part II describes the transition from semantic representations for words to compositional semantics. Chapter 4 covers the relevant literature in this field. Following this, Chapter 5 investigates the role of syntax in semantic composition. For this, we discuss a series of neural network-based models and learning mechanisms, and demonstrate how syntactic information can be incorporated into semantic composition. This study allows us to establish the effectiveness of syntactic information as a guiding parameter for semantic composition, and answer questions about the link between syntax and semantics. Following these discoveries regarding the role of syntax, Chapter 6 investigates whether it is possible to further reduce the impact of monolingual surface forms and syntax when attempting to capture semantics. Asking how machines can best approximate human signals of semantics, we propose multilingual information as one method for grounding semantics, and develop an extension to the distributional hypothesis for multilingual representations. Finally, Part III summarizes our findings and discusses future work.

We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.

We present a new approach for documentlevel sentiment inference, where the goal is to predict directed opinions (who feels positively or negatively towards whom) for all entities mentioned in a text. To encourage more complete and consistent predictions, we introduce an ILP that jointly models (1) sentenceand discourse-level sentiment cues, (2) factual evidence about entity factions, and (3) global constraints based on social science theories such as homophily, social balance, and reciprocity. Together, these cues allow for rich inference across groups of entities, including for example that CEOs and the companies they lead are likely to have similar sentiment towards others. We evaluate performance on new, densely labeled data that provides supervision for all pairs, complementing previous work that only labeled pairs mentioned in the same sentence. Experiments demonstrate that the global model outperforms sentence-level baselines, by providing more coherent predictions across sets of related entities.

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.

This thesis is about the problem of representing sentential meaning in distributional semantics. Distributional semantics obtains the meanings of words through their usage, based on the hypothesis that words occurring in similar contexts will have similar meanings. In this framework, words are modeled as distributions over contexts and are represented as vectors in high dimensional space. Compositional distributional semantics attempts to extend this approach to higher linguistics structures. Some basic composition models proposed in literature to obtain the meaning of phrases or possibly sentences show promising results in modeling simple phrases. The goal of the thesis is to further extend these composition models to obtain sentence meaning representations. The thesis puts more focus on unsupervised methods which make use of the context of phrases and sentences to optimize the parameters of a model. Three different methods are presented. The first model is the PLF model, a practical composition and linguistically motivated model which is based on the lexical function model introduced by Baroni and Zamparelli (2010) and Coecke et al. (2010). The second model is the Chunk-based Smoothed Tree Kernels (CSTKs) model, extending Smoothed Tree Kernels (Mehdad et al., 2010) by utilizing vector representations of chunks. The final model is the C-PHRASE model, a neural network-based approach, which jointly optimizes the vector representations of words and phrases using a context predicting objective. The thesis makes three principal contributions to the field of compositional distributional semantics. The first is to propose a general framework to estimate the parameters and evaluate the basic composition models. This provides a fair way to comparing the models using a set of phrasal datasets. The second is to extend these basic models to the sentence level, using syntactic information to build up the sentence vectors. The third contribution is to evaluate all the proposed models, showing that they perform on par with or outperform competing models presented in the literature. Thesis Supervisor: Marco Baroni Title: Associate Professor

Syntactic simplification is the process of reducing the grammatical complexity of a text, while retaining its information content and meaning. The aim of syntactic simplification is to make text easier to comprehend for human readers, or process by programs. In this thesis, I describe how syntactic simplification can be achieved using shallow robust analysis, a small set of hand-crafted simplification rules and a detailed analysis of the discourse-level aspects of syntactically rewriting text. I offer a treatment of relative clauses, apposition, coordination and subordination. I present novel techniques for relative clause and appositive attachment. I argue that these attachment decisions are not purely syntactic. My approaches rely on a shallow discourse model and on animacy information obtained from a lexical knowledge base. I also show how clause and appositive boundaries can be determined reliably using a decision procedure based on local context, represented by part-of-speech tags and noun chunks. I then formalise the interactions that take place between syntax and discourse during the simplification process. This is important because the usefulness of syntactic simplification in making a text accessible to a wider audience can be undermined if the rewritten text lacks cohesion. I describe how various generation issues like sentence ordering, cue-word selection, referring-expression generation, determiner choice and pronominal use can be resolved so as to preserve conjunctive and anaphoric cohesive-relations during syntactic simplification. In order to perform syntactic simplification, I have had to address various natural language processing problems, including clause and appositive identification and attachment, pronoun resolution and referring-expression generation. I evaluate my approaches to solving each problem individually, and also present a holistic evaluation of my syntactic simplification system.

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.

ConceptNet is a very large semantic network of commonsense knowledge suitable for making various kinds of practical inferences over text. ConceptNet captures a wide range of commonsense concepts and relations like those in Cyc, while its simple semantic network structure lends it an ease-of-use comparable to WordNet. To meet the dual challenge of having to encode complex higher-order concepts, and maintaining ease-of-use, we introduce a novel use of semi-structured natural language fragments as the knowledge representation of commonsense concepts. In this paper, we present a methodology for reasoning flexibly about these semi-structured natural language fragments. We also examine the tradeoffs associated with representing commonsense knowledge in formal logic versus in natural language. We conclude that the flexibility of natural language makes it a highly suitable representation for achieving practical inferences over text, such as context finding, inference chaining, and conceptual analogy. 1 What is ConceptNet? ConceptNet (www.conceptnet.org) is the largest freely available, machine-useable commonsense resource. Structured as a network of semi-structured natural language fragments, ConceptNet presently consists of over 250,000 elements of commonsense knowledge. We were inspired dually by the range of commonsense concepts and relations in Cyc (Lenat, 1995), and by the ease-of-use of WordNet (Fellbaum, 1998), and hoped to combine the best of both worlds. As a result, we adopted the semantic network representation of WordNet, but extended the representation in several key ways. First, we extended WordNet’s lexical notion of nodes to a conceptual notion of nodes, but we kept the nodes in natural language, because one of the primary strengths of WordNet in the textual domain is that its knowledge representation is itself textual. ConceptNet’s nodes are thus natural language fragments which are semi-structured according to an ontology of allowable syntactic patterns, and accommodate both first-order concepts given as noun phrases (e.g. “potato chips”), and second-order concepts given as verb phrases (e.g. “buy potato chips”). Second, we extended WordNet’s small ontology of semantic relations, which are primarily taxonomic in nature, to include a richer set of relations appropriate to concept-level nodes. At present there are 19 semantic relations used in ConceptNet, representing categories of, inter alia, temporal, spatial, causal, and functional knowledge. By combining higher order nodes with this relational ontology, it is possible to represent richer kinds of knowledge in ConceptNet beyond what can be represented in WordNet (Fig 1.). For example, we can represent a layman’s common sense observation that “you may be hurt if you get into an accident” in ConceptNet as EffectOf(“get into accident”, “be hurt”). Note that because the knowledge representation is semi-structured natural language, there are often various ways to represent the same knowledge. This is a source of ambiguity, but as we will argue in this paper, maintaining some ambiguity lends us greater flexibility for reasoning.

This work uses parallel monolingual corpora for a detailed study of the task of sub-sentential paraphrase acquisition. We argue that the scarcity of this type of resource is compensated by the fact that it is the most suited type for studies on paraphrasing. We propose a large exploration of this task with experiments on two languages with five different acquisition techniques, selected for their complementarity, their combinations, as well as four monolingual corpus types of varying comparability. We report, under all conditions, a significant improvement over all techniques by validating candidate paraphrases using a maximum entropy classifier. An important result of our study is the identification of difficult-to-acquire paraphrase pairs, which are classified and quantified in a bilingual typology.

We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.


Question: Optimal design and analysis of tradeoffs for planar transformers in high-power DC-DC converters.

This document presents an enhanced methodology for accurately predicting ferrite core losses when dealing with nonsinusoidal waveforms, utilizing solely the Steinmetz parameters. The proposed approach introduces a novel recursive algorithm that divides the flux trajectory into major and minor loops, significantly enhancing the precision of the loss calculation. Compared to two existing techniques, our method demonstrates superior performance based on empirical data. Notably, the algorithm's effectiveness is achieved using only the conventional Steinmetz equation parameters, which are standard material characteristics. This innovation offers a more refined and efficient way to estimate core losses in ferrite components, particularly under conditions involving nonsinusoidal signals, thus contributing to the optimization of high-frequency power electronics designs.

In this paper, a new soft switching flyback dc-dc converter with a simple zero-voltage and zero-current switching (ZVZCS) cell is proposed. The auxiliary resonant cell consists of one switch, one diode, one inductor, and two capacitors. It provides ZVZCS not only for the main switch but also for the auxiliary switch at both turn-on and turn-off times. In addition, all the auxiliary diodes and body diodes of the switches operate under full ZVZCS except the output diode which turns off only with zero current switching. This soft switching technique makes the proposed converter have less switching losses which leads the converter to operate in higher frequencies and efficiency. Another outstanding feature of the proposed converter is using the auxiliary resonant cell out of the main power path to have lower voltage stress on the switches. In this paper, pulse width modulation (PWM) is employed to control the main switch. A detailed mode analysis of the proposed converter operation is presented along with design procedure. Finally, the accuracy performance of the converter is verified through a 60 W experimental prototype results.

This paper presents the description and analysis of a phase-shift-modulated full-bridge converter with a novel robust passive low-rms-current resonant auxiliary circuit for zero-voltage switching (ZVS) operation of both the leading and lagging switch legs. Detailed time-domain analysis describes the steady-state behavior of the auxiliary circuit in different operating conditions. An in-depth comparative study between a fully specified baseline converter and the equivalent converter using the proposed resonant auxiliary circuit is presented. For a similar peak auxiliary current to ensure ZVS operation, a minimum of 20% reduction in rms current is obtained, which decreases the conduction losses. Key characteristics and design considerations are also fully discussed. Experimental results from a 750-W prototype confirm the predicted enhancements using the proposed resonant auxiliary circuit.

This paper presents a detailed comparison of the high-speed operating characteristics of four synchronous PM machines for applications that require wide speed ranges of constant-power operation. These machines include surface PM machines with both distributed and fractional-slot concentrated windings, and two interior PM machine with distributed windings. These two versions of the interior PM machine include one with and a tight constraint on the machine's back-emf voltage at top speed and one without this constraint The target application is an automotive direct-drive starter/alternator requiring a very wide 10:1 constant power speed ratio (CPSR). Detailed comparisons of the performance characteristics of the machines are presented that include important issues such as the back-emf voltage at top speed, machine mass and cost, and eddy current losses in the magnets. Analytical results are verified using finite element analysis (FEA). Guidelines are developed to help drive designers decide which type of machine is most suitable for high-CPSR applications. Tradeoffs associated with choosing each of these machines are presented.

Operating a bridge-type PWM (pulse width modulation) switch mode power converter with asymmetrical duty ratios can eliminate switching losses with no increase in conduction loss. This circuit topology combines the best features of resonant (zero switching loss) and switch mode (low conclusion loss) circuits. Design equations are presented for just such a circuit.<<ETX>>

This paper is the 2nd part of the survey titled “High Voltage Direct Current Transmission - A Review, Part I”. The main converter technologies and HVDC systems' components will be discussed in this complementary paper.

The hybrid integration of an unipolar and a bipolar power semiconductor in parallel combines their advantages whilst avoiding their disadvantages. The drawback of this approach is an increased complexity of the gate drive. This paper proposes a simple hardware solution utilising a transductor to transmit both drive and control energy together with a slow optocoupler providing an enable signal for the respective hybrid switch. Questions regarding the choice of an optimal switching frequency are addressed

A novel multi-stack CMOS device structure is proposed, the operation of the structure is fully analyzed for high power CMOS switch design. The structure is also implemented in a standard 0.18-um triple-well CMOS process, and its performance is fully characterized. The proposed switch device incorporates multi-stack NMOS switches, one of which has a switch at the bulk and the others of which have a connection between the bulk and the source in order to provide high power handling capability to the transmit switch side. In order to demonstrate the improvement of power handling capability, performance of the conventional structure and the proposed structure are fully analyzed, compared from various simulation results, and verified with measurement results in detail. Experimental data show that the isolation of the proposed test structure is 25 dB higher at 30 dBm input power level than that of conventional structures, which was caused by the significant reduction of leakage current of the switch device in OFF state. In addition, insertion loss of the Rx switch can be maintained as 1.5 dB by applying body switching technique in 900 MHz.

The rapidly growing use of digital control in Distributed Power Architectures (DPA) has been primarily driven by the increasing need of sophisticated Power Management functions. However, specifically designed control algorithms can also be exploited for optimizing the system performance. In this paper we present a new auto-tuning method and system that makes it possible to operate an isolated DC-DC converter at maximum efficiency. The auto-tuning performs an optimum adjustment of both primary side dead time and secondary side conduction times based on the measurement of the input current. It does not require any additional external components since current sensing functions are already implemented for power management purposes. Experimental measurements performed on an asymmetrical driven half-bridge DC-DC converter demonstrate the effectiveness of our solution and its robustness to component variations.

This work reports on the design and test of a CMOS-based Switched Capacitor (SC) DC-DC conversion integrated circuit (IC) for the point-of-load (POL) application conventionally addressed with the buck converter. A 12V-to-1.5V converter is fabricated in a 0.18µm technology, with an active die area of 3mm2. There is a significant reduction in printed circuit board (PCB) footprint, passive component height and cost when compared to surveyed buck converters. The converter achieves 93% peak efficiency and an efficiency of 80% over an output current range of 7mA to 1A. This work demonstrates the vast potential of SC converters for wide-range voltage conversion in deep-submicron CMOS technologies, when cost and efficiency are of critical importance.

This paper analyzes the two-way Wilkinson power dividers using coupled lines as λ/4 impedance transformer to have more compact layout. The analysis shows that the input port matching is only influenced by the even mode impedance of the coupled lines. Once the even mode impedance is fixed, all other specifications of the power divider are determined by the odd mode impedance of the coupled lines. Design tradeoffs among the output matching, isolation and the odd mode impedance are shown. By using smaller odd mode impedance, layout constraint can be relaxed. To validate the analysis, Wilkinson power dividers using coupled lines with areas of 1.5 cm2 and 1.25 cm2 are designed together with a conventional divider with an area of 3.8 cm2. The experimental results of the three designs are comparable. When coupled lines are used, the layout is more compact with a reduction in size of more than 50% compared to the conventional design.


Question: Optimal design and analysis of tradeoffs for planar transformers in high-power DC-DC converters.

This article delves into the impact of various parallel winding strategies for planar transformers, specifically focusing on the one-dimensional (1D) field approach. A specialized software tool has been created to accurately assess the alternating current (AC) losses and leakage inductance associated with these winding configurations. The article also addresses the issue of uneven current distribution across parallel layers, exploring the underlying reasons for this phenomenon and proposing solutions to ensure more balanced current sharing.

In the context of planar transformer design for high-power DC-DC converters, the article highlights the significance of considering different paralleling strategies. By adopting a 1D field analysis, the software developed can effectively calculate the AC loss and leakage inductance, which are critical parameters for optimizing transformer performance. The uneven current sharing among parallel layers is a common problem that can lead to reduced efficiency and reliability. The article critically analyzes this issue, providing insights into its root causes, such as winding design and magnetic field interactions. It also suggests practical approaches to mitigate this effect, aiming to achieve more uniform current distribution and thus enhance the overall efficiency of the transformer in high-power applications.

Modern high-frequency induction heating technology puts forward higher requirements for load matching of the induction heating power supply. LLC resonant load circuit can replace the traditional induction heating load matching transformer, and increase power density. This paper analyzes the load matching characteristics of LLC load resonant circuit in high frequency induction heating power supply, and gives the parameter selection method of matching components. At last, combining with phase-shifting pulse-width modulation (PWM) control method, the phase-shifting induction heating experiment platform based on LLC load resonant circuit is established. Experimental results show that the performance of LLC circuit in high frequency induction heating power supply load matching is excellent.

This paper presents a high step-down tranformerless single-stage single-switch ac/dc converter suitable for universal line applications (90-270 Vrms) . The topology integrates a buck-type power-factor correction (PFC) cell with a buck-boost dc/dc cell and part of the input power is coupled to the output directly after the first power processing. With this direct power transfer feature and sharing capacitor voltages, the converter is able to achieve efficient power conversion, high power factor, low voltage stress on intermediate bus (less than 130 V) and low output voltage without a high step-down transformer. The absence of transformer reduces the component counts and cost of the converter. Unlike most of the boost-type PFC cell, the main switch of the proposed converter only handles the peak inductor current of dc/dc cell rather than the superposition of both inductor currents. Detailed analysis and design procedures of the proposed circuit are given and verified by experimental results.

This paper analyzes the two-way Wilkinson power dividers using coupled lines as λ/4 impedance transformer to have more compact layout. The analysis shows that the input port matching is only influenced by the even mode impedance of the coupled lines. Once the even mode impedance is fixed, all other specifications of the power divider are determined by the odd mode impedance of the coupled lines. Design tradeoffs among the output matching, isolation and the odd mode impedance are shown. By using smaller odd mode impedance, layout constraint can be relaxed. To validate the analysis, Wilkinson power dividers using coupled lines with areas of 1.5 cm2 and 1.25 cm2 are designed together with a conventional divider with an area of 3.8 cm2. The experimental results of the three designs are comparable. When coupled lines are used, the layout is more compact with a reduction in size of more than 50% compared to the conventional design.

This paper presents the design and implementation of a series resonant inverter operating at high frequency for induction heating applications. The main advantage of this work is focused in applying power electronics, particularly a series resonant inverter based on an IGBT's full bridge and a series RLC Load matched with a high frequency coupling transformer. The series resonant inverter is designed to deliver a current load up to 120A with a one phase AC source. Due to the inherent high speed operation and high efficiency of switching converters, other advantages of the induction furnace presented in this paper are that it require less time to start melting, and require less physical space than other traditional furnaces.

This paper presented the design of a capacitive power transfer (CPT) system by implementing a Class-E inverter due to its high efficiency, which in theory can reach close to 100% performance. However, the Class-E inverter is highly sensitive to its circuit parameters under the scenario of having small capacitance at the coupling plate. As a solution, an additional capacitor can be integrated into the Class-E inverter for increased coupling capacitance for a better performance. Both simulation and experimental investigations were carried out to verify the high efficiency CPT system based on Class-E inverter with additional capacitor. The outcome of the investigation exhibited 96% of overall DC-DC power transfer efficiency with 0.97 W output at 1MHz operating frequency.

This paper presents a dc–dc boost converter topology for low input and high output voltage applications, such as photovoltaic systems, fuel cell systems, high-intensity discharge lamp, and electric vehicles. The suggested configuration consists of a three-winding coupled-inductor, a single switch and two hybrid voltage multiplier cells. Furthermore, two independent hybrid voltage multiplier cells are in parallel when the single switch S is turned on, and they are in series when the switch S is turned off. So, the advantages of the proposed converter structure are summarized as follows: 1) A coupled inductor with three windings is introduced in the presented converter structure. The two secondary windings of the coupled inductor are, respectively, used to form a hybrid multiplier cell on the one hand, on the other hand, it increases the control freedom of the voltage gain, enhances the utility rate of magnetic core and power density, and reduces the stress of power components to provide a stable constant dc output voltage. 2) The two hybrid multiplier cells can absorb synchronously the energy of stray inductance, which not only reduces the current stress of corresponding diodes, but also greatly alleviates the spike voltage of the main switch, which improves the efficiency. 3) The two hybrid multiplier cells are connected in series to supply power energy for the load, so the voltage gain is extended greatly due to this particular structure. Thus, low-voltage low-conduction-loss devices can be selected and the reverse-recovery currents within the diodes are inhibited. The operating principles and the steady state analyses of the proposed converter are discussed in detail. Finally, a test prototype has been implemented in the laboratory, and the simulated and experimental results show satisfactory agreement with the theoretical analysis.

In this paper, the main concepts related to the development of a single-stage three-phase high power factor rectifier, with high-frequency isolation and regulated DC bus are described. The structure operation is presented, being based on the DC-DC SEPIC converter operating in the discontinuous conduction mode. This operational mode provides to the rectifier a high power factor feature, with sinusoidal input current, without the use of any current sensors and current loop control. A design example and simulation results are presented in order to validate the theoretical analysis.

An inductively coupled contactless dc connector has been proposed for the next-generation 380-V dc distribution system in data centers. A LLC resonant dc-dc converter topology with gallium nitride (GaN) power transistors has been applied to realize the short-distance highly efficient contactless power transfer. A prototype of a 1.2-kW 384- to 192-V connector has been fabricated and the conversion efficiency of over 95% with the power density of 8.1 W/cm3 has been confirmed experimentally under 1000-kHz operation. The design consideration has been carried out and the potential to achieve 10.0 W/cm3 has been also shown taking the feature of the GaN power device and the characteristics of the magnetic core material for the transformer into account. The contactless dc connector integrates the functioning of an isolated dc-dc converter into a connector for space saving, and the dc current can be cut off without arc because of the inductive coupling. The proposed connector contributes to realizing a highly efficient, space saving, and reliable future 380-V dc distribution system.

A power converter is very necessary in the system of high-speed permanent magnet generators (HSPMG), since the frequency of the output electrical energy is generally hundreds of Hertz or even more than 1000 Hz. However, the converter with switches in fast on-off transitions will have some effects on the generator and, therewith, some current harmonics will be also induced in the device. Taking a 117 kW 60 000 r/min HSPMG as an example, the harmonics in this generator were analyzed. And then, the voltage harmonic content, the current harmonic content, and the current total harmonic distortion could be obtained. In addition, stator core losses and rotor eddy current losses were analyzed comparatively, and the influence of the converter on the generator power factor was further studied. Based on the 3-D coupling field between fluid and temperature, the temperature distributions were obtained when the generator was connected to different loads, and the influence of the rectifier on the generator temperature field could be obtained, which was also compared with the test data.

The eGaN HEMTs with ZVS technique have emerged in high frequency converters to reduce the switching loss. This paper investigates an eGaN HEMT ZVS isolated SEPIC converter running up to 2 MHz. Furthermore, an inductor optimization method is proposed to ensure ZVS and reduce reverse conduction loss of the SR. The efficiency curve is optimized based on the trade-off method between the switching and conduction losses. Moreover, the embedded inductor is proposed to reduce the conduction loss of the inductor. Finally, two 2 MHz eGaN HEMT prototypes with the commercial and embedded inductor with 18–36 V input and 5 V/2 A output are built respectively to verify the effectiveness of the proposed methods. The converters achieve a peak efficiency of over 88.2% and power density of 52.9 W/in3, outperforming the state-of-art power module products.


Question: Optimal design and analysis of tradeoffs for planar transformers in high-power DC-DC converters.

This paper introduces a novel dynamic core loss model tailored for soft ferromagnetic and power ferrite materials, designed to accurately predict core loss under various flux waveforms. The model relies on standard core loss coefficients, which can be sourced directly from manufacturers or derived from loss curves associated with sinusoidal excitation. Its versatility is demonstrated through application in both two-dimensional and three-dimensional transient finite element analysis, with outcomes validated against empirical data, showcasing its precision and reliability in estimating core losses in diverse materials and conditions. This dynamic core loss model offers a significant step forward in the analysis and optimization of magnetic components in high-frequency power electronics, enabling more accurate predictions and improved design efficiency.

A family of switched-capacitor resonant circuits using only two transistors is presented. The circuit operates under zero-current switching and, therefore, the switching loss is zero. It also offers a wide choice of voltage conversions including fractional as well as multiple and inverted voltage conversion ratios.

This paper presents an improved broadband balun using a coplanar-waveguide(CPW)-to-slotline field transformation. It operates at very wide frequency, and is of compact size since it does not depend on a resonant structure. The measured results show a passband of 200 MHz to 2 GHz, insertion loss less than 0.75 dB and a size of 20 mm × 14 mm. The amplitude imbalance is approximately 0.3 dB and the phase imbalance is less than 6o over the entire operation range.

LLC resonant converter is widely used in industry. However, up to now, no simple and accurate small-signal equivalent circuit model is available. This paper proposes an equivalent circuit model of LLC resonant converter. The simple equivalent circuit model is derived based on modification and simplification of extended describing function method. The model can well predicts the small-signal behaviors observed in LLC resonant converter, whenever switching frequency is below, close to or above the resonant frequency. For the first time, analytical expressions for control to output voltage, input to output voltage, input impedance and output impedance are provided to aid close loop feedback design. Simplis simulation and experimental results are presented to prove the accuracy of the model.

A design methodology to transmit power using a chip-to-chip wireless interface is proposed. The proposed power transmission system is based on magnetic coupling, and the power transmission of 5mW/mm2 was verified. The transmission efficiency trade-off with the transmitted power is also discussed.

This paper proposes the design and implementation of a high power full bridge bidirectional isolated DC-DC converter (BIDC) which comprises of two symmetrical voltage source converters and a high frequency transformer. In the proposed BIDC, a well-known PI controller based single phase-shift (SPS) modulation technique is used in order to achieve high power transfer. Besides, different phase shift methods such as extended phase-shift (EPS) and double phase-shift (DPS) are compared with SPS. Both simulation and experimental results are caried out to verify PI controller based simple phase-shift controlled BIDC prototype that is designed for 300-V 2.4-kW and operating at 20 kHz.

This paper presents the description and analysis of a phase-shift-modulated full-bridge converter with a novel robust passive low-rms-current resonant auxiliary circuit for zero-voltage switching (ZVS) operation of both the leading and lagging switch legs. Detailed time-domain analysis describes the steady-state behavior of the auxiliary circuit in different operating conditions. An in-depth comparative study between a fully specified baseline converter and the equivalent converter using the proposed resonant auxiliary circuit is presented. For a similar peak auxiliary current to ensure ZVS operation, a minimum of 20% reduction in rms current is obtained, which decreases the conduction losses. Key characteristics and design considerations are also fully discussed. Experimental results from a 750-W prototype confirm the predicted enhancements using the proposed resonant auxiliary circuit.

Whenever an electrical machine is meant to be fed by a power converter, the design should be approached as a system optimization, more than a simple machine sizing. A great variety of electrical machines is available to accomplish this goal, and the task of comparing the different options can be very difficult. A general purpose sizing equation, easily adjustable for every topology, that could take into account different waveforms and machine characteristics, would be a very desirable tool. In this paper, a general approach is presented to develop and to discuss such an equation. Sample applications of the sizing and power density equations are utilized to compare the induction machine and the doubly salient permanent magnet (DSPM) machine.

This paper presents the design and implementation of a 40-kV, 20-kJ/s high-voltage capacitor charger based on a series/parallel resonant converter. The inclusion of the parallel resonant capacitor component in this circuit results in the production of a trapezoidal resonant current, which reduces conduction loss. This capacitor is practically realized as a part of the balancing network of the high-voltage rectifiers in the secondary side of the circuit. Particular attention in this paper is paid to the high-voltage transformer, which must be carefully designed to provide the required functionality without negative impact on the resonant circuit. A PSpice simulation has been used to prove that the proposed control method for the circuit is valid. This is supported with the experimental results, which verify that the operation of the circuit is as expected and that the converter is able to meet all the design criteria with an efficiency of up to 96%.

A novel DC/AC current sensor works on the fluxgate principle. The core in the form of a 7/10-mm ring made of electrodeposited permalloy is sandwiched in the middle of a printed circuit board (PCB), whereas the sensor excitation winding is also integrated in the copper layers of the PCB. To lower the sensor power consumption, the excitation winding was tuned by a parallel capacitor. The peak-peak/rms ratio of 5.2 was achieved for the excitation frequency of 25 kHz. The open-loop sensor has 100-mV/A sensitivity; the characteristics have a linearity error of 10% and hysteresis below 0.1% in the 1-A range.

Detailed analysis and parameter design for a resistorcapacitor-diode (RCD) clamped snubber used in the output rectifier of phase-shift full-bridge zero-voltage-switching (PS-FB-ZVS) converters are presented. Design equations and some properties of the clamped circuit are highlighted.


Question: Optimal design strategy and tradeoffs evaluation for planar transformers in high-power DC-DC converters.

This paper introduces an innovative isolated current-fed pulsewidth modulation (PWM) DC-DC converter, the Current-Fed Dual-Bridge DC-DC Converter, which boasts a reduced inductance and eliminates the need for deadtime operation. Compared to the conventional current-fed full-bridge converter, this novel topology exhibits over three times less inductance, thereby significantly enhancing transient response speed. Additional features include simplified self-driven synchronous rectification, an uncomplicated housekeeping power supply, and minimized output filter capacitance. Comprehensive analysis reveals that the proposed converter can either decrease voltage stress on all primary side power switches or achieve soft switching, depending on the applied driving scheme. To validate the theoretical concepts, a 48-V/125-W dual-output prototype DC-DC converter was constructed and tested. Experimental results, corroborated by simulations, confirm the practicality and superior attributes of the proposed topology.

This new topology, with its minimized inductance and absence of deadtime operation, represents a significant advancement in current-fed DC-DC converter design. Its reduced inductance translates to faster transient response speeds, a critical factor in high-performance applications. The simplified self-driven synchronous rectification and housekeeping power supply contribute to a streamlined design, reducing overall complexity and potentially lowering costs. The decreased output filter capacitance further optimizes the converter's efficiency and size. By offering either reduced voltage stress on primary side power switches or soft switching capabilities, the converter demonstrates versatility in addressing different operational requirements. The successful testing of a 48-V/125-W dual-output prototype converter validates the theoretical predictions, showcasing the practical benefits and advantages of the proposed topology.

This paper presents a detailed comparison of the high-speed operating characteristics of four synchronous PM machines for applications that require wide speed ranges of constant-power operation. These machines include surface PM machines with both distributed and fractional-slot concentrated windings, and two interior PM machine with distributed windings. These two versions of the interior PM machine include one with and a tight constraint on the machine's back-emf voltage at top speed and one without this constraint The target application is an automotive direct-drive starter/alternator requiring a very wide 10:1 constant power speed ratio (CPSR). Detailed comparisons of the performance characteristics of the machines are presented that include important issues such as the back-emf voltage at top speed, machine mass and cost, and eddy current losses in the magnets. Analytical results are verified using finite element analysis (FEA). Guidelines are developed to help drive designers decide which type of machine is most suitable for high-CPSR applications. Tradeoffs associated with choosing each of these machines are presented.

We present a method for optimizing and automating component and transistor sizing for CMOS operational amplifiers. We observe that a wide variety of performance measures can be formulated as posynomial functions of the design variables. As a result, amplifier design problems can be formulated as a geometric program, a special type of convex optimization problem for which very efficient global optimization methods have recently been developed. The synthesis method is therefore fast, and determines the globally optimal design; in particular the final solution is completely independent of the starting point (which can even be infeasible), and infeasible specifications are unambiguously detected. After briefly introducing the method, which is described in more detail by M. Hershenson et al., we show how the method can be applied to six common op-amp architectures, and give several example designs.

A novel magnetic coupler of large r r gap IS presented It IS developed for the eleehlo relucle s aumahc mduchve charger The ncw induchve coupler proposed hqe has sUmcient mung ~oducmnce ven rf I has a large a s gap Calculated exrung inductance 18 40 pH at 2 nuns of w d m g and 5 mm ay gap, whch apees well with measured value In order to assess the gmerahon of heat c w e d by the eddy cment, the magneuc flux densrhes in the lnduchve chaser and also a flat non plate, to wbch the inductive charger IS attached, Bre calculated The conyersion efXciency wth the coupler and a MOSFETs full-bndge inverier of IW lrHq IS 97% at 8 3 kW output

This paper presents a comprehensive evaluation of several direct torque control (DTC) strategies for permanent magnet synchronous machines (PMSMs), namely DTC, model predictive DTC, and duty ratio modulated DTC. Moreover, field-oriented control is also included in this study. The aforementioned control strategies are reviewed and their control performances are analyzed and compared. The comparison is carried out through simulation, finite-element analysis, and experimental results of a PMSM fed by a two-level voltage source inverter. With the intent to fully reveal the advantages and disadvantages of each control strategy, critical evaluation has been conducted on the basis of several criteria: Torque ripple, stator flux ripple, switching frequency of inverter, steady-state control performance, dynamic response, machine losses, parameter sensitivity, algorithm complexity, and stator current total harmonic distortion.

The eGaN HEMTs with ZVS technique have emerged in high frequency converters to reduce the switching loss. This paper investigates an eGaN HEMT ZVS isolated SEPIC converter running up to 2 MHz. Furthermore, an inductor optimization method is proposed to ensure ZVS and reduce reverse conduction loss of the SR. The efficiency curve is optimized based on the trade-off method between the switching and conduction losses. Moreover, the embedded inductor is proposed to reduce the conduction loss of the inductor. Finally, two 2 MHz eGaN HEMT prototypes with the commercial and embedded inductor with 18–36 V input and 5 V/2 A output are built respectively to verify the effectiveness of the proposed methods. The converters achieve a peak efficiency of over 88.2% and power density of 52.9 W/in3, outperforming the state-of-art power module products.

This paper proposes a modularized global architecture using multi-winding transformers for battery cell balancing. The global balancing for a series-connected battery string is achieved based on forward conversion in each battery module and based on flyback conversion among modules. The demagnetization of the multiwinding transformers is also simultaneously achieved by the flyback conversion among modules without the need of additional demagnetizing circuits. Moreover, all MOSFET switches are driven by two complementary pulse width modulation signals without the requirement of cell voltage sensors, and energy can be automatically and simultaneously delivered from any high voltage cells to any low voltage cells. Compared with existing equalizers requiring additional balancing circuits for battery modules, the proposed modularized equalizer shares one circuit for the balancing among cells and modules. The balancing performance of the proposed equalizer is perfectly verified through experimental results, and the maximum balancing efficiency is up to 91.3%. In summary, the proposed modularized equalizer has the advantages of easier modularization, simpler control, higher efficiency, smaller size, and lower cost, ensuring the battery system higher reliability and easier implementation.

Electrical machines will always be a subcomponent in a larger system and should therefore be designed in conjunction with other major parts. Only a limited set of parameters, such as weight, size, or speed, is often relevant at a system level, but accurately determining those parameters requires performing a time-consuming detailed design of the electrical machine. This conflicts with a top down system design approach, where initially many different system topologies need to be considered. Hence, there is a need for a consistent and detailed description of the dependency of the interface parameters on machine properties. This paper proposes a method to obtain detailed design trends of electrical machines with a very high level of detail, based on large-scale application of multiobjective optimization using finite element models. The method is demonstrated by quantifying the power density of surface-mounted permanent magnet (SPM) machines versus rotor surface speed, power level, and cooling approach. It is found that power density strongly depends on cooling approach, followed by rotor surface speed, but barely on power level. Ultimately, this paper can be used both as an example when performing a similar design study and as a reference on detailed high-level and low-level machine design trends.

A Rogowski current transducer is an invaluable tool for semiconductor and power electronic circuit development since it is non-intrusive and does not saturate at high currents. This paper reviews the operating principles, performance limitations and development of this measurement technology and outlines improvements to the integrator design that enables bandwidths of 10MHz to be achieved.

This paper presents a new power regulation method for contactless power transfer systems that combines current magnitude control of a series tuned resonant converter with energy injection concepts suitable for constant load or single pick-up applications. In the latter, the control of the primary converter operates with asymmetrical control, to regulate the magnitude of the high frequency primary track current according to the load requirement of the secondary power pickup. The feedback control algorithm is developed by monitoring the output voltage of the secondary power pickup. Switching losses and EMI are reduced due to the ZCS (Zero Current Switching) operation of the converter. Simulation and experimental results show that the primary converter has a fast and smooth start-up transient response, and the magnitudes of the primary current and the output voltage of a secondary power pickup are both fully controllable against load variations.

A High Efficiency two-stage AC/DC converter is presented and discussed in this paper, which can be applied in on-board charger for electric vehicles. The front-end converter is a high efficiency ZVS half bridge power factor correction (PFC) topology with only one device in the current flowing path. Owing to the high voltage stress of the half bridge rectifier, the SiC MOSFET is applied to reduce the conduction and switching losses and achieve high frequency. The downstream stage is a LLC resonant DC/DC converter. The peak efficiency reaches 97.5%.


Question: Optimal design strategy and tradeoffs evaluation for planar transformers in high-power DC-DC converter systems.

This paper introduces a novel three-phase current-fed DC/DC converter with an active clamp, designed specifically for low-DC renewable energy sources, such as fuel cells. The converter is engineered to interface efficiently between a low-voltage fuel-cell source and a high-voltage DC bus, suitable for inverters. It boasts a high efficiency and voltage boosting capability, facilitated by achieving zero-voltage switching in all active components and zero current switching in the rectifier diodes. The discontinuous current conduction on the secondary side enables this, making the converter suitable for high-power transfer. Its three-phase configuration not only increases power transfer capacity but also reduces the RMS current per phase, thereby minimizing conduction losses. Additionally, a delta-delta connection on the three-phase transformer provides parallel current paths, further reducing conduction losses in the transformer windings. The converter achieves an efficiency of over 93%, attributed to improvements in switching dynamics and reduced conduction losses. The high voltage ratio is a result of combining the inherent voltage boost characteristics of the current-fed converter with the transformer turns ratio. The proposed converter and three-phase PWM strategy are thoroughly analyzed, simulated, and validated through hardware implementation. Experimental results from a 500-W prototype confirm the design's efficiency and performance, supporting its application in high-power DC-DC conversion scenarios.

This paper presents a comprehensive evaluation of several direct torque control (DTC) strategies for permanent magnet synchronous machines (PMSMs), namely DTC, model predictive DTC, and duty ratio modulated DTC. Moreover, field-oriented control is also included in this study. The aforementioned control strategies are reviewed and their control performances are analyzed and compared. The comparison is carried out through simulation, finite-element analysis, and experimental results of a PMSM fed by a two-level voltage source inverter. With the intent to fully reveal the advantages and disadvantages of each control strategy, critical evaluation has been conducted on the basis of several criteria: Torque ripple, stator flux ripple, switching frequency of inverter, steady-state control performance, dynamic response, machine losses, parameter sensitivity, algorithm complexity, and stator current total harmonic distortion.

The asymmetrical half-bridge (AHB) flyback converter is an attractive topology for operation at higher switching frequencies because it can operate with zero-voltage switching of the primary-side switches and zero-current switching of the secondary-side rectifier. In this paper, a detailed analysis and design procedure of the AHB flyback converter for the universal-line-voltage-range applications is presented. The performance of the AHB flyback converter is evaluated by loss analysis based on the simulation waveforms obtained in Simplis and experimentally verified on a laboratory prototype of a 65-W (19.5-V, 3.33-A) universal-line-voltage-range adapter.

This paper is the 2nd part of the survey titled “High Voltage Direct Current Transmission - A Review, Part I”. The main converter technologies and HVDC systems' components will be discussed in this complementary paper.

This paper presents the design and implementation of a 100 kW three-level T-type (3L-T2), single-phase power electronics building block (PEBB). The PEBB features switching power devices with associated gate drivers, DC-link capacitors and interconnection busbar. A hybrid switch (HyS), which includes a Si IGBT and a SiC MOSFET in parallel, forms the switching power device in the PEBB. An low-inductive multi-layer laminated busbar was designed to have symmetrical current commutation loops. A major contribution is that this is achieved while no three-level T-type power module was used. A loop inductance of 29 nH was achieved which is lower than the existing state-of-the-art three-level designs in literature. The fabricated prototype PEBB achieves a specific power density of 27.7 kW/kg and a volumetric power density of 308.61 W/in3. Single-phase operation of the PEBB has been demonstrated at the switching frequency of 28 kHz.

A new isolated current-fed pulsewidth modulation dc-dc converter-current-fed dual-bridge dc-dc converter-with small inductance and no deadtime operation is presented and analyzed. The new topology has more than 3times smaller inductance than that of current-fed full-bridge converter, thus having faster transient response speed. Other characteristics include simple self-driven synchronous rectification, simple housekeeping power supply, and smaller output filter capacitance. Detailed analysis shows the proposed converter can have either lower voltage stress on all primary side power switches or soft switching properties when different driving schemes are applied. A 48-V/125-W prototype dc-dc converter with dual output has been tested for the verification of the principles. Both simulations and experiments verify the feasibility and advantages of the new topology

When low ripple is required from a switched mode dc-dc converter, dissipative active filters offer an alternative to passive LC and coupled inductor filters. Analytical and experimental results are presented for a simple active ripple filter. The filter employs a pair of current transformers as sensors for feedforward and feedback control and two metal-oxide-semiconductor field-effect transistors (MOSFETs) as cancellation current drivers. Measurements demonstrate good ripple attenuation up to 5 MHz, more than 70 dB being obtained at 100 kHz, the switching frequency of a test converter. The overall efficiency was measured as 95%, with room for further improvement. The filter is suitable for input and output smoothing in dc-dc converters for aerospace and other critical applications.

This paper proposes a new soft-switched continuous-conduction-mode (CCM) boost converter suitable for high-power applications such as power factor correction, hybrid electric vehicles, and fuel cell power conversion systems. The proposed converter achieves zero-voltage-switched (ZVS) turn-on of active switches in CCM and zero-current-switched turn-off of diodes leading to negligible reverse-recovery loss. The components' voltage ratings and energy volumes of passive components of the proposed converter are greatly reduced compared to the conventional zero-voltage-transition converter. Voltage conversion ratio is almost doubled compared to the conventional boost converter. Extension of the proposed concept to realize multiphase dc-dc converters is discussed. Experimental results from a 1.5-kW prototype are provided to validate the proposed concept.

In this paper, a simplified optimal trajectory control (SOTC) for the LLC resonant converter is proposed. During the steady state, a linear compensator such as proportional-integral or proportional-integral-derivative (PID) is used, controlling the switching frequency fs to eliminate the steady-state error. However, during load transients, the SOTC method takes over, immediately changing the pulse widths of the gate-driving signals. Using the state-plane analysis, the pulse widths are estimated, letting the state variables track the optimal trajectory locus in the minimum period of time. The proposed solution is implemented in a digital controller, and experimental results show that while the digital logic requirement is very small, the performance improvement is significant.

An improved version of a single-ended primary inductor converter (SEPIC) is presented. The converter consists of a conventional SEPIC converter plus an additional high-frequency transformer and diode to maintain a freewheeling mode of the DC inductor currents during the switch on state. The voltage conversion ratio characteristics and semiconductor device voltage and current stresses are characterized. The main advantages of this converter are the continuous output current, smaller output voltage ripple, and lower semiconductors current stress compared with the conventional SEPIC converter. The design and simulation of the concept is verified by an experiment with a 48-V input and 12-V/3.75-A output converter.

A novel magnetic coupler of large r r gap IS presented It IS developed for the eleehlo relucle s aumahc mduchve charger The ncw induchve coupler proposed hqe has sUmcient mung ~oducmnce ven rf I has a large a s gap Calculated exrung inductance 18 40 pH at 2 nuns of w d m g and 5 mm ay gap, whch apees well with measured value In order to assess the gmerahon of heat c w e d by the eddy cment, the magneuc flux densrhes in the lnduchve chaser and also a flat non plate, to wbch the inductive charger IS attached, Bre calculated The conyersion efXciency wth the coupler and a MOSFETs full-bndge inverier of IW lrHq IS 97% at 8 3 kW output