Search

In academic conferences, the structure of the review process has always been considered a critical aspect of ensuring the quality of the conferences. Assigning reviews manually, by either the TPC chairs or Area Chairs, is time-consuming, and the process does not scale well to the number of submitted papers. Organizing a conference with multiple symposia (or tracks) helps its scalability, but predetermined boundaries between tracks may lead to inefficient use of reviewer expertise and suboptimal review assignment. Inspired by a rich literature on the problem of automated review assignment, we have designed and implemented a new review assignment system, called Erie, and successfully deployed it for IEEE INFOCOM 2015 and INFOCOM 2016. Implemented in Python, Erie is designed to use Latent Semantic Indexing to compute the suitability score between a submitted paper and a reviewer's representative papers, and to solve an optimization problem that maximizes the total suitability score across all submitted papers to the conference. Anecdotal evidence shows that Erie outperformed the accuracy of manual assignments by Area Chairs, and helped to improve the percentage of expert reviewers by a substantial margin. [ABSTRACT FROM PUBLISHER]

Affective computing (AC), a field that bridges the gap between human affect and computational technology, has witnessed remarkable technical advancement. However, theoretical underpinnings of affective computing are rarely discussed and reviewed. This paper provides a thorough conceptual analysis of the literature to understand theoretical questions essential to affective computing and current answers. Inspired by emotion theories, we proposed the function-component-representation (FCR) framework to organize different conceptions of affect along three dimensions that each address an important question: function of affect (why compute affect), component of affect (how to compute affect), and representation of affect (what affect to compute). We coded each paper by its underlying conception of affect and found preferences towards affect detection, behavioral component, and categorical representation. We also observed coupling of certain conceptions. For example, papers using the behavioral component tend to adopt the categorical representation, whereas papers using the physiological component tend to adopt the dimensional representation. The FCR framework is not only the first attempt to organize different theoretical perspectives in a systematic and quantitative way, but also a blueprint to help conceptualize an AC project and pinpoint new possibilities. Future work may explore how the identified frequencies of FCR framework combinations may be applied in practice. [ABSTRACT FROM AUTHOR]

Recognizing actions from still images has been popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial sum–product network (SPN). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside subimages and models the correlation of subimages via extra layers of SPN. Our method is shown to be effective on two benchmark data sets. [ABSTRACT FROM PUBLISHER]

Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling, and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This paper highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space. We focus on the means by that to leverage and coalesce these multiple disciplines to advance translational science and computational cardiology. Analyzing the scientific trends and understanding the current needs we present our perspective for the future of cardiovascular treatment. [ABSTRACT FROM AUTHOR]

This paper addresses the questions and concerns raised in the comment paper entitled “Comments on ‘Traffic Sign Recognition Using Kernel Extreme Leaning Machines With Deep Perceptual Features”’. It is analyzed that the major questions and concerns in the comment paper are due to some misunderstanding on the statements and the main contributions of the original paper by Zeng et al.. The main clarifications are as follows: (1) The weight update rule used by Zeng et al. is a standard form of kernel extreme learning machine (KELM) but not the rule of reduced KELM mentioned by the comment paper. (2) The aim and main contribution of the original paper by Zeng et al. are to improve the classification precision of traffic sign recognition (TSR) based on convolutional neural networks without dramatically increasing the scale or complexity of the deep neural network model, which leads to a relatively less training cost. The authors of the comment paper focused on the reduction of KELM’s computational costs but it is beyond the topic of the original paper. In addition, the reduction of KELM’s computational costs has been well studied in the literature. (3) The supplementary experiment is provided to show that the TSR method proposed by Zeng et al. is valid whether or not the training samples are much more than the needed kernels. [ABSTRACT FROM AUTHOR]

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations. [ABSTRACT FROM AUTHOR]

In a series of papers by Pawlikowski and his team, they warned about a deep crisis of credibility in simulation studies for the performance evaluation of telecommunication networks. The goal of this article is to revisit the investigation on simulation credibility and to extend the previous research in this area by presenting the results of a comprehensive survey of over 8370 IEEE publications (2007?2009) on telecommunication networks which appeared in the proceedings of IEEE Transactions on Communications (TC), IEEE/ACM Transactions on Networking (TN), IEEE Infocom, IEEE ICC, and IEEE Globecom. These proceedings were selected based on their popularity and relevance to our study. Besides simulation credibility, we also report on the commonly used simulation tools and performance analysis methodology adopted by the authors of the surveyed papers. Our findings show that a significant number of authors did not provide information of the simulation tools used or, even when they did, there was not enough information about the CI or SE of simulation results presented in the surveyed papers. Thus, there is no significant change with respect to quality and credibility of the simulation studies revised and the deep crisis of credibility still remains. The research design most authors prefer is a hybrid one where both simulation and analytic methods are being used for network performance analysis. Finally, we provide guidelines for the best practice of using simulation tools for the performance evaluation of telecommunication networks. [ABSTRACT FROM PUBLISHER]

Surface defect is a serious problem in real-world manufacturing system and it is important to use vision-based recognition to ensure the surface quality of products. Currently, due to the ability of automatic feature extraction, deep learning models, such as convolutional neural network (CNN), have been widely used in this area. However, these CNN-based models may not solve a problem well - inter-class similarities and intra-class variations (ISIV), which affect their ability of feature extraction and thus influence their recognition performance. To address this problem, this paper introduces a graph guidance mechanism into CNN to improve the ability of feature extraction, called Graph guided Convolutional Neural Network (GCNN). Firstly, GCNN defines a graph by computing the similarities between training samples. Secondly, the graph is introduced into VGG11, a popular CNN structure, to increase the inter-class distances and decrease the intra-class distances between defect samples. Meanwhile, a learnable coefficient is introduced into the training process to balance the effect of graph guidance automatically. The experimental results on four famous surface defect datasets demonstrate that the graph guidance helps CNN models have better ability of feature extraction and thus achieve better performance. Compared with state-of-the-art models, the proposed method can achieve the best performance. Furthermore, the final discussion shows that the learnable coefficient can help the proposed model to gain better performance, and that the proposed model increases a little computation cost compared to its original CNN model. Note to Practitioners—This paper is motivated by the problem in real-world manufacturing process – inter-class similarities and intra-class variations. Most of current CNN-based models may not solve this problem well, which limits their applications in real-world system. This paper proposes a graph guidance mechanism and introduces it to the training of CNN. The proposal can improve the ability of feature extraction, and thus have better performance than those without the graph guidance. By applying the proposal to four examples, the results show that the proposal is more feasible and effective than the state-of-the-art models in surface defect recognition. Furthermore, the mechanism assists the training of CNN and is removed in the process of recognition, so it will not increase time and occupied memory in recognition, and meanwhile, only limited computation cost is increased in training process. [ABSTRACT FROM AUTHOR]

One of the tasks for the SC19 Student Cluster Competition is to reproduce the results in the reproducibility challenge article “Computing Planetary Interior Normal Modes with a Highly Parallel Polynomial Filtering Eigensolver”, by J. Shi et al., which describes a highly parallel algorithm for computing planetary normal modes. In running experiments from the article, we study the weak and strong scalability of the algorithm, as well as the relationship between model size, degree of polynomial filter, and execution time. We investigate these findings on a two-node, 64-core Intel Skylake-based Xeon cluster. Unfortunately, we are able to confirm some, but not all, of the original findings, with discrepancies possibly due to a low number of experimental runs due to competition time limits as well as nonuniform scaling of compute resources. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to speed up electromagnetic-transients (EMT) simulation, using graphics-processing-unit (GPU)-based computing. This paper extends earlier published works in the area, by exploiting additional parallelism inside EMT simulation. A 2D-parallel matrix-vector multiplication is used that is faster than previous 1D-methods. Also, this paper implements a GPU-specific sparsity technique to further speed up the simulations, as the available CPU-based sparsity techniques are not suitable for GPUs. In addition, as an extension to previous works, this paper demonstrates modelling a power-electronic subsystem. The efficacy of the approach is demonstrated using two different scalable test systems. A low granularity system, that is, one with a large cluster of buses connected to others with a few transmission lines is considered, as is also a high granularity where a small cluster of buses is connected to other clusters, thereby requiring more interconnecting transmission lines. Computation times for GPU-based computing are compared with the computation times for sequential implementations on the CPU. This paper shows two surprising differences of GPU simulation in comparison with CPU simulation. First, the inclusion of sparsity only makes minor reductions in the GPU-based simulation time. Second, excessive granularity, even though it appears to increase the number of parallel-computable subsystems, significantly slows down the GPU-based simulation. [ABSTRACT FROM PUBLISHER]

Marbling creates stone-like or intricate abstract decorations from inks floating on water or gel. Instead of using fluid dynamics to simulate marbling, a proposed method takes a mathematical approach with closed-form expressions. This method improves control, ease of implementation, parallelism, and speed, enabling real-time visual feedback and creation of vivid flowing animations. Users can start designs from a blank sheet, raster images, or videos. When starting with a blank sheet, this method can produce compact resolution-independent vector output. The transforms for the marbling operations all have inverse transforms. Forward application generates compact resolution-independent vector-based output; inverse application generates pixel-based output. In both cases, the closed-form expressions preserve the designs' quality and sharpness. Extensive comparisons with other digital marbling methods demonstrates this method's efficiency and effectiveness. Applications of this method include surface detail rendering for 3D objects, interactive video processing, and scene decoration. The video at http://youtu.be/ZgVbIaKhC_4 shows the application of simulated marbling to a video stream. [ABSTRACT FROM PUBLISHER]

Time-domain indirect methods based on Lyapunov stability theory and linear matrix inequality techniques (LMIs) have been applied for delay-dependent stability analysis of large-scale load frequency control (LFC) schemes. This paper aims to enhance the numerical tractability of large-scale LMIs by exploiting the special characteristics of the LFC loops. First, in the typical LFC model, only a few delayed states that are directly influenced by transmission delays are distinguished from other normal system states. Hence, an improved reconstruction model is formed, based on which the delay-dependent stability condition is established with the decreased order of the LMIs and decision variables. Then, to further improve the numerical tractability of the developed stability criterion, all weighting matrices required in the augmented Lyapunov functional are enforced to have structural restrictions by proposing an extended symmetry-exploiting technique. Case studies show that the method proposed in this paper significantly improves the calculation efficiency of stability criterion established for multi-area power systems at the cost of only a minor reduction in computational accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review of learning analytics dashboards (LADs) research that reports empirical findings to assess the impact on learning and teaching. Several previous literature reviews identified self-regulated learning as a primary focus of LADs. However, there has been much less understanding how learning analytics are grounded in the literature on self-regulated learning and how self-regulated learning is supported. To address this limitation, this review analyzed the existing empirical studies on LADs based on the well-known model of self-regulated learning proposed by Winne and Hadwin. The results show that existing LADs are rarely grounded in learning theory, cannot be suggested to support metacognition, do not offer any information about effective learning tactics and strategies, and have significant limitations in how their evaluation is conducted and reported. Based on the findings of the study and through the synthesis of the literature, the paper proposes that future research and development should not make any a priori design decisions about representation of data and analytic results in learning analytics systems such as LADs. To formalize this proposal, the paper defines the model for user-centered learning analytics systems (MULAS). The MULAS consists of the four dimensions that are cyclically and recursively interconnected including: theory, design, feedback, and evaluation. [ABSTRACT FROM AUTHOR]

The Marching Cubes paper by Bill Lorensen and Harvey Cline, "Marching Cubes: A High Resolution 3D Surface Construction Algorithm," was published at SIGGRAPH 1987.1 According to Google Scholar, their paper has 15,667 citations (as of January 17, 2020), the most highly cited paper in computer graphics. Sadly, while writing this article Bill Lorensen passed away on December 12, 2019. Origins Department Editor Chris Johnson contributed the text in italics. EARLY [ABSTRACT FROM AUTHOR]

Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

Recognition of aggressive driving behavior helps future research and practices in Intelligent Transport Systems. This paper tries to briefly explain the concepts related to aggressive driving behavior and introduces a system integrated with a novel machine learning algorithm for the recognition of trip-based aggressive driving behavior. The algorithm is a Gaussian Mixture Model (GMM) structured with Factor Analysis (FA), and Hierarchical Clustering (HC): common factors were extracted using FA, which is further applied to HC and GMM in the recognition of trip-based aggressive driving. The system is applied in a case study using data from the Shanghai Naturalistic Driving Study, for simulating data collection using the Advanced Driving Assistance System (ADAS) system in a real-traffic situation. Three behavior types (cautious, regular, and aggressive driving) were successfully clustered. For validity, the real aggressive driving behavior records were extracted based on the video, and the proposed system was compared with existing recognition methods. Results indicate that the accuracy of aggressive driving recognition of the system is higher than others (accuracy = 87%). This paper provides a reference in defining and determining aggressive driving, and a robust system for aggressive driving behavior recognition along with the trained algorithm, which can be used in real-world applications for improving driving safety with the applications in ADAS systems, auto-insurance industry. [ABSTRACT FROM AUTHOR]

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

Intelligent public transport systems are a key direction for the study of intelligent transportation systems (ITSs), and they perform the following functions: location and tracking, aided navigation, dispatch and command, and dynamic information release; these systems also help travelers determine optimal routes. This paper mainly studies intelligent bus operation optimization by integrating cases and data from business chains, including the optimization of public transport vehicle scheduling according to the characteristics of vehicle scheduling, considers the interests of passengers and public transport companies, and adopts a true-value encoding method with the departure time as the variable goal optimization. In addition, this paper builds a model for the intelligent scheduling problem in public transport based on an enhanced quantum genetic algorithm (EQGA) to find the optimal timetable. This model sets the minimum waiting time cost of passengers and the maximum interests of public transport companies as the goals, restricts the departure interval and two adjacent intervals, and constrains the load factor of passengers. Moreover, based on the analysis of public transport travel characteristics and passenger flow data, this paper evaluates the efficiency of public transport operation, reasonably analyzes the utilization of public transport resources and the travel time of public transport passengers, and thoroughly studies the public transport operation system. This paper selects actual bus line data for empirical analysis, and the empirical results show that the proposed algorithm and model can meet the requirements of many aspects and provides good intelligence, applicability, and optimization. [ABSTRACT FROM AUTHOR]

Direct-drive wave energy converter (WEC) and buoy control algorithms have shown great potential for renewable wave energy extraction in ideal conditions. However the actual power take-off (PTO) impacts are barely considered in the WEC design. This paper highlights the demands of designing the WEC wave-to-wire control from a global point of view by studying the actual PTO impacts. A permanent magnet linear electrical machine (LEM) PTO unit is simulated and controlled to fulfill the WEC buoy control requirements. Several state of the art control algorithms, which include singular-arc (SA) control, shape-based (SB) control, model predictive control (MPC), and proportional-derivative (PD) control, are applied to maximize the wave energy production (mechanical energy). Multiple types of electrical PTOs, including ideal PTO, unlimited PTO and limited PTO, are all implemented to evaluate WEC wave-to-wire performances. Further, the PTO copper loss model and the PTO actual efficiency maps are introduced and studied to improve the electrical PTO operation efficiency. To further assess the control schemes in various wave conditions, one-year PacWave ground-truth data is applied as well. Numerical simulations are conducted using MATLAB/Simulink and the Simscape toolbox. The electrical PTO unit is composed of a LEM, an ideal inverter, and an ideal energy storage system. The results show that the actual PTO will impact the constrained controls (MPC and SB) less comapring to unconstrained controls (SA and PD). Although SB can produce the maximum energy with the limited PTOs, it is not robust for all wave conditions. At the end of the paper, the possible solutions for improving the WEC wave-to-wire performances are also provided. [ABSTRACT FROM AUTHOR]

The study on the scattering mechanism of breaking ship waves provides important theoretical basis to the ship detection and radar imaging in a marine environment. This paper presents a new procedure for evaluating the electromagnetic scattering field of breaking ship waves over a perfect electric conductor sea surface. In this paper, different forms of the near-field ship waves are obtained by the computational fluid dynamics (CFD) method, which provides a new means of analyzing a fully coupled fluid-structure interaction system in a detailed manner. Sufficient grids are used in the CFD numerical simulation to obtain the flow field information of the breaking waves. The accuracy of the breaking wave geometry has been improved obviously, which will lead to a better understanding of the electromagnetic scattering characteristics of breaking ship waves. Considering the multiple scattering between the ship hull and the breaking waves, the scattering field of the sea surface and the ship hull are evaluated by the forward–backward iterated physical optics algorithm. Both scattering characteristics of the breaking ship waves and distribution of the scattering coefficients are obtained. By comparing the bistatic scattering field in different situations, the numerical results show some important effects of the breaking ship waves, which are quite different from the effects of ship models. What is more, the time varying radar cross section of the breaking ship waves are presented based on the dynamic ship waves. [ABSTRACT FROM AUTHOR]

Two decades have passed since the introduction of Markov chain Monte Carlo (MCMC) into light transport simulation by Veach and Guibas, and numerous follow-up works have been published since then. However, up until now no survey has attempted to cover the majority of these methods. The aim of this paper is therefore to offer a first comprehensive survey of MCMC algorithms for light transport simulation. The methods presented in this paper are categorized by their objectives and properties, while we point out their strengths and weaknesses. We discuss how the methods handle the main issues of MCMC and how they could be combined or improved in the near future. To make the paper suitable for readers unacquainted with MCMC methods, we include an introduction to general MCMC and its demonstration on a simple example. [ABSTRACT FROM AUTHOR]

This paper is concerned with a fuzzy analysis of power-flow (PF) involving uncertainties of load demands and network parameters. The crux of this paper is to propose an advanced fuzzy arithmetic. Fuzzy transformation method merges with backward–forward sweep in order to evaluate the contribution and propagation of uncertainty in IEEE 33-bus and 69-bus distribution systems. Results are validated by true intervals and random ranges. To determine true intervals, Global Optimization Problems (GOPs) are defined and solved through derivative-based and free techniques. To estimate random ranges, Monte-Carlo Simulations (MCSs) are employed. Our findings confirm that the sharpness of fuzzy intervals, tractability of computations, and applicability of possibility distributions. Following scenario-based evaluations, this paper discusses new implications of power losses, voltage profiles, optimal re-configuration, feeder extension, and reactive power compensation so that results would be beneficial to system planners and operators. Altogether, this paper provides a blueprint for a new way to handle uncertainties in a wide variety of power system problems without global optimization, linearization, and randomized simulations. [ABSTRACT FROM AUTHOR]

Emotional information plays an important role in various multimedia applications. Movies, as a widely available form of multimedia content, can induce multiple positive emotions and stimulate people's pursuit of a better life. Different from negative emotions, positive emotions are highly correlated and difficult to distinguish in the emotional space. Since different positive emotions are often induced simultaneously by movies, traditional single-target or multi-class methods are not suitable for the classification of movie-induced positive emotions. In this paper, we propose TransEEG, a model for multi-label positive emotion classification from a viewer's brain activities when watching emotional movies. The key features of TransEEG include (1) explicitly modeling the spatial correlation and temporal dependencies of multi-channel EEG signals using the Transformer structure based model, which effectively addresses long-distance dependencies, (2) exploiting the label-label correlations to guide the discriminative EEG representation learning, for that we design an Inter-Emotion Mask for guiding the Multi-Head Attention to learn the inter-emotion correlations, and (3) constructing an attention score vector from the representation-label correlation matrix to refine emotion-relevant EEG features. To evaluate the ability of our model for multi-label positive emotion classification, we demonstrate our model on a state-of-the-art positive emotion database CPED. Extensive experimental results show that our proposed method achieves superior performance over the competitive approaches. [ABSTRACT FROM AUTHOR]

Capturing the ‘mutual gaze’ of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net++ takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net++ to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net++ to a social network, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO, and show that LAEO can be a useful tool for guided search of human interactions in videos. [ABSTRACT FROM AUTHOR]

Online healthcare applications have grown more popular over the years. For instance, telehealth is an online healthcare application that allows patients and doctors to schedule consultations, prescribe medication, share medical documents, and monitor health conditions conveniently. Apart from this, telehealth can also be used to store a patient's personal and medical information. With its rise in usage due to COVID-19, given the amount of sensitive data it stores, security measures are necessary. A simple way of making these applications more secure is through user authentication. One of the most common and often used authentications is face recognition. It is convenient and easy to use. However, face recognition systems are not foolproof. They are prone to malicious attacks like printed photos, paper cutouts, replayed videos, and 3D masks. The goal of face anti-spoofing is to differentiate real users (live) from attackers (spoof). Although effective in terms of performance, existing methods use a significant amount of parameters, making them resource-heavy and unsuitable for handheld devices. Apart from this, they fail to generalize well to new environments like changes in lighting or background. This paper proposes a lightweight face anti-spoofing framework that does not compromise on performance. Our proposed method achieves good performance with the help of an ArcFace Classifier (AC). The AC encourages differentiation between spoof and live samples by making clear boundaries between them. With clear boundaries, classification becomes more accurate. We further demonstrate our model's capabilities by comparing the number of parameters, FLOPS, and performance with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Exposure to stimuli gives rise to sensory and affective experiences. Computing the relationships between these experiences is instrumental to designing affectively appealing products and understanding human experiences. Hierarchical structures of sensory and affective responses, which can be built through adjective rating tasks, are regarded as an effective means for expressing relationships between sensory and affective responses. Naturally, these hierarchical structures depend on the type of stimulus; however, so far, their dependencies on material categories have yet to be examined. We therefore investigated how hierarchical structures of affective and sensory responses depend on seven material categories: fabric, leather, wood, paper, foliage, stone, and glass. Each material category had 100 visual representations selected from the Flickr Material Database. Thirty-nine participants were asked to rate 368 pictures across a set of materials. The questionnaire adjectives included visually- and haptically-centered items although the stimuli were purely visual. We found that the structures differed substantially among the material categories, although there were some commonalities. Particularly, the positions of polysemic or multimodal adjectives such as “light” and “uncomfortable” in the hierarchy were highly dependent on the material category. For example, “light” has both physical (lightweight) and psychological (e.g., non-solemn, cheerful) meanings. For stone and glass (generally considered to be of heavy weight), the psychological meanings were primarily considered. Conversely, the physical meanings were predominant for fabric, leather, wood, paper, and foliage, for which weight is a factor in judging quality. The present study helps interpret affective descriptors whose meanings vary among types of stimuli. [ABSTRACT FROM AUTHOR]

The authors introduce The Journal of Open Source Software (JOSS), a publication that focuses on research software and its place in the scholarly publishing ecosystem. JOSSs goal is to make it easy for authors to publish a paper about their software, mostly focused on the software itself, and then be credited when this software is used based on users citing the JOSS paper. [ABSTRACT FROM AUTHOR]

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX. [ABSTRACT FROM AUTHOR]

Chemical mechanical polishing/planarization (CMP) combines physical grinding and chemical reactions to planarize the wafer surface. The complex mechanism of CMP brings great challenges to the mechanism-based modeling process. The data-driven CMP modeling process is limited by insufficient datasets. At the same time, these two types of models generally have high computational complexity. In this paper, we introduce the group method of data handling (GMDH)-type polynomial network to build the CMP model to address the above challenges. We designed and manufactured the test chip using a 28nm process. The measurement data from the test chip shows that compared with the mechanism-based CMP model, the trained CMP model based on GMDH-type polynomial network has higher accuracy and lower computational complexity, with the average simulation speed being 115x faster. Experiments based on silicon data show that this modeling method has a small demand for data, and 20 randomly selected sets of data can meet the needs for modeling the current CMP process. [ABSTRACT FROM AUTHOR]

Engagement is the process by which participants establish, maintain, and end their perceived connection. Automatic engagement inference is one of the tasks required to develop successful human-centered HMI applications. Engagement is a multi-faceted multimodal construct requiring high accuracy in interpretating contextual, verbal and non-verbal cues, making the development of an intelligent automated engagement inference system challenging. Existing surveys concentrate on specific application settings, and a comprehensive survey covering the different engagement facets, definition and inference across various contexts is lacking. Moreover, despite the importance of context-aware modeling, the literature lacks a systematic context-aware overview on the topic. This paper presents a comprehensive survey on previous work in engagement for HMI, entailing interdisciplinary definition, engagement components, publicly available datasets, ground truth assessment, and commonly used features and methods, serving as a guide for the development of future HMI interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement is studied sets apart this survey from existing ones. Our findings suggest four important directions for future research: 1) context-aware computational modeling, 2) temporal dynamics, 3) personalised computing, and 4) bias and fairness of engagement inference systems. [ABSTRACT FROM AUTHOR]

A logic diagnosis procedure provides information about the defects that are present in a faulty unit as a set of candidate faults. To obtain smaller, and more accurate, sets of candidate faults, a diagnostic test generation procedure produces a test set that distinguishes fault pairs. This paper observes that large sets of candidate faults are obtained when multiple defects are present in a faulty unit, even if a diagnostic test set is used for logic diagnosis. This points to the possibility that fault pairs do not provide a complete set of targets for diagnostic test generation. This paper analyzes the conditions that cause a large set of candidate faults to be formed under a particular logic diagnosis procedure and suggests new targets for diagnostic test generation. The experimental results for benchmark circuits demonstrate that a diagnostic test set can be improved by adding diagnostic tests for the new targets. [ABSTRACT FROM AUTHOR]

Many applications of hyperspectral remote sensing involve automatic detection of solid targets. The design and applicability of such systems depend on their detection performance under various deployment scenarios; thus, it is useful to develop an accurate and realistic performance model. In this paper, we present a hyperspectral target detection performance prediction model for the common matched filter and normalized matched filter detection algorithms. A statistical model for hyperspectral data is discussed, which includes a replacement model for target pixels and a finite mixture of $t$ -elliptically contoured distributions for background materials. For this general input model, analytic forms for detector output distributions are not always available. The main contribution of this paper is to develop an efficient and robust performance simulation technique using Monte Carlo (MC) with importance sampling. Compared to standard MC, our simulation technique requires a substantially smaller sample size to obtain accurate performance estimates at low false alarm rates. The proposed technique is useful for predicting detection performance over a wide range of input models when analytic solutions are unavailable. [ABSTRACT FROM AUTHOR]

Firing accuracy is a critical quantity of interest in the performance analysis of electromagnetic railgun (EMRG) exterior ballistics. Since the simulation technique is always used to simulate the complex physical system at a low cost, it is employed to evaluate the firing accuracy of railgun projectile with uncertainty in this paper. An exterior ballistics simulation model of the railgun, including 3-DOF flight mechanical model, relative kinematic model, and guidance law model, is developed to simulate the ballistics behavior in the missile target intercept mission. The firing accuracy is studied in this paper to compare the capability of EMRG with different ranges and muzzle velocities. In order to explore the firing accuracy of the guided projectile, the guidance precision analysis and ballistics uncertainty quantification are studied. In the guidance precision analysis, the shot range and efficiency are used to make a comparison. In addition, the sensitivity analysis and firing accuracy analysis are also performed in terms of uncertainty quantification. The simulation results illustrate that the simulation of the guided projectile can give some practical guide to the projectile design of EMRG. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like cruise control. The experimental results using the next generation simulation datasets demonstrate its superior fitting accuracy over conventional models. [ABSTRACT FROM AUTHOR]

This paper investigates the deployment issue of an emergency medical service (EMS) system to maintain the preferred service coverages under different considerations. Specifically, two coverage levels are introduced to reflect the requirements under the regular situation and the situation with ambulance unavailable. We propose the two-stage robust optimization (RO) models to design a reliable ambulance system subject to unavailability of the ambulances, with and without the ambulance relocation. For the RO problem with mixed-integer recourse for relocation, we customize the column and constraint generation method with an approximation strategy to handle the computational challenge. Our numerical study: 1) demonstrates that our RO formulations have a strong modeling capacity on designing the EMS system; 2) shows that our approximation algorithm performs very well; and 3) provides a quantitative evaluation of, including, relocation operations on the system performance. Note to Practitioners—This paper presents the novel optimization models to help ambulance deployment. Due to ambulance unavailability and relocation operations, traditional optimization formulations might not be sufficient for modeling or might be hard for computation. In this paper, we provide an uncertainty set-based approach to capture ambulance unavailability and to build the robust optimization models (with relocation recourse decisions). Also, efficient algorithms are designed to support practical instances. Numerical results are very supportive to our new models and computational methods. [ABSTRACT FROM AUTHOR]

Ranking and selection (R&S) procedures are powerful tools to enhance the efficiency of simulation-based optimization. In this paper, we consider the R&S problem subject to stochastic constraints and seek to improve the selection efficiency by incorporating the information from across the domain into quadratic regression metamodels. To better fulfill the quadratic assumption of the regression metamodel used in this paper, we divide the solution space into adjacent partitions such that the underlying functions of both the objective and constraint measures in each partition are approximately quadratic with homogeneous noise. Using the large deviations theory, we characterize the asymptotically optimal allocation rule by maximizing the rate at which the probability of false selection tends to zero. Numerical experiments demonstrate that our approach dramatically improves the selection efficiency by 50%–90% on some typical selection examples compared with the existing approaches. Note to Practitioners—Simulation is widely used for designing and analyzing complex discrete-event systems such as manufacturing production systems, transportation systems, and supply chain operations. However, efficiency is still a significant concern when using simulation for stochastic optimization problems, especially in the presence of stochastic constraints. This paper was motivated by the problem of selecting the best feasible design (solution) from a finite set of alternatives given anobjective and stochastic constraints, where the performances of the objective and constraint measures can be estimated via simulation. Different from the existing approaches in the literature, in this paper, we propose a new R&S procedure using regression metamodels. Given a fixed amount of computing budget, our approach determines the asymptotic optimal number of simulation replications for each design. It can be shown through numerical experiments that the proposed approach can significantly improve the selection efficiency by more than 50% over the existing methods on some typical benchmark examples. [ABSTRACT FROM AUTHOR]

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL. [ABSTRACT FROM AUTHOR]

The problem under consideration in this paper is how to avoid deadlocks and livelocks in the paradigm of Petri nets (PNs). Although deadlock and livelock avoidance has been extensively studied in existing literature, fewer results can be applied to practical automated manufacturing systems (AMSs) due to expensive computations and reduced permissiveness. In this paper, we propose an efficient and maximally permissive control scheme to avoid deadlocks and livelocks by using critical distance. From the perspective of the reachability graph of a PN, critical distance is equal to the maximum number of transitions among all transition sequences from any critical state to its corresponding deadlock or livelock state. First, we show how to calculate the critical distance of a PN in an efficient way by using a portion of reachable states. Then, local reachability graphs are established based on critical distance to avoid deadlocks and livelocks. The provided policy is maximally permissive since only the necessary minimum number of illegal transitions are forbidden at each state. Several representative examples are presented to illustrate our approach. Note to Practitioners—Deadlock and livelock avoidance is a crucial problem in automated manufacturing systems (AMSs). Various methods have been proposed to deal with deadlocks and livelocks by researchers and practitioners. However, there exist fewer works that focus on deep exploration regarding how long a deadlock or livelock will occur from a safe state. As a consequence, the existing works suffer from the disadvantages of being too aggressive or conservative, i.e., checking the whole state space or forbidding many acceptable states when avoiding deadlocks and livelocks. Through the analysis of a partial of reachable states, this paper derives the critical distance of Petri nets modeling AMSs, which is equal to the maximum number of transitions among all transition sequences from any critical marking to its corresponding deadlock or livelock marking. All deadlocks and livelocks, if exist, can be detected at any given marking within the critical distance. Our approach not only is maximally permissive but also explores only a partial of states, thereby mitigating state explosion problem. [ABSTRACT FROM AUTHOR]

This paper considers distributed estimation methods to enable the formation of Unmanned-Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with communication devices to receive a beacon signal from the target and share information with neighboring UAVs. The shared information includes the time-of-arrival (TOA) of the beacon signal from the target and estimates on the target’s location. Every UAV processes the received information from the neighbors using a single-time-scale distributed estimation protocol. This differs from multi-time-scale protocols that require (i) many consensus iterations on a-priori estimates, (ii) fast communication among agents (in general, much faster than the sampling rate of the target dynamics), and thus, more-costly communication equipment and processing units. Further, our approach outperforms most single-time-scale methods in terms of observability assumption as these methods assume that the target is observable via the measurement data received from neighboring UAVs (referred to as local-observability). This requires more communications among the sensors. In contrast, our approach is only based on global-observability assumption, and thus, requires less networking (only strong-connectivity) and communication traffic along with less computational load by data-processing once at the same time-scale of sampling target dynamics. We consider modified time-difference-of-arrival (TDOA) measurements with a constant output matrix for the linearized model. UAVs make a pre-specified formation, and by estimating the target’s location via these measurements, move along with the target. Note to Practitioners—Inspired by recent development in industrial UAVs along with emerging progress in low-cost processing units, fog computing systems, and wireless communications, this paper considers mobile tracking of a moving target via a group of wireless-connected autonomous drones. In the classical tracking methods, which are prone to single-point-of-failure, all the sensors need to send their information to a ground central station over a long-range and costly data-transmission channel. In contrast, by collaborative tracking the processing and decision-making are distributed among a swarm of drones equipped with onboard miniaturized electronic parts such as sensors, microcontrollers, microprocessors, and communication units. This article provides an efficient algorithm to enable such drones to autonomously track the moving target in real-time. Note that the cost and tracking ability of the UAV swarm are directly determined by the computational efficiency and communication burden of the estimation algorithm. In this regard, most available estimation algorithms are over budget and even infeasible due to the need for fast data-transmission channels, fast CPUs, and high network traffic. Our proposed estimation technique outperforms similar algorithms in terms of required communication bandwidth, data-transmission rate, and computational resources, which considerably reduces the hardware cost and improves tracking efficiency in real-time large-scale applications. We show the feasibility and efficiency of our distributed tracking method by simulation. [ABSTRACT FROM AUTHOR]

Automatic modulation classification (AMC) identifies a received signal’s modulation scheme without prior knowledge of the intercepted signal, which enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), lots of neural networks are introduced into AMC. To further improve classification performance, various complementary cues including in-phase/quadrature (I/Q), amplitude/phase (A/P), constellation, and other formats are used together to enhance the discrimination of the DL model, where only outputs of the last layer are used. In this paper, we find that different layers’ outputs in the DL model are also complementary to each other. As a result, a hierarchical classification head based convolutional gated deep neural network (HCGDNN) is proposed by utilizing different layers’ output, which only uses the I/Q cue. The proposed HCGDNN consists of three groups of convolutional neural networks (CNN) blocks, two groups of bidirectional gated recurrent units (BiGRU), and a hierarchical classification head. Compared to the long short-term memory (LSTM), the BiGRU has a smaller computational complexity and also releases the gradient dispersion and explosion in the training phase. With the help of the hierarchical classification head, three groups of modulation predictions are made for a received I/Q signal. After that, a novel nonlinear optimization fusion method is derived to generate fusion weights to fuse different groups, then a final classification decision is made. Compared to AMC methods using various cues, the proposed HCGDNN only uses I/Q cue and has low computational overhead. Numerical results suggest that the newly developed HCGDNN achieves superior performance on the public benchmark.To help other researchers, the source code will be uploaded to the github as long as the paper is published. [ABSTRACT FROM AUTHOR]

In this article we present our results from the SC19 Student Cluster Competition Reproducibility Challenge. The challenge entails reproducing the article entitled “Computing Planetary Interior Normal Modes with A Highly Parallel Polynomial Filtering Eigensolver” presented at SC’18, which proposes a parallel polynomial filtered Lanczos algorithm to directly calculate the planetary normal modes of heterogeneous planets. The proposed algorithm showed excellent performance with relatively low memory consumption and high parallel efficiency. In this work, we reproduce the scaling tests in that article on a cluster using Intel Cascade Lake architecture and use the proposed algorithm to illustrate specific normal modes of Mars. We compare the results obtained on our cluster with those in the original article. We also design a new metric to better analyze the results. In addition, we use the profiling tool Intel VTune Amplifier to explain our discoveries. Our results demonstrate that the given models show great scalability, which is similar to the original article. The required normal modes of Mars are also successfully calculated and visualized. [ABSTRACT FROM AUTHOR]

This paper considers propagation of low-frequency broadband pulses in shallow water. It focuses on across-slope propagation in wedge-shaped oceanic waveguides and on single hydrophone receiving configurations. In a low-frequency shallow-water context, propagation is dispersive and usually described by modal theory. However, in the presence of a tilted bottom, propagation is also affected by 3-D effects (horizontal refractions). The paper shows that time-frequency analysis is a suitable tool to illustrate and understand 3-D propagation. It illustrates the pertinence of the time-frequency analysis of 3-D signal by proposing an algorithm to estimate the seabed slope using a single receiver. The method is benchmarked on numerical simulations, and successfully applied on small-scale experimental data. [ABSTRACT FROM PUBLISHER]

This paper presents the theoretical keystone for a decentralization of model-based diagnosis by proving the equivalence between decentralized and centralized residual generation. The proof is based on structural analysis and graph-theoretical concepts. The second contribution of this paper is the design of a decentralized fault-focused residual generation scheme advantageously implementing a strategy of fault isolation on request. Algorithms are tested on the attitude determination and control system of a low Earth orbit satellite. [ABSTRACT FROM PUBLISHER]

Cycle time, which is of order of ten weeks for most products in semiconductor wafer fabrication facilities (wafer fabs), must be explicitly considered in production planning models. Cycle times depend nonlinearly on the resource workload in a wafer fab. Different data-driven (DD) production planning formulations are studied in this paper. Such formulations are based on a set of system states representing the congestion behavior of a wafer fab with work in process (WIP) and resulting output levels. Different WIP-output relations in the DD formulations are investigated. In addition, different methods are proposed to gather representative sets of system states. The DD formulations are compared with the Allocated Clearing Function (ACF) formulation with respect to profit and cost values using simulation models of a scaled-down and a large-sized multi-product wafer fab, respectively. The simulation results show that some DD variants are able to outperform the ACF formulation under several experimental conditions. [ABSTRACT FROM AUTHOR]

With the coming of the Internet of Things (IoT) era, malware attacks targeting IoT networks have posed serious threats to users. Recently, the emerging of edge computing have paved the way for new data processing paradigms in IoT networks, but it is still a challenge for deploying malware detection systems on the IoT devices. This paper develops an IoT malware detection system based on trust hybrid user-edge evaluation, namely MDHE. This system decomposes a large and complex deep learning model into two parts, which are deployed on edge servers and end devices, respectively. Specifically, a trust evaluation mechanism is used to select the trusted devices to participate the model training. Moreover, we develop a private feature generation that leverages a graph mining technology to extract the subgraph features, which then are perturbed by leveraging the differential privacy technology to prevent user privacy from leaking. Finally, we reconstruct the perturbed features on edge server, and propose a Capsule Network (CapsNet) to identify malware. Experimental results show that MDHE can effectively detect malware. Specifically, it can reduce sensitive inference while maintaining the utility of data. [ABSTRACT FROM AUTHOR]

Understanding diffusion processes in networks has emerged as an important research topic because of its wide range of applications. Analysis of diffusion traces can help us answer important questions such as the source(s) of diffusion and the role of each node during the diffusion process. However, in large-scale networks, due to the cost and privacy concerns, it is almost impossible to monitor the entire network and collect the complete diffusion trace. In this paper, we tackle the problem of reconstructing the diffusion history from a partial observation. We formulate the diffusion history reconstruction problem as a maximum a posteriori (MAP) problem and prove the problem is NP-hard. Then, we propose a step-by-step reconstruction algorithm, which can always produce a diffusion history that is consistent with the partial observation. Our experimental results based on synthetic and real networks show that the algorithm significantly outperforms some existing methods. [ABSTRACT FROM AUTHOR]

This paper describes a Markov decision process approach to a robust spacecraft mission control policy that maximizes the expected value of science reward assuming a circular orbit. The control policy that governs mission steps can be computed off-board or onboard depending upon the availability of communication bandwidth and on-board computational resources. This paper considers a sample science mission, where the spacecraft collects data from celestial objects viewable only within a certain orbit true anomaly window. Science data collection requires the spacecraft to slew its instrument(s) toward each target, and continue pointing in the direction of the target while the spacecraft traverses its orbit. Robustness and stochastic optimization of scientific reward, is achieved at the cost of computational complexity. Approximate dynamic programming (ADP) is exploited to reduce the computational time and effort to manageable levels and to treat larger problem sizes. The proposed ADP algorithm partitions the state-space based on true anomaly regions, enabling grouping of adjacent science targets. Results of a simulation case study demonstrate that our proposed ADP approach performs quite well for reasonable ranges of key problem parameters. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

In this paper we present a solution for real-time multi-FPGA simulation of energy conversion systems based on the Latency-Based Linear Multistep Compound (LB-LMC) simulation approach. The approach relies on a nodal decomposition approach derived from multiport network analysis, and fast parallel communication between Field Programmable Gate Array (FPGA) devices. The results presented in this paper –using a microgrid example– demonstrate how the developed real-time platform overcame the resource scalability limit of single FPGA solvers while achieving a time step size of 100 nanoseconds. [ABSTRACT FROM AUTHOR]

Few of the challenges with development of a single on–board motor for direct–drive electric vehicles include high torque density and low torque ripple. Therefore, in this paper, a 36–slot, 34–pole consequent pole six–phase permanent magnet synchronous machine (PMSM) has been optimized to address the aforementioned challenges for direct–drive application. Existing literature on optimization processes that rely solely on finite element models are restricted to three–phase machines only and also take longer computation time. Therefore, this paper proposes a novel optimization approach based on supervised machine learning for six–phase PMSM. In this approach, a non–conventional extended dual dq–frame model that accounts for higher order space harmonics in inductances and flux linkages has been developed and used for accurate computation of average torque and torque ripple of six–phase PMSM. Using the performance characteristics obtained from the extended dual dq–frame model for a set of initial design candidates, support vector regression algorithm is employed for supervised machine learning and increasing solutions in the design space. Furthermore, pareto front is used for selecting optimal machine models with maximum torque density and reduced torque ripple. Multi–objective trade–offs and comparison of initial and optimized designs based on average torque, torque ripple, efficiency and cost are performed. [ABSTRACT FROM AUTHOR]