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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]

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]

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]

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]

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 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]

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]

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]

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]

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]

This paper devises an algorithm for finding the minimal set of axial lines that can represent a geometry of building and urban layout in two dimensions. Although axial lines are useful to analyze spatial configuration in the Space Syntax, existing methods for selecting axial lines seldom address the optimality of their solutions. The proposed algorithm uses linear programming to obtain a minimal set of axial lines. To minimize the number of axial lines that represent the entire geometry of building and urban layout, a linear programming problem is established in which a set of axial lines represents the entire geometry. The axial lines must have at least one intersection with every extension line of the wall edges to the sides of the reflex angles. If a solution to this linear programming problem exists, it will be guaranteed to be an optimum. However, some solutions of this general linear programming problem may include isolated lines, which are undesirable for an axial line analysis. To avoid isolated axial lines, this paper states a new formulation by adding a group of constraints to the original formulation. By examining the modified linear programming problem in various two-dimensional building maps and spatial layouts, this paper demonstrates that the proposed algorithm can guarantee a minimum set of axial lines to represent a two-dimensional geometry. This modified linear programming problem prevents isolated axial lines in the process of axial line reduction. [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]

Binary neural networks (BNNs) are variations of artificial/deep neural network (ANN/DNN) architectures that constrain the real values of weights to the binary set of numbers {−1,1}. By using binary values, BNNs can convert matrix multiplications into bitwise operations, which accelerates both training and inference and reduces hardware complexity and model sizes for implementation. Compared to traditional deep learning architectures, BNNs are a good choice for implementation in resource-constrained devices like FPGAs and ASICs. However, BNNs have the disadvantage of reduced performance and accuracy because of the tradeoff due to binarization. Over the years, this has attracted the attention of the research community to overcome the performance gap of BNNs, and several architectures have been proposed. In this paper, we provide a comprehensive review of BNNs for implementation in FPGA hardware. The survey covers different aspects, such as BNN architectures and variants, design and tool flows for FPGAs, and various applications for BNNs. The final part of the paper gives some benchmark works and design tools for implementing BNNs in FPGAs based on established datasets used by the research community. [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]

In group decision making, it is inevitable that the individual decision maker’s subjectivity is involved, which causes difficulty in reaching a group decision. One of the difficulties is to aggregate a small set of expert opinions with the individual subjectivity or uncertainty modeled with probability theory. This difficult problem is called probability distribution function aggregation (DFA). This paper presents a simple and efficient approach to the DFA problem. The main idea of the proposed approach is that the DFA problem is modeled as a nonlinear function of a set of probability distribution functions, and then a linear feedback iteration scheme is proposed to solve the nonlinear function, leading to a group judgment or decision. Illustration of this new approach is given by a well-known DFA example which was solved with the Delphi method. The DFA problem is a part of the group decision problem. Therefore, the proposed algorithm is also useful to the decision making problem in general. Another contribution of the this paper is the proposed notation of systematically representing the imprecise group decision problem with the classification of imprecise information into three classes, namely incomplete information, vague information, and uncertain information. The particular DFA problem dealt with in this paper is then characterized with this general notation. [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]

This paper presents a comprehensive review of natural fiber-reinforced composites (NFRCs) for lower-limb prosthetic designs. It covers the characteristics, types, and properties of natural fiber-reinforced composites as well as their advantages and drawbacks in prosthetic designs. This review also discusses successful prosthetic designs that incorporate NFRCs and the factors that make them effective. Additionally, this study explores the use of computational biomechanical models to evaluate the effectiveness of prosthetic devices and the key factors that are considered. Overall, this document provides a valuable resource for anyone interested in using NFRCs for lower-limb prosthetic designs. [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]

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]

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 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]

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]

In this paper, a novel technique to improve the fault detection sensitivity in a three-phase transformer using the tank current method is reported. The tank current method was first published by the first author in recent past for single isolated transformer winding. A fault in single isolated transformer winding was uniquely identified employing the typical resonance behavior of tank current, but for three-phase transformer, such implementation is yet to be explored. The present scope of this paper is only for uniformly wound three-phase transformer windings. Implementing the tank current method for such a three-phase transformer may not be very simple, since the neighboring windings, unlike in the case of isolated winding, strongly influence the measurement of the tank current. Therefore, this paper appropriately considers the effect of neighboring windings in a three-phase transformer and investigates the resonance behavior of its tank current to propose a promising innovative extension approach of the method. Experimental results on actual transformers are also presented for practical validation of the proposed method. Results imply that the tank current method is indeed very sensitive to identify the fault in an actual transformer compared to the conventional neutral current method. The authors also believe that the method needs to be reinforced by field measurements on several transformers to ascertain its viability. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent modeling and optimization method to set up the smart energy hub model for the multiple energy system and optimize the operation of its associated energy hub model automatically. The core idea of the proposed method is to divide the complex energy hub model into several simple energy hub models. In this paper, the proposed computerized algorithm, which is applied to the smart energy hub model division, includes an energy loop intelligent searching method, a real nodes arrangement method, and a virtual nodes insertion method. One case study for the multiple energy system of a residential district in Guangzhou city is performed to validate the effectiveness of the proposed method. Compared with the traditional calculation method, the proposed method guarantees global optimal operation decision for the whole multiple energy system and the computational burden is significantly reduced. [ABSTRACT FROM AUTHOR]

The lack of information regarding the systemic behavior of new software and hardware before introducing them for controlling and operating power networks consists of a major challenge for power distribution utilities towards a smart grid implementation. In this context, this paper describes an innovative and cost-effective test platform for advanced distribution automation (ADA) functionalities. The platform integrates a steady-state power system simulator, intelligent electronic devices, smart meters, communication infrastructure, and typical operation systems, using a cyber-physical system approach. The development of such platform originated a novel smart grid emulator, aiming at the execution of systemic tests and validation of ADA functionalities in a controlled environment, allowing systemic analysis rarely found in hardware-in-the-loop (HIL) testbeds. Through such an approach, it is possible to predict and solve typical issues that would be typically noticed only in field implementation. The study case presents tests of a specific algorithm for downed conductor location, considering the environment that the power distribution utility would have through the deployment of a smart grid infrastructure. The paper also discusses the robustness of the event-driven synchronization as an interface technique for HIL and software-in-the-loop environment, which is responsible for integrating operation systems, field equipment, and the power system simulator. [ABSTRACT FROM AUTHOR]

This paper deals with stabilization of an ideal transformer model (ITM) circuit, used as an interfacing algorithm for hardware-in-the-loop simulations. The ITM allows for the distribution of simulation model computations over multiple processors or can be used in power hardware-in-the-loop simulations. When specific impedance conditions of the partitioned circuit are not met, this interfacing algorithm becomes unstable. This paper first presents a new theoretical approach for representing the ITM interfacing algorithm that is based on delay differential equations. With a stability analysis of the obtained time-delay system, a stabilization method is proposed, which increases the stability boundaries of the ITM algorithm. Simulation examples, including the scenario of a two-level inverter system, are provided. [ABSTRACT FROM AUTHOR]

Test generation procedures target a variety of fault models in order to produce test sets that are effective for defect detection. This paper considers the likely scenario where two-cycle skewed-load tests are generated to detect single transition faults, and the test set is complemented with tests for single stuck-at faults that are not detected by the transition fault test set. For this scenario, this paper makes several unique observations that can be utilized to produce a single compact test set that consists only of two-cycle skewed-load tests for both fault models. The first observation is that a single-cycle test for a stuck-at fault can be transformed into a skewed-load test that is guaranteed to detect the stuck-at fault without performing logic or fault simulation. The second observation is that skewed-load tests, which are transformed from single-cycle tests for stuck-at faults, sometimes detect more transition and stuck-at faults than tests that were generated for transition faults. The third observation is that a static test compaction procedure, which is based on the modification and removal of tests, is effective in this context because it allows tests for stuck-at faults to detect more transition faults and vice versa. This paper describes a test compaction procedure based on these observations and presents experimental results for benchmark circuits to demonstrate the effectiveness of the procedure. [ABSTRACT FROM AUTHOR]

Lithography simulation is one of the key steps in physical verification, enabled by the substantial optical and resist models. A resist model bridges the aerial image simulation to printed patterns. While the effectiveness of learning-based solutions for resist modeling has been demonstrated, they are considerably data-demanding. Meanwhile, a set of manufactured data for a specific lithography configuration is only valid for the training of one single model, indicating low data efficiency. Due to the complexity of the manufacturing process, obtaining enough data for acceptable accuracy becomes very expensive in terms of both time and cost, especially during the evolution of technology generations when the design space is intensively explored. In this paper, we propose a new resist modeling framework for contact layers, utilizing existing data from old technology nodes and active selection of data in a target technology node, to reduce the amount of data required from the target lithography configuration. Our framework based on transfer learning and active learning techniques is effective within a competitive range of accuracy, i.e., $3 \times -10 \times $ reduction on the amount of training data with comparable accuracy to the state-of-the-art learning approach. [ABSTRACT FROM AUTHOR]

Classical numerical techniques remain very computationally expensive to model the magnetization of twisted multifilamentary superconductors in three dimensions because they require solving a large system of equations. In this paper, a reduced model is proposed to approximate the ac losses of these types of wires. It is established through the projection of the physical problem in a helical Frenet basis and the use of periodic boundary conditions on a slice of the wire. The reduced model on the slice leads to systems of more reasonable size leading to smaller and faster simulations than when the whole wire is modeled. Simulation results show that the proposed method leads to computed losses similar to those obtained on a whole wire. Several simulation cases are proposed to prove the validity of the proposed model, and a comparison to some ac loss measurements is proposed to show the effectiveness of the proposed approach. The implementation relies on the H-formulation and is solved with the open-source solver, Getdp. [ABSTRACT FROM AUTHOR]

This paper presents an active learning strategy for robotic systems that takes into account task information, enables fast learning, and allows control to be readily synthesized by taking advantage of the Koopman operator representation. We first motivate the use of representing nonlinear systems as linear Koopman operator systems by illustrating the improved model-based control performance with an actuated Van der Pol system. Information-theoretic methods are then applied to the Koopman operator formulation of dynamical systems where we derive a controller for active learning of robot dynamics. The active learning controller is shown to increase the rate of information about the Koopman operator. In addition, our active learning controller can readily incorporate policies built on the Koopman dynamics, enabling the benefits of fast active learning and improved control. Results using a quadcopter illustrate single-execution active learning and stabilization capabilities during free fall. The results for active learning are extended for automating Koopman observables and we implement our method on real robotic systems. [ABSTRACT FROM AUTHOR]

Product lifecycle management (PLM) is an engineering management approach which helps organizations to manage product-related technical data for its complete lifecycle. The right product data enables better business functioning in organizations. PLM systems are recent and upcoming information technology tools in manufacturing firms. Although PLM systems were initially introduced as the on-premise tools, the current cloud technology is taking it from organizational boundaries to a global level through the Internet. Most enterprise applications have already started moving to clouds; PLM is also all set to be utilized through clouds. But it brings various challenges during cloud PLM adoption. Hence, this paper aims to empirically investigate the critical challenges that manufacturing firms face while adopting cloud PLM. There is rare academic research in the cloud PLM domain. This paper results in the three most significant challenges viz., “Data security on clouds,” “Lack of trust on services,” and “Lack of customization.” These critical challenges not only help the managers in manufacturing firms in planning to migrate on clouds but also the technical cloud providers to improve the cloud services and systems appropriately in order to increase the compatibility of cloud PLM in firms. The present paper uniquely identifies the critical challenges for cloud PLM adoption in large manufacturing firms. [ABSTRACT FROM AUTHOR]