Search

In-plane shear deformation is a highly important mechanic that controls the formation of wrinkles during the forming of textile composites. This paper presents two computational formulations that describe the in-plane shear behavior of woven thermoset prepreg fabric. The bias-extension characterization test was used to validate the computational models. One of the two computational models presented is based on linear visco-elasticity and the other is based on visco-hyperelasticity. The comparison of simulation and experiments shows that a visco-hyperelastic model is necessary to accurately capture the behavior of woven composite prepregs. This paper also compares the experimental measurements with calculations based on the pin-jointed net theory to develop a criterion that helps to determine the range of the bias-extension experiment data that can be used to validate and calibrate computational models. [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]

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]

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]

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]

Replicability and reproducibility of computational models has been somewhat understudied by "the replication movement." In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code. [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]

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]

Work sharing and work stealing are two scheduling paradigms to redistribute work when performing distributed computations. In work sharing, processors attempt to migrate pending jobs to other processors in the hope of reducing response times. In work stealing, on the other hand, underutilized processors attempt to steal jobs from other processors. Both paradigms generate a certain communication overhead and the question addressed in this paper is which of the two reduces the response time the most given that they use the same amount of communication overhead. Prior work presented explicit bounds, for large scale systems, on when randomized work sharing outperforms randomized work stealing in case of Poisson arrivals and exponential job sizes and indicated that work sharing is best when the load is below $\phi -1 \approx 0.6180$ , with $\phi $ being the golden ratio. In this paper we revisit this problem and study the impact of the job size distribution using a mean field model. We present an efficient method to determine the boundary between the regions where sharing or stealing is best for a given job size distribution, as well as bounds that apply to any (phase-type) job size distribution. The main insight is that work stealing benefits significantly from having more variable job sizes and work sharing may become inferior to work stealing for loads as small as $1/2 + \epsilon $ for any $\epsilon > 0$. [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]

Agent-based approaches have gained popularity in engineering applications, but its potential for advanced traffic controls has not been sufficiently explored. This paper presents a multi-agent framework that models traffic control instruments and their interactions with road traffic. A constrained Markov decision process (CMDP) model is used to represent agent decision making in the context of multi-objective policy goals, where the policy goal with the highest priority becomes the single optimization objective and the other goals are transformed as constraints. A reinforcement learning-based computational framework is developed for control applications. To implement the multi-objective decision model, a threshold lexicographic ordering method is introduced and integrated with the learning-based algorithm. Moreover, a two-stage hybrid framework is established to improve the learning efficiency of the model. While the proposed approach is potentially applicable for different road traffic operations, this paper applies the framework for traffic signal control in a network of Stockholm based on traffic simulation. The computational results show that the proposed control approach can handle a complex case of multiple policy requirements. Meanwhile, the agent-based intelligent control has shown superior performance when compared to other optimized signal control methods. [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]

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]