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Utility-Scale Energy Storage Systems: Converters and Control IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-25 Sebastian Stynski; Wensheng Luo; Andrii Chub; Leopoldo G. Franquelo; Mariusz Malinowski; Dmitri Vinnikov
Energy storage systems (ESSs) facilitate utility grid operations on various levels, which include power generation, power transmission, and power distribution. The benefits of these systems produce an overall improvement of grid stability, security, and resilience; cost reductions resulting from the need for less expensive reserve equipment; enhanced power quality; and customer satisfaction. In line
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Front Cover IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-21
Presents the front cover for this issue of the publication.
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Table of Contents IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18
Presents the table of contents for this issue of the publication.
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Masthead IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Industrial Electronics Is Ubiquitous [Editor's Column] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Peter Palensky
Presents the introductory editorial for this issue of the publication.
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Continued Adaptation [Message From the President] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Terry Martin
Presents the President’s message for this issue of the publication.
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Can Electric Vehicles Meet Highway-Trip Requirements?: Exploration of the Real-World Impact on Highway Driving Range Derating IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Xinmei Yuan; Jiangbiao He; Shuai Li; Lili Li; Shuanglong Shi; Bo He
Battery electric vehicles (BEVs) have limited driving ranges compared to conventional vehicles (CVs). Therefore, whether BEVs meet highway-trip requirements poses a critical concern for consumers. The real-world driving range is significantly different from laboratory test results due to a lack of real-world factors. Here, a hybrid physical-stochastic model is proposed to assess the real-world performance
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Next-Generation Battery Management Systems: Dynamic Reconfiguration IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-21 Weiji Han; Torsten Wik; Anton Kersten; Guangzhong Dong; Changfu Zou
Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, and so forth. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications
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Developing More Efficient Wind Turbines: A Survey of Control Challenges and Opportunities IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Alireza Shourangiz-Haghighi; Matias Diazd; Yichen Zhang; Jiale Li; Yuan Yuan; Rasoul Faraji; Lei Ding; Josep M. Guerrero
Wind energy has received considerable attention during the past two decades, owing to its vital role in battling both the energy crisis and global climate change. Wind energy has grown to become the most significant nonconventional renewable energy source. Today, modern wind turbines (WTs) are controlled to offer several functionalities that improve their flexibility at a level similar to conventional
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On the Stability of the Power Electronics-Dominated Grid: A New Energy Paradigm IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-21 Ahmad Khan; Mohsen Hosseinzadehtaher; Mohammad B. Shadmand; Sertac Bayhan; Haitham Abu-Rub
The energy paradigm is making the modern power grid more difficult to study, design, and control. Precisely speaking, the pace of the new energy paradigm involves the high penetration of power electronics systems in the power grid, which becomes a challenge from stability, vulnerability, and power-quality points of views. Therefore, various literature have focused on defining the modern power grid
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Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-21 Ming Liu; Dongpeng Liu; Guangyu Sun; Yi Zhao; Duolin Wang; Fangxing Liu; Xiang Fang; Qing He; Dong Xu
Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deeplearning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. From the significant difference between the predicted trajectory and the observed one
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Convergence and Interoperability for the Energy Internet: From Ubiquitous Connection to Distributed Automation IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Ying Wu; Yanpeng Wu; Josep M. Guerrero; Juan C. Vasquez; Emilio J. Palacios-Garcia; Jiao Li
The Energy Internet is proposed to enhance the collaborative utilization of distributed renewable energy resources; enable a flexible, customer-engaged energy transaction network; and achieve real-time balancing of supply and demand. This allows integrating advanced Internet of Things (IoT)-based architectures, information and communications technology (ICT)-based end-to-end digital energy chains;
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Power Electronics and Drives: Applications to Modern Ship Propulsion Systems IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Carlos A. Reusser; Hector A. Young; Joel R. Perez Osses; Marcelo A. Perez; Oliver J. Simmonds
Electrically propelled ships gained popularity by the early 20th century, with the rapid development of submarines and mediumcapacity container ships, mainly using dc motors [1]. Synchronous ac motors have since been employed for naval propulsion systems, but due to the restricted operation of the available power electronic devices at that time, these configurations were too expensive and unreliable
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Single-Transmitter Multiple-Pickup Wireless Power Transfer: Advantages, Challenges, and Corresponding Technical Solutions IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Yu Gu; Jiang Wang; Zhenyan Liang; Yitong Wu; Carlo Cecati; Zhen Zhang
As an emerging research topic, wireless power transfer (WPT) with a single transmitter for multiple pickups (STMP) shows salient advantages of reducing the number of power inverters, increasing the power density, and so on, which has profound implications for various applications, such as wireless charging for electric vehicles (EVs) and smart homes. First, this article introduces the concept of STMP-WPT
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Private 5G: The Future of Industrial Wireless IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Adnan Aijaz
High-performance wireless communication is crucial to the digital transformation of industrial systems, which is driven by Industry 4.0 and Industrial Internet initiatives. Among the candidate industrial wireless technologies, 5G (cellular/mobile) holds significant potential. The operation of private (nonpublic) 5G networks in industrial environments is promising to fully unleash this potential. This
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Toward the Plug-and-Produce Capability for Industry 4.0: An Asset Administration Shell Approach IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Xun Ye; Junhui Jiang; Changdae Lee; Namhyeok Kim; Mengmeng Yu; Seung Ho Hong
The fourth industrial revolution, also termed Industry 4.0 (I4.0), will digitalize manufacturing operations and decentralize them from the shop floor to the office and across entire enterprise networks [1]. The proposed system-level digitalization solutions for I4.0 include cyber-physical systems [2] and digital twins [3]. Standardized I4.0 technologies are urgently required to change industry and
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Condition Monitoring of Industrial Electric Machines: State of the Art and Future Challenges IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Sang Bin Lee; Greg C. Stone; Jose Antonino-Daviu; Konstantinos N. Gyftakis; Elias G. Strangas; Pascal Maussion; Carlos A. Platero
The limitations of the thermal, vibration, or electrical monitoring of electric machines such as false indications, low sensitivity, and difficulty of fault interpretation have recently been exposed. This has led to a shift in the direction in research toward applying new techniques for improving the reliability of condition monitoring. With the changing environment, the purpose of this article is
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Industrial Electronics Society Awards 2020 [Society News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Eric Monmasson
Presents the recipients of IES society awards in 2020.
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Toward Future Sustainable Industrial Cyber-Physical Systems: ICPS 2020 in Finland [Society News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Jose L. Martinez Lastra; Armando W. Colombo; Stamatis Karnouskos; Shiyan Hu
Presents information on the ICPS 2020 Conference.
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ETFA 2020: The First Hybrid IES Conference in Challenging Times [Society News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Thilo Sauter; Francisco Vasques
Presents information on the EFTA 2020 Conference.
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The Second IEEE International Conference on Industrial Electronics for Sustainable Energy Systems [Society News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18
Presents information on the Second IEEE International Conference on Industrial Electronics for Sustainable Energy.
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A New IES TC on Technology, Ethics, and Society [Society News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18
Reports on the new IES Technical Committee on Technology, Ethics, and Society.
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The 100th-Anniversary Harbin Institute of Technology?IES Lectures on Industrial Technologies [Society News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Victor Huang; Huijun Gao
Reports on the 100th-Anniversary Harbin Institute of Technology-IES Lectures on Industrial Technologies.
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Breaking News?IES Standards Has Successfully Completed a New IEEE Standard! [Society News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Victor Huang
Reports on new standards development work completed by te IAS society.
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IEEE Macau Section IES Chapter Holds 2020 Undergraduate Project Contest [Chapter News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Chi-Seng Lam
Presents information on various IES Society chapters.
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[Erratum] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18
Presents corrections to production errors in the article, “Protection testing for multiterminal high-voltage dc grid: Procedures and assessments,” (Liu, Z., et al), IEEE Ind. Electron. Mag., vol. 14, no. 3, pp. 46–64, 2020. doi: 10.1109/ MIE.2020.2977150.
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Plans and presence: students and young professionals in a new virtual reality [students and young professionals news] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Marek Jasinski
Reports on IES activities using virtual meetings and events.
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The 2020 Women in Engineering Workshop in Italy [Women in IES News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Lucia Lo Bello
Presents information on the 2020 Women in Engineering Workshop.
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Induction Machines Handbook, Third Edition [Book News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Marian P. Kazmierkowski
The author is world-renowned and known for his 27 previous books elated to electrical machines, power electronics, and drives. This new offering offers a third edition of a handbook devoted to induction machines as a response to the significant progress observed in the last decade in technology and design for better precision and performance. Just like the second edition, the new third edition consists
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Power electronic converter configuration and control for dc microgrid systems [book news] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18 Fernando A. Silva
The book is a wonderful collection of peer-reviewed papers on power electronic converters and control for dc microgrid systems. After a preface by Prof. Frede Blaabjerg recommending the book, the chapters review the state of the art in wind energy conversion systems. Then, research outcomes are presented, such as multichannelbased microgrids; large-scale renewable energy integration issues, such as
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[Calendar] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-12-18
Presents the IES society calendar of upcoming events and meetings.
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Share Your Preprint Research with the World! IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-10-15
Advertisement: TechRxiv is a free preprint server for unpublished research in electrical engineering, computer science, and related technology. Powered by IEEE, TechRxiv provides researchers across a broad range of fields the opportunity to share early results of their work ahead of formal peer review and publication. Researchers are asked to upload their unpublished research.
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Protection Testing for Multiterminal High-Voltage dc Grid: Procedures and IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-25 Zhou Liu; Seyed Sattar Mirhosseini; Marjan Popov; Yash Audichya; Daniele Colangelo; Sadegh Jamali; Peter Palensky; Weihao Hu; Zhe Chen
eAssessment The application of multiterminal (MT), high-voltage dc (HVdc) (MTdc) grid technology requires test procedures for the operation and implementation of the protection solutions. The test procedures are usually derived from experience and from extensive measurement data, which, at present, are still not widely available. Based on a hardware-inthe- loop (HIL) method, advanced dc protection
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Cyber-Physical Microgrids: Toward Future Resilient Communities IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-24 Tuyen V. Vu; Bang L.H. Nguyen; Zheyuan Cheng; Mo-Yuen Chow; Bin Zhang
Microgrids can be isolated from large-scale power transmission/distribution systems (macrogrids) to deliver energy to their local communities using local energy resources and distribution systems when power outages occur in the macrogrids. In such situations, microgrids could be considered the last available resource to provide energy to critical infrastructure.
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Industrial Agents as a Key Enabler for Realizing Industrial Cyber-Physical Systems: Multiagent Systems Entering Industry 4.0 IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-24 Stamatis Karnouskos; Paulo Leitao; Luis Ribeiro; Armando Walter Colombo
Industrial agents (IAs) [1] are multiagentbased systems (MASs) [2] that, for many years, have been advocated as a promising and realistic solution for an emerging set of industrial challenges. In the past, MASs fell into the scope of enterprise agility [3]-[8], and now, more than ever, pertain to the industrial digital transformation and sustainability spheres. MAS technology is being applied to several
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Power Routing: A New Paradigm for Maintenance Scheduling IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-24 Marco Liserre; Giampaolo Buticchi; Jose Ignacio Leon; Abraham Marquez Alcaide; Vivek Raveendran; Youngjong Ko; Markus Andresen; Vito Giuseppe Monopoli; Leopoldo Franquelo
Currently, the necessity of efficient and reliable power systems is also increasing because of the strict requirements that standards and regulations impose, but still costs have to remain low. The monitoring and control of the components' lifetime can lead to reduce maintenance costs.However, overcoming the related challenges is not a straightforward task, as it involves knowledge of power device
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Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-24 Xiaosong Hu; Kai Zhang; Kailong Liu; Xianke Lin; Satadru Dey; Simona Onori
Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article
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Early Italian Computers: Pier Giorgio Perotto's P101 [Historical] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-24 Massimo Guarnieri
This historical column follows the article on Mario Tchou's ELEA 9003, which was published in the June 2020 issue of IEEE Industrial Electronics Magazine, and recounts events that also took place at the Olivetti Company. We know that much of the early progress in personal computers took place in the United States, and particularly on the West Coast, in the early 1970s. Nevertheless, trailblazers were
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Summing Up [Women in IES News] IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-24 Lucia Lo Bello
Worldwide, the current year has been quite challenging due to the COVID-19 pandemic and its dramatic effects. All of us have had to suddenly change our lives and habits to face such an unexpected global emergency for which we were not prepared. From one day to the next, the luckiest ones, that is, those of us who have not lost our jobs, have had to switch from working on site to smart working. In many
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Robust Formation Control for Cooperative Underactuated Quadrotors via Reinforcement Learning. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-24 Wanbing Zhao,Hao Liu,Frank L Lewis
In this article, the model-free robust formation control problem is addressed for cooperative underactuated quadrotors involving unknown nonlinear dynamics and disturbances. Based on the hierarchical control scheme and the reinforcement learning theory, a robust controller is proposed without knowledge of each quadrotor dynamics, consisting of a distributed observer to estimate the position state of
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Disturbance-Aware Neuro-Optimal System Control Using Generative Adversarial Control Networks. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-23 Kai-Fung Chu,Albert Y S Lam,Chenchen Fan,Victor O K Li
Disturbance, which is generally unknown to the controller, is unavoidable in real-world systems and it may affect the expected system state and output. Existing control methods, like robust model predictive control, can produce robust solutions to maintain the system stability. However, these robust methods trade the solution optimality for stability. In this article, a method called generative adversarial
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Online Learning With Adaptive Rebalancing in Nonstationary Environments. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-22 Kleanthis Malialis,Christos G Panayiotou,Marios M Polycarpou
An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely
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An Uplink Communication-Efficient Approach to Featurewise Distributed Sparse Optimization With Differential Privacy. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-17 Jian Lou,Yiu-Ming Cheung
In sparse empirical risk minimization (ERM) models, when sensitive personal data are used, e.g., genetic, healthcare, and financial data, it is crucial to preserve the differential privacy (DP) in training. In many applications, the information (i.e., features) of an individual is held by different organizations, which give rise to the prevalent yet challenging setting of the featurewise distributed
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Gated Value Network for Multilabel Classification. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-17 Yimin Hou,Sen Wan,Feng Bao,Zhiquan Ren,Yunfeng Dong,Qionghai Dai,Yue Deng
We introduce a gated value network (GVN) for general multilabel classification (MLC) tasks. GVN was motivated by deep value network (DVN) that directly exploits the ``compatibility'' metric as the learning pursuit for MLC. Meanwhile, it further improves traditional DVN on twofold. First, GVN relaxes the complex variable optimization steps in DVN inference by incorporating a feedforward predictor for
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Distributed Min-Max Learning Scheme for Neural Networks With Applications to High-Dimensional Classification. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-17 Krishnan Raghavan,Shweta Garg,Sarangapani Jagannathan,V A Samaranayake
In this article, a novel learning methodology is introduced for the problem of classification in the context of high-dimensional data. In particular, the challenges introduced by high-dimensional data sets are addressed by formulating a L₁ regularized zero-sum game where optimal sparsity is estimated through a two-player game between the penalty coefficients/sparsity parameters and the deep neural
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Robust Neurooptimal Control for a Robot via Adaptive Dynamic Programming. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-17 Linghuan Kong,Wei He,Chenguang Yang,Changyin Sun
We aim at the optimization of the tracking control of a robot to improve the robustness, under the effect of unknown nonlinear perturbations. First, an auxiliary system is introduced, and optimal control of the auxiliary system can be seen as an approximate optimal control of the robot. Then, neural networks (NNs) are employed to approximate the solution of the Hamilton-Jacobi-Isaacs equation under
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Optimal Impulsive Control Using Adaptive Dynamic Programming and Its Application in Spacecraft Rendezvous. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-17 Ali Heydari
Optimal control of nonlinear impulsive systems with free impulse instants and the number of impulses is investigated in this study. A scheme based on adaptive dynamic programming is developed, which leads to a feedback (approximate) solution to the defined optimal impulsive control problem. This is done by proposing a learning algorithm for tuning parameters of a function approximator, which, once
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Terminal Sliding Mode Control of MEMS Gyroscopes With Finite-Time Learning. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-17 Yuyan Guo,Bin Xu,Rui Zhang
This article proposes a neural terminal sliding mode controller (TSMC) with finite-time (FT) convergence for the uncertain MEMS gyroscope dynamics. To address the uncertainty, considering the periodic tracking property of MEMS gyroscopes, a composite learning mechanism driven by the learning performance evaluation signal is applied to learn the system dynamics. By selecting the terminal sliding mode
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Rich Visual Knowledge-Based Augmentation Network for Visual Question Answering. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-17 Liyang Zhang,Shuaicheng Liu,Donghao Liu,Pengpeng Zeng,Xiangpeng Li,Jingkuan Song,Lianli Gao
Visual question answering (VQA) that involves understanding an image and paired questions develops very quickly with the boost of deep learning in relevant research fields, such as natural language processing and computer vision. Existing works highly rely on the knowledge of the data set. However, some questions require more professional cues other than the data set knowledge to answer questions correctly
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Effective Visual Domain Adaptation via Generative Adversarial Distribution Matching. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-10 Qi Kang,SiYa Yao,MengChu Zhou,Kai Zhang,Abdullah Abusorrah
In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. However, through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks
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Attention in Natural Language Processing. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-10 Andrea Galassi,Marco Lippi,Paolo Torroni
Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed
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Corrections to ``Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning''. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-10 Nan Lai,Meina Kan,Chunrui Han,Xingguang Song,Shiguang Shan
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Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-09 Haishan Ye,Luo Luo,Zhihua Zhang
Composite function optimization problem often arises in machine learning known as regularized empirical minimization. We introduce the acceleration technique to the Newton-type proximal method and propose a novel algorithm called accelerated proximal subsampled Newton method (APSSN). APSSN only subsamples a small subset of samples to construct an approximate Hessian that achieves computational efficiency
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Off-Policy Reinforcement Learning for Tracking in Continuous-Time Systems on Two Time Scales. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-09 Wenqian Xue,Jialu Fan,Victor G Lopez,Yi Jiang,Tianyou Chai,Frank L Lewis
This article applies a singular perturbation theory to solve an optimal linear quadratic tracker problem for a continuous-time two-time-scale process. Previously, singular perturbation was applied for system regulation. It is shown that the two-time-scale tracking problem can be separated into a linear-quadratic tracker (LQT) problem for the slow system and a linear-quadratic regulator (LQR) problem
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Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control With Actuator Fault. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-09 Xinsong Yang,Xiaoxiao Wan,Cheng Zunshui,Jinde Cao,Yang Liu,Leszek Rutkowski
This article considers global exponential synchronization almost surely (GES a.s.) for a class of switched discrete-time neural networks (DTNNs). The considered system switches from one mode to another according to transition probability (TP) and evolves with mode-dependent average dwell time (MDADT), i.e., TP-based MDADT switching, which is more practical than classical average dwell time (ADT) switching
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Deep Learning-Based Model Predictive Control for Continuous Stirred-Tank Reactor System. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-09 Gongming Wang,Qing-Shan Jia,Junfei Qiao,Jing Bi,MengChu Zhou
A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. The proposed DeepMPC consists of a growing deep belief
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Inductive Structure Consistent Hashing via Flexible Semantic Calibration. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-09 Zheng Zhang,Luyao Liu,Yadan Luo,Zi Huang,Fumin Shen,Heng Tao Shen,Guangming Lu
Semantic-preserving hashing establishes efficient multimedia retrieval by transferring knowledge from original data to hash codes so that the latter can preserve the underlying visual and semantic similarities. However, it becomes a crucial bottleneck: how to effectively bridge the trilateral domain gaps (i.e., the visual, semantic, and hashing spaces) to further improve the retrieval accuracy. In
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Synchronous Fault-Tolerant Near-Optimal Control for Discrete-Time Nonlinear PE Game. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-09 Yuan Yuan,Peng Zhang,Xuelong Li
In this article, the synchronous fault-tolerant near-optimal control strategy design problem is studied for a class of discrete-time nonlinear pursuit-evasion (PE) games. In the studied PE game, the input saturation phenomenon and possible actuator fault are simultaneously taken into consideration. To accelerate the estimation speed, a novel nonlinear fault estimator is designed by introducing a nonlinear
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Exponential Stability of Fractional-Order Complex Multi-Links Networks With Aperiodically Intermittent Control. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-07 Yao Xu,Shang Gao,Wenxue Li
In this article, the exponential stability problem for fractional-order complex multi-links networks with aperiodically intermittent control is considered. Using the graph theory and Lyapunov method, two theorems, including a Lyapunov-type theorem and a coefficient-type theorem, are given to ensure the exponential stability of the underlying networks. The theoretical results show that the exponential
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Faceted Text Segmentation via Multitask Learning. IEEE Trans. Neural Netw. Learn. Syst. (IF 8.793) Pub Date : 2020-09-07 Bei Wu,Bifan Wei,Jun Liu,Kewei Wu,Meng Wang
Text segmentation is a fundamental step in natural language processing (NLP) and information retrieval (IR) tasks. Most existing approaches do not explicitly take into account the facet information of documents for segmentation. Text segmentation and facet annotation are often addressed as separate problems, but they operate in a common input space. This article proposes FTS, which is a novel model