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Self-adaptive Bat Algorithm With Genetic Operations

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1284-1294页

作者： Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou H.Vincent Poor School of Software Engineering Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Electrical Engineering Princeton UniversityPrinceton NJ 08544 USA IEEE

Swarm intelligence in a bat algorithm(BA)provides social *** operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization *** integration provides an opportunity for improved search ***,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions *** from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated ***,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and *** by these exemplars,SBAGO improves both BA’s efficiency and global search *** evaluate this approach by using 29 widely-adopted problems from four test *** is also evaluated by a real-life optimization problem in mobile edge computing *** results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

关键词： Bat algorithm(BA) genetic algorithm(GA) hybrid algorithm learning mechanism meta-heuristic optimization algorithms

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Visual cognition of birds and its underlying neural mechanism:A review

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Avian Research 2022年第2期13卷 216-225页

作者： Xiaoke Niu Zhenyang Jiang Yanyan Peng Shuman Huang Zhizhong Wang Li Shi Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical EngineeringZhengzhou UniversityZhengzhou450001China Department of Automation Tsinghua UniversityBeijing100084China

Birds have acute vision and many remarkable visual cognition abilities,due to their unique living *** underlying neural mechanisms have also attracted interests of researchers in ***,we firstly summarize the visual cognition abilities of birds,and make a comparison with ***,the underlying neural mechanisms are presented,including histological structure of avian brain and visual pathways,typical experimental results and conclusions in electrochemistry and *** latter mainly focuses on several higher brain areas related to visual cognition,including mesopallium ventrolaterale,entopallium,visual Wulst,and nidopallium ***,we make a conclusion and provide a suggestion about future studies on revealing the neural mechanisms of avian visual *** review presents a detailed understanding of avian visual cognition and would be helpful in ornithology studies in the field of cognitive neuroscience.

关键词： Avian Electrophysiology Neural mechanism Visual cognition

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Electromagnetic Characteristic Analysis and Design of a Novel Intensifying-Flux Hybrid Excited Permanent Magnet Machine 6

Electromagnetic Characteristic Analysis and Design of a Nove...

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6th IEEE Student Conference on Electric Machines and Systems, SCEMS 2023

作者： Lu, Ruipan Xu, Pengchong Liu, Zhangqi Liu, Xiping Jiangxi University of Science and Technology School of Electrical Engineering and Automation Jiangxi China The State University of New Jersey Department of Electrical and Computer Engineering Rutgers PiscatawayNJ United States

ISBN: (纸本)9798350329346

In this paper, a novel intensifying-flux hybrid excitation machine (IF-HEPMSM) is proposed in conjunction with the latest development requirements of electric vehicles. Therefore, the proposed IF-HEPMSM design can simultaneously obtain the synergy of strong overload capability and wide speed regulation range. Firstly, the topology, characteristics and working principle of IF-HEPMSM are introduced to reveal the improvement of the machine performance, based on which, the tooth width and yoke thickness of the machine stator are optimized to ensure the output torque of the machine while effectively improving the wide flux regulation range. The electromagnetic characteristics of the IF-HEPMSM are investigated and compared with the IF-PMSM, conventional permanent magnet machine to highlight the superiority of the IF-HEPMSM. © 2023 IEEE.

关键词： Permanent magnets

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ComESN: Compressive Echo State Network for Multi-task Time-series Prediction

ComESN: Compressive Echo State Network for Multi-task Time-s...

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2023 IEEE International Symposium on Product Compliance engineering - Asia 2023, ISPCE-AS 2023

作者： Jiang, Ruiqi Wu, Zhou Zhang, Yongfeng Ye, Xianming Chongqing University School of Automation Chongqing China University of Jinan School of Electrical Engineering Jinan China University of Pretoria Department of Electrical Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798350361209

Time-series prediction has been widely studied for single-variable applications, but shows poor performance for multi-variable applications due to the complex correlation. Multivariable prediction is often transferred into many single-variable tasks, and results into high computational cost. The prediction performance decreases exponentially as the dimensions increase. The compression of multiple prediction tasks is necessary to provide a trade-off between computational cost and prediction accuracy. For this purpose, compressive echo state neural network (ComESN) is proposed, which consists of a compressive module and a multi-task echo state network (MT-ESN). In this study, the locally linear embedding algorithm is adopted as compression method to reduce the number of prediction tasks for improving the computational efficiency. A novel MT-ESN is developed to further improve the performance of ESN for multi-task prediction. Due to highly efficient compression and unveiling correlation of multiple tasks, ComESN could cut down the computational cost while providing a promising prediction accuracy. Experiments on three real-world datasets illustrate that the proposed ComESN could compete with state-of-the-art methods with regard to computational cost and accuracy. © 2023 IEEE.

关键词： Forecasting

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An overview on the distributed internal model approach and its applications

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Control Theory and Technology 2024年第3期22卷 345-359页

作者： Youfeng Su He Cai Jie Huang College of Computer and Data Science Fuzhou UniversityFuzhou 350116China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Mechanical and Automation Engineering The Chinese University of Hong KongShatinHong KongChina

The cooperative output regulation problem has been studied by two approaches:the distributed observer(DO)approach and the distributed internal model(DIM)approach,*** of these two approaches has its own merits and ***,we presented an overview on the cooperative output regulation problem by the DO *** paper further surveys the cooperative output regulation problem by the DIM *** first summarize the constructions and the roles of two different versions of the internal models:the distributed p-copy internal model and the distributed canonical internal ***,we describe an integrated framework that combines the DO approach and the DIM *** and variants of the DIM and their applications will also be highlighted.

关键词： Cooperative output regulation Distributed p-copy internal model Distributed canonical internal model Integrated approach

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Customized Touch UI Development for E-Cockpit in Automobile 3

Customized Touch UI Development for E-Cockpit in Automobile

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Sivaraj, D. Rathika, P.D. Jayasree, D. Dwarkesh Psg College of Technology Department of Electronics and Communication Engineering Coimbatore India Psg College of Technology Department of Robotics and Automation Engineering Coimbatore India Psg Polytechnic College Department of Electronics and Communication Engineering Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370867

In recent times, the automotive industry has made significant steps towards digitization by focusing on electronic cockpits (E-Cockpits) to enhance the driving experience, comfort and safety. The incorporation of Touch-Based User Interface (UI) technologies has played a crucial role in revolutionizing Driver-Vehicle interaction [10]. The project of E-Cockpit represents a concept that consolidates various internal car systems onto one unified digital platform, enabling seamless control and access to essential infotainment, safety features, and connectivity services [14]. For the realization of the complete potential of an E-Cockpit, it is important to develop an innovative and User-Friendly touch UI as this directly influences both user experience and safety concerns. © 2023 IEEE.

关键词： User interfaces

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Machine Learning in Academic Performance Prediction: Analyzing Attendance and Marks to Forecast Future Results

Machine Learning in Academic Performance Prediction: Analyzi...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Kanmani Divakar, Divyesh Keshaveni, N. Ramesh, E. Prathibha, M. Canara Engineering College Mangaluru Visvesvaraya Technological University Department of Artificial Intelligence and Machine Learning Belagavi India Mangaluru Visvesvaraya Technological University Department of Robotics & Automation Engineering SCEM Belagavi India Canara Engineering College Mangaluru Visvesvaraya Technological University Department of Electronics & Communication Engineering Belagavi India Canara Engineering College Mangaluru Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517892

In the realm of higher education, predicting student performance is crucial for enhancing educational outcomes and institutional efficiency. This project aims to develop a predictive model to forecast students upcoming semester performance based on their past academic records and attendance. This paper makes use of machine learning regression techniques, including Random Forest, Gradient Boosting, and Voting Regressor, to analyze students' scores and attendance data. The dataset comprises marks and attendance records for analytical, theoretical, and practical subjects, with target grades categorized into seven classes: A+, A, B+, B, C, P and F. To evaluate model performance, continuous predictions were discretized into these grade categories, allowing for the calculation of Precision, Recall, F1-Score, and the generation of confusion matrices. The results indicate the efficacy of different models in accurately predicting student grades. Visualizations such as bar charts and heatmaps were used to present and compare the performance metrics across various models and subject types. The insights from this project are expected to assist educators in identifying at-risk students and improving educational strategies, ultimately contributing to enhanced student success and institutional reputation. © 2024 IEEE.

关键词： Prediction models

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Delay-Guaranteed Path Selection and Scheduling in IAB Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Eftetahi, Pooria Seyed Cai, Lin Ren, Xiangyu Department of Electrical and Computer Engineering University of Victoria VictoriaBC Canada Department of Electrical & Computer Engineering the University of Victoria Canada Department of Automation Engineering University of Electronic Science and Technology of China Chengdu China

Integrated access and backhaul (IAB) is a promising solution to improve coverage at low deployment costs. In IAB networks, due to wireless channel variations, guaranteeing delay for delay-sensitive applications is a major challenge. Given the random traffic arrivals and channel variations, using a central controller for packet-level delay management becomes infeasible due to the added delay from the central controller. In this paper, we propose a distributed cross-layer method to provide delay-guaranteed path selection and scheduling for the IAB network where priority queues and weighted round robin are adopted to deliver differentiated services. Our goal is to determine the optimal path and scheduling decisions to maximize the total utility of the IAB network. We deploy an iterative approach in a distributed manner to solve the maximization problem at each IAB-node. Through simulation, we show that our proposed solution guarantees the delay at the packet-level while achieving considerable gains in terms of delay and packet delivery ratio compared to the state-of-the-art. © 2015 IEEE.

关键词： Delay-sensitive applications

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Reinforcement Learning for Online Dispatching Policy in Real-Time Train Timetable Rescheduling

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 478-490页

作者： Yue, Peng Jin, Yaochu Dai, Xuewu Feng, Zhenhua Cui, Dongliang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Bielefeld Nature Inspired Computing and Engineering Group Bielefeld33619 Germany University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Surrey School of Computer Science and Electronic Engineering GuildfordGU2 7XH United Kingdom

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the presence of unexpected disturbances. However, it is challenging to promptly create a rescheduled timetable in real time. In this study, we propose a reinforcement-learning-based method for real-time rescheduling of high-speed trains. The key innovation of the proposed method is to learn a well-generalized dispatching policy from a large amount of samples, which can be applied to the TTR task directly. At first, the problem is transformed into a multi-stage decision process, and the decision agent is designed to predict dispatching rules. To enhance the training efficiency, we generate a small yet good-quality action set to reduce invalid explorations. Besides, we propose an action sampling strategy for action selection, which implements forward planning with consideration of evaluation uncertainty, thus improving search efficiency. Extensive experimental results demonstrate the effectiveness and competitiveness of the proposed method. It has been proven that the local policies trained by the proposed method can be applied to numerous problem instances directly, rendering it unnecessary to use human-designed rules. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 127-139页

作者： Tao, Xian Yan, Shaohua Gong, Xinyi Adak, Chandranath Institute of Automation Chinese Academy of Sciences Beijing100190 China Binzhou Institute of Technology Binzhou256606 China CAS Engineering Laboratory for Intelligent Industrial Vision Beijing100190 China School of Artificial Intelligence University of ChineseAcademy of Sciences Beijing100049 China Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

In industrial quality assessment, monitoring whether the textured product contains defects is a critical step. Compared to a large number of defect-free images that are easy to obtain, anomaly samples are limited and vary randomly in size and type. It is challenging to develop an automatic and accurate texture defect localization system that only uses normal images for training. In this article, a multiresolution feature learning network is proposed to detect various texture defects in an unsupervised manner. A robust pretrained model is first employed to extract the perceptual features from the input image, then the perceptual features of various layers are fed to the corresponding multiscale autoencoder framework. This hierarchical alignment strategy aids in receiving multilevel information for locating anomalies of various sizes. Moreover, a residual attention module is embedded in the architecture to further improve the detection performance. Our proposed method has achieved state-of-the-art performance on the texture dataset of MVTecAD. We also extended the experiment to the real industrial texture datasets, and its detection result is better than the major existing advanced techniques. © 2020 IEEE.

关键词： Textures

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