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Real-time behavior recognition of animal: an IoT-based system design using acceleration data

Multimedia Tools and Applications 2024年 1-26页

作者： Tran, Duc-Nghia Manh, Do Viet Van Thanh, Pham Shankar, A. Achyut Joshi, Kireet Tran, Duc-Tan Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam University of Fire Prevention and Fighting Hanoi100000 Viet Nam Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Center of Research Impact and Outcome Chitkara University Punjab Chandigarh India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam

By analyzing large amounts of animal behavior data, humans can assess cattle’s condition. It leads to research and development a field using accelerometers and machine learning algorithms to ‘study’ behavior from acceleration data. It is challenging to analyze animal behavior due to the activity complexity. For example, acceleration data of some cow behaviors (e.g., feeding and standing) are similar if these behaviors contain similar gestures. In practical, real-time applications even bring more challenging since it is necessary to minimize a data window for behavior consideration. Using short data window often reduces the performance of classification algorithms. This study proposes a new solution for the mentioned challenges by combining neck-mounted and leg-mounted accelerometers, then using the vector of the dynamic body acceleration (VeDBA) and Mean features of synchronized acceleration data for the Decision Tree algorithm. The proposed method is demonstrated that performs well (0.94 accuracy, 0.91 sensitivity, 0.90 positive predictive value) within a low computational complexity algorithm and short data window (3-second data window, at the rate of one sample per second). It is particularly suited for the case of a real-time IoT-based system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Accelerometers

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Deep Reinforcement Learning-Based Multi-Task Scheduling in Cloud Manufacturing Under Different Task Arrival Modes

Deep Reinforcement Learning-Based Multi-Task Scheduling in C...

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作者： Ping, Yaoyao Liu, Yongkui Zhang, Lin Wang, Lihui Xu, Xun School of Mechano-Electronic Engineering Xidian University Xi'an710071 China School of Automation Science and Electrical Engineering Engineering Research Center of Complex Product Advanced Manufacturing Systems Ministry of Education Beihang University Beijing100191 China Department of Production Engineering KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering The University of Auckland Auckland1142 New Zealand

Cloud manufacturing is a service-oriented networked manufacturing model that aims to provide manufacturing resources as services in an on-demand manner. Scheduling is one of the key techniques for cloud manufacturing to achieve the aim. Multi-task scheduling with dynamical task arrivals is a critical problem in cloud manufacturing. Many traditional algorithms such as the genetic algorithm (GA) and ant colony optimization algorithm (ACO) have been used to address the issue, which, however, either are incapable of or perform poorly in tackling the problem. Deep reinforcement learning (DRL) as the combination of deep learning (DL) and reinforcement learning (RL) provides an effective technique in this regard. In view of this, we employ a typical DRL algorithm - Deep Q-network (DQN) - and propose a DQN-based approach for multitask scheduling in cloud manufacturing. Three different task arrival modes - arriving at the same time, arriving in random batches, and arriving one by one sequentially - are considered. Four baseline methods including random scheduling, round-robin scheduling, earliest scheduling, and minimum execution time (min-time) scheduling are investigated. A comparison of results indicates that the DQN-based scheduling approach is effective and performs best among all approaches in addressing the multitask scheduling problem in cloud manufacturing. © 2023 by ASME.

关键词： Deep reinforcement learning

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Forty Plus Years of Model Reduction and Still Learning 63

Forty Plus Years of Model Reduction and Still Learning

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Astolff, A. Beck, C.L. Bhattacharjee, D. Kawano, Y. Moreschini, A. Sandberg, H. Scherpen, J.M.A. Imperial College London Department of Electrical and Electronic Engineering United Kingdom Università di Roma 'Tor Vergata' Dipartimento di Ingegneria Civile e Ingegneria Informatica Italy Univ. of Illinois at Urbana-Champaign IL United States Hiroshima University Graduate School of Advanced Science and Engineering Japan Kth Royal Institute of Technology Stockholm Sweden Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Jan C. Willems Center for Systems and Control Netherlands

ISBN: (纸本)9798350316339

The approximation of complex dynamical systems models by reduced order models has been considered an important research problem for over four decades, not only in the field of control, but also in economics, image processing, circuit analysis, statistical mechanics, aircraft structures, and more recently in hybrid energy systems, to name just a sampling of fields. In this paper, we provide an overview of the development of balanced truncation and interpolation approaches for reducing linear and non-linear dynamical systems models for the purpose of control analysis and design. © 2024 IEEE.

关键词： Importance sampling

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Superconducting isolators based on time-modulated coupled-resonator systems

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Physical Review Applied 2024年第5期21卷 054061-054061页

作者： Yi Zhuang Chandrashekhar Gaikwad Daria Kowsari Kater Murch Aravind Nagulu Department of Electrical and Systems Engineering Department of Physics Department of Physics & Astronomy Dornsife College of Letters Arts & Sciences Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA

We present a unified approach for designing isolators based on temporally modulated coupled resonator networks. Our method leverages standard superconducting quantum interference device–based resonators as building blocks, arranged in series-coupled resonators to realize a wide range of on-chip nonreciprocal devices. The devices operate by translating microwave power into out-of-band intermodulation products in a direction-dependent way. Our theoretical studies demonstrate the effectiveness of the proposed approach, achieving isolators with near-zero insertion losses and isolation greater than 20 dB. To validate our findings, we implemented and measured a series-coupled three-resonator superconducting isolator using a single-layer superconducting process. At a base temperature of 20 mK, our device exhibited an insertion loss of 1.3 dB in the forward direction and isolation of up to 25 dB at the center frequency and greater than 15 dB across a bandwidth of 250 MHz in the reverse direction. Our approach promises to enable the design of a broad range of high-performance nonreciprocal devices for superconducting circuits.

关键词： Non-reciprocal propagation Optoelectronics Quantum circuits Quantum control Quantum information with solid state qubits Superconducting devices Cavity resonators Microwave techniques

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High-Dimensional Quantum Key Distribution by a Spin-Orbit Microlaser

High-Dimensional Quantum Key Distribution by a Spin-Orbit Mi...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Zhang, Yichi Zhao, Haoqi Wu, Tianwei Gao, Zihe Ge, Li Feng, Liang Department of Materials Science and Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics and Astronomy College of Staten Island CUNY Staten IslandNY10314 United States The Graduate Center CUNY New YorkNY10016 United States

High-dimensional quantum key distribution (HD-QKD) promises to enhance information capacity and noise-resilience. Here we report an integrated spin-orbit microlaser enabled HD-QKD through decoy-state BB84 protocol, demonstrating robust and compact real-time secret key generation strategy. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Quantum cryptography

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Design of Distributed Nonlinear Observers Without Using Global Inputs

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IEEE Transactions on Automatic Control 2025年

作者： Cao, Ganghui Wang, Jinzhi Polycarpou, Marios M. Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Cyprus KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This paper investigates the design of distributed observers for a class of nonlinear systems. The designed distributed observers reside in a network of sensor nodes. The communication links in the network enable each sensor node to estimate all states of the nonlinear system, even though each one can only collect a small portion of the system's outputs. However, all inputs of the system are still needed at each node to carry out a correct state estimation. For this problem, the paper further proposes a distributed nonlinear observer without using global inputs, in which each sensor node only needs partial input information of the nonlinear system. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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High-Dimensional Quantum Key Distribution by a Spin-Orbit Microlaser

High-Dimensional Quantum Key Distribution by a Spin-Orbit Mi...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

High-dimensional quantum key distribution (HD-QKD) promises to enhance information capacity and noise-resilience. Here we report an integrated spin-orbit microlaser enabled HD-QKD through decoy-state BB84 protocol, demonstrating robust and compact real-time secret key generation strategy. CLEO 2024 © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Quantum cryptography

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images 5

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

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