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arXiv 2024年

作者： Zhao, Songhan Gong, Shimin Gu, Bo Li, Lanhua Lyu, Bin Hoang, Dinh Thai Yi, Changyan The School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China The Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China The School of Communications and Information Engineering Nanjing University of Posts and Telecommunications China The School of Electrical and Data Engineering University of Technology Sydney Australia The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs’ trajectory planning and operating modes, the ARIS’s passive beamforming, and the GUs’ transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs’ trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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DeCASA in AgriVerse: Parallel Agriculture for Smart Villages in Metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2055-2062页

作者： Xiujuan Wang Mengzhen Kang Hequan Sun Philippe de Reffye Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Beijing Engineering Research Center of Intelligent Systems and Technology Beijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Department of Chromosome Biology Max Planck Institute for Plant Breeding ResearchBeijing 100049China Faculty of Biology LMU MunichGroßhaderner Str.2Planegg-Martinsried D-82152Germany AMAP Univ MontpellierCIRADCNRSINRAEIRDMontpellier F-34398France IEEE Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

The demand for food is tremendously increasing with the growth of the world population,which necessitates the development of sustainable agriculture under the impact of various factors,such as climate *** fulfill this challenge,we are developing Metaverses for agriculture,referred to as Agri Verse,under our Decentralized Complex Adaptive systems in Agriculture(De CASA)project,which is a digital world of smart villages created alongside the development of Decentralized sciences(De Sci)and Decentralized Autonomous Organizations(DAO)for Cyber-Physical-Social systems(CPSSs).Additionally,we provide the architectures,operating modes and major applications of De CASA in *** achieving sustainable agriculture,a foundation model based on ACP theory and federated intelligence is ***,we discuss the challenges and opportunities.

关键词： Parallel Agriculture Management and Control AgriVerse Agriculture CPSS ACP DAO-Based Platform Precision Agriculture

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Partial Label Feature Selection: An Adaptive Approach

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IEEE Transactions on Knowledge and data engineering 2024年第8期36卷 4178-4191页

作者： Zan Zhang Jialu Yao Lin Liu Jiuyong Li Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei Anhui China UniSA STEM University of South Australia Adelaide SA Australia Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei Anhui China

As an emerging weakly supervised learning framework, partial label learning aims to induce a multi-class classifier from ambiguous supervision information where each training example is associated with a set of candidate labels, among which only one is the true label. Traditional feature selection methods, either for single label and multiple label problems, are not applicable to partial label learning as the ambiguous information contained in the label space obfuscates the importance of features and misleads the selection process. This makes the selection of a proper feature subset from partial label examples particularly challenging, and therefore has rarely been investigated. In this paper, we propose a novel feature selection algorithm for partial label learning, named PLFS, which considers not only the relationships between features and labels, but also exploits the relationships between instances to select the most informative and important features to enhance the performance of partial label learning. PLFS constructs an adaptive weighted graph to exploit the similarity information among instances, differentiate the label space and weight the feature space, which leads to the selection of a proper feature subset. Extensive experiments over a broad range of benchmark data sets clearly validate the effectiveness of our proposed feature selection approach.

关键词： Feature extraction Supervised learning Training Knowledge engineering Labeling Classification algorithms Big data

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Application of Over-Sampling Techniques and Fuzzy ARTMAP to Condition Monitoring of a Power Generation System

Application of Over-Sampling Techniques and Fuzzy ARTMAP to ...

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Power engineering Application (ICPEA), IEEE International Conference in

作者： Timothy Zhi Wei Chang Shing Chiang Tan Kok Swee Sim Chee Peng Lim Pey Yun Goh Faculty of Engineering and Technology Multimedia University Melaka Malaysia Faculty of Information Science and Technology Multimedia University Melaka Malaysia Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia

Condition monitoring is a process of assessing the health status of a system, process, or machine. Monitoring and identifying any potential fault can be conducted by leveraging measurements from the installed sensors that provide information on the state of the system. In this respect, machine learning models are useful for processing and analyzing the sensor data for fault detection. However, the imbalanced nature of these sensory data can cause misleading high accuracy scores. In this study, we employ an over-sampling method to tackle the imbalanced class problem. Specifically, both Synthetic Minority Over-sampling Technique (SMOTE) and Gaussian SMOTE are used to generate minority class samples. The balanced data set is used by the Fuzzy ARTMAP (FAM) model for fault classification. The effectiveness of the developed method is evaluated using a real-world circulating water system in a power generation plant. The results indicate that both SMOTE variants can improve the performance of FAM in detecting faults corresponding to operating conditions of the circulating water system for efficient power generation.

关键词： Condition monitoring Fault diagnosis Power engineering Machine learning algorithms Fault detection data models Sensor systems

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Analysis of Typical Features Using an Angle-to-sEMG Decoder Model

Analysis of Typical Features Using an Angle-to-sEMG Decoder ...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Zehao Liu Hai Jiang Yusuke Yamanoi Peiji Chen Xiaobei Jing Guanglin Li Hiroshi Yokoi Xu Yong Department of Mechanical Engineering and Intelligent Systems Univiersity of Electro-Communications Tokyo Japan Chinese Academy of Sciences (CAS) Shenzhen Institute of Advanced Technology (SIAT) Shenzhen China Department of Elecrical Engineering Faculty of Engineering Tokyo University of Science Tokyo Japan

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Current prosthetic control methods mainly rely on extensive data collection and the training of classification models. Nevertheless, achieving a balance between learning efficiency and classification accuracy remains a persistent challenge. In this paper, we introduce a novel prosthetic control approach that divides the processing of surface electromyography (sEMG) signals into two stages to predict corresponding joint angles. Initially, a pretrained model is utilized to infer the associated joint angles based on typical features. This methodology necessitates the mapping of individual sEMG signals to these typical features through a compact mapping network during application. we think that it will enhance hand motion recognition accuracy while minimizing learning duration though a pre-training approach. This study represents the initial phase, which involves the acquisition and evaluation of the similarity among typical features linked to joint angles. To this end, we constructed angle-to-sEMG models (decoders) and input the same unprocessed continuous joint angle data into the decoders of five participants. Subsequently, we utilized the resultant typical features as inputs for the encoders of different individuals. The findings indicate that, notwithstanding interindividual variability, there exists a degree of resemblance in the typical features associated with joint angles, thus enabling the generation of angle data resembling the true angles even across diverse subjects.

关键词： Training Analytical models Accuracy Biological system modeling Signal processing Electromyography data models Decoding Surface treatment Prosthetics

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Postural Synergy Analysis of Continuous Hand Movements for Application to Prosthetic Hands

Postural Synergy Analysis of Continuous Hand Movements for A...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Hai Jiang Yusuke Yamanoi Peiji Chen Xu Yong Guanglin Li Hiroshi Yokoi Xiaobei Jing Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen China Department of Mechanical Engineering and Intelligent Systems The Univiersity of Electro-Communications Tokyo Japan Department of Elecrical Engineering Faculty of Engineering Tokyo University of Science Tokyo Japan

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

This paper focuses on exploring the coordinated movements used to control dexterous myoelectric prosthetic hands by hand motion analysis inspired by the motor synergy theory of the human hand. Recently, prosthetic hands have become multi-degree-of-freedom, and simultaneous proportional control of each finger is the most required challenge. In response to this problem, motor synergy is expected to play a key role in constructing the control method because it reduces the amount of information on the control object. In the experiment, we used a Leap Motion Controller to measure 60 continuous hand movement data from four healthy individuals, analyzed based on principal component analysis methods, and extracted several principal component data used by humans to control various grasping movements. We produced a hand model with 19 degrees of freedom for visualizing the principal component data. The results of the visualization showed similarities in postural synergies among the four healthy individuals used to control continuous hand movements. Based on the acquired synergies, the number of learning data for the control method of myoelectric prosthetic hands will be greatly reduced.

关键词： Hands Proportional control data visualization Signal processing Motors Motion measurement Motion analysis Informatics Prosthetic hand Principal component analysis

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Evaluation of the Montenegrin Academic Digital Innovation Hub 27

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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27th International Conference on Information Technology, IT 2023

作者： Lakovic, Luka Kara, Peter A. Ognjanovic, Ivana Bokor, Laszlo Sendelj, Ramo Reich, Christoph Roganovic, Milovan Zoulias, Emmanouil Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada Simon, Aniko University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Faculty of Science Engineering and Computing Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro Sigma Technology Budapest1093 Hungary

ISBN: (纸本)9798350397512

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro. © 2023 IEEE.

关键词： Life cycle

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ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System

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Procedia Computer science 2023年 217卷 1266-1279页

作者： M.O. Okwu L.K. Tartibu E.O. Ojo S. Adume J.O. Gidiagba J. Fadeyi Department of Mechanical and Industrial Engineering Tech. University of Johannesburg South Africa Department of Mechanical Engineering Federal University of Petroleum Resources Effurun Nigeria Mathematics and Data Science Dept. Computing Science and Mathematics Division Faculty of Natural Science University of Stirling UK Faculty of Engineering and Applied Sc. Memorial University Newfoundland Canada Department of Industrial and Systems Engineering University of Buffalo USA

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach.

关键词： Adaptive Neuro-Fuzzy Inference System Dual Source Multi-Echelon Multi-Destinations

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Assistive Technical Communication of Montenegrin eServices: a Case Study 27

Assistive Technical Communication of Montenegrin eServices: ...

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27th International Conference on Information Technology, IT 2023

作者： Simon, Aniko Lakovic, Luka Kovacevic, Pavle Kara, Peter A. Ognjanovic, Ivana Sendelj, Ramo Reich, Christoph Roganovic, Milovan Mantas, John Bokor, Laszlo Sigma Technology Budapest1093 Hungary University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing LondonKT1 2EE United Kingdom University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinical Centre of Montenegro Clinic for Neurology Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi 11527 Greece

ISBN: (纸本)9798350397512

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity. © 2023 IEEE.

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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