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IEEE Sensors Letters 2023年第9期7卷 1页

作者： Tarekegn, Getaneh Berie Tai, Li-Chia Lin, Hsin-Piao Tesfaw, Belayneh Abebe Juang, Rong-Terng Hsu, Huan-Chia Huang, Kai-Lun Singh, Kanishk National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30010 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan Feng Chia University Department of Electronic Engineering Taichung40724 Taiwan Industrial Technology Research Institute Information and Communications Research Laboratories Hsinchu310401 Taiwan

Large-scale location estimation is crucial for many artificial intelligence Internet of Things (IoT) applications in the era of smart cities. This letter proposes a deep learning-based outdoor positioning scheme for large-scale wireless settings using fingerprinting techniques. We first developed a feature extraction technique using t-distributed stochastic neighbor embedding (t-SNE) to extract dominant and distinguishable features while eliminating noises from the radio fingerprints. Afterward, we developed a deep learning-based coarse-fine localization framework to improve positioning performance significantly. Based on our numerical analysis, the proposed scheme reduces computation time by 64.41%, and the average positioning error is 38 cm. Therefore, the proposed approach significantly improved positioning accuracy and reduced computation time. © 2017 IEEE.

关键词： Feature extraction

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Machine Learning based Design of Energy Management System using Non-Intrusive Load Monitoring Strategy

Machine Learning based Design of Energy Management System us...

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2024 International Conference on control, automation and Diagnosis, ICCAD 2024

作者： Shahid, Kamal Gulzar, Muhammad Majid Raza, Muhammad Ali Habib, Salman Ahmad, Hasnain University of The Punjab Institute of Electrical Electronics and Computer Engineering Lahore Pakistan King Fahd University of Petroleum & Minerals Department of Control & Instrumentation Engineering Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350361025

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings. © 2024 IEEE.

关键词： Deep neural networks

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Editorial: The Second International Workshop on Artificial Intelligence Systems in Education - AIxEDU 2

Editorial: The Second International Workshop on Artificial I...

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2nd International Workshop on Artificial INtelligent Systems in Education, AIxEDU 2024

作者： Casalino, Gabriella Limongelli, Carla Schicchi, Daniele Taibi, Davide Temperini, Marco Department of Computer Science University of Bari Aldo Moro Bari Italy Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome Italy Institute for Educational Technology Italian National Research Council CNR-ITD Palermo Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

The Second International Workshop on Artificial Intelligence Systems in Education (AIxEDU) marks its 2024 edition in conjunction with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA), held from 25-28 November 2024 in Bolzano, Italy. This workshop brings together Artificial Intelligence and Education experts to explore the latest innovations and best practices in applying AI to educational contexts. Participants discuss recent trends, research initiatives, and emerging developments in AI technologies that aim to enhance teaching methodologies, support personalized learning, and address pressing educational challenges. The workshop emphasizes a multidisciplinary approach to understanding how AI systems can promote inclusion, improve learning outcomes, and empower educators while addressing ethical considerations and ensuring equitable access to education aligned with global sustainable development goals. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Adversarial machine learning

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An Efficient Hybrid Red Panda Optimization-Similarity-Navigated Graph Neural Network with Blockchain Technology for Shared Electric Vehicle Charging 4

An Efficient Hybrid Red Panda Optimization-Similarity-Naviga...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sammy, F. Rajeshwari, S. Prashanth, G.K. Thirumalaimuthu, Babu Pokhariya, Hemant Singh Natrayan, L. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522 302 India Bangalore Institute of Technology Department of Mathematics Karnataka Bengaluru560004 India Siddaganga Institute Technology Department of Mca Karnataka Tumkur-572103 India St. Joseph's College of Engineering Department of Instrumentation and Control Engineering Tamil Nandu Chennai600119 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Blockchain technology for shared electric vehicle (EV) charging makes use of a distributed ledger in handling different transactions which promotes authenticity in charging. However, this technology can be non-scalable at times which slows down the transaction rates as the scale increases in an organization and may also demand high computational as well as energy requirements which increases the inefficiency of the total system, the operating expenses of EV charging station, costs of energy utilized for charging, cost of maintenance and cost of station infrastructure may prove to be a weakness, impacting the feasibility of infrastructure deployment and upkeep expenses of the charging stations. To overcome these drawbacks, this paper proposes a hybrid method for blockchain technology with shared EV charging. The proposed approach combines the use of the Red Panda Optimization (RPO) with Similarity-Navigated Graph Neural Network (SNGNN). Hence, it is named as hybrid RPO-SNGNN approach. Reducing the operating cost of EV charging position and maximizes the security efficiency is the main aim of the proposed approach. To optimize scheduling and resource allocation by finding efficient paths by RPO and predict future demand and user behaviour patterns by SNGNN. By then, the proposed model has been put into practice on the MATLAB work platform and contrasted with other existing methods. The proposed technique outperforms all existing techniques, including Fuzzy-based Cross Hierarchical Reinforcement Q Learning (FCHRQL), Non-Dominated Sorting Genetic Algorithm-Long Short-Term Memory Network (NSGA-II-LSTMN), and Ant Colony Optimization-Proximal Policy Optimization (ACO-PPO). The proposed method shows the operating cost of $550 and the security rate is 97%. From the result, the proposed approach operating cost is low and the security rate is high when compared to the existing approaches. © 2024 IEEE.

关键词： Charging stations

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Receding-Horizon control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators

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SN computer Science 2023年第1期4卷 1-16页

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 71-73 Kassel 34121 Germany

This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach. © 2022, The Author(s).

关键词： Model approximation Neural networks Online control Optimization Reinforcement learning Stability

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C. M. Chongqing University School of Big Data & Software Engineering Chongqing400000 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. © 2002-2012 IEEE.

关键词： Federated learning

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A Non-Iterative LMI-Based Design of Static Output Feedback control with L2 Gain Performance for Linear Systems 6

A Non-Iterative LMI-Based Design of Static Output Feedback C...

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6th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2024

作者： Oya, Hidetoshi Asada, Daiki Nagai, Shunya Nakano, Kazushi Tokyo City University Department of Computer Science Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (纸本)9798350380040

In this paper, we present a new LMI-based non-iterative design strategy for static output feedback controllers with L2 Gain Performance for linear systems. In our approach, on the basis of the well-known necessary and sufficient condition for the existence of the static output feedback control for linear systems, the non-iterative LMI-based design procedure for the proposed static output feedback controller is derived. In this paper, we show the LMI-based non-iterative design procedure for static output feedback controllers with L2 Gain Performance and the effectiveness of the proposed static output feedback control is presented through a simple illustrative example. © 2024 IEEE.

关键词： Feedback control

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Distributed Event-Triggered Adaptive Cooperative Circumnavigation control of Networked Unmanned Aerial Vehicles 20

Distributed Event-Triggered Adaptive Cooperative Circumnavig...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Ghommam, Jawhar Fareh, Raouf Al Naimi, Ibrahim Sultan Qaboos University College of Engineering Department of Electrical and Computer Engineering Oman Laboratory of Control Energy Management ENIS Tunisia University of Sharjah Electrical Engineering Dept. United Arab Emirates

ISBN: (纸本)9798350332568

In this paper, the objective for a group of unmanned aerial vehicle agents (UAVs) to achieve three dimensional circumnavigation around a moving target which information is made available to all agents in the group. The cooperative circumnavigation is to drive the UAVs to orbit around the target according to a given elliptical desired spatial formation. Due to the thrust limitation needed to fly the drone, existing cyclic pursuit algorithms cannot be extended directly to achieve this objective. Thus the proposed algorithm is worked out to take into account this constraint in order to achieve such objective. The drones are subject to unknown external disturbance, also the masses of those agent drones are assumed to be unknown. Furthermore, the communication cost can be decreased and the Zeno behavior is shown to be excluded. The proposed controller guarantees the bounded control effort irrespective of the external disturbance and model uncertainties of the drone. Numerical simulations are conducted to illustrate the efficacy of the approach. © 2023 IEEE.

关键词： Drones

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New Skills and Knowledge for Digital Entrepreneurs in the age of Artificial Intelligence 19

New Skills and Knowledge for Digital Entrepreneurs in the ag...

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19th European Conference on Innovation and Entrepreneurship, ECIE 2024

作者： Orozova, Daniela Angelova, Nadezhda Zlatev, Zlatin Department of Informatics and Mathematics Faculty of Economics Trakia University Stara Zagora Bulgaria Department of Electrical Engineering Automation Computer Systems and Communications Faculty of Technics and Technologies Trakia University Yambol Bulgaria

ISBN: (纸本)9781917204149

Digital technologies are becoming increasingly complex and integrated, leading to significant transformations in society and the economy. The article aims to explore and summarize the new opportunities and potential risks of the widespread use of artificial intelligence (AI) in all aspects of life, to define the new skills and necessary knowledge of digital entrepreneurs and to highlight the need for transformation in modern education. Recognizing that the relationship between technology and business is two-way and becoming stronger, revealing that well-prepared employees are a guarantee of success and prosperity of companies in various fields, we try to focus on the main groups of qualities, skills and basic knowledge of students in the age of artificial intelligence. The development of the Internet, expansion of connectivity through social networks, the advent of AI, 3D printing, and immersive technologies like Augmented Reality, and Virtual Reality, require new knowledge and skills, leading to new challenges in education. Qualified personnel in this modern world must have solid professional training and systemic thinking (knowledge, skills, accumulated information), developed cognitive abilities, and personal skills based on collecting and analyzing large amounts of diverse information from heterogeneous sources. Questions arise: how can multiple information sources be combined effectively, and how can the fusion of multiple sources provide additional information to support decision-making processes? Combining information obtained from the real world makes the results heterogeneous and more informative. It follows the need to develop machine learning methods to extract relevant information from increasingly complex data sets. The goal is to improve the accuracy of the applied classification algorithms by combining predictions from multiple models, as well as obtaining a more stable final classification evaluation, effective handling of noisy data, adaptation to c

关键词： Adversarial machine learning

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Designed Steering Law for the Hybrid Actuators of Single Gimbal control Moment Gyroscopes and Reaction Wheels Array 41

Designed Steering Law for the Hybrid Actuators of Single Gim...

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第41届中国控制会议

作者： Feng Liu Dongping Wang Kedong LYU School of electrical information engineering North Minzu University College of Computer and Information Engineering Tianjin Agricultural University Department of Automation Engineering Tianjin Electronic Information Technician College

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Single Gimbal control Momentum Gyroscopes(SGCMG) are widely used with the ability of large torque output and high accuracy control of a *** singularity problem(especially the internal elliptical singularity) of SGCMG becoming a major obstacle in successful implementing the task of the attitude *** singularity can be effectively avoided by additional reaction wheel(RW) array in the field of the actual ***,the saturation problem is existed in the *** this paper,the hybrid actuators of the SGCMG with pyramid configuration and RW array with orthogonal configuration is *** new steering law is ***,the internal elliptical singularity and the saturation problem of the RW can be effectively avoided by the new steering law at the same time.

关键词： singularity avoidance new designed steering law hybrid actuators of SGCMG and RW array

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