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14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Wu, Min Guo, Kefeng Lin, Zhi Xia, Huiyun An, Kang Yang, Liang Wang, Jiangzhou School of Space Information Space Engineering University Beijing101407 China College of Electronic Engineering National University of Defense Technology Hefei230037 China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Sixty third Research Institute National University of Defense Technology Nanjing210007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

ISBN: (纸本)9783031861956

Amid the escalating demand for accessible users and security insurance in satellite aerial terrestrial integrated networks (SATINs), security and energy efficiency emerge as pivotal indicators. This paper proposes a secure beamforming scheme in reconfigurable intelligent surface (RIS) aided SATINs, in presence with multiple eavesdroppers, where rate splitting multiple access (RSMA) and RIS are adopted at the secondary UAV networks for achieving multiuser diversity and antijamming. To optimize the secrecy energy efficiency (SEE) for secondary networks while adhering to constraints on ground earth station (GES) secrecy rate, a deep reinforcement learning (DRL) framework is proposed to address the coupling between optimization variables through the improved proximal policy optimization (PPO) method, of which from existing DRL scheme is that the proposed one builds a unified learning framework. Simulation results indicate that the SEE derived by the proposed DRL scheme is superior to that of benchmark schemes, which validate the advantage of this work. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Satellite antennas

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Interdependency patterns of potential seaport risk factors in relation to supply chain disruption in Indonesia

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Journal of Shipping and Trade 2023年第1期8卷 6页

作者： Do. Bagus, Muhammad Reza Hanaoka, Shinya Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8550 Japan Department of Information System College of Informatics and Computer Management Tidore Mandiri (STMIK Tidore Mandiri) Tidore Indonesia

As an integral part of the global supply chain network, Indonesian supply chain entities should understand conditional seaport risk factors that could lead to seaport threats that affect supply chain continuity. This study aims to provide a procedure for evaluating the interdependencies, implications, and correlations among various seaport risk factors for supply chain threats, specifically by investigating current practices in the developed economic region of Indonesian seaport operations. The study uses a rough set method to solve feature selection problems and multivariate analysis of variance to assess the correlation between dependent and independent variables. We find 39 conditional seaport risk factors that are potentially influenced by about 21 dependent factors related to seaport-fulcrum supply chain entities. Furthermore, threats from the planning process, infrastructure, seaport service process, distribution process, financial costs of nuclear enterprises and security existed and affluent highest potential risk in Indonesia. © 2023, The Author(s).

关键词： Association rule mining Genetic algorithm Interdependency risk analysis Rule rough set theory Seaport risk factors

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Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach

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IEEE Transactions on Wireless Communications 2023年第3期22卷 1808-1822页

作者： He, Hengtao Wang, Rui Jin, Weijie Jin, Shi Wen, Chao-Kai Li, Geoffrey Ye Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2BX United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs. © 2002-2012 IEEE.

关键词： Channel estimation

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Low Noise Wireless Sensing in Termite Detection

Low Noise Wireless Sensing in Termite Detection

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2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Zhang, Wei Qi, Xiangshu Luo, Yunlong Qi, Alex Qi, Yihong Feng, Gang Changsha University College of Computer Science and Engineering Changsha410022 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Pontosense Inc. KitchenerONN2H 5G5 Canada Southwest Jiaotong University School of Information and Technology Chengdu611756 China Missouri University of Science and Technology Electromagnetic Compatibility Laboratory RollaMO65409 United States Christie Digital Systems Canada Inc. Kitchener Canada

ISBN: (纸本)9798350360394

Termites cause enormous financial damages all around the world every year. The majority of research on wireless sensing is always concerned with the various human-centric applications, however, the vast potential of wireless sensing for termite detection is often disregarded. In this article, a Wireless Intelligent Sensing (WISe) millimeter-wave (mmWave) radar system for termite detection is proposed. Here, we design a three-layer architecture with some intelligent resources to monitor the movement signs of the termites. In addition, we present a low noise sensing architecture that minimizes the noise in hardware and digital signal processing, which can reduce the impact of environment factors on termite detection. Experimental results demonstrate that, the proposed system is capable of detecting termites and differentiating them from other insects. © 2024 IEEE.

关键词： Digital signal processing

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A Method to Classify Emotions Based on BERT 4

A Method to Classify Emotions Based on BERT

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4th International Signal Processing, Communications and engineering Management Conference, ISPCEM 2024

作者： Chen, Zeli Lin, Jian Jiang, Jie Jing, Di Department of Computer Science Lafayette College EastonPA United States School of Information Technology and Artificial Intelligence Zhejiang University of Finance and Economics Zhejiang Hangzhou China Department of Mathematical and Statistical Sciences University of Alberta EdmontonABT6G 2G1 Canada School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9798331528676

Nowadays, as science and technology continue to advance, addressing people's emotional issues is of particular significance, and how to accurately and efficiently recognize emotions has become increasingly prominent. We explores sentiment recognition and classification methods, including rule-based approaches, traditional machine learning models, deep learning models, and multi-modal sentiment analysis. Through comparative analysis, we will evaluate the effectiveness of these methods in handling social media texts. Additionally, we has developed a BERT-based two-stage model that first determines sentiment polarity (positive or negative) and then performs detailed classification of negative emotions. This two-stage model significantly improves accuracy and detail recognition compared to traditional one-stage models and other baseline methods. The results show that our approach not only enhances sentiment analysis accuracy but also provides deeper insights into the emotional subtleties of social media texts. This advancement holds significant practical implications for real-time sentiment monitoring and more effective crisis management strategies. ©2024 IEEE.

关键词： Contrastive Learning

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OPTIMAL LOAD MANAGEMENT OF A PROSUMER WITH PV POWER PLANT AND BATTERY STORAGE 13

OPTIMAL LOAD MANAGEMENT OF A PROSUMER WITH PV POWER PLANT AN...

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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2022

作者： Šimić, Zvonimir Klaić, Zvonimir Knežević, Goran Topić, Danijel J.J. Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Kneza Trpimira 2b Osijek Croatia

ISBN: (纸本)9781839538445

In this paper, a method for optimal load management of prosumer with a photovoltaic (PV) power plant and battery storage is presented. Three scenarios are observed, scenario 1 is a prosumer with a PV power plant without the ability of load time-shifting, scenario 2 is a prosumer with a PV power plant and ability of load time-shifting and scenario 3 is a prosumer with a PV power plant and battery storage (BS) with the ability of load time-shifting. Shiftable loads in the household observed in this paper are a washing machine, dishwasher, tumble dryer, and two air conditioners. Optimization is done for one month and a comparison of electricity exchanged with the grid and monthly electricity bills for three different scenarios are presented. In scenario 1, 441 kWh is received from the grid, 130 kWh is delivered to the grid and the electricity cost is 47.44 €. In scenario 2, 380 kWh is received from the grid, 70 kWh is delivered to the grid and the electricity cost is 44.64 €. In scenario 3, 348 kWh is received from the grid and the electricity cost is 34.12 €. The combination of a PV power plant and BS with optimal load management leads to the reduction of electricity costs. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Battery storage

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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computer Modeling in engineering & sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Deep Reinforcement Learning for Multi-Objective Resource All...

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作者： Xu, Yuanyuan Zhu, Kun Xu, Hu Ji, Jiequ Hohai University College of Computer and Information Nanjing211100 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China University of Victoria Department of Electrical and Computer Engineering VictoriaV8P 5C2 Canada

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success ratio of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a contribution-based dual-clip proximal policy optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all subproblems are solved. Simulation results reveal that the proposed algorithm can outperform other algorithms in terms of the MOS and transmission success ratio. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Energy-Aware Sensor Scheduling and Opportunistic Transmission for State Estimation in Industrial IoT Systems

Energy-Aware Sensor Scheduling and Opportunistic Transmissio...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Qiao, Zexin Lyu, Ling Dai, Yanpeng Cheng, Nan Shen, Xuemin Dalian Maritime University School of Information Science and Technology Dalian116026 China Xidian University School of Telecommunications Engineering Xi'an710071 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350345384

The wide application of Internet of Things (IoT) technology in industrial automation promotes the emergence of industrial IoT systems, where state estimation plays a critical role in accurately tracking the state of control system and enabling informed decisions and appropriate actions. State estimation is generally performed with the sensory data delivered over IoT. Therefore, the transmission performance of sensory data plays a crucial role in the accuracy of state estimation. However, it is challenging to ensure the required transmission reliability for state estimation due to spectrum scarcity, energy limitation, and the harsh industrial radio environment. To address this issue, we propose an energy-aware sensor scheduling and opportunistic transmission scheme to balance the estimation performance and resource consumption. In particular, we define a novel performance metric, estimation error ratio, to evaluate the impact of sensor scheduling on estimation performance. Then, a spectrum and energy resources constrained minimization problem is formulated to reduce the weighted sum of estimation error and energy consumption. In order to effectively solve the non-convex problem, an estimation error ratio-based algorithm is designed to determine the scheduled sensors. Moreover, we propose an iterative method to obtain the near-optimal solution for the opportunistic transmission problem. Finally, simulation results demonstrate that the proposed scheme can effectively reduce energy consumption with the estimation accuracy guarantee. © 2023 IEEE.

关键词： Energy utilization

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