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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiali Wang Yuning Jiang Xin Liu Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China Automatic Control Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings.

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Design of High Temperature Anticorrosion Diagnosis System for Atmospheric and Vacuum Distillation Unit Based on Forcecontrol and SQL Sever2014 5

Design of High Temperature Anticorrosion Diagnosis System fo...

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5th International Conference on Automation, control and Robotics engineering, CACRE 2020

作者： Zhao, Liang Zhang, Zexin Wang, Lida Liu, Guichang Dalian University of Technology School of Control Science and Engineering Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China Dalian University of Technology School of Chemical Engineering Dalian China

ISBN: (纸本)9781728198880

Due to the complex composition of the oil flowing through the refining unit, the equipment anticorrosion work has always been a major problem in petrochemical enterprises, and the corrosion problem is a key factor related to shortening the service life of the atmospheric and vacuum distillation unit. Aiming at the problem of high temperature corrosion of atmospheric and vacuum unit in a petrochemical enterprise and the current situation of corrosion management in the enterprise, this paper designs a high temperature corrosion diagnosis system of atmospheric and vacuum unit based on Forcecontrol configuration development platform. The system uses three dimensional interpolation algorithm to calculate the corrosion rate, and uses Microsoft SQL sever2014 database technology and Forcecontrol 7.1 configuration development platform to realize the comprehensive management of corrosion data, corrosion rate calculation, early warning, expert advice and other functions. Through the function test of each module in the system, and using the actual corrosion data provided by the enterprise to simulate and verify the system, the test results show that the system can achieve the expected functions, and ensure the calculation accuracy of corrosion rate. © 2020 IEEE.

关键词： Distillation

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Federated Reinforcement Learning for Electric Vehicles Charging control on Distribution Networks

arXiv

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arXiv 2023年

作者： Qian, Junkai Jiang, Yuning Liu, Xin Wang, Qiong Wang, Ting Shi, Yuanming Chen, Wei The MoE Engineering Research Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University China The Automatic Control Laboratory EPFL Switzerland The School of Information Science and Technology ShanghaiTech University China The State Grid Beijing Electric Power Company China The Department of Electronic Engineering Tsinghua University China

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov Decision Process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Over-the-Air Federated Learning via Second-Order Optimization

arXiv

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arXiv 2022年

作者： Yang, Peng Jiang, Yuning Wang, Ting Zhou, Yong Shi, Yuanming Jones, Colin N. The Shanghai Key Lab. of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai200062 China The Automatic Control Laboratory EPFL Lausanne1015 Switzerland The School of Information Science and Technology ShanghaiTech University Shanghai201210 China

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in excessive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Wireless networks

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intelligent Mobile Power Sharing and Rental System Based on BeiDou Navigation

Intelligent Mobile Power Sharing and Rental System Based on ...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Hui Hu Xinhu Li Jiaxin Wu Qingli Zeng Renmin Zhang College of Communication and Electronic Engineering Jishou University Jishou Hunan P.R. China Hunan Provincial Key Lab. of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Huaihua Hunan P.R. China

ISBN: (纸本)9781665464024

Aiming at the problems that the mobile power supply in the market cannot output AC voltage and manually adjustable DC voltage at the same time, expensive and cannot be accurately located, an intelligent mobile power sharing and rental system based on BeiDou navigation is designed. The device mainly consists of three major parts: power circuit module, controller module and mobile APP, which solves a series of problems such as location finding, rental use, visual power management and positioning return of shared power by users. The power circuit module converts the battery voltage into 220V AC output and adjustable DC precision output. The controller module realizes the functions of power supply DC output voltage collection, Battery power Detection, Beidou positioning and data uploading to the server. The mobile APP realizes the functions such as displaying the location, voltage, power and other data of multiple power supply units and sharing the rental under the shared rental mode by the APP. The system realized in this paper can greatly promote the development of China's shared energy storage industry and effectively change the current dilemma that people have difficulties in using electricity outdoors.

关键词： Smart agriculture Visualization Technological innovation Costs Power supplies Navigation Voltage

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Research on Dynamic Maintenance Strategy of Complex Equipment System 11

Research on Dynamic Maintenance Strategy of Complex Equipmen...

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11th International Conference on Prognostics and System Health Management, PHM-Jinan 2020

作者： Li, Yingshun Wang, Aina Yi, Xiaojian Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China Beijing Institute of Technology The School of Mechatronical Engineering Department of Overall Technology Beijing China

ISBN: (纸本)9781728151816

In recent years, the complex equipment system presents the rapid development of integration and intelligence, and it is easy to break down when it works in bad environment for a long time. The maintenance support ability and comprehensive maintenance economic benefit of the complex equipment system play an important role in the defense ability. Therefore, in order to improve the combat readiness of equipment, solve the current situation of economic loss and low operation ability caused by insufficient or excessive maintenance. In this paper, a dynamic maintenance strategy for complex equipment system is proposed. The goal of this maintenance strategy is to minimize the average cost in a short period of time,to update the running state of the system and the execution time of fault maintenance in real time. Finally, the effectiveness of the dynamic maintenance strategy proposed in this paper is verified by the analysis of maintenance model parameters. The dynamic maintenance strategy proposed in this paper considers the operation state and economic cost of complex equipment system, which is more applicable to practical engineering application. © 2020 IEEE.

关键词： Maintenance

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Orthogonal Waveform Design of MIMO Radar Based on Niche Genetic Algorithm

Orthogonal Waveform Design of MIMO Radar Based on Niche Gene...

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2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020

作者： Pei, Bingnan Pei, Tengda Zhang, Haosheng Quanzhou University of Information Engineering Fujian Key Lab. of Modern Communication and BeiDou Positioning Technology Quanzhou China Dalian University Dalian Key Laboratory of Environmental Perception and Intelligent Control Dalian China

ISBN: (纸本)9781728172019

In order to optimize the orthogonal design of MIMO radar waveform, an improved algorithm based on niche genetic algorithm is proposed. The algorithm is able to eliminate individuals with similar structures in the population or data, maintain a certain distance between individuals, and effectively hold the diversity of them It thus eliminates the phenomenon of premature convergence, strengthens the global search ability of the algorithm, and improves its convergence speed. It is shown that the algorithm has both the advantages of overall search ability of genetic algorithm and the ability of the niche genetic algorithm to discover multiple optimal solutions. Simulation was given to show that the algorithm was cable to reduce the side-lobe of the auto-correlation function of the orthogonal polyphase code and the peak value of the cross-correlation function of the code with good performance of autocorrelation and cross-correlation obtained © 2020 IEEE.

关键词： Genetic algorithms

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Deep and Localized Transcranial Magnetic Stimulation in Rat Using Conventional Round Coil

Deep and Localized Transcranial Magnetic Stimulation in Rat ...

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General Assembly and Scientific Symposium, URSI

作者： Mai. Lu Shoogo Ueno Key Lab. of Opt-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou Gansu China Graduate School of Engineering Kyushu University Fukuoka Japan

Transcranial magnetic stimulation (TMS) shows significant values in both brain research and therapeutic applications in the treatment of various psychiatric and neurological disorders. Animal studies of TMS plays an important role in understanding of the cellular and molecular mechanisms underlying TMS induced effects in the brain. This work investigated TMS in rat brain by employing conventional round coil with varied coil positions. Three dimensional distributions of induced electric field in realistic rat model were calculated by 3-D impedance method. Simulations results indicated that the conventional round coil can be applied to rat TMS with improved focality while also keeping high stimulation intensities in deep brain regions.

关键词： Neurological diseases Solid modeling Impedance measurement Transcranial magnetic stimulation Simulation Rats Brain modeling

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Attention-Based Knowledge-aware Multi-Interest intelligent Model for Sequential Recommendation

Attention-Based Knowledge-aware Multi-Interest Intelligent M...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yang Li Qianmu Li Shunmei Meng Jun Hou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Intelligent Manufacturing Department Wuyi University Jiangmen China State Key Lab. for Novel Software Technology Nanjing University Nanjing China School of Social Science Nanjing Vocational University of Industry Technology Nanjing China

As a fundamental means of intelligent service, recommender systems have shown a trend of diversification. Sequential recommendation, a class of time-aware methods, aims to predict the user’s next click. Multi-interest representation is a hot topic in recommender systems, but the research on how to apply multi-interest into sequences is still in the minority. And many methods will introduce knowledge graph to improve the representation of items, but how to consider both knowledge graph and user’s multi-interest in sequential recommendation has become a temporary blank. In this paper, we propose an attentive sequential recommendation with knowledge-aware multi-interest module (AKAMI). Items are embedded from the sequence’s structure and user’s interests. Specifically, we represent items by stacking self-attention layers to mine the internal correlation of the sequence. Meanwhile, we segment the sequence via the common neighbors in the knowledge graph and cast the user’s multi-interest based on the aggregation of subsequences. Multi-interests are integrated into item representation by the gating network adaptively. Comprehensive experiments on two real-world datasets show that the proposed model performs better than various representative methods under different evaluation metrics. Additionally, we also visually explain the model through a case study.

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StMMR: Accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation

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GigaScience 2024年第13期13卷 giae089页

作者： Zhang, Daoliang Yu, Na Yuan, Zhiyuan Li, Wenrui Sun, Xue Zou, Qi Li, Xiangyu Liu, Zhiping Zhang, Wei Gao, Rui Center of Intelligent Medicine School of Control Science and Engineering Shandong University Jinan 250061 China Institute of Science and Technology for Brain-Inspired Intelligence Center for Medical Research and Innovation Shanghai Pudong Hospital Fudan University Pudong Medical Center Fudan University Shanghai 200433 China MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST Department of Automation Tsinghua University Beijing 100084 China School of Software Engineering Beijing Jiaotong University Beijing 100044 China

Background: Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value for characterizing and understanding tissue architecture. However, the inherent heterogeneity and varying spatial resolutions present challenges in the joint analysis of multimodal SRT data. Results: We introduce a multimodal geometric deep learning method, named stMMR, to effectively integrate gene expression, spatial location, and histological information for accurate identifying spatial domains from SRT data. stMMR uses graph convolutional networks and a self-attention module for deep embedding of features within unimodality and incorporates similarity contrastive learning for integrating features across modalities. Conclusions: Comprehensive benchmark analysis on various types of spatial data shows superior performance of stMMR in multiple analyses, including spatial domain identification, pseudo-spatiotemporal analysis, and domain-specific gene discovery. In chicken heart development, stMMR reconstructed the spatiotemporal lineage structures, indicating an accurate developmental sequence. In breast cancer and lung cancer, stMMR clearly delineated the tumor microenvironment and identified marker genes associated with diagnosis and prognosis. Overall, stMMR is capable of effectively utilizing the multimodal information of various SRT data to explore and characterize tissue architectures of homeostasis, development, and tumor. © 2024 The Author(s).

关键词： domain identification geometric deep learning multimodal integration similarity contrastive learning spatially resolved transcriptomics

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