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A game incentive mechanism for energy efficient federated learning in computing power networks

Digital communications and Networks 2024年第6期10卷 1741-1747页

作者： Xiao Lin Ruolin Wu Haibo Mei Kun Yang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengduChina Chengdu Tongfei Technology Co.LtdChengduChina

Computing Power Network(CPN)is emerging as one of the important research interests in beyond 5G(B5G)or *** paper constructs a CPN based on Federated Learning(FL),where all Multi-access Edge Computing(MEC)servers are linked to a computing power center via wireless *** this FL procedure,each MEC server in CPN can independently train the learning models using localized data,thus preserving data ***,it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC *** address these issues,we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC ***,we formulate a comprehensive algorithm to jointly optimize the communication resource(wireless bandwidth and transmission power)allocations and the computation resource(computation capacity of MEC servers)allocations while ensuring the local accuracy of the training of each MEC *** numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.

关键词： Computing power network Federated learning Energy efficiency Stackelberg game Resource allocation

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Research on Task Scheduling Model of Ant Colony Optimization Cloud Computing Platform for Online Practical Customer-training Application

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IEIE Transactions on Smart Processing and Computing 2024年第3期13卷 243-253页

作者： Wang, Hongtao School of Electronic and Communication Engineering Jilin Technology College of Electronic Information Jilin132021 China

With the continuous development of internet information technology, cloud-computing task-scheduling platform technology is gradually maturing. Cloud computing is profoundly changing every aspect of people's lives and providing many conveniences. With the application of cloud computing in more fields, more extensive applications and efficient task scheduling algorithms have become increasingly important. This research focuses on the problem of task-scheduling methods for cloud computing platforms in customer-oriented online training systems. Based on the optimization of the ant colony algorithm, an ant colony optimization (ACO) cloud-computing task-scheduling algorithm is proposed. The research results indicate that when the number of tasks is 300, the makespan value of the optimized ant colony cloud scheduling algorithm (OACC) is 340, that of the discrete firefly algorithm (DFA) is 350, that of multi-objective differential evolution (MODE) is 380, and that of improved group search optimization (IGSO) is 409. The overall performance of OACC was 20.3% higher than that of IGSO. OACC maintained a low and stable degree of imbalance (DI) in different task count tests. At a task volume of 300, the overall utility evaluation of the ACO cloud-computing task-scheduling algorithm was 146, which is 31.5% higher than ACO, 18.7% higher than TACO, and 8.1% higher than LB-AACO. The experimental results meet expectations and indicate that the OACC cloud-computing task-scheduling algorithm proposed in the study has high task-processing ability and efficiency and is capable of scheduling tasks on cloud computing platforms for customer-oriented online training systems. Copyrights © 2024 The Institute of Electronics and information Engineers.

关键词： Ant colony optimization

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Contrastive Clustering for Unsupervised Recognition of Interference Signals

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Computer Systems Science & engineering 2023年第8期46卷 1385-1400页

作者： Xiangwei Chen Zhijin Zhao Xueyi Ye Shilian Zheng Caiyi Lou Xiaoniu Yang The School of Communication Engineering Hangzhou Dianzi UniversityHangzhou310018China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314001China

Interference signals recognition plays an important role in anti-jamming *** the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition ***,there is no unsupervised interference signals recognition algorithm at *** this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is ***,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive *** the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive *** addition,a dynamic entropy parameter strategy is *** simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC *** unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other *** particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 *** addition,our method is close to the supervised learning algorithm.

关键词： Interference signals recognition unsupervised clustering contrastive learning deep learning k-nearest neighbor

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Process Industries Soft sensor modeling based on Semi-supervised Growing Echo States Gaussian Process

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IEEE Sensors Journal 2025年第11期25卷 20695-20707页

作者： Li, Wenyi Dai, Wei Li, Biao Nan, Jing China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Suzhou University School of Mechanical and Information Engineering Suzhou234000 China

The echo state Gaussian process (ESGP) is an efficient method for modeling dynamical systems and has been successfully employed in soft sensor modeling within the process industry. However, the ESGP operates as a supervised learner, necessitating manual configuration of the reservoir. In practical process industries, many industrial samples lack labels, resulting in incomplete data. To address these challenges, this paper introduces a semi-supervised growing ESGP (SS-GESGP). Firstly, we propose a growing ESGP (GESGP) for incremental automatic reservoir construction. Subsequently, to tackle soft sensing modeling with incomplete data, we integrate a state resampling technique into the GESGP learning process. This technique resamples the echo state, incorporating both labeled and unlabeled spatiotemporal information. By leveraging the resampled states for Gaussian process, we convert semi-supervised problems into supervised learning scenarios. Finally, the SS-GESGP is evaluated using both time-series benchmarks and real-world process industry data. Experimental results demonstrate that the proposed method exhibits superior soft sensing performance compared to state-of-the-art approaches. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Fine-Grained Emotion Prediction for Movie and Television scene images

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The Journal of China Universities of Posts and Telecommunications 2024年第3期31卷 43-55页

作者： Su Zhibin Zhou Xuanye Liu Bing Ren Hui State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing 100024China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of ChinaBeijing 100024China School of Information and Communication Engineering Communication University of ChinaBeijing 100024China

For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great *** this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the *** addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional *** experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image *** study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.

关键词： fine-grained emotion prediction movie and television scene images stacking model linear regression

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Performance-controlled Relay Selection for Non-Orthogonal Multiple Access System Under Imperfect Successive Interference Cancellation

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Journal of Shanghai Jiaotong university(Science) 2024年第6期29卷 1181-1190页

作者： DU Yunhai LI Enyu MA Long School of Information and Control Engineering Qingdao University of TechnologyQingdao 266525ShandongChina

In order to improve the performance of non-orthogonal multiple access(NOMA)system,a downlink NOMA system with two symmetric users is *** noting that the requirement of reliability for these two users is different in some practical transmission scenarios,a performance-controlled max-min relay selection scheme for near-user and far-user is *** to the practical transmission requirements,the outage performance of these two users can be controlled by different settings in the considered *** the influence of the imperfect successive interference cancellation(SIC)in the practical scenarios,the exact outage performance of the far and near users in the system is analyzed,and the approximation results in high signalto-noise ratio are also *** on the analysis of the approximate results,the optimal power allocation factor for the outage performance of these two users is ***,the correctness of the theoretical results is verified by simulation analysis.

关键词： non-orthogonal multiple access(NOMA) cooperative communications outage probability power allocation Rayleigh fading

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Structure design and coordinated motion analysis of bionic crocodile robot

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Biomimetic Intelligence & Robotics 2024年第2期4卷 50-62页

作者： Jun Wang Jingya Zheng Yuhang Zhao Kai Yang School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Crocodiles,one of the oldest and most resilient species on Earth,have demonstrated remarkable locomotor abilities both on land and in water,evolving over millennia to adapt to diverse *** this study,we draw inspiration from crocodiles and design a highly biomimetic crocodile robot equipped with multiple degrees of freedom and articulated trunk *** design is based on comprehensive analysis of the structural and motion characteristics of real *** bionic crocodile robot has a problem of limb-torso incoordination during *** solve this problem,we used the D-H method for both forward and inverse kinematics analysis of the robot's legs and *** a series of simulation experiments,we investigated the robot's motion stability,fault tolerance,and adaptability to environments in two motor patterns:with and without spine and tail *** experimental results show that the bionic crocodile robot exhibits superior motion performance when the spine and tail cooperate with the *** study not only demonstrates the potential of biomimicry in robotics but also underscores the significance of understanding how nature's designs can inform and enhance technological innovations.

关键词： Bionic crocodile robot Kinematic modeling Motion simulation Multipart coordination

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Adaptive control of a Flexible Manipulator With Unknown Hysteresis and Intermittent Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 148-158页

作者： Shouyan Chen Weitian He Zhijia Zhao Yun Feng Zhijie Liu Keum-Shik Hong School of Mechanical and Electrical Engineering Guangzhou University the School of Control Science and Engineering Shandong University IEEE the College of Electrical and Information Engineering Hunan University the National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University the School of Intelligence Science and Technology University of Science and Technology Beijing (USTB) the Institute for Future the School of AutomationQingdao University the School of Mechanical Engineering Pusan National University

In this study, we consider a single-link flexible manipulator in the presence of an unknown Bouc-Wen type of hysteresis and intermittent actuator faults. First, an inverse hysteresis dynamics model is introduced, and then the control input is divided into an expected input and an error compensator. Second,a novel adaptive neural network-based control scheme is proposed to cancel the unknown input hysteresis. Subsequently,by modifying the adaptive laws and local control laws, a fault-tolerant control strategy is applied to address uncertain intermittent actuator faults in a flexible manipulator system. Through the direct Lyapunov theory, the proposed scheme allows the state errors to asymptotically converge to a specified interval. Finally,the effectiveness of the proposed scheme is verified through numerical simulations and experiments.

关键词： Adaptive control flexible manipulator intermittent actuator faults inverse hysteresis dynamics vibration control

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Effective Chirp Modulation communication System Design for LEO Satellite IoT

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IEEE Internet of Things Journal 2025年第11期12卷 16962-16976页

作者： Zhao, Wenqing Zhang, Haozhe Zou, Jun Liu, Guangzu Shu, Feng Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing China Hainan University School of Information and Communication Engineering Haikou China

The high-dynamic Doppler frequency offset presents great challenges to communications in the Low-Earth Orbit (LEO) satellite Internet of Things (IoT) scenarios, including the high complexity receiver synchronization algorithm, frequency tracking capability and high receiver sensitivity. In this paper, we propose an effective communication system architecture based on chirp modulation for LEO satellite IoT with reference to LoRa system. Our approach introduces an improved chirp modulation based on Low-Rate Optimization (LRO) with specified frequency interval and a CRC-assisted decoding scheme. Simulation results show that the proposed optimal LRO factor can improve the spectral efficiency. Furthermore, we propose a low complexity synchronization algorithm and LRO-based frequency tracking strategy. The simulation results confirm the feasibility of the proposed communication system for LEO satellite IoT, which demonstrates robust performance under high-dynamic Doppler frequency offsets. © 2014 IEEE.

关键词： Chirp modulation

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Combining first principles and machine learning for rapid assessment response of WO_(3) based gas sensors

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International Journal of Mining Science and technology 2024年第12期34卷 1765-1772页

作者： Ran Zhang Guo Chen Shasha Gao Lu Chen Yongchao Cheng Xiuquan Gu Yue Wang School of Medical Information and Engineering Xuzhou Medical UniversityXuzhou 221000China School of Materials Science and Physics China University of Mining and TechnologyXuzhou 221116China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

The rapid advancement of gas sensitive properties in metal oxides is crucial for detecting hazardous gases in industrial and coal mining ***,the conventional experimental trial and error approach poses significant challenges and resource consumption for the high throughput screening of gas sensitive ***,this paper introduced a novel screening approach that integrates first principles with machine learning(ML)to rapidly predict the gas sensitivity of ***,a comprehensive database of multi-physical parameters was established by modeling various adsorption sites on the surface of WO3,which serves as a representative *** density functional theory(DFT)is one of the first principles,DFT calculations were conducted to derive essential multi-physical parameters,including bandgap,density of states(DOS),Fermi level,adsorption energy,and structural modifications resulting from *** collected data was subsequently utilized to develop a cor-relation model linking the multi-physical parameters to gas sensitive performance using intelligent *** model’s performance was assessed through receiver operating characteristic(ROC)curves,confusion matrices,and other evaluation metrics,ultimately achieving a prediction accuracy of 90%for identifying key features influencing gas adsorption *** proposed strategy for predicting the gas sensitive characteristics of materials holds significant potential for application in identifying addi-tional gas sensitive properties across various materials.

关键词： Machine learning Density functional theory Rapid assessment Gas sensor

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