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3rd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Xue, Hao Hu, Bojun Yue, Xiaoyu Zhu, Gege Jiangsu University of Science and Technology College of Automation Zhenjiang China Jiangsu University of Science and Technology Ocean College Zhenjiang China Jiangsu University of Science and Technology School of Computer Zhenjiang China

ISBN: (纸本)9798350398410

In view of the difficulty of measuring the photoelectric volume pulse wave(PPG)signal during motion, the log-normalized step-step)algorithm is proposed. The algorithm uses angular velocity to correct the acceleration and removing the gravitational component via a Kalman filter. The corrected acceleration was then used as a reference signal for the log-normalized variable step-length adaptive filtering to filter out the motion artifacts in the PPG signal, and finally the heartbeat information was estimated by spectral spike tracking. The results showed that the mean absolute error of heart rate information estimated by the algorithm decreased by 1.4 times/min and the mean relative error by 1.55%.It can be seen that the algorithm in this paper can effectively remove interference from motion artifacts and obtain more accurate heartbeat information. © 2023 IEEE.

关键词： Adaptive filtering

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A Logistic-Regression-Based Reduced Model for Predicting Online News Popularity 2

A Logistic-Regression-Based Reduced Model for Predicting Onl...

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2nd International Conference on Computing, Communication, Perception and Quantum technology, CCPQT 2023

作者： Lou, Lan Wang, Jingping Liu, Xinya University of Toronto The Department of Computer and Mathematical Sciences Toronto Canada Wuhan University of Technology College of Automation Wuhan China Harbin Institute of Technology The Department of Computer Science and Technology Shen Zhen China

ISBN: (纸本)9798350342697

Consuming news through social media has become an integral part of our lives, thus online news popularity prediction is now a crucial issue for marketing strategies and social media platforms. This paper proposes a real-time prediction model, based on a dataset from Mashable, it uses regression analysis to find out whether an article is of high-popularity or low-popularity. In this paper, we first solve the problem of imbalanced data via Synthetic Minority Oversampling Technique (SMOTE);then various feature selection and engineering methods are employed to extract key features. A comparison experiment is conducted to verify the validity of the data enhancement. And the results show that the reduced logistic model with SMOTE and dimension reduction outperforms other models with an Accuracy of 80.6% and a Recall of 78.6%. Our work can help the software application on evaluating the popularity of online news. © 2023 IEEE.

关键词： Feature Selection

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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An Efficient Privacy-Preserving Asynchronous Federated Approach for Intelligent Decision Making in Equipment Maintenance 7

An Efficient Privacy-Preserving Asynchronous Federated Appro...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Dou, Kangkang He, Fan Shang, Fanshu Li, Xingyi Dong, Yuhao Liu, Jiayu Jiangsu Automation Research Institute Nanjing University of Aeronautics and Astronautics Department of Information Technology Research Lianyungang China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350352009

Federated learning enables collaborative training of a high-quality global model across multiple participants without sharing raw local data. Its advantages, such as decentralization, data isolation, and high computational performance, have made it a popular research direction in various fields. However, federated learning faces privacy risks during model parameter transmission. Additionally, reducing communication overhead and computational resource consumption while ensuring data privacy protection remains a critical challenge. To address these issues, this paper proposes an efficient privacy-preserving asynchronous federated learning method tailored for equipment maintenance scenarios. Leveraging asynchronous federated learning techniques, this method enables efficient collaborative training among multiple participants. It also employs homomorphic encryption to protect local data information. By utilizing time-series models, we construct a predictive model for equipment spare parts consumption, providing valuable insights for intelligent maintenance decision-making. Experimental results demonstrate that our proposed method has faster convergence. More importantly, the root mean square error decreases by at least 0.004, the mean square error decreases by at least 0.024, and the relative error decreases by at least 0.008 compared to the baseline. © 2024 IEEE.

关键词： Federated learning

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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Science China(Technological Sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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Control Structures and Algorithms for Force Feedback Bilateral Teleoperation Systems: A Comprehensive Review

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computer Modeling in Engineering & Sciences 2025年第2期142卷 973-1019页

作者： Jiawei Tian Yu Zhou Lirong Yin Salman A.AlQahtani Minyi Tang Siyu Lu Ruiyang Wang Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Force feedback bilateral teleoperation represents a pivotal advancement in control technology,finding widespread application in hazardous material transportation,perilous environments,space and deep-sea exploration,and healthcare *** paper traces the evolutionary trajectory of force feedback bilateral teleoperation from its conceptual inception to its current *** elucidates the fundamental principles underpinning interaction forces and tactile exchanges,with a specific emphasis on the crucial role of tactile *** this review,a quantitative analysis of force feedback bilateral teleoperation development trends from 2011 to 2024 has been conducted,utilizing published journal article data as the primary source of *** review accentuates classical control frameworks and algorithms,while also delving into existing research advancements and prospec-tive breakthrough ***,it explores specific practical scenarios ranging from intricate surgeries to hazardous environment exploration,underscoring the technology’s potential to revolutionize industries by augmenting human manipulation of remote *** underscores the pivotal role of force feedback bilateral teleoperation as a transformative human-machine interface,capable of shaping flexible control strategies and addressing technological *** research endeavors in force feedback bilateral teleoperation are expected to prioritize the creation of more immersive experiences,overcoming technical hurdles,fortifying human-machine collaboration,and broadening application domains,particularly within the realms of medical intervention and hazardous *** the continuous progression of technology,the integration of human intelligence and robotic capabilities is expected to produce more innovations and breakthroughs in the field of automatic control.

关键词： Force feedback bilateral teleoperation robot development history application prospect

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Learning-based Big Data Sharing Incentive in Mobile AIGC Networks

Learning-based Big Data Sharing Incentive in Mobile AIGC Net...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wen, Jinbo Zhang, Yang Chen, Yulin Zhong, Weifeng Huang, Xumin Liu, Lei Niyato, Dusit Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Guangdong University of Technology School of Automation China Xidian University School of Telecommunications Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350351255

Rapid advancements in wireless communication have led to a dramatic upsurge in data volumes within mobile edge networks. These substantial data volumes offer opportunities for training Artificial Intelligence-Generated Content (AIGC) models to possess strong prediction and decision-making capabilities. AIGC represents an innovative approach that utilizes sophisticated generative AI algorithms to automatically generate diverse content based on user inputs. Leveraging mobile edge networks, mobile AIGC networks enable customized and real-time AIGC services for users by deploying AIGC models on edge devices. Nonetheless, several challenges hinder the provision of high-quality AIGC services, including issues related to the quality of sensing data for AIGC model training and the establishment of incentives for big data sharing from mobile devices to edge devices amidst information asymmetry. In this paper, we initially define a Quality of Data (QoD) metric based on the age of information to quantify the quality of sensing data. Subsequently, we propose a contract theoretic model aimed at motivating mobile devices for big data sharing. Furthermore, we employ a Proximal Policy Optimization (PPO) algorithm to determine the optimal contract. Numerical results demonstrate the efficacy and reliability of the proposed PPO-based contract model. © 2024 IEEE.

关键词： age of information big data sharing contract theory Mobile AIGC networks proximal policy optimization

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Tracking Control of Unmanned Surface Vessels Based on Fully Actuated System Approaches

Tracking Control of Unmanned Surface Vessels Based on Fully ...

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2023 China automation Congress, CAC 2023

作者： Li, Zijing Zhu, Jing Zhai, Xiangping College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350303759

Considering the complication and high requirements of controller design in unmanned surface vessels (USVs) from the perspective of under-actuated model, this paper investigates the tracking control of USVs in terms of fully actuated system approaches (FASA). We firstly propose a high-order fully actuated (HOFA) model of USV for path tracking. Then, using the idea and method of FASA controller design, a control law is proposed to make the closed-loop system obtain the dynamic characteristics we expect and decouple it into two linear systems, which can be achieved with simple and easy computation. The proposed USV path tracking control law achieves global asymptotic stability without continuous angular and forward velocity excitations. The effectiveness of this method is verified through MATLAB simulation. © 2023 IEEE.

关键词： MATLAB

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Accumulating Priority Queue for Charging of Unmanned Aerial Vehicles in Cognitive Radio Networks 6

Accumulating Priority Queue for Charging of Unmanned Aerial ...

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6th International Symposium on Autonomous Systems, ISAS 2023

作者： Li, Yuran Zhai, Xiangping Bryce Wang, Ran Zhu, Jing Yao, Chenghua Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China

ISBN: (纸本)9798350316155

The combination of cognitive radio networks and unmanned aerial vehicles (UAVs) can overcome the scarcity of spectrum for wireless networks and provide important benefits for large-scale deployment of UAVs. However, energy constraint is an inevitable challenge for UAV communication. In order to prolong the lifetime of UAVs, we apply the accumulating priority queue (APQ) discipline for the first time to the queuing charging process of UAVs in cognitive radio networks. The APQ is based on the classic priority queue with the accumulation rate added to it, which means that the priority of a user is a function of the waiting time. By analyzing the values for different arrival rates and priorities, we discuss the factors that affect the average waiting times and key performance indicators. We also consider the related optimization problem and propose an improved bisection algorithm. According to the requirements of different classes of users, we can adjust the accumulation rates and arrival rates to meet the performance indicators. © 2023 IEEE.

关键词： Wireless networks

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