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Stackelberg Game for Wireless Powered and Backscattering Enabled Sensor Networks

China Communications 2024年第3期21卷 189-204页

作者： Lyu Bin Cao Yi Wang Shuai Guo Haiyan Hao Chengyao Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou 215006China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

关键词： backscatter communication energy interaction stackelberg game wireless powered sensor network

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Out-of-Distribution Detection by Principal Component Correspondence

Out-of-Distribution Detection by Principal Component Corresp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoyuan Guan Zhiyong Gan Ling Deng Wei Shi Jiankang Chen Shenshen Bu Chunliang Zhao Jianfang Hu Yuren Zhou Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Network Business Group China Unicom Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China School of Data and Science Qingdao University of Science and Technology Qingdao China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.

关键词： Training Measurement Analytical models Training data Vectors Data models Intelligent systems Facial features Principal component analysis Least mean squares methods

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Feature Fusion of Gramian Angular Field Deep Learning EEG-Based Epileptic Seizure Classification 17

Feature Fusion of Gramian Angular Field Deep Learning EEG-Ba...

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17th International Convention on Rehabilitation Engineering and Assistive Technology, i-CREATe 2024

作者： Aboyeji, Sunday Timothy Wang, Xin Chen, Yan Ahmad, Ijaz Li, Lin Liu, Zhenzhen Yao, Chen Zhang, Yu Zhao, Guoru Li, Guanglin Chen, Shixiong Shenzhen Institute of Advanced Technology Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Chinese Academy of Sciences Guangdong Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China Surgery Division Epilepsy Center Shenzhen Children's Hospital Shenzhen518036 China First Affiliated Hospital of Shenzhen University Shenzhen Second People's Hospital The National Key Clinic Specialty Shenzhen Key Laboratory of Neurosurgery Department of Neurosurgery Shenzhen518035 China Naval Medical University Translational Medicine Center Shanghai200433 China The Chinese Univ of Hong Kong School of Medicine Guangdong Shenzhen518172 China

ISBN: (纸本)9798350355154

Deep learning, particularly convolutional neural networks (CNNs), is increasingly used for epileptic seizure detection, leveraging their ability to directly extract features from EEG signals. Since CNNs excel with image data, various methods have been developed to convert signals into images, including the Gramian Angular Field (GAF) transformation algorithm. The algorithm is subtyped into the Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF). Each of these transformation techniques has been employed individually in earlier studies. However, each method focuses on unique properties of the EEG signal during image construction. Therefore, we proposed a feature fusion of the two transformation methods using the CNN model to learn richer representations. The experiment was conducted on publicly available Bonn datasets. Initially, the datasets are segmented into epochs of length 0.5s and step size 0.2s. Images are generated for each of the segments, resulting in 8600 images for each set of Bonn datasets. The generated images from each of the transformations are fed into parallel CNNs, and the extracted features are fused for improved performance. We conducted binary, ternary, and multiclass (5-class) classifications. The results show that feature fusion of GASF and GADF improves the classification performance with an average accuracy increase of 0.255%, 0.295%, and 0.660% for binary, ternary, and multiclass, respectively. This feature fusion of the GAF transformation approach shows promising potential for distinguishing various EEG patterns, underscoring its potential in seizure classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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An intelligent prosthetic system for EMG pattern recognition based prosthesis control*

An intelligent prosthetic system for EMG pattern recognition...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Lan Tian Yue Zheng Naifu Jiang Haoshi Zhang Yan Liu Xiangxin Li Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China

Fitting an appropriate artificial limb is an effective method to help the Upper limb amputees to restore their motor function. In decades, scientists have conducted a series of studies to decode the motor intent of amputees to achieve naturally and dexterous control of prosthetic limbs. Among them, Movement intention by using EMG-based pattern recognition (PR) algorithms from the residual muscle electrophysiological information of the limbs is one of the important methods of multi-functional prosthetic control. However, the EMG-PR based prosthesis is still not widely applied in clinical. One of the major reasons is the real-time performance of various PR algorithms is not robustness enough for clinical application since laboratory performance metrics used to evaluate PR algorithms may be poorly associated with the clinical outcomes. And another important reason is the lack of bio-imitability of prostheses, because the developed bionic prostheses with five independently controlled fingers are heavy and lack of active wrist. Therefore, this paper proposes a multi-degree-of-freedom intelligent prosthetic system for EMG PR algorithms assessment, including the PR algorithm system software named REHPS, a Real-time Control System for Prosthesis (RCSP) and a lightweight prosthetic hand named S-HAND. Both able-bodied subjects and amputees could wear the prosthetic system to carry out tests which are mimicking the daily use of prosthesis. These tests would provide clinic performance metrics for the research of motion intent recognition algorithm. The system integrates advanced motion intent recognition algorithms into the prosthesis and also provides more effective evaluation methods for myoelectric pattern recognition algorithm research.

关键词： Measurement Wrist Prosthetic limbs Software algorithms Electromyography Real-time systems Robustness

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Zhang Neural Network Models and Tables Summarized and Compared for Different Time-Dependent Problems Solving in Addition to DDD (Direct Derivative Dynamics) Method

Zhang Neural Network Models and Tables Summarized and Compar...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zanyu Tang Zhiyuan Qi Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Jishou University Jishou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781665409858

Zhang neural network (ZNN, also called zeroing neural network) is considered as an effective method to solve time-dependent problems (i.e., time-varying problems). Different continuous-time ZNN models of various time-dependent problems solving are obtained by exploiting the ZNN design formula. Before applying on digital equipment, the acquired continuous-time ZNN models are generally discretized by using Zhang time discretization (ZTD) formula. To provide readers with the choice of various ZNN models and different ZTD formulas for time-dependent problems solving, this paper summarizes ZNN models and ZTD formulas, and presents them in tables. Specifically, four categories and seventeen subcategories of time-dependent problems are summarized and presented after studying related literatures. In addition, ZTD formulas from the 2-instant ZTD through the 10-instant ZTD formula are also summarized and compared in a table. With the direct derivative dynamics (DDD) method as a special situation, this paper also introduces the process of solving time-dependent problems with the ZNN method, especially the time-dependent matrix problem.

关键词： Industrial electronics Electric potential Neural networks Numerical models Problem-solving

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Inspection of EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis

Inspection of EEG Signals for Noninvasive Blood Glucose Moni...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Igbe, Tobore Samuel, Oluwarotimi Williams Li, Jingzhen Kulwa, Frank Kandwal, Abhishek Nie, Zedong University of Virginia Center for Diabetes Technology Department of Psychiatry and Neurobehavioural Sciences School of Medicine CharlottesvilleVA United States University of Derby School of Computing and Engineering DerbyDE22 1GB United Kingdom Center for Minimally Invasive and Surgical Devices Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guandong Shenzhen China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems and Shenzhen College of Advanced Technology Ucas Guangdong Shenzhen518055 China Shoolini University Department of Physics and Materials Science Himachal Pradesh India

ISBN: (纸本)9781665493840

Prediabetes is a metabolic disorder where the blood glucose (BG) level is higher than normal but not high as diabetes;early diagnosis can prevent health complications and death. However, to determine the BG level, it is required to prick the finger, which causes pain and discomfort. To eliminate this problem, there is a need to investigate noninvasive techniques to estimate BG values and continuous BG monitoring. In this paper, we investigated the changes in electroencephalogram (EEG) frequency parameters that have been scarcely considered for prediabetes diagnosis. We analyzed EEG signals after carrying out an oral glucose tolerance test on 25 participants. Five frequency bands of EEG signals were obtained continuously in 3 positions;frontal (F), occipital (O), and parental (P). The analysis is performed using a boxplot to examine the pattern of the recorded signals. The result shows that the EEG signal from the O position has a sensitivity of 95.3% in the left hemisphere, the P location has 90.3% in the right hemisphere, and F has 93% in the left hemisphere. This observation shows the appropriate location and the combination of EEG frequency parameters, such as the alpha and beta mean power from O and P, which can be integrated into a wearable device to provide a promising clinical solution for noninvasive blood glucose monitoring and prediabetes diagnosis. © 2023 IEEE.

关键词： Electroencephalography

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Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation

Graph Contrastive Learning with Adaptive Augmentation for Kn...

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International Conference on Computer Supported Cooperative Work in Design

作者： Mei Yu Zhaoyuan Ding Jian Yu Wenbin Zhang Ming Yang Mankun Zhao College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking(TANK Lab) Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University Marietta GA USA

Knowledge concept recommendation is a kind of fine-grained recommendation in massive open online courses (MOOCs) scenario, user interaction data has the characteristics of strong collaborative signals and imbalanced interactions. This leads to a single recommendation and reduced accuracy. Recently, the ability of contrastive learning (CL) in mitigating interaction imbalance in recommender systems has received widespread attention. CL requires the use of augmentation methods to generate different views. Existing augmentation methods (1) augment only at the topology or feature level ignoring semantic or structural information, and (2) undifferentiated augmentation tends to lose the critical information. In this paper, we propose Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation (GCARec). Specifically, (1) topology level adaptive augmentation based on degree centrality captures critical structural information, and then (2) feature level adaptive augmentation based on degree centrality captures critical semantic information. Comprehensive experiments show that our proposed approach can outperform other baselines. Our implementations are available at https://***/DingZhaoyuan/GCARec.

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MASZSL: A Multi-Block Attention-Based Description Generative Adversarial Network for Knowledge Graph Zero-Shot Relational Learning

MASZSL: A Multi-Block Attention-Based Description Generative...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mei Yu Pengtao Fan Mankun Zhao Wenbin Zhang Yue Zhao Ming Yang Jian Yu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking(TANK Lab) Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University Marietta GA USA

In the real world, the Knowledge Graph(KG) is dynamic and new entities are added at any time. Therefore, open-world Knowledge Graph Completion(KGC) was proposed to approach new-added entities, but previous approaches often introduced too much noise when introducing external text resources for new entities. To alleviate this problem, knowledge graph zero-shot relational learning (KGZSL) has recently attracted more attention. Generative Adversarial Networks (GANs) are frequently used in KGZSL to connect existing relation descriptions to the domain of knowledge graphs. However, these methods ignore the impact of existing entities on embeddings for unidentified relational representations and in-stead concentrate on examining the connection between relational textual texts and knowledge network structures. In this work, we propose a multi-block attention framework using relation Description Generative Adversarial Networks (desGAN) jointing KG and text representation and address model collapse and training stability problems in previous studies. The core idea of our method is to obtain the background knowledge graph information and the relation representation through and multi-block attention layer and the desGAN, then a connection between the structured KG semantic space and the unstructured text semantic space of the new entity is established, forcing the entity pair to be closer to their real relation. Experimental results on the knowledge graph zeroshot relational learning dataset demonstrate that our MASZSL has a faster convergence speed and achieves state-of-the-art performance on this task.

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High-performance Zn-graphite battery based on LiPF6 single-salt electrolyte with high working voltage and long cycling life

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Journal of Energy Chemistry 2021年第7期30卷 602-609页

作者： Yong Wang Luojiang Zhang Fan Zhang Xuan Ding Kyungsoo Shin Yongbing Tang Functional Thin Films Research Center Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangdongChina University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Advanced Materials Processing&Mold Ministry of EducationZhengzhou UniversityZhengzhou 450002HenanChina Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangdongChina

Zinc-ion batteries(ZIBs) are promising alternative energy storage devices to lithium-ion batteries owing to the merits of large abundance,high theoretical capacity,and environmental ***,critical challenges including low working voltage(below 2 V),low energy density as well as dendrites formation during long cycling caused by aqueous ZIB systems still hinder their practical ***,a high-voltage Zn-graphite battery(ZGB) based on a non-zinc ion single-salt electrolyte(2.5 M LiPF6 in carbonate solvent) is ***,we surprisingly found that Zn^(2+) is dissolved in the LiPF6 single-salt electrolyte during resting and discharging processes,thus enabling reversible Zn plating/stripping mechanism on the Zn foil anode in the ZGB over the voltage window of 1.0-3.1 *** a result,the ZGB achieves long-term cycling performance with a capacity retention of ~100% for over1200 cycles at 3 C and high Coulombic efficiency of ~100% in 1.0-3.1 V with no dendrites ***,the ZGB exhibits a high working voltage of up to 2.2 V,thus contributing to both high energy density(up to 210 Wh kg^(-1)) and high power density(up to 1013 W kg^(-1)),superior than most reported ZIBs.

关键词： Zinc-ion battery Zn anode Graphite cathode LiPF6 single-salt electrolyte High voltage

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A quantitative biomechanical study for precise orthopedic intervention in idiopathic scoliosis

A quantitative biomechanical study for precise orthopedic in...

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2022 IEEE International Conference on Real-Time computing and Robotics, RCAR 2022

作者： Feng, Ruliang Tao, Huiren Ye, Canhua Li, Guanglin Bai, Xueling Wang, Lin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macau Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Institute of Biomedical and Health Engineering Chinese Academy of Sciences Center for Translational Medicine Research and Development Shenzhen518055 China Shenzhen University General Hospital Department of Spine Surgery Shenzhen China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

The scoliosis with a prevalence of about 2.4% has become the top 3 major 'killer' of children and adolescents' health. Only 0.02% of scoliosis would be severe enough to require surgical intervention, while the rest was treated with orthotics or exercise training. However, the location of the orthotic forces and its effects were still not clear, resulting in a great deal of blindness in the fabrication of precise individualized orthoses and the later applied orthopedic forces. In this paper, we built a 3D spine model of a patient with idiopathic scoliosis based on CT tomography data, applied different orthopedic forces to the spine model to compare the results and clarify the relationship between them in order to determine the optimal location and magnitude of the orthopedic force, which were necessary for precise interventions in patients. The present results showed that 1) the greater the applied force, the better the correction effect (within reasonable limits) and 2) the effect of multiple forces applied for correction was better than that of a single force applied, as reflected by a greater displacement of the vertebrae and almost identical mean Von Mises stress in the discs, which could support the production of effective personalized orthopedic robots. © 2022 IEEE.

关键词： Finite element method

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