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Semantic Feature Division Multiple Access for Digital Semantic Multiple Access Channels

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Shen, Bin Ma, Shuai Chen, Rui Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China Peng cheng Laboratory Shenzhen518066 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

To solve the multi-user semantic communication network capacity bottlenecks, this paper proposes a semantic feature domain multiple access (SFDMA) scheme. By extracting and encoding semantic features and jointly processing multi-user data, the scheme can realize the encoding and transmission of multi-user semantic information in the separated feature subspace. Based on this scheme, a semantic digital multiple access network model is proposed to perform multi-user inference tasks, and an efficient semantic encoding and decoding scheme is developed by using variational information bottleneck (VIB) theory to ensure both the orthogonality in the feature domain and the classification accuracy. Furthermore, a SFDMA model for image reconstruction is designed based on the Swin Transformer. The modified model also considers the reconstruction quality and orthogonality of semantic feature domain. In addition, we design an optimization target for multiple access to implement an adaptive power allocation method for multi-user semantic communications. Simulation results show that our proposed SFDMA scheme exhibits semantic feature domain orthogonality in different applications, and the adaptive power allocation method shows excellent performance in the classification task, and effectively guarantee the quality of service (QoS) of the semantic communications. © 2015 IEEE.

关键词： Image reconstruction

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Time-varying group formation-tracking control for heterogeneous multi-agent systems with switching topologies and time-varying delays

Time-varying group formation-tracking control for heterogene...

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第40届中国控制会议

作者： Shiyu Zhou Xiaobao Wei Xiwang Dong Yongzhao Hua Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education of ChinaChongqing University Institute of Artificial Intelligence Beihang University

Group formation-tracking problem for heterogeneous multi-agent systems（HMASs） with both switching networks and communication delays is investigated in this paper. In order to achieve different tasks, the agents are classified into various groups. The followers are allowed to realize the formation and track the trajectory of the leader in each group. Firstly, by utilizing the consensus control, an observer is proposed to estimate the state of the leader. Then, an observer-based control protocol is put forward to solve the group formation-tracking problem with both communication delays and switching networks. Moreover, an algorithm to determine the gain feedbacks is demonstrated, in the meantime, the observer’s error systems as well as the group formation-tracking error systems are proved to be convergent. Finally, an example in the simulation prat is presented to verify the theoretical results.

关键词： Group formation-tracking problem general linear multi-agent systems time-varying delays switching topologies

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Short-Term Load Forecasting with TDCI: Temporal Decomposition and Channel Independence

Short-Term Load Forecasting with TDCI: Temporal Decompositio...

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2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Wenxue Li Chi Cheng Peihua Xu Renfeng Liu Yun Zhang Huabing Zhou College of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Meteorological Service Center Wuhan China Faculty of Artificial Intelligence Education School of Educational Information Technology Central China Normal University Wuhan China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models.

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A Large Capacity Expansion and Robust Recognition Method of Dot-Dispersing Coded Targets with Binary Step-Response Serial Encoding

SSRN

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SSRN 2023年

作者： Song, Limei Zheng, Tenglong Li, Yunpeng Deng, Sanpeng Yang, Yangang Zhu, Xinjun Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin300387 China School of Control Science and Engineering Tiangong University Tianjin300387 China School of Mechanical Engineering Tiangong University Tianjin300387 China School of Artificial Intelligence Tiangong University Tianjin300387 China School of Mechanical Engineering Tianjin University of Technology and Education Tianjin300222 China Institute of Robotics and Intelligent Equipment Tianjin University of Technology and Education Tianjin300222 China Tianjin Key Laboratory of Intelligent Robot Technology and Application Tianjin300350 China Tianjin Bonus Robotics Technology Co. Ltd Tianjin300350 China

In close-range photogrammetry, it is difficult to meet the measurement requirements of large scenes in actual engineering due to the limited capacity of coded targets. To expand the capacity of the coded target, we propose a binary step-response serial-coded target (BSSCT). The BSSCT introduces periodic binary wave information as an additional feature in the dot-dispersing coded target. Also, a robust recognition algorithm for the BSSCT is developed, the P2-Invariant, and the step period is used for decoding. The capacity of the coded target reaches 7 magnitudes without increasing the auxiliary points. Under different lighting conditions and viewing angles, the measurement experiments show that the BSSCT outperforms other state-of-the-art coded targets. Our BSSCT is a promising standard method for large field system calibration and object measurement. © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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Spectroscopic techniques combined with DD-SIMCA model and explainable artificial intelligence for rapidly and accurately identifying the quality of Astragali Radix

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Industrial Crops and Products 2025年 231卷

作者： Bai, Lei Zhang, Zhi-Tong Yuan, Dongping Hu, Ziliang Qi, Yali Liu, Wenjian Guan, Huanhuan Chen, Li Shi, Zhiqi Hu, Chenjun Xue, Mei Li, Jindong Yan, Guojun State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture Nanjing University of Chinese Medicine Nanjing210023 China Taizhou School of Clinical Medicine The Affiliated Taizhou People's Hospital of Nanjing Medical University Taizhou225300 China Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine Wuxi214071 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Jiangsu Province Engineering Research Center of TCM Intelligence Health Service Nanjing210023 China

Astragali Radix (AR) is a traditional Chinese medicine (TCM) widely used worldwide for its nutritional and medicinal benefits, but it is facing increasing quality issues. This article developed a novel method for quickly and accurately identifying AR's quality. In this study, spectroscopic techniques combined with data-driven soft independent modelling of class analogy (DD-SIMCA) and explainable artificial intelligence (XAI) were used to determine AR's geographic origins and predict its antioxidant activity. The results showed that preprocessed near-infrared (NIR) or raw visible (VIS) spectra with DD-SIMCA could accurately identify AR's authentic regions, with 100 % sensitivity, specificity, and accuracy. Additionally, XAI identified 80 features from NIR spectra and 33 from VIS spectra, both strongly correlated with AR's antioxidant activity. Fusing these features and integrating them with support vector machine led to significantly better model performance, with Rp2 of 0.9760, RPDP of 6.7447, RMSEP of 1.3824, and MAEP of 1.1088. Overall, this study provided valuable insights for the quality assessment of TCMs and other medicinal plants. © 2025 The Authors

关键词： Damage detection

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Constitution identification model in traditional Chinese medicine based on multiple features

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Digital Chinese Medicine 2024年第2期7卷 108-119页

作者： XU Anying WANG Tianshu YANG Tao HAN Xiao ZHANG Xiaoyu WANG Ziyan ZHANG Qi LI Xiao SHANG Hongcai HU Kongfa School of Artificial Intelligence and Information Technology Nanjing University of Chinese MedicineNanjingJiangsu 210023China Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijing 100700China Key Laboratory of Internal Medicine of Chinese Medicine Ministry of EducationBeijing University of Chinese MedicineBeijing 100700China Dongfang Hospital Beijing University of Chinese MedicineBeijing 100078China Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor NanjingJiangsu 210023China Jiangsu Province Engineering Research Center of TCM Intelligence Health Service Nanjing University of Chinese MedicineNanjingJiangsu 210023China Jiangsu Research Center for Major Health Risk Management and TCM Control Policy Nanjing University of Chinese MedicineNanjingJiangsu 210023China

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment *** First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was ***;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body *** addition;a dual-branch deep network was employed to collect deep features from the full-body standing ***;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification ***;precision;and F1 score were the three measures selected to assess the perfor-mance of the *** It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;*** comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;*** The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep *** addition;by employing the proposed model;the objectification and intel-ligence o

关键词： Traditional Chinese medicine(TCM) Constitution identification Deep feature Facial complexion feature Body shape feature Multiple features

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Research on Power-Assisted Strategy and Device Based on Muscle Synergy

Research on Power-Assisted Strategy and Device Based on Musc...

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IEEE International Conference on Computational intelligence and Virtual Environments for Measurement Systems and Applications

作者： Lintao Hu Xiuying Luo Shangjie Tang Xiaoying Wu Lin Chen Xiaolin Zheng Wensheng Hou Key Laboratory of Biorheological Science and Technology of Ministry of Education Collaborative Innovation Center for Brain Science Chongqing University Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology Chongqing China Chongqing Medical Electronics Engineering Technology Research Center Chongqing University

ISBN: (数字)9781538683446

ISBN: (纸本)9781538683453

Surface EMG (sEMG) signals are non-invasive means of recording muscle activity that reflect the activation of human skeletal muscles. The purpose of this research is to study a dynamic assisted strategy based on muscle synergy, and to design an upper limb motion assisted device to achieve different assist tasks. Eleven healthy participants were recruited for the study. The participants were asked to perform grasping tasks at 9 target locations in the space and sEMG signals of the eight involved muscles were recorded. The non-negative matrix factorization (NMF) algorithm was applied to extract muscle coordination information during each corresponding experimental task. According to the muscle coordination information of all the participants' sEMG signals extracted by NMF decomposition, the corresponding upper limb motion tasks were decoded. The proposed method can decode the movement pattern of the human arm by considering the mapping relationship between the muscle coordination information and the joint motion, which may provide less effortful control of the robotic exoskeleton for rehabilitation training of individuals with neurological disorders or arm impairment.

关键词： Exoskeletons Muscles Task analysis Motion control Human computer interaction robot sensing systems Man-machine systems

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Design of Visual Gaze Target Locating Device Based on Depth Camera

Design of Visual Gaze Target Locating Device Based on Depth ...

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IEEE International Conference on Computational intelligence and Virtual Environments for Measurement Systems and Applications

作者： Yuxin. Liu Lintao. Hu Bowen. Xiao Xiao Y. Wu Yanhan. Chen Donghai. Ye Wen S. Hou Xiaolin. Zheng Key Laboratory of Biorheological Science and Technology of Ministry of Education & Collaborative Innovation Center for Brain Science Chongqing University Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology College of art Chongqing University Chongqing China Chongqing Medical Electronics Engineering Technology Research Center

ISBN: (数字)9781538683446

ISBN: (纸本)9781538683453

Based on the research of machine vision positioning application in recent years, this paper discusses the common machine vision positioning system and its principle, and designs a visual target positioning device to detect people's target intention. It mainly includes dynamic following and target positioning functions. Dynamic following can determine the fixation direction of the tester, and target positioning can obtain the spatial position information of the fixation target. The device can recognize the intention of people by observing the visual fixation direction of the subject and obtain the spatial position information of the target, which is a human-computer interaction path with a widely used scene.

关键词： Cameras Visualization Neck Microprocessors Target recognition Head

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TSW-FD: A Novel Temporal and Spatial Domain Weight Analysis of Feature Difference for Micro-Expression Spotting

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Zhihao Zhang Fan Mo Ke Zhao Tong Chen Xiaolan Fu State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences Beijing 100101 China Department of Psychology University of Chinese Academy of Sciences Beijing 100049 China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University Chongqing 400715 China Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology Chongqing 400715 China

The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.

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