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arXiv 2022年

作者： Shi, Zheng Wang, Hong Fu, Yaru Yang, Guanghua Ma, Shaodan Ye, Xinrong School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Science and Technology The Open University of Hong Kong Hong Kong State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of Macau 999078 China School of Physics and Electronic Information Anhui Normal University Wuhu241002 China

This paper focuses on boosting the performance of small cell networks (SCNs) by integrating multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) in consideration of imperfect channel-state information (CSI). The estimation error and the spatial randomness of base stations (BSs) are characterized by using Kronecker model and Poisson point process (PPP), respectively. The outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies, including random grouping and distance-based grouping. It is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime, while the outage performance deteriorates if the intensity is sufficiently low. Besides, as the channel uncertainty lessens, the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell interference. Moreover, highly correlated estimation error ameliorates the outage performance under a low quality of CSI, otherwise it behaves oppositely. Afterwards, the goodput is maximized by choosing appropriate precoding matrix, receiver filters and transmission rates. In the end, the numerical results verify our analysis and corroborate the superiority of our proposed algorithm. © 2022, CC BY.

关键词： Channel state information

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Ferroelectrets:Heterogenous polymer electrets with high piezoelectric sensitivity for transducers

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Journal of Advanced Dielectrics 2023年第4期13卷 3-26页

作者： Xunlin Qiu Peng Fang Axel Mellinger Ruy Alberto Pisani Altafim Werner Wirges Gunnar Gidion Dmitry Rychkov Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237P.R.China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055P.R.China Department of Physics Central Michigan University 223 Dow Science ComplexMount PleasantMI 48859USA Computer Systems Department Informatics Center Federal University of ParáıbaJoão Pessoa-PBBrazil Department of Physics and Astronomy University of Potsdam PotsdamBrandenburgGermany Laboratory for Electrical Instrumentation Department of Microsystems Engineering-IMTEK University of FreiburgGeorges-Köhler-Allee 106Freiburg 79110Germany Technology and Study Center Weissenburg Deggendorf Institute of Technology WeissenburgBavariaGermany

Nowadays,the demand for advanced functional materials in transducer technology is growing *** materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)and vice *** are interesting from both fundamental and application points of ***(also called piezoelectrets)are a relatively young group of piezo-,pyro-and ferroelectric *** exhibit ferroic behavior phenomenologically undistinguishable from that of traditional ferroelectrics,although the materials per se are essentially non-polar space-charge electrets with artificial macroscopic dipoles(i.e.,internally charged cavities).A lot of work has been done on ferroelectrets and their applications up to *** this paper,we review and discuss mostly the work done at University of Potsdam on the research and development of *** will,however,also mention important results from other teams,and prospect the challenges and future progress trend of the field of ferroelectret research.

关键词： Ferroelectret piezoelectret space-charge electret transducer advanced functional materials

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Large language models for diabetes training:a prospective study

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Science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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A Novel Scalable Framework For Constructing Dynamic Multi-objective Optimization Problems

A Novel Scalable Framework For Constructing Dynamic Multi-ob...

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Congress on Evolutionary Computation

作者： Qingshan Tan Changhe Li Hai Xia Sanyou Zeng Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world applications are not taken into account: different objectives may have different function models and variables to be optimized; and the number of conflicting variables should be independent from the number of objectives; the time-linkage property is not considered. In order to overcome the above issues, a novel framework for constructing DMOPs is proposed, where all objectives can be designed independently, and the number of the conflicting variables can be tuned by users. Moreover, it is easy to add new dynamic features to this framework. Several classical dynamic multi-objective optimization algorithms are tested on four scenarios, results show that these characteristics are challenging for the existing algorithms.

关键词： Schedules Analytical models Heuristic algorithms Object oriented modeling Evolutionary computation Benchmark testing Dynamic scheduling

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An Accelerated Iterative Learning control Approach for X-Y precision plane motion stage

An Accelerated Iterative Learning Control Approach for X-Y p...

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作者： Tao Zhang Hongyu Yan Danping Jia Hualiang Zhang Bingjie Zhao Xu Lu Yang Liu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Dalian University of Technology School of Artificial Intelligence Shenyang University of Technology School of Information Science and Engineering Shenyang University of Technology

X-Y precision plane motion stage is widely used in precision motion control fields such as lithography machine workpiece stage,CNC machine tool,Laser measuring system,etc.,which requires high accuracy of the position *** X-Y precision plane motion stage uses two permanent magnet linear synchronous motors(PMLSM) as drivers to drive the motion table to perform XY two-degree-of-freedom *** open-closedloop PD-type iterative learning controller(ILC) approach based on accelerated convergence was proposed to improve trajectory tracking performance of *** interval was changed by real-time correction of the exponential gain matrix in the interval to accelerate the convergence *** to the λ norm,the monotone convergence of the proportion differentiation(PD) was developed for the *** controller consists of two terms:a PID controller which enhances robustness to disturbances and system parameter variability and an ILC which generally improves the performance in the repetitive tasks by using the information from previous *** experimental results show that tracking precision and convergence speed of PMLSM can be effectively improved by the scheme.

关键词： X-Y precision plane motion stage Iterative learning control tracking precision convergence rate

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Gong, Xiaoyan Tang, Ying Liu, Xiwei Jing, Sifeng Cui, Wei Liang, Joleen Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100084 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

Nowadays, AI education from Kindergarten to 9th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts. © 2020 IEEE.

关键词： Curricula

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A comprehensive survey for Hyperspectral Image Classification: The evolution from conventional to transformers and Mamba models

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Neurocomputing 2025年 644卷

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia School of Mathematics Southeast University Nanjing211189 China Department of Geography National University of Singapore 117568 Singapore Department of Infrastructure Engineering University of Melbourne Parkville VIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi'an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSIC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSIC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSIC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSIC, detailing their advantages and challenges. Emerging trends in HSIC are explored, including in-depth discussions on Explainable AI and interpretability concepts, alongside Diffusion Models for denoising, feature extraction, and fusion. Comprehensive experimental results were conducted on three Hyperspectral datasets to substantiate the efficacy of various conventional DL models. Additionally, we identify several open challenges and pertinent research questions in the field of HSIC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and efficiency of HSIC. The Source code is available at https://***/mahmad000/HSIC-2024. © 2025

关键词： Convolutional neural networks

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Spectral2Spectral: Image-spectral Similarity Assisted Deep Spectral CT Reconstruction without Reference

arXiv

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arXiv 2022年

作者： Guo, Xiaodong Li, Yonghui Chang, Dingyue He, Peng Feng, Peng Yu, Hengyong Wu, Weiwen The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China Chongqing University Chongqing China The China Academy of Engineering Physics Institute of Materials Mianyang China The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The School of Biomedical Engineering Sun Yat-Sen University Guangdong Shenzhen China The Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Zhengzhou China

Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy bins leads to imaging results of low signal-noise ratio. The existing supervised deep reconstruction networks for CT reconstruction are difficult to address these challenges because it is usually impossible to acquire noise-free clinical images with clear structures as references. In this paper, we propose an iterative deep reconstruction network to synergize unsupervised method and data priors into a unified framework, named as Spectral2Spectral. Our Spectral2Spectral employs an unsupervised deep training strategy to obtain high-quality images from noisy data in an end-to-end fashion. The structural similarity prior within image-spectral domain is refined as a regularization term to further constrain the network training. The weights of neural network are automatically updated to capture image features and structures within the iterative process. Three large-scale preclinical datasets experiments demonstrate that the Spectral2spectral reconstructs better image quality than other the state-of-the-art methods. © 2022, CC0.

关键词： Image reconstruction

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What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid?

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Journal of Integrative Agriculture 2020年第7期19卷 1789-1801页

作者： WANG Xiu-juan KANG Meng-zhen FAN Xing-rong YANG Li-li ZHANG Bao-gui HUANG San-wen Philippe DE REFFYE WANG Fei-yue The State Key Laboratory of Management and Control for Cormplex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China Bejjing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBejing 100190P.R.China Innovation Center for Parallel Agriculture Qingdao Academy of Intelligent IndustriesQingdao 266109P.R.China School of Computer Science and Information Engineering Chongqing Technology and Business UniversityChongqing 400067P.R.China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083P.R.China College of Land Science and Technology China Agricultural UniversityBeijing 100193P.R.China Agricultural Genomes Institute at Shenzhen Chinese Academy of Agricultural SciencesShenzhen 518124P.R.China AMAP University MontpellierCIRADCNRSINRAIRDMontpellier 34000France The School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049P.R.China

To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model(FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm(PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organlevel model may provide a computational approach to define the target of breeding by combination with a genetic model.

关键词： cucumber biomass production functional-structural plant model source-sink ratio fruit-setting PSO heterosis

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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