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Banach空间中太阳集对无穷序列的最佳同时逼近

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数学学报（中文版） 2002年第2期45卷 287-294页

作者：罗先发何金苏李冲吉首大学数学与计算机科学系湖南吉首416000 浙江师范大学数理信息学院浙江金华321004 东南大学应用数学系江苏南京210096

本文研究太阳集对一列无穷序列的最佳同时逼近问题，建立了特征及唯一性结果．

关键词：最佳同时逼近同时太阳集严格凸唯一性 RS集 Banach空间

基于DiffServ的自适应IP QoS管理框架研究

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系统仿真学报 2001年第3期13卷 394-399页

作者：汤庸杨学良区海翔李显济彭重嘉中国科学技术大学研究生院计算机系北京100039 广东工业大学计算机学院广州510090

IP QoS已成为IP网络的核心技术。目前实现IP QoS的机制主要包括有多种，例如子带宽管理、MPLS、IntServ、DiffServ等，可以结合起来实现更完善的IP QoS。目前，比较一致的看法是在网络边缘采用IntServ，在网络主干采用DiffServ。我们在... 详细信息

IP QoS已成为IP网络的核心技术。目前实现IP QoS的机制主要包括有多种，例如子带宽管理、MPLS、IntServ、DiffServ等，可以结合起来实现更完善的IP QoS。目前，比较一致的看法是在网络边缘采用IntServ，在网络主干采用DiffServ。我们在局域网自适应IP QoS基础上，进一步研究了基于DiffServ的IP QoS。本文给出一种DiffServ结合IntServ实现彻底的端到端的IP QoS网络框架，提出了DiffServ实现自适应IP QoS管理的框架与方案，讨论了内部路由节点自适应QoS控制策略。最后讨论基于网络仿真软件NS2的一个简单实验结果。

关键词： DiffServ IntServ 服务质量 IP网局域网 Internet

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基于神经网络的导弹自适应滑模控制器设计

系统仿真学报 2008年第20期20卷 5589-5592页

作者：曾宪法张磊申功璋北京航空航天大学自动化科学与电气工程学院北京100083

针对导弹六自由度非线性模型,根据时标分离原理将导弹系统分为快慢不同的四个回路。针对快、慢回路,提出了一种基于神经网络的自适应滑模控制器设计方案。首先分别在快、慢回路中采用反馈线性化实现解耦控制,然后设计基于神经网络的自... 详细信息

针对导弹六自由度非线性模型,根据时标分离原理将导弹系统分为快慢不同的四个回路。针对快、慢回路,提出了一种基于神经网络的自适应滑模控制器设计方案。首先分别在快、慢回路中采用反馈线性化实现解耦控制,然后设计基于神经网络的自适应滑模控制器来保证系统鲁棒性及性能,其中神经网络用来逼近系统的不确定性。理论分析及计算机仿真都表明,按照该方法设计的控制器不仅具有较强的鲁棒性,而且保证了闭环系统的渐近稳定性。

关键词：导弹神经网络自适应控制滑模控制

EEG-based Emotion Recognition Using Multiple Kernel Learning

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Machine Intelligence Research 2022年第5期19卷 472-484页

作者： Qian Cai Guo-chong Cui Hai-xian Wang school of Statistics and Data science Nanjing Audit UniversityNanjing 211815China Key Laboratory of Child Development and Learning science of Ministry of Education School of Biological Science&Medical EngineeringSoutheast UniversityNanjing 210096China Institute of Artificial Intelligence of Hefei Comprehensive National science Center Hefei 230094China

Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent ***,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high ***,there is little research on MKL in EEG-based emotion ***,this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion ***,we proposed a support vector machine(SVM)classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition *** designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical ***,tri-categorization experiments were performed for neutral,negative and positive emotions based on a commonly used dataset,the Shanghai Jiao Tong university emotional EEG dataset(SEED).The average classification accuracies for random division and sequential division were 92.25%and 74.37%,respectively,which shows better classification performance than the traditional single kernel *** final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further *** the analysis of the experimental results,we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better *** is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion *** summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EE

关键词： Emotion recognition electroencephalography(EEG) multiple kernel learning machine learning brain computer interface

CEPC Technical Design Report

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Radiation Detection Technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi Yuanjie Bi Tianjian Bian fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-Yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH university of science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui university HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu university Institute of ScienceVaranasi Bandirma Onyedi Eylul university Bandirma Beihang university Beijing Beijing Computational science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal university Beijing Brown university ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad university Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo university Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central china Normal university WuhanHubei Central South university ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan china academy of engineering physics MianyangSichuan china building materials academy Beijing china Center of Advanced science and Technology Beijing china Institute of Atomic Energy Beijing china Jiliang university HangzhouZhejiang china Nuclear Instrument CO. LTDBeijing china university of Geosciences Beijing china university of Geosciences WuhanHubei china university of Mining and Technology XuzhouJiangsu china West Normal university NanchongSichuan chongqing university Department of PhysicsChongqing Chulalongkorn university Bangkok City university of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell university IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu university DalianLiaoning Dalian university of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East china university Of science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by china,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

关键词： initiated exceeding precise

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区块链交易数据隐私保护方法

计算机科学 2020年第3期47卷 281-286页

作者：许重建李险峰北京大学深圳研究生院

区块链具有开放性、不可篡改、分布式共享全局账本等优点,但同时这些特性也造成了交易数据隐私泄露问题,严重影响其在许多业务领域的应用,特别是在企业联盟链领域的应用,随着区块链应用的不断发展,如何在区块链平台上对交易数据进行隐... 详细信息

区块链具有开放性、不可篡改、分布式共享全局账本等优点,但同时这些特性也造成了交易数据隐私泄露问题,严重影响其在许多业务领域的应用,特别是在企业联盟链领域的应用,随着区块链应用的不断发展,如何在区块链平台上对交易数据进行隐私保护是一个非常值得研究的问题。为此,首先对现有的区块链交易数据隐私保护方法进行研究并指出其不足,其次对区块链交易数据隐私保护需求进行定性分析,将每一笔交易数据分为敏感数据和基础数据两部分,建立需求分析矩阵,得出交易隐私保护的本质需求和隐含需求以及可能的应用场景;然后结合对称加密与非对称加密各自的特点以及智能合约的共识特性,设计了一套基于双重加密的区块链交易数据隐私保护方法,该方法主要包括私密数据提供方加密存储交易数据、私密数据使用方解密读取交易数据、私密数据可访问方共享交易数据3个模块,同时对每个模块的工作流程进行了详细论述;最后在蚂蚁区块链平台上结合国际贸易多方共同参与的实际业务对该方法进行验证。测评结果表明,该方法能够实现字段级别细粒度的交易数据隐私保护,能够在链上高效稳定地进行私密数据共享和完成私密数据的全链路操作;在使用4个节点搭建的区块链平台上完成了超过100万笔的交易测试,平均TPS达到了800;相比原来没有使用隐私保护的系统,交易性能并没有明显降低,相比比特币、以太坊等区块链平台,文中使用的区块链平台通过加密后的交易性能得到了几十倍的提升。

关键词：区块链全局账本智能合约数据共享隐私保护双重加密

Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework:more accurate and more convenient

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Burns & Trauma 2019年第1期7卷 44-57页

作者： chong Jiao Kehua Su Weiguo Xie Ziqing Ye school of computer science Wuhan UniversityWuhan 430072China Institute of Burns Wuhan Hospital No.3 and Tongren Hospital of Wuhan UniversityWuhan 430060China

Background:Burns are life-threatening with high morbidity and *** diagnosis supported by accurate burn area and depth assessment is critical to the success of the treatment decision and,in some cases,can save the patient’s *** techniques such as straight-ruler method,aseptic film trimming method,and digital camera photography method are not repeatable and comparable,which lead to a great difference in the judgment of burn wounds and impede the establishment of the same evaluation ***,in order to semi-automate the burn diagnosis process,reduce the impact of human error,and improve the accuracy of burn diagnosis,we include the deep learning technology into the diagnosis of ***:This article proposes a novel method employing a state-of-the-art deep learning technique to segment the burn wounds in the *** designed this deep learning segmentation framework based on the Mask Regions with Convolutional Neural Network(Mask R-CNN).For training our framework,we labeled 1150 pictures with the format of the Common Objects in Context(COCO)data set and trained our model on 1000 *** the evaluation,we compared the different backbone networks in our *** backbone networks are Residual Network-101 with Atrous Convolution in Feature Pyramid Network(R101fa),Residual Network-101 with Atrous Convolution(R101A),and InceptionV2-Residual Network with Atrous Convolution(IV2RA).Finally,we used the Dice coefficient(DC)value to assess the model ***:The R101fa backbone network gains the highest accuracy 84.51%in 150 ***,we chose different burn depth pictures to evaluate these three backbone *** R101fa backbone network gains the best segmentation effect in superficial,superficial thickness,and deep partial *** R101A backbone network gains the best segmentation effect in full-thickness ***:This deep learning framework shows excellent segmentation in burn wound and extremely

关键词： Burn image Deep learning Mask R-CNN Image segmentation

Advances in nuclear detection and readout techniques

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Nuclear science and Techniques 2023年第12期34卷 281-358页

作者： Rui He Xiao‑Yang Niu Yi Wang Hong‑Wei liang Hong‑Bang liu Ye Tian Hong‑lin Zhang Chao‑Jie Zou Zhi‑Yi liu Yun‑Long Zhang Hai‑Bo Yang Ju Huang Hong‑Kai Wang Wei‑Jia Han Bei Cao Gang Chen Cong Dai li‑Min Duan Rui‑Rui fan fang‑fa Fu Jian‑Hua Guo Dong Han Wei Jiang xian‑Qin li Xin li Zhuo‑Dai li Yu‑Tie liang Shun liao De‑Xu lin Cheng‑Ming liu Guo‑Rui liu Jun‑Tao liu Ze Long Meng‑Chen Niu Hao Qiu Hu Ran xiang‑Ming Sun Bo‑Tan Wang Jia Wang Jin‑xiang Wang Qi‑lin Wang Yong‑Sheng Wang Xiao‑Chuan Xia Hao‑Qing Xie He‑Run Yang Hong Yin Hong Yuan Chun‑Hui Zhang Rui‑Guang Zhao Ran Zheng Cheng‑Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China school of Nuclear science and Technology University of Chinese Academy of SciencesBeijing 100049China Advanced Energy science and Technology Guangdong Laboratory Huizhou 516000China Key Laboratory of Particle and Radiation Imaging Department of Engineering PhysicsTsinghua UniversityBeijing 100084China school of Integrated Circuits Dalian University of TechnologyDalian 116020China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China school of Nuclear science and Technology Lanzhou UniversityLanzhou 730000China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Radiation Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China Electronic Engineering school Heilongjiang UniversityHarbin 150080China Spallation Neutron Source science Center Dongguan 523803China Micro‑electronic Department Harbin Institute of TechnologyHarbin 150001China Purple Mountain Observatory Chinese Academy of SciencesNanjing 210034China liaoning Academy of Materials Shenyang 110167China Istituto Nazionale di Fisica Nucleare Sezione di PerugiaPerugiaItaly school of mathematics and Physics Chengdu University of TechnologyChengdu 620035China PLAC Key Laboratory of Quark and Lepton Physics(MOE)Central China Normal UniversityWuhan 430079China school of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China State Key Lab of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China school of computer science Northwestern Polytechnical UniversityXi’an 710129China

“A Craftsman Must Sharpen His Tools to Do His Job,”said *** detection and readout techniques are the foundation of particle physics,nuclear physics,and particle astrophysics to reveal the nature of the ***,they are being increasingly used in other disciplines like nuclear power generation,life sciences,environmental sciences,medical sciences,*** article reviews the short history,recent development,and trend of nuclear detection and readout techniques,covering Semiconductor Detector,Gaseous Detector,Scintillation Detector,Cherenkov Detector,Transition Radiation Detector,and Readout *** explaining the principle and using examples,we hope to help the interested reader underst and this research field and bring exciting information to the community.

关键词： Silicon detector Wide bandgap detector Time projection chamber(TPC) Multigap resistive plate chamber detector(MRPC) Scintillation detector Calorimeters Cherenkov detector Transition radiation detector Artificial intelligence Trigger-less readout

A network pharmacological study on the potential mechanism of Yinchenhao Decoction in treatment of chronic hepatitis B

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Drug Combination Therapy 2019年第3期1卷 134-152页

作者： Jin-Yu Zhang xian-fa Cai school of Public Health Guangdong Pharmaceutical UniversityGuangzhou510310China school of Medical Information Engineering Guangdong Pharmaceutical UniversityGuangzhou510006China

Background: Yinchenhao Decoction (YCHD), an ancient Chinese herbal medicinal compound prescribe for the treatment of jaundice, which long-term clinical experience and randomized trials have shown is effective against chronic hepatitis B (CHB). However, its mechanism remains unknown. With the gradual separation of pharmacodynamic components in YCHD and the development of computer virtual technology, network pharmacology provides an opportunity. Our study is to explore the underlying mechanism of YCHD for the treatment of CHB. Methods: The active compounds of YCHD were screened from TCMSP database, whose targets were predicted based on the ligands structures and the targets of CHB was selected from the TTD, DisGeNET, and Drugbank databases to obtain the potential targets of YCHD for CHB treating. Cytoscape 3.6.1 was applied for network analysis to identify key targets, and gene enrichment of the targets shared by both YCHD and CHB was performed by DAVID database. The virtual verification of binding abilities between the target protein and the small molecule was finally performed by the Sytems Dock platform. Results: The potentially important targets associated with CHB corresponding to 33 compound components from YCHD included ESR1, CYP1A2, PTGS2, CYP1A1, ABCG2, MMP9. Besides, enriched were the related 8 KEGG pathways (Pling pathway. Conclusions: This study used network pharmacology method to reveal the characteristics,“multi-component and multi-target and multi-pathway”, of YCHD on CHB treatment, providing thoughts for further research.

关键词： Network pharmacology Yinchenhao Decoction Chronic hepatitis B