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computer Modeling in Engineering & sciences 2024年第8期140卷 1205-1231页

作者： xiaoli You Ruiyu Bai kai Xue Zimo Zhang Minghao Wang Xuanqi Wang Jiahao Wang jinku Guo Qiang Shen Honglong Chang Xu long Bowen Ji Unmanned System Research Institute Northwestern Polytechnical UniversityXi’an710072China National Key Laboratory of Unmanned Aerial Vehicle technology Northwestern Polytechnical UniversityXi’an710072China Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China School of Automobile Chang’an UniversityXi’an710018China MOE Engineering Research Center of Smart Microsensors and Microsystems College of Electronics and InformationHangzhou Dianzi UniversityHangzhou310018China School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an710129China

Implanted neural probes can detect weak discharges of neurons in the brain by piercing soft brain tissue,thus as important tools for brain science research,as well as diagnosis and treatment of brain ***,the rigid neural probes,such as Utah arrays,Michigan probes,and metal microfilament electrodes,are mechanically unmatched with brain tissue and are prone to rejection and glial scarring after implantation,which leads to a significant degradation in the signal quality with the implantation *** recent years,flexible neural electrodes are rapidly developed with less damage to biological tissues,excellent biocompatibility,and mechanical compliance to alleviate *** them,the mechanical modeling is important for the optimization of the structure and the implantation *** this review,the theoretical calculation of the flexible neural probes is firstly summarized with the processes of buckling,insertion,and relative interaction with soft brain tissue for flexible probes from outside to ***,the corresponding mechanical simulation methods are organized considering multiple impact factors to realize minimally invasive ***,the technical difficulties and future trends of mechanical modeling are discussed for the next-generation flexible neural probes,which is critical to realize low-invasiveness and long-term coexistence in vivo.

关键词： Mechanical modeling flexible neural probes invasive theoretical calculation simulation

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基于时态测试器的实时分支时态逻辑模型检测

软件学报 2022年第8期33卷 2930-2946页

作者：骆翔宇黄欣玥古天龙苏开乐陈祖希郑黎晓华侨大学计算机科学与技术学院福建厦门361021 广西可信软件重点实验室(桂林电子科技大学) 广西桂林541004 暨南大学信息科学技术学院/网络空间安全学院广东广州510632 南京信息工程大学人工智能学院江苏南京210044

基于自动机理论的模型检测技术在形式化验证领域处于核心地位,然而传统自动机在时态算子上不具备可组合性,导致各种时态逻辑的模型检测算法不能有机整合.为了实现集成限界时态算子的实时分支时态逻辑RTCTL*的高效模型检测,提出一种RTCTL... 详细信息

基于自动机理论的模型检测技术在形式化验证领域处于核心地位,然而传统自动机在时态算子上不具备可组合性,导致各种时态逻辑的模型检测算法不能有机整合.为了实现集成限界时态算子的实时分支时态逻辑RTCTL*的高效模型检测,提出一种RTCTL*正时态测试器构造方法以及相关符号化模型检测算法,既证明了所提出的RTCTL*正时态测试器构造方法是完备的,也证明了该算法时间复杂度与被验证系统呈线性关系,与公式长度呈指数关系.基于JavaBDD软件包成功开发了该算法的模型检测工具MCTK2.0.0.完成了MCTK与著名的符号化模型检测工具nu Xmv之间的实验对比分析工作,结果表明:MCTK虽然在内存消耗上要多于nu Xmv,但是MCTK的时间复杂度双指数级小于nuXmv,使得利用MCTK验证大规模系统的实时时态性质成为可能.

关键词：符号化模型检测公平离散系统正时态测试器实时分支时态逻辑二元决策图

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基于可重随机化混淆电路的可验证计算

软件学报 2019年第2期30卷 399-415页

作者：赵青松曾庆凯刘西蒙徐焕良计算机软件新技术国家重点实验室(南京大学) 江苏南京210023 南京大学计算机科学与技术系江苏南京210023 南京农业大学信息科技学院江苏南京210095 福州大学数学与计算机科学学院福建福州350117 School of Information Systems Singapore Management University

Yao的混淆电路可用于客户端将函数计算外包给服务器,并可验证其正确性.然而,混淆电路仅能使用1次.Gennaro等人组合使用全同态加密和混淆电路,可实现客户端和服务器在多次输入上重用混淆电路.但是,所有已知的全同态加密在效率的提高上似... 详细信息

Yao的混淆电路可用于客户端将函数计算外包给服务器,并可验证其正确性.然而,混淆电路仅能使用1次.Gennaro等人组合使用全同态加密和混淆电路,可实现客户端和服务器在多次输入上重用混淆电路.但是,所有已知的全同态加密在效率的提高上似乎仍有很大的空间,并且需要较强的困难性假设.另一方面,Gennaro等人的方案只能在敌手不能对客户端发起任何数量的验证查询这种较弱的模型下被证明是安全的.部分同态加密的困难性假设要弱于全同态加密,虽然只支持数量有限的同态操作,但比全同态加密运行速度更快、更加紧凑.提出了一个使用加同态加密的可验证计算方案.它基于DDH假设,能够容忍任意数量的恶意验证查询,采用的主要技术是可重随机化的混淆电路.该技术可以实现重随机化的混淆电路分布与原有的混淆电路分布在计算上是不可区分的.另外,也给出了一种使用可重随机化的混淆电路构造密码转置防火墙方案,称为可重用密码转置防火墙.也就是说,混淆电路可生成1次,接下来,密码转置防火墙可安全地重随机化和重用多次.

关键词：可验证计算可重随机化混淆电路同态加密密码转置防火墙

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基于随机森林算法的脑电情绪识别研究

电脑与信息技术 2022年第3期30卷 1-4页

作者：廖健熙吕勇王振宇赵凯嘉兴学院信息科学与工程学院浙江嘉兴314001 万科思自控信息(中国)有限公司浙江嘉兴314006

为了提高脑电情绪识别的准确率与效率,针对EEG信号数据冗余导致分类精确度低和效率低等不足,训练了一个随机森林(Random Forest,RF)模型进行脑电情绪识别,利用采集到的脑电信号特征提取后进行测试,并与长短期记忆网络(long Short-Term M... 详细信息

为了提高脑电情绪识别的准确率与效率,针对EEG信号数据冗余导致分类精确度低和效率低等不足,训练了一个随机森林(Random Forest,RF)模型进行脑电情绪识别,利用采集到的脑电信号特征提取后进行测试,并与长短期记忆网络(long Short-Term Memory,LSTM)、K近邻(K-Nearest Neighbor,KNN)和logistic回归(Logistic Regression,LR)进行了比较。实验结果表明,RF识别分类的准确率达到97%以上,优于另外三种分类器,且平均每次训练RF用时521±10.3ms,预测用时仅为29.3±1.5ms,情绪识别效率较高,具有一定的理论与实际应用价值。

关键词：脑电情绪识别随机森林特征提取机器学习

基于药效团模型的PPAR激动剂的筛选及活性研究

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中南药学 2024年第7期22卷 1705-1711页

作者：李琦魏朝张鑫磊郭凯蕾马丽莎何金穗孙定康梁佳龙林佳艳张彭湃刘雪英河南大学生命科学学院河南开封475004 空军军医大学药学系西安710000 陕西中医药大学药学院陕西咸阳712046 中国人民解放军陆军第九四六医院医疗保障中心新疆伊宁835000

目的筛选具有PPARδ激动活性的化合物。方法通过构建PPARδ药效团模型对Top science数据库中的780万个小分子进行筛选,并采用对接分析通过化合物与受体之间的结合作用模式及其靶点选择性进行筛选;通过分子动力学模拟的方法,确定苗头化合... 详细信息

目的筛选具有PPARδ激动活性的化合物。方法通过构建PPARδ药效团模型对Top science数据库中的780万个小分子进行筛选,并采用对接分析通过化合物与受体之间的结合作用模式及其靶点选择性进行筛选;通过分子动力学模拟的方法,确定苗头化合物,进而通过体外活性实验获得PPARδ激动剂。结果通过计算机虚拟筛选获得了24个潜在PPARδ激动剂,分子对接结果表明,化合物3、5、9为潜在的PPARδ选择性激动剂。体外激动活性实验表明化合物3、5、9具有一定的PPARδ激动活性。结论与化合物3、5相比,化合物9拥有更强的体外激动活性(EC_(50)=17.66μmol·L^(-1)),后续可进行结构迭代优化,为进一步开发PPARδ激动剂奠定了基础。

关键词：虚拟筛选药效团模型分子对接分子动力学模拟

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

Video Polyp Segmentation: A Deep Learning Perspective

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Machine Intelligence Research 2022年第6期19卷 531-549页

作者： Ge-Peng Ji Guobao xiao Yu-Cheng Chou Deng-Ping Fan kai Zhao Geng Chen Luc Van Gool Research School of Engineering Australian National UniversityCanberra 2601Australia college of computer and Control Engineering Minjiang UniversityFuzhou 350108China Department of computer science Johns Hopkins UniversityBaltimore 21218USA computer Vision Laboratory ETH ZürichZürich 8092Switzerland Department of Radiological sciences University of CaliforniaLos Angeles 90095USA School of computer science and Engineering Northwestern Polytechnical UniversityXi′an 710072China

We present the first comprehensive video polyp segmentation(VPS)study in the deep learning *** the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation *** address this issue,we first introduce a high-quality frame-by-frame annotated VPS dataset,named SUN-SEG,which contains 158690colonoscopy video frames from the well-known *** provide additional annotation covering diverse types,i.e.,attribute,object mask,boundary,scribble,and ***,we design a simple but efficient baseline,named PNS+,which consists of a global encoder,a local encoder,and normalized self-attention(NS)*** global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations,which are then progressively refined by two NS *** experiments show that PNS+achieves the best performance and real-time inference speed(170 fps),making it a promising solution for the VPS ***,we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based ***,we discuss several open issues and suggest possible research directions for the VPS *** project and dataset are publicly available at https://***/GewelsJI/VPS.

关键词： Video polyp segmentation(VPS) dataset self-attention colonoscopy abdomen

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

注意力机制和CNN结合的雷达辐射源个体识别

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兵器装备工程学报 2023年第4期44卷 290-296页

作者：杨海宇郭文普康凯何婧媛边强火箭军工程大学作战保障学院西安710025 延安大学数学与计算机科学学院陕西延安716000

针对雷达辐射源个体识别准确率低、抗噪性能差和训练时间长的问题,提出了一种注意力机制和卷积神经网络相结合的方法。首先根据雷达发射机功率放大器的硬件差异,建立雷达辐射源的系统模型;其次,对雷达信号进行双谱分析,得到的双谱图作... 详细信息

针对雷达辐射源个体识别准确率低、抗噪性能差和训练时间长的问题,提出了一种注意力机制和卷积神经网络相结合的方法。首先根据雷达发射机功率放大器的硬件差异,建立雷达辐射源的系统模型;其次,对雷达信号进行双谱分析,得到的双谱图作为网络输入;然后,将注意力机制引入优化后的卷积神经网络,提高对个体特征的学习能力;最后,与现有方法对比,验证算法的有效性。实验证明,相比卷积神经网络,所提方法识别准确率提高5%,训练时间缩短一半。

关键词：注意力机制卷积神经网络雷达辐射源个体识别双谱分析