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2020 5th International Seminar on Computer technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Ma, Haocong Zhang, Ji Zhou, Junhua Zhai, Xiang Xue, Junjie Ji, Hang State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing100854 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing100854 China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing100854 China

Malicious code is characterized by a large number of types, rapid increase in number, continuous update of transmission routes, and continuous enhancement of back analysis and back detection methods. Therefore, how to effectively detect and analyze malicious code has been a problem of great concern. This paper studies the features of binary file and disassembly file of malicious code, introduces the concept of information gain, and proposes a method to construct the multi-dimensional characteristic graph of malicious code. Finally, the convolutional neural network is used to classify the multi-dimensional feature graph of malicious code, which provides a new idea for the feature extraction of malicious code. © Published under licence by IOP Publishing Ltd.

关键词： Malware

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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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From Fourier to neural ODEs: flow matching for modeling complex systems 24

From Fourier to neural ODEs: flow matching for modeling comp...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xin Li Jingdong Zhang Qunxi Zhu Chengli Zhao Xue Zhang Xiaojun Duan Wei Lin College of Science National University of Defense Technology Changsha Hunan China School of Mathematical Sciences LMNS and SCMS and Research Institute of Intelligent Complex Systems Fudan University China Research Institute of Intelligent Complex Systems Fudan University China School of Mathematical Sciences LMNS and SCMS and Research Institute of Intelligent Complex Systems and State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University China and Shanghai Artificial Intelligence Laboratory China

Modeling complex systems using standard neural ordinary differential equations (NODEs) often faces some essential challenges, including high computational costs and susceptibility to local optima. To address these challenges, we propose a simulation-free framework, called Fourier NODEs (FNODEs), that effectively trains NODEs by directly matching the target vector field based on Fourier analysis. Specifically, we employ the Fourier analysis to estimate temporal and potential high-order spatial gradients from noisy observational data. We then incorporate the estimated spatial gradients as additional inputs to a neural network. Furthermore, we utilize the estimated temporal gradient as the optimization objective for the output of the neural network. Later, the trained neural network generates more data points through an ODE solver without participating in the computational graph, facilitating more accurate estimations of gradients based on Fourier analysis. These two steps form a positive feedback loop, enabling accurate dynamics modeling in our framework. Consequently, our approach outperforms state-of-the-art methods in terms of training time, dynamics prediction, and robustness. Finally, we demonstrate the superior performance of our framework using a number of representative complex systems.

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A Parkinson's Bradykinesia Recognition System Based on Deep Learning Method 12

A Parkinson's Bradykinesia Recognition System Based on Deep ...

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12th International Conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Tong, Lina Liu, Daisong Zhang, Mingjia Peng, Liang Department of State Key Laboratory of Management and Control for Complex Systems Institute of Automation China Academy of Science Beijing China School of Mechanical Electronic and Information Engineering Chinese University of Mining and Technology-Beijing Beijing China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Parkinson's disease is a neurodegenerative disease common in middle-aged and elderly people, and bradykinesia is one of the most obvious symptoms. In this study, A deep learning-based Parkinson's bradykinesia recognition system is designed which has an inertial sensor-based data acquisition system. It can wirelessly collect bradykinesia kinematic information by wearing it on the wrist and ankle of Parkinson's patients. In addition, a method for identifying the symptom of Parkinson's bradykinesia based on Long Short Term Memory (LSTM) Network is proposed, with an accuracy of 98.6%. It provides a feasible method to identify Parkinson bradykinesia to assist the diagnosis and treatment. © 2022 IEEE.

关键词： Neurodegenerative diseases

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xiang, Tian-Yu Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Yang, Hong-Jun Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology The Joint Laboratory of Intelligence Science and Technology Taipa China

ISBN: (纸本)9781665481090

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios. © 2022 IEEE.

关键词： Convolution

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Towards Intelligent and Trustworthy Crowdsourcing systems with Cyber-physical Social Intelligence via Retrieval-augmented Generation

Towards Intelligent and Trustworthy Crowdsourcing Systems wi...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jing Yang Xingxia Wang Qinghua Ni Yuhang Liu Jing Wang Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

In recent years, crowdsourcing systems that tackle complex tasks through the collective efforts of many individuals have garnered substantial attention. However, the existing crowd-sourcing systems face some challenges such as overreliance on human experience and effort, information overload, and risks of privacy breaches. To overcome these challenges, this paper proposes an intelligent and trustworthy crowdsourcing frame-work based on blockchain and retrieval-augmented generation. The framework centers around foundational models to integrate the three distinct and sequentially interdependent phases: task decomposition, task allocation or recommendation, and task execution, enabling the automatic provision of a personalized, comfortable, and efficient user experience. Blockchain and smart contracts are utilized to protect user privacy and interests without leaning on any third trusted authority. Additionally, the operational process of the framework is explained and validated by taking printed circuit board design and manufacturing as a case study and ChatGPT as its generator.

关键词： Crowdsourcing Retrieval augmented generation Smart contracts Privacy breach User experience Stability analysis Blockchains Social intelligence Resource management Reliability

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Motion Planning of 3D Nonholonomic Robots via Curvature-Constrained Vector Fields

Motion Planning of 3D Nonholonomic Robots via Curvature-Cons...

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IEEE Conference on Decision and Control

作者： Yike Qiao Xiaodong He Zhongkui Li Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Vector-field-based methods are typical feedback planning algorithms, especially eligible for the motion planning of nonholonomic robots. Nevertheless, most existing vector fields (VF) do not account for the prevalent constraints on robot’s kinematics. This paper addresses the motion planning problem for 3D nonholonomic robots with trajectories featuring upper bounded curvature. To this end, a curvature-constrained VF over $\mathbb{R}^{3}$ is proposed, whose integral curves guarantee an upperbound of curvature as well as an almost-global attraction region towards the desired position with a specified heading direction. Moreover, a control strategy is presented to determine the robot’s control inputs subject to the curvature constraint. Under the designed control laws, the robot is guaranteed to track the VF while ensuring that the actual trajectory adheres to the curvature constraint. Finally, the efficacy of the presented motion planning algorithm is validated by numerical simulations.

关键词： Three-dimensional displays Tracking Velocity control Kinematics Numerical simulation Vectors Planning Trajectory Collision avoidance Robots

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Large Eddy simulation of Flow and Heat Transfer Performance in Periodically Inward Corrugated Tubes

SSRN

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SSRN 2023年

作者： Zhong, Yuzhou Song, Yueheng Zhao, Lei Mi, Fei Zhao, Jingquan Zhu, Xiaowei School of Aeronautic Science and Engineering Beihang University Beijing100191 China Science & Technology on Complex Aviation System Simulation Laboratory Beijing100070 China Co. Ltd. Henan453049 China

Corrugated tubes are extensively unitized in enhancing heat transfer within tubular heat exchange devices. The efficiency of these corrugated tubes is closely linked to the geometric characteristics of the surface corrugations. In this study, we conducted an extensive series of Large Eddy simulations on a representative type of enhanced tube featuring periodic inward corrugations (PIC). Our goal was to compile a comprehensive and precise dataset of the thermal and hydraulic properties specific to this design. The key design parameters that define the corrugated patterns are corrugation height, corrugation width, and the length of the straight section. We systematically explored these parameters to understand their impact on heat transfer and fluid dynamics. Among these parameters, corrugation height has been identified as the most influential factor affecting flow and heat transfer properties. As the corrugation height varies from 1/8D to 3/8D, we observed a substantial increase in the friction factor and Nusselt number by a factor of 209 and 5, respectively, at Re = 1000;while the critical Reynolds number decreased roughly from 800 to 100. These findings underscore the significant role of corrugation height in shaping the flow and heat transfer performance of PIC tubes. The other two parameters, corrugation width and straight section length, were also found to be crucial in determining the overall heat transfer coefficient and pressure drop. The results of our study suggest that when utilizing this type of corrugated tubes, careful consideration is required to strike an optimal balance between heat transfer efficiency and pressure loss. The large-sets LES results presented in this paper can serve as valuable inputs for optimizing the design of PIC tubes. © 2023, The Authors. All rights reserved.

关键词： Large eddy simulation

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Cryo-EM structures of human m6A writer complexes

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细胞研究（英文版） 2022年第11期32卷 982-994页

作者： Shichen Su Shanshan Li Ting Deng Minsong Gao Yue Yin Baixing Wu Chao Peng Jianzhao Liu Jinbiao Ma Kaiming Zhang State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and DevelopmentMultiscale Research Institute of Complex SystemsDepartment of Biochemistry and BiophysicsSchool of Life SciencesFudan UniversityShanghaiChina MOE Key Laboratory for Cellular Dynamics and Division of Life Sciences and Medicine University of Science and Technology of ChinaHefeiAnhuiChina MOE Key Laboratory of Macromolecular Synthesis and Functionalization Department of Polymer Science and EngineeringZhejiang UniversityHangzhouZhejiangChina National Facility for Protein Science in Shanghai Zhangjiang LaboratoryShanghai Advanced Research InstituteChinese Academy of ScienceShanghaiChina State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and DevelopmentMultiscale Research Institute of Complex SystemsDepartment of Biochemistry and BiophysicsSchool of Life SciencesFudan UniversityShanghaiChina Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Guangdong-Hong Kong Joint Laboratory for RNA MedicineRNA Biomedical InstituteMedical Research CenterSun Yat-Sen Memorial HospitalSun Yat-Sen UniversityGuangzhouGuangdongChina

N6-methyladenosine(m6A)is the most abundant ribonucleotide modification among eukaryotic messenger *** m6A"writer"consists of the catalytic subunit m6A-METTL complex(MAC)and the regulatory subunit m6A-METTL-associated complex(MACOM),the latter being essential for enzymatic ***,we report the cryo-electron microscopy(cryo-EM)structures of MACOM at a 3.0-A resolution,uncovering that WTAP and VIRMA form the core structure of MACOM and that ZC3H13 stretches the conformation by binding ***,the 4.4-? resolution cryo-EM map of the MACOM-MAC complex,combined with crosslinking mass spectrometry and GST pull-down analysis,elucidates a plausible model of the m6A writer complex,in which MACOM binds to MAC mainly through WTAP and METTL3 *** combination with in vitro RNA substrate binding and m6A methyltransferase activity assays,our results illustrate the molecular basis of how MACOM assembles and interacts with MAC to form an active m6A writer complex.

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Discrete unified gas kinetic scheme for continuum compressible flows

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Physical Review E 2023年第2期107卷 025304-025304页

作者： Zhaoli Guo (郭照立) Lian-Ping Wang (王连平) Yiming Qi (岐亦铭) Institute of Multidisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan 430074 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 China State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing 100871 China

In this paper, a discrete unified gas kinetic scheme (DUGKS) is proposed for continuum compressible gas flows based on the total energy kinetic model [Guo et al., Phys. Rev. E 75, 036704 (2007)]. The proposed DUGKS can be viewed as a special finite-volume lattice Boltzmann method for the compressible Navier-Stokes equations in the double distribution function formulation, in which the mass and momentum transport are described by the kinetic equation for a density distribution function (g), and the energy transport is described by the other one for an energy distribution function (h). To recover the full compressible Navier-Stokes equations exactly, the corresponding equilibrium distribution functions geq and heq are expanded as Hermite polynomials up to third and second orders, respectively. The velocity spaces for the kinetic equations are discretized according to the seventh and fifth Gauss-Hermite quadratures. Consequently, the computational efficiency of the present DUGKS can be much improved in comparison with previous versions using more discrete velocities required by the ninth Gauss-Hermite quadrature.

关键词： Compressible flows Kinetic theory Boltzmann theory Numerical techniques

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