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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Li, Qiang Ju, Zhuoya Fan, Lili Liu, Xin Liu, Wenhua Wang, Feiyue Scientific and Technological Innovation Center Beijing China Beijing Institute of Technology The School of Automation Beijing China China University of Petroleum School of Big Data & Cyber Security Qingdao China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

As social production and living spaces gradually expand from the physical and information domains to the cognitive and social domains, this article addresses the new changes brought about by these expansions. Starting from the new impact of era of intelligent connectivity on social cognitive management, it analyzes the urgent need to enhance social cognitive management capabilities centered on cognitive decision-making, identifying the core key as Adversarial Generated Intelligence (AGI). It delves into the conceptual characteristics, operating mechanisms, and winning strategies of AGI, identifies the new challenges it faces, and proposes a Parallel Intelligence empowering AGI (PiAGI) system architecture through the empowerment of parallel intelligence. This provides theoretical support for improving social cognitive management capabilities centered on cognitive decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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An Improved Machine Learning Model for Pure Component Property Estimation

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Engineering 2024年第8期39卷 61-73页

作者： Xinyu Cao Ming Gong Anjan Tula Xi Chen Rafiqul Gani Venkat Venkatasubramanian State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Physics Bard College at Simon’s RockGreat BarringtonMA 01230USA PSE for SPEED Company Charlottenlund DK-2920Denmark Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 510000China Department of Applied Sustainability Széchenyi István UniversityGyör 9026Hungary Complex Resilient Intelligent Systems Laboratory Department of Chemical EngineeringColumbia UniversityNew YorkNJ 10027USA

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product ***,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these ***,accuracy and predictive capabilities still limit the scope and applicability of most property estimation *** paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different *** selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research *** framework is assessed using datasets of varying sizes for 20 pure component *** 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

关键词： Group contribution Gaussian process Warping function Prior predictive checking

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神经网络增强SED-SL建模应用于翼型绕流湍流计算

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Acta Mechanica Sinica 2024年第3期40卷 72-86页

作者：黄文霄刘溢浪毕卫涛高毅卓陈军 State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing100871China School of Aeronautics Northwestern Polytechnical UniversityXi’an710072China School of Aeronautic Science and Engineering Beihang UniversityBeijing100191China

本文采用SED-SL-RBF的新型建模方法,利用有限NACA机翼的空气动力学数据增强了SED-SL(壁湍流的结构系综动力学-应力长)模型,构建了机翼上湍流边界层的多层结构(MLS),并利用机器学习从实验数据中重建模型参数.该方法应用于九种不同类型的N... 详细信息

本文采用SED-SL-RBF的新型建模方法,利用有限NACA机翼的空气动力学数据增强了SED-SL(壁湍流的结构系综动力学-应力长)模型,构建了机翼上湍流边界层的多层结构(MLS),并利用机器学习从实验数据中重建模型参数.该方法应用于九种不同类型的NACA机翼上的湍流,具有广泛的雷诺数和攻角范围.研究采用RBF(径向基函数)神经网络重建模型参数(l^(∞)_(0)和y^(∞)_(buf)),并将其应用于SED-SL的CFD数值计算.相较Menter SST湍流模型,SED-SL-RBF模型提升了在同样几何形状和流动条件下升力和阻力系数的预测精度.预测升力系数C_(L)的精确度超过了95%,而预测阻力系数C_(D)的误差则小于6 count.神经网络增强的SED-SL模型对压力场的预测精度也非常高.NACA 2421的MLS参数表现出不随攻角变化的相似性,并可视其为由雷诺数刻画的函数.该结果表明,NACA2421的MLS参数与失速前的攻角大小无关.该相似行为为模拟各种物理条件下的机翼流动提供了一种可行的方案.未来期望整合数据以揭示模型参数方面的模型内在差异,从而将SED-SL-RBF模型的适用性扩展到更广泛的流动场景.

关键词： Structural ensemble dynamics RANS model Turbulent boundary layer Machine learning Neural network

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Voter model on adaptive networks

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Chinese Physics B 2022年第5期31卷 780-790页

作者： Jinming Du Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Institute of Industrial and Systems Engineering College of Information Science and EngineeringNortheastern UniversityShenyang 110819China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China

Voter model is an important basic model in statistical *** recent years,it has been more and more used to describe the process of opinion formation in *** real complex systems,the interactive network of individuals is dynamically adjusted,and the evolving network topology and individual behaviors affect each ***,we propose a linking dynamics to describe the coevolution of network topology and individual behaviors in this paper,and study the voter model on the adaptive *** theoretically analyze the properties of the voter model,including consensus probability and *** evolution of opinions on dynamic networks is further analyzed from the perspective of evolutionary ***,a case study of real data is shown to verify the effectiveness of the theory.

关键词： statistical physics opinion dynamics evolutionary game theory complex systems

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Ion conduction path in composite solid electrolytes for lithium metal batteries: from polymer rich to ceramic rich

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Frontiers in Energy 2022年第5期16卷 706-733页

作者： Zhouyu ZHANG Hao CHEN Zhenglin HU Shoubin ZHOU Lan ZHANG Jiayan LUO Key Laboratory for Green Chemical Technology of the Ministry of Education School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China Shanghai Key Laboratory of Advanced High-Temperature Materials and Precision Forming School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huafu High Technology Energy Storage Co. Ltd.Yangzhou 225600China CAS Key Laboratory of Green Process and Engineering Beijing Key Laboratory of Ionic Liquids Clean ProcessState Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Solid-state electrolytes(SSEs)can address the safety issue of organic electrolyte in rechargeable lithium ***,neither polymer nor ceramic SSEs used alone can meet the demand although great progress has been made in the past few *** solid electrolytes(CSEs)composed of flexible polymers and brittle but more conducting ceramics can take advantage of the individual system for solid-state lithium metal batteries(SSLMBs).CSEs can be largely divided into two categories by the mass fraction of the components:“polymer rich”(PR)and“ceramic rich”(CR)systems with different internal structures and electrochemical *** review provides a comprehensive and in-depth understanding of recent advances and limitations of both PR and CR electrolytes,with a special focus on the ion conduction path based on polymer-ceramic interaction mechanisms and structural designs of ceramic fillers/*** addition,it highlights the PR and CR which bring the leverage between the electrochemical property and the mechanical ***,it further prospects the possible route for future development of CSEs according to their rational design,which is expected to accelerate the practical application of SSLMBs.

关键词： composite solid electrolytes active filler/framework ion conduction path interphase compatibility multilayer design

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A Deep Learning Based Personal Authentication Method Using sEMG Signal 41

A Deep Learning Based Personal Authentication Method Using s...

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第41届中国控制会议

作者： Lina Tong Mingjia Zhang Daisong Liu Hanghang Ma Yuxiang Zhang Liang Peng School of Mechanical Electronic & Information Engineering Chinese University of Mining and Technology-Beijing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChina Academy of Science

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Personal authentication means that the relevant system confirms whether the identity of the user is real,legal and unique.A safe and convenient personal authentication method is a core for guarantee the security of our information and *** Electromyography(sEMG),which exists on the surface of the skin,is a good signal to achieve personal authentication,for it is difficult to be extracted and *** this paper,a new personal authentication method using forearm sEMG is ***,8-channel sEMG signals are recorded by Myo armband that is placed on right forearm of 14 subjects who perform the same hand open ***,two different deep learning models are presented to classify the *** multi-layers Convolution Neural Network(ML-CNN) and two stages Long Short Term Memory(2 s-LSTM) Network can achieve accuracy of 97.50% and 93.60% respectively,shows it is feasible to achieve the goal of personal *** the time cost of ML-CNN is higher than the 2 s-LSTM,both are lower than human reaction *** the architecture of ML-CNN is more suitable to achieving the goal of personal authentication.

关键词： Personal Authentication Surface Electromyography(sEMG) Myo multi-layers Convolution Neural Network(CNN) LSTM Network

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Temporal Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1586-1598页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Deng, Fuhu Chen, Leduo Qin, Zhiguang Raymond Choo, Kim-Kwang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu Sichuan610054 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610054 China School of Advanced Technology Suzhou Jiangsu215123 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fuzing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the interframe variances and generalizing graph convolutional network (GCN) operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatial-temporal cascaded aggregation (STCA) module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120. © 2020 IEEE.

关键词： Convolution

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Energy spectrum of two-dimensional isotropic rapidly rotating turbulence

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Physical Review Fluids 2025年第5期10卷 054608-054608页

作者： Pei-Yang Li Jin-Han Xie Department of Mechanics and Engineering Science at College of Engineering and State Key Laboratory for Turbulence and Complex Systems Peking University Beijing 100871 People's Republic of China

We study a two-dimensional isotropic rotating system and obtain both theoretically and numerically a K−2 energy spectrum under the rapidly rotating condition (Ro≪1), which was initially obtained by Zeman [Phys. Fluids 6, 3221 (1994)] and Zhou [Phys. Fluids 7, 2092 (1995)]. In rotating turbulence, the K−2 energy spectrum was proposed under the assumption of isotropy; however, the direction selectivity of rotation breaks isotropy, making this K−2 spectrum not easily observable. To fill the gap between theoretical assumptions and realizability, we study the turbulence of inertial waves in an artificial two-dimensional isotropic rotating turbulence system. In the limit of a small Rossby number, we asymptotically derive a nonlinear amplitude equation for inertial waves, which gives the K−2 spectrum using a strong turbulence argument. This scaling is justified by numerical simulations of both the amplitude equation and the original system.

关键词： Nonlinear equations

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From Fourier to Neural ODEs: Flow Matching for Modeling complex systems 41

From Fourier to Neural ODEs: Flow Matching for Modeling Comp...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Xin Zhang, Jingdong Zhu, Qunxi Zhao, Chengli Zhang, Xue Duan, Xiaojun Lin, Wei College of Science National University of Defense Technology Hunan Changsha410073 China School of Mathematical Sciences LMNS SCMS Fudan University China Research Institute of Intelligent Complex Systems Fudan University China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University China 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. Copyright 2024 by the author(s)

关键词： Large scale systems

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