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Control Policy Learning Design for Vehicle Urban Positioning via BeiDou Navigation

Journal of Systems Science & Complexity 2024年第1期37卷 114-135页

作者： QIN Yahang ZHANG chengye chen Ci XIE Shengli LEWIS frank L School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China. Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China. UTA Research Institute The University of Texas at ArlingtonFort WorthTX 76019USA

This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via BeiDou *** navigating in urban canyons,the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou ***,the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system,and the point-to-point localization control is modeled and handled by leveraging off-policy reinforcement learning for feedback *** proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system,without the full knowledge of the vehicle *** is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate.A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment.

关键词： BeiDou navigation multipath effect prescribed convergence rate reinforcement learning urban localization.

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Multi-population particle swarm optimization algorithm for automatic design of steel frames

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 89-103页

作者： Wenchen SHAN Jiepeng LIU Yao DING Y.frank chen Junwen ZHOU Institute of Smart City and Intelligent Transportation Southwest Jiaotong UniversityChengdu 610000China School of Civil Engineering Chongqing UniversityChongqing 400045China Department of Civil Engineering the Pennsylvania State UniversityMiddletownPA 17057USA

Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost ***,a multi-population particle swarm optimization(MPPSO)algorithm is developed to optimize the weight of steel frames according to standard design *** are made to improve the algorithm performances including the constraint-based strategy,piecewise mean learning strategy and multi-population cooperative *** proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design *** related parameter influences on optimization results are *** the representative frame,MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4%lighter.A similar trend can also be observed in cases subjected to Chinese design *** addition,a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO *** purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design *** aims to provide a reference for automatic structural optimization design problems even with dimensional *** proposed method can be easily generalized to the optimization problem of other structural systems.

关键词： steel frame multi-population particle swarm optimization automatic structural optimization design

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Empirically evaluating virtual reality’s effect on reservoir engineering tasks

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Visual Informatics 2024年第1期8卷 26-46页

作者： Bryson Lawton Nanjia Wang Steven Samoil Parisa Daeijavad Siqi Xie Zhangxin chen frank Maurer Department of Computer Science University of Calgary2500 University Dr NWCalgaryT2N 1N4AlbertaCanada Department of Chemical&Petroleum Engineering University of Calgary2500 University Dr NWCalgaryT2N 1N4AlbertaCanada

To help determine in what ways virtual reality(VR)technologies may benefit reservoir engineering workflows,we conducted a usability study on a prototype VR tool for performing reservoir model analysis *** leveraging the strengths of VR technologies,this tool’s aim is to help advance reservoir analysis workflows beyond conventional methods by improving how one understands,analyzes,and interacts with reservoir model *** evaluate our tool’s VR approach to this,the study presented herein was conducted with reservoir engineering experts who used the VR tool to perform three common reservoir model analysis tasks:the spatial filtering of model cells using movable planes,the cross-comparison of multiple models,and well path *** study found that accomplishing these tasks with the VR tool was generally regarded as easier,quicker,more effective,and more intuitive than traditional model analysis software while maintaining a feeling of low task workload on ***,participants provided positive feedback regarding their experience with using VR to perform reservoir engineering work tasks,and in general,it was found to improve multi-model cross-analysis and rough object manipulation in *** indicates the potential for VR to be better than conventional means for some work tasks and participants also expressed they could see it best utilized as an addition to current software in their reservoir model analysis *** were,however,some concerns voiced when considering the full adoption of VR into their work that would be best first addressed before this took place.

关键词： Immersive analytics Visual analytics Data visualization Reservoir engineering Virtual reality VR

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Application of machine learning in perovskite materials and devices:A review

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Journal of Energy Chemistry 2024年第7期94卷 254-272页

作者： Ming chen Zhenhua Yin Zhicheng Shan Xiaokai Zheng Lei Liu Zhonghua Dai Jun Zhang Shengzhong(frank)Liu Zhuo Xu School of Electric Power Civil Engineering and ArchitectureCollege of Physics and Electronics EngineeringState Key Laboratory of Quantum Optics and Quantum Optics DevicesShanxiUniversityTaiyuan030006ShanxiChina Key Laboratory of Applied Surface and Colloid Chemistry National Ministry of EducationShanxi Engineering Lab for Advanced Energy TechnologySchool of Materials Science and EngineeringShaanxi Normal UniversityXi'an 710119ShaanxiChina State Key Laboratory of Catalysis Dalian National Laboratory for Clean EnergyDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina

Metal-halide hybrid perovskite materials are excellent candidates for solar cells and photoelectric *** recent years,machine learning(ML)techniques have developed rapidly in many fields and provided ideas for material discovery and *** can be applied to discover new materials quickly and effectively,with significant savings in resources and time compared with traditional experiments and density functional theory(DFT)*** this review,we present the application of ML in per-ovskites and briefly review the recent works in the field of ML-assisted perovskite ***,the advantages of perovskites in solar cells and the merits of ML applied to perovskites are ***,the workflow of ML in perovskite design and some basic ML algorithms are ***,the applications of ML in predicting various properties of perovskite materials and devices are ***,we propose some prospects for the future development of this *** rapid devel-opment of ML technology will largely promote the process of materials science,and ML will become an increasingly popular method for predicting the target properties of materials and devices.

关键词： Machine learning Perovskite Materials design Bandgap engineering Stability Crystal structure

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Construction Activity Analysis of Workers Based on Human Posture Estimation Information

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Engineering 2024年第2期33卷 225-236页

作者： Xuhong Zhou Shuai Li Jiepeng Liu Zhou Wu Yohchia frank chen School of Civil Engineering Chongqing UniversityChongqing 400044China School of Automation Chongqing UniversityChongqing 400044China Department of Civil Engineering The Pennsylvania State UniversityMiddletownPA 17057USA

Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction ***,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor *** have focused on monitoring on-site construction activities of ***,when multiple workers are working together,current research cannot accu rately and automatically identify the construction *** research proposes a deep learning framework for the automated analysis of the construction activities of multiple *** this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity *** designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.

关键词： Pose estimation Activity analysis Object tracking Construction workers Automatic systems

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Modulating the alloying mode in the doping-induced synthesis of Au-Pd nanowires

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Nano Research 2024年第4期17卷 3334-3343页

作者： Hui Jin Xiaoliang Wei Lecheng Zhao Jialong Yu Qiao Pan Shumin Li Qian Wang Zhaotong Yuan Dan yang Donghui Zhao Hongyu chen Yawen Wang Institute of Advanced Synthesis School of Chemistry and Molecular EngineeringJiangsu National Synergistic Innovation Center for Advanced MaterialsNanjing Tech UniversityNanjing 211816China School of Science Westlake UniversityHangzhou 310064China Institute of Natural Sciences Westlake Institute for Advanced StudyHangzhou 310064China

Heterogeneous doping is one effective strategy for synthesizing metal alloy ***,the heterogeneous doping processes of Pd on the ultrathin Au nanowires were systematically modulated and ***-Pd alloy nanowires with various morphologies and lattice structures can be obtained by adjusting the morphology of the precursor Au nanowires and the kinetics of the heterogeneous doping *** effects of the rate of Pd reduction and the concentration of the ligand oleylamine(OAm)on the Pd deposition and alloying mode were ***,as the Pd deposition rate decreases,the Pd deposition and alloying mode switches from the island-forming Stransky–Krastanov(SK)mode to the epitaxial frank-van der Merwe(FM)mode,and eventually to an unconventional twisting alloying mode,where the interdiffusion of Pd and Au causes drastic rearrangement of the lattice structure and formation of helical *** kinetics-related variation of alloying mode could also be observed in the Au-Ag nanowires,demonstrating a general design principle for the synthesis of alloy *** addition,the electrocatalytic performance of various Au-Pd nanowires was evaluated,and the alloy nanowire formed via the SK mode was found to be an excellent electrocatalyst for oxygen reduction and ethanol oxidation.

关键词： gold nanowires Au-Pd alloy oleylamine post-synthetic modification

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瑞士苏黎世Hardbrücke车站更新

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建筑技艺 2011年第2期17卷 218-219页

作者： Hannes Henz Roger Frei frank wie-yang chen Marc Holle Fabian Hrmann Jrn Küsters Claudia Meier Mathias Müller Daniel Niggli Marcellus Schwarz Basil Spiess Nina Strck WGG Schnetzer Puskas 朱晓琳 EM2N Architekten

Hardbrücke车站位于瑞士的苏黎世,用于Hardbrücke火车站的更新费用约为3350000瑞士法郎,总建筑面积5650m2.建筑师试图以选择性的干预手段赋予车站一种新的身份,使人们能够很容易地找到车站所在的方位,同时增加车站站前空间的吸引力.

关键词：苏黎世 cke 车站 Hardbr 瑞士

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A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2154-2165页

作者： Yalin Zhang Zhongxin Liu Zengqiang chen College of Artificial Intelligence Nankai UniversityTianjin 300350 Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai UniversityTianjin 300350China IEEE

Battery energy storage systems(BESSs)are widely used in smart ***,power consumed by inner impedance and the capacity degradation of each battery unit become particularly severe,which has resulted in an increase in operating *** general economic dispatch(ED)algorithm based on marginal cost(MC)consensus is usually a proportional(P)controller,which encounters the defects of slow convergence speed and low control *** order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance,we attempt to design a proportional integral(PI)controller with a reset mechanism(PI+R)to asymptotically promote MC consensus and total power mismatch towards 0 in this *** be frank,the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing,which accelerates convergence,improves control accuracy,and avoids *** eigenvalues of the system under a PI+R controller is well analyzed,ensuring the regularity of the system and enabling the reset *** ensure supply and demand balance within the isolated BESSs,a centralized reset mechanism is introduced,so that the controller is distributed in a flow set and centralized in a jump *** cope with Zeno behavior and input delay,a dwell time that the system resides in a flow set is *** on this,the system with input delays can be reduced to a time-delay free *** the capacity limitation of the battery,a modified MC scheme with PI+R controller is *** correctness of the designed scheme is verified through relevant simulations.

关键词： Battery energy storage system(BESS) distributed control economic dispatch multi-agent system reset control

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Learning the continuous-time optimal decision law from discrete-time rewards

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National Science Open 2024年第5期3卷 130-147页

作者： Ci chen Lihua Xie Kan Xie frank Leroy Lewis Yilu Liu Shengli Xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou 510006China UTA Research Institute the University of Texas at ArlingtonFort Worth 76118USA Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA Oak Ridge National Laboratory Oak Ridge 37830USA Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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Study on thermal decomposition kinetics of azobenzene-4,4′-dicarboxylic acid by using compensation parameter method and nonlinear fitting evaluation

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Chinese Journal of Chemical Engineering 2024年第6期70卷 269-279页

作者： Shuyi Shen Song Guo Sining chen Jinhua Zhao Nanjing University of Science&Technology Nanjing 210094China China Academy of Safety Science and Technology Beijing 100012China

Recently,azobenzene-4,4'-dicarboxylic acid(ADCA)has been produced gradually for use as an organic synthesis or pharmaceutical intermediate due to its eminent *** large quantities put into application in the future,the thermal stability of this substance during storage,transportation,and use will become quite ***,in this work,the thermal decomposition behavior,thermal decomposition kinetics,and thermal hazard of ADCA were *** were conducted by using a SENSYS evo DSC device.A combination of differential iso-conversion method,compensation parameter method,and nonlinear fitting evaluation were also used to analyze thermal kinetics and mechanism of ADCA *** results show that when conversion rate α increases,the activation energies of ADCA's first and main decomposition peaks *** amount of heat released during decomposition varies between 182.46 and 231.16 J·g^(-1).The proposed kinetic equation is based on the Avrami-Erofeev model,which is consistent with the decomposition *** the frank-Kamenetskii model,a calculated self-accelerating decomposition temperature of 287.0℃is obtained.

关键词： Azobenzene-4,4′-dicarboxylic acid Thermal decomposition behavior Kinetic mechanism Thermal hazard evaluation Compensation parameter effect

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