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Coalition formation problem: a capability-centric analysis and general model

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Science China(Information Sciences) 2024年第11期67卷 180-193页

作者： Jie CHEN Miao GUO Bin XIN Qing WANG Shengyu LU Yipeng WANG Yulong DING School of Automation Beijing Institute of Technology National Key Lab of Autonomous intelligent Unmanned systems Department of Control Science and Engineering Tongji University

Coalition formation(CF) refers to reasonably organizing robots and/or humans to form coalitions that can satisfy mission requirements, attracting more and more attention in many fields such as multirobot collaboration and human-robot collaboration. However, the analysis on CF problems remains *** provide a valuable study reference for researchers interested in CF, this paper proposed a capabilitycentric analysis of the CF problem. The key problem elements of CF are firstly extracted by referencing the concepts of the 5W1H method. That is, objects(who) form coalitions(what) to accomplish missions(why) by aggregating capabilities(how) in a specific environment(where-when). Then, a multi-view analysis of these elements and their correlation in terms of capabilities is proposed through various logic diagrams, structure charts, etc. Finally, to facilitate a deeper understanding of capability-centric CF, a general mathematical model is constructed, demonstrating how the different concepts discussed in this analysis contribute to the overall model.

关键词： coalition formation capability aggregation capability metric mission requirement environmental effect

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Non-destructive Testing and intelligent Evaluation of Road Structural Conditions using GPR and FWD

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Journal of Traffic and transportation Engineering(English Edition) 2024年

作者： Zhen Liu Siqi Wang Xingyu Gu Qiao Dong National Demonstration center for Experimental Road and Traffic Engineering Education (Southeast University) Department of Roadway Engineering， School of transportation， Southeast University

Aiming at the assessment of road structural conditions， a method of combining ground penetrating radar (GPR) and falling weight deflectometer (FWD) for assessing the pavement structural integrity and strength was proposed in this study. First， 3D GPR was performed to detect the thickness and internal distress of pavement structural layers using the canny edge detection and you only look once version eight (YOLOv8) detection algorithms. Results showed that the error of thickness extraction was approximately 3%， and the distress detection achieved a mean average precision (mAP) of 0.859 and an inference time of 11.43 ms with a GTX 1070 GPU. Then， the extracted thickness was used for the modulus (E) inversion of pavement structure layers based on FWD test data and regression analysis. Finally， the distress ratio inside pavement structures (DRIPS) was proposed as a structural integrity index. The relationship between E and DRIPS was revealed and showed a good correlation. The greater the DRIPS value， the worse the pavement structural integrity and strength. It provides a reference for the evaluation of road structural conditions. This strategy proved to be reliable for nondestructive testing and evaluation of road structures， which could improve the comprehensiveness and effectiveness for evaluating road structural conditions.

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Dynamical pattern recognition for univariate time series and its application to an axial compressor

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Control Theory and Technology 2024年第1期22卷 39-55页

作者： Jingtao Hu Weiming Wu Zejian Zhu Cong Wang center for intelligent Medical Engineering School of Control Science and TechnologyShandong UniversityJinan250061ShandongChina School of Automation Science and Engineering South China University of TechnologyGuangzhou510641GuangdongChina

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.

关键词： Dynamical pattern recognition Deterministic learning Stall warning Radial basis function network Sampled-data observer

The Immense Impact of Reverse Edges on Large Hierarchical Networks

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Engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research center of Autonomous intelligent Unmanned systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

Surface roughness classification using light scattering matrix and deep learning

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Science China(Technological Sciences) 2024年第2期67卷 520-535页

作者： SUN Hao TAN Wei RUAN YiXiao BAI Long XU JianFeng State Key Laboratory of intelligent Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Wuhan Digital Design and Manufacturing Innovation center Co. LtdWuhan 430074China

High-quality optical components have been widely used in various applications;thus,extremely high beam quality is ***,surface roughness is a key indicator of the surface *** this study,the angular distribution of light scattering field intensity was obtained for surfaces having different roughness profiles based on the finite difference time domain(FDTD)method,and the results were compared with those obtained using the generalized Harvey-Shack(GHS)*** was shown that the FDTD approach can be used for an accurate simulation of the scattered field of a rough surface,and the superposition of results obtained from many surfaces that have the same roughness level was in good agreement with the result given by the analytic GHS model.A light scattering matrix(LSM)method was proposed based on the FDTD simulation results that could obtain rich surface roughness *** classification effect of LSM was compared with that of the single-incidence scattering distribution(SISD)based on a ResNet-50 deep learning *** classification accuracy of the model trained with the LSM dataset was obtained as 95.74%,which was 23.40%higher than that trained using the SISD ***,the effects of different noise types and filtering methods on the classification performance were analyzed,and the LSM was also shown to improve the robustness and generalizability of the trained surface roughness ***,the proposed LSM method has important implications for improving the data acquisition scheme of current light scattering measurement systems,and it also has the potential to be used for detection and characterization of surface defects of optical components.

关键词： surface roughness FDTD simulation GHS theory deep learning light scattering matrix

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Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

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Journal of systems Engineering and Electronics 2024年第2期35卷 495-508页

作者： YANG Luyi LI Haiyang ZHANG Jin ZHU Yuehe College of Aerospace Science and Engineering National University of Defense TechnologyChangsha 410073China China Astronauts Research and Training center Beijing 100094China Hunan Key Laboratory of intelligent Planning and Simulation for Aerospace Missions Changsha 410073China

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

关键词： manned lunar mission free return orbit reachable domain(RD) deep neural network computation efficiency

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CPS architecture design for urban roadway intersections based on MBSE

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Journal of Intelligent and Connected Vehicles

Journal of intelligent and Connected Vehicles 2024年第3期7卷 190-204页

作者： Chen Wang Xiaoping Ma Limin Jia Zheng Lai Zhexuan Yang Han Yan Jing Zhao School of Traffic and transportation State Key Laboratory of Advanced Rail Autonomous OperationBeijing Jiaotong UniversityBeijing 100044China School of Traffic and transportation State Key Laboratory of Advanced Rail Autonomous OperationKey Laboratory of Railway Industry of Proactive Safety and Risk ControlBeijing Jiaotong UniversityBeijing 100044China China intelligent and Connected Vehicles(Beijing)Research Institute Co. Ltd.Beijing 100176China

With the rapid growth of urbanization and the increasing demand for transportation, urban traffic congestion has become a hindrance to individuals’ travel experience. Urban intersections are one of the primary sources of traffic congestion, and these bottlenecks have a negative impact not only on traffic efficacy but also on the surrounding road traffic in the region. To alleviate urban traffic congestion, cyber-physical systems have been widely implemented in the transportation industry, allowing for the perception, analysis, calculation, and dispatching of urban traffic flow, as well as making urban transportation safe, efficient, and quick. As the system scale and functions increase, system design has become increasingly complex, necessitating a deeper comprehension of the system’s structure and interaction relationships to construct a stable and reliable system. Therefore, this study proposes a method for designing cyber-physical systems for urban traffic intersections based on Model-Based systems Engineering (MBSE). This method models and analyses exhaustively the system’s requirements, functions, and logical architecture using System Modeling Language (SysML). After the architecture design has been completed, an architecture verification and optimization method based on Failure Mode and Effect Analysis (FMEA) for urban road intersection cyber-physical systems is utilized to analyze the architecture’s reliability by analyzing the failure modes of activities and to optimize the system architecture to improve the design’s efficiency and reliability.

关键词： Model-Based systems Engineering(MBSE) urban intersection cyber-physical system architecture design System Modeling Language(SysML)

L-Type Calcium Channel Modulates Low-Intensity Pulsed Ultrasound-Induced Excitation in Cultured Hippocampal Neurons

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Neuroscience Bulletin 2024年第7期40卷 921-936页

作者： Wen-Yong Fan Yi-Ming Chen Yi-Fan Wang Yu-Qi Wang Jia-Qi Hu Wen-Xu Tang Yi Feng Qian Cheng Lei Xue State Key Laboratory of Medical Neurobiology and MOE Frontiers center for Brain Science Fudan UniversityShanghai 200433China Department of Physiology and Neurobiology School of Life SciencesFudan UniversityShanghai 200438China Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education Tongji UniversityShanghai 200070China center for Rehabilitation Medicine Department of Pain ManagementZhejiang Provincial People's HospitalAffliated People's HospitalHangzhou Medical CollegeHangzhou 310014China Department of Critical Care Medicine Shanghai General HospitalShanghai Jiaotong University School of MedicineShanghai 200080China Shanghai Research Institute for intelligent Autonomous systems Tongji UniversityShanghai 201210China Research Institute of intelligent Complex systems Fudan UniversityShanghai 200433China

As a noninvasive technique,ultrasound stimulation is known to modulate neuronal activity both in vitro and in *** latest explanation of this phenomenon is that the acoustic wave can activate the ion channels and further impact the electrophysiological properties of targeted ***,the underlying mechanism of low-intensity pulsed ultrasound(LIPUS)-induced neuro-modulation effects is still ***,we characterize the excitatory effects of LIPUS on spontaneous activity and the intracellular Ca^(2+)homeostasis in cultured hippocampal *** whole-cell patch clamp recording,we found that 15 min of 1-MHz LIPUS boosts the frequency of both spontaneous action potentials and spontaneous excitatory synaptic currents(sEPSCs)and also increases the amplitude of sEPSCs in hippocampal *** phenomenon lasts for>10 min after LIPUS *** with Ca^(2+)imaging,we clarified that LIPUS increases the[Ca^(2+)]cyto level by facilitating L-type Ca^(2+)channels(LTCCs).In addition,due to the[Ca^(2+)]cyto elevation by LIPUS exposure,the Ca^(2+)-dependent CaMKII-CREB pathway can be activated within 30 min to further regulate the gene transcription and protein *** work suggests that LIPUS regulates neuronal activity in a Ca^(2+)-dependent manner via *** may also explain the multi-activation effects of LIPUS beyond *** stimulation potentiates spontaneous neuronal activity by increasing Ca^(2+)influx.

关键词： Low-intensity pulsed ultrasound Neuromodulation L-type calcium channel Hippocampal neuron

Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms

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Advances in Manufacturing 2024年第2期12卷 227-251页

作者： Si-Xiao Gao Hui Liu Jun Ota Key Laboratory of Traffic Safety on Track of Ministry of Education Institute of Artificial Intelligence and Robotics(IAIR)School of Traffic and Transportation EngineeringCentral South UniversityChangsha410075People’s Republic of China Research into Artifacts center for Engineering School of EngineeringThe University of TokyoTokyoJapan

Currently,simultaneous buffer and service rate allocation is a topic of interest in the optimization of manufacturing *** allocation problems have been solved previously to satisfy economic requirements;however,owing to the progress of green manufacturing,energy conservation and environmental protection have become increasingly ***,an energy-efficient approach is developed to maximize the throughput and minimize the energy consumption of manufacturing systems,subject to the total buffer capacity,total service rate,and predefined energy *** energy-efficient approach integrates the simulated annealing-non-dominated sorting genetic algorithm-II with the honey badger algorithm-histogram-based gradient boosting regression *** former algorithm searches for Pareto-optimal solutions of sufficient *** latter algorithm builds prediction models to rapidly calculate the throughput,energy consumption,and energy *** examples show that the proposed hybrid approach can achieve a better solution quality compared with previously reported ***,the prediction models can rapidly evaluate manufacturing systems with sufficient *** study benefits the multi-objective optimization of green manufacturing systems.

关键词： Energy-efficient allocation Multi-objective optimization Energy efficiency Energy consumption Machine learning