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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Pengfei Yang Lei Cai Wenjing Guo Wenfeng Li School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan China

Dynamic Berth Allocation Problem (DBAP) is an essential problem in container terminal operations. Most studies focus on discrete or continuous berths in DBAP. However, affected by the geographical conditions, the mixture of discrete and continuous berths which are called hybrid berths often appear in real port container terminals. Moreover, the arrival time of vessels is often fluctuant due to the influence of environmental factors. To solve such a Dynamic Hybrid Berth Allocation Problem (DHBAP) under vessels' arrival delay, this study develops a proactive-reactive approach. Specifically, we establish a mixed-integer programming model with a buffer as the proactive strategy to obtain a baseline schedule. Then, we propose a hybrid berth reactive strategy (HBRS) to adjust the baseline schedule for vessels that are delayed. To get a better solution in a short time, a genetic algorithm is designed. We verify the effectiveness of the proposed HBRS by comparing it with the most commonly used right-shift strategy. Experimental results show that the longer the buffer is, the better the robustness of the model is, but the total time of the vessel in terminals will also increase. Compared with the right-shift strategy, the proposed HBRS can obtain an allocation plan with similar robustness in a shorter total time of the vessel in terminals.

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CAMInterHand: Cooperative Attention for Multi-View Interactive Hand Pose and Mesh Reconstruction

CAMInterHand: Cooperative Attention for Multi-View Interacti...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Guwen Han Qi Ye Anjun Chen Jiming Chen College of Computer Science and Technology Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineeringthe State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Interactive hand mesh reconstruction from singleview images poses a significant challenge with the severe occlusion and depth ambiguity inherent in interactive hand gestures. Recent approaches that employ probabilistic models and tokenpruned techniques have shown decent results in multi-view human body reconstruction. Nevertheless, these methods have not fully utilized multi-scale semantic information from multiview images and are not applicable in scenarios involving severe occlusion during dual-hand interactions. Simultaneously, current single-view methods independently reconstruct the left and right hands, which are ineffective in enhancing the interaction between both hands. To address these challenges, we propose CAMInterHand, a cooperative attention-based method for multi-view interactive hand pose and mesh reconstruction. Specifically, CAMInterHand extracts local pyramid features and global vertex features from multi-scale feature maps of multi-view images, enabling the exploration of rich local semantic information and facilitating effective feature alignment. Furthermore, CAMInterHand employs the cooperative attention fusion module to fuse all features from multi-view images, enhancing interactions among vertices of dual hands within global and local contexts. We conduct extensive experiments on the large-scale multi-view dataset InterHand2.6M and CAMInterHand achieves a substantial performance improvement over existing methods for multi-view and single-view interactive hand reconstruction.

关键词： Fuses Semantics Feature extraction Probabilistic logic Data mining Robotics and automation Image reconstruction

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8th International Conference on Advances in Energy Resources and Environment engineering, ICAESEE 2022

作者： Xin, Lin Wu, Jing Wang, Bowei Niu, Maofei Li, Jiaze Xu, Weihao Wang, Xin Shang, Zhenjie Li, Hualong Ma, Yan College of Safety and Environmental Engineering Shandong University of Science and Technology Shandong Qingdao266590 China Key Laboratory of Ministry of Education for Mine Disaster Prevention and Control Shandong University of Science and Technology Shandong Qingdao266590 China Shandong Luxi Power Generation Co. Ltd. Shandong Jining408300 China

ISBN: (纸本)9783031425622

To provide a reference for the model tests of UCG, river sand was used as the aggregate, cement and gypsum was used as the cementing agent according to the similarity principle, and a combination of comprehensive test and orthogonal test was applied to obtain the suitable ratio of similar materials for the surrounding rock. The mechanical properties and damage variation law during uniaxial compression are studied and analyzed. The results showed that the compressive strength of similar materials with different proportions is between 0.71 MPa and 2.1 MPa, which provides a reference for the selection of similar materials for surrounding rock of UCG. There are four groups of similar proportions of similar specimens, which meet the theoretical requirements and can be used in model tests. The compressive strength decreases with the increase of sand binder ratio, and with the increase of cement gypsum ratio, the compressive strength of the specimens first increases and then decreases. The failure modes of specimens are mainly shear failure and splitting. The main stages of damage increase of similar specimens under uniaxial compression can be divided into three categories, namely, the stage before peak stress, the stage after peak stress and the stage before and after peak stress. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Compressive strength

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Adaptive Deep Reinforcement Learning for Critical Boundary Scenario Generation 43

Adaptive Deep Reinforcement Learning for Critical Boundary S...

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43rd Chinese control Conference, CCC 2024

作者： Zhou, Junjie Wang, Lin Wang, Xiaofan Meng, Qiang Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai202400 China Ministry of Education of China Key Laboratory of System Control and Information Process Shanghai202400 China National University of Singapore Department of Civil and Environmental Engineering Singapore117576 Singapore Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai202400 China

ISBN: (纸本)9789887581581

Critical boundary scenarios play a vital role in the comprehensive testing of autonomous vehicles, as they can significantly expedite testing processes and reduce economic and time costs. To tackle the intricate, multidimensional, and diverse nature of intelligent driving scenarios, this paper proposes an innovative bi-level adaptive deep reinforcement learning (BADRL) approach aimed at generating authentic and diverse critical boundary scenarios. Leveraging naturalistic driving data, background agents are trained to exhibit naturalistic and realistic driving behaviors using a neural-based naturalistic driving behavior model. To address the intricacies of multi-interaction, high-dimensional environments, the scenario complexity model is introduced to evaluate the relative complexity between the traffic environment and the tested autonomous vehicle in real time. By integrating the scenario complexity model, the naturalistic driving behavior learning, intelligent driving testing, and critical boundary scenario generation are concatenated together to form a closed loop. BADRL facilitates the upper-level neural network in learning to adaptively increase the complexity of test scenarios, which are then fed into lower-level models to optimize the behavior of principal traffic participants, thereby generating naturalistic and critical boundary test scenarios. Extensive simulations were carried out to verify the efficacy of the BADRL technique in complex intersection environments. Results indicate that the BADRL approach boosts the efficiency of critical boundary scenario generation by approximately 10% compared to the state-of-the-art method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Deep reinforcement learning

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Magnetically tunable zero-index metamaterials

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Photonics Research 2023年第10期11卷 1613-1626页

作者： YUCONG YANG YUEYANG LIU JUN QIN SONGGANG CAI JIEJUN SU PEIHENG ZHOU LONGJIANG DENG YANG LI LEI BI National Engineering Research Centre of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of ChinaChengdu 611731China Key Laboratory of Multi-spectral Absorbing Materials and Structures of Ministry of Education University of Electronic Science and Technology of ChinaChengdu 611731China State Key Laboratory of Precision Measurement Technology and Instrument Department of Precision InstrumentTsinghua UniversityBeijing 100084China

Zero-index metamaterials(ZIMs)feature a uniform electromagnetic mode over a large area in arbitrary shapes,enabling many applications including high-transmission supercouplers with arbitrary shapes,directionindependent phase matching for nonlinear optics,and collective emission of many quantum ***,most ZIMs reported to date are passive;active ZIMs that allow for dynamic modulation of their electromagnetic properties have rarely been ***,we design and fabricate a magnetically tunable ZIM consisting of yttrium iron garnet(YIG)pillars sandwiched between two copper clad laminates in the microwave *** harnessing the Cotton–Mouton effect of YIG,the metamaterial was successfully toggled between gapless and bandgap states,leading to a“phase transition”between a zero-index phase and a single negative phase of the *** an S-shaped ZIM supercoupler,we experimentally demonstrated a tunable supercoupling state with a low intrinsic loss of 0.95 d B and a high extinction ratio of up to 30.63 d B at 9 *** have also engineered a transition between the supercoupling state and the topological one-way transmission state at10.6 *** work enables dynamic modulation of the electromagnetic characteristics of ZIMs,enabling various applications in tunable linear,nonlinear,quantum,and nonreciprocal electromagnetic devices.

关键词： index collective shaped

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Multi-objective Optimization of Repulsive Force Actuator for Vacuum Circuit Breaker 30

Multi-objective Optimization of Repulsive Force Actuator for...

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30th International Symposium on Discharges and Electrical Insulation in Vacuum, ISDEIV 2023

作者： Li, Peiyuan Liu, Xiaoming Chen, Hai Jiang, Wentao Zhou, Yongjie Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment Tianjin China Tiangong University School of Control Science and Engineering Tianjin China Shanghai Qiteng Electric Co. Ltd. Shanghai China

ISBN: (纸本)9784886864369

The fast DC vacuum switch driven by repulsive force actuator is the key equipment for the control and protection of the new energy power system. Its operating speed directly determines the interruption time of the DC vacuum switch. This paper takes fast 400V DC vacuum switch as the research object, studies the dynamic output characteristics, establishes the finite element numerical analysis model of electromagnetic repulsive force actuator, simulates and analyzes the influence of opening capacitors and charging voltage on the dynamic performance of fast switching. Moreover, the multi-objective artificial jellyfish optimizer is applied to design the repulsive force actuator. Based on the optimized 400V DC vacuum switch, the dynamic output performance of the fast switch has been obtained and analyzed. And the numerical simulation results show that the dynamic motion characteristics and energy conversion efficiency of the fast switch can be improved within 2.0ms. © 2023 IEEJ.

关键词： Electric circuit breakers

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Multi-Agent Q-Value Mixing Network with Covariance Matrix Adaptation Strategy for the Voltage Regulation Problem 42

Multi-Agent Q-Value Mixing Network with Covariance Matrix Ad...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Yiwen Zhang, Senlin Liu, Meiqin Dong, Shanling Zheng, Ronghao College of Electrical Engineering Zhejiang University Hangzhou310027 China Zhejiang University National Key Laboratory of Industrial Control Technology Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9789887581543

control and optimization of power systems typically involves schemes that utilize optimal power flow techniques and comprehensive modeling of various electrical components. However, the extensive integration of renewable energy sources and distributed energy resources makes it difficult to obtain accurate models, making the traditional model-based approaches more challenging. In this paper, we propose a Covariance Matrix Adaptation Q-value network mixing method (CMAQMIX), which is a novel model-free multi-agent reinforcement learning method that combines the advantages of Covariance Matrix Adaptation Evolution Strategy and the Q-value network mixing method to solve the problem of continuous action space. We establish a new multi-agent voltage regulation environment based on CityLearn framework to test the CMAQMIX method. The results show that our proposed method outperforms the Independent Proximal Policy Optimization method and can give immediate action response without the complete domain knowledge of the power system. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Optimization

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Fracture behaviour of fibre-reinforced composite materials subjected to shear loading: An experimental and numerical study

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International Journal of Lightweight Materials and Manufacture 2023年第1期6卷 108-116页

作者： Liu, Haibao Zhou, Jin Kong, Xiangshao Li, Shipeng Centre for Aeronautics School of Aerospace Transport and Manufacturing Cranfield University CranfieldMK43 0AL United Kingdom School of Mechanical Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China Departments of Naval Architecture Ocean and Structural Engineering School of Transportation Wuhan University of Technology Hubei Wuhan430063 China Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education Tianjin University Tianjin300350 China

Compared to fibre-dominated behaviour, a response dominated by the matrix under shear loading can considerably limit the load-bearing capability and restrict the utilisation of composite materials. It is therefore practically significative to understand the shear response of composite materials. This paper presents a detailed experimental and numerical investigation on the fracture behaviour of composite laminates subjected to shear-dominated loading. Composite specimen with a lay-up of [(0/90)4/0]S are tested using the V-Notched Rail (VRS) shear test method. Subsequently, the Scanning Electron Microscope (SEM) investigation is also conducted on the fracture surfaces of the tested specimens to get insights into the damage mechanisms due to shear loading. In addition, a high-fidelity computational damage model is developed to predict the shear fracture behaviour of fibre-reinforced composites. Simulation results, including loading response, strain distribution and shear fracture, well correlated with experimental results, which demonstrates the predictive capability of the developed elastic-plastic damage model. © 2022 The Authors

关键词： Finite element method

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Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2882-2896页

作者： He, Lijun Jia, Ziye Wang, Juncheng Lansard, Erick Han, Zhu Yuen, Chau China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Hong Kong Baptist University Department of Computer Science Kowloon Tsai Hong Kong Nanyang Technological University School of Electrical and Electronics Engineering Jurong West 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature. © 2004-2012 IEEE.

关键词： Geostationary satellites

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A Method for Automatic Identification of Natural Fracture Based on Machine Learning: A Case Study on the Dahebian Block of the Liupanshui Basin in Guizhou Province 13th

A Method for Automatic Identification of Natural Fracture Ba...

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13th International Field Exploration and Development Conference, IFEDC 2023

作者： Zhao, Wei-Guang Sang, Shu-Xun Cheng, De-Qiang Han, Si-Jie Zhou, Xiao-Zhi Zhang, Jin-Chao Zhao, Fu-Ping School of Resources and Geosciences China University of Mining and Technology Xuzhou221116 China Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Controland Utilization China University of Mining and Technology Xuzhou221008 China Carbon Neutrality Institute China University of Mining and Technology Xuzhou221008 China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process Ministry of Education China University of Mining and Technology Xuzhou221116 China Key Laboratory of Unconventional Natural Gas Evaluation and Development in Complex Tectonic Areas Ministry of Natural Resources Guiyang550009 China Guizhou Engineering Research Institute of Oil & Gas Exploration and Development Guiyang550009 China

ISBN: (纸本)9789819702718

Natural fractures are effective storage spaces and important seepage channels for oil and gas reservoirs. Accurately identifying natural fractures in reservoirs is crucial for the exploration and development of oil and gas resources. This article combines conventional and imaging logging data and uses machine learning to automatically identify natural fractures in reservoirs. The fracture labels of conventional logging come from imaging logging. Conventional logging data is decomposed through multi-scale wavelet to extract components that reflect fracture information, and further build the original data set. The AdaBoost model is trained based on a modified dataset of balanced samples for automatic fractures recognition in logging. The research results indicate that the approximate component and high-frequency component reflect the fluctuation of the formation and noise information respectively, and have little impact on the reservoir fractures identification;The medium frequency component can reflect the characteristic information of fractures and can be used for model training;After hyper-parameter optimization, the AdaBoost model has high accuracy and generalization ability, and can still accurately identify the types and distribution of natural fractures from the actual unbalanced logging data. This research has important guiding significance for the accurate characterization and construction of reservoir. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wavelet transforms

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