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2022 IEEE International Conference on industrial technology, ICIT 2022

作者： Wen, Weixiang Chen, Kang Wang, Jinlong Xu, Zhezhuang Wang, Rong Chen, Boyu Zhang, Qingdong Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Fujian Sanming353000 China

ISBN: (纸本)9781728119489

The vehicle scheduling in the logistics system of the steel park is a critical factor determining the scheduling efficiency and logistics throughput. The limited space resources at the entrance of the steel park make vehicles shall be arranged in reasonable batches to guarantee the scheduling consistency. To achieve the purpose, the paper first formulates the model of vehicle batching. Then, the algorithm CCVB (constrained clustering of vehicle batching) is proposed to solve the problem from a clustering perspective. Comparative experiments are carried out to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed mechanism can sufficiently improve the scheduling efficiency of steel logistics under limited time and space resources. © 2022 IEEE.

关键词： Vehicles

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Optimization of Stacking Location for Mixed Loading of Steel Products

Optimization of Stacking Location for Mixed Loading of Steel...

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2022 IEEE International Conference on industrial technology, ICIT 2022

作者： Wang, Rong Zheng, Song Wang, Jinlong Xu, Zhezhuang Wen, Weixiang Chen, Boyu Zhang, Qingdong Fuzhou University College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming Fujian353000 China

ISBN: (纸本)9781728119489

In the steel logistics park, the operational efficiency is repeatedly compromised due to excessive truck turnaround. For solving this problem fundamentally, the mixed loading relationship of multi-type steel products is excavated and the optimum solution for storage yard is proposed in the paper. Due to the limited stacking space, some products with small outbound demand have to be stacked together. A two-stage model is established to meet the stacking requirements for different outbound quantity of steel products. In the first stage, products with a small amount of stock will be mixed and stacked together according to certain rules. Then all products are allocated to specific yards in the second stage with the goal of maximizing the mixed correlation of steel products in the same yard to reduce the number of transitions for pick-up trucks. At the end, products in large quantities are placed in the stacking position close to the pick-up position. Experiments demonstrate that the proposed scheme is of great benefit to improve the operation efficiency of the logistics park. © 2022 IEEE.

关键词： Trucks

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A Dynamic Safe Arbitrator for Human-Machine Shared control

A Dynamic Safe Arbitrator for Human-Machine Shared Control

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2022 Chinese automation Congress, CAC 2022

作者： Chen, Haocong Chen, Yutao Wang, Wu Lin, Dingci Huang, Jie Fuzhou University 5G+Industrial Internet Institute Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China Fuzhou University College of Electrical Engineering and Automation Fuzhou350108 China

ISBN: (纸本)9781665465335

Shared control has become a popular subject of human-machine hybrid intelligence. This paper presents a dynamic safety arbitrator for shared control in an automated vehicle. The vehicle is controlled autonomously by a model predictive controller (MPC) tracking a given trajectory, while at the same time human input is provided via a joystick. Unlike traditional mixed-initiative control methods that blend weighted human and vehicle inputs, the proposed arbitrator first adopts control barrier function (CBF) to generate weights for human and vehicle inputs under safety critical situations, and then employs the null-space based control (NSBC) to merge the two inputs with priorities determined by the weights. As a result, the final input to the vehicle has a clear physical meaning that represents dynamic priorities of human and vehicle autonomous inputs, in which the higher priority input is fully adopted and the lower priority input is partially adopted without intervening the higher priority input. An immediate advantage of the proposed arbitrator is that there is no need to tune the weights between human and autonomous inputs. Simulation results show that the dynamic safe arbitrator is effective for shared control. © 2022 IEEE.

关键词： Safety engineering

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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer

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Computers in Biology and Medicine 2024年 179卷 108771-108771页

作者： Wang, Wenqing He, Ji Liu, Han School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relationships, and due to direct aggregation of the extracted features, they introduce unnecessary implicit noise into the fused images. To solve the above problems, this paper proposes a novel dual-branch hybrid fusion network called EMOST for medical image fusion that combines a convolutional neural network (CNN) and a transformer. First, to extract more comprehensive feature information, an effective feature extraction module is proposed, which consists of an efficient dense block (EDB), an attention module (AM), a multiscale convolution block (MCB), and three sparse transformer blocks (STB). Meanwhile, a lightweight efficient model (EMO) is used in the feature extraction module to exploit the efficiency of the CNN with the dynamic modeling capability of the transformer. Additionally, the STB is incorporated to adaptively maintain the most useful self-attention values and remove as much redundant noise as possible by developing the top-k selection operator. Moreover, a novel feature fusion rule is designed to efficiently integrate the features. Experiments are conducted on four types of multimodal medical images. The proposed method shows higher performance than the art-of-the-state methods in terms of quantitative and qualitative evaluations. The code of the proposed method is available at https://***/XUTauto/EMOST. © 2024 Elsevier Ltd

关键词： Feature extraction

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Transfer learning with a spatiotemporal graph convolution network for city flowprediction

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Frontiers of information technology & Electronic Engineering 2025年第1期26卷 79-92页

作者： Binkun LIU Yu KANG Yang CAO Yunbo ZHAO Zhenyi XU Department of Automation University of Science and Technology of ChinaHefei 230026China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230088China

Recently,deep learning based city flow prediction has been extensively used in the establishment of *** methods are data-hungry,making them unscalable to areas lacking *** transfer learningcan use data-rich source domains to assist target domain cities in city flow prediction,the performance of existingmethods cannot meet the needs of actual use,because the long-distance road network connectivity is *** this problem,we propose a transfer learning method based on spatiotemporal graph convolution,in which weconstruct a co-occurrence space between the source and target domains,and then align the mapping of the sourceand target domains’data in this space,to achieve the transfer learning of the source city flow prediction modelon the target ***,a dynamic spatiotemporal graph convolution module along with a temporalencoder is devised to simultaneously capture the concurrent spatiotemporal features,which implies the inherentrelationship among the road network structures,human travel habits,and city bike ***,these concurrentfeatures are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence *** domain features are thereby aligned with the source domain features in the co-occurrence space by using aMahalanobis distance loss,to achieve cross-city bike flow *** proposed method is evaluated on the publicbike flow datasets in Chicago,New York,and Washington in 2015,and significantly outperforms state-of-the-arttechniques.

关键词： Transfer learning City flow prediction Spatiotemporal graph convolution

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic control Society.

关键词： Trucks

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A New True Random Number Generator Based on Chaotic Circuit Architecture

A New True Random Number Generator Based on Chaotic Circuit ...

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2022 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2022

作者： Lv, Fei Diao, Jin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan China

ISBN: (纸本)9781510657304

In the fields of information security, the application of random numbers is becoming more and more widespread. True random numbers (TRNs) tend to have better randomness and unpredictability than pseudo random numbers (PRNs). Chaotic systems are very suitable for constructing true random number generators (TRNG) due to their periodic infinity, randomicity, long-term unpredictability, high sensitivity to initial values and parameters, etc. However, when chaotic systems are implemented by finite state machines, their dynamic characteristics will disappear, such as periodic infinity and initial value sensitivity, and there are serious safety concerns about the random number generator designed on this basis. In order to address the degradation problem of the chaotic systems, this paper proposes a true random number generator based on the chaotic circuit, implements a complete hardware system, and tests its randomness. © 2022 SPIE.

关键词： Chaotic systems

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A Comparison of Large-Scale MOEAs with Informed Initialization for Voltage Transformer Ratio Error Estimation 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Li, Lianghao He, Cheng Li, Hongbin Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications. The large number of decision variables in LSMOP leads to a tremendous high-dimensional search space, which is still challenging for existing multiobjective evolutionary algorithms (MOEAs). The voltage transformer ratio error estimation (TREE) problem is a typical LSMOP in real-world applications. A number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been adopted for solving the TREE problem, but so far there is still considerable potential for improvement in their results. In this paper, we propose a informed initialization for solving the TREE problem. The physical properties of the TREE problem are used to construct an initial set of solutions with specific characteristics. The proposed initialization method is tested against the default initialization method on five TREE problems by being applied on six state-of-the-art LSMOEAs and a classical MOEA. The experimental results demonstrate that the initialization method has a significant effect on the performance of LSMOEAs in solving the TREE problems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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A Neural Approach Towards Real-time Management for Integrated Energy System Incorporating Carbon Trading and Electrical Vehicle Scheduling 4th

A Neural Approach Towards Real-time Management for Integrat...

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Proceedings of the 4th International Conference on Neural Computing for Advanced Applications, NCAA 2023

作者： Li, Yiying Wang, Bo Zhang, Lijun Liu, Lei Fan, Huijin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan430074 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819958467

This paper proposes a real-time integrated energy system (IES) management approach which aims at promoting overall energy efficiency, increasing renewable energy penetration, and smoothing load fluctuation. The electric vehicles (EVs) charging scheduling is incorporated into the IES management, where the uncertain arrivals and departures of multiple EVs are considered as a stochastic but flexible load to the IES. Furthermore, towards the carbon neutralization target, a carbon emissions trading mechanism is introduced into the IES management to incentivize the system to operate in an eco-friendlier manner. To tackle the computational complexity induced by the stochastic and intermittent nature of the renewable energy sources and EVs load, the scheduling of the IES is realized in a neural network based real-time manner, driven by a deep reinforcement learning approach that guarantees safe training and operation. The case study verifies the effectiveness of the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Distributed Robust Adaptive control for Cooperative Formation of Uncertain Multiple Fixed-Wing UAVs with Disturbances 5th

Distributed Robust Adaptive Control for Cooperative Formati...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Zhi, Yongran Zheng, Zongzhun Guan, Bin Liu, Lei Fan, Huijin National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Beijing Aerospace Automatic Control Institute Beijing100854 China Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811939976

This paper investigates the formation problem of multiple fixed-wing unmanned aircraft vehicles (UAVs) based on virtual leader structure, subjecting to unknown uncertainties and bounded disturbances. A distributed model reference robust adaptive controller (DMRRAC) is proposed to swiftly construct the formation under the existence of flight dynamical uncertainties and external bounded disturbances. An adaptive projection formation operator and a novel high-frequency robust term can guarantee the system to be uniformly asymptotically stable in a complex situation, which can also enhance the performance of the system such as robustness. Simulation confirms the feasibility and effectiveness of the proposed distributed control algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fixed wings

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