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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Sun, Shengdi Wang, Zhangyu Liu, Runsen Yang, Songyue Wang, Liyun Liu, Wentao School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements. © 2024 IEEE.

关键词： Trucks

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Design of Regenerative Braking Strategy for Hybrid Electric Vehicle Based on Sensitivity Ratio

Design of Regenerative Braking Strategy for Hybrid Electric ...

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第35届中国控制与决策会议

作者： Qinyu Xu Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology

Based on the braking system of hybrid electric vehicles(HEVs),a method based on sensitivity ratio decisionmaking is proposed to coordinate the multi-objective requirements of stability and economy in vehicle regenerative ***,the grid method is used to calculate the fitness of the braking force distribution coefficient of each front and rear wheel under each braking intensity,and the Pareto solution set is obtained through non-dominated *** the decision method of nonlinear variable weight sensitivity ratio is adopted offline to obtain the best *** the one-dimensional mapping table(1-D table) of and is built,and embedded into the braking energy strategy to achieve the coordination of stability and ***,the simulation is carried out in ADVISOR simulation software,and the results show that the stability of this strategy is better than the traditional ADVISOR strategy,and the brake recovery rate is increased by 43.55%.

关键词： Hybrid electric vehicle regenerative braking strategy multi-objective optimization nonlinear variable weight sensitivity ratio decision

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Low-light image enhancement based on Transformer and CNN architecture

Low-light image enhancement based on Transformer and CNN arc...

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第35届中国控制与决策会议

作者： Keyuan Chen Bin Chen Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology

Affected by low photon counts and noise,images captured in low-light environments commonly have low signalto-noise ratios,details losing,low contrast and other ***-light image enhancement which restores clear images from low-light images with unsatisfactory quality is *** this paper,we propose a strong baseline model based on transformer and CNN architecture for low light image enhancement(TrCLLE).In TrCLLE,the advantages of transformer in effectively modeling long-distance dependence and convolutional neural network in modeling local features through inductive bias are *** consists of three parts:shallow feature extraction module,deep feature extraction module and high-quality image reconstruction *** last,the factors affecting performance are analyzed in detail and the experimental results demonstrate that the proposed method achieves enhanced images with higher quality compared to other popular approaches.

关键词： transformer self-attention mechanism convolutional neural network low-light image enhancement

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Anomaly Detection and Fault Diagnosis Method for Autonomous Transport Vehicles on Unstructured Roads 22

Anomaly Detection and Fault Diagnosis Method for Autonomous ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhang, Yifang Yu, Guizhen Li, Han Zhang, Chaoqi Li, Lecong Zhang, Chuanying School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

Autonomous vehicles in mining areas undertake substantial production tasks and are prone to various faults during operation. Early detection of abnormalities, along with timely fault warnings and diagnoses, can enhance transportation safety and increase vehicle turnout rates. This study utilizes driving data from autonomous vehicles in mining areas and considers the characteristics of unstructured road scenes. The driving area is segmented into distinct intervals, and Kullback-Leibler (KL) divergence is applied within each interval to detect anomalies in the vehicle's lateral deviation during operation. Experimental results demonstrate that the proposed method achieves an anomaly detection accuracy of 91.4%, with a false negative rate of 8.3% and a false positive rate of 8.7%. © 2024 IEEE.

关键词： Vehicle detection

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Towards DDIs Identification by Knowledge Graph with BiRW and Back Aggregation

Towards DDIs Identification by Knowledge Graph with BiRW and...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Yin, Zhuang Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9781665468190

Effective identification of potential drug-drug interactions (DDIs) can prevent adverse effects caused by DDIs to a certain extent. This paper proposes the new hybrid method for predicting DDIs, which is called BiRW-KGBAN that combines bidirectional random walk and back aggregation based on knowledge graph. BiRW-KGBAN first constructs two knowledge graph KG-DrugBank and KG-KEGG that integrate data related to drugs. Then, an improved sampling method based on bidirectional random walk (BiRW) is used to sample the neighborhood information of drugs, including directly connected entities, related semantic relations and potential information. In addition, the path with drug as the center node is also extracted. Finally, two improved back aggregation model KGBAN1 and KGBAN2 are used to obtain the final embedded representation of the drugs. The experiments show that, compared with other existing methods, our method could obtain the higher-order topological information and the deeper potential neighborhood information of drugs, and has great improvements in DDIs prediction. The case study also shows that the proposed method has the potential for actual DDIs prediction. © 2022 IEEE.

关键词： Knowledge graph

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A chattering free consensus controller for multiple Lur'e systems with a non-autonomous leader and directed switching topology

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science China(Technological sciences) 2023年第4期66卷 1110-1118页

作者： WANG PeiJun YU WenWu WEN GuangHui YU XingHuo HUANG TingWen School of Mathematics and Statistics Anhui Normal UniversityWuhu 241000China Anhui Province Engineering Laboratory of Intelligent Robot's Information Fusion and Control Anhui Normal UniversityWuhu 241000China School of Mathematics Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 21111lChina Department of Electrical Engineering Nantong UniversityNantong 226019China Department of Systems Science School of MathematicsSoutheast UniversityNanjing 210096China School of Engineering RMIT UniversityMelbourne VIC 3001Australia Science Program Texas A&M University at QatarDoha 23874Qatar

Achieving asymptotical cooperative goal for multi-agent systems(MASs)with non-autonomous leaders(i.e.,leaders with nonzero inputs)is a critical but challenging *** approach is to use discontinuous controllers which may cause chattering phenomenon in practical *** to achieve the asymptotical goal via a chattering free cooperative controller remains to be open so *** this paper,an adaptive continuous controller is designed to achieve zero error consensus tracking in multiple Lur’e systems with a non-autonomous leader under directed switching ***,an unknown input observer(UIO)based on relative outputs is given to estimate the relative full *** an adaptive continuous controller is designed by introducing a decay function which remains positive into the term that plays the role of eliminating the impacts of leader’s nonzero ***,by using multiple Lyapunov functions(MLFs)technique,it is proven that zero error consensus tracking can be achieved if the average dwell time(ADT)is greater than a positive ***,theoretical result is verified by performing simulations on Chua’s *** with existing work,the proposed controller can not only achieve asymptotical consensus,but also is chattering free.

关键词： multi-agent system consensus chattering free non-autonomous leader directed switching topology

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A Vision-based Bird’s Eye View Representation Network for 3D Objects in Open-pit Mining Area 22

A Vision-based Bird’s Eye View Representation Network for 3...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Tai, Mengen Liu, Wentao Wang, Zhangyu Zhou, Bin Yu, Guizhen Yang, Songyue School of Transportation Science and Engineering Ministry of Industry and Information Technolody Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technolody Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technolody Beihang University Beijing China

ISBN: (纸本)9798331527471

Autonomous transportation systems, which have reconstructed open-pit mining operations, depend on accurate and real-time obstacle detection in challenging environments. Existing methods often exhibit limitations in accuracy due to their reliance on traditional image-based techniques, leading to errors and incomplete results. To address these issues, we developed a vision-based 3D object detection algorithm designed for open-pit mining environments. Our approach leverages the BEVDepth model, enabling accurate 3D object recognition using monocular camera input. Moreover, camera parameters are integrated to enhance resilience and flexibility across diverse mining environments. Last, our algorithm was implemented on a newly customized open-pit mining dataset. The experimental results verify the algorithm efficiency by attaining a high mean Average Precision score (mAP) of 67% while offering real-time performance with inference times as short as 25ms per frame. It enables accurate and efficient obstacle detection in autonomous mining vehicles and contributes to the development of safer and more productive mining operations. © 2024 IEEE.

关键词： Obstacle detectors

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Design of RBF Neural Network Based on Entropy and Sensitivity Analysis

Design of RBF Neural Network Based on Entropy and Sensitivit...

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第35届中国控制与决策会议

作者： Zhenlin Dong Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology

Aiming at the problem of how to determine the hidden layer structure of radial basis function(RBF) neural network,an RBF neural network algorithm based on entropy and sensitivity analysis is proposed to optimize the network ***,an entropy-based initial clustering is proposed to get the number and position of the initial clustering centers which serve as the input to the K-means ***,a hidden node deletion method based on sensitivity analysis is proposed to analyze the weight and standard deviation between the hidden layer with the output layer,and the hidden nodes which have low contributions to the network will be *** experiments on the nonlinear function approximation and the nonlinear dynamic system identification are set to verify the proposed *** results show that the proposed method achieves stronger approximation ability and better identification effect than the state-of-the-art methods.

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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The Influence of Digital Financial Literacy on Interest in Investing in the Capital Market of Millennial Generation Students 1

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4th International Conference on Inventive Computation and information Technologies, ICICIT 2022

作者： Gaol, Ford Lumban Christopher Dermawan, Gerry Rafif, Muhammad Resnandya Rubianto, Rully Matsuo, Tokuro Doctor of Computer Science Binus Graduate Program Jakarta Indonesia School of Information System Bina Nusantara University Alam Sutera Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9789811974021

ISBN: (纸本)9789811974014

In today's environment, financial knowledge is vital and should be possessed by everyone. Numerous people are financially disadvantaged as a result of the COVID-19 pandemic. This serves as a reminder of the critical nature of financial investment in the future. However, many Indonesians, particularly students, are still unfamiliar with capital market investing. In reality, investing in the stock market can provide a variety of benefits, including financial security, capital appreciation, and passive income. Financial literacy concerns can result in a lack of financial awareness among pupils. Indeed, financial literacy is widely available online and can be accessed at any time and from any location. We employed a quantitative method with a non-experimental research design for this study. A questionnaire survey is used to obtain data. The goal of this study is to determine whether there is a correlation between student interest in capital market investment and their digital financial literacy. As a result, extensive action is required to increase student interest in the capital market, which is precisely what this study wants to do. According to our findings, students who have access to digital financial and investment information have increased their financial literacy and developed a greater interest in investing in the capital market. Individuals are also encouraged by social media trends to try their hand at investing in the stock market. Additionally, students prefer to invest in stocks and mutual funds over other investing options. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Investments

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