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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhang, Xiangyu Xu, Guoyan Li, Han Chen, Peng Xia, Qi Cai, Han 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

In this study, we tackle the path-tracking problem of a nonlinear four-wheel steering vehicle dynamics model subject to model mismatches and propose a model predictive control (MPC) algorithm based on an event-triggered mechanism (ET-MPC). The goal is to maintain closed-loop control performance while reducing the computational and communication burdens of traditional MPC. We introduce an ET-MPC framework utilizing a model-free reinforcement learning agent with proximal policy optimization (PPO). This agent interacts with the MPC system, progressively learning to determine the optimal event-triggered mechanism. To enhance exploration and training efficiency, we incorporate the Long Short-Term Memory (LSTM) technique into PPO. Experimental results show that the proposed ET-MPC framework, combined with reinforcement learning for reward optimization, demonstrates superior overall performance in path-tracking control of four-wheel steering vehicles. © 2024 IEEE.

关键词： Four wheel steering

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Cable segmentation based on Mask2Former in open-pit mining area 22

Cable segmentation based on Mask2Former in open-pit mining a...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Liyun Zhou, Bin Yang, Songyue Liu, Wentao Li, Huazhi Sun, Shengdi 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

The development of unmanned transportation technology has improved operational efficiency and safety in open-pit mining areas. However, there remain significant challenges to be addressed. One pressing issue is the need for mining trucks to pass through the area where the cable is laid on the ground. Accidentally crushing or damaging these cables would lead to significant risk to the mining area operation. However, due to the complexity of the mining environment and the characteristics of cables being thin and curved, the existing methods such as edge segmentation are difficult to meet the requirements for cable segmentation. This paper introduces a cable segmentation method based on Mask2Former and carries out comprehensive experiments to examine the effectiveness of the method. Experimental results show that the proposed method achieves IoU of 66.04% and PA of 80.08%, which can meet the requirements of practical applications. © 2024 IEEE.

关键词： Mine trucks

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Real-time 3D Object Detection based on Dynamic Sparse Voxel Transformer in Mining Area 22

Real-time 3D Object Detection based on Dynamic Sparse Voxel ...

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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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A LiDAR-Camera Fusion Network for Small Object Detection in Open-pit Mining Areas 22

A LiDAR-Camera Fusion Network for Small Object Detection in ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Liu, Muzhuo Wang, Jie Zhou, Bin Wang, Zhangyu Liu, Wentao Wang, Mingyuan School of Transportation Science and Engineering Ministry of Industr and Information Technology Beihang University Beijing China State Key Lab of Intellgent Transportation System Ministry of Industr and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industr and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

As intelligent and autonomous driving technologies advance, the secure and productive functioning of mining trucks during autonomous operations is critically dependent on precise object detection within open-pit mining environments. Due to the sparse nature of point cloud data for small objects such as stones, cones, and equipment parts, they are susceptible to generating false negatives and false positives. Although camera images are rich in semantic content, the absence of precise depth cues renders them vulnerable to interference from background noise. Currently, the technology of fusing LiDAR point cloud and camera image data for 3D object detection is gaining popularity in this field. In this paper, we introduce a query feature generation strategy based on multi-scale image features in the cross-attention-based feature fusion module to generate high-quality fusion features for small objects. Additionally, within the traditional non-maximum suppression strategy, we introduce size factors and shape factors to alleviate the sensitivity of small objects to bounding box offsets. Notably, our network model has achieved an mAP of 88.0 and an NDS of 77.9 on the mining dataset, significantly enhancing the precision of detecting small objects over previous approaches. © 2024 IEEE.

关键词： Open pit mining

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Efects of Defect,Mean Stress and Lower Loading on High Cycle and Very High Cycle Fatigue Behavior of Ti‑6Al‑4V Alloy

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Acta Metallurgica Sinica(English Letters) 2025年第3期38卷 435-448页

作者： Yiyun Guo Lei Wu Yibo Shang Chengqi Sun State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Engineering Science University of Chinese Academy of SciencesBeijing 100049China Guobiao(Beijing)Testing&Certifcation Co. Ltd.Beijing 101400China Fundamentals Department Air Force Engineering UniversityXi’an 710051China National Key Lab of Aerospace Power System and Plasma Technology Air Force Engineering UniversityXi’an 710038China

In this study,the efects of defect,mean stress and lower loading are investigated for high cycle(HCF)and very high cycle fatigue(VHCF)behavior of Ti-6Al-4V *** indicates that the S-N curve of Ti-6Al-4V alloy exhibits a linear decreasing trend or a plateau characteristic in HCF and VHCF regimes,which depends on the defect size and stress *** strength decreases with increasing the defect size,and it is irrespective of stress *** fatigue crack initiates from specimen surface at R=−1 in both HCF and VHCF *** the fatigue crack initiates from the subsurface or the interior of the specimen at R=0.1 in VHCF regime.A sequence of lower stress amplitude below the fatigue strength at 10^(9) cyc has no or negligible infuence on the fatigue life of 10^(5)-10^(9) *** lower stress amplitude in variable amplitude loadings does not afect the failure *** residual compressive stress relaxation is not observed after a large number of lower loadings under ultrasonic frequency fatigue *** formula and Goodman formula give dangerous predictions of VHCF strength for both smooth specimens and specimens with defects.

关键词： Ti-6Al-4V titanium alloy Very high cycle fatigue Defect Stress ratio Lower stress amplitude

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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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Prediction of Driving Departure of Mining Autonomous Transport Vehicles Based on GRU Network 22

Prediction of Driving Departure of Mining Autonomous Transpo...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Li, Lecong Yu, Guizhen Li, Han Xia, Qi Zhang, Xiangyu Cai, Han 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

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms. © 2024 IEEE.

关键词： Mine transportation

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Retrieval of high spatial resolution mountainous land surface temperature considering topographic and adjacency effects

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Science China Earth Sciences 2024年第11期67卷 3610-3625页

作者： Zhiwei HE Bohui TANG Zhaoliang LI Faculty of Land Resources Engineering Kunming University of Science and TechnologyKunming 650093China Key Laboratory of Plateau Remote Sensing Department of Education of Yunnan ProvinceKunming 650093China State Key Lab of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture and Rural AffairsInstitute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081China

Land surface temperature(LST) is a key parameter reflecting the interaction between land and atmosphere. Currently,thermal infrared(TIR) quantitative remote sensing technology is the only means to obtain large-scale, high spatial resolution LST. Accurately retrieving high spatial resolution mountainous LST(MLST) plays an important role in the study of mountain climate change. The complex terrain and strong spatial heterogeneity in mountainous areas change the geometric relationship between the surface and satellite sensors, affecting the radiation received by the sensors, and rendering the assumption of planar parallelism invalid. In this study, considering the influence of complex terrain in mountainous areas on atmospheric downward radiation and the thermal radiation contribution of adjacent pixels, a mountainous TIR radiative transfer model based on the sky view factor was developed. Combining with the atmospheric radiative transfer model MODTRAN 5.2, a nonlinear generalized split-window algorithm suitable for high spatial resolution MLST retrieval was constructed and applied to Landsat-9 TIRS-2satellite TIR remote sensing data. The analysis results indicate that neglecting the topographic and adjacency effects would lead to significant discrepancies in LST retrieval, with simulated data showing LST differences of up to 2.5 K. Furthermore, due to the lack of measured MLST in the field, the MLST accuracy obtained by this retrieval method was indirectly validated using the currently recognized highest-accuracy forward 3D radiative transfer model DART. The MLST and emissivity were input into the DART model to simulate the brightness temperature at the top of the atmosphere(TOA) of Landsat-9 band 10, and compared with the brightness temperature at TOA of Landsat-9 band 10. The RMSE(Root Mean Square Error) for the two subregions was0.50 and 0.61 K, respectively, indicating that the method proposed can retrieve high-precision MLST.

关键词： Mountainous land surface temperature Topographic and adjacency effects Nonlinear generalized split-window algorithm Landsat-9 data DART model

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Stackelberg-Game Computation Offloading Scheme for Parked Vehicle-Assisted VEC and Experiment Analysis

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-12页

作者： Liu, Chunhong Zhao, Mengdi Wang, Huaichen Cheng, Bo Liu, Jialei Yuan, Peiyan Engineering Lab of Intelligence Business & Internet of Things and School of Computer and Information Engineering Henan Normal University Xinxiang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle and School of Computer Engineering Hubei University of Arts and Science Xiangyang China

Vehicle edge computing (VEC) offers users low-latency and high-reliability services by using computational resources at the network's edge. Nevertheless, because of inadequate infrastructure and limited resources, computation-intensive and delay-sensitive vehicle applications cannot be performed efficiently at the edge. Therefore, several studies have used the idle resources of parked vehicles to assist in computation offloading. In this paper, we propose a parked vehicle-assisted vehicle edge computing architecture considering multi-agent collaboration, including intelligent vehicles and edge servers. Additionally, we propose a framework for a parallel Internet of Vehicles (IoV) utilizing computational experiment. The service provider is assigned the role of owning VEC resources and recruiting parking vehicle resources. The model was constructed by using the resource consumption-service relationship of both offloading parties to ensure service quality. First, a Stackelberg game model was constructed based on the interaction between requesting vehicles and a service provider. The latter was the leader, and the requesting vehicles were the followers. The Nash equilibrium for optimal pricing and offloading allocations was attained and verified, and a distributed gradient-based equilibrium algorithm was designed to solve the Stackelberg game model and obtain the final decision through mutual communication. The method also protects the privacy of participants and respects the willingness of requesting vehicles to offload. Finally, the simulation experiments confirmed that the proposed algorithm can achieve game equilibrium. Furthermore, it outperformed state-of-the-art algorithms in improving the service provider's utility. IEEE

关键词： Vehicles

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High-efficiency crystalline carbon nitride photocatalysts:Status and perspectives

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Nano Research 2024年第9期17卷 7840-7863页

作者： Wenji Pu Yunqiao Zhou Lingfeng Yang Haifeng Gong Yuhan Li Qingyu Yang Dieqing Zhang Engineering Research Center for Waste Oil Recovery Technology and Equipment Ministry of EducationChongqing Key Laboratory of Catalysis and New Environmental MaterialsChongqing Technology and Business UniversityChongqing 400067China The Education Ministry Key Lab of Resource Chemistry Joint International Research Laboratory of Resource Chemistry of Ministry of EducationShanghai Key Laboratory of Rare Earth Functional Materialsand Shanghai Frontiers Science Center of Biomimetic CatalysisShanghai Normal UniversityShanghai 200234China State Key Laboratory of Tibetan Plateau Earth System Resources and EnvironmentInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China

Crystallinity and crystal structure greatly influence the photocatalytic behavior of *** g-C3N4 produced by traditional thermal-induced polycondensation reaction bears low crystallinity and thus poor photoactivity,which originates from the incomplete polymerization of the precursor containing amine groups,abundant hydrogen bonds,and unreacted amino,as well as cyanide functional groups in the *** photocatalytic process,these residual functional groups often work as electron trap sites,which may hinder the transfer of electrons on the plane,resulting in low ***,crystalline carbon nitride(CCN)was reported as a promising photocatalyst because its increased crystallinity not only reduces the number of carriers recombination centers,but also increases charge conductivity and improves light utilization due to extendedπ-conjugated systems and delocalizedπ-*** such,we summarize the recent studies on CCN-based photocatalysts for the photoactivity ***,the unique structure and properties of CCN materials are ***,the preparation methods and modification strategies are well *** also sum up the applications of CCN-based materials in the environmental purification and energy ***,this review concerning CNN materials ends with prospects and challenges in the obtainment of high crystallinity by effective techniques,and the deep understanding of photocatalytic mechanism.

关键词： crystalline carbon nitride preparation method modification strategy photocatalytic application

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