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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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Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

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Computers, Materials & Continua 2022年第4期71卷 573-592页

作者： Najib Ahmed Mohammed Ali Mohammed Mansoor Rodina Binti Ahmad Saaidal Razalli Bin Azzuhri Department of Software Engineering Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala Lumpur Malaysia 2Department of Computer System&TechnologyFaculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research *** is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected *** the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper *** mcMTC has a significant technical impact on the design of all layers of the communication protocol *** this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission *** achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet *** Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,***,a new PHY layer for mcMTC has been *** reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)***-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar *** results also show that the general-purpose wireless net

关键词： Machine-type communication mission-critical applications MTC URLLC M2M wireless networks polar codes

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Anole: A Lightweight and Verifiable Learned-Based Index for Time Range Query on Blockchain systems 28th

Anole: A Lightweight and Verifiable Learned-Based Index for...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Chang, Jian Li, Binhong Xiao, Jiang Lin, Licheng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031306365

Time range query is essential to facilitate a wide range of blockchain applications such as data provenance in the supply chain. Existing blockchain systems adopt the storage-consuming tree-based index structure for better query performance, however, fail to efficiently work for most blockchain nodes with limited resources. In this paper, we propose Anole, a lightweight and verifiable time range query mechanism, to present the feasibility of building up a learned-based index to achieve high performance and low storage costs on blockchain systems. The key idea of Anole is to exploit the temporal characteristics of blockchain data distribution and design a tailored lightweight index to reduce storage costs. Moreover, it uses a digital signature to guarantee the correctness and completeness of query results by considering the learned index’s error bounds, and applies batch verification to further improve verification performance. Experimental results demonstrate that Anole improves the query performance by up to 10 × and reduces the storage overhead by 99.4 % compared with the state-of-the-art vChain+. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Personalized Collaborative Search Engine for Decision Making

Personalized Collaborative Search Engine for Decision Making

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2024 International Conference on Communication, Computer sciences and engineering, IC3SE 2024

作者： Murshleen, Mohd. Prabhu, A. Jain, Garima Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Computer Science & Business System Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Web search engines are used for retrieving information but still there exists ambiguity in their retrieval process. It is so because traditional search engines laid emphasis on matching of keywords rather than maintaining semantic relations among terms lack of ability to produce precise results, presence of ambiguous keywords and verifying needs of users are few hindrances that are acting as scenario for web search engines. The paper focuses on improving search quality of users by collecting user experiences through creating hierarchical user profile it leads to collaborative search engine that helps in reaching at particular decision then the collected user's data are uploaded to recommendation server which are than displayed a search engines like Google/Yahoo. © 2024 IEEE.

关键词： Search engines

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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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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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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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Investigation of stimulated Raman scattering in magnesium fluoride microcavities 28

Investigation of stimulated Raman scattering in magnesium fl...

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28th Microoptics Conference, MOC 2023

作者： Lin, Guoping Harbin Institute of Technology Min. of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System School of Science Shenzhen518055 China

ISBN: (纸本)9784863488045

We have successfully manufactured two magnesium fluoride whispering gallery mode microcavities with diameters as small as a few hundred micrometers. Stimulated Raman scattering experiments conducted using these microcavities revealed low threshold levels and ultrahigh quality factors, suggesting a promising potential for their application in nonlinear photonics. © 2023 The Japan Society of Applied Physics.

关键词： Microcavities

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Fuzzy identification of nonlinear dynamic system based on selection of important input variables

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Journal of systems engineering and Electronics 2022年第3期33卷 737-747页

作者： LYU Jinfeng LIU Fucai REN Yaxue Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan UniversityQinhuangdao 066004China School of Mathematics and Information Science and Technology Hebei Normal University of Science and TechnologyQinhuangdao 066004China

Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the ***, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.

关键词： Takagi-Sugeno(T-S)fuzzy modeling input variable selection(IVS) fuzzy identification fuzzy c-means clustering algorithm

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