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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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A Review:From Aquatic Lives Locomotion to Bio-inspired Robot Mechanical Designations

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Journal of Bionic Engineering 2023年第6期20卷 2487-2511页

作者： Pengxiao Bao Liwei Shi Lijie Duan Shuxiang Guo Zhengyu Li School of Medical Technology Beijing Institute of TechnologyBeijing100081China School of Life Science Beijing Institute of TechnologyBeijing100081China The Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information TechnologyBeijing Institute of TechnologyNo.5ZhongguancunSouth StreetBeijing100081China The Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhen518055GuangdongChina

With the development of camera technology,high-speed cameras have greatly contributed to capturing the movement and posture of animals,which has dramatically promoted experimental biology *** the same time,with the concept of bionics gradually gaining popularity among researchers,the design of robots is absorbing more and more biological features,where the interest in the bio-inspired robot is hewed *** with the traditional robot,the bio-inspired robot imitates the motion pattern to achieve similar propulsion features,which may be more effective and *** this paper,the motion patterns of aquatic animals are divided into four categories according to their propulsion mechanisms:drag-based,lift-based,jet-based,and *** bio-inspired robots imitating aquatic prototypes are introduced and ***,the prospect of aquatic bio-inspired robots is discussed.

关键词： Bionic Bio-inspired robot Aquatic animal Propelling mechanism Structure design

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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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2T1C DRAM based on semiconducting MoS_(2) and semimetallic graphene for in-memory computing

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National science Open 2023年第4期2卷 65-75页

作者： Saifei Gou Yin Wang Xiangqi Dong Zihan Xu Xinyu Wang Qicheng Sun Yufeng Xie Peng Zhou Wenzhong Bao State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityZhangjiang Fudan International Innovation CenterShanghai 200433China Shenzhen Six Carbon Technology Shenzhen 518055China

In-memory computing is an alternative method to effectively accelerate the massive data-computing tasks of artificial intelligence(AI)and break the memory *** this work,we propose a 2T1C DRAM structure for in-memory *** integrates a monolayer graphene transistor,a monolayer MoS_(2)transistor,and a capacitor in a two-transistor-onecapacitor(2T1C)*** this structure,the storage node is in a similar position to that of one-transistor-one-capacitor(1T1C)dynamic random-access memory(DRAM),while an additional graphene transistor is used to accomplish the nondestructive readout of the stored ***,the ultralow leakage current of the MoS_(2)transistor enables the storage of multi-level voltages on the capacitor with a long retention *** stored charges can effectually tune the channel conductance of the graphene transistor due to its excellent linearity so that linear analog multiplication can be *** of the almost unlimited cycling endurance of DRAM,our 2T1C DRAM has great potential for in situ training and recognition,which can significantly improve the recognition accuracy of neural networks.

关键词： molybdenum disulfide(MoS_(2)) graphene DRAM in-memory computing

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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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Effect of Ti on microstructure and properties of electromagnetic stirred Al-18wt.%Mg_(2)Si alloy

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China Foundry 2023年第1期20卷 78-82页

作者： Yu-yan Ren Sheng-yuan Chen Tong-yu Liu Song Zhang Ying-min Li Faculty of Electromechanical Information Weifang University of Science and TechnologyShouguang 262700ShandongChina Weifang Key Laboratory of High-temperature Materials and Single Crystal Components Fabrication Technology for Advanced Engine System Weifang 262700ShandongChina School of Materials Science and Engineering Shenyang University of TechnologyShenyang 110870China

By adding different amounts of Ti into the electromagnetic stirred Al-18wt.%Mg_(2)Si alloy,the effect of Ti element on the microstructure and mechanical properties of the alloy was *** experimental results show that the microstructure is refined after modification with Ti,which is related to the heterogeneous nucleation of TiAl_(3) particles on theα-Al *** the increase of Ti content and holding time after stirring,the primary Mg_(2)Si phase is refined firstly and then coarsened,and correspondingly,the mechanical properties of the alloy show a trend of increasing at first and then *** the addition of Ti is 0.5wt.%and the holding time is about 20 min,the refinement effect of primary Mg_(2)Si phase is the most significant and the mechanical properties of the alloy are optimal.

关键词： Al-18wt.%Mg_(2)Si alloy Ti modification electromagnetic stirring mechanical property

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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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Hierarchical Optimization Algorithm for the Automatic Design of Analog Integrated Circuit

Hierarchical Optimization Algorithm for the Automatic Design...

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China Semiconductor technology International Conference (CSTIC)

作者： Yu Zong Jing Wan State key lab of ASIC and System School of Information Science and Engineering Fudan University Shanghai China

ISBN: (数字)9798331513351

ISBN: (纸本)9798331513368

In this work, we have explored the use of hierarchical optimization technique to automatically design low dropout regulator. When we use genetic algorithm for multi-objective optimization, the increase of design variables will greatly increase the difficulty of optimization. Therefore, we divide the whole circuit into different levels and only upload the sub circuits’ combination of design parameters to reduce the design variables and shorten the optimization time. In our work, we have explored the optimization results under different constraints, and compared them with the direct optimization results systematically.

关键词： Regulators Optimization Genetic algorithms Analog integrated circuits

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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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Video Conference system in Mixed Reality Using a Hololens

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Computer Modeling in Engineering & sciences 2023年第1期134卷 383-403页

作者： Baolin Sun Xuesong Gao Weiqiang Chen Qihao Sun Xiaoxiao Cui HaoGuo Cishahayo Remesha Kevin Shuaishuai Liu Zhi Liu School of Information Science and Engineering Shandong UniversityQingdao266000China State Key Laboratory of Digital Multi-Media Technology Hisense Co.Ltd.Qingdao266000China

The mixed reality conference system proposed in this paper is a robust,real-time video conference application software that makes up for the simple interaction and lack of immersion and realism of traditional video conference,which realizes the entire process of holographic video conference from client to cloud to the *** paper mainly focuses on designing and implementing a video conference system based on AI segmentation technology and mixed *** mixed reality conference system components are discussed,including data collection,data transmission,processing,and mixed reality *** data layer is mainly used for data collection,integration,and video and audio *** network layer uses Web-RTC to realize peer-to-peer data *** data processing layer is the core part of the system,mainly for human video matting and human-computer interaction,which is the key to realizing mixed reality conferences and improving the interactive *** presentation layer explicitly includes the login interface of the mixed reality conference system,the presentation of real-time matting of human subjects,and the presentation *** the mixed reality conference system,conference participants in different places can see each other in real-time in their mixed reality scene and share presentation content and 3D models based on mixed reality technology to have a more interactive and immersive experience.

关键词： Mixed reality AI segmentation hologram video conference Web-RTC

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