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5th IEEE International Conference on Computer and Communication engineering technology, CCET 2022

作者： Jin, Xin Wang, Xinnian Ding, Ran Wu, Yunchao Science and Technology on Information System Engineering Laboratory Nanjing China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Wargame, as a tool to generate sample data for analysis and model training, has vast application in fields of training, command & control, and tactical research. Traditional wargame technologies greatly rely on human wisdom in the loop, impossible to generate large scale sample data. Reinforcement learning technology can generate large scale sample data, but it is not competent for the decision complexity above campaign level. This paper proposes a hybrid intelligence wargame method, which can generate large scale sample data using AI algorithms under the guidance of human wisdom. It has wide applications, which provides data analysis functions that existing wargame methods cannot provide. Prototype software has been developed based on the method, with feasibility and effectiveness verified through experiments, which has certain reference value. © 2022 IEEE.

关键词： Reinforcement learning

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年第13期12卷 23678-23693页

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Facial expression recognition under occlusion conditions based on multi-feature cross-attention

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Journal of Intelligent and Fuzzy systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

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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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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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Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

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Plant Phenomics 2023年第4期5卷 779-789页

作者： Jianqing Zhao Yucheng Cai Suwan Wang Jiawei Yan Xiaolei Qiu Xia Yao Yongchao Tian Yan Zhu Weixing Cao Xiaohu Zhang National Engineering and Technology Center for Information Agriculture Nanjing Agricultural UniversityNanjing 210095China Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture and Rural AffairsNanjing 210095China Jiangsu Key Laboratory for Information Agriculture Nanjing 210095China Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing 210095China

Accurate wheat spike detection is crucial in wheat field phenotyping for precision *** in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat ***,wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the *** models for wheat spike detection are typically designed for a specific growth *** adaptability to other growth stages or field scenes is *** models cannot detect wheat spikes accurately caused by the difference in color,size,and morphological features between growth *** paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity *** constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection ***,a Detection Network is designed to improve wheat spike detection capability.A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery.A new micro-scale detection layer is added to the network to extract the features of small *** loss is improved by Complete Intersection over Union to reduce the impact of the *** results show that WheatNet can achieve greater accuracy than classical detection *** detection accuracy with average precision of spike detection at the filling stage is 90.1%,while it is 88.6%at the maturity *** suggests that WheatNet is a promising tool for detection of wheat spikes.

关键词： Wheat maturity wheat

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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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Effect of lanthanum oxide on microstructure and mechanical properties of ZK60 magnesium alloy

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Journal of Rare Earths 2024年第12期42卷 2270-2278,I0003页

作者： Wei Qiu Zhaoyuan Lu Senmao Zhang Shuanglong Li Jian Chen Wei Chen Wei Li Yanjie Ren Jun Luo Maohai Yao Wen Xie School of Energy and Power Engineering Changsha University of Science&TechnologyChangsha410114China Key Laboratory of Energy Efficiency and Clean Utilization The Education Department of Hunan ProvinceChangsha University of Science&TechnologyChangsha410114China School of Mechanical&Energy Engineering Zhejiang University of Science&TechnologyHangzhou310023China Hunan Provincial Key Laboratory of Vehicle Power and Transmission System Hunan Institute of EngineeringXiangtan411104China Hunan Rare Earth Metal Material Research Institute Institute of Rare Earth Light AlloysChangsha410126China

Different amounts of La_(2)O_(3)were added to ZK60 alloy to observe the changes in microstructure and mechanical properties of ZK60-XLa_(2)O_(3)(X=0,0.2 wt%,0.5 wt%)*** findings reveal that within the as-cast ZK60 alloy,the Mg-Zn binary phase is manifested as rod-shaped precipitates,exhibiting a dispersed distribution predominantly along the grain boundary *** the increase of La_(2)O_(3)addition,the rod-like Mg-Zn binary phases are gradually replaced by Mg-Zn-La ternary phases,and these Mg-Zn-La ternary phases are semi-continuously distributed around the grain *** ZK602 and ZK605 alloys result in a corresponding improvement in ductility after *** is noteworthy that the ductility of the as-extruded ZK605 alloy is increased by 59.4%compared to ZK60 at the expense of a reduction of only 3.9%in yield *** together,the significant increase in ductility of ZK605 is mainly due to the weakening of the basal texture,which is weakened by the redox reaction of La_(2)O_(3)with the a-Mg matrix,generating the La *** decrease in yield strength of ZK605 in the extruded state is closely related to the observed grain coarsening phenomenon and the basal texture *** the same time,the ultimate tensile strength of ZK605 shows a slight increase,which is mainly due to the second phase *** Mg-Zn-La ternary phase exhibits a notably hard character,and it is evident that the distribution of this phase becomes progressively denser as the La_(2)O_(3)content increases.

关键词： ZK60 La_(2)O_(3) Microstructure Mechanical properties Basal texture Rare earths

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Lengthening of the CHURC1 3'untranslated region by alternative polyadenylation is associated with the progression of acute myeloid leukemia

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Genes & Diseases 2025年第1期12卷 106-109页

作者： Xi Hu Panxiang Cao Fang Wang Huqin Zhang Tong Wang Hongxing Liu Xiaoming Wu The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and TechnologyXi'an Jiaotong UniversityXi'anShaanxi 710049China Division of Pathology and Laboratory Medicine Hebei Yanda Lu Daopei HospitalLangfangHebei 065201China

Alternative polyadenylation(APA)is a post-transcriptional process that typically determines the length of mature mRNAs'3'untranslated region(3'UTR).The global shortened 3'UTR alters mRNA stability,localization,and translation and contributes to cancer progression.1 The APA characteristics and its effects on acute myeloid leukemia(AML)remain to be comprehensively ***,we identified churchill domain containing 1(CHURC1)as an upregulated gene with a lengthened 3'UTR in CD34+enriched AML cells compared with healthy hematopoietic stem and progenitor *** for APA regulation of CHURC1,we found that rs6745 and RNA binding protein TIA1 potentially contribute to the distal poly(A)site usage in cis-and transways,***,a lengthened CHURC13'UTR affected the apoptosis of AML cells by disrupting the expression of DICER1 by sponging *** chemotherapeutic sensitivity of 8 drugs was associated with CHURC1 poly(A)site ***,our analysis indicated a previously undetected gene,CHURC1,that affected AML progression through 3'UTR length change resulting from *** collection and detailed analysis methods were described in the supplementary material.

关键词： URC myeloid acute

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Experimental deformation of shales at elevated temperature and pressure:Pore-crack system evolution and its effects on shale gas reservoirs

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Petroleum science 2024年第6期21卷 3754-3773页

作者： Yi-Wen Ju Xin-Gao Hou Kui Han Yu Song Lei Xiao Cheng Huang Hong-Jian Zhu Li-Ru Tao Key Laboratory of Earth System Numerical Modeling and Application College of Earth and Planetary SciencesUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China Key Laboratory of Earth Exploration and Information Technique Ministry of EducationCollege of GeophysicsChengdu University of TechnologyChengdu610059SichuanChina Key Laboratory of Coalbed Methane Resource&Reservoir Formation Process Ministry of EducationSchool of Resources&Earth ScienceChina University of Mining and TechnologyXuzhou221116JiangsuChina

Although many studies based on naturally deformed samples have been carried out to investigate the pore-crack characteristics of shales,studies based on high temperature(T)and high pressure(P)deformation experiments,which can exclude sample heterogeneity factors,simulate deep T-P conditions,and generate a continuous deformation sequence,are still *** this study,shales with different deformation levels are generated by triaxial compression experiments,and methods including scanning electron microscopy,mercury injection,and gas sorption are utilized to characterize their influence factors and pore-crack *** indicate that T is the primary factor influencing shale deformation when P is low,while P is dominant under high P *** T<90℃ and P<60 MPa,shales undergo brittle deformation and their macropores decrease due to the compaction of primary pores,while mesopores increase because of the interconnection of *** 90℃200℃ and P>110 MPa,shales are subjected to ductile deformation,and their micro-and mesopores drop significantly due to the intense compaction in the matrix while macropores continuously increase with crack *** permeability of shale increases with the degree of deformation and ductile material contents are predicted to be a key factor determining whether open microcracks can be preserved after ductile *** account for these experimental results,an ideal model of micro pore-crack system evolution in deformed shales is further proposed,which can provide guidance for the exploration of shale gas resources in the deep or structurally complex zones.

关键词： Shale Deformation experiment Microscopic structure Pore-crack system Shale gas reservoir

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