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Railway switch fault diagnosis based on Multi-heads Channel Self Attention,residual connection and Deep CNN

Transportation Safety and Environment 2023年第1期5卷 58-65页

作者： Xirui Chen Hui Liu Zhu Duan Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075HunanChina.

A novel switch diagnosis method based on self-attention and residual deep convolutional neural networks(CNNs)is proposed.Because of the imbalanced dataset,the K-means synthetic minority oversampling technique(SMOTE)is applied to balancing the dataset at first.Then,the deep CNN is utilized to extract local features from long power curves,and the residual connection is performed to handle the performance degeneration.In the end,the multi-heads channel self attention focuses on those important local features.The ablation and comparison experiments are applied to verifying the effectiveness of the proposed methods.With the residual connection and multi-heads channel self attention,the proposed method has achieved an impressive accuracy of 99.83%.The t-SNE based visualizations for features of the middle layers enhance the trustworthiness.

关键词： fault diagnosis railway switch residual connection channel self-attention deep convolutional neural network

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Highway icing time prediction with deep learning approaches based on data from road sensors

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Science China(Technological Sciences) 2023年第7期66卷 1987-1999页

作者： WANG ShiHong WANG TianLe PEI Xuan WANG Hao ZHU Qiang TANG Tao HOU TaoGang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Integrated Circuit Science and Engineering Beihang UniversityBeijing 100191China School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China CATS Highway Engineering&Technology Co. Ltd.Beijing 100029China

In harsh climates,highway icing poses a hazard to traffic safety and increases road maintenance costs.It is of great significance to predict when the highway icing may occur and take a preventive plan.However,there are few studies on highway icing time prediction due to the scarcity and complexity of data.In this study,variables of icing temperature,friction,ice percentage,road surface temperature,water film height,saline concentration,and road condition were collected by road sensors distributed on a highway in China.A large-scale time series highway surface information dataset called HighwayIce is formed.Furthermore,a deep learning approach called IceAlarm,composed of long short-term memory neural network(LSTM),multilayer perceptron(MLP),and residual connection,has been developed to predict when the highway will ice.The LSTM is used to process dynamic variables,the MLP is used to process static variables,and the fully-connected layers with residual connections are used to make a deep fusion.The experimental results show that the average mean absolute error before icing using the IceAlarm model is about 6min and outperforms all baseline models.The HighwayIce dataset and IceAlarm model can help improve the prediction accuracy and efficiency of forecasting real-world road icing time,therefore reducing the impact of icy road conditions on traffic.

关键词： road icing time prediction road surface condition multilayer perceptron(MLP) long short-term memory(LSTM) residual connection

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U-shaped Vision Transformer and Its Application in Gear Pitting Measurement

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Journal of Dynamics, Monitoring and Diagnostics 2022年第4期1卷 223-228页

作者： Sijun Wang Yi Qin Dejun Xi Chen Liang College of Mechanical and Vehicle Engineering Chongqing UniversityChongqingChina

Although convolutional neural networks have become the mainstream segmentation model,the locality of convolution makes them cannot well learn global and long-range semantic information.To further improve the performance of segmentation models,we propose U-shaped vision Transformer(UsViT),a model based on Transformer and convolution.Specifically,residual Transformer blocks are designed in the encoder of UsViT,which take advantages of residual network and Transformer backbone at the same time.What is more,transpositions in each Transformer layer achieve the information interaction between spatial locations and feature channels,enhancing the capability of feature learning.In the decoder,for enhancing receptive field,different dilation rates are introduced to each convolutional layer.In addition,residual connections are applied to make the information propagation smoother when training the model.We first verify the superiority of UsViT on automatic portrait matting public dataset,which achieves 90.43%accuracy(Acc),95.56%Dice similarity coefficient,and 94.66%Intersection over Union with relatively fewer parameters.Finally,UsViT is applied to gear pitting measurement in gear contact fatigue test,and the comparative results indicate that UsViT can improve the Acc of pitting detection.

关键词： vision Transformer residual connection dilation rate information interaction pitting measurement

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Automatic Segmentation Method for Cone-Beam Computed Tomography Image of the Bone Graft Region within Maxillary Sinus Based on the Atrous Spatial Pyramid Convolution Network

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Journal of Shanghai Jiaotong university(Science) 2021年第3期26卷 298-305页

作者：许江长何莎敏于德栋吴轶群陈晓军 Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and VibrationSchool of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai200240China Department of Second Dental Centre Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineCollege of StomatologyShanghai Jiao Tong UniversityNational Center for StomatologyNational Clinical Research Center for Oral DiseasesShanghai Key Laboratory of StomatologyShanghai200011China Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai200240China

Sinus floor elevation with a lateral window approach requires bone graft(BG)to ensure sufficient bone mass,and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.However,the BG region from cone-beam computed tomography(CBCT)images is connected to the margin of the maxillary sinus,and its boundary is blurred.Common segmentation methods are usually performed manually by experienced doctors,and are complicated by challenges such as low efficiency and low precision.In this study,an auto-segmentation approach was applied to the BG region within the maxillary sinus based on an atrous spatial pyramid convolution(ASPC)network.The ASPC module was adopted using residual connections to compose multiple atrous convolutions,which could extract more features on multiple scales.Subsequently,a segmentation network of the BG region with multiple ASPC modules was established,which effectively improved the segmentation performance.Although the training data were insufficient,our networks still achieved good auto-segmentation results,with a dice coefficient(Dice)of 87.13%,an Intersection over Union(Iou)of 78.01%,and a sensitivity of 95.02%.Compared with other methods,our method achieved a better segmentation effect,and effectively reduced the misjudgement of segmentation.Our method can thus be used to implement automatic segmentation of the BG region and improve doctors’work efficiency,which is of great importance for developing preliminary studies on the measurement of postoperative BG within the maxillary sinus.

关键词： atrous spatial pyramid convolution(ASPC) bone graft(BG)region medical image segmentation residual connection

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A CNN-based Vocal Recognition Algorithm for IARC Mission 8

A CNN-based Vocal Recognition Algorithm for IARC Mission 8

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第三十九届中国控制会议

作者： Zibo Lyu Yinbao Niu Zhaochen Lin Xiyang Liu Harbin Institute of Technology

In this paper, a vocal command recognition problem is investigated to meet the rule of the International Aerial Robot Competition（IARC） Mission 8, which requires the participant to interact with UAVs through non-electrical methods. First, the audio signal description is given, and the preprocessing of the data is provided. Then, a CNN-based vocal recognition algorithm with model of residual connection is proposed in which feature extraction with preprocessing method is applied instead of taking the raw data as the input. Moreover, the proposed algorithm is implemented on an android platform. Then, simulation experiments are conducted to show the effectiveness of the proposed algorithm. Besides, comparisons and analysis are given for different methods of data processing.

关键词： Vocal Recognition Spectrogram MFCC CNN residual connection

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