2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)最新文献_第10页

Instance-Wise Causal Feature Selection Explainer for Rotating Machinery Fault Diagnosis 基于实例的旋转机械故障诊断因果特征选择解释器

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058059

Chang Guo, Zuogang Shang, Jiaxin Ren, Zhibin Zhao, Shibin Wang, Xuefeng Chen

{"title":"Instance-Wise Causal Feature Selection Explainer for Rotating Machinery Fault Diagnosis","authors":"Chang Guo, Zuogang Shang, Jiaxin Ren, Zhibin Zhao, Shibin Wang, Xuefeng Chen","doi":"10.1109/ICSMD57530.2022.10058059","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058059","url":null,"abstract":"Artificial neural networks in prognostics and health management (PHM), especially in intelligent fault diagnosis (IFD) have made great progress but possess black-box nature, leading to lack of interpretability and weak robustness when facing complex environment variations. When environment changes, the model tends to make wrong decisions leading to a cost, especially for major equipment if easily trusted by the users. Researchers have made studies on eXplainable Artificial Intelligence (XAI) based IFD to better understand the models. Most of them express their interpretability in the way of drawing gradient-based saliency maps to show where the model focuses on, which is of little consideration for causal effect and not sparse enough without quantitative metrics. To address these issues, we design an XAI method that utilizes a neural network as an instance-wise feature selector to select frequency bands that have stronger causal strength with the diagnosis result than others and further explain the diagnosis model. We quantify causal strength with the relative entropy distance (RED) and treat the simplified RED as the objective function for the optimization of the selector model. Finally, our experiments demonstrate the superiority of our method over another algorithm L2X measured by post-hoc accuracy (PHA), variant average causal effect (ACE), and vision plots.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126985339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-task unmanned swarm control method based on dynamic optimal path planning 基于动态最优路径规划的多任务无人群体控制方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058284

Chao Qu, Hongrui Lin, Xiaoyang Jin

引用次数: 0

Control and Optimization of Power Dispatching System Based on New Energy Mode 基于新能源模式的电力调度系统控制与优化

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058324

Jiashuo Zheng, Yuwen Li, Dong Guo, Tiejun Yan, Jialin Wang, Eerdun Li

{"title":"Control and Optimization of Power Dispatching System Based on New Energy Mode","authors":"Jiashuo Zheng, Yuwen Li, Dong Guo, Tiejun Yan, Jialin Wang, Eerdun Li","doi":"10.1109/ICSMD57530.2022.10058324","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058324","url":null,"abstract":"At present, new energy sources such as wind or solar energy are connected to the power grid on a large scale. Due to the characteristics of regionalism, intermittency, volatility, and low controllability, it is not conducive to the safe and stable operation of the power system [1]. New energy can also not be completely absorbed. Based on the output characteristics of new energy and the operation mode of power grid dispatching, this paper analyzes the influence of new energy generation on the power grid dispatching system. To achieve the lowest generation cost, the highest operating efficiency, the best new energy consumption, and the best control mode. This paper summarizes and puts forward four optimal dispatching theoretical models for the above objectives. These models generally meet the demand for new energy power generation structures. They can realize the interaction of multiple sources of the power grid and improve the dispatch ability of the power grid. In the load forecasting model, it proposed a 96 times node forecasting way based on the analysis of historical data. This method can comprehensively improve the proportion of grid-connected power generation of new energy from both sides. In other models, this paper proposed several strategies for optimizing power grid dispatching modes in the new energy system. The related forecasting model, dispatching model, and improving new energy consumption model in this paper will have a certain reference value for the joint dispatching management of multiple generation modes.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116850773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MT-ONet: Mixed Transformer O-Net for Medical Image Segmentation MT-ONet:用于医学图像分割的混合变压器O-Net

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058445

Pengfei Zheng

{"title":"MT-ONet: Mixed Transformer O-Net for Medical Image Segmentation","authors":"Pengfei Zheng","doi":"10.1109/ICSMD57530.2022.10058445","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058445","url":null,"abstract":"In the past few years, the deep learning is widely used in the medical industry due to its advantage. Constructed using Convolutional Neural Networks (CNN), the U-Net framework has become the industry standard for solving medical image segmentation tasks. Nonetheless, this framework is incapable of entirely learning all global and remote semantic information. It has been demonstrated that the transformer structure collects more global information than U-Net but less local information than CNN. To improve the performance of segmentation and classification in medical images while maximizing global and local data, we integrate O-Net with Mixed Transformer [1], this fuses the advantages of CNN and Transformer. This enables us to maximize both types of data. We combine CNN, Mixed Transformer, and Local-Global Gaussian-Weighted Self-Attention (LGG-SA) in the encoder component of our proposed O-Net architecture to obtain more global and local background information. The decoder part combines the Mixed Transformer and CNN blocks to obtain the results. The segmentation capability of the proposed network is evaluated by the multi-organ CT dataset containing synaptic information. The results of our trials demonstrate that the proposed MT-ONet can deliver superior segmentation performance relative to cutting-edge methods, resulting in improved classification precision.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115658896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dc series arc fault detection based on random forest combined with entropy weight method 基于随机森林结合熵权法的直流串联电弧故障检测

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058444

Yujiao Liu, Yan Li, Guoliang Li, Hui Zhou, Mengwen Li

{"title":"Dc series arc fault detection based on random forest combined with entropy weight method","authors":"Yujiao Liu, Yan Li, Guoliang Li, Hui Zhou, Mengwen Li","doi":"10.1109/ICSMD57530.2022.10058444","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058444","url":null,"abstract":"In this paper, a fault detection method for DC series arc based on entropy weight method and random forest algorithm is proposed, which can be effectively applied to dc series arc fault identification for series resistive, capacitive and inductive loads. Firstly, the short-time Fourier transform (FFT) is used for frequency domain analysis of the data collected by accessing different loads. By comparing and analyzing the spectrum graphs under normal and fault conditions, the spectrum segment with the strongest frequency influence is selected for analysis. Time domain feature selection peak-to-peak value, mean value and standard deviation using entropy weight method to determine the weight, to form a comprehensive time domain feature, to avoid the instability of a single index; Spectrum standard deviation and mean value are selected for frequency domain features, and finally the time domain criterion and frequency domain criterion are taken as the input of random forest, and the random forest algorithm is used to achieve accurate detection of arc faults. The experimental results show that the proposed method can effectively distinguish the current characteristics of arc fault from those of normal operation, and the accuracy is higher than that of single criterion.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122634561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Series AC Arc Fault Detection Model Based on Hybrid Time and Frequency Analysis 基于时频混合分析的串联交流电弧故障检测模型

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058442

Xue Zhou, Wenhao Geng, Jianing He, G. Zhai

{"title":"Series AC Arc Fault Detection Model Based on Hybrid Time and Frequency Analysis","authors":"Xue Zhou, Wenhao Geng, Jianing He, G. Zhai","doi":"10.1109/ICSMD57530.2022.10058442","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058442","url":null,"abstract":"Series arc fault cannot be protected by a common purpose circuit breaker for its lower fault current amplitude compared with its normal one, hence arc fault circuit interrupters are required for avoiding potential fire hazards. This paper presents an ac series arc fault detection method based on hybrid time and frequency analysis and softmax classification neural network (HTFSCNN). An experimental platform capable of automatically recording normal and arc fault current waveforms is designed in order to collect data set. In this paper, four indicators in time-domain and six indicators in frequency-domain are selected as inputs of the HTFSCNN classifier, according to the characteristics of the current waveforms and frequency spectra. The conjugate gradient method was applied to train the backwards-propagation algorithm. The loss function was cross entropy and the output function was softmax. Experimental results show that this method can effectively separate the fault currents from the normal ones with accuracy of 98.74% under seven loads specified in IEC standards. Finally, the trained HTFSCNN model was implanted into a microcontroller and its feasibility is verified.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123276657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MT-U2Net: Mixed Transformed Base U2Net for MRI Segmentation MT-U2Net:用于MRI分割的混合变换基U2Net

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058354

Cangyi Jiang

{"title":"MT-U2Net: Mixed Transformed Base U2Net for MRI Segmentation","authors":"Cangyi Jiang","doi":"10.1109/ICSMD57530.2022.10058354","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058354","url":null,"abstract":"In this paper, we propose a powerful while efficient net architecture for deep learning, MT-U2Net for Magnetic Resonance (MR) image segmentation or other fields of computer vision like semantic segmentation, Salient Object Detection (SOD). As U-Net has made a lot of contribution to computer vision tasks, it is obvious that the network architecture can still be improved. Thus, we mainly target two weaknesses: one is the weakness of explicitly modeling long-range-dependencies, the other is missing details and features on multi-scale. We took the strengthen of MT-UNet and U2-Net so that we can handle with both the weaknesses. Thus, it is named Mixed Transformed U2Net. We coordinated the net architecture and turned it to another configuration with fewer layers to Maintain the net structural stability. However, we used the novel Transformer module named Mixed Transformer Module (MTM) supported by Local-Global Gaussian-Weighted Self-Attention (LGG-SA) and External Attention (EA) to mine the inter-connections while calculate affinities to themselves efficiently, ReSidual U-blocks (RSU) to ensure the architecture can be deeper. We completed our network so that we can segmentation images accurately.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129661148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synchro-Reassigned Extracting Transform and Its Application to Bearing Fault Diagnosis under Variable Speed Condition 同步重分配提取变换及其在变速条件下轴承故障诊断中的应用

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058207

Hong-Yi Wu, Yong Lv, Rui Yuan, Xu Yang, Bowen Li

{"title":"Synchro-Reassigned Extracting Transform and Its Application to Bearing Fault Diagnosis under Variable Speed Condition","authors":"Hong-Yi Wu, Yong Lv, Rui Yuan, Xu Yang, Bowen Li","doi":"10.1109/ICSMD57530.2022.10058207","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058207","url":null,"abstract":"High-resolution time-frequency representation is critical for signal analysis and condition monitoring. synchroextracting transform based on frequency or time reassignment are new types of nonstationary signal processing methods, and their performance is better than that of conventional methods when analyzing time-varying signals. However, the limitation is that they cannot accurately analyze signals that contain both “slowly-varying” and “rapidly-varying” features. To avoid the disadvantages of SET, this paper proposes a novel strategy called Synchro-Reassigned Extracting Transform (SRET) to process nonstationary signals with different modulation characteristics. By using the instantaneous frequency operator and the group delay operator, SRET reassigns and extracts the time-frequency coefficients synchronously in the frequency and time directions to achieve sharpening of energy ridges. To use the computer for fast calculation, the paper also provides a discretization implementation algorithm. Finally, the proposed approach has been applied to numerical simulations and application research. The results show that SRET can accurately estimate the time-varying characteristics of nonstationary signals, and has the potential for fault diagnosis of rotating machinery.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128359994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Diesel Engine Fault Diagnosis Method Based on Time-Frequency-Nonconvex Robust Principal Component Analysis 基于时频非凸鲁棒主成分分析的柴油机故障智能诊断方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058265

Fang Wang, Lida Wang, Yufang Wen, Fei Ha, Jia Lu, Wenbin Jiao

{"title":"Intelligent Diesel Engine Fault Diagnosis Method Based on Time-Frequency-Nonconvex Robust Principal Component Analysis","authors":"Fang Wang, Lida Wang, Yufang Wen, Fei Ha, Jia Lu, Wenbin Jiao","doi":"10.1109/ICSMD57530.2022.10058265","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058265","url":null,"abstract":"Intelligent fault diagnosis is an effective method to ensure the continuous and efficient operation of machinery. In practical industrial applications, noise is unavoidable, which leads to serious degradation of the performance of intelligent fault diagnosis methods. Given this, an intelligent diesel engine fault diagnosis method based on Time-Frequency Distribution (TFD) and Non-Convex Robust Principal Component Analysis (NCRPCA) method is studied in this paper, aiming to provide a way to accurately diagnose faults in a noisy environment. Firstly, the original vibration signals were analyzed to obtain the time-frequency training matrix, and then the NCRPCA method was used to automatically extract the fault features. And the Support Vector Machine (SVM) method is used to identify the fault. The method is trained directly on the original dataset and has strong adaptability to noise. The method is applied to the diesel engine fault diagnosis experiment, and the results show that the method is effective for the performance evaluation of diesel valve clearance fault, and has high fault diagnosis accuracy.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127813249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Condition Monitoring by Online Updated Optimized Weights Spectrum: An Industrial Motor Case Study 基于在线更新优化权重谱的机器状态监测:以工业电机为例研究

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058298

Bingchang Hou, Jin-Zhen Kong, Yikai Chen, Jie Liu, D. Wang

引用次数: 0