2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)最新文献

Fault Diagnosis of High-speed Train Bogie Based on Spectrogram and Multi-channel Voting 基于谱图和多通道投票的高速列车转向架故障诊断

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-11-01 DOI: 10.1109/DDCLS.2018.8516061

L. Su, Lei Ma, N. Qin, Deqing Huang, Andrew H. Kemp

{"title":"Fault Diagnosis of High-speed Train Bogie Based on Spectrogram and Multi-channel Voting","authors":"L. Su, Lei Ma, N. Qin, Deqing Huang, Andrew H. Kemp","doi":"10.1109/DDCLS.2018.8516061","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516061","url":null,"abstract":"Fault diagnosis of high-speed train bogie is of great importance in ensuring the safety of train operation. The multichannel vibration signals measured at different positions on the bogies characterize the dynamics of the vehicle and contain key information describing the performance of the bogie components. However, due to the complexity and uncertainty of the signals, it is hard to extract stable features that represent the characteristics of the signals. Besides, manual selection of reliable channels is indispensable in existing works. This paper presents an ensemble of methods for fault type recognition of high-speed train bogie based on spectrogram images and voting method. First, vibration signals of bogies are transformed to spectrogram images that are then taken as the input of Random Forests (RFs). In the next, four voting methods including Plurality Voting (PV), Classification Entropy (CE), Winner Takes All (WTA), as well as a novel method we proposed using neural network (NN) is applied for combining all the channels’ classification results to give a final decision on fault type. The proposed method not only avoid complicated feature extraction procedures by using a simple transform, but also make the best of multiple channels by automatic combination. Experiments conducted on the dataset based on SIMPACK simulations have verified the efficacy of the presented method in classifying key component(s) failures, with accuracy near 100%. Further, a more complex fault state in which the components of bogies only lose their effectiveness partially, instead of fully, has been tested and analyzed, where near 90% of accuracy is achieved. These results demonstrate the high robustness of the new method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"36 1","pages":"22-26"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88436584","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: 4

Optimization Parameters of PID Controller for Powered Ankle-foot Prosthesis Based on CMA Evolution Strategy 基于CMA进化策略的动力踝足假体PID控制器参数优化

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8515918

Kaiyang Yin, Muye Pang, Kui Xiang, Chen Jing

引用次数: 5

Routing Algorithm based on Energy and Hop Number for Linear Distributed WSN 基于能量和跳数的线性分布式WSN路由算法

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8515912

Pengfei Wu, M. Wang

引用次数: 5

On the Design and Analysis of a Learning Control Algorithm for Point-to-point Tracking Tasks 点对点跟踪任务的学习控制算法设计与分析

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8516011

Na Lin, R. Chi, Ruikun Zhang

引用次数: 0

A Comparative Study of Adaptive Soft Sensors for Quality Prediction in an Industrial Refining Hydrocracking Process 自适应软传感器在工业精炼加氢裂化过程质量预测中的比较研究

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8516025

Xiaofeng Yuan, Jiao Zhou, Yalin Wang

引用次数: 12

Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep Convolution Neural Networks 基于深度卷积神经网络的YOLOV2色织织物缺陷检测

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8516094

Hongwei Zhang, Ling-jie Zhang, Pengfei Li, De Gu

引用次数: 50

Feature Extraction and Classification of Hyperspectral Image Based on 3D- Convolution Neural Network 基于三维卷积神经网络的高光谱图像特征提取与分类

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8515930

Xuefeng Liu, Qiaoqiao Sun, Y. Meng, Congcong Wang, Min Fu

引用次数: 6

Active Disturbance Rejection Based Iterative Learning Control for Variable Air Volume Central Air-Conditioning System 基于自抗扰迭代学习的变风量中央空调系统控制

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8515974

Shiying Lu, W. Ai, Xiangyang Li

引用次数: 5

Short-Term Traffic Flow Prediction Based on XGBoost 基于XGBoost的短期交通流量预测

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8516114

Xuchen Dong, Ting Lei, S. Jin, Z. Hou

{"title":"Short-Term Traffic Flow Prediction Based on XGBoost","authors":"Xuchen Dong, Ting Lei, S. Jin, Z. Hou","doi":"10.1109/DDCLS.2018.8516114","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516114","url":null,"abstract":"Fast and accurate short-term traffic flow prediction is an important precondition for traffic analysis and control. Due to the fact that the short-term traffic flow has nonlinear characteristic and changes randomly, concurrent computation is difficult for traditional machine learning algorithms. In this paper, a traffic flow prediction model combining wavelets decomposition and reconstruction with the extreme gradient boosting (XGBoost) algorithm is proposed to predict the short-term traffic flow. First, in the training part, wavelet de-noising algorithm is utilized to obtain the high and low frequency information of target traffic flow. Secondly, the high frequency information of traffic flow is processed by threshold method. After that, the high and low frequency information is reconstituted as the training label. Finally, the de-noised target flow is sent to the XGBoost algorithm for training to predict traffic flow. In this way, the trend of the traffic flow in each sample period is retained, and the influence of the short-term high frequency noise is reduced. The proposed traffic flow prediction method is tested base on the traffic flow detector data collected in Beijing, and the proposed method is compared with support vector machine (SVM) algorithm. The result shows that the prediction accuracy of the proposed algorithm is much higher than SVM, which is of great importance in the field of traffic flow prediction.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"69 1","pages":"854-859"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89558128","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: 47

Quantum Noise Protection via Weak Measurement for Quantum Mixed States 基于量子混合态弱测量的量子噪声保护

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI: 10.1109/DDCLS.2018.8515903

Sajede Harraz, S. Cong, Shuang Feng

引用次数: 5