Pub Date : 2018-11-01DOI: 10.1109/DDCLS.2018.8516061
L. Su, Lei Ma, N. Qin, Deqing Huang, Andrew H. Kemp
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.
{"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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8515918
Kaiyang Yin, Muye Pang, Kui Xiang, Chen Jing
Optimization parameters of PID controller based on Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is presented in this paper. It is used to solve the problem of torque control for powered ankle-foot prosthesis. Original optimization parameters method of PID controller for powered ankle-foot is time-consuming and cannot get satisfied control effect. The parameters of PID control are used as an individual of CMA-ES in this paper. Appropriate fitness function is selected to adjust the PID parameters online. Step signal and torque approximation are used as the system input to verify the controller performance. In unit-step response, the overshoot of original PID is 15 times as much as it of CMA-ES PID, the setting time of original PID is 6 times as much as it of CMA-ES PID. In device torque response, the output of CMA-ES PID is stabilized throughout the control process. These indicates that CMA-ES PID is an effective control strategy for torque control of powered ankle-foot prosthesis.
{"title":"Optimization Parameters of PID Controller for Powered Ankle-foot Prosthesis Based on CMA Evolution Strategy","authors":"Kaiyang Yin, Muye Pang, Kui Xiang, Chen Jing","doi":"10.1109/DDCLS.2018.8515918","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515918","url":null,"abstract":"Optimization parameters of PID controller based on Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is presented in this paper. It is used to solve the problem of torque control for powered ankle-foot prosthesis. Original optimization parameters method of PID controller for powered ankle-foot is time-consuming and cannot get satisfied control effect. The parameters of PID control are used as an individual of CMA-ES in this paper. Appropriate fitness function is selected to adjust the PID parameters online. Step signal and torque approximation are used as the system input to verify the controller performance. In unit-step response, the overshoot of original PID is 15 times as much as it of CMA-ES PID, the setting time of original PID is 6 times as much as it of CMA-ES PID. In device torque response, the output of CMA-ES PID is stabilized throughout the control process. These indicates that CMA-ES PID is an effective control strategy for torque control of powered ankle-foot prosthesis.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"175-179"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89125564","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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8515912
Pengfei Wu, M. Wang
Monitoring nodes are usually linear distributed along river and canal in irrigation area, which constructs linear distributed WSN. Aiming at linear distributed WSN, Flooding routing protocol based on energy and hop number (BEH-Flooding) is proposed. This protocol realizes efficient and stable wireless data transmission for irrigation area. According to the principle of same hop number, nodes are divided into multiple levels. In each level, two routing nodes are selected based on the principle of optimal residual energy. In the transmission stage, data packets are only transferred between routing nodes of upper level and routing nodes of lower level. By this, the protocol not only has the robustness of Flooding protocol, but also reduces extra data transmission. The simulation results validate the effectiveness of the proposed routing protocol. This method provides an approach to data acquisition for monitoring system in irrigation area.
{"title":"Routing Algorithm based on Energy and Hop Number for Linear Distributed WSN","authors":"Pengfei Wu, M. Wang","doi":"10.1109/DDCLS.2018.8515912","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515912","url":null,"abstract":"Monitoring nodes are usually linear distributed along river and canal in irrigation area, which constructs linear distributed WSN. Aiming at linear distributed WSN, Flooding routing protocol based on energy and hop number (BEH-Flooding) is proposed. This protocol realizes efficient and stable wireless data transmission for irrigation area. According to the principle of same hop number, nodes are divided into multiple levels. In each level, two routing nodes are selected based on the principle of optimal residual energy. In the transmission stage, data packets are only transferred between routing nodes of upper level and routing nodes of lower level. By this, the protocol not only has the robustness of Flooding protocol, but also reduces extra data transmission. The simulation results validate the effectiveness of the proposed routing protocol. This method provides an approach to data acquisition for monitoring system in irrigation area.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"10 1","pages":"194-198"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82599682","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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8516011
Na Lin, R. Chi, Ruikun Zhang
A simple iterative learning control approach is proposed to track specific target points in this work. For a general linear system, a P-type point-to-point ILC and a PD-type point-to-point ILC laws are designed, respectively. The two control laws only use the tracking error at the specified point to update the input signal at the corresponding specified point. The input signal between two consecutive specified points remains the same as the input signal at the previous specified point. The proposed method has the advantages of simple structure and easy application. The convergence analysis and simulation results further confirmed the availability of the method.
{"title":"On the Design and Analysis of a Learning Control Algorithm for Point-to-point Tracking Tasks","authors":"Na Lin, R. Chi, Ruikun Zhang","doi":"10.1109/DDCLS.2018.8516011","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516011","url":null,"abstract":"A simple iterative learning control approach is proposed to track specific target points in this work. For a general linear system, a P-type point-to-point ILC and a PD-type point-to-point ILC laws are designed, respectively. The two control laws only use the tracking error at the specified point to update the input signal at the corresponding specified point. The input signal between two consecutive specified points remains the same as the input signal at the previous specified point. The proposed method has the advantages of simple structure and easy application. The convergence analysis and simulation results further confirmed the availability of the method.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"76 1","pages":"189-193"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80847214","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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8516025
Xiaofeng Yuan, Jiao Zhou, Yalin Wang
Soft sensors have played indispensable roles in modern refining industry, which can provide significant information for process modeling, control, monitoring and optimization. However, the prediction performance often gradually deteriorates due to process time-varying problem caused by reasons like catalyst deactivation. Therefore, it is very important to update the inferential models regularly in order to keep good prediction performance. In this paper, a comparative study of adaptive soft sensors is carried out for quality prediction in a real hydrocracking process. Recursive partial least squares (RPLS), moving window RPLS (MWRPLS), locally weighted partial least squares (LWPLS) and moving window LWPLS (MWLWPLS) models are built to predict the 10% boiling point of the aviation kerosene product. The results show that RPLS and MWRPLS can provide better prediction performance.
{"title":"A Comparative Study of Adaptive Soft Sensors for Quality Prediction in an Industrial Refining Hydrocracking Process","authors":"Xiaofeng Yuan, Jiao Zhou, Yalin Wang","doi":"10.1109/DDCLS.2018.8516025","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516025","url":null,"abstract":"Soft sensors have played indispensable roles in modern refining industry, which can provide significant information for process modeling, control, monitoring and optimization. However, the prediction performance often gradually deteriorates due to process time-varying problem caused by reasons like catalyst deactivation. Therefore, it is very important to update the inferential models regularly in order to keep good prediction performance. In this paper, a comparative study of adaptive soft sensors is carried out for quality prediction in a real hydrocracking process. Recursive partial least squares (RPLS), moving window RPLS (MWRPLS), locally weighted partial least squares (LWPLS) and moving window LWPLS (MWLWPLS) models are built to predict the 10% boiling point of the aviation kerosene product. The results show that RPLS and MWRPLS can provide better prediction performance.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"24 1","pages":"1064-1068"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81188939","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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8516094
Hongwei Zhang, Ling-jie Zhang, Pengfei Li, De Gu
To reduce labor costs for manual extract image features of yarn-dyed fabric defects, a method based on YOLOV2 is proposed for yarn-dyed fabric defect automatic localization and classification. First, 276 yarn-dyed fabric defect images are collected, preprocessed and labelled. Then, YOLO9000, YOLO-VOC and Tiny YOLO are used to construct fabric defect detection models. Through comparative study, YOLO-VOC is selected to further model improvement by optimize super-parameters of deep convolutional neural network. Finally, the improved deep convolutional neural network is tested for yarn-dyed fabric defect detection on practical fabric images. The experimental results indicate the proposed method is effective and low labor cost for yarn-dyed fabric defect detection.
{"title":"Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep Convolution Neural Networks","authors":"Hongwei Zhang, Ling-jie Zhang, Pengfei Li, De Gu","doi":"10.1109/DDCLS.2018.8516094","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8516094","url":null,"abstract":"To reduce labor costs for manual extract image features of yarn-dyed fabric defects, a method based on YOLOV2 is proposed for yarn-dyed fabric defect automatic localization and classification. First, 276 yarn-dyed fabric defect images are collected, preprocessed and labelled. Then, YOLO9000, YOLO-VOC and Tiny YOLO are used to construct fabric defect detection models. Through comparative study, YOLO-VOC is selected to further model improvement by optimize super-parameters of deep convolutional neural network. Finally, the improved deep convolutional neural network is tested for yarn-dyed fabric defect detection on practical fabric images. The experimental results indicate the proposed method is effective and low labor cost for yarn-dyed fabric defect detection.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"65 1","pages":"170-174"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80200296","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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8515930
Xuefeng Liu, Qiaoqiao Sun, Y. Meng, Congcong Wang, Min Fu
Deep learning has huge potential for hyperspectral image (HSI) classification. In order to fully exploit the information in HSI and improve the classification accuracy, a new classification method based on 3D-convolutional neural network (3D-CNN) is proposed. In the meantime, virtual samples are introduced to solve the problem of insufficient samples of HSI. The experimental results show that the proposed method has a good application prospect in HSI classification.
{"title":"Feature Extraction and Classification of Hyperspectral Image Based on 3D- Convolution Neural Network","authors":"Xuefeng Liu, Qiaoqiao Sun, Y. Meng, Congcong Wang, Min Fu","doi":"10.1109/DDCLS.2018.8515930","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515930","url":null,"abstract":"Deep learning has huge potential for hyperspectral image (HSI) classification. In order to fully exploit the information in HSI and improve the classification accuracy, a new classification method based on 3D-convolutional neural network (3D-CNN) is proposed. In the meantime, virtual samples are introduced to solve the problem of insufficient samples of HSI. The experimental results show that the proposed method has a good application prospect in HSI classification.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"918-922"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84725333","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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8515974
Shiying Lu, W. Ai, Xiangyang Li
The Variable Air Volume (VAV) Central Air-Conditioning system is a complicated system with non-linearity, large-time delay and strong inertia, thus it is difficult to design an effective controller. Iterative Learning Control (ILC) takes good effect in controlled process with repeatability and periodicity, but it cannot cope with uncertain disturbance explicitly. A creative algorithm, Active Disturbance Rejection based Iterative Learning Control (ADR-Based ILC), is proposed to improve ILC’s performance in VAV control system. ADR-Based ILC compensates the disturbance explicitly caused by ambient temperature, heat from people and machines, and makes it higher control precision and higher energy-efficiency. An accurate model of VAV system is built in TRNSYS platform, and ADR-Based ILC is proved to be more effective than fuzzy PID and ILC.
{"title":"Active Disturbance Rejection Based Iterative Learning Control for Variable Air Volume Central Air-Conditioning System","authors":"Shiying Lu, W. Ai, Xiangyang Li","doi":"10.1109/DDCLS.2018.8515974","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515974","url":null,"abstract":"The Variable Air Volume (VAV) Central Air-Conditioning system is a complicated system with non-linearity, large-time delay and strong inertia, thus it is difficult to design an effective controller. Iterative Learning Control (ILC) takes good effect in controlled process with repeatability and periodicity, but it cannot cope with uncertain disturbance explicitly. A creative algorithm, Active Disturbance Rejection based Iterative Learning Control (ADR-Based ILC), is proposed to improve ILC’s performance in VAV control system. ADR-Based ILC compensates the disturbance explicitly caused by ambient temperature, heat from people and machines, and makes it higher control precision and higher energy-efficiency. An accurate model of VAV system is built in TRNSYS platform, and ADR-Based ILC is proved to be more effective than fuzzy PID and ILC.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"82 1","pages":"1073-1078"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83744215","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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8516114
Xuchen Dong, Ting Lei, S. Jin, Z. Hou
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.
{"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}
Pub Date : 2018-05-25DOI: 10.1109/DDCLS.2018.8515903
Sajede Harraz, S. Cong, Shuang Feng
Due to the interaction with the environment, a quantum state is often affected by the different types of noises which becomes to one of the biggest problems for practical quantum computation. We study the possibility of protecting the mixed state of a quantum system that goes through noise by weak measurements and control operations. The aim is to find the optimal measurement strength and control operations and make the input and output states as close as possible. We show that our scheme can effectively protect arbitrary mixed states against typical types of noise sources: amplitude damping, phase damping and amplitude-phase damping. The optimal measurement and control operators are deduced in different bases of the Bloch sphere to find the best control scheme for each type of noise. The effectiveness of our control scheme is demonstrated by simulation results.
{"title":"Quantum Noise Protection via Weak Measurement for Quantum Mixed States","authors":"Sajede Harraz, S. Cong, Shuang Feng","doi":"10.1109/DDCLS.2018.8515903","DOIUrl":"https://doi.org/10.1109/DDCLS.2018.8515903","url":null,"abstract":"Due to the interaction with the environment, a quantum state is often affected by the different types of noises which becomes to one of the biggest problems for practical quantum computation. We study the possibility of protecting the mixed state of a quantum system that goes through noise by weak measurements and control operations. The aim is to find the optimal measurement strength and control operations and make the input and output states as close as possible. We show that our scheme can effectively protect arbitrary mixed states against typical types of noise sources: amplitude damping, phase damping and amplitude-phase damping. The optimal measurement and control operators are deduced in different bases of the Bloch sphere to find the best control scheme for each type of noise. The effectiveness of our control scheme is demonstrated by simulation results.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"30 1","pages":"302-307"},"PeriodicalIF":0.0,"publicationDate":"2018-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84996321","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}
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