Pub Date : 2019-05-01DOI: 10.1109/DDCLS.2019.8908965
Xinghe Ma, Yaguang Ma, Junying Zhao, Dan Xu
According to the disadvantages of traditional three-level space vector control strategy with the complexity of the selection of switching vector order and the large computational complexity of the algorithm, a simplified space vector control strategy applied to the three-level indirect matrix converter is proposed. On the device, this method adopts a control strategy with no zero vector in the rectification stage to ensure that the input voltage power factor reaches a maximum, the inverter stage uses space vector control strategy of the simplified sector reconstruction to rotate the reference voltage into the first sector. Then reference voltage correction and level-down processing are performed. Compared with the traditional control strategy, this control strategy not only reduces the computational complexity of the algorithm but also eliminates the need to store a large amount of data in advance, and also reduces the harmonic distortion ratio. The simplified space vector control strategy is verified on simulation and an experimental prototype platform is built. The correctness and feasibility of the control strategy are verified on experimental methods.
{"title":"Simplified Strategy of Three-level Indirect Matrix Converter Based on Space Vector","authors":"Xinghe Ma, Yaguang Ma, Junying Zhao, Dan Xu","doi":"10.1109/DDCLS.2019.8908965","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908965","url":null,"abstract":"According to the disadvantages of traditional three-level space vector control strategy with the complexity of the selection of switching vector order and the large computational complexity of the algorithm, a simplified space vector control strategy applied to the three-level indirect matrix converter is proposed. On the device, this method adopts a control strategy with no zero vector in the rectification stage to ensure that the input voltage power factor reaches a maximum, the inverter stage uses space vector control strategy of the simplified sector reconstruction to rotate the reference voltage into the first sector. Then reference voltage correction and level-down processing are performed. Compared with the traditional control strategy, this control strategy not only reduces the computational complexity of the algorithm but also eliminates the need to store a large amount of data in advance, and also reduces the harmonic distortion ratio. The simplified space vector control strategy is verified on simulation and an experimental prototype platform is built. The correctness and feasibility of the control strategy are verified on experimental methods.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"66 1","pages":"93-98"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79777409","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908875
Shan Ding, F. Long, Huijin Fan, Lei Liu, Yongji Wang
Obstacle detection is an important issue in the study of an unmanned airship, which helps the airship to avoid obstacles and reduces the risk of accidences. This paper establishes an obstacle detection network, which is obtained by inserting wisely some $1times 1$ and $3times 3$ convolutional layers at the beginning and the end of the YOLOv3-tiny network. The experimental results show that our novel network leads to a higher accuracy compared to YOLOv3-tiny while with a satisfied processing speed.
{"title":"A Novel YOLOv3-tiny Network for Unmanned Airship Obstacle Detection","authors":"Shan Ding, F. Long, Huijin Fan, Lei Liu, Yongji Wang","doi":"10.1109/DDCLS.2019.8908875","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908875","url":null,"abstract":"Obstacle detection is an important issue in the study of an unmanned airship, which helps the airship to avoid obstacles and reduces the risk of accidences. This paper establishes an obstacle detection network, which is obtained by inserting wisely some $1times 1$ and $3times 3$ convolutional layers at the beginning and the end of the YOLOv3-tiny network. The experimental results show that our novel network leads to a higher accuracy compared to YOLOv3-tiny while with a satisfied processing speed.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"17 1","pages":"277-281"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85277435","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908943
Pengfei Xia, Wei Wei, Zaiwen Liu, Min Zuo
Positioning control of a nano-positioner driven by a piezoelectric actuator is discussed. Robust adaptive control with extended state observer is presented for the trajectory tracking control. Radial basis function neural network (RBFNN) is utilized to estimate the unknown nonlinearities. Extended state observer (ESO) is also taken to observe the total disturbance, which includes external disturbances and hysteresis. Both the RBFNN and the ESO are utilized to reduce the dependence on model information. A nano-positioner model is established. Simulations confirm the robust adaptive control with ESO is effective in improving positioning accuracy.
{"title":"A Robust Adaptive Control with Extended State Observer for a Piezo-actuated Nano-positioner","authors":"Pengfei Xia, Wei Wei, Zaiwen Liu, Min Zuo","doi":"10.1109/DDCLS.2019.8908943","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908943","url":null,"abstract":"Positioning control of a nano-positioner driven by a piezoelectric actuator is discussed. Robust adaptive control with extended state observer is presented for the trajectory tracking control. Radial basis function neural network (RBFNN) is utilized to estimate the unknown nonlinearities. Extended state observer (ESO) is also taken to observe the total disturbance, which includes external disturbances and hysteresis. Both the RBFNN and the ESO are utilized to reduce the dependence on model information. A nano-positioner model is established. Simulations confirm the robust adaptive control with ESO is effective in improving positioning accuracy.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"64 1","pages":"1003-1007"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83775791","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909032
Saim Ahmed, Haoping Wang, Yang Tian
Generally model-reference adaptive control (MRAC) is designed using known regression matrix. However, the formulation of regression matrix is difficult for more degree of freedoms (DOFs) of robot manipulator and sometime impossible to compute for many applications. In this work, MRAC using time delay estimation (TDE) named (MRAC-TDE) is proposed to avoid complex calculation of regression matrix and provides model-free control. Therefore, TDE is devised to estimate the unknown dynamics and MRAC is used to update the control gains. The closed-loop stability of system is investigated using the Lyapunov stability criterion. In the end, to validate the effectiveness of the proposed method, simulations are illustrated the appropriateness of proposed MRAC-TDE.
{"title":"Time Delay Estimation Based Model Reference Adaptive Control for Robot Manipulators","authors":"Saim Ahmed, Haoping Wang, Yang Tian","doi":"10.1109/DDCLS.2019.8909032","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909032","url":null,"abstract":"Generally model-reference adaptive control (MRAC) is designed using known regression matrix. However, the formulation of regression matrix is difficult for more degree of freedoms (DOFs) of robot manipulator and sometime impossible to compute for many applications. In this work, MRAC using time delay estimation (TDE) named (MRAC-TDE) is proposed to avoid complex calculation of regression matrix and provides model-free control. Therefore, TDE is devised to estimate the unknown dynamics and MRAC is used to update the control gains. The closed-loop stability of system is investigated using the Lyapunov stability criterion. In the end, to validate the effectiveness of the proposed method, simulations are illustrated the appropriateness of proposed MRAC-TDE.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"95 1","pages":"261-265"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83919698","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909058
Lingyu Zhang, Dehui Sun, Li Wang, Haibo Zhang
Aiming at the problems of heavy workload of basic data collection, complicated manual parameters calibration and inaccurate calibration in the traditional microscopic traffic simulation model parameter calibration, an adaptive microscopic traffic simulation model parameter calibration method based on floating car data is proposed. First, the basic data of the simulation road network were obtained by using the floating car technology. Secondly, the parameters calibration process of the microscopic traffic simulation model was constructed, and the self- adaptive orthogonal genetic was used to achieve the model parameters calibration. Finally, using the actual data of the South Ring Road main line, District Changping, Beijing to simulate and verify. The results show that the proposed method in this paper can not only reduce the workload of manual calibration, but also the model parameter calibration is more accurate, which proves the feasibility and effectiveness of the method.
{"title":"Parameter Calibration of Microscopic Traffic Simulation Model Based on Floating Car Data","authors":"Lingyu Zhang, Dehui Sun, Li Wang, Haibo Zhang","doi":"10.1109/DDCLS.2019.8909058","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909058","url":null,"abstract":"Aiming at the problems of heavy workload of basic data collection, complicated manual parameters calibration and inaccurate calibration in the traditional microscopic traffic simulation model parameter calibration, an adaptive microscopic traffic simulation model parameter calibration method based on floating car data is proposed. First, the basic data of the simulation road network were obtained by using the floating car technology. Secondly, the parameters calibration process of the microscopic traffic simulation model was constructed, and the self- adaptive orthogonal genetic was used to achieve the model parameters calibration. Finally, using the actual data of the South Ring Road main line, District Changping, Beijing to simulate and verify. The results show that the proposed method in this paper can not only reduce the workload of manual calibration, but also the model parameter calibration is more accurate, which proves the feasibility and effectiveness of the method.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"255 1","pages":"1219-1224"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78216164","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}
With the advent of online social networks, the approach of recommendation based on social network has emerged. However, some recommendation algorithms based on the trust network do not fully mine the information of user's trust relationships. To alleviate such problems, we propose a socialRT method, which is a social recommendation trust method based on joint matrix decomposition. The proposed socialRT method collective factorizes the following relationship matrix and the social trust relationship matrix to obtain the recommendation model. We have conducted experiments on Sina Weibo dataset, the experimental results demonstrate that the proposed recommendation method leads to a substantial increase in recommendation quality.
{"title":"SocialRT: A Recommendation Method Based On Social Trust","authors":"Xing Xing, Zhixin Meng, Hanting Chu, Jianyan Luo, Tiansheng Qu, Zhichun Jia","doi":"10.1109/DDCLS.2019.8908972","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908972","url":null,"abstract":"With the advent of online social networks, the approach of recommendation based on social network has emerged. However, some recommendation algorithms based on the trust network do not fully mine the information of user's trust relationships. To alleviate such problems, we propose a socialRT method, which is a social recommendation trust method based on joint matrix decomposition. The proposed socialRT method collective factorizes the following relationship matrix and the social trust relationship matrix to obtain the recommendation model. We have conducted experiments on Sina Weibo dataset, the experimental results demonstrate that the proposed recommendation method leads to a substantial increase in recommendation quality.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"12 1","pages":"443-447"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78354847","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909080
Yuwei Zhang, Ke Deng, Yongcai Pan, Qingzheng Liu
In view of the inadequacy of Itti's computational model of visual attention mechanism in significance region detection, an improved computational model of visual attention based on edge detection was proposed here. It is based on the human eye's perception advantage of the edge shape information of the target object. On the basis of Itti model, this model can improve the extraction effect of significant regions in visual attention computing model by introducing edge information, and can segment significant regions more accurately. The experiment shows that the success rate of target object contour detection in this method can reach 91%, which is higher than the traditional detection method in calculation speed, and the target object contour recognition effect is better.
{"title":"A Visual Attention Computational Model Based on Edge Detection","authors":"Yuwei Zhang, Ke Deng, Yongcai Pan, Qingzheng Liu","doi":"10.1109/DDCLS.2019.8909080","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909080","url":null,"abstract":"In view of the inadequacy of Itti's computational model of visual attention mechanism in significance region detection, an improved computational model of visual attention based on edge detection was proposed here. It is based on the human eye's perception advantage of the edge shape information of the target object. On the basis of Itti model, this model can improve the extraction effect of significant regions in visual attention computing model by introducing edge information, and can segment significant regions more accurately. The experiment shows that the success rate of target object contour detection in this method can reach 91%, which is higher than the traditional detection method in calculation speed, and the target object contour recognition effect is better.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"16 1","pages":"957-961"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78363291","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909074
Zeqing Ma, Jinrui Ren, Yi-Wei Lin, Q. Quan
In this paper, an auxiliary modeling method for system identification is proposed for multicopter dynamic modeling. By utilizing an auxiliary controller called damper during the modeling and identification process, the unstable attitude angle and angular rate channels of multicopters can turn to be stable so as to obtain the parameterized dynamic model without safety problems led by traditional methods and large-space requirement. Through an application to an indoor fixed quadcopter system, simulation results demonstrate the feasibility of the proposed method for the multicopter dynamic modeling.
{"title":"An Auxiliary Model Construction Method for System Identification and Its Application to An Indoor Multicopter Platform","authors":"Zeqing Ma, Jinrui Ren, Yi-Wei Lin, Q. Quan","doi":"10.1109/DDCLS.2019.8909074","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909074","url":null,"abstract":"In this paper, an auxiliary modeling method for system identification is proposed for multicopter dynamic modeling. By utilizing an auxiliary controller called damper during the modeling and identification process, the unstable attitude angle and angular rate channels of multicopters can turn to be stable so as to obtain the parameterized dynamic model without safety problems led by traditional methods and large-space requirement. Through an application to an indoor fixed quadcopter system, simulation results demonstrate the feasibility of the proposed method for the multicopter dynamic modeling.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"2 1","pages":"1189-1195"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78449116","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908958
Yi Liu, Jiu-sun Zeng, Lei Xie, Xun Lang, Shihua Luo, H. Su
This paper focus on developing an effective method to monitor the industrial process with multiple operation conditions. By utilizing the technique of probabilistic linear discriminant analysis (PLDA), the between- and within-class latent variables can extract more useful information. The proposed method, the modified PLDA (MPLDA), transforms the centralized samples into a new type of between-class latent variables. The current mode operation condition can be identified by comparing a series of cosine similarities deduced by the original and the new between-class latent variables. The online monitoring procedures are built on the basis of this mode identification. Unlike the conventional $T^{2}$ and $Q$ statistics designed for within-class latent variable, the proposed monitoring statistics take both between- and within-class latent variables into consideration. For the model training, the joint updating expectation-maximization (EM) algorithm is developed. The enhanced performance of the MPLDA based method is illustrated by the application of Tennessee Eastman (TE) process.
{"title":"Multimode Process Monitoring Based on Modified Probabilistic Linear Discriminant Analysis","authors":"Yi Liu, Jiu-sun Zeng, Lei Xie, Xun Lang, Shihua Luo, H. Su","doi":"10.1109/DDCLS.2019.8908958","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908958","url":null,"abstract":"This paper focus on developing an effective method to monitor the industrial process with multiple operation conditions. By utilizing the technique of probabilistic linear discriminant analysis (PLDA), the between- and within-class latent variables can extract more useful information. The proposed method, the modified PLDA (MPLDA), transforms the centralized samples into a new type of between-class latent variables. The current mode operation condition can be identified by comparing a series of cosine similarities deduced by the original and the new between-class latent variables. The online monitoring procedures are built on the basis of this mode identification. Unlike the conventional $T^{2}$ and $Q$ statistics designed for within-class latent variable, the proposed monitoring statistics take both between- and within-class latent variables into consideration. For the model training, the joint updating expectation-maximization (EM) algorithm is developed. The enhanced performance of the MPLDA based method is illustrated by the application of Tennessee Eastman (TE) process.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"49 1","pages":"604-609"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85442785","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908850
Feifan Shen, Lingjian Ye, Saite Fan, Zhiqiang Ge, Zhihuan Song
This paper handles with the problem of the run-to-run trajectory prediction of batch processes with uneven batch length. Most current data-driven works focus on the run-to-run variations during both batch trajectory modeling and prediction stages. However, batch-to-batch correlations should be drawn extreme attentions to when gradual changes exist in batch sequence. To obtain a better batch trajectory prediction performance of uneven-length batch processes, dynamic time warping (DTW) and long-short term memory (LSTM) neural network are introduced in this work to extract batch-to-batch correlations. Firstly, the recursive DTW is used to synchronize uneven batch samples. Then, the LSTM neural network is introduced to extract the run-to-run batch correlations during the trajectory modeling stage. Finally, online batch trajectory prediction can be implemented according to the offline LSTM model. A simulation based on the fed-batch penicillin fermentation process is provided to testify the effectiveness of the proposed method.
{"title":"Run-to-run Trajectory Prediction of Uneven-length Batch Processes Using DTW-LSTM","authors":"Feifan Shen, Lingjian Ye, Saite Fan, Zhiqiang Ge, Zhihuan Song","doi":"10.1109/DDCLS.2019.8908850","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908850","url":null,"abstract":"This paper handles with the problem of the run-to-run trajectory prediction of batch processes with uneven batch length. Most current data-driven works focus on the run-to-run variations during both batch trajectory modeling and prediction stages. However, batch-to-batch correlations should be drawn extreme attentions to when gradual changes exist in batch sequence. To obtain a better batch trajectory prediction performance of uneven-length batch processes, dynamic time warping (DTW) and long-short term memory (LSTM) neural network are introduced in this work to extract batch-to-batch correlations. Firstly, the recursive DTW is used to synchronize uneven batch samples. Then, the LSTM neural network is introduced to extract the run-to-run batch correlations during the trajectory modeling stage. Finally, online batch trajectory prediction can be implemented according to the offline LSTM model. A simulation based on the fed-batch penicillin fermentation process is provided to testify the effectiveness of the proposed method.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"17 1","pages":"1183-1188"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86929132","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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