Pub Date : 2016-11-01DOI: 10.1109/ICSENST.2016.7796240
Nasrin Afsarimanesh, M. Alahi, S. Mukhopadhyay, M. Kruger, P. Yu
This paper presents a label-free and non-invasive technique for selective detection of C-terminal telopeptide type I collagen (CTx-I) by employing Electrochemical Impedance Spectroscopy to measure sample impedance. Molecular imprinted polymer, containing artificial recognition sites for CTx-was prepared by precipitation polymerization using CTx-I peptide as a template, methacrylic acid as a functional monomer and ethylene glycol methacrylate as the cross-linker. A high penetration depth planar interdigital sensor was functionalized by a self-assembled monolayer along with the synthesized MIP. Different concentrations of CTx-I sample solutions were tested using the proposed sensing system. High-Performance Liquid chromatography diode array system was used to validate the results.
{"title":"Development of molecular imprinted polymer interdigital sensor for C-terminal telopeptide of type I collagen","authors":"Nasrin Afsarimanesh, M. Alahi, S. Mukhopadhyay, M. Kruger, P. Yu","doi":"10.1109/ICSENST.2016.7796240","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796240","url":null,"abstract":"This paper presents a label-free and non-invasive technique for selective detection of C-terminal telopeptide type I collagen (CTx-I) by employing Electrochemical Impedance Spectroscopy to measure sample impedance. Molecular imprinted polymer, containing artificial recognition sites for CTx-was prepared by precipitation polymerization using CTx-I peptide as a template, methacrylic acid as a functional monomer and ethylene glycol methacrylate as the cross-linker. A high penetration depth planar interdigital sensor was functionalized by a self-assembled monolayer along with the synthesized MIP. Different concentrations of CTx-I sample solutions were tested using the proposed sensing system. High-Performance Liquid chromatography diode array system was used to validate the results.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126083673","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 : 2016-11-01DOI: 10.1109/ICSENST.2016.7796266
Rui Zhao, Jinjiang Wang, Ruqiang Yan, K. Mao
Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions due to its noise, varying length and irregular sampling. A majority of previous models focus on feature extraction/fusion methods that involve expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, representation learning from raw data has been redefined. Among deep learning models, Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data. Therefore, LSTMs is able to work on the sensory data of machine condition. Here, the first study about a empirical evaluation of LSTMs-based machine health monitoring systems is presented. A real life tool wear test is introduced. Basic and deep LSTMs are designed to predict the actual tool wear based on raw sensory data. The experimental results have shown that our models, especially deep LSTMs, are able to outperform several state-of-arts baseline methods.
{"title":"Machine health monitoring with LSTM networks","authors":"Rui Zhao, Jinjiang Wang, Ruqiang Yan, K. Mao","doi":"10.1109/ICSENST.2016.7796266","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796266","url":null,"abstract":"Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions due to its noise, varying length and irregular sampling. A majority of previous models focus on feature extraction/fusion methods that involve expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, representation learning from raw data has been redefined. Among deep learning models, Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data. Therefore, LSTMs is able to work on the sensory data of machine condition. Here, the first study about a empirical evaluation of LSTMs-based machine health monitoring systems is presented. A real life tool wear test is introduced. Basic and deep LSTMs are designed to predict the actual tool wear based on raw sensory data. The experimental results have shown that our models, especially deep LSTMs, are able to outperform several state-of-arts baseline methods.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124025002","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 : 2016-11-01DOI: 10.1109/ICSENST.2016.7796313
Li Yuehua, Zhou Jingbo, Huang Fengshan, L. Lijian
Center extraction of structured light stripe is an essential problem for the development of line structured light sensors (LSLS). To obtain the sub-pixel center coordinates precisely, an improved gray weighted method (IGWM) is proposed with an adaptive sampling region. Firstly, the center of the stripe is computed using gray weighted method (GWM) for each pixel column. Then these center points are fitted using moving least squares algorithm to estimate the tangential vector, the normal vector and the radius of curvature. For each center point, a rectangular region is defined with two sides parallel with the normal vector. The other two sides that parallel with the tangential vector alter their length automatically according to the radius of curvature. After that, the center coordinate at this point is recalculated based on the GWM, but in the normal vector direction and only takes into account the pixels within the rectangular region. The experimental results show that this method is not only suited for the center extraction of smooth laser stripes, but also the ones with sharp corners. The noise can also be obviously suppressed than that of the traditional GWM.
{"title":"An improved gray weighted method for sub-pixel center extraction of structured light stripe","authors":"Li Yuehua, Zhou Jingbo, Huang Fengshan, L. Lijian","doi":"10.1109/ICSENST.2016.7796313","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796313","url":null,"abstract":"Center extraction of structured light stripe is an essential problem for the development of line structured light sensors (LSLS). To obtain the sub-pixel center coordinates precisely, an improved gray weighted method (IGWM) is proposed with an adaptive sampling region. Firstly, the center of the stripe is computed using gray weighted method (GWM) for each pixel column. Then these center points are fitted using moving least squares algorithm to estimate the tangential vector, the normal vector and the radius of curvature. For each center point, a rectangular region is defined with two sides parallel with the normal vector. The other two sides that parallel with the tangential vector alter their length automatically according to the radius of curvature. After that, the center coordinate at this point is recalculated based on the GWM, but in the normal vector direction and only takes into account the pixels within the rectangular region. The experimental results show that this method is not only suited for the center extraction of smooth laser stripes, but also the ones with sharp corners. The noise can also be obviously suppressed than that of the traditional GWM.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129169805","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}
This paper presents a foot-mounted inertial sensor system for pedestrian localization, and aims to find an adaptive stance detection method for pedestrian gait analysis. The approach is based on a Support Vector Machine (SVM) classifier, which divides the gaits into two types: walking and running. For walking, the algorithm uses two threshold conditions and a median filter to detect stance and still phases. For running, a new step detection method based on Extended Kalman Filter (EKF) is used to roughly identify every step of running at first, and then empirical formulas are summarized between the average velocity of each step and thresholds. The corrected thresholds based on empirical formulas are used in the second-round accurate stance detection. The localization accuracy for running is largely improved in this algorithm.
{"title":"A SVM-based adaptive stance detection method for pedestrian inertial navigation","authors":"Zhechen Zhang, Hongyu Wang, Zhonghua Zhao, Zhejun Wu","doi":"10.1109/ICSENST.2016.7796271","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796271","url":null,"abstract":"This paper presents a foot-mounted inertial sensor system for pedestrian localization, and aims to find an adaptive stance detection method for pedestrian gait analysis. The approach is based on a Support Vector Machine (SVM) classifier, which divides the gaits into two types: walking and running. For walking, the algorithm uses two threshold conditions and a median filter to detect stance and still phases. For running, a new step detection method based on Extended Kalman Filter (EKF) is used to roughly identify every step of running at first, and then empirical formulas are summarized between the average velocity of each step and thresholds. The corrected thresholds based on empirical formulas are used in the second-round accurate stance detection. The localization accuracy for running is largely improved in this algorithm.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"58 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130660881","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 : 2016-11-01DOI: 10.1109/ICSENST.2016.7796321
Wenzhong Liu, Shiqiang Pi
Magnetic nanothermometry is of great promising in future biomedical and industrial applications. However, the diameters of the used thermosensitive materials, magnetic nanoparticles, are commonly nonuniform, which will impact the performance of the magnetic nanothermometry. To achieve future high performance magnetic nanothermometer, we study the influences of the particle size distribution of magnetic nanoparticles on the response signal and the temperature estimation error in this paper.
{"title":"AC magnetic nanothermometry: The influence of particle size distribution","authors":"Wenzhong Liu, Shiqiang Pi","doi":"10.1109/ICSENST.2016.7796321","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796321","url":null,"abstract":"Magnetic nanothermometry is of great promising in future biomedical and industrial applications. However, the diameters of the used thermosensitive materials, magnetic nanoparticles, are commonly nonuniform, which will impact the performance of the magnetic nanothermometry. To achieve future high performance magnetic nanothermometer, we study the influences of the particle size distribution of magnetic nanoparticles on the response signal and the temperature estimation error in this paper.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129072323","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 : 2016-11-01DOI: 10.1109/ICSENST.2016.7796232
Y. Qin, L. Mo, Jing Ye, Z. Du
Human activity recognition has been widely used in many fields, especially in video surveillance and virtual reality, etc. The paper investigates a general feature combination method for a relatively new 3D CNNs and LSTMs fusion model in human activity recognition. All the features used in this combination method are from human activity videos without manually extracting features or any prior knowledge, and the model has good generalization performance. Through extracting multi-channel features of the motion optical flow vector, grayscale and body edge, putting them to 3D convolutional neural network, and processing time characteristics within Long-Short Term Memory neural network, the recognition rate of the model rises greatly. The experiment selects KTH dataset as the data source. The model based on RGB is used to compare with the model based on multi-channel features. It shows that multi-channel features can improve recognition accuracy rate obviously, and have great robustness in different scenes, which proves that it is an efficient feature combination method fitted 3D CNNs and LSTMs.
{"title":"Multi-channel features fitted 3D CNNs and LSTMs for human activity recognition","authors":"Y. Qin, L. Mo, Jing Ye, Z. Du","doi":"10.1109/ICSENST.2016.7796232","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796232","url":null,"abstract":"Human activity recognition has been widely used in many fields, especially in video surveillance and virtual reality, etc. The paper investigates a general feature combination method for a relatively new 3D CNNs and LSTMs fusion model in human activity recognition. All the features used in this combination method are from human activity videos without manually extracting features or any prior knowledge, and the model has good generalization performance. Through extracting multi-channel features of the motion optical flow vector, grayscale and body edge, putting them to 3D convolutional neural network, and processing time characteristics within Long-Short Term Memory neural network, the recognition rate of the model rises greatly. The experiment selects KTH dataset as the data source. The model based on RGB is used to compare with the model based on multi-channel features. It shows that multi-channel features can improve recognition accuracy rate obviously, and have great robustness in different scenes, which proves that it is an efficient feature combination method fitted 3D CNNs and LSTMs.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116353791","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 : 2016-11-01DOI: 10.1109/ICSENST.2016.7796340
Xu Jun, Tang Ya-nan, Li Xin
In order to realize remote monitoring and centralized management of combustible gas. A monitoring system of high performance fuel gas based on RS485 bus communication mode was presented in this paper. The system was composed of center controller and unit controller. The hardware design of unit controller and center controller was introduced, and the software design scheme of the control and communication between each controller were given in this paper. The center controller used Modbus protocol to communicate with the unit controller, to realize the display, storage and query of alarm signal of combustible gas, and complete the automatic zero adjustment, calibration and fault detection of the sensor signal of the unit controller. The unit controller collected the concentration value and temperature value of the combustible gas in real time, but due to effect of temperature, so I used RBF neural network to solve the problem of measuring deviation. The experimental results show that the measurement error is less than 2% LEL, and the alarm error is less than 3% LEL. The presented system has advantages such as easiness of operation, real-time fixing, relativity low cost and a wide prospect of applications.
{"title":"High performance combustible gas monitoring system","authors":"Xu Jun, Tang Ya-nan, Li Xin","doi":"10.1109/ICSENST.2016.7796340","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796340","url":null,"abstract":"In order to realize remote monitoring and centralized management of combustible gas. A monitoring system of high performance fuel gas based on RS485 bus communication mode was presented in this paper. The system was composed of center controller and unit controller. The hardware design of unit controller and center controller was introduced, and the software design scheme of the control and communication between each controller were given in this paper. The center controller used Modbus protocol to communicate with the unit controller, to realize the display, storage and query of alarm signal of combustible gas, and complete the automatic zero adjustment, calibration and fault detection of the sensor signal of the unit controller. The unit controller collected the concentration value and temperature value of the combustible gas in real time, but due to effect of temperature, so I used RBF neural network to solve the problem of measuring deviation. The experimental results show that the measurement error is less than 2% LEL, and the alarm error is less than 3% LEL. The presented system has advantages such as easiness of operation, real-time fixing, relativity low cost and a wide prospect of applications.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115152604","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 : 2016-11-01DOI: 10.1109/ICSENST.2016.7796332
I. Platt, A. Tan, I. Woodhead, K. Eccleston
In this paper we derive a robust Markov Chain Monte Carlo formulation to determine the suitable driver amplitudes for a microwave antenna to generate a Bessel beam. We show that the resulting solutions provide a robust driver for a well collimated beam with high SNR over a region of 0.5-3 m, easily sufficient for close proximity sampling.
{"title":"A Monte Carlo approach to determining bessel beam source parameters","authors":"I. Platt, A. Tan, I. Woodhead, K. Eccleston","doi":"10.1109/ICSENST.2016.7796332","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796332","url":null,"abstract":"In this paper we derive a robust Markov Chain Monte Carlo formulation to determine the suitable driver amplitudes for a microwave antenna to generate a Bessel beam. We show that the resulting solutions provide a robust driver for a well collimated beam with high SNR over a region of 0.5-3 m, easily sufficient for close proximity sampling.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121733026","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 : 2016-11-01DOI: 10.1109/ICSENST.2016.7796252
Lei Jiang, Yu Wang, Xin-hua Zhu, Yan Su
The major challenge of inertial navigation system (INS) is the rapid navigation error drift when aiding sensors are unavailable. However, if the dynamics of land vehicle can be detected, these errors can be corrected or restrained. A method based on support vector machine (SVM) using the outputs of MIMU is proposed here to identify the dynamics of land vehicle. This method computes part of the time-domain features and frequency-domain features. Then, a subset of these features is selected based on wrapper evaluation criteria. Afterwards, SVM is trained based on these selected features. Finally, the trained SVM is used in identification tests. The identification results show that this method can correctly identify the stationary, straight-line and cornering states.
{"title":"A novel vehicle dynamics identification method utilizing MIMU sensors based on support vector machine","authors":"Lei Jiang, Yu Wang, Xin-hua Zhu, Yan Su","doi":"10.1109/ICSENST.2016.7796252","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796252","url":null,"abstract":"The major challenge of inertial navigation system (INS) is the rapid navigation error drift when aiding sensors are unavailable. However, if the dynamics of land vehicle can be detected, these errors can be corrected or restrained. A method based on support vector machine (SVM) using the outputs of MIMU is proposed here to identify the dynamics of land vehicle. This method computes part of the time-domain features and frequency-domain features. Then, a subset of these features is selected based on wrapper evaluation criteria. Afterwards, SVM is trained based on these selected features. Finally, the trained SVM is used in identification tests. The identification results show that this method can correctly identify the stationary, straight-line and cornering states.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124666718","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 : 2016-11-01DOI: 10.1109/ICSENST.2016.7796274
Xu Jing, Wang Yutian, Zhang Lijuan, Zhao Xu, Wu Xijun, Pan Zhao
Fluorescence technique serves as a soft sensor with the ability to estimate the shape of emission and excitation spectrum and the information of concentrations of each fluorophore in multi-component fluorescent substances. Noise exist in each measurement inevitably. The impact of smoothing on parallel factor model of fluorescence emission excitation matrix is studied by compare nine methods. Smoothing can obtain more smooth resolved spectra, while the advantage of predication concentrations is not so obviously with the methods and processes used in this paper. Other more smoothing methods need to attempt to discover the obvious advantage of predication concentrations.
{"title":"A study of the impact of smoothing on parallel factor model of fluorescence emission excitation matrix","authors":"Xu Jing, Wang Yutian, Zhang Lijuan, Zhao Xu, Wu Xijun, Pan Zhao","doi":"10.1109/ICSENST.2016.7796274","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796274","url":null,"abstract":"Fluorescence technique serves as a soft sensor with the ability to estimate the shape of emission and excitation spectrum and the information of concentrations of each fluorophore in multi-component fluorescent substances. Noise exist in each measurement inevitably. The impact of smoothing on parallel factor model of fluorescence emission excitation matrix is studied by compare nine methods. Smoothing can obtain more smooth resolved spectra, while the advantage of predication concentrations is not so obviously with the methods and processes used in this paper. Other more smoothing methods need to attempt to discover the obvious advantage of predication concentrations.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133764561","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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