Pub Date : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455609
Yong-Gyu Song, S. Zeng, Yutao Zhang
In the repetitive control of tracking periodic signals based on the principle of internal model, the control effect has a great relationship with the parameters of the controlled system. If the system is affected by noise and causes the internal parameters to change, failure to obtain the repeated control of the internal parameters in time will cause the system to lose stability. Therefore, how to quickly identify the parameters of the controlled system is particularly important in the field of repetitive control. In the actual process, the traditional least square method is often used to identify the parameters of the controlled system. However, the convergence of the algorithm to parameter identification is very slow. Once the controlled system parameters are changed, the parameter information provided by the new data cannot be updated in time, and the convergence of the identification results is very slow. In order to overcome the data saturation phenomenon of the least squares algorithm, this paper uses three methods of forgetting factor algorithm, variable gain matrix algorithm, and introducing additional matrix R algorithm to improve the traditional least squares identification algorithm, and verified these three through MATLAB simulation. Effectiveness of the method. Compared with traditional methods, the improved three identification methods can speed up the convergence of parameter identification and improve the accuracy of parameter identification.
{"title":"The problem of identification parameter data saturation in repetitive control and its solution","authors":"Yong-Gyu Song, S. Zeng, Yutao Zhang","doi":"10.1109/DDCLS52934.2021.9455609","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455609","url":null,"abstract":"In the repetitive control of tracking periodic signals based on the principle of internal model, the control effect has a great relationship with the parameters of the controlled system. If the system is affected by noise and causes the internal parameters to change, failure to obtain the repeated control of the internal parameters in time will cause the system to lose stability. Therefore, how to quickly identify the parameters of the controlled system is particularly important in the field of repetitive control. In the actual process, the traditional least square method is often used to identify the parameters of the controlled system. However, the convergence of the algorithm to parameter identification is very slow. Once the controlled system parameters are changed, the parameter information provided by the new data cannot be updated in time, and the convergence of the identification results is very slow. In order to overcome the data saturation phenomenon of the least squares algorithm, this paper uses three methods of forgetting factor algorithm, variable gain matrix algorithm, and introducing additional matrix R algorithm to improve the traditional least squares identification algorithm, and verified these three through MATLAB simulation. Effectiveness of the method. Compared with traditional methods, the improved three identification methods can speed up the convergence of parameter identification and improve the accuracy of parameter identification.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126383315","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455525
Xinjian Zhu, Feng Li, Chenghao Li, L. Jia, Qingfeng Cao
A parameter estimation method based on multi-signal processing is developed that aims at the Hammerstein output error model in this paper. The multi-signal processing is devised to estimate independently parameters of nonlinear block and linear block for Hammerstein output error model. Firstly, using input-output data of binary signal, the linear block parameters are computed by means of auxiliary model recursive least square method, the unmeasurable variables of the Hammerstein model are effectively handled using auxiliary model technology. In addition, model error probability density function technology is applied to estimate parameters of nonlinear block measurable input-output data of random signal, which not only can control space state distribution of model error, but also make error distribution tend to normal distribution. The results verify that proposed parameter estimation method can effectively estimate the Hammerstein output error model.
{"title":"Parameter Estimation of the Hammerstein Output Error Model Using Multi-signal Processing","authors":"Xinjian Zhu, Feng Li, Chenghao Li, L. Jia, Qingfeng Cao","doi":"10.1109/DDCLS52934.2021.9455525","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455525","url":null,"abstract":"A parameter estimation method based on multi-signal processing is developed that aims at the Hammerstein output error model in this paper. The multi-signal processing is devised to estimate independently parameters of nonlinear block and linear block for Hammerstein output error model. Firstly, using input-output data of binary signal, the linear block parameters are computed by means of auxiliary model recursive least square method, the unmeasurable variables of the Hammerstein model are effectively handled using auxiliary model technology. In addition, model error probability density function technology is applied to estimate parameters of nonlinear block measurable input-output data of random signal, which not only can control space state distribution of model error, but also make error distribution tend to normal distribution. The results verify that proposed parameter estimation method can effectively estimate the Hammerstein output error model.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131397492","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455519
Zhiqiang Feng, Li Guo, Darong Huang, Runze Li
For electrical transmission lines, insulator inspection is an important indicator for power system safety operation. Manual visual inspection activities are usually performed in insulator statue recognition and maintenance, but it is time-consuming, unsafe, and low-efficient. As the development of image processing and machine learning, automatic insulator defect detection has been drawn more attention in electrical equipment inspection in recent years. This paper proposes an automatic insulator detection method using YOLOv5 object detection model. By comparing performance with 4 different versions of YOLOv5, experimental results show that YOLOv5x model with K-means clustering can achieve highest accuracy at 86.8%, and MAP is 95.5%. In addition, this model can efficiently identify and locate the insulator defects across transmission lines, so as to avoid unsafe manual detection and improve the detection efficiency.
{"title":"Electrical Insulator Defects Detection Method Based on YOLOv5","authors":"Zhiqiang Feng, Li Guo, Darong Huang, Runze Li","doi":"10.1109/DDCLS52934.2021.9455519","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455519","url":null,"abstract":"For electrical transmission lines, insulator inspection is an important indicator for power system safety operation. Manual visual inspection activities are usually performed in insulator statue recognition and maintenance, but it is time-consuming, unsafe, and low-efficient. As the development of image processing and machine learning, automatic insulator defect detection has been drawn more attention in electrical equipment inspection in recent years. This paper proposes an automatic insulator detection method using YOLOv5 object detection model. By comparing performance with 4 different versions of YOLOv5, experimental results show that YOLOv5x model with K-means clustering can achieve highest accuracy at 86.8%, and MAP is 95.5%. In addition, this model can efficiently identify and locate the insulator defects across transmission lines, so as to avoid unsafe manual detection and improve the detection efficiency.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134217632","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}
For human health, medical diagnosis plays an irreplaceable role, conventional medical diagnosis methods cannot ensure the accuracy of diagnosis due to the interference of various external factors. Therefore, this paper proposes a data-driven intelligent medical management system via neural networks(MMS-ID). The essence of this method is to predict the survival time of cancer patients with the aid of gradient boosting decision tree (GBDT) and hybrid neural network model. Firstly, GBDT screens the feature factors of matching conditions according to the set value domain, and inputs them into the neural network. Subsequently, a hybrid neural network that combines the convolutional neural network (CNN) and the long short-term memory (LSTM) model is employed to predict survival length of cancer patients. Finally, the stability of MMS-ID is analyzed and compared with a series of baseline methods. A series of experiments prove that MMS-ID has excellent performance.
{"title":"A Data-Driven Intelligent Medical Management System via Neural Networks","authors":"Jinhui Yang, Jianhui Wang, Xuhong Cheng, Zhiwei Guo, Yu Shen, Xu Gao","doi":"10.1109/DDCLS52934.2021.9455708","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455708","url":null,"abstract":"For human health, medical diagnosis plays an irreplaceable role, conventional medical diagnosis methods cannot ensure the accuracy of diagnosis due to the interference of various external factors. Therefore, this paper proposes a data-driven intelligent medical management system via neural networks(MMS-ID). The essence of this method is to predict the survival time of cancer patients with the aid of gradient boosting decision tree (GBDT) and hybrid neural network model. Firstly, GBDT screens the feature factors of matching conditions according to the set value domain, and inputs them into the neural network. Subsequently, a hybrid neural network that combines the convolutional neural network (CNN) and the long short-term memory (LSTM) model is employed to predict survival length of cancer patients. Finally, the stability of MMS-ID is analyzed and compared with a series of baseline methods. A series of experiments prove that MMS-ID has excellent performance.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134423173","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455533
Pengjuan Ma, Sen Chen, Zhi-liang Zhao
This paper proposes a new three-dimensional guidance law in order to achieve high-precision interception against the highly maneuvering targets in terminal guidance phase. Firstly, a reduced-order extended state observer is proposed to estimate the line-of-sight angle rate and the total disturbances composed of internal nonlinear dynamics and external disturbances. Secondly, a three-dimensional guidance law based on the reduced-order extended state observer is designed to actively compensate for the total disturbances and guarantee high-precision interception. The convergence and stability of the closed-loop interception system are analyzed rigorously. Different from the existing extended state observer based method, this paper only uses the line-of-sight angle as the output signal in the guidance law design. At the same time, the upper bounds of the system states are carefully analyzed to avoid the singularity of the closed-loop interception systems, which is not considered in existing results. Simulation results illustrate the effectiveness of the proposed method.
{"title":"A new three-dimensional guidance law based on reduced-order extended state observer for highly maneuvering targets","authors":"Pengjuan Ma, Sen Chen, Zhi-liang Zhao","doi":"10.1109/DDCLS52934.2021.9455533","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455533","url":null,"abstract":"This paper proposes a new three-dimensional guidance law in order to achieve high-precision interception against the highly maneuvering targets in terminal guidance phase. Firstly, a reduced-order extended state observer is proposed to estimate the line-of-sight angle rate and the total disturbances composed of internal nonlinear dynamics and external disturbances. Secondly, a three-dimensional guidance law based on the reduced-order extended state observer is designed to actively compensate for the total disturbances and guarantee high-precision interception. The convergence and stability of the closed-loop interception system are analyzed rigorously. Different from the existing extended state observer based method, this paper only uses the line-of-sight angle as the output signal in the guidance law design. At the same time, the upper bounds of the system states are carefully analyzed to avoid the singularity of the closed-loop interception systems, which is not considered in existing results. Simulation results illustrate the effectiveness of the proposed method.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131553587","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455498
Na Dong, Jianfang Chang, Wenjing Lv, Shuo Zhu
To alleviate the noise in the temperature tracking of the LiBr-H2O absorption refrigeration system, denoising model-free adaptive control algorithm has been proposed in this paper. Firstly, the improved tracking differentiator is mainly used to alleviate the phase delay. Secondly, the model-free adaptive control with single input and single output has been extended to dual input and single output, equipped with the improved tracking differentiator, the denoising control of temperature tracking for LiBr-H2O absorption refrigeration system has been constructed. Thirdly, the stability of denoising model-free adaptive control algorithm has been proven. Finally, the proposed algorithm is applied to the mathematical model and actual system, the experimental results prove the rapidity and immunity of the proposed algorithm in different systems.
{"title":"Denoising Control of Temperature Tracking for LiBr-H2O Absorption Refrigeration System","authors":"Na Dong, Jianfang Chang, Wenjing Lv, Shuo Zhu","doi":"10.1109/DDCLS52934.2021.9455498","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455498","url":null,"abstract":"To alleviate the noise in the temperature tracking of the LiBr-H2O absorption refrigeration system, denoising model-free adaptive control algorithm has been proposed in this paper. Firstly, the improved tracking differentiator is mainly used to alleviate the phase delay. Secondly, the model-free adaptive control with single input and single output has been extended to dual input and single output, equipped with the improved tracking differentiator, the denoising control of temperature tracking for LiBr-H2O absorption refrigeration system has been constructed. Thirdly, the stability of denoising model-free adaptive control algorithm has been proven. Finally, the proposed algorithm is applied to the mathematical model and actual system, the experimental results prove the rapidity and immunity of the proposed algorithm in different systems.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131176548","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455517
Lingyun Sun, Xuelian Wang, Yuqing Qin, Lei Su, Hao Shen, Jing Wang
This paper considers the passivity analysis of inertial neural networks with Markov jump parameters and reaction-diffusion terms. The original second-order differential system, by utilizing a suitable variable transformation, is transformed into a first-order one. The focus is on investigating the passive property of Markov jump reaction-diffusion neural networks. Then, based on Lyapunov stability theory, some sufficient criteria in terms of linear matrix inequality are established to guarantee the desired passive performance of neural networks.
{"title":"Passivity Analysis of Markov Jump Inertial Neural Networks Subject to Reaction-Diffusion","authors":"Lingyun Sun, Xuelian Wang, Yuqing Qin, Lei Su, Hao Shen, Jing Wang","doi":"10.1109/DDCLS52934.2021.9455517","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455517","url":null,"abstract":"This paper considers the passivity analysis of inertial neural networks with Markov jump parameters and reaction-diffusion terms. The original second-order differential system, by utilizing a suitable variable transformation, is transformed into a first-order one. The focus is on investigating the passive property of Markov jump reaction-diffusion neural networks. Then, based on Lyapunov stability theory, some sufficient criteria in terms of linear matrix inequality are established to guarantee the desired passive performance of neural networks.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134464405","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455610
Min Fan, Gang Peng, Bo Zhang, Meng Zhou, Shitao Jia
With the rapid development of Power Internet of Things, power grid monitoring data and analysis methods are increasing, so real-time dynamic monitoring of power equipment becomes possible. This paper presents a data driven method for evaluation and trend prediction of distribution transformer operation state. The key features reflecting dynamic change of operation state are extracted from voltage and current data of distribution transformer, and characteristic data flow is input into dynamic evaluation model to make real-time portrait description of distribution transformer operation state. According to time order and change trend of characteristic data flow, Long Short-Term Memory network (LSTM) is used to analysis regulation of characteristic data, and Support Vector Regression model (SVR) for its prediction. The future characteristic data flow is obtained, which is input into the dynamic evaluation model to realize the future operation trend prediction of the distribution transformer. Finally, examples are given to illustrate the feasibility, advanced nature and applicability of the method.
{"title":"Operation State Assessment and Prediction of Distribution Transformer Based on Data Driven","authors":"Min Fan, Gang Peng, Bo Zhang, Meng Zhou, Shitao Jia","doi":"10.1109/DDCLS52934.2021.9455610","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455610","url":null,"abstract":"With the rapid development of Power Internet of Things, power grid monitoring data and analysis methods are increasing, so real-time dynamic monitoring of power equipment becomes possible. This paper presents a data driven method for evaluation and trend prediction of distribution transformer operation state. The key features reflecting dynamic change of operation state are extracted from voltage and current data of distribution transformer, and characteristic data flow is input into dynamic evaluation model to make real-time portrait description of distribution transformer operation state. According to time order and change trend of characteristic data flow, Long Short-Term Memory network (LSTM) is used to analysis regulation of characteristic data, and Support Vector Regression model (SVR) for its prediction. The future characteristic data flow is obtained, which is input into the dynamic evaluation model to realize the future operation trend prediction of the distribution transformer. Finally, examples are given to illustrate the feasibility, advanced nature and applicability of the method.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131328497","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455548
Lingyun Zhu, Wei Dong, Qingyun Sun, Esteban Vargas, Xin Du
In this paper, an innovative SEIR(Susceptible-Exposed-Infective-Recovered) model is proposed to estimate the true infectivity and lethality of the COVID-19 epidemic in Wuhan, China. Segmented parameters are used in the model to prove the effectiveness of improved public health interventions such as city lockdown and extreme social distancing.And the generally polynomial chaos method is used to increase the reliability of the model results in the case of parameter estimation. The accuracy and validity of the proposed SEIR model are proved according to the official reported data.Also, according to the epidemic trend reflected by the model, the effectiveness and timeliness of the epidemic prevention policies formulated by the government can be reflected.
{"title":"Estimation of the Unreported Infections of COVID-19 based on an Extended Stochastic Susceptible-Exposed-Infective-Recovered Model","authors":"Lingyun Zhu, Wei Dong, Qingyun Sun, Esteban Vargas, Xin Du","doi":"10.1109/DDCLS52934.2021.9455548","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455548","url":null,"abstract":"In this paper, an innovative SEIR(Susceptible-Exposed-Infective-Recovered) model is proposed to estimate the true infectivity and lethality of the COVID-19 epidemic in Wuhan, China. Segmented parameters are used in the model to prove the effectiveness of improved public health interventions such as city lockdown and extreme social distancing.And the generally polynomial chaos method is used to increase the reliability of the model results in the case of parameter estimation. The accuracy and validity of the proposed SEIR model are proved according to the official reported data.Also, according to the epidemic trend reflected by the model, the effectiveness and timeliness of the epidemic prevention policies formulated by the government can be reflected.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115552162","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455676
Chunmei Duan, Meizhen Liu
In the paper, we propose a 3D shape descriptor which can be applied to areas such as non-rigid 3D shape analysis and retrieval. We start with the calculation of the Wave Kernel Signature (WKS) and the scale-invariant Heat Kernel Signature (siHKS) of surface points belong to a 3D shape. Then we combine them together and obtain their principle components by PCA (principle component analysis), which are employed as our own point signatures. We take a weighted average of all the point signatures over a 3D surface to obtain our own shape descriptor. Different from other approaches, we employ shape curvature as the element of weight in the construction of the shape descriptor. Moreover, our shape descriptor is also trained in a machine learning framework and then used to a non-rigid 3D shape retrieval application. The results of the experiments in the end of the paper show that our 3D shape descriptor is efficient and feasible for applications such as analysis of non-rigid 3D shape, non-rigid 3D shape matching and 3D shape retrieval, etc..
{"title":"3D Shape Descriptor by Principal Component Analysis Embedding for Non-rigid 3D Shape Retrieval in A Learning Framework","authors":"Chunmei Duan, Meizhen Liu","doi":"10.1109/DDCLS52934.2021.9455676","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455676","url":null,"abstract":"In the paper, we propose a 3D shape descriptor which can be applied to areas such as non-rigid 3D shape analysis and retrieval. We start with the calculation of the Wave Kernel Signature (WKS) and the scale-invariant Heat Kernel Signature (siHKS) of surface points belong to a 3D shape. Then we combine them together and obtain their principle components by PCA (principle component analysis), which are employed as our own point signatures. We take a weighted average of all the point signatures over a 3D surface to obtain our own shape descriptor. Different from other approaches, we employ shape curvature as the element of weight in the construction of the shape descriptor. Moreover, our shape descriptor is also trained in a machine learning framework and then used to a non-rigid 3D shape retrieval application. The results of the experiments in the end of the paper show that our 3D shape descriptor is efficient and feasible for applications such as analysis of non-rigid 3D shape, non-rigid 3D shape matching and 3D shape retrieval, etc..","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115895861","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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