Pub Date : 2021-11-08DOI: 10.1109/IAI53119.2021.9619277
Jun Yang, Di Wu, Ximing Sun
This paper proposes a novel integral sliding mode robust control method for tracking control of robot manipulators based on Hamilton-Jacobi Inequality theory and a nonlinear disturbance observer. Firstly, the dynamic model of the manipulator considering the uncertainty and external disturbance is established through the Lagrange method. Secondly, a nonlinear disturbance observer is designed to estimate and compensate the composite interference. Hamilton-Jacobi Inequality theory and the designed disturbance observer are then applied to design the integral sliding mode robust control law with a new integral sliding mode surface. Finally, the proposed controller is employed for tracking control of a two-degree-of-freedom manipulator and compared with the conventional sliding mode controller. The comparison results demonstrate that the proposed approach can provide superior performance such as high tracking accuracy, fast transient response, and low chattering.
{"title":"Integral Sliding Mode Robust Control of Manipulator Based on Disturbance Observer and HJI Theory","authors":"Jun Yang, Di Wu, Ximing Sun","doi":"10.1109/IAI53119.2021.9619277","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619277","url":null,"abstract":"This paper proposes a novel integral sliding mode robust control method for tracking control of robot manipulators based on Hamilton-Jacobi Inequality theory and a nonlinear disturbance observer. Firstly, the dynamic model of the manipulator considering the uncertainty and external disturbance is established through the Lagrange method. Secondly, a nonlinear disturbance observer is designed to estimate and compensate the composite interference. Hamilton-Jacobi Inequality theory and the designed disturbance observer are then applied to design the integral sliding mode robust control law with a new integral sliding mode surface. Finally, the proposed controller is employed for tracking control of a two-degree-of-freedom manipulator and compared with the conventional sliding mode controller. The comparison results demonstrate that the proposed approach can provide superior performance such as high tracking accuracy, fast transient response, and low chattering.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121537914","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619284
Weiru Yuan, Zhenhao Tang, Bing Bu, Shengxian Cao
A machine learning based framework involving data-mining method was proposed in this paper. To begin with, a powerful signal decomposition technique (singular spectrum analysis, SSA) was used to divide the original wind sequence into several sub-series to form a potential feature set. Then, the optimal sub-series is screened as the input feature set based on a novel swarm intelligence optimization algorithm (adaptive genetic algorithm based on improved harmony search algorithm, IAGA). Finally, a more appropriate sub-feature set together with the corresponding machine learning model (deep belief network, DBN) were established. A series of simulations is conducted by utilizing actual dataset to validate the proposed method. Comparison results represent that the proposed SSA-IAGA-DBN method achieves high prediction accuracy and robustness in short term wind power prediction tasks.
{"title":"A Novel Hybrid Short-Term Wind Power Prediction Framework Based on Singular Spectrum Analysis and Deep Belief Network Utilized Improved Adaptive Genetic Algorithm","authors":"Weiru Yuan, Zhenhao Tang, Bing Bu, Shengxian Cao","doi":"10.1109/IAI53119.2021.9619284","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619284","url":null,"abstract":"A machine learning based framework involving data-mining method was proposed in this paper. To begin with, a powerful signal decomposition technique (singular spectrum analysis, SSA) was used to divide the original wind sequence into several sub-series to form a potential feature set. Then, the optimal sub-series is screened as the input feature set based on a novel swarm intelligence optimization algorithm (adaptive genetic algorithm based on improved harmony search algorithm, IAGA). Finally, a more appropriate sub-feature set together with the corresponding machine learning model (deep belief network, DBN) were established. A series of simulations is conducted by utilizing actual dataset to validate the proposed method. Comparison results represent that the proposed SSA-IAGA-DBN method achieves high prediction accuracy and robustness in short term wind power prediction tasks.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117256412","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619301
Qi Tang, Ziyao Ding, Kun Liu, Ximing Sun
The data collected by various sensors in monitoring the operating status of aero-engines can be used to predict the Remaining Useful Life (RUL) of aero-engines. This dataset has characterisitcs of high dimensions and large scale, which increase the difficulty of accurately predicting RUL. To obtain more accurate prediction results, this paper proposes a prediction model based on dynamic ensemble learning to predict RUL of aero-engines. The model selects the K nearest neighbor samples of one testing sample, dynamically determines the weight of each learner by evaluating the local performance of this learner in the neighbor samples, and constructs a weighted kernel density estimation function based on previously calculated weights to achieve integrated prediction of multiple base learners dynamically. In order to better determine the similarity between the data, an improved adaptive KNN (K-Nearest Neighbor) algorithm is introduced, and the importance of each sensor is introduced into the traditional distance measurement, and the adaptive K value selection is realized through the relationship between the global average density and the local density. In order to reflect the short-term and long-term dependencies between samples in dataset better, neural network LSTM (Long Short-Term Memory) is selected as the base learner of the dynamic ensemble learning model. Finally, the aircraft engine simulation data set C-MAPSS released by NASA is used for simulation verification. The experimental results show that the model proposed in this paper can improve the forecast precision of aero-engines’ RUL.
{"title":"Remaining Useful Life Prediction for Aero-engines based on Improved Dynamic Ensemble Learning","authors":"Qi Tang, Ziyao Ding, Kun Liu, Ximing Sun","doi":"10.1109/IAI53119.2021.9619301","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619301","url":null,"abstract":"The data collected by various sensors in monitoring the operating status of aero-engines can be used to predict the Remaining Useful Life (RUL) of aero-engines. This dataset has characterisitcs of high dimensions and large scale, which increase the difficulty of accurately predicting RUL. To obtain more accurate prediction results, this paper proposes a prediction model based on dynamic ensemble learning to predict RUL of aero-engines. The model selects the K nearest neighbor samples of one testing sample, dynamically determines the weight of each learner by evaluating the local performance of this learner in the neighbor samples, and constructs a weighted kernel density estimation function based on previously calculated weights to achieve integrated prediction of multiple base learners dynamically. In order to better determine the similarity between the data, an improved adaptive KNN (K-Nearest Neighbor) algorithm is introduced, and the importance of each sensor is introduced into the traditional distance measurement, and the adaptive K value selection is realized through the relationship between the global average density and the local density. In order to reflect the short-term and long-term dependencies between samples in dataset better, neural network LSTM (Long Short-Term Memory) is selected as the base learner of the dynamic ensemble learning model. Finally, the aircraft engine simulation data set C-MAPSS released by NASA is used for simulation verification. The experimental results show that the model proposed in this paper can improve the forecast precision of aero-engines’ RUL.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126060836","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619300
Yi Wu, Jing Wang, Yangquan Chen
Many intelligent methods have been proposed and applied in the field of autonomous manufacturing inspection. These advanced algorithms with high requirements on computing power and network may lead to time delay, high cost and energy consumption in practical applications with massive data to be processed. We carry out an efficient defect detection system in an end-edge-cloud architecture with the concept of edge computing to process the big data quickly and effectively. A branchy deep learning model with early exit capability of inference is proposed to detect the category and location of the defect in printed circuit boards. We offload part of the computing tasks to the edge nodes by segmenting and deploying the DL model. Therefore, our system has high detection efficiency and makes real-time defect detection possible.
{"title":"An Efficient Defect Detection System for Printed Circuit Boards with Edge-Cloud Fusion Computing","authors":"Yi Wu, Jing Wang, Yangquan Chen","doi":"10.1109/IAI53119.2021.9619300","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619300","url":null,"abstract":"Many intelligent methods have been proposed and applied in the field of autonomous manufacturing inspection. These advanced algorithms with high requirements on computing power and network may lead to time delay, high cost and energy consumption in practical applications with massive data to be processed. We carry out an efficient defect detection system in an end-edge-cloud architecture with the concept of edge computing to process the big data quickly and effectively. A branchy deep learning model with early exit capability of inference is proposed to detect the category and location of the defect in printed circuit boards. We offload part of the computing tasks to the edge nodes by segmenting and deploying the DL model. Therefore, our system has high detection efficiency and makes real-time defect detection possible.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122799189","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619222
Liu Kangju, Sun Weitang, Li Yefeng, Zhao Yuan
Self-maintenance strategy of CNC machine tool is one of the key technologies to realize intelligent manufacturing. The main difficulties of this technology are: how to effectively collect and summarize the possible faults of CNC machine tools; how to collect and analyze the execution status of CNC machine tools in real time; how to put forward and set the feasible and best fault maintenance strategy and expert scheme according to the collected information. For this reason, this paper proposes a solution for CNC machine tool maintenance: first, the CNC system needs to have the function of fault maintenance strategy screening, when the machine tool failure occurs, the CNC system can quickly select the best matching maintenance scheme; second, the CNC system needs to have the function of fault early warning, according to the historical fault data, it can send early warning before the failure occurs. Information, timely remind the operation and maintenance personnel to protect. Finally, the practical application verifies the application effect of the autonomous maintenance strategy.
{"title":"Research on self-maintenance strategy of CNC machine tools based on case-based reasoning","authors":"Liu Kangju, Sun Weitang, Li Yefeng, Zhao Yuan","doi":"10.1109/IAI53119.2021.9619222","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619222","url":null,"abstract":"Self-maintenance strategy of CNC machine tool is one of the key technologies to realize intelligent manufacturing. The main difficulties of this technology are: how to effectively collect and summarize the possible faults of CNC machine tools; how to collect and analyze the execution status of CNC machine tools in real time; how to put forward and set the feasible and best fault maintenance strategy and expert scheme according to the collected information. For this reason, this paper proposes a solution for CNC machine tool maintenance: first, the CNC system needs to have the function of fault maintenance strategy screening, when the machine tool failure occurs, the CNC system can quickly select the best matching maintenance scheme; second, the CNC system needs to have the function of fault early warning, according to the historical fault data, it can send early warning before the failure occurs. Information, timely remind the operation and maintenance personnel to protect. Finally, the practical application verifies the application effect of the autonomous maintenance strategy.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"2 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114091925","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619262
Yipeng Wang, Wei Wang, Shuangshuang Li
In order to solve the problem of insufficient data in the process of constructing concentration monitoring model of ammonia nitrogen in intensive aquaculture, a new improved data generation model of TableGAN is proposed based on the model optimization algorithm. The method generates synthetic data with the same distribution characteristics as the original data by confrontation training, and makes the generated data more effective in the optimization model by adding classifiers and optimization functions. The field data of a breeding enterprise show that the accuracy of the ammonia nitrogen concentration soft sensing model trained by the synthetic data set is better than that of the model trained by the original data set in terms of root mean square error and maximum absolute error, and the test effect of the model is also improved significantly.
{"title":"Application of data generation model in aquaculture water quality monitoring","authors":"Yipeng Wang, Wei Wang, Shuangshuang Li","doi":"10.1109/IAI53119.2021.9619262","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619262","url":null,"abstract":"In order to solve the problem of insufficient data in the process of constructing concentration monitoring model of ammonia nitrogen in intensive aquaculture, a new improved data generation model of TableGAN is proposed based on the model optimization algorithm. The method generates synthetic data with the same distribution characteristics as the original data by confrontation training, and makes the generated data more effective in the optimization model by adding classifiers and optimization functions. The field data of a breeding enterprise show that the accuracy of the ammonia nitrogen concentration soft sensing model trained by the synthetic data set is better than that of the model trained by the original data set in terms of root mean square error and maximum absolute error, and the test effect of the model is also improved significantly.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679516","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619401
Ryo Chikaraishi, M. Deng
This paper proposes a calorimetric measurement system that takes into account the temperature dependence of the thermal conductivity of Peltier devices. Calorimetry is a method to measure the power loss from the amount of heat released from a power conversion device. In this paper, the Peltier device is used as a calorimetric measurement system and the temperature dependence of the thermal conductivity of the Peltier device is focused. By using Funnel Control as the tracking compensator of the control system that takes into account the temperature dependence of the thermal conductivity in the Peltier device, the measurement time of the power loss can be reduced by about 400 s compared with the previous method.
{"title":"Nonlinear Funnel control of calorimetric system considering temperature dependence of thermal conductivity","authors":"Ryo Chikaraishi, M. Deng","doi":"10.1109/IAI53119.2021.9619401","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619401","url":null,"abstract":"This paper proposes a calorimetric measurement system that takes into account the temperature dependence of the thermal conductivity of Peltier devices. Calorimetry is a method to measure the power loss from the amount of heat released from a power conversion device. In this paper, the Peltier device is used as a calorimetric measurement system and the temperature dependence of the thermal conductivity of the Peltier device is focused. By using Funnel Control as the tracking compensator of the control system that takes into account the temperature dependence of the thermal conductivity in the Peltier device, the measurement time of the power loss can be reduced by about 400 s compared with the previous method.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124714860","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619242
He Wang, Weile Chen, Haibo Du
In this paper, the discrete-time optimization problem for transmission line planning for power systems is studied, in which the local cost function is considered. Firstly, a global cost function is constructed by using penalty function method. Secondly, for the optimization problem of intelligent line selection system, a discrete-time optimization algorithm is proposed. In the optimization algorithm design, the gradient of approximate cost function is used. In the proposed algorithm, the global optimal advantage of each sub-stage is selected, and the optimal advantage can be adjusted by penalty parameters. Compared with the traditional optimization algorithm, the convergence time and accuracy are improved. Finally, the example simulation results verify the effectiveness and superiority of the proposed discrete-time optimization algorithm.
{"title":"Discrete-Time Approximate Optimization Algorithm for Intelligent Line Selection System","authors":"He Wang, Weile Chen, Haibo Du","doi":"10.1109/IAI53119.2021.9619242","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619242","url":null,"abstract":"In this paper, the discrete-time optimization problem for transmission line planning for power systems is studied, in which the local cost function is considered. Firstly, a global cost function is constructed by using penalty function method. Secondly, for the optimization problem of intelligent line selection system, a discrete-time optimization algorithm is proposed. In the optimization algorithm design, the gradient of approximate cost function is used. In the proposed algorithm, the global optimal advantage of each sub-stage is selected, and the optimal advantage can be adjusted by penalty parameters. Compared with the traditional optimization algorithm, the convergence time and accuracy are improved. Finally, the example simulation results verify the effectiveness and superiority of the proposed discrete-time optimization algorithm.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"80 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130766852","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 : 2021-11-08DOI: 10.1109/iai53119.2021.9619379
{"title":"IAI 2021 Table of contents","authors":"","doi":"10.1109/iai53119.2021.9619379","DOIUrl":"https://doi.org/10.1109/iai53119.2021.9619379","url":null,"abstract":"","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133452745","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619197
Yuan Zhao, Hai Yang, Yefeng Liu, Hong Zhu
China's independently developed Beidou 3 system has been fully operational and has achieved global positioning. In order to further improve the satellite navigation and positioning function of the ground mobile robot terminal, the influence of the high-frequency oscillating random disturbance signal received by the mobile robot data and the high-order nonlinear dynamics of the system on the navigation and positioning accuracy was analyzed, and the time-varying characteristics of the dynamic adaptive RTK-GPS positioning algorithm were used. A process neural network (PNN) based on empirical pattern decomposition (EMD) is proposed. Firstly, the existing input signal of the satellite positioning terminal is decomposed into several intrinsic mode functions (IMFs) using the EMD method. Then, for each IMF, the neural network model is constructed, and the dynamic error data is used as the sample for the neural network model correction training. For the satellite signal interference or lock loss process, the trained neural network is used to predict the output divergence to suppress the position and speed errors, so as to improve the accuracy of positioning and navigation. Experimental results show that this method is still suitable to improve the positioning accuracy in non-stationary environment, enhances the acquisition and tracking characteristics of the system, especially when the observation satellite is maneuvering, and the error of positioning results can be significantly reduced.
{"title":"Research on Navigation and Positioning of Mobile Robot in Non-stationary Environment Based on Process Neural Network","authors":"Yuan Zhao, Hai Yang, Yefeng Liu, Hong Zhu","doi":"10.1109/IAI53119.2021.9619197","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619197","url":null,"abstract":"China's independently developed Beidou 3 system has been fully operational and has achieved global positioning. In order to further improve the satellite navigation and positioning function of the ground mobile robot terminal, the influence of the high-frequency oscillating random disturbance signal received by the mobile robot data and the high-order nonlinear dynamics of the system on the navigation and positioning accuracy was analyzed, and the time-varying characteristics of the dynamic adaptive RTK-GPS positioning algorithm were used. A process neural network (PNN) based on empirical pattern decomposition (EMD) is proposed. Firstly, the existing input signal of the satellite positioning terminal is decomposed into several intrinsic mode functions (IMFs) using the EMD method. Then, for each IMF, the neural network model is constructed, and the dynamic error data is used as the sample for the neural network model correction training. For the satellite signal interference or lock loss process, the trained neural network is used to predict the output divergence to suppress the position and speed errors, so as to improve the accuracy of positioning and navigation. Experimental results show that this method is still suitable to improve the positioning accuracy in non-stationary environment, enhances the acquisition and tracking characteristics of the system, especially when the observation satellite is maneuvering, and the error of positioning results can be significantly reduced.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133526676","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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