This paper addresses the autonomous landing of a quadrotor equipped with sensors and an on-board computer on a moving platform. An autonomous landing framework is proposed, including modules of target detection, state estimation, trajectory planning and tracking control. Specifically, the target detection module utilizes an on-board camera to recognize targets with Apriltags, owing to the high efficiency and accuracy. The state estimation module combines the detected target information and the measurements from the on-board sensors to estimate the absolute motion state of the quadrotor and the platform at the current moment, but also predicts the platform state at the target moment. A dynamically feasible and non-conservative trajectory is generated by the trajectory planning module based on the state estimation. According to the results of the trajectory planning and the state estimation modules, the tracking control module issues control commands to make the quadrotor track the trajectory efficiently. Simulation tests are conducted to validate the feasibility and effectiveness of the proposed framework.
{"title":"A Novel Framework for Autonomous Landing of the Quadrotor on the Moving Platform by Onboard Vision Sensors","authors":"Qianqian Cao, Haixin Yu, Yongchun Fang, Xiao Liang","doi":"10.1109/IAI53119.2021.9619430","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619430","url":null,"abstract":"This paper addresses the autonomous landing of a quadrotor equipped with sensors and an on-board computer on a moving platform. An autonomous landing framework is proposed, including modules of target detection, state estimation, trajectory planning and tracking control. Specifically, the target detection module utilizes an on-board camera to recognize targets with Apriltags, owing to the high efficiency and accuracy. The state estimation module combines the detected target information and the measurements from the on-board sensors to estimate the absolute motion state of the quadrotor and the platform at the current moment, but also predicts the platform state at the target moment. A dynamically feasible and non-conservative trajectory is generated by the trajectory planning module based on the state estimation. According to the results of the trajectory planning and the state estimation modules, the tracking control module issues control commands to make the quadrotor track the trajectory efficiently. Simulation tests are conducted to validate the feasibility and effectiveness of the proposed framework.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"65 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":"132689902","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/IAI53119.2021.9619456
Meng-Wei, Min-Ye, Qiao-Wang, Gaoqi-Lian, Jiabo-Li
Remaining useful life (RUL) prediction of lithium-ion batteries is one of the key technologies on prognostics and health management. Highly accurate RUL prediction of lithium-ion batteries is a prerequisite to ensure the safety and reliability for electric vehicles. To describe the accurate RUL prediction, the RUL indirect prediction framework based on Gaussian mixture regression (GMR) is proposed. Firstly, the discharging voltage and current indirect health indicators are extracted, and grey relation analysis (GRA) is used to analyze the relation with capacity. Then, to improve the RUL prediction performance, GMR method is proposed for reducing the impact of external disturbances. Finally, the proposed method is compared with existing methods. The results show that the proposed method is superior to traditional methods.
{"title":"Remaining Useful Life Indirect Prediction of Lithium-ion Batteries Based on Gaussian Mixture Regression","authors":"Meng-Wei, Min-Ye, Qiao-Wang, Gaoqi-Lian, Jiabo-Li","doi":"10.1109/IAI53119.2021.9619456","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619456","url":null,"abstract":"Remaining useful life (RUL) prediction of lithium-ion batteries is one of the key technologies on prognostics and health management. Highly accurate RUL prediction of lithium-ion batteries is a prerequisite to ensure the safety and reliability for electric vehicles. To describe the accurate RUL prediction, the RUL indirect prediction framework based on Gaussian mixture regression (GMR) is proposed. Firstly, the discharging voltage and current indirect health indicators are extracted, and grey relation analysis (GRA) is used to analyze the relation with capacity. Then, to improve the RUL prediction performance, GMR method is proposed for reducing the impact of external disturbances. Finally, the proposed method is compared with existing methods. The results show that the proposed method is superior to traditional methods.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"12 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":"131325695","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/IAI53119.2021.9619282
Tian Jiang, Delin Kong
With the development of artificial intelligence, the application of computer technology for analyzing the objects in images and videos has been more and more widely used. This paper describes the human head and shoulders based on Haar-like features, and realizes the detection of the head and shoulders in the video using machine learning. Firstly, this paper selects the ViBe background model method to extract the moving area in the video. Then, Haar-like features are selected to describe the human head and shoulders. Finally, the head and shoulders in the video are detected by the soft cascade classifier. The algorithm is programmed using C++ language, and selects traffic intersection videos for experiment. Experimental results demonstrate that above 90% detection rate is achieved, which meets the design requirements.
{"title":"Video Based Human Head and Shoulders Detection Using Machine Learning","authors":"Tian Jiang, Delin Kong","doi":"10.1109/IAI53119.2021.9619282","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619282","url":null,"abstract":"With the development of artificial intelligence, the application of computer technology for analyzing the objects in images and videos has been more and more widely used. This paper describes the human head and shoulders based on Haar-like features, and realizes the detection of the head and shoulders in the video using machine learning. Firstly, this paper selects the ViBe background model method to extract the moving area in the video. Then, Haar-like features are selected to describe the human head and shoulders. Finally, the head and shoulders in the video are detected by the soft cascade classifier. The algorithm is programmed using C++ language, and selects traffic intersection videos for experiment. Experimental results demonstrate that above 90% detection rate is achieved, which meets the design requirements.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"12 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":"122894055","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}
A new consensus problem of first-order leaderless multi-agent systems with external disturbance is presented. The proposed consensus algorithm, based on sampled data, combines data-sample control with finite-time theory. The new discrete-time finite-time (DTFT) consensus algorithm enables the consensus errors of agents converge to a small region within a limited time due to the robustness and fast convergence ability of finite time theory. Simulation results verify the effectiveness of the proposed method.
{"title":"Discrete-time finite-time consensus algorithm for first-order multi-agent systems","authors":"Yuping He, Shijie Zhang, Weijian Zhang, Weile Chen","doi":"10.1109/IAI53119.2021.9619391","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619391","url":null,"abstract":"A new consensus problem of first-order leaderless multi-agent systems with external disturbance is presented. The proposed consensus algorithm, based on sampled data, combines data-sample control with finite-time theory. The new discrete-time finite-time (DTFT) consensus algorithm enables the consensus errors of agents converge to a small region within a limited time due to the robustness and fast convergence ability of finite time theory. Simulation results verify the effectiveness of the proposed method.","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":"121297708","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/IAI53119.2021.9619296
Xiang Li, Xiangyang Xu, Hui Zhang
This paper investigates a robust anti-windup control of a cyber physical tracking system subject to random attack-induced delays described by Markov chains. The openness of communication network makes cyber physical systems (CPSs) vulnerable to a network attack. The random attack signals occupy the transmission resources of legitimate signals. In order to actively compensate for the communication delay caused by the presence of attack signals, a CPS closed-loop tracking system is described as a discrete-time Markovian jump system. A nominal robust tracking controller considering $H_{infty}$ performance is designed to ensure the stability and tracking performance of the Markovian jump system when the saturation limit is not reached. The tracking performance degradation caused by physical saturation of the actuator is tackled by an anti-windup control. The gain matrices of the anti-windup controller are obtained by solving a set of matrix inequalities. Numerical simulations are conducted to show the effectiveness of the proposed control technique.
{"title":"Robust Anti-windup H∞ Control of a Cyber Physical Tracking System Subject to Replay Attacks","authors":"Xiang Li, Xiangyang Xu, Hui Zhang","doi":"10.1109/IAI53119.2021.9619296","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619296","url":null,"abstract":"This paper investigates a robust anti-windup control of a cyber physical tracking system subject to random attack-induced delays described by Markov chains. The openness of communication network makes cyber physical systems (CPSs) vulnerable to a network attack. The random attack signals occupy the transmission resources of legitimate signals. In order to actively compensate for the communication delay caused by the presence of attack signals, a CPS closed-loop tracking system is described as a discrete-time Markovian jump system. A nominal robust tracking controller considering $H_{infty}$ performance is designed to ensure the stability and tracking performance of the Markovian jump system when the saturation limit is not reached. The tracking performance degradation caused by physical saturation of the actuator is tackled by an anti-windup control. The gain matrices of the anti-windup controller are obtained by solving a set of matrix inequalities. Numerical simulations are conducted to show the effectiveness of the proposed control technique.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 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":"129156672","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/IAI53119.2021.9619440
Jieji Ren, Xiangchao Yan, Lijian Sun, M. Ren
Contact measurements are significant for surface metrology and can provide highly precise results. However, the point-by-point touch sampling process is less efficient, which seriously limits their applications in manufacture process, especially for the measurement of multi-scale complex workpieces. On the other hand, the lack of high-quality labeled datasets in manufacturing industries prevents advanced supervised learning approaches from modeling and accelerating the measurement process. To address these problems, this paper proposed a highly efficient sparse sampling strategy to accelerate the measurement efficiency and a self-learning based approach to reconstruct precise dense results, that can not only dramatically reduce the number of sampling points but also eliminate the dataset demand to train the reconstruction algorithm. The proposed method can learn the prior of sparse samples and then reconstruct dense accurate measurements with self-supervised behavior based on the optimization process of encoder-decoder convolutional neural networks. Intensive experiments show that the proposed approach outperforms blind interpolated methods and even close to supervised learning approaches.
{"title":"Self-Learning based Highly Efficient Sampling Strategy for Complex Surface Reconstruction on Contact Measurements","authors":"Jieji Ren, Xiangchao Yan, Lijian Sun, M. Ren","doi":"10.1109/IAI53119.2021.9619440","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619440","url":null,"abstract":"Contact measurements are significant for surface metrology and can provide highly precise results. However, the point-by-point touch sampling process is less efficient, which seriously limits their applications in manufacture process, especially for the measurement of multi-scale complex workpieces. On the other hand, the lack of high-quality labeled datasets in manufacturing industries prevents advanced supervised learning approaches from modeling and accelerating the measurement process. To address these problems, this paper proposed a highly efficient sparse sampling strategy to accelerate the measurement efficiency and a self-learning based approach to reconstruct precise dense results, that can not only dramatically reduce the number of sampling points but also eliminate the dataset demand to train the reconstruction algorithm. The proposed method can learn the prior of sparse samples and then reconstruct dense accurate measurements with self-supervised behavior based on the optimization process of encoder-decoder convolutional neural networks. Intensive experiments show that the proposed approach outperforms blind interpolated methods and even close to supervised learning approaches.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"20 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":"117091188","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/IAI53119.2021.9619390
Haixu Ding, Jian Tang, J. Qiao
With the advancement of science and technology, more and more complex systems require the model to have the ability to output multiple parameters simultaneously. Fuzzy neural network (FNN) is widely used in complex system modeling because of its combination of the nonlinear analysis ability of artificial neural network (ANN) and the fuzzy inference ability of fuzzy system. Therefore, this paper constructs a multi-input and multi-output (MIMO) model based on T-S (Takagi-Sugeno) FNN. First, according to the construction mechanism of TS-FNN, the MIMO network structure is designed. Then, a multi-output parameter update algorithm is designed, which takes into account the global performance and local performance of the network. Finally, simulation experiments are designed through benchmark experiments and modeling problems in an industrial process, which proves the feasibility and effectiveness of the neural network model.
{"title":"Multi-input and multi-output modeling method based on T-S fuzzy neural network and its application","authors":"Haixu Ding, Jian Tang, J. Qiao","doi":"10.1109/IAI53119.2021.9619390","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619390","url":null,"abstract":"With the advancement of science and technology, more and more complex systems require the model to have the ability to output multiple parameters simultaneously. Fuzzy neural network (FNN) is widely used in complex system modeling because of its combination of the nonlinear analysis ability of artificial neural network (ANN) and the fuzzy inference ability of fuzzy system. Therefore, this paper constructs a multi-input and multi-output (MIMO) model based on T-S (Takagi-Sugeno) FNN. First, according to the construction mechanism of TS-FNN, the MIMO network structure is designed. Then, a multi-output parameter update algorithm is designed, which takes into account the global performance and local performance of the network. Finally, simulation experiments are designed through benchmark experiments and modeling problems in an industrial process, which proves the feasibility and effectiveness of the neural network model.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"56 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":"126643311","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/IAI53119.2021.9619349
Zhiyang Ju, Hui Zhang, Zhi Qi, Qianyu Luo
Path following of duty-cycled spinning bevel tip needle is addressed in this paper. A model aiming for tracking reference paths is developed for steering control of the needle. Instead of dividing the reference path into multiple target points, which render the path following task into multiple regulation problems, the whole path is considered in this model. Furthermore, the nonlinear model developed is transformed into a linear time varying (LTV) system in a polytopic paradigm and thus a RMPC is applied which deals with the parameter uncertainties and the control input constraints. The simulation results verify the effectiveness of the proposed path following RMPC algorithm.
{"title":"Path Following Model Predictive Control of Duty-Cycled Spinning Bevel Tip Needle","authors":"Zhiyang Ju, Hui Zhang, Zhi Qi, Qianyu Luo","doi":"10.1109/IAI53119.2021.9619349","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619349","url":null,"abstract":"Path following of duty-cycled spinning bevel tip needle is addressed in this paper. A model aiming for tracking reference paths is developed for steering control of the needle. Instead of dividing the reference path into multiple target points, which render the path following task into multiple regulation problems, the whole path is considered in this model. Furthermore, the nonlinear model developed is transformed into a linear time varying (LTV) system in a polytopic paradigm and thus a RMPC is applied which deals with the parameter uncertainties and the control input constraints. The simulation results verify the effectiveness of the proposed path following RMPC algorithm.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"66 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":"128088905","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/IAI53119.2021.9619275
Xiaosong Li, Xinran Peng, Chao Ma, Tian Liu, Zenghua Li
The software development project implementation effect evaluation is important for assessing the software development. From the aspects of progress, cost and performance, the software development project implementation effect evaluation indicator was constructed, and the software development project implementation effect evaluation process based on support vector machine was proposed, and three support vector machine classifiers (excellent/qualified, excellent/unqualified, qualified/unqualified) were used to train and test the model with sample sets. The accuracy of the tested model was 92%, and a case analysis was carried out. The research conclusions provide effective methods for carrying out the implementation effect evaluation of software development projects.
{"title":"Research on Software Development Project Implementation Effect Evaluation Based on Support Vector Machine","authors":"Xiaosong Li, Xinran Peng, Chao Ma, Tian Liu, Zenghua Li","doi":"10.1109/IAI53119.2021.9619275","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619275","url":null,"abstract":"The software development project implementation effect evaluation is important for assessing the software development. From the aspects of progress, cost and performance, the software development project implementation effect evaluation indicator was constructed, and the software development project implementation effect evaluation process based on support vector machine was proposed, and three support vector machine classifiers (excellent/qualified, excellent/unqualified, qualified/unqualified) were used to train and test the model with sample sets. The accuracy of the tested model was 92%, and a case analysis was carried out. The research conclusions provide effective methods for carrying out the implementation effect evaluation of software development projects.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"20 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":"133213070","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}
Aircraft fuel system, which provides a continuous source of fuel to the engine, is an important component of the aircraft. Although the sequential fuel feed strategy for the symmetric arranged multi-tanks aircraft is widely used in today’s aircraft, the fuel feed strategy of the asymmetric arranged multi-tanks is still a challenge. In this article, a two layer offline approach is developed to obtain the fuel feed strategy to minimize the difference between the actual center of gravity (CG) and the desired CG. The performance of the proposed approach is tested in a case study based on the test data of aircraft pitch movement. The result indicates that the proposed approach solves the problem with an offline manner from the optimization perspective.
{"title":"Optimal Fuel Feed Strategy for Asymmetric Arranged Multi-tanks Aircraft","authors":"Haoyu Miao, Zikai Ouyang, Shunpeng Yang, Weichao Yan, Mengfan Cao, Shibo Chen, Zaiyue Yang","doi":"10.1109/IAI53119.2021.9619403","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619403","url":null,"abstract":"Aircraft fuel system, which provides a continuous source of fuel to the engine, is an important component of the aircraft. Although the sequential fuel feed strategy for the symmetric arranged multi-tanks aircraft is widely used in today’s aircraft, the fuel feed strategy of the asymmetric arranged multi-tanks is still a challenge. In this article, a two layer offline approach is developed to obtain the fuel feed strategy to minimize the difference between the actual center of gravity (CG) and the desired CG. The performance of the proposed approach is tested in a case study based on the test data of aircraft pitch movement. The result indicates that the proposed approach solves the problem with an offline manner from the optimization perspective.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 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":"130117245","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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