Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164544
Xindi Wang, Bin Jiang, Lingfei Xiao, Leiming Ma
The transfer learning method performs better than conventional deep learning when dealing with the few-shot diagnosis situation where obtaining the true bearing defect signal is challenging. In order to leverage transfer learning to overcome the few-shot challenge of variable-condition bearing failure diagnosis, we propose the few-shot fault diagnosis approach based on enhanced meta-transfer learning. First, the network parameters are optimized based on a meta-learner. Second, a meta-learning-based transfer network model is constructed, combined with domain-adaptive methods to obtain a meta-learner with strong generalization ability. Meanwhile, the channel attention module is applied to the feature layer to strengthen the model’s feature expression ability. The proposed method Take advantage of the limited fault feature on small-sample data, while avoiding overfitting and improving the generalization ability. The performance of the proposed approach is verified on the fault data from the low-speed dynamic balance test bench. The consequences indicate that the diagnosis approach based on meta-transfer learning can accurately classify the bearing failures under variable conditions. Contrasted to other approaches, the proposed approaches possess better accuracy and generalization capability.
{"title":"Enhanced Meta-Transfer Learning for Few-Shot Fault Diagnosis of Bearings with Variable Conditions","authors":"Xindi Wang, Bin Jiang, Lingfei Xiao, Leiming Ma","doi":"10.1109/ISAS59543.2023.10164544","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164544","url":null,"abstract":"The transfer learning method performs better than conventional deep learning when dealing with the few-shot diagnosis situation where obtaining the true bearing defect signal is challenging. In order to leverage transfer learning to overcome the few-shot challenge of variable-condition bearing failure diagnosis, we propose the few-shot fault diagnosis approach based on enhanced meta-transfer learning. First, the network parameters are optimized based on a meta-learner. Second, a meta-learning-based transfer network model is constructed, combined with domain-adaptive methods to obtain a meta-learner with strong generalization ability. Meanwhile, the channel attention module is applied to the feature layer to strengthen the model’s feature expression ability. The proposed method Take advantage of the limited fault feature on small-sample data, while avoiding overfitting and improving the generalization ability. The performance of the proposed approach is verified on the fault data from the low-speed dynamic balance test bench. The consequences indicate that the diagnosis approach based on meta-transfer learning can accurately classify the bearing failures under variable conditions. Contrasted to other approaches, the proposed approaches possess better accuracy and generalization capability.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"501 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127719578","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}
Autonomous landing for unmanned seaplanes in complex sea conditions has been a challenge for along time. Seaplanes have become one of the key areas of development in the design and use of our aircraft due to their unique usage characteristics. This paper presents a new autonomous landing system for unmanned seaplanes based on Active Disturbance Rejection Control (ADRC). The controller is divided into longitudinal speed control subsystem and attitude control subsystem, the speed control subsystem is composed of the ADRC control and throttle switch modules, and the attitude subsystem is composed of the pitch angle ADRC controller and the altitude PID controller. Simulations are performed in irregular waves. Simulation results show that the proposed control system can successfully land the unmanned seaplane with satisfactory performance.
{"title":"Autonomous Landing for Unmanned Seaplanes","authors":"Shiwang Song, Xinhua Wang, Fukang Zhao, Guoyao Huan","doi":"10.1109/ISAS59543.2023.10164624","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164624","url":null,"abstract":"Autonomous landing for unmanned seaplanes in complex sea conditions has been a challenge for along time. Seaplanes have become one of the key areas of development in the design and use of our aircraft due to their unique usage characteristics. This paper presents a new autonomous landing system for unmanned seaplanes based on Active Disturbance Rejection Control (ADRC). The controller is divided into longitudinal speed control subsystem and attitude control subsystem, the speed control subsystem is composed of the ADRC control and throttle switch modules, and the attitude subsystem is composed of the pitch angle ADRC controller and the altitude PID controller. Simulations are performed in irregular waves. Simulation results show that the proposed control system can successfully land the unmanned seaplane with satisfactory performance.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128101574","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 : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164486
Sun Xingjun
At present, the aircraft fault decision-making function only deals with a single fault, but the aircraft fault has concurrency. The existing aircraft fault decision-making function lacks the ability to deal with multiple fault concurrence situations. How to trace the source of multiple fault alarm information and excavate the original fault is of great significance for simplifying the alarm display and improving the pilot’s fault handling efficiency. In this paper, a fault diagnosis and comprehensive suppression function is designed, which consists of a fast fault location method based on prior knowledge and a comprehensive diagnosis and suppression method based on fault tree knowledge. The fast fault location method based on prior knowledge is based on case reasoning, which writes the past troubleshooting cases and many elements into the fault case base. When new faults occur, the matching degree of similar cases in the case base is obtained through retrieval model, so as to quickly obtain the current fault processing method. The comprehensive diagnosis method based on fault tree knowledge converts the fault tree into a binary decision diagram, and uses Huffman coding to realize computer programming. The probability of each cut set event in the binary decision graph is the probability product of its contained bottom event, so as to determine the risk degree of the failure to locate the cause of the failure. The fault sup-pression method classifies and processes the alarm information when multiple faults occur in a single system and multiple faults occur in multiple systems. The original fault and derivative fault are filtered by using the fault correlation value, the original fault is displayed, and the corresponding derivative fault is suppressed. The fault diagnosis and comprehensive suppression function of the aircraft airborne system designed in this paper provides sup-port for the development of the large aircraft alarm system.
{"title":"Research on fault diagnosis and comprehensive suppression function of airborne system","authors":"Sun Xingjun","doi":"10.1109/ISAS59543.2023.10164486","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164486","url":null,"abstract":"At present, the aircraft fault decision-making function only deals with a single fault, but the aircraft fault has concurrency. The existing aircraft fault decision-making function lacks the ability to deal with multiple fault concurrence situations. How to trace the source of multiple fault alarm information and excavate the original fault is of great significance for simplifying the alarm display and improving the pilot’s fault handling efficiency. In this paper, a fault diagnosis and comprehensive suppression function is designed, which consists of a fast fault location method based on prior knowledge and a comprehensive diagnosis and suppression method based on fault tree knowledge. The fast fault location method based on prior knowledge is based on case reasoning, which writes the past troubleshooting cases and many elements into the fault case base. When new faults occur, the matching degree of similar cases in the case base is obtained through retrieval model, so as to quickly obtain the current fault processing method. The comprehensive diagnosis method based on fault tree knowledge converts the fault tree into a binary decision diagram, and uses Huffman coding to realize computer programming. The probability of each cut set event in the binary decision graph is the probability product of its contained bottom event, so as to determine the risk degree of the failure to locate the cause of the failure. The fault sup-pression method classifies and processes the alarm information when multiple faults occur in a single system and multiple faults occur in multiple systems. The original fault and derivative fault are filtered by using the fault correlation value, the original fault is displayed, and the corresponding derivative fault is suppressed. The fault diagnosis and comprehensive suppression function of the aircraft airborne system designed in this paper provides sup-port for the development of the large aircraft alarm system.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125800150","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 : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164435
Chen Yuquan, Wang Fumian, Cheng Songsong, Du Bin, Wang Bing
Different from existing protocols for achieving fixed-time convergence, a novel fixed-time protocol with an adaptive gain is proposed for solving the distributed optimization problem over networks. Based on the zero-gradient-sum straetgy, the problem will reduced to the fixed-time consensus problem and the convergence time is determined by the frequency of a sine function. Besides, some comments on the practical implementation are also given and it is found that the proposed protocol maintains strong robustness to input saturations. Two illustrative examples are finally provided to validate all the mentioned results.
{"title":"A Novel Protocol for Fixed-Time Distributed Optimization Over Networks Based on Zero-Gradient-Sum Strategy","authors":"Chen Yuquan, Wang Fumian, Cheng Songsong, Du Bin, Wang Bing","doi":"10.1109/ISAS59543.2023.10164435","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164435","url":null,"abstract":"Different from existing protocols for achieving fixed-time convergence, a novel fixed-time protocol with an adaptive gain is proposed for solving the distributed optimization problem over networks. Based on the zero-gradient-sum straetgy, the problem will reduced to the fixed-time consensus problem and the convergence time is determined by the frequency of a sine function. Besides, some comments on the practical implementation are also given and it is found that the proposed protocol maintains strong robustness to input saturations. Two illustrative examples are finally provided to validate all the mentioned results.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124827023","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 : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164579
Bing Sun, Wei Zhang, Zinan Su, Hongyi Wang
With the growing interest in ocean exploration, accurately tracking underwater targets has become increasingly important for resource exploitation and environmental protection. This paper explores the application of deep learning algorithms for multi-target tracking in underwater environments. The challenges of image processing in this context are discussed, and the YOLOv3 target detection algorithm is utilized to train a real-time underwater target tracking model with image enhancement techniques. Furthermore, the advantages and disadvantages of the YOLOv3 and PP-YOLO algorithms are compared by training the PP-YOLO model in the cloud. This study contributes to the development of more efficient and reliable methods for underwater target tracking.
{"title":"Real-time Underwater Target Tracking Using PP-YOLO and Cloud Computing","authors":"Bing Sun, Wei Zhang, Zinan Su, Hongyi Wang","doi":"10.1109/ISAS59543.2023.10164579","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164579","url":null,"abstract":"With the growing interest in ocean exploration, accurately tracking underwater targets has become increasingly important for resource exploitation and environmental protection. This paper explores the application of deep learning algorithms for multi-target tracking in underwater environments. The challenges of image processing in this context are discussed, and the YOLOv3 target detection algorithm is utilized to train a real-time underwater target tracking model with image enhancement techniques. Furthermore, the advantages and disadvantages of the YOLOv3 and PP-YOLO algorithms are compared by training the PP-YOLO model in the cloud. This study contributes to the development of more efficient and reliable methods for underwater target tracking.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125668652","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 : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164339
C. Wan, Xunhong Lv, Zehui Mao, Zhiwei Wang, Yunrui Li, Chengang Ni
When an unmanned surface vehicle (USV) equiped with a LiDAR conducts obstacle detection, the swaying of the hull and the water splashes generated during navigation can cause disturbance and deviation in the scanned point cloud data, resulting in an increased rate of missed detection of static obstacles such as reefs and trees. This paper proposes an online obstacle detection algorithm for USV based on an improved Random Sample Consensus (RANSAC) algorithm. To address the large amount of point cloud data generated during the USV’s navigation process, a point cloud preprocessing based on voxel filtering is proposed to achieve denoising and compression of the original point cloud data while retaining its features. Considering that ground point cloud data will be disturbed during USV navigation, a RANSAC-based improved algorithm based on the grid projection method is designed, and ground segmentation is performed based on the results of static obstacle classification to generate a grid map. Clustering processing is performed using the grid clustering algorithm to obtain the detected obstacles and mark their location and size using bounding boxes. Finally, a trial run is conducted on a USV equipped with LiDAR, and the experimental results show that the proposed improved algorithm can reduce the missed detection rate and meet the real-time requirements of the algorithm, effectively improving the detection rate of nearby static obstacles.
{"title":"Online Obstacle Detection for USV based on Improved RANSAC Algorithm","authors":"C. Wan, Xunhong Lv, Zehui Mao, Zhiwei Wang, Yunrui Li, Chengang Ni","doi":"10.1109/ISAS59543.2023.10164339","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164339","url":null,"abstract":"When an unmanned surface vehicle (USV) equiped with a LiDAR conducts obstacle detection, the swaying of the hull and the water splashes generated during navigation can cause disturbance and deviation in the scanned point cloud data, resulting in an increased rate of missed detection of static obstacles such as reefs and trees. This paper proposes an online obstacle detection algorithm for USV based on an improved Random Sample Consensus (RANSAC) algorithm. To address the large amount of point cloud data generated during the USV’s navigation process, a point cloud preprocessing based on voxel filtering is proposed to achieve denoising and compression of the original point cloud data while retaining its features. Considering that ground point cloud data will be disturbed during USV navigation, a RANSAC-based improved algorithm based on the grid projection method is designed, and ground segmentation is performed based on the results of static obstacle classification to generate a grid map. Clustering processing is performed using the grid clustering algorithm to obtain the detected obstacles and mark their location and size using bounding boxes. Finally, a trial run is conducted on a USV equipped with LiDAR, and the experimental results show that the proposed improved algorithm can reduce the missed detection rate and meet the real-time requirements of the algorithm, effectively improving the detection rate of nearby static obstacles.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116520453","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 : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164521
Jiwei Du, Kun Yan, Song Gao, Chaobo Chen, Dongbin Zhao, Haidong Shen
In this paper, an extended state observer (ESO)-based sliding mode tracking control method is designed for the quadrotor unmanned aerial vehicle (UAV) with external disturbances and actuator faults. Firstly, the nonlinear model of the quadrotor UAV is established. Then the ESO is constructed to tackle the unknown differentiable disturbances and the sliding mode control technique is combined with adaptive estimation to address the unknown nondifferentiable actuator faults, respectively. Finally, a robust fault-tolerant tracking control method is proposed to make sure that all closed-loop system errors are uniformly ultimate bounded via Lyapunov stability analysis, and the efficiency of the proposed approach is confirmed by the numerical simulation.
{"title":"Robust Trajectory Tracking Control for Unmanned Aerial Vehicle with Actuator Faults","authors":"Jiwei Du, Kun Yan, Song Gao, Chaobo Chen, Dongbin Zhao, Haidong Shen","doi":"10.1109/ISAS59543.2023.10164521","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164521","url":null,"abstract":"In this paper, an extended state observer (ESO)-based sliding mode tracking control method is designed for the quadrotor unmanned aerial vehicle (UAV) with external disturbances and actuator faults. Firstly, the nonlinear model of the quadrotor UAV is established. Then the ESO is constructed to tackle the unknown differentiable disturbances and the sliding mode control technique is combined with adaptive estimation to address the unknown nondifferentiable actuator faults, respectively. Finally, a robust fault-tolerant tracking control method is proposed to make sure that all closed-loop system errors are uniformly ultimate bounded via Lyapunov stability analysis, and the efficiency of the proposed approach is confirmed by the numerical simulation.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129770774","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 : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164461
Chao Ren, Bin Jiang, N. Lu
Few-shot fault diagnosis is a challenging issue in manufacturing area, which rely on knowledge learned from historical data and limited data in new work condition. Nevertheless, the unbalanced distribution in historical working condition data and the distribution discrepancy between the finite small data and historical data lead to the poor generalization and low reliability of few-shot model. This study proposes a task adaptation meta learning framework. First, target domain is selected from historical working condition by relative entropy. Then, domain-adversarial training of neural networks is applied in historical samples for data distribution alignment to make tasks easy to learn. Finally, the fault diagnosis model trained with gradient based meta learning is adapted to new condition quickly with few data. On the Bearing Dataset under time-varying rotational speed conditions, the proposed framework has a good performance compared with the state-of-art method.
{"title":"Task Adaptation Meta Learning for Few-Shot Fault Diagnosis under Multiple Working Conditions","authors":"Chao Ren, Bin Jiang, N. Lu","doi":"10.1109/ISAS59543.2023.10164461","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164461","url":null,"abstract":"Few-shot fault diagnosis is a challenging issue in manufacturing area, which rely on knowledge learned from historical data and limited data in new work condition. Nevertheless, the unbalanced distribution in historical working condition data and the distribution discrepancy between the finite small data and historical data lead to the poor generalization and low reliability of few-shot model. This study proposes a task adaptation meta learning framework. First, target domain is selected from historical working condition by relative entropy. Then, domain-adversarial training of neural networks is applied in historical samples for data distribution alignment to make tasks easy to learn. Finally, the fault diagnosis model trained with gradient based meta learning is adapted to new condition quickly with few data. On the Bearing Dataset under time-varying rotational speed conditions, the proposed framework has a good performance compared with the state-of-art method.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132771631","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 : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164406
Ruotong Qu, B. Jiang, Yuehua Cheng
This paper presents a novel quantitative evaluation method for fault diagnosability, which is independent of specific fault diagnosis schemes. The results of detectability and separability of faults can be obtained by analyzing system models, providing theoretical guidance and reference for fault diagnosis design in engineering. Firstly, the fault diagnosability evaluation problem of dynamic system described by state space is transformed into the distance determination problem of multivariate distribution in statistics. Then, diagnosability quantitative evaluation indexes based on Fisher information distance are designed, the proposed method and index are used to realize the quantitative evaluation of UAV fault diagnosability, and the effectiveness is verified by digital simulation. Finally, the geodesic of fault manifold is studied, which is used as a supplement of the index proposed in this paper, helping to obtain stable and comprehensive fault diagnosability determination, and the visual results of fault diagnosability and fault development process are shown.
{"title":"Research of System Diagnosability on Fault Information Manifold","authors":"Ruotong Qu, B. Jiang, Yuehua Cheng","doi":"10.1109/ISAS59543.2023.10164406","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164406","url":null,"abstract":"This paper presents a novel quantitative evaluation method for fault diagnosability, which is independent of specific fault diagnosis schemes. The results of detectability and separability of faults can be obtained by analyzing system models, providing theoretical guidance and reference for fault diagnosis design in engineering. Firstly, the fault diagnosability evaluation problem of dynamic system described by state space is transformed into the distance determination problem of multivariate distribution in statistics. Then, diagnosability quantitative evaluation indexes based on Fisher information distance are designed, the proposed method and index are used to realize the quantitative evaluation of UAV fault diagnosability, and the effectiveness is verified by digital simulation. Finally, the geodesic of fault manifold is studied, which is used as a supplement of the index proposed in this paper, helping to obtain stable and comprehensive fault diagnosability determination, and the visual results of fault diagnosability and fault development process are shown.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":" September","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131976822","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 : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164320
Lianxu Hao, Chunxi Yang, Xincai Li
Soi1 temperature in the tillage layer has a significant impact on crop growth, so the accurate prediction of its change trend can help intelligent agricultural systems to make autonomous decisions and ensure the normal growth of plants. In this paper, an accurate prediction model of soil temperature in the tillage layer is established based on PSO-LSTM. First, the particle swarm optimization algorithm is used to optimize the key parameters of the LSTM model, which effectively improves the model performance. Then, kriging interpolation is used to estimate the soil temperature distribution in the tillage layer, and uneven distribution results are obtained. Finally, an experiment is conducted with the soil data actually collected from the Panax notoginseng cultivation layer. The results show that the proposed soil temperature prediction model in this paper has higher accuracy, which can achieve accurate prediction of soil temperature and effectively guide the intelligent agricultural system to make autonomous decisions on soil temperature.
{"title":"Prediction of Soil Temperature Field in Panax Notoginseng Plough Layer Based on PSO-LSTM Neural Network","authors":"Lianxu Hao, Chunxi Yang, Xincai Li","doi":"10.1109/ISAS59543.2023.10164320","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164320","url":null,"abstract":"Soi1 temperature in the tillage layer has a significant impact on crop growth, so the accurate prediction of its change trend can help intelligent agricultural systems to make autonomous decisions and ensure the normal growth of plants. In this paper, an accurate prediction model of soil temperature in the tillage layer is established based on PSO-LSTM. First, the particle swarm optimization algorithm is used to optimize the key parameters of the LSTM model, which effectively improves the model performance. Then, kriging interpolation is used to estimate the soil temperature distribution in the tillage layer, and uneven distribution results are obtained. Finally, an experiment is conducted with the soil data actually collected from the Panax notoginseng cultivation layer. The results show that the proposed soil temperature prediction model in this paper has higher accuracy, which can achieve accurate prediction of soil temperature and effectively guide the intelligent agricultural system to make autonomous decisions on soil temperature.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116264926","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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