Pub Date : 2023-03-01DOI: 10.1109/PRMVIA58252.2023.00018
Yin Liu, Fangqiang Yu, Jinglin Xu, Peikang Xin
Indentify dangerous houses in rural areas isn’t very efficient, considering the large workload to visit the rural area, patchy and untimely manual document’s registration management. This study first uses UAV oblique photography technology to quickly obtain high-resolution aerial photographic images of villages and reconstruct three-dimensional reality models. Then, based on the YOLOv5 algorithm, the features of dangerous houses in aerial photography images are automatically detected, and the features of dangerous houses are mapped to the real 3D model to accurately locate the dangerous buildings. Finally, a digital management platform for rural dangerous houses is developed to support rural managers in identifying, measuring and tracking dangerous houses. The application results in a village along the coast of southern Fujian province showed that the accuracy rate of the final dangerous house screening rate of this method was 92%, and the coverage rate was 95%, which could greatly improve the efficiency, accuracy and coverage of dangerous house screening and reduce the workload of manual screening; and improve management efficiency through platform-based and visual methods.
{"title":"Identification of Dangerous Rural Houses Using Oblique Photogrammetry and Photo Recognition Technology","authors":"Yin Liu, Fangqiang Yu, Jinglin Xu, Peikang Xin","doi":"10.1109/PRMVIA58252.2023.00018","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00018","url":null,"abstract":"Indentify dangerous houses in rural areas isn’t very efficient, considering the large workload to visit the rural area, patchy and untimely manual document’s registration management. This study first uses UAV oblique photography technology to quickly obtain high-resolution aerial photographic images of villages and reconstruct three-dimensional reality models. Then, based on the YOLOv5 algorithm, the features of dangerous houses in aerial photography images are automatically detected, and the features of dangerous houses are mapped to the real 3D model to accurately locate the dangerous buildings. Finally, a digital management platform for rural dangerous houses is developed to support rural managers in identifying, measuring and tracking dangerous houses. The application results in a village along the coast of southern Fujian province showed that the accuracy rate of the final dangerous house screening rate of this method was 92%, and the coverage rate was 95%, which could greatly improve the efficiency, accuracy and coverage of dangerous house screening and reduce the workload of manual screening; and improve management efficiency through platform-based and visual methods.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122565982","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 : 2023-03-01DOI: 10.1109/prmvia58252.2023.00017
Jian-qi Li, Yincong Liang, Rui Du, Jingying Wan, Bin-fang Cao, Hui Liu
Aiming at the problem that the defects generated in the production and transportation of punched nickel-plated steel strips are not easy to be detected by deep learning methods, a lightweight, low-redundancy, and high-precision detection method is proposed in this paper. Firstly, a feature extraction network based on GhostNet is constructed, which reduces the amount of computation and feature redundancy while ensuring accuracy. Then the ECA module is applied to the detection head to perform weighted fusion of the features of different channels for better differentiation. Finally, the YOLO detection head is used for multi-scale detection. In the experiment, the mAP of 84.86% was obtained by this method, which proves that this method can be applied to the actual steel strip defect: detection.
{"title":"Lightweight defect detection method of punched nickel-plated steel strip based on GhostNet","authors":"Jian-qi Li, Yincong Liang, Rui Du, Jingying Wan, Bin-fang Cao, Hui Liu","doi":"10.1109/prmvia58252.2023.00017","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00017","url":null,"abstract":"Aiming at the problem that the defects generated in the production and transportation of punched nickel-plated steel strips are not easy to be detected by deep learning methods, a lightweight, low-redundancy, and high-precision detection method is proposed in this paper. Firstly, a feature extraction network based on GhostNet is constructed, which reduces the amount of computation and feature redundancy while ensuring accuracy. Then the ECA module is applied to the detection head to perform weighted fusion of the features of different channels for better differentiation. Finally, the YOLO detection head is used for multi-scale detection. In the experiment, the mAP of 84.86% was obtained by this method, which proves that this method can be applied to the actual steel strip defect: detection.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115539164","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 : 2023-03-01DOI: 10.1109/PRMVIA58252.2023.00011
Min Xiong, Wenming Cao, Jianqi Zhong
With the vigorous development of image classification technology in the field of computer vision, Few-shot learning (FSL) has become a research hotspot for solving classification task model training with a small number of samples. FSL aims to achieve efficient identification and processing of new category samples with few annotations. Previous works focus on information extraction based on one single model for FSL, lacking the distinction of the differences between data samples. Therefore, we present a meta-learning-based dual model with knowledge clustering for few-shot image classification, trying to learn the correlation between dual models and capture the information embedded in the data samples. In addition, we introduce the center loss to cluster the same sort of samples and to maximize the similarity among the intraclass and the difference among the inter-class. We adopt multiple tasks based on Meta-learning during the training stage. For each task, the training of dual models divides into two phases, which depend on each other under the guidance of the center loss. At the first phase, the first model is trained with a soft label obtained by the predicted label of the second model. The second phase repeats the information exchange of the first phase. We find that the optimal predictions of the active model are close to the soft and actual labels. Extensive experimental results on three general benchmarks illustrate the effectiveness of our proposed methods on few-shot classification tasks.
{"title":"Collaborative Learning-based Dual Network for Few-Shot Image Classification","authors":"Min Xiong, Wenming Cao, Jianqi Zhong","doi":"10.1109/PRMVIA58252.2023.00011","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00011","url":null,"abstract":"With the vigorous development of image classification technology in the field of computer vision, Few-shot learning (FSL) has become a research hotspot for solving classification task model training with a small number of samples. FSL aims to achieve efficient identification and processing of new category samples with few annotations. Previous works focus on information extraction based on one single model for FSL, lacking the distinction of the differences between data samples. Therefore, we present a meta-learning-based dual model with knowledge clustering for few-shot image classification, trying to learn the correlation between dual models and capture the information embedded in the data samples. In addition, we introduce the center loss to cluster the same sort of samples and to maximize the similarity among the intraclass and the difference among the inter-class. We adopt multiple tasks based on Meta-learning during the training stage. For each task, the training of dual models divides into two phases, which depend on each other under the guidance of the center loss. At the first phase, the first model is trained with a soft label obtained by the predicted label of the second model. The second phase repeats the information exchange of the first phase. We find that the optimal predictions of the active model are close to the soft and actual labels. Extensive experimental results on three general benchmarks illustrate the effectiveness of our proposed methods on few-shot classification tasks.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"339 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116472575","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 : 2023-03-01DOI: 10.1109/prmvia58252.2023.00048
Zhilin Zhang, Ting Zhang, Zhaoying Liu, Yujian Li
Fine-grained ship image recognition is to discriminate different subcategories of ship categories. Because of the lack of ship data sets and the particularity of the identification task, fine-grained ship recognition is a challenging task. We designed a part assignment module, which has the function of part assignment and extracting import part information. Then, we added the module to the SimCLR contrastive learning framework. This method uses the module to assignment the information in the feature map, extract the key information of key regions, increase the learning ability of contrast learning for key information, in the end, the accuracy of fine-grained classification can be improved.
{"title":"Contrastive Learning with Part Assignment for Fine-grained Ship Image Recognition","authors":"Zhilin Zhang, Ting Zhang, Zhaoying Liu, Yujian Li","doi":"10.1109/prmvia58252.2023.00048","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00048","url":null,"abstract":"Fine-grained ship image recognition is to discriminate different subcategories of ship categories. Because of the lack of ship data sets and the particularity of the identification task, fine-grained ship recognition is a challenging task. We designed a part assignment module, which has the function of part assignment and extracting import part information. Then, we added the module to the SimCLR contrastive learning framework. This method uses the module to assignment the information in the feature map, extract the key information of key regions, increase the learning ability of contrast learning for key information, in the end, the accuracy of fine-grained classification can be improved.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127820837","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 : 2023-03-01DOI: 10.1109/prmvia58252.2023.00015
Xin Xu, Haixia Pan, Hongqiang Wang, Yefan Cao
The driver-assistance system tends to fuse multi-modal sensor data, for instance, the infrared and RGB sensors, to detect intrusion objects to enhance driving safety. However, the semantic misalignment dilemma and the spectral imb-alance between infrared and RGB images make it hard to exp-loit the advantages of multi-sensors in the end-to-end learning system. To solve these problems, we employ the widely used affine transformation on our railway dataset to solve the se-mantic-misalignment issue, in addition, we propose a fusion module, DMF, to fuse the well-aligned features, which can bri-dge the domain gap among different sensors. To this end, we propose an efficient railway invasive object detection network, YOLOv5s-DMF. Compared with the state-of-the-art metho-ds, the YOLOv5s-DMF substantially reduces the MR by 14.23% by employing the well-established decouple head. And our YOLOv5s-DMF further increases the mAP@0.5 by 5.7% and the mAP@0.5:0.95by4.1%.
{"title":"Object Detection Algorithm for Railway Scenes Based on Infrared and RGB Image Fusion","authors":"Xin Xu, Haixia Pan, Hongqiang Wang, Yefan Cao","doi":"10.1109/prmvia58252.2023.00015","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00015","url":null,"abstract":"The driver-assistance system tends to fuse multi-modal sensor data, for instance, the infrared and RGB sensors, to detect intrusion objects to enhance driving safety. However, the semantic misalignment dilemma and the spectral imb-alance between infrared and RGB images make it hard to exp-loit the advantages of multi-sensors in the end-to-end learning system. To solve these problems, we employ the widely used affine transformation on our railway dataset to solve the se-mantic-misalignment issue, in addition, we propose a fusion module, DMF, to fuse the well-aligned features, which can bri-dge the domain gap among different sensors. To this end, we propose an efficient railway invasive object detection network, YOLOv5s-DMF. Compared with the state-of-the-art metho-ds, the YOLOv5s-DMF substantially reduces the MR by 14.23% by employing the well-established decouple head. And our YOLOv5s-DMF further increases the mAP@0.5 by 5.7% and the mAP@0.5:0.95by4.1%.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123444729","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 : 2023-03-01DOI: 10.1109/PRMVIA58252.2023.00031
Xi Zhang, W. Wang, Jing Chen
Dynamic tolling of toll roads is a way to dynamically adjust the toll rates according to the changing road traffic conditions in order to alleviate traffic congestion and improve commuting efficiency. Aiming at the dynamic toll collection problem of Chinese expressway, we design a reinforcement learning simulation environment for China’s expressway network and propose a reinforcement learning dynamic toll model based on a priori lane selection strategy that adapts to the characteristics of the network and travelers’ travel habits. Experiments show that the reinforcement learning-based dynamic tolling can increase the total revenue by more than 10% compared with the fixed- rate tolling scheme and keep the congestion rate at a low level. In addition, the ablation experiments demonstrate that the priori knowledge-based lane selection model can better weigh the "total revenue", "system throughput" and "total system travel time" of the optimized road network under the joint optimization objective
{"title":"A Priori Lane Selection Strategy for Reinforcement Learning of Dynamic Expressway Tolling","authors":"Xi Zhang, W. Wang, Jing Chen","doi":"10.1109/PRMVIA58252.2023.00031","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00031","url":null,"abstract":"Dynamic tolling of toll roads is a way to dynamically adjust the toll rates according to the changing road traffic conditions in order to alleviate traffic congestion and improve commuting efficiency. Aiming at the dynamic toll collection problem of Chinese expressway, we design a reinforcement learning simulation environment for China’s expressway network and propose a reinforcement learning dynamic toll model based on a priori lane selection strategy that adapts to the characteristics of the network and travelers’ travel habits. Experiments show that the reinforcement learning-based dynamic tolling can increase the total revenue by more than 10% compared with the fixed- rate tolling scheme and keep the congestion rate at a low level. In addition, the ablation experiments demonstrate that the priori knowledge-based lane selection model can better weigh the \"total revenue\", \"system throughput\" and \"total system travel time\" of the optimized road network under the joint optimization objective","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116653910","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 : 2023-03-01DOI: 10.1109/PRMVIA58252.2023.00008
Xiaosheng Wen, Ping Jian
Traditional Dense Captioning intends to describe local details of image with natural language. It usually uses target detection first and then describes the contents in the detected bounding box, which will make the description content rich. But captioning based on target detection often lacks the attention to the association between objects and the environment, or between the objects. And for now, there is no dense captioning method has the ability to deal with irregular areas. To solve these problems, we propose a visual-saliency based region division method. It focuses more on areas than just on objects. Based on the division, the local description of the irregular region is carried out. For each area, we combine the image with the target area to generate features, which are put into the caption model. We used the Visual Genome dataset for training and testing. Through experiments, our model is comparable to the baseline under the traditional bounding box. And the description of irregular region generated by our method is equally good. Our model performs well in image retrieval experiments and has less information redundancy. In the application, we support to manually select the region of interest on the image for description, for assist in expanding the dataset.
{"title":"Image Dense Captioning of Irregular Regions Based on Visual Saliency","authors":"Xiaosheng Wen, Ping Jian","doi":"10.1109/PRMVIA58252.2023.00008","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00008","url":null,"abstract":"Traditional Dense Captioning intends to describe local details of image with natural language. It usually uses target detection first and then describes the contents in the detected bounding box, which will make the description content rich. But captioning based on target detection often lacks the attention to the association between objects and the environment, or between the objects. And for now, there is no dense captioning method has the ability to deal with irregular areas. To solve these problems, we propose a visual-saliency based region division method. It focuses more on areas than just on objects. Based on the division, the local description of the irregular region is carried out. For each area, we combine the image with the target area to generate features, which are put into the caption model. We used the Visual Genome dataset for training and testing. Through experiments, our model is comparable to the baseline under the traditional bounding box. And the description of irregular region generated by our method is equally good. Our model performs well in image retrieval experiments and has less information redundancy. In the application, we support to manually select the region of interest on the image for description, for assist in expanding the dataset.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128857147","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 : 2023-03-01DOI: 10.1109/prmvia58252.2023.00055
Yunhao Pan, Chenhong Sui, Haipeng Wang, Hao Liu, Guobin Yang, Ao Wang, Q. Gong
Deep salient object detection has experienced noticeable progress. Unfortunately, most existing methods focus on clean samples regardless of the noise disturbance induced by human or natural factors. This results in the detection performance being extremely vulnerable to small perturbations. To this end, this paper proposes robust salient object detection via adversarial training (ATSOD). In specific, we introduce the classical DSS algorithm and inject it into an adversarial training framework favoring salient object detection. This ensures that, apart from clean samples, adversarial examples involving tiny disturbances are also explored for model training. Comparative experiments are conducted on five popular benchmarks. Experimental results show that despite the slight performance degradation for natural examples, there is a significant performance improvement for adversarial examples.
{"title":"Robust Salient Object Detection via Adversarial Training","authors":"Yunhao Pan, Chenhong Sui, Haipeng Wang, Hao Liu, Guobin Yang, Ao Wang, Q. Gong","doi":"10.1109/prmvia58252.2023.00055","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00055","url":null,"abstract":"Deep salient object detection has experienced noticeable progress. Unfortunately, most existing methods focus on clean samples regardless of the noise disturbance induced by human or natural factors. This results in the detection performance being extremely vulnerable to small perturbations. To this end, this paper proposes robust salient object detection via adversarial training (ATSOD). In specific, we introduce the classical DSS algorithm and inject it into an adversarial training framework favoring salient object detection. This ensures that, apart from clean samples, adversarial examples involving tiny disturbances are also explored for model training. Comparative experiments are conducted on five popular benchmarks. Experimental results show that despite the slight performance degradation for natural examples, there is a significant performance improvement for adversarial examples.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"os-16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127765750","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 : 2023-03-01DOI: 10.1109/PRMVIA58252.2023.00023
Yongkang Lan
A new real coding genetic algorithm is proposed, which discretizes the continuous feasible region and then makes it continuous and complete by mutation operator and local search operator, thus achieving the uniformity of the discretization and continuity of the genetic algorithm. By comparison with binary genetic algorithm, differential evolution algorithm (DE), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), and artificial bee colony algorithm (ABC), the results show that the proposed algorithm outperforms the others in all test functions. The algorithm is applied to the case of optimizing the weights of neural networks and excellent results are obtained, which validates the effectiveness of the algorithm.
{"title":"Binary-like Real Coding Genetic Algorithm","authors":"Yongkang Lan","doi":"10.1109/PRMVIA58252.2023.00023","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00023","url":null,"abstract":"A new real coding genetic algorithm is proposed, which discretizes the continuous feasible region and then makes it continuous and complete by mutation operator and local search operator, thus achieving the uniformity of the discretization and continuity of the genetic algorithm. By comparison with binary genetic algorithm, differential evolution algorithm (DE), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), and artificial bee colony algorithm (ABC), the results show that the proposed algorithm outperforms the others in all test functions. The algorithm is applied to the case of optimizing the weights of neural networks and excellent results are obtained, which validates the effectiveness of the algorithm.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115809572","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 : 2023-03-01DOI: 10.1109/prmvia58252.2023.00043
Ying Zhang, Di Peng, Gong Meng, Qian Zhao, Tiantian Li
This paper studies the fault diagnosis model of aeronautical multivariate heterogeneous input data. Because of the gyroscope’s powerful nonlinear mapping capabilities, it is a natural fit for modeling failure detection, this article combined with a variety of aviation gyro input data with fault monitoring methods, a model simulation method for multivariate heterogeneous input data in different states is proposed, which are one-dimensional and multi-dimensional data fault diagnosis in the standby state of the aircraft, and multi-sensor fault detection in the flight state or stationary state, which can effectively meet the needs of managing the fault diagnosis of multi-heterogeneous input of aviation.
{"title":"Simulation of Fault Diagnosis Model for Managing Aeronautical Multivariate Heterogeneous Inputs","authors":"Ying Zhang, Di Peng, Gong Meng, Qian Zhao, Tiantian Li","doi":"10.1109/prmvia58252.2023.00043","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00043","url":null,"abstract":"This paper studies the fault diagnosis model of aeronautical multivariate heterogeneous input data. Because of the gyroscope’s powerful nonlinear mapping capabilities, it is a natural fit for modeling failure detection, this article combined with a variety of aviation gyro input data with fault monitoring methods, a model simulation method for multivariate heterogeneous input data in different states is proposed, which are one-dimensional and multi-dimensional data fault diagnosis in the standby state of the aircraft, and multi-sensor fault detection in the flight state or stationary state, which can effectively meet the needs of managing the fault diagnosis of multi-heterogeneous input of aviation.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127704518","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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