Combinatorial action recognition has recently attracted the attention of researchers in the field of computer vision. It focuses on the effective representation and discrimination of spatio-temporal interactions occurring between different actions and objects in video data. Existing work tends to strengthen the framework's object recognition capabilities and relationship modeling capabilities, e.g., attention mechanisms, and graph structures. We find that existing algorithms can be influenced by interaction-independent video segments in a video, misleading the algorithm to focus on additional information in the vision. For the algorithm to analyze the spatio-temporal interactions of causally related video segments in a video, a Causal Slice Recognition Network (CSRN) is proposed. This method can effectively remove the interference of video background segments by explicitly recognizing and extracting the causally related segments in the video. We validate the method on the Something-else dataset and obtain the best results.
{"title":"Combinatorial action recognition based on causal segment intervention","authors":"Xiaozhou Sun","doi":"10.1117/12.3014465","DOIUrl":"https://doi.org/10.1117/12.3014465","url":null,"abstract":"Combinatorial action recognition has recently attracted the attention of researchers in the field of computer vision. It focuses on the effective representation and discrimination of spatio-temporal interactions occurring between different actions and objects in video data. Existing work tends to strengthen the framework's object recognition capabilities and relationship modeling capabilities, e.g., attention mechanisms, and graph structures. We find that existing algorithms can be influenced by interaction-independent video segments in a video, misleading the algorithm to focus on additional information in the vision. For the algorithm to analyze the spatio-temporal interactions of causally related video segments in a video, a Causal Slice Recognition Network (CSRN) is proposed. This method can effectively remove the interference of video background segments by explicitly recognizing and extracting the causally related segments in the video. We validate the method on the Something-else dataset and obtain the best results.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511858","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}
Jinwei Cheng, Jie Yuan, Xiaoning Hu, Baorong Xie, Junrui Wang
In view of the fault and leak detection problems caused by complex scenes of offshore area in remote sensing image ship detection, a lightweight ship classification detection method is proposed based on improved YOLOv7-tiny. On the one hand, this method stacks a lightweight feature extraction module and applies it to the backbone feature extraction network, which significantly reduces the parameter and computational complexity and does not weaken the network's ability of feature extraction. On the other hand, this method introduces spatial information into the feature pyramid, raising the discrimination of features at different scales, to improve the classification and detection ability of the network. This method has been tested on the remote sensing image ship data set. The experimental results show that the average accuracy of ship classification detection based on the improved network is increased by 2.9%. Meanwhile, the parameter quantity and computational complexity are better than YOLOv7-tiny, with a 15% reduction in parameter quantity and a 24% reduction in computational complexity.
{"title":"The ship classification and detection method of optical remote sensing image based on improved YOLOv7-tiny","authors":"Jinwei Cheng, Jie Yuan, Xiaoning Hu, Baorong Xie, Junrui Wang","doi":"10.1117/12.3014371","DOIUrl":"https://doi.org/10.1117/12.3014371","url":null,"abstract":"In view of the fault and leak detection problems caused by complex scenes of offshore area in remote sensing image ship detection, a lightweight ship classification detection method is proposed based on improved YOLOv7-tiny. On the one hand, this method stacks a lightweight feature extraction module and applies it to the backbone feature extraction network, which significantly reduces the parameter and computational complexity and does not weaken the network's ability of feature extraction. On the other hand, this method introduces spatial information into the feature pyramid, raising the discrimination of features at different scales, to improve the classification and detection ability of the network. This method has been tested on the remote sensing image ship data set. The experimental results show that the average accuracy of ship classification detection based on the improved network is increased by 2.9%. Meanwhile, the parameter quantity and computational complexity are better than YOLOv7-tiny, with a 15% reduction in parameter quantity and a 24% reduction in computational complexity.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640382","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}
In view of the problems such as unclear target direction, low search efficiency, and slow convergence speed of the basic ant colony algorithm in AGV two-dimensional path planning, an improved ant colony algorithm based on artificial gravity field and triangle pruning method is proposed. The algorithm first uses the attractive strength provided by the gravity field to construct heuristic information, enhancing the guidance of the target point on the planning direction and improving the directionality and search efficiency. Then, based on the concentration enhancement mechanism of the elite ant model's pheromone, an adaptive reward update mechanism for increments is proposed to improve the convergence speed. Next, an adaptive adjustment mechanism of the pheromone heuristic factor value correlated with the iteration number is discussed to balance the randomness and search efficiency of the entire planning process. Finally, the triangle pruning method is applied to global path optimization based on global path planning, effectively reducing the number of turning nodes and improving the actual motion efficiency. Comparative experiments on path planning in two-dimensional static maps using matlab validate the effectiveness of the improved algorithm in AGV global dynamic path planning.
{"title":"Improved ant colony algorithm based on artificial gravity field for adaptive dynamic path planning","authors":"Shuo Wang, Lutao Yan, Haiyuan Li, Jian Li","doi":"10.1117/12.3014563","DOIUrl":"https://doi.org/10.1117/12.3014563","url":null,"abstract":"In view of the problems such as unclear target direction, low search efficiency, and slow convergence speed of the basic ant colony algorithm in AGV two-dimensional path planning, an improved ant colony algorithm based on artificial gravity field and triangle pruning method is proposed. The algorithm first uses the attractive strength provided by the gravity field to construct heuristic information, enhancing the guidance of the target point on the planning direction and improving the directionality and search efficiency. Then, based on the concentration enhancement mechanism of the elite ant model's pheromone, an adaptive reward update mechanism for increments is proposed to improve the convergence speed. Next, an adaptive adjustment mechanism of the pheromone heuristic factor value correlated with the iteration number is discussed to balance the randomness and search efficiency of the entire planning process. Finally, the triangle pruning method is applied to global path optimization based on global path planning, effectively reducing the number of turning nodes and improving the actual motion efficiency. Comparative experiments on path planning in two-dimensional static maps using matlab validate the effectiveness of the improved algorithm in AGV global dynamic path planning.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640392","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}
In order to ensure the power supply reliability of the power system, most of the power line maintenance operations use live work to complete wire breaking, wiring, replacement of fall insurance and other work. The distribution network live working robot can keep the operator away from the dangerous environment, ensure the safety of personnel, reduce the labor intensity of the operator, improve the work efficiency of live working, and has a broad application prospect. In order to work safety, more and more power technicians use the way of teleoperation robot arm to carry out live work. In this paper, a virtual reality synchronization system combined with binocular cameras is proposed. By reconstructing the live working environment in the virtual scene and combining with the remote operation mode of the mechanical arm, the system provides a new working mode for the electric power operators, so that they are no longer limited by the field of vision, and thus can complete the live working task more flexibly.
{"title":"Development and application of virtual synchronization system for live working robots based on binocular vision","authors":"JunHui Yan, JianFang Shen","doi":"10.1117/12.3014626","DOIUrl":"https://doi.org/10.1117/12.3014626","url":null,"abstract":"In order to ensure the power supply reliability of the power system, most of the power line maintenance operations use live work to complete wire breaking, wiring, replacement of fall insurance and other work. The distribution network live working robot can keep the operator away from the dangerous environment, ensure the safety of personnel, reduce the labor intensity of the operator, improve the work efficiency of live working, and has a broad application prospect. In order to work safety, more and more power technicians use the way of teleoperation robot arm to carry out live work. In this paper, a virtual reality synchronization system combined with binocular cameras is proposed. By reconstructing the live working environment in the virtual scene and combining with the remote operation mode of the mechanical arm, the system provides a new working mode for the electric power operators, so that they are no longer limited by the field of vision, and thus can complete the live working task more flexibly.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640408","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}
The rule engine is an important part of the industry-education integrated Internet of Things teaching platform, and it is the basis for realizing the dynamic configuration of business rules in the practical teaching function. Combined with the data characteristics of the Internet of Things application scenario, this paper proposes a rule engine optimization algorithm based on Rete, and designs a pre-sorting algorithm based on rule frequency, which pre-sorts the order of nodes according to the frequency of use of rule patterns, with priority Match frequently used patterns, increases the sharing rate of nodes, and reduce the memory usage of the inference network. Through experimental simulation, the improved algorithm is verified, and the experimental results prove the effectiveness of the algorithm.
{"title":"Research on rule engine optimization algorithm in internet of things teaching platform","authors":"JianZhong Li, Qiang Wan, ZhiQiang Zhang","doi":"10.1117/12.3014592","DOIUrl":"https://doi.org/10.1117/12.3014592","url":null,"abstract":"The rule engine is an important part of the industry-education integrated Internet of Things teaching platform, and it is the basis for realizing the dynamic configuration of business rules in the practical teaching function. Combined with the data characteristics of the Internet of Things application scenario, this paper proposes a rule engine optimization algorithm based on Rete, and designs a pre-sorting algorithm based on rule frequency, which pre-sorts the order of nodes according to the frequency of use of rule patterns, with priority Match frequently used patterns, increases the sharing rate of nodes, and reduce the memory usage of the inference network. Through experimental simulation, the improved algorithm is verified, and the experimental results prove the effectiveness of the algorithm.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511477","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}
3D reconstruction is 3 d reconstruction in aerial reconstruction, industrial measurement, medical image reconstruction, cultural relics preservation and restoration, virtual reality and other fields. However, the traditional 3 D reconstruction algorithm will have a poor reconstruction effect due to the transient foreground influence and the limitation of feature point identification in the scene. To solve the above problems, this paper uses LoFT R-SIFT algorithm to extract the feature points in weak texture area, increase the number of feature points matching in weak texture area, then introduces ExtremeC3Net algorithm to eliminate the feature points on the dynamic portrait in the scene; Finally, DPT improves the MVS algorithm to make deep compensation. The experimental results prove that the feature point matching accuracy of the algorithm is improved by 55%, which can better capture the details of the scene
{"title":"Research on 3D reconstruction algorithms for small scenes with weak texture","authors":"Changrui Nai","doi":"10.1117/12.3014370","DOIUrl":"https://doi.org/10.1117/12.3014370","url":null,"abstract":"3D reconstruction is 3 d reconstruction in aerial reconstruction, industrial measurement, medical image reconstruction, cultural relics preservation and restoration, virtual reality and other fields. However, the traditional 3 D reconstruction algorithm will have a poor reconstruction effect due to the transient foreground influence and the limitation of feature point identification in the scene. To solve the above problems, this paper uses LoFT R-SIFT algorithm to extract the feature points in weak texture area, increase the number of feature points matching in weak texture area, then introduces ExtremeC3Net algorithm to eliminate the feature points on the dynamic portrait in the scene; Finally, DPT improves the MVS algorithm to make deep compensation. The experimental results prove that the feature point matching accuracy of the algorithm is improved by 55%, which can better capture the details of the scene","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511517","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}
To address the low efficiency of manual inspection methods used for Citrus Huanglongbing prevention and control, a system design of citrus huanglongbing in-field detection with AI edge computing device is proposed and evaluated. The system consist of Image Capture Robotic Devices, AI Edge Computing Service, Cloud Service, and Remote Control Client. A citrus Huanglongbing detection neural network model was trained with 84.1%mAP, which can be deployed on an AI edge computing device, such as Jetson Nano to detect HLB with lower delay than using a cloud-based AI approach. Therefore, robotic devices such as UAVs, surveillance cameras can be used to efficiently inspect citrus orchard, process images of citrus leaves collected from cameras in real-time. Experimental result shows that this system has great potential to apply on Citrus Huanglongbing field detection scenario to enhance the inspection efficiency of citrus orchards.
{"title":"Towards Huanglongbing In-field detection system with AI edge computing","authors":"Xuefeng Rao, Quanyou Zhao, Dingming Huang","doi":"10.1117/12.3014425","DOIUrl":"https://doi.org/10.1117/12.3014425","url":null,"abstract":"To address the low efficiency of manual inspection methods used for Citrus Huanglongbing prevention and control, a system design of citrus huanglongbing in-field detection with AI edge computing device is proposed and evaluated. The system consist of Image Capture Robotic Devices, AI Edge Computing Service, Cloud Service, and Remote Control Client. A citrus Huanglongbing detection neural network model was trained with 84.1%mAP, which can be deployed on an AI edge computing device, such as Jetson Nano to detect HLB with lower delay than using a cloud-based AI approach. Therefore, robotic devices such as UAVs, surveillance cameras can be used to efficiently inspect citrus orchard, process images of citrus leaves collected from cameras in real-time. Experimental result shows that this system has great potential to apply on Citrus Huanglongbing field detection scenario to enhance the inspection efficiency of citrus orchards.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511429","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}
Looking at the traditional interior space design industry, the traditional design method is mainly manual design and the use of interactive modeling software and its design process mainly relies on trial and error. This paper takes the interior space design software platform as the background to study the collocation recommendation algorithm of the 3D home model, aim at improve the efficiency of the intelligent design algorithm. The recommendation idea of collaborative filtering is simple to implement, does not need to consider the inherent attribute characteristics of three-dimensional home projects, and is fast to calculate. After constructing the image feature database, this article uses the similarity between images to measure the visual similarity of the indoor space model; uses similar home projects to predict the collocation data of adjacent projects, and densifies the sparse collocation data; constructs each image separately Feature database, and use this to build its similarity table. According to the similarity table corresponding to each item, the first simNum items of the same category that are similar to the current item can be found. The experimental results show that compared with the traditional algorithm, the algorithm in this paper has greatly improved the accuracy of collocation recommendation.
{"title":"Research on intelligent design algorithm of indoor space based on hybrid recommendation model","authors":"Huaxue He","doi":"10.1117/12.3014657","DOIUrl":"https://doi.org/10.1117/12.3014657","url":null,"abstract":"Looking at the traditional interior space design industry, the traditional design method is mainly manual design and the use of interactive modeling software and its design process mainly relies on trial and error. This paper takes the interior space design software platform as the background to study the collocation recommendation algorithm of the 3D home model, aim at improve the efficiency of the intelligent design algorithm. The recommendation idea of collaborative filtering is simple to implement, does not need to consider the inherent attribute characteristics of three-dimensional home projects, and is fast to calculate. After constructing the image feature database, this article uses the similarity between images to measure the visual similarity of the indoor space model; uses similar home projects to predict the collocation data of adjacent projects, and densifies the sparse collocation data; constructs each image separately Feature database, and use this to build its similarity table. According to the similarity table corresponding to each item, the first simNum items of the same category that are similar to the current item can be found. The experimental results show that compared with the traditional algorithm, the algorithm in this paper has greatly improved the accuracy of collocation recommendation.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511380","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}
The rapid development of artificial intelligence facilitates the improvement of image processing algorithms. For an intelligent inspection robot, the ability to analyze the environment through image collection plays an important role. It needs to collect multiple images of the same scene from different angles of view so as to make a thorough analysis about the environment it locates and generate further decisions. Therefore, a technique called image stitching is used. Currently, the development of image stitching algorithms is getting mature – multiple algorithms have already been proposed based feature extraction techniques. However, these existing algorithms are usually unable to handle the problem of parallax existing in real world image. Therefore, in order to solve it, we proposed an unsupervised deep learning image stitching algorithm, which uses infrared images to provide auxiliary information. We utilized our own equipment to collect real world images in visible light and infrared. Finally, we implemented our own model and other popular existing image stitching algorithms and compared and contrasted their performance on our dataset. The results showed that our model has the best performance in all aspects than other algorithms on the dataset, indicating the strong advantages of deep learning methods on image stitching tasks
{"title":"Unsupervised deep learning image stitching model assisted with infrared images","authors":"Ming Zhu, Chengkun Li, Xueying He, Xiao Xiao","doi":"10.1117/12.3014359","DOIUrl":"https://doi.org/10.1117/12.3014359","url":null,"abstract":"The rapid development of artificial intelligence facilitates the improvement of image processing algorithms. For an intelligent inspection robot, the ability to analyze the environment through image collection plays an important role. It needs to collect multiple images of the same scene from different angles of view so as to make a thorough analysis about the environment it locates and generate further decisions. Therefore, a technique called image stitching is used. Currently, the development of image stitching algorithms is getting mature – multiple algorithms have already been proposed based feature extraction techniques. However, these existing algorithms are usually unable to handle the problem of parallax existing in real world image. Therefore, in order to solve it, we proposed an unsupervised deep learning image stitching algorithm, which uses infrared images to provide auxiliary information. We utilized our own equipment to collect real world images in visible light and infrared. Finally, we implemented our own model and other popular existing image stitching algorithms and compared and contrasted their performance on our dataset. The results showed that our model has the best performance in all aspects than other algorithms on the dataset, indicating the strong advantages of deep learning methods on image stitching tasks","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511400","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}
Graph model layout technology is an important cornerstone in graph visualization. Although the present graph model layout methods have been well studied, there are obvious problems: (1) excessively high initial state correlation; (2) excessive reliance on local optimal solutions; (3) limitation on the number of nodes. In this paper, we propose a new graph layout method on a graph grammar framework. First, the input graph model is parsed by graph grammar, with the reduction process recorded. Next, in the reverse order of reduction, the derivation operation starts from the initial graph and ends at a redrawn graph, with a new layout that meets the required specifications. Compared with other methods, regardless of the initial state, this method combines global and local layout specifications in productions and provides an intuitive yet effective way for the graph layout adjustment.
{"title":"A grammar-based layout method for graph models","authors":"Yufeng Liu, Yang Zhou, Fan Yang, Song Li","doi":"10.1117/12.3014367","DOIUrl":"https://doi.org/10.1117/12.3014367","url":null,"abstract":"Graph model layout technology is an important cornerstone in graph visualization. Although the present graph model layout methods have been well studied, there are obvious problems: (1) excessively high initial state correlation; (2) excessive reliance on local optimal solutions; (3) limitation on the number of nodes. In this paper, we propose a new graph layout method on a graph grammar framework. First, the input graph model is parsed by graph grammar, with the reduction process recorded. Next, in the reverse order of reduction, the derivation operation starts from the initial graph and ends at a redrawn graph, with a new layout that meets the required specifications. Compared with other methods, regardless of the initial state, this method combines global and local layout specifications in productions and provides an intuitive yet effective way for the graph layout adjustment.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511884","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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