The inter-symbol interference caused by channel distortion in the communication process seriously affects the communication quality, and this problem is often solved by equalization technology. The principle of the traditional blind equalization constant modulus algorithm (CMA) with fixed step is introduced, and the problem of fast convergence speed and small steady-state error is analyzed by simulation. In order to solve this problem, a blind equalization algorithm with variable step size based on CMA is proposed. In order to solve the problem of fast convergence speed and small steady-state error, the CMA algorithm is improved and the principle of the improved algorithm is described, and the influence of the step on the performance of the algorithm is analyzed. Finally, the simulation experiment proves that the improved algorithm can speed up the convergence speed and keep a small steady-state error at the same time.
{"title":"Study on Variable Step Size Blind Equalization Algorithm Based on CMA","authors":"Mingyu Yang, Dongming Xu","doi":"10.1145/3573942.3573997","DOIUrl":"https://doi.org/10.1145/3573942.3573997","url":null,"abstract":"The inter-symbol interference caused by channel distortion in the communication process seriously affects the communication quality, and this problem is often solved by equalization technology. The principle of the traditional blind equalization constant modulus algorithm (CMA) with fixed step is introduced, and the problem of fast convergence speed and small steady-state error is analyzed by simulation. In order to solve this problem, a blind equalization algorithm with variable step size based on CMA is proposed. In order to solve the problem of fast convergence speed and small steady-state error, the CMA algorithm is improved and the principle of the improved algorithm is described, and the influence of the step on the performance of the algorithm is analyzed. Finally, the simulation experiment proves that the improved algorithm can speed up the convergence speed and keep a small steady-state error at the same time.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115643709","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}
As a lightweight target detection network, YOLOv5 is popular in the industry for its advantages of fast speed and small model, but the detection accuracy is not very high. In response to this problem, we propose an improved model SN-YOLO based on YOLOv5. First, we introduce Softer-NMS as the post-processing method of the model, which will make the prediction box more accurate. Secondly, we improved the loss function of the original algorithm and introduced the SIOU loss function to optimize the model and improve the accuracy of the algorithm. Finally, in order to improve the feature extraction ability of the backbone, we implanted the CBAM (Convolutional block attention module) module into the algorithm. We validate the model using the 2007 and 2012 datasets of PASCAL VOC. The experimental results show that SN-YOLO has a great improvement over the original model in all aspects. The effectiveness of the algorithm is verified.
{"title":"SN-YOLO: Improved YOLOv5 with Softer-NMS and SIOU for Object Detection","authors":"Wanyu Deng, Zhen Wang","doi":"10.1145/3573942.3574029","DOIUrl":"https://doi.org/10.1145/3573942.3574029","url":null,"abstract":"As a lightweight target detection network, YOLOv5 is popular in the industry for its advantages of fast speed and small model, but the detection accuracy is not very high. In response to this problem, we propose an improved model SN-YOLO based on YOLOv5. First, we introduce Softer-NMS as the post-processing method of the model, which will make the prediction box more accurate. Secondly, we improved the loss function of the original algorithm and introduced the SIOU loss function to optimize the model and improve the accuracy of the algorithm. Finally, in order to improve the feature extraction ability of the backbone, we implanted the CBAM (Convolutional block attention module) module into the algorithm. We validate the model using the 2007 and 2012 datasets of PASCAL VOC. The experimental results show that SN-YOLO has a great improvement over the original model in all aspects. The effectiveness of the algorithm is verified.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134236159","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}
Linfeng Yang, Zhixiang Zhu, Chenwu Wang, Pei Wang, Shaobo Hei
For action recognition, two-stream networks consisting of RGB and optical flow has been widely used, showing high recognition accuracy. However, optical flow computation is time-consuming and requires a large amount of storage space, and the recognition efficiency is very low. To alleviate this problem, we propose an Adaptive Multi-Scale Residual (AMSR) module and a Long Short Term Motion Squeeze (LSMS) module, which are inserted into the 2D convolutional neural network to improve the accuracy of action recognition and achieve a balance of accuracy and speed. The AMSR module adaptively fuses multi-scale feature maps to fully utilize the semantic information provided by deep feature maps and the detailed information provided by shallow feature maps. The LSMS module is a learnable lightweight motion feature extractor for learning long-term motion features of adjacent and non-adjacent frames, thus replacing the traditional optical flow and improving the accuracy of action recognition. Experimental results on UCF-101 and HMDB-51 datasets demonstrate that the method proposed in this paper achieves competitive performance compared to state-of-the-art methods with only a small increase in parameters and computational cost.
{"title":"Joint Multi-Scale Residual and Motion Feature Learning for Action Recognition","authors":"Linfeng Yang, Zhixiang Zhu, Chenwu Wang, Pei Wang, Shaobo Hei","doi":"10.1145/3573942.3574082","DOIUrl":"https://doi.org/10.1145/3573942.3574082","url":null,"abstract":"For action recognition, two-stream networks consisting of RGB and optical flow has been widely used, showing high recognition accuracy. However, optical flow computation is time-consuming and requires a large amount of storage space, and the recognition efficiency is very low. To alleviate this problem, we propose an Adaptive Multi-Scale Residual (AMSR) module and a Long Short Term Motion Squeeze (LSMS) module, which are inserted into the 2D convolutional neural network to improve the accuracy of action recognition and achieve a balance of accuracy and speed. The AMSR module adaptively fuses multi-scale feature maps to fully utilize the semantic information provided by deep feature maps and the detailed information provided by shallow feature maps. The LSMS module is a learnable lightweight motion feature extractor for learning long-term motion features of adjacent and non-adjacent frames, thus replacing the traditional optical flow and improving the accuracy of action recognition. Experimental results on UCF-101 and HMDB-51 datasets demonstrate that the method proposed in this paper achieves competitive performance compared to state-of-the-art methods with only a small increase in parameters and computational cost.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134520985","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 current face forgery methods based on deep learning are becoming more mature and abundant, and existing detection techniques have some limitations and applicability issues that make it difficult to effectively detect such behaviour. In this paper, we propose an enhanced dual-stream FC_2_stream network model based on dual-stream networks to detect forged regions in manipulated face images through end-to-end training of the images. The RGB stream is used to extract features from the RGB image to find the forged traces; the noise stream uses the filtering layer of the SRM (Steganalysis Rich Model) model to extract the noise features and find the inconsistency between the noise in the real region and the forged region in the fake face, then the features of the two streams are fused with a bilinear pooling layer to predict the forged region, and finally the forged region is determined by whether the blending boundary of the forged image is displayed to determine the image authenticity. Experiments conducted on four benchmark datasets show that our model is still effective against forgeries generated by unknown face manipulation methods, and also demonstrate the superior generalisation capability of our model.
{"title":"A Novel Face Forgery Detection Method Based on Augmented Dual-Stream Networks","authors":"Yumei Liu, Yong Zhang, Weiran Liu","doi":"10.1145/3573942.3574030","DOIUrl":"https://doi.org/10.1145/3573942.3574030","url":null,"abstract":"The current face forgery methods based on deep learning are becoming more mature and abundant, and existing detection techniques have some limitations and applicability issues that make it difficult to effectively detect such behaviour. In this paper, we propose an enhanced dual-stream FC_2_stream network model based on dual-stream networks to detect forged regions in manipulated face images through end-to-end training of the images. The RGB stream is used to extract features from the RGB image to find the forged traces; the noise stream uses the filtering layer of the SRM (Steganalysis Rich Model) model to extract the noise features and find the inconsistency between the noise in the real region and the forged region in the fake face, then the features of the two streams are fused with a bilinear pooling layer to predict the forged region, and finally the forged region is determined by whether the blending boundary of the forged image is displayed to determine the image authenticity. Experiments conducted on four benchmark datasets show that our model is still effective against forgeries generated by unknown face manipulation methods, and also demonstrate the superior generalisation capability of our model.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130377352","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}
For most existing crowd counting methods, image-based methods are still used for crowd counting in the presence of video datasets, ignoring powerful time information. Thus, a novel spatiotemporal attention convolutional neural network is proposed to solve the video-based crowd counting problem. Firstly, the first ten layers of VGG-16 are used as the backbone network to extract features, and a single layer of ConvLSTM captures the time correlation of adjacent frames. Then, stacked dilated convolutional layers are used to enlarge the receptive field without increasing the computational load. Finally, a convolutional block attention module is introduced with the adaptive refinement of feature mapping. Its ability to emphasize or suppress information in the channel and spatial dimensions aids information dissemination. Experimental results on the two reference datasets (i.e., Mall and WorldExpo'10) show that the proposed method further improves the accuracy of crowd counting and is superior to the other existing crowd counting methods.
{"title":"A Novel Spatiotemporal Attention Convolutional Neural Network for Video Crowd Counting","authors":"Shangjie Zhang, Yuelei Xiao","doi":"10.1145/3573942.3574069","DOIUrl":"https://doi.org/10.1145/3573942.3574069","url":null,"abstract":"For most existing crowd counting methods, image-based methods are still used for crowd counting in the presence of video datasets, ignoring powerful time information. Thus, a novel spatiotemporal attention convolutional neural network is proposed to solve the video-based crowd counting problem. Firstly, the first ten layers of VGG-16 are used as the backbone network to extract features, and a single layer of ConvLSTM captures the time correlation of adjacent frames. Then, stacked dilated convolutional layers are used to enlarge the receptive field without increasing the computational load. Finally, a convolutional block attention module is introduced with the adaptive refinement of feature mapping. Its ability to emphasize or suppress information in the channel and spatial dimensions aids information dissemination. Experimental results on the two reference datasets (i.e., Mall and WorldExpo'10) show that the proposed method further improves the accuracy of crowd counting and is superior to the other existing crowd counting methods.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115537956","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}
Due to VANET (vehicle ad-hoc network, VANET) has the characteristics of fast node movement and unstable network topology, the data transmission in the network faces the problems of disconnection of communication links and difficult to guarantee delay. Therefore, it is very important to design a routing algorithm that can ensure the stability of the communication link and the efficient data transmission. Based on the traditional GPSR protocol (greedy perimeter stateless routing, GPSR), this paper proposes an improved VANET routing method CL-GPSR, which makes forwarding decisions based on the established link connection time prediction model and delay estimation model. Simulation results show that the proposed CL-GPSR routing method can provide higher packet delivery rate and lower average delay.
{"title":"Routing Method Based on Connectivity and Latency in VANET","authors":"Hua Liu, Wujun Yang, Zhixian Chang, Min Shi","doi":"10.1145/3573942.3573991","DOIUrl":"https://doi.org/10.1145/3573942.3573991","url":null,"abstract":"Due to VANET (vehicle ad-hoc network, VANET) has the characteristics of fast node movement and unstable network topology, the data transmission in the network faces the problems of disconnection of communication links and difficult to guarantee delay. Therefore, it is very important to design a routing algorithm that can ensure the stability of the communication link and the efficient data transmission. Based on the traditional GPSR protocol (greedy perimeter stateless routing, GPSR), this paper proposes an improved VANET routing method CL-GPSR, which makes forwarding decisions based on the established link connection time prediction model and delay estimation model. Simulation results show that the proposed CL-GPSR routing method can provide higher packet delivery rate and lower average delay.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123396947","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 enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene.
{"title":"UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism","authors":"Sugang Ma, Zixian Zhang, Zhixian Zhao, Xiaobao Yang, Zhiqiang Hou","doi":"10.1145/3573942.3574035","DOIUrl":"https://doi.org/10.1145/3573942.3574035","url":null,"abstract":"To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124706587","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 output of the Micro Electro-mechanical System (MEMS) gyroscope is susceptible affected by temperature drift, which reduces the measurement accuracy of the gyroscope. In this paper, a gyroscope temperature compensation method based on sparrow search algorithm (SSA) and radial basis function (RBF) neural network is proposed to reduce the temperature drift error of gyroscope. Firstly, we utilize the RBF neural network to establish the model of temperature error on the original output of gyroscope; then SSA is employed to find the optimal parameters of the RBF neural network in order to improve its search speed and generalization performance; finally, the optimized RBF neural network is applied to the temperature compensation of the gyroscope. The numerical simulation and comparison results under different temperatures demonstrate that, compared with polynomial and RBF neural network, the SSA-RBF neural network compensation method has superior compensation accuracy and faster convergence speed, which significantly reduces the maximum error, mean value and the standard deviation of gyroscope. Thus, the proposed SSA-RBF method can obtain more accurate fitting performance, effectively compensate the temperature error of MEMS gyroscope, and improve the MEMS gyroscope measurement accuracy.
{"title":"MEMS Gyroscope Temperature Compensation Based on SSA-RBF Neural Network","authors":"Yuanhua Liu, Ziwei Wang, Xinliang Niu","doi":"10.1145/3573942.3573959","DOIUrl":"https://doi.org/10.1145/3573942.3573959","url":null,"abstract":"The output of the Micro Electro-mechanical System (MEMS) gyroscope is susceptible affected by temperature drift, which reduces the measurement accuracy of the gyroscope. In this paper, a gyroscope temperature compensation method based on sparrow search algorithm (SSA) and radial basis function (RBF) neural network is proposed to reduce the temperature drift error of gyroscope. Firstly, we utilize the RBF neural network to establish the model of temperature error on the original output of gyroscope; then SSA is employed to find the optimal parameters of the RBF neural network in order to improve its search speed and generalization performance; finally, the optimized RBF neural network is applied to the temperature compensation of the gyroscope. The numerical simulation and comparison results under different temperatures demonstrate that, compared with polynomial and RBF neural network, the SSA-RBF neural network compensation method has superior compensation accuracy and faster convergence speed, which significantly reduces the maximum error, mean value and the standard deviation of gyroscope. Thus, the proposed SSA-RBF method can obtain more accurate fitting performance, effectively compensate the temperature error of MEMS gyroscope, and improve the MEMS gyroscope measurement accuracy.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258745","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 single feature cannot adapt to the dynamic changes of the scene during video target tracking. This paper, to address this issue, first studies the tracking algorithm of multi-feature fusion, which uses the complementarity between different features to better adapt to the scene changes. On this basis, the APCE anti-occlusion criterion is added to enable the algorithm to resist the influence of target occlusion on tracking to a certain extent. The experimental results show that the average tracking accuracy of the proposed algorithm is about 0.779, which is about 2% higher than that of the SAMF algorithm, and the tracking success rate can be as high as 72%.
{"title":"Research and Implementation of Multi-feature Tracking Algorithms","authors":"Xinyue Zhang, Yao Tang","doi":"10.1145/3573942.3574040","DOIUrl":"https://doi.org/10.1145/3573942.3574040","url":null,"abstract":"A single feature cannot adapt to the dynamic changes of the scene during video target tracking. This paper, to address this issue, first studies the tracking algorithm of multi-feature fusion, which uses the complementarity between different features to better adapt to the scene changes. On this basis, the APCE anti-occlusion criterion is added to enable the algorithm to resist the influence of target occlusion on tracking to a certain extent. The experimental results show that the average tracking accuracy of the proposed algorithm is about 0.779, which is about 2% higher than that of the SAMF algorithm, and the tracking success rate can be as high as 72%.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128557896","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}
An improved residual network model is proposed to deal with the complex and changeable characteristics of one-dimensional electrocardiogram. In this model, firstly, in order to avoid the network degradation problem of the model along with the deepening of the number of layers, when extracting various deep-level features of ECG signals using multiple convolution layers in CNN, the residual module is integrated into the network, and an appropriate shortcut connection is selected to connect the input with the superposition output of the corresponding convolution layer to construct a deep residual network to extract more abstract signal features. Secondly, the output of the last residual module is sent to the GAP layer, and the parameters of this layer are greatly reduced compared with those of the full connection layer, which is equivalent to the compression of the model, and thus the over-fitting of the model is avoided to a certain extent. Finally, the original ECG signals were automatically classified based on the PCinCC2017 database to complete the recognition of atrial fibrillation. Experimental results show that the proposed algorithm has a classification accuracy of 86% and a F1 measure of 83%, which prove the feasibility of the model and the effectiveness of the algorithm.
{"title":"Improved atrial fibrillation recognition algorithm based on residual network","authors":"Zhiqiang Bao, Ting Ai, Ying Bai","doi":"10.1145/3573942.3574118","DOIUrl":"https://doi.org/10.1145/3573942.3574118","url":null,"abstract":"An improved residual network model is proposed to deal with the complex and changeable characteristics of one-dimensional electrocardiogram. In this model, firstly, in order to avoid the network degradation problem of the model along with the deepening of the number of layers, when extracting various deep-level features of ECG signals using multiple convolution layers in CNN, the residual module is integrated into the network, and an appropriate shortcut connection is selected to connect the input with the superposition output of the corresponding convolution layer to construct a deep residual network to extract more abstract signal features. Secondly, the output of the last residual module is sent to the GAP layer, and the parameters of this layer are greatly reduced compared with those of the full connection layer, which is equivalent to the compression of the model, and thus the over-fitting of the model is avoided to a certain extent. Finally, the original ECG signals were automatically classified based on the PCinCC2017 database to complete the recognition of atrial fibrillation. Experimental results show that the proposed algorithm has a classification accuracy of 86% and a F1 measure of 83%, which prove the feasibility of the model and the effectiveness of the algorithm.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129896713","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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