Xianguo Wang, Chunxi Guan, Juan Ding, Huazhang Liu, Meixia Dong, Min Huang
The health bracelet composed of multiple sensors has large data acquisition, low data accuracy and poor fault tolerance. Therefore, the market application of health bracelet is limited. To solve the above problems, a multi-sensor data fusion method based on BP neural network is proposed. The simulation results show that the BP neural network model for multi-sensor data fusion processing, greatly improving the data accuracy, operation speed and robustness of multi-sensor.
{"title":"Research on health bracelet based on BP neural network algorithm","authors":"Xianguo Wang, Chunxi Guan, Juan Ding, Huazhang Liu, Meixia Dong, Min Huang","doi":"10.1117/12.2667287","DOIUrl":"https://doi.org/10.1117/12.2667287","url":null,"abstract":"The health bracelet composed of multiple sensors has large data acquisition, low data accuracy and poor fault tolerance. Therefore, the market application of health bracelet is limited. To solve the above problems, a multi-sensor data fusion method based on BP neural network is proposed. The simulation results show that the BP neural network model for multi-sensor data fusion processing, greatly improving the data accuracy, operation speed and robustness of multi-sensor.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121985253","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}
Given the excellent globality and parallelism, Transformer has been widely applied to image tasks. Visual Transformers demand modeling the spatial correlations among visual tokens. However, those existing methods either only emphasize the relative position between two tokens, or only concern on their contexts. Intuitively, a rational attention distribution should hinge on both. To this end, this paper proposes Reference Aware Attention (RAA). RAA decomposes inner-tokens dependency into three intuitive factors, in which reference bias is introduced to model how a reference token attends to a region. Experimental results suggest that RAA can effectively promote the performances of visual Transformers on various medical image diagnosis tasks.
{"title":"Reference aware attention based medical image diagnosis","authors":"Qidan Dai, Wenhui Shen, Pike Xu, Heng Xiao, Xiao Qin","doi":"10.1117/12.2667605","DOIUrl":"https://doi.org/10.1117/12.2667605","url":null,"abstract":"Given the excellent globality and parallelism, Transformer has been widely applied to image tasks. Visual Transformers demand modeling the spatial correlations among visual tokens. However, those existing methods either only emphasize the relative position between two tokens, or only concern on their contexts. Intuitively, a rational attention distribution should hinge on both. To this end, this paper proposes Reference Aware Attention (RAA). RAA decomposes inner-tokens dependency into three intuitive factors, in which reference bias is introduced to model how a reference token attends to a region. Experimental results suggest that RAA can effectively promote the performances of visual Transformers on various medical image diagnosis tasks.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126263192","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}
This paper introduces a design and implementation method of an access control management system based on remote control, which consists of the identity collection device and the database server access controller and realizes access control through remote reading and background data identification. When the target person wants to open the door, the door lock control device sends the acquired personnel data to the database server. The database server compares the data with the time information stored in the database in advance by the person, and quickly determines whether the door needs to be opened. If required, the access controller sends a signal to the electric control lock to open the door and the person can enter the house. Otherwise, the person cannot enter the house. This design integrates the human-computer interaction technology, data transmission technology, and communication technology to implement automatic management of intelligent devices through remote time control. Therefore, this design can efficiently identify and authenticate personal identity. In addition, this design features high reliability, low cost, small volume, complete functionality, and strong scalability on information collection. The time-based access control system fully realizes the automatic management of personnel who attempt to pass the access control system based on the identity information of the personnel stored on the database server and the time node to be reserved and by using the computer as the background processing tool.
{"title":"Application design of the time-based access control system","authors":"Kefeng Li, Luhua Cao, D. Xu, Zichun Chen","doi":"10.1117/12.2667526","DOIUrl":"https://doi.org/10.1117/12.2667526","url":null,"abstract":"This paper introduces a design and implementation method of an access control management system based on remote control, which consists of the identity collection device and the database server access controller and realizes access control through remote reading and background data identification. When the target person wants to open the door, the door lock control device sends the acquired personnel data to the database server. The database server compares the data with the time information stored in the database in advance by the person, and quickly determines whether the door needs to be opened. If required, the access controller sends a signal to the electric control lock to open the door and the person can enter the house. Otherwise, the person cannot enter the house. This design integrates the human-computer interaction technology, data transmission technology, and communication technology to implement automatic management of intelligent devices through remote time control. Therefore, this design can efficiently identify and authenticate personal identity. In addition, this design features high reliability, low cost, small volume, complete functionality, and strong scalability on information collection. The time-based access control system fully realizes the automatic management of personnel who attempt to pass the access control system based on the identity information of the personnel stored on the database server and the time node to be reserved and by using the computer as the background processing tool.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130591298","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 the current oil fields in China, the horizontal well technology with a long horizontal interval has gradually become the core technology to develop conventional oil and gas reservoirs, and the accurate determination of the drag and torque of the drill string is the key. However, the determination of the friction coefficient is affected by many factors, and it is difficult to describe it clearly by mathematical formulas. According to the characteristics of friction factors, the method of calculating the friction coefficient of drill string is studied, and a prediction model of friction coefficient based on BP algorithm is established. Based on the predicted friction coefficient, the calculation method of drag and torque is analyzed, and a drag and torque prediction model based on BP algorithm is established. The experimental results show that the use of BP neural network can accurately predict the friction coefficient and torque, and the prediction of the friction coefficient can characterize the risk of sticking of the drill string to a certain extent, which facilitates the adjustment of drilling parameters on site to improve the safety during drilling.
{"title":"Research on the prediction of drag and torque based on BP algorithm","authors":"Wenqi Wu, Sen Fan, Lulu Hua, X. Wang","doi":"10.1117/12.2667453","DOIUrl":"https://doi.org/10.1117/12.2667453","url":null,"abstract":"In the current oil fields in China, the horizontal well technology with a long horizontal interval has gradually become the core technology to develop conventional oil and gas reservoirs, and the accurate determination of the drag and torque of the drill string is the key. However, the determination of the friction coefficient is affected by many factors, and it is difficult to describe it clearly by mathematical formulas. According to the characteristics of friction factors, the method of calculating the friction coefficient of drill string is studied, and a prediction model of friction coefficient based on BP algorithm is established. Based on the predicted friction coefficient, the calculation method of drag and torque is analyzed, and a drag and torque prediction model based on BP algorithm is established. The experimental results show that the use of BP neural network can accurately predict the friction coefficient and torque, and the prediction of the friction coefficient can characterize the risk of sticking of the drill string to a certain extent, which facilitates the adjustment of drilling parameters on site to improve the safety during drilling.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122625186","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}
Shuai-yi Cao, Chenguang Qiu, Chaojie Ding, Yao Wang
In according with nonlinear identification problem, an improved interval type-2 fuzzy c-mean clustering algorithm is proposed. A novel objective function is adapted in improved interval type-2 fuzzy c-mean clustering algorithm, which can reduce the influence of noise on clustering results. The proposed clustering algorithm is applied to T-S fuzzy model premise parameters identification and least squares is used for consequent parameters identification. The proposed identification algorithm is applied to double input single output model and actual thermal power unit main steam temperature data model, the identification results show that, the proposed algorithm has higher identification accuracy.
{"title":"T-S fuzzy model identification based on improved interval type-2 fuzzy c-means clustering algorithm","authors":"Shuai-yi Cao, Chenguang Qiu, Chaojie Ding, Yao Wang","doi":"10.1117/12.2667629","DOIUrl":"https://doi.org/10.1117/12.2667629","url":null,"abstract":"In according with nonlinear identification problem, an improved interval type-2 fuzzy c-mean clustering algorithm is proposed. A novel objective function is adapted in improved interval type-2 fuzzy c-mean clustering algorithm, which can reduce the influence of noise on clustering results. The proposed clustering algorithm is applied to T-S fuzzy model premise parameters identification and least squares is used for consequent parameters identification. The proposed identification algorithm is applied to double input single output model and actual thermal power unit main steam temperature data model, the identification results show that, the proposed algorithm has higher identification accuracy.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"113 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133784780","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 problem of low detection accuracy of YOLOv5s target detection algorithm in foggy traffic environment, an improved YOLOv5s-based pedestrian detection algorithm for foggy skies is proposed. The algorithm uses image defogging techniques to preprocess the data, expands the sample size by manually generating the Foggy Cityscapes-Person dataset through a new fog simulation pipeline algorithm, and enhances the network's ability to sense small targets under foggy skies by adjusting the loss function and the training method to improve the detection accuracy of pedestrians under foggy skies, resulting in an increase of the mAP value from 64.97% to The mAP value increases from 64.97% to 81.29%. The experimental results show that the YOLOv5s-ACE network model proposed in this paper effectively reduces the missing detection rate and false detection rate, and the model can quickly and accurately detect pedestrian targets in foggy sky scenes.
{"title":"Improved foggy pedestrian detection algorithm based on YOLOv5s","authors":"Xiaoning Feng, Wenrong Jiang","doi":"10.1117/12.2667416","DOIUrl":"https://doi.org/10.1117/12.2667416","url":null,"abstract":"To address the problem of low detection accuracy of YOLOv5s target detection algorithm in foggy traffic environment, an improved YOLOv5s-based pedestrian detection algorithm for foggy skies is proposed. The algorithm uses image defogging techniques to preprocess the data, expands the sample size by manually generating the Foggy Cityscapes-Person dataset through a new fog simulation pipeline algorithm, and enhances the network's ability to sense small targets under foggy skies by adjusting the loss function and the training method to improve the detection accuracy of pedestrians under foggy skies, resulting in an increase of the mAP value from 64.97% to The mAP value increases from 64.97% to 81.29%. The experimental results show that the YOLOv5s-ACE network model proposed in this paper effectively reduces the missing detection rate and false detection rate, and the model can quickly and accurately detect pedestrian targets in foggy sky scenes.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132798436","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}
With the increasing awareness of privacy protection in recent years, various encryption techniques are gradually applied to network traffic, which makes encrypted traffic classification an indispensable part of network management. Recent studies show that the approaches based on deep learning are compelling for the traffic classification task. However, most of them take the encrypted payload as input, which not only requires high computational overhead to make classification, but also limits the performance improvement due to the unavailability of the plaintext. In this paper, we treat the encrypted traffic as sequences and solve the classification task from the perspective of sequence modeling, which only depends on several sequence fields obtained from the traffic header. We properly design a lightweight model and name it TGA by its structure, which consists of a temporal convolutional network (TCN), a gated recurrent unit (GRU) and the attention mechanism. TGA first extracts short-term features from sequences by applying the TCN, and then captures the long-term dependencies by exploiting the GRU, and finally focuses on valuable features through dynamic assignment of attention weights. Through these three steps, TGA is expected to obtain the most effective but lightest temporal features. Experimental results on the public dataset demonstrate that TGA shows superiority in terms of classification accuracy and time efficiency, while the number of parameters is reduced to at most 30% of the state-of-the-art models.
{"title":"A lightweight model for encrypted traffic classification through sequence modeling","authors":"Yanliang Jin, Yantao Chen, Yuan Gao","doi":"10.1117/12.2667332","DOIUrl":"https://doi.org/10.1117/12.2667332","url":null,"abstract":"With the increasing awareness of privacy protection in recent years, various encryption techniques are gradually applied to network traffic, which makes encrypted traffic classification an indispensable part of network management. Recent studies show that the approaches based on deep learning are compelling for the traffic classification task. However, most of them take the encrypted payload as input, which not only requires high computational overhead to make classification, but also limits the performance improvement due to the unavailability of the plaintext. In this paper, we treat the encrypted traffic as sequences and solve the classification task from the perspective of sequence modeling, which only depends on several sequence fields obtained from the traffic header. We properly design a lightweight model and name it TGA by its structure, which consists of a temporal convolutional network (TCN), a gated recurrent unit (GRU) and the attention mechanism. TGA first extracts short-term features from sequences by applying the TCN, and then captures the long-term dependencies by exploiting the GRU, and finally focuses on valuable features through dynamic assignment of attention weights. Through these three steps, TGA is expected to obtain the most effective but lightest temporal features. Experimental results on the public dataset demonstrate that TGA shows superiority in terms of classification accuracy and time efficiency, while the number of parameters is reduced to at most 30% of the state-of-the-art models.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114706329","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}
Most existing video denoising methods based on the PatchMatch algorithm and optical flow estimation often lead to artifacts blurring and poor denoising effect on scale-varying data. To tackle these issues, we propose a multi-scale feature fusion network based on different pyramid blocks and adaptive spatial-channel attention, which enables to effectively extract multi-scale feature information from noisy video data. Furthermore, we develop a spatial-temporal alignment module with deformable convolution to align the implicit features and reduce blurring artifacts. The results show that the proposed method outperforms the state-of-the-art algorithms in visual and objective quality metrics on the public datasets DAVIS and Set8.
{"title":"Multi-scale feature fusion network with spatial-temporal alignment for video denoising","authors":"Yushan Lv, Di Wu, Yuhang Li, Youdong Ding","doi":"10.1117/12.2667325","DOIUrl":"https://doi.org/10.1117/12.2667325","url":null,"abstract":"Most existing video denoising methods based on the PatchMatch algorithm and optical flow estimation often lead to artifacts blurring and poor denoising effect on scale-varying data. To tackle these issues, we propose a multi-scale feature fusion network based on different pyramid blocks and adaptive spatial-channel attention, which enables to effectively extract multi-scale feature information from noisy video data. Furthermore, we develop a spatial-temporal alignment module with deformable convolution to align the implicit features and reduce blurring artifacts. The results show that the proposed method outperforms the state-of-the-art algorithms in visual and objective quality metrics on the public datasets DAVIS and Set8.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125956219","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}
Adversarial training proves to be the most effective measure to classify adversarial perturbation, which is imperceptible but can drastically alter the output of the classifier. We review various theories behind the relationship between generalization gap and adversarial robustness and then raise the question: is it the input near the decision boundary that provides guidance for the classifier to learn the ideal decision boundary and therefore yield a more desired outcome? We provide quantitative confirmation that the expected required sample size correlates favorably with sample distance and further investigate the relationship between the robust classification error and the expected distance from the decision boundary to samples. Experimental results reveal that applying the data near the decision boundary as training sets can significantly promote adversarial generalization, which keeps consistence with the main conjectures presented in this work.
{"title":"Support samples guided adversarial generalization","authors":"En Yang, Tong Sun, Jun Liu","doi":"10.1117/12.2667635","DOIUrl":"https://doi.org/10.1117/12.2667635","url":null,"abstract":"Adversarial training proves to be the most effective measure to classify adversarial perturbation, which is imperceptible but can drastically alter the output of the classifier. We review various theories behind the relationship between generalization gap and adversarial robustness and then raise the question: is it the input near the decision boundary that provides guidance for the classifier to learn the ideal decision boundary and therefore yield a more desired outcome? We provide quantitative confirmation that the expected required sample size correlates favorably with sample distance and further investigate the relationship between the robust classification error and the expected distance from the decision boundary to samples. Experimental results reveal that applying the data near the decision boundary as training sets can significantly promote adversarial generalization, which keeps consistence with the main conjectures presented in this work.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519690","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 recent years, the expansion of colleges and universities has led to a sharp rise in the number of students, and the number of poor students has also increased, which has greatly increased the difficulty and workload of financial aid for poor students. In order to improve the accuracy and efficiency of poor student identification, there is an urgent need to adopt digital and intelligent measures to assist poor student identification. In this paper, we propose a method to identify needy students using SVM and decision tree algorithm. Firstly, students' campus card consumption information is preprocessed to obtain the consumption poverty index of each student by SVM classification model. Then the decision tree algorithm is used to derive the student's family poverty index based on the student's family information. Finally, the comprehensive poverty index is calculated by weighted summation. The experimental results show that the proposed method realizes the statistics and analysis of students' consumption and family information, and it can identify poor students more accurately, which effectively improves the efficiency and accuracy of poor students' identification.
{"title":"Research on the identification method of poor students based on SVM and decision tree algorithm","authors":"Shuqing Hao, Yinming Zhang, Yun Qing","doi":"10.1117/12.2667195","DOIUrl":"https://doi.org/10.1117/12.2667195","url":null,"abstract":"In recent years, the expansion of colleges and universities has led to a sharp rise in the number of students, and the number of poor students has also increased, which has greatly increased the difficulty and workload of financial aid for poor students. In order to improve the accuracy and efficiency of poor student identification, there is an urgent need to adopt digital and intelligent measures to assist poor student identification. In this paper, we propose a method to identify needy students using SVM and decision tree algorithm. Firstly, students' campus card consumption information is preprocessed to obtain the consumption poverty index of each student by SVM classification model. Then the decision tree algorithm is used to derive the student's family poverty index based on the student's family information. Finally, the comprehensive poverty index is calculated by weighted summation. The experimental results show that the proposed method realizes the statistics and analysis of students' consumption and family information, and it can identify poor students more accurately, which effectively improves the efficiency and accuracy of poor students' identification.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128378430","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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