Aiming at the problems of poor detection results and low reading accuracy caused by small targets and oblique shooting angles of pointer instruments in complex background environments, a combined method of depth learning, perspective transformation, and Canny edge detection was proposed to perform pointer instrument readings. This recognition method uses an improved YOLO V7 target detection algorithm to detect and extract instruments in complex environments, and then corrects the extracted instruments through perspective transformation. Finally, the Canny edge detection algorithm and Hough transform are used to determine the center and pointer characteristics to obtain pointer readings. Through experimental comparison and verification, this method is more accurate and reliable than traditional methods, with a certain speed. It provides a more accurate and faster method for identifying pointer type instrument readings for subsequent work.
{"title":"Research on the method of quickly identifying and reading pointer meter","authors":"Xinqing Song, Xiaoxiang Pu, Xuyang Liu","doi":"10.1117/12.2682461","DOIUrl":"https://doi.org/10.1117/12.2682461","url":null,"abstract":"Aiming at the problems of poor detection results and low reading accuracy caused by small targets and oblique shooting angles of pointer instruments in complex background environments, a combined method of depth learning, perspective transformation, and Canny edge detection was proposed to perform pointer instrument readings. This recognition method uses an improved YOLO V7 target detection algorithm to detect and extract instruments in complex environments, and then corrects the extracted instruments through perspective transformation. Finally, the Canny edge detection algorithm and Hough transform are used to determine the center and pointer characteristics to obtain pointer readings. Through experimental comparison and verification, this method is more accurate and reliable than traditional methods, with a certain speed. It provides a more accurate and faster method for identifying pointer type instrument readings for subsequent work.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128160020","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}
Ye Wang, Xuezhi Yang, Xuenan Liu, Rencheng Song, J Zhang
In this study, we present a new method for remote detection of mental stress via webcam. The system is based on remote Photoplethysmograph (rPPG) obtained from face video frames of heart rate, breathing rate, and pulse rate variability (PRV). The experiment collected pulse wave data from 14 healthy students with a stress distribution consisting of four phases: Rest, Stroop-Color-Word Test, Mental Arithmetic Task, and Recovery. We combined the stress questionnaire to select data to assess the human autonomic response to stress and recovery, the results showed significant differences in frequency domain characteristics and nonlinear parameters between phases. The average classification accuracy under different stress sources was 80.31%. The results demonstrate the applicability and convenience of the remote stress detection method. It can be used without disturbing a person’s daily life and provides an alternative to traditional contact techniques for those who want to monitor stress levels regularly.
{"title":"Remote assessment of physiological parameters by non-contact methods to detect mental stress","authors":"Ye Wang, Xuezhi Yang, Xuenan Liu, Rencheng Song, J Zhang","doi":"10.1117/12.2682510","DOIUrl":"https://doi.org/10.1117/12.2682510","url":null,"abstract":"In this study, we present a new method for remote detection of mental stress via webcam. The system is based on remote Photoplethysmograph (rPPG) obtained from face video frames of heart rate, breathing rate, and pulse rate variability (PRV). The experiment collected pulse wave data from 14 healthy students with a stress distribution consisting of four phases: Rest, Stroop-Color-Word Test, Mental Arithmetic Task, and Recovery. We combined the stress questionnaire to select data to assess the human autonomic response to stress and recovery, the results showed significant differences in frequency domain characteristics and nonlinear parameters between phases. The average classification accuracy under different stress sources was 80.31%. The results demonstrate the applicability and convenience of the remote stress detection method. It can be used without disturbing a person’s daily life and provides an alternative to traditional contact techniques for those who want to monitor stress levels regularly.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127414239","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 existing public face datasets, the horizontal frontal and left-right rotation poses are the majority, and the models trained by them can not meet the requirements of face detection in the overlooking situation. Aiming at this phenomenon, the Tilt-angle face dataset TFD is cited and further expanded, and the Tilt-angle face dataset TFD-B is manually collected. The RetinaFace algorithm is adopted to carry out multiple face detection experiments. Typical experiment A shows that compared with WiderFace, the average detection precision of TFD+TFD-B as training set is improved by 4.81% when looking down at 15°, 9.87% when looking down at 30°, 10.56% when looking down at 45°,12.63% when looking down at 60°, and 15.62% when looking down at 75°, which indicates that TFD+TFD-B can effectively improve the precision of face detection in the overlooking situation. At the same time, the experiments carried out further show that expanding the training dataset can improve the precision of face detection. TFD+TFD-B can be obtained at https://github.com/huang1204510135/DFD.
{"title":"Face detection research based on a tilt-angle dataset","authors":"Sichao Cheng, Lei Yuab, Xin-chen Zhang","doi":"10.1117/12.2682549","DOIUrl":"https://doi.org/10.1117/12.2682549","url":null,"abstract":"In the existing public face datasets, the horizontal frontal and left-right rotation poses are the majority, and the models trained by them can not meet the requirements of face detection in the overlooking situation. Aiming at this phenomenon, the Tilt-angle face dataset TFD is cited and further expanded, and the Tilt-angle face dataset TFD-B is manually collected. The RetinaFace algorithm is adopted to carry out multiple face detection experiments. Typical experiment A shows that compared with WiderFace, the average detection precision of TFD+TFD-B as training set is improved by 4.81% when looking down at 15°, 9.87% when looking down at 30°, 10.56% when looking down at 45°,12.63% when looking down at 60°, and 15.62% when looking down at 75°, which indicates that TFD+TFD-B can effectively improve the precision of face detection in the overlooking situation. At the same time, the experiments carried out further show that expanding the training dataset can improve the precision of face detection. TFD+TFD-B can be obtained at https://github.com/huang1204510135/DFD.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134395509","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 main variable temperature controller has not achieved an automatic testing function so far, which consumes great labor and capital costs. The innovation of this paper is to achieve fully automatic and intelligent detection of the main variable temperature controller. We construct an intelligent testing equipment remote control system, including a remote monitoring and management system for testing equipment backstage, a mobile APP for testing equipment, an APP for receiving terminals, and an application service for the testing report interface, which realizes the function of remote management and viewing of reports. A fully automatic calibration device for the main substation thermostat is designed, including the inspected thermostat, constant temperature oil tank, calibrator, and graphic conversion device, which realizes the device to automatically complete the testing of substation-related devices. By designing the communication serial port of the device and developing the corresponding device on the cloud, the software and hardware are well integrated. The technical solution promotes the turnover rate and utilization rate of testing equipment and enhances the comprehensive management capability of testing equipment.
{"title":"A detection system and device for main variable temperature controller based on cloud platform","authors":"Guiliang Li, Jinhui Yan, Xin Luo, Yongsheng Luo, Yousong Ren, Chengjiang Zhou","doi":"10.1117/12.2682390","DOIUrl":"https://doi.org/10.1117/12.2682390","url":null,"abstract":"The main variable temperature controller has not achieved an automatic testing function so far, which consumes great labor and capital costs. The innovation of this paper is to achieve fully automatic and intelligent detection of the main variable temperature controller. We construct an intelligent testing equipment remote control system, including a remote monitoring and management system for testing equipment backstage, a mobile APP for testing equipment, an APP for receiving terminals, and an application service for the testing report interface, which realizes the function of remote management and viewing of reports. A fully automatic calibration device for the main substation thermostat is designed, including the inspected thermostat, constant temperature oil tank, calibrator, and graphic conversion device, which realizes the device to automatically complete the testing of substation-related devices. By designing the communication serial port of the device and developing the corresponding device on the cloud, the software and hardware are well integrated. The technical solution promotes the turnover rate and utilization rate of testing equipment and enhances the comprehensive management capability of testing equipment.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"64 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133077154","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}
Nowadays, Wordle became almost everyone's current obsession. To study the reason for Wordle’s explosion, look for the secret behind Wordle. It is beneficial to develop a forecasting model to measure the fluctuations and distributions of the results based on time series and words. In the text used the context processing of words in text sequences in natural language processing to analogize that the same rule can be used for the composition and structure of words, so as to establish a percentage prediction model for the number of attempts of players with the character mechanism of letter position and structure in words. The error uncertainty of the model is evaluated by the MAPE error value. Through the analysis of the MAPE value, the error of the model to the predicted value is about 1.92%, so it is confident that the model can complete the prediction task with an error not exceeding 1.92%. Through this model, Predicting the result of the word "EERIE" as (2.16, 10.90 14.06, 24.49, 25.79, 14.41, 3.45).
{"title":"Insight into wordle's data set based on deep learning","authors":"Jia Song, Shuwei Peng, Haopeng Du, Guitang Wang","doi":"10.1117/12.2682565","DOIUrl":"https://doi.org/10.1117/12.2682565","url":null,"abstract":"Nowadays, Wordle became almost everyone's current obsession. To study the reason for Wordle’s explosion, look for the secret behind Wordle. It is beneficial to develop a forecasting model to measure the fluctuations and distributions of the results based on time series and words. In the text used the context processing of words in text sequences in natural language processing to analogize that the same rule can be used for the composition and structure of words, so as to establish a percentage prediction model for the number of attempts of players with the character mechanism of letter position and structure in words. The error uncertainty of the model is evaluated by the MAPE error value. Through the analysis of the MAPE value, the error of the model to the predicted value is about 1.92%, so it is confident that the model can complete the prediction task with an error not exceeding 1.92%. Through this model, Predicting the result of the word \"EERIE\" as (2.16, 10.90 14.06, 24.49, 25.79, 14.41, 3.45).","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"12715 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131046263","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}
Currently, DeepLab is unable to utilize multiscale feature information at multiple levels, and there are often problems such as blurred segmentation boundaries, unclear detail extraction, and incorrect segmentation.This article optimizes the DeepLabv3 plus model The backbone network has been converted to a lightweight MobileNetV2 network. In Atrous Spatial Pyramid Pooling (ASPP), stripe pooling has been used to replace global average pooling, and the original hole ratio combination of 6, 12, and 18 has been changed to 3, 7, 9, and 17. A branch with R=3 has been added, as well as the use of dense connections. The improved ASPP has the advantage of higher acceptability. The experiment shows that the average intersection ratio of the improved DeepLabv3 plus model on the dataset is 69.71%, and the average pixel accuracy is 79.45%. Compared with the original network model, the improved average intersection ratio is increased by 3.2%. Using the above improved methods has improved the performance of DeepLabv3 plus, enabling more detailed information to be obtained, and improving the resolution of the model.
{"title":"Stripe pooling and densely connected DeepLabv3 plus efficient semantic segmentation","authors":"Jiafei Wang, Yanyan Liu, Guoning Li","doi":"10.1117/12.2682538","DOIUrl":"https://doi.org/10.1117/12.2682538","url":null,"abstract":"Currently, DeepLab is unable to utilize multiscale feature information at multiple levels, and there are often problems such as blurred segmentation boundaries, unclear detail extraction, and incorrect segmentation.This article optimizes the DeepLabv3 plus model The backbone network has been converted to a lightweight MobileNetV2 network. In Atrous Spatial Pyramid Pooling (ASPP), stripe pooling has been used to replace global average pooling, and the original hole ratio combination of 6, 12, and 18 has been changed to 3, 7, 9, and 17. A branch with R=3 has been added, as well as the use of dense connections. The improved ASPP has the advantage of higher acceptability. The experiment shows that the average intersection ratio of the improved DeepLabv3 plus model on the dataset is 69.71%, and the average pixel accuracy is 79.45%. Compared with the original network model, the improved average intersection ratio is increased by 3.2%. Using the above improved methods has improved the performance of DeepLabv3 plus, enabling more detailed information to be obtained, and improving the resolution of the model.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121143737","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 development of deep learning techniques, computer vision techniques have also been significantly improved. Image retrieval is a common technique used to retrieve images of interest from image databases, which can help users find the desired images more quickly. However, traditional image retrieval methods often fail to meet user needs because they often ignore complex scale information, e.g., features may differ at different scales. Therefore, an image retrieval based on a region-attention feature fusion mechanism can overcome this drawback, and it can improve the performance of image retrieval by emphasizing multi-scale features through a region-attention mechanism. In this paper, we propose an image retrieval method based on regional attention based multi-scale feature fusion, which can effectively use multiscale features. The effectiveness of RMFF is demonstrated by conducting experiments on mainstream image retrieval datasets.
{"title":"Regional attention based multi-scale feature fusion for image retrieval","authors":"Rui Jixiang, Sia Chen","doi":"10.1117/12.2682315","DOIUrl":"https://doi.org/10.1117/12.2682315","url":null,"abstract":"With the development of deep learning techniques, computer vision techniques have also been significantly improved. Image retrieval is a common technique used to retrieve images of interest from image databases, which can help users find the desired images more quickly. However, traditional image retrieval methods often fail to meet user needs because they often ignore complex scale information, e.g., features may differ at different scales. Therefore, an image retrieval based on a region-attention feature fusion mechanism can overcome this drawback, and it can improve the performance of image retrieval by emphasizing multi-scale features through a region-attention mechanism. In this paper, we propose an image retrieval method based on regional attention based multi-scale feature fusion, which can effectively use multiscale features. The effectiveness of RMFF is demonstrated by conducting experiments on mainstream image retrieval datasets.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116608235","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 traditional test of the system using graphical interface determines the test results by manual. With the higher accuracy requirements of the test software system in current industrial domain, the traditional manual testing has the problems of high costs, low accuracy and efficiency. Then an automatic test system based on image matching algorithm is proposed to simulate manual automatic test result. Firstly, the architecture of automatic test system is researched, which reduces the test cost. Then, an automatically selected method by template matching algorithm and ORB algorithm for the complexity of the target images, which improves the matching rate and accuracy. Finally, an image similarity calculation algorithm based on perceptual hash algorithm is built, which improves the test accuracy and efficiency twice. The experimental results show that the accuracy achieved to 97%, which meets the accuracy requirements for the industrial field.
{"title":"Research on automatic test system based on image matching algorithm","authors":"Zhenhao Wu, Chunjie Xu, Shitao Zhao","doi":"10.1117/12.2682447","DOIUrl":"https://doi.org/10.1117/12.2682447","url":null,"abstract":"The traditional test of the system using graphical interface determines the test results by manual. With the higher accuracy requirements of the test software system in current industrial domain, the traditional manual testing has the problems of high costs, low accuracy and efficiency. Then an automatic test system based on image matching algorithm is proposed to simulate manual automatic test result. Firstly, the architecture of automatic test system is researched, which reduces the test cost. Then, an automatically selected method by template matching algorithm and ORB algorithm for the complexity of the target images, which improves the matching rate and accuracy. Finally, an image similarity calculation algorithm based on perceptual hash algorithm is built, which improves the test accuracy and efficiency twice. The experimental results show that the accuracy achieved to 97%, which meets the accuracy requirements for the industrial field.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130824222","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, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance.
{"title":"Wind farm combination forecasting model based on dynamic graph attention","authors":"X. Liao, Yiqun Cheng","doi":"10.1117/12.2682328","DOIUrl":"https://doi.org/10.1117/12.2682328","url":null,"abstract":"In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129608740","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 improve the self-service convenience of campus teachers and students payment, and enhance the ability of campus smart services. This article proposes a smart campus payment service system based on Microservice architecture, which mainly relies on the basic capabilities of the Spring Cloud framework and Docker containers to build the entire Microservice platform. According to the principle of micro-services, the system is divided into micro-services according to functional modules, so as to reduce mutual calls between micro-services, achieve low coupling and high cohesion, improve the stability and scalability of the system, and make the payment process configurable and payment data visualization.
{"title":"Design and implementation of smart campus payment system based on microservice architecture","authors":"C. Liu, Zhihai Suo, Qiuyue Mao, Ying Zhu","doi":"10.1117/12.2682497","DOIUrl":"https://doi.org/10.1117/12.2682497","url":null,"abstract":"In order to improve the self-service convenience of campus teachers and students payment, and enhance the ability of campus smart services. This article proposes a smart campus payment service system based on Microservice architecture, which mainly relies on the basic capabilities of the Spring Cloud framework and Docker containers to build the entire Microservice platform. According to the principle of micro-services, the system is divided into micro-services according to functional modules, so as to reduce mutual calls between micro-services, achieve low coupling and high cohesion, improve the stability and scalability of the system, and make the payment process configurable and payment data visualization.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123109661","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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