Analog-to-digital converters (ADCs) are moving toward high speed and high resolution for low-cost testing. Based on the theory of intelligent sensor network, this paper designs a low-cost test solution for high-precision ADC chips, which solves the problems related to signal integrity. It mainly includes the following: designing an appropriate circuit connection scheme, planning an appropriate PCB stack-up structure, formulating detailed layout and wiring constraints, etc., and building a high-speed ADC test platform to obtain static and dynamic performance; based on the existing instruments in the laboratory, the effects of different signal sources, different input powers, and the presence or absence of filters on the dynamic performance of high-speed ADCs are studied. In the simulation process, the HyperLynx simulation platform is used to design and simulate the signal integrity of the high-speed acquisition board. Combined with the relevant theoretical knowledge of the signal integrity of high-speed digital circuits, the signal integrity analysis and simulation of the ADC module circuit and the DDR3 high-speed memory circuit are carried out, respectively. The results show that, taking the histogram method as a reference, when the optimal 30 windows are selected, the integral nonlinearity (INL) error of the proposed method is 0.12 LSB, the highest sampling frequency is up to 5GSps, and 61440 sampling points are required. The time is reduced by about 30% compared with the excitation error identification and removal (SEIR) method, which effectively improves the low-cost test effect of the ADC chip.
{"title":"Development and Implementation of a Low-Cost Test Solution for High-Precision ADC Chips Based on Intelligent Sensor Networks","authors":"Xiangjun Liu, Jinkun Sun","doi":"10.1155/2022/3453468","DOIUrl":"https://doi.org/10.1155/2022/3453468","url":null,"abstract":"Analog-to-digital converters (ADCs) are moving toward high speed and high resolution for low-cost testing. Based on the theory of intelligent sensor network, this paper designs a low-cost test solution for high-precision ADC chips, which solves the problems related to signal integrity. It mainly includes the following: designing an appropriate circuit connection scheme, planning an appropriate PCB stack-up structure, formulating detailed layout and wiring constraints, etc., and building a high-speed ADC test platform to obtain static and dynamic performance; based on the existing instruments in the laboratory, the effects of different signal sources, different input powers, and the presence or absence of filters on the dynamic performance of high-speed ADCs are studied. In the simulation process, the HyperLynx simulation platform is used to design and simulate the signal integrity of the high-speed acquisition board. Combined with the relevant theoretical knowledge of the signal integrity of high-speed digital circuits, the signal integrity analysis and simulation of the ADC module circuit and the DDR3 high-speed memory circuit are carried out, respectively. The results show that, taking the histogram method as a reference, when the optimal 30 windows are selected, the integral nonlinearity (INL) error of the proposed method is 0.12 LSB, the highest sampling frequency is up to 5GSps, and 61440 sampling points are required. The time is reduced by about 30% compared with the excitation error identification and removal (SEIR) method, which effectively improves the low-cost test effect of the ADC chip.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"18 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87043686","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 whole textile industry chain, yarn production is one of the key links, which has a great impact on the quality of textile and clothing products. For a long time, the textile industry has been hoping for a yarn quality prediction technology, which can accurately predict the final yarn quality indicators according to the known conditions such as raw materials and production processes. CNN-LSTM yarn prediction model is a deep neural network model based on the assumption that the influence of textile processing time series on yarn quality is considered. CNN optimizes the input eigenvalues through one-dimensional convolution and pooling, and LSTM matches the optimized fiber performance indexes and process parameters in time series according to the processing sequence and excavates their laws, thus realizing the goal of predicting yarn quality indexes. The effects of input fiber performance index, process parameters, convolution kernel parameters, pool kernel parameters, LSTM unit number, LSTM layer number, and optimization algorithm on prediction accuracy were studied, and the parameters of CNN-LSTM model were determined. Experiments on the data set of spinning yarn show that the mean square error (MSE) of CNN-LSTM model in predicting yarn strength, Dan Qiang unevenness, evenness unevenness, and total neps is lower than that of linear regression model and BP neural network. At the same time, it is found that the prediction accuracy of CNN-LSTM model is greatly influenced by process parameters and optimization algorithm.
{"title":"Prediction Model of Rotor Yarn Quality Based on CNN-LSTM","authors":"Zhenlong Hu","doi":"10.1155/2022/3955047","DOIUrl":"https://doi.org/10.1155/2022/3955047","url":null,"abstract":"In the whole textile industry chain, yarn production is one of the key links, which has a great impact on the quality of textile and clothing products. For a long time, the textile industry has been hoping for a yarn quality prediction technology, which can accurately predict the final yarn quality indicators according to the known conditions such as raw materials and production processes. CNN-LSTM yarn prediction model is a deep neural network model based on the assumption that the influence of textile processing time series on yarn quality is considered. CNN optimizes the input eigenvalues through one-dimensional convolution and pooling, and LSTM matches the optimized fiber performance indexes and process parameters in time series according to the processing sequence and excavates their laws, thus realizing the goal of predicting yarn quality indexes. The effects of input fiber performance index, process parameters, convolution kernel parameters, pool kernel parameters, LSTM unit number, LSTM layer number, and optimization algorithm on prediction accuracy were studied, and the parameters of CNN-LSTM model were determined. Experiments on the data set of spinning yarn show that the mean square error (MSE) of CNN-LSTM model in predicting yarn strength, Dan Qiang unevenness, evenness unevenness, and total neps is lower than that of linear regression model and BP neural network. At the same time, it is found that the prediction accuracy of CNN-LSTM model is greatly influenced by process parameters and optimization algorithm.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"30 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83857453","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}
At present, the teaching, training, and assessment programs of English speaking courses in schools are basically carried out by the teaching method of face-to-face teaching by teachers, especially in the English speaking assessment; students and teachers communicate with each other to answer questions, while the students’ performance is subjectively judged by the school English teachers. This model not only consumes the teaching resources of school English teachers but also cannot ensure the uniformity of grading standards. To this end, this paper designs and proposes an English speaking test system with the aim of building an intelligent speaking test system by designing a speech recognition deepening frequency processing function through SSM framework combined with JSP technology and deep learning theory. The English speaking test system designed in this paper integrates several open source frameworks, adopts modular design, and divides it into two subsystems, student-side module system and teacher-side module system for system framework design, and finally, this system is tested for functionality. The system was designed and implemented through the framework description, functional description, and functional testing of this system, and finally, the English speaking test system was designed and implemented. The system is designed and implemented to change the traditional one-to-one mode of school teaching, save teachers’ manpower, strengthen the content of intensive training for students’ English proficiency and improve the frequency of exams, facilitate school administrators and English teachers to evaluate the effectiveness of their teaching implementation, and develop corresponding effective individualized teaching and guidance strategies for schools to provide convenience, which can greatly reduce the recognized labor intensity of English teachers and provide a correct evaluation of students, a correct evaluation of the level, and other roles.
{"title":"A Study on the Design of English Speaking Examination System Based on SSM Framework","authors":"Hongying Zheng","doi":"10.1155/2022/5239463","DOIUrl":"https://doi.org/10.1155/2022/5239463","url":null,"abstract":"At present, the teaching, training, and assessment programs of English speaking courses in schools are basically carried out by the teaching method of face-to-face teaching by teachers, especially in the English speaking assessment; students and teachers communicate with each other to answer questions, while the students’ performance is subjectively judged by the school English teachers. This model not only consumes the teaching resources of school English teachers but also cannot ensure the uniformity of grading standards. To this end, this paper designs and proposes an English speaking test system with the aim of building an intelligent speaking test system by designing a speech recognition deepening frequency processing function through SSM framework combined with JSP technology and deep learning theory. The English speaking test system designed in this paper integrates several open source frameworks, adopts modular design, and divides it into two subsystems, student-side module system and teacher-side module system for system framework design, and finally, this system is tested for functionality. The system was designed and implemented through the framework description, functional description, and functional testing of this system, and finally, the English speaking test system was designed and implemented. The system is designed and implemented to change the traditional one-to-one mode of school teaching, save teachers’ manpower, strengthen the content of intensive training for students’ English proficiency and improve the frequency of exams, facilitate school administrators and English teachers to evaluate the effectiveness of their teaching implementation, and develop corresponding effective individualized teaching and guidance strategies for schools to provide convenience, which can greatly reduce the recognized labor intensity of English teachers and provide a correct evaluation of students, a correct evaluation of the level, and other roles.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"33 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86629410","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}
Coal ash blast is a potential hazard that causes serious disasters in coal mines. In explosion control, research work on coal ash sensitivity prediction is of practical importance to improve accuracy, reduce blindness of explosion protection measures, and strengthen targets. The potential and destructive characteristics of coal ash blast vary greatly from coal to coal, especially in coal mines with complex and changing environments, where the characteristics of coal ash blast show great variability under the influence of various factors. In addition, due to the lack of systematic and comprehensive understanding of the occurrence mechanism of coal ash blast, it is necessary to conduct systematic research on the occurrence mechanism of coal ash blast. Current coal ash blast sensitivity summarizes and concludes prediction methods to create reliable predictions for coal ash blast. A new general learning method, support vector machine (SVM), has been developed, which provides a unified framework for solving limited sample training problems and can better solve small sample training problems. With the purpose of determining the coal mine problem and coal ash sensitivity prediction sensitivity indicators and thresholds, the SVM method is used to set the sensitivity function of each prediction indicator, and the sensitivity of each prediction indicator for the proposed study mine is expressed quantitatively. The experimental results show that the prediction accuracy of SVM for positive and negative categories is 15.6% higher than that of BP neural network and 35.1% higher than that of Apriori algorithm. Therefore, the prediction effectiveness of the SVM algorithm is proved. Therefore, it is practical to adopt SVM method for prediction on sensitivity to coal ash blast and apply the latest statistical learning theory SVM to predict the risk of coal ash.
{"title":"A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration","authors":"J. Zhang, Qingxia Wang, Wannian Guo, L. Li","doi":"10.1155/2022/7604338","DOIUrl":"https://doi.org/10.1155/2022/7604338","url":null,"abstract":"Coal ash blast is a potential hazard that causes serious disasters in coal mines. In explosion control, research work on coal ash sensitivity prediction is of practical importance to improve accuracy, reduce blindness of explosion protection measures, and strengthen targets. The potential and destructive characteristics of coal ash blast vary greatly from coal to coal, especially in coal mines with complex and changing environments, where the characteristics of coal ash blast show great variability under the influence of various factors. In addition, due to the lack of systematic and comprehensive understanding of the occurrence mechanism of coal ash blast, it is necessary to conduct systematic research on the occurrence mechanism of coal ash blast. Current coal ash blast sensitivity summarizes and concludes prediction methods to create reliable predictions for coal ash blast. A new general learning method, support vector machine (SVM), has been developed, which provides a unified framework for solving limited sample training problems and can better solve small sample training problems. With the purpose of determining the coal mine problem and coal ash sensitivity prediction sensitivity indicators and thresholds, the SVM method is used to set the sensitivity function of each prediction indicator, and the sensitivity of each prediction indicator for the proposed study mine is expressed quantitatively. The experimental results show that the prediction accuracy of SVM for positive and negative categories is 15.6% higher than that of BP neural network and 35.1% higher than that of Apriori algorithm. Therefore, the prediction effectiveness of the SVM algorithm is proved. Therefore, it is practical to adopt SVM method for prediction on sensitivity to coal ash blast and apply the latest statistical learning theory SVM to predict the risk of coal ash.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"35 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85850731","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}
Ecological vulnerability is the focus of research on global environmental impact, regional sustainable development, ecological civilization, and green development. There are eight deserts and four sandy lands in northern China. The ecological environment is sensitive to climate change and human activities. It is of great significance to carry out long-term sequential ecological vulnerability assessments. Therefore, taking northern China as the research area, this paper selects 13 data indicators such as climate, topography, and soil based on the ecological sensitivity-ecological recovery-ecological pressure model (SPR) and uses the spatial principal component analysis method (SPCA) to quantitatively evaluate the spatial and temporal differentiation characteristics and driving forces of ecological vulnerability in this area from 1980 to 2020. The results showed that areas with extreme, severe, and moderate vulnerability dominated northern China, accounting for 74.58% of the total area. The analysis revealed a decrease in ecological vulnerability from west to east and north to south. Meanwhile, from the perspective of timing, the overall level of ecological vulnerability showed an upward trend before 2000, and the overall level of ecological vulnerability continued to decline after 2000, and the quality of the ecological environment improved. During the study period, areas in northern China with severe vulnerability and slight vulnerability showed a change of 15.53% and -14.01%, respectively. The main reason for the change in ecological vulnerability is the frequent transformation between forest land, grassland, water, and cultivated land. In addition, the study found a spatial autocorrelation of ecological vulnerability of northern China and a significantly positive correlation. After 2000, the spatial aggregation of vulnerability was high-high cluster, which was mainly distributed in northwest China. The study’s findings will provide a robust scientific basis for ecosystem management and sustainable development.
{"title":"Spatial and Temporal Variations in the Ecological Vulnerability of Northern China","authors":"Chunwei Song, Huishi Du","doi":"10.1155/2022/7232830","DOIUrl":"https://doi.org/10.1155/2022/7232830","url":null,"abstract":"Ecological vulnerability is the focus of research on global environmental impact, regional sustainable development, ecological civilization, and green development. There are eight deserts and four sandy lands in northern China. The ecological environment is sensitive to climate change and human activities. It is of great significance to carry out long-term sequential ecological vulnerability assessments. Therefore, taking northern China as the research area, this paper selects 13 data indicators such as climate, topography, and soil based on the ecological sensitivity-ecological recovery-ecological pressure model (SPR) and uses the spatial principal component analysis method (SPCA) to quantitatively evaluate the spatial and temporal differentiation characteristics and driving forces of ecological vulnerability in this area from 1980 to 2020. The results showed that areas with extreme, severe, and moderate vulnerability dominated northern China, accounting for 74.58% of the total area. The analysis revealed a decrease in ecological vulnerability from west to east and north to south. Meanwhile, from the perspective of timing, the overall level of ecological vulnerability showed an upward trend before 2000, and the overall level of ecological vulnerability continued to decline after 2000, and the quality of the ecological environment improved. During the study period, areas in northern China with severe vulnerability and slight vulnerability showed a change of 15.53% and -14.01%, respectively. The main reason for the change in ecological vulnerability is the frequent transformation between forest land, grassland, water, and cultivated land. In addition, the study found a spatial autocorrelation of ecological vulnerability of northern China and a significantly positive correlation. After 2000, the spatial aggregation of vulnerability was high-high cluster, which was mainly distributed in northwest China. The study’s findings will provide a robust scientific basis for ecosystem management and sustainable development.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"7 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83462586","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 change the current piano teaching mode and develop towards digitalization, this paper puts forward the Internet of Things remote piano information teaching system. The digital electric piano teaching system is controlled by multimedia computer. It is a kind of music teaching form composed of electric piano and other electronic keyboard instruments, music auxiliary teaching system, and music production software. It integrates viewing, listening, and practicing and changes the traditional one-to-one teaching mode. Its core structure adopts professional audio processing chip and processor to realize controllable digital audio communication channel, which solves the interference problem well, and realizes classroom simulation functions such as centralized teaching, personal guidance, personal demonstration, group demonstration, and group practice. The application results show that the average learning time of the students who pass the intelligent digital electric piano teaching system is reduced by 14%; After two months of study, 46 students still like the piano through the intelligent digital electric piano teaching system, with a retention rate of 92%, which is significantly higher than the 38 students who study the traditional piano, with a retention rate of 76%. Conclusion. The system makes teaching easier and more efficient.
{"title":"Internet of Things Remote Piano Information Teaching System and Its Control Method","authors":"Yang-Wu Fan","doi":"10.1155/2022/4730550","DOIUrl":"https://doi.org/10.1155/2022/4730550","url":null,"abstract":"In order to change the current piano teaching mode and develop towards digitalization, this paper puts forward the Internet of Things remote piano information teaching system. The digital electric piano teaching system is controlled by multimedia computer. It is a kind of music teaching form composed of electric piano and other electronic keyboard instruments, music auxiliary teaching system, and music production software. It integrates viewing, listening, and practicing and changes the traditional one-to-one teaching mode. Its core structure adopts professional audio processing chip and processor to realize controllable digital audio communication channel, which solves the interference problem well, and realizes classroom simulation functions such as centralized teaching, personal guidance, personal demonstration, group demonstration, and group practice. The application results show that the average learning time of the students who pass the intelligent digital electric piano teaching system is reduced by 14%; After two months of study, 46 students still like the piano through the intelligent digital electric piano teaching system, with a retention rate of 92%, which is significantly higher than the 38 students who study the traditional piano, with a retention rate of 76%. Conclusion. The system makes teaching easier and more efficient.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"44 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88286093","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}
Introduction. The development of network technology is promoting the process of digitalization. The digital museum, an emerging museum display mode, is gradually highlighting its great value in the wave of digitalization. In the context of the rapid development of digital museums and mobile applications, the user interface of digital museums, as the integration point of human-computer interaction, the artistic expression of its visual design is also more important. This paper takes the digital museum application (APP) user interface design as the research direction. By optimizing the visual design of the interface, the user’s operating experience is improved, so that users can enjoy an orderly, time-saving, efficient, comfortable, and interesting interactive experience. Methods. User interface design to analyze related theories such as target-oriented design and visual hierarchy design and dig out the manifestation of user goals in the museum’s APP visual hierarchy and clarify the design content. Use qualitative and quantitative user research methods to conduct demand research for museum users and build user role models. Build user role target task model. Determine the function settings of the museum’s APP and establish an information structure. Secondly, build a low-fidelity model through the level analysis of the visual elements in the interface and use the low-fidelity prototype test to guide the museum’s APP visual level design. The interface visual level elements are integrated into the museum’s APP visual level framework and integrated with the museum’s characteristics Together, put forward the visual hierarchical design strategy of the museum APP. Results. A new model for the dissemination and display of cultural information resources by transposing the display of museum information content fusion to the works of the digital platform. The ease of use, fun, and artistic quality of the user interface of the digital museum are the keys to attracting users. Through the summary of the theory and the construction of the design strategy, we build and design a museum APP that meets the user’s experience, meets the user’s goals, and has a good visual hierarchy. The experimental results show that the largest number of people, 62.6%, wanted to learn about local culture by visiting museums. The number of people whose purpose was to travel and the study was the next highest, and they accounted for an equal share, 43.1% and 40.6%, respectively. A smaller number, 23.4%, attended museums for hobbies and interests. The number of people who visit museums for research purposes is even lower, at 11.9%. Through usability testing and user satisfaction analysis of the design model, the rationality and effectiveness of the design strategy are verified, which can provide appropriate guidance for museum app design. Conclusion. The problems encountered in the visual design and production of digital museums and the solutions were discussed, and through a comparative
{"title":"Multimedia Analysis of Digital Museum User Interface Based on Goal-Oriented Theory and Information Fusion and Intelligent Sensing","authors":"Qing-Yun Zhuang, Wanni Xu, Danyang Yang, Ning Wei","doi":"10.1155/2022/9656817","DOIUrl":"https://doi.org/10.1155/2022/9656817","url":null,"abstract":"Introduction. The development of network technology is promoting the process of digitalization. The digital museum, an emerging museum display mode, is gradually highlighting its great value in the wave of digitalization. In the context of the rapid development of digital museums and mobile applications, the user interface of digital museums, as the integration point of human-computer interaction, the artistic expression of its visual design is also more important. This paper takes the digital museum application (APP) user interface design as the research direction. By optimizing the visual design of the interface, the user’s operating experience is improved, so that users can enjoy an orderly, time-saving, efficient, comfortable, and interesting interactive experience. Methods. User interface design to analyze related theories such as target-oriented design and visual hierarchy design and dig out the manifestation of user goals in the museum’s APP visual hierarchy and clarify the design content. Use qualitative and quantitative user research methods to conduct demand research for museum users and build user role models. Build user role target task model. Determine the function settings of the museum’s APP and establish an information structure. Secondly, build a low-fidelity model through the level analysis of the visual elements in the interface and use the low-fidelity prototype test to guide the museum’s APP visual level design. The interface visual level elements are integrated into the museum’s APP visual level framework and integrated with the museum’s characteristics Together, put forward the visual hierarchical design strategy of the museum APP. Results. A new model for the dissemination and display of cultural information resources by transposing the display of museum information content fusion to the works of the digital platform. The ease of use, fun, and artistic quality of the user interface of the digital museum are the keys to attracting users. Through the summary of the theory and the construction of the design strategy, we build and design a museum APP that meets the user’s experience, meets the user’s goals, and has a good visual hierarchy. The experimental results show that the largest number of people, 62.6%, wanted to learn about local culture by visiting museums. The number of people whose purpose was to travel and the study was the next highest, and they accounted for an equal share, 43.1% and 40.6%, respectively. A smaller number, 23.4%, attended museums for hobbies and interests. The number of people who visit museums for research purposes is even lower, at 11.9%. Through usability testing and user satisfaction analysis of the design model, the rationality and effectiveness of the design strategy are verified, which can provide appropriate guidance for museum app design. Conclusion. The problems encountered in the visual design and production of digital museums and the solutions were discussed, and through a comparative ","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"35 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79668513","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}
Obesity is a global public health problem in modern society. Body mass index (BMI) can measure whether a person’s body is in obesity and health. Body mass is the quality of human body, which is a comprehensive and relatively stable characteristic of human body structure, physiological function, and psychological factors, and body mass is the basis of heredity and acquisition. Physical exercise is a kind of physical activity that people do in their spare time in order to exercise their body and mind. In this paper, we take the physical health problem of obesity as the background and combine the fuzzy breakpoint regression method to design the body mass index and physical exercise participation rate. The relationship between the body mass index and physical exercise participation rate design based on fuzzy breakpoint regression in this paper is mainly discussed as follows: background: the obesity rate is rising and chronic diseases caused by obesity affect health; results: jumps were observed for body health indices above 24.9 kg/m2, while participation rates in physical activity decreased compared to the normal weight range; and conclusion: the greater the weight of the group, the less willingness to participate in physical activity and the lower the rate of participation in physical activity.
{"title":"The Relationship between Body Mass Index and Physical Activity Participation Rate Design Based on Fuzzy Breakpoint Regression Design","authors":"Jinhao Wu, L. He","doi":"10.1155/2022/3721659","DOIUrl":"https://doi.org/10.1155/2022/3721659","url":null,"abstract":"Obesity is a global public health problem in modern society. Body mass index (BMI) can measure whether a person’s body is in obesity and health. Body mass is the quality of human body, which is a comprehensive and relatively stable characteristic of human body structure, physiological function, and psychological factors, and body mass is the basis of heredity and acquisition. Physical exercise is a kind of physical activity that people do in their spare time in order to exercise their body and mind. In this paper, we take the physical health problem of obesity as the background and combine the fuzzy breakpoint regression method to design the body mass index and physical exercise participation rate. The relationship between the body mass index and physical exercise participation rate design based on fuzzy breakpoint regression in this paper is mainly discussed as follows: background: the obesity rate is rising and chronic diseases caused by obesity affect health; results: jumps were observed for body health indices above 24.9 kg/m2, while participation rates in physical activity decreased compared to the normal weight range; and conclusion: the greater the weight of the group, the less willingness to participate in physical activity and the lower the rate of participation in physical activity.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"57 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78795535","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}
Under the modern design concept, consider ergonomics to design home products. With the progress of civilization and technology, the improvement of life quality in the process of urbanization, and the increasing abundance of home life and home products, people’s requirements for living environment and environmental products are continuously improving. In order to further meet the necessities of life and solve the reasons such as limited living space at home, people are no longer satisfied with purchasing household products in large quantities but are more suitable for household needs. According to the user’s requirements for ergonomic home product design, a criterion layer is established, and the weight of the criterion layer is calculated to obtain its corresponding weight value. It can be obtained that consumers think that safety is the most important, followed by ease of use, functionality, and aesthetics. In the second criterion level, the order of importance is stable operation, safe use of materials, invisible circuit, strong practicability, massage function, safety guardrail, convenient installation, easy cleaning, intelligent operation, home style, structural strength, easy to move, natural materials, air purification, easy disassembly, suitable size, simple shape, convenient function, timely after-sales, soft color tone, noise reduction, simple decoration, single color matching, and comfortable function. The addition of the nearest neighbors improves the accuracy of the CFCNN-CL algorithm and the REPREDICT PCC algorithm in terms of smart algorithm recommendations for home products considering ergonomics. But compared between the two, the CFCNN-CL algorithm has better performance and better accuracy than the REPREDICT PCC algorithm. In terms of the influence of data sparseness, UCF-Jaccard has a smaller MAE value than other methods in general and is less susceptible to the influence of sparse data, and the MAE value does not change much. Among the group filtering methods, the RRP-UICL method has better prediction accuracy than the commonly used group filtering methods.
{"title":"Research on Home Product Design and Intelligent Algorithm Recommendation considering Ergonomics","authors":"Xianya Wang","doi":"10.1155/2022/1791269","DOIUrl":"https://doi.org/10.1155/2022/1791269","url":null,"abstract":"Under the modern design concept, consider ergonomics to design home products. With the progress of civilization and technology, the improvement of life quality in the process of urbanization, and the increasing abundance of home life and home products, people’s requirements for living environment and environmental products are continuously improving. In order to further meet the necessities of life and solve the reasons such as limited living space at home, people are no longer satisfied with purchasing household products in large quantities but are more suitable for household needs. According to the user’s requirements for ergonomic home product design, a criterion layer is established, and the weight of the criterion layer is calculated to obtain its corresponding weight value. It can be obtained that consumers think that safety is the most important, followed by ease of use, functionality, and aesthetics. In the second criterion level, the order of importance is stable operation, safe use of materials, invisible circuit, strong practicability, massage function, safety guardrail, convenient installation, easy cleaning, intelligent operation, home style, structural strength, easy to move, natural materials, air purification, easy disassembly, suitable size, simple shape, convenient function, timely after-sales, soft color tone, noise reduction, simple decoration, single color matching, and comfortable function. The addition of the nearest neighbors improves the accuracy of the CFCNN-CL algorithm and the REPREDICT PCC algorithm in terms of smart algorithm recommendations for home products considering ergonomics. But compared between the two, the CFCNN-CL algorithm has better performance and better accuracy than the REPREDICT PCC algorithm. In terms of the influence of data sparseness, UCF-Jaccard has a smaller MAE value than other methods in general and is less susceptible to the influence of sparse data, and the MAE value does not change much. Among the group filtering methods, the RRP-UICL method has better prediction accuracy than the commonly used group filtering methods.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"2022 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74476037","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 presents an in-depth study and analysis of the localization and tracking of multiple targets in soccer training using a distributed intelligent sensor approach. An event-triggered mechanism is used to drive the acoustic array sensors in the distributed acoustic array sensor network, which solves the problem of increased communication load caused by frequent communication of microphones and effectively reduces the communication load between microphones as well as the energy consumption of the acoustic array sensor network. By designing a suitable state estimation equation for the acoustic source target and fully utilizing the measurement and state estimation information of its nodes as well as the state estimation information of neighboring nodes, the next moment state of the acoustic source target can be accurately predicted. A correlation filtering tracking algorithm based on multiscale spatial co-localization is proposed. In the proposed algorithm, the tracker contains a total of several subfilters with different sampling ranges. Then, this paper also proposes a collaborative discrimination method to judge the spatial response of the target image samples of each filter and jointly localize the target online. Based on this, this paper further explores the potential of correlation filter tracking algorithms in complex environments and proposes a robust correlation filter tracking algorithm that fuses multiscale spatial views. The cross-view geometric similarity measure based on multiframe pose information is proposed, and the matching effect is better than that based on single-frame cross-view geometric similarity; to solve the problem of player appearance similarity interference, a graph model-based cross-view appearance similarity measure learning method is further proposed, with players in each view as nodes, player appearance depth features as node attributes, and connections between cross-view players as edges to construct a cross-view player graph. The similarity obtained by the graph convolutional neural network training is better than the appearance similarity calculated based on simple cosine distance.
{"title":"Distributed Soccer Training Smart Sensors for Multitarget Localization and Tracking","authors":"Jian Jiang, Zhiqun Qiu","doi":"10.1155/2022/4772636","DOIUrl":"https://doi.org/10.1155/2022/4772636","url":null,"abstract":"This paper presents an in-depth study and analysis of the localization and tracking of multiple targets in soccer training using a distributed intelligent sensor approach. An event-triggered mechanism is used to drive the acoustic array sensors in the distributed acoustic array sensor network, which solves the problem of increased communication load caused by frequent communication of microphones and effectively reduces the communication load between microphones as well as the energy consumption of the acoustic array sensor network. By designing a suitable state estimation equation for the acoustic source target and fully utilizing the measurement and state estimation information of its nodes as well as the state estimation information of neighboring nodes, the next moment state of the acoustic source target can be accurately predicted. A correlation filtering tracking algorithm based on multiscale spatial co-localization is proposed. In the proposed algorithm, the tracker contains a total of several subfilters with different sampling ranges. Then, this paper also proposes a collaborative discrimination method to judge the spatial response of the target image samples of each filter and jointly localize the target online. Based on this, this paper further explores the potential of correlation filter tracking algorithms in complex environments and proposes a robust correlation filter tracking algorithm that fuses multiscale spatial views. The cross-view geometric similarity measure based on multiframe pose information is proposed, and the matching effect is better than that based on single-frame cross-view geometric similarity; to solve the problem of player appearance similarity interference, a graph model-based cross-view appearance similarity measure learning method is further proposed, with players in each view as nodes, player appearance depth features as node attributes, and connections between cross-view players as edges to construct a cross-view player graph. The similarity obtained by the graph convolutional neural network training is better than the appearance similarity calculated based on simple cosine distance.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"26 11 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77099967","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：