Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01422
Qiang Du
Abstract This paper is based on the use of recurrent neural networks and LSTM deep neural networks to obtain the financial risk prediction feature sequence in the context of big data. The financial risk prediction feature sequence is used as the input value of the input gate of the LSTM deep neural network model after data filtering, normalization and loss function optimization, and then the financial risk prediction for the output gate of the LSTM deep neural network model. Considering the availability of data, small and medium-sized enterprises listed in A-share companies in the Wind database are selected as sample enterprises, and evaluation indexes are constructed and detected at the same time so as to complete the experimental design of enterprise financial risk prediction in the context of big data. The prediction of enterprise financial risk is empirically analyzed using simulation analysis and statistical analysis. The results show that in the model performance analysis, the average value of ten years of data, the highest value is still the result obtained by LSTM training, 0.761, compared with other models of LSTM deep neural network in static financial risk prediction in the overall best performance. In the case study of Yibai Pharmaceutical, the minimum value of the rate of return, return on total assets, and return on assets were -10.02%, 2.56%, -20.72%, which reflects the fact that the private enterprises still have large profitability space to be mined. This study helps investors or financial institutions such as funds to find out the possible financial risk crisis of listed companies as early as possible to avoid the parties from incurring large financial losses.
{"title":"Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks","authors":"Qiang Du","doi":"10.2478/amns.2023.2.01422","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01422","url":null,"abstract":"Abstract This paper is based on the use of recurrent neural networks and LSTM deep neural networks to obtain the financial risk prediction feature sequence in the context of big data. The financial risk prediction feature sequence is used as the input value of the input gate of the LSTM deep neural network model after data filtering, normalization and loss function optimization, and then the financial risk prediction for the output gate of the LSTM deep neural network model. Considering the availability of data, small and medium-sized enterprises listed in A-share companies in the Wind database are selected as sample enterprises, and evaluation indexes are constructed and detected at the same time so as to complete the experimental design of enterprise financial risk prediction in the context of big data. The prediction of enterprise financial risk is empirically analyzed using simulation analysis and statistical analysis. The results show that in the model performance analysis, the average value of ten years of data, the highest value is still the result obtained by LSTM training, 0.761, compared with other models of LSTM deep neural network in static financial risk prediction in the overall best performance. In the case study of Yibai Pharmaceutical, the minimum value of the rate of return, return on total assets, and return on assets were -10.02%, 2.56%, -20.72%, which reflects the fact that the private enterprises still have large profitability space to be mined. This study helps investors or financial institutions such as funds to find out the possible financial risk crisis of listed companies as early as possible to avoid the parties from incurring large financial losses.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"5 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138586182","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}
Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01421
Lei Guo, Xujie Guo
Abstract In this paper, a two-dimensional panel data model of economic policy uncertainty is investigated based on the individual fixed effects of panel quantile regression, and a nonparametric panel model with individual fixed effects is established. The unfolding of nonparametric penalized spline and the introduction of Bayesian in stratified quantile are utilized to construct regression models applicable to accounting robustness, respectively. In the empirical study, the economic policy uncertainty index, accounting robustness and commercial credit supply are measured respectively. The annual data of China’s Shenzhen and Shanghai A-share listed companies during the period from 2012 to 2021 were selected as the research basis, and Bayesian quantile regression was made on the basis of correlation analysis. The coefficient of commercial credit supply is found to be -0.0821, and the variable RD1 is negatively correlated with economic policy uncertainty. This regression result confirms hypothesis H1 of this paper, suggesting that private firms invest less in innovation when economic policy uncertainty is higher. In the test of economic policy uncertainty by type, the regression coefficients of RD2, EPU, and SIZE are negative, respectively -0.0368, −0.2124, and -0.1458, which indicates that fiscal policy, monetary policy, and exchange rate and capital account policy uncertainty are negatively correlated with the supply of business credit to enterprises. Based on this correlation, this study provides guidance for the development of business credit for enterprises.
{"title":"Economic Policy Uncertainty, Accounting Robustness and Commercial Credit Supply - A Big Data Analysis Based on Accounts Receivable","authors":"Lei Guo, Xujie Guo","doi":"10.2478/amns.2023.2.01421","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01421","url":null,"abstract":"Abstract In this paper, a two-dimensional panel data model of economic policy uncertainty is investigated based on the individual fixed effects of panel quantile regression, and a nonparametric panel model with individual fixed effects is established. The unfolding of nonparametric penalized spline and the introduction of Bayesian in stratified quantile are utilized to construct regression models applicable to accounting robustness, respectively. In the empirical study, the economic policy uncertainty index, accounting robustness and commercial credit supply are measured respectively. The annual data of China’s Shenzhen and Shanghai A-share listed companies during the period from 2012 to 2021 were selected as the research basis, and Bayesian quantile regression was made on the basis of correlation analysis. The coefficient of commercial credit supply is found to be -0.0821, and the variable RD1 is negatively correlated with economic policy uncertainty. This regression result confirms hypothesis H1 of this paper, suggesting that private firms invest less in innovation when economic policy uncertainty is higher. In the test of economic policy uncertainty by type, the regression coefficients of RD2, EPU, and SIZE are negative, respectively -0.0368, −0.2124, and -0.1458, which indicates that fiscal policy, monetary policy, and exchange rate and capital account policy uncertainty are negatively correlated with the supply of business credit to enterprises. Based on this correlation, this study provides guidance for the development of business credit for enterprises.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"17 12","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585450","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}
Abstract In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster analysis method, we convert the variable types of customer demand indexes into different clustering effects. Fuzzy cluster analysis is used to establish the mapping relationship between customer demand, functional requirements of the product, and design parameters. Use the idea of customer demand analysis and transformation and the module division method to build the framework system of product configuration design and complete the construction of a customer demand-oriented product configuration visualization platform. By dividing different customer requirements, the best classification of customer requirements is obtained, and the technical optimization design of washing machine products is taken as an example to analyze the practicability of the platform constructed in this paper. Among the 12 technical characteristics of the washing machine, the importance of EG11 is 0.1395, the importance of EG1 is 0.1116, and the importance of EG5 is 0.1017, which indicates that customers are most concerned about the energy-saving function of the product, and thus the enterprise should design the product based on the customer needs to satisfy the customer’s demands.
{"title":"Research on the construction of a visualization platform for customer demand analysis based on big data technology","authors":"Shengping Yan, Hongbang Su, Guisheng Ma, Xiaoxuan Qi, Yuling Li, Liang Cheng","doi":"10.2478/amns.2023.2.01414","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01414","url":null,"abstract":"Abstract In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster analysis method, we convert the variable types of customer demand indexes into different clustering effects. Fuzzy cluster analysis is used to establish the mapping relationship between customer demand, functional requirements of the product, and design parameters. Use the idea of customer demand analysis and transformation and the module division method to build the framework system of product configuration design and complete the construction of a customer demand-oriented product configuration visualization platform. By dividing different customer requirements, the best classification of customer requirements is obtained, and the technical optimization design of washing machine products is taken as an example to analyze the practicability of the platform constructed in this paper. Among the 12 technical characteristics of the washing machine, the importance of EG11 is 0.1395, the importance of EG1 is 0.1116, and the importance of EG5 is 0.1017, which indicates that customers are most concerned about the energy-saving function of the product, and thus the enterprise should design the product based on the customer needs to satisfy the customer’s demands.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"14 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585645","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}
Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01419
Huazhong Hu
Abstract This paper takes garden landscape as the research object, discusses the application of digital media art in assisting garden landscape design, and obtains the optimization probability of various types of garden landscape types in landscape spatial layout through patch generation using the change simulation (PLUS) model. And after using the multi-scale Retinex algorithm to make image enhancement, a fusion of single-scale enhancement results in achieving the initial optimization of the image, after a dynamic interception and stretching operation to restore the enhancement effect to achieve the optimization of the landscape image. Finally, a group of landscape design images are selected as experimental objects to test the effectiveness of digital media art-assisted landscape design. The results show that with the assistance of digital media art, the modulus of the change distance of neighboring units is between 2.21 and 10.89, and the relative change rate takes the value between 0.45 and 5.21. The method is capable of balancing the ups and downs and repetitive rhythms in the design, ensuring that the landscape has good brightness and visual effects.
{"title":"Research based on the use of digital media art in garden landscape design","authors":"Huazhong Hu","doi":"10.2478/amns.2023.2.01419","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01419","url":null,"abstract":"Abstract This paper takes garden landscape as the research object, discusses the application of digital media art in assisting garden landscape design, and obtains the optimization probability of various types of garden landscape types in landscape spatial layout through patch generation using the change simulation (PLUS) model. And after using the multi-scale Retinex algorithm to make image enhancement, a fusion of single-scale enhancement results in achieving the initial optimization of the image, after a dynamic interception and stretching operation to restore the enhancement effect to achieve the optimization of the landscape image. Finally, a group of landscape design images are selected as experimental objects to test the effectiveness of digital media art-assisted landscape design. The results show that with the assistance of digital media art, the modulus of the change distance of neighboring units is between 2.21 and 10.89, and the relative change rate takes the value between 0.45 and 5.21. The method is capable of balancing the ups and downs and repetitive rhythms in the design, ensuring that the landscape has good brightness and visual effects.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"5 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585742","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}
Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01426
Pan Jun
Abstract This paper analyzes the fission information dissemination mode from the digital information media mode of the film and television media industry. Using the correlation algorithm to analyze the influence of TV drama ratings and broadcasting accounted for and selecting cluster analysis to explore the relationship between TV drama broadcasting and TV type and rating. Take the ratings as the dependent variable, set the independent variables, establish the multivariate statistical model, and use SPSS software to calculate factor analysis of TV drama ratings. By combining user opinions, optimize the heterogeneous graph neural network film and television communication model based on attribute information. Test the MAE value and effect of the propagation algorithm proposed in this paper using the real Movies Lens dataset. When N=5, the recall, precision and F1 of this paper’s algorithm are 0.295, 0.751, and 0.425, respectively. The difference of the three metrics with the resource diffusion algorithm based on the three-step graph is 0.25, 0.634, and 0.36. When N=50, the difference of the three metrics between this paper’s algorithm and the social diffusion algorithm based on labels is 0.197, 0.071, and 0.101.
{"title":"Exploration of Digital Communication Mechanism of Film and Television Media Industry in the Background of Artificial Intelligence","authors":"Pan Jun","doi":"10.2478/amns.2023.2.01426","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01426","url":null,"abstract":"Abstract This paper analyzes the fission information dissemination mode from the digital information media mode of the film and television media industry. Using the correlation algorithm to analyze the influence of TV drama ratings and broadcasting accounted for and selecting cluster analysis to explore the relationship between TV drama broadcasting and TV type and rating. Take the ratings as the dependent variable, set the independent variables, establish the multivariate statistical model, and use SPSS software to calculate factor analysis of TV drama ratings. By combining user opinions, optimize the heterogeneous graph neural network film and television communication model based on attribute information. Test the MAE value and effect of the propagation algorithm proposed in this paper using the real Movies Lens dataset. When N=5, the recall, precision and F1 of this paper’s algorithm are 0.295, 0.751, and 0.425, respectively. The difference of the three metrics with the resource diffusion algorithm based on the three-step graph is 0.25, 0.634, and 0.36. When N=50, the difference of the three metrics between this paper’s algorithm and the social diffusion algorithm based on labels is 0.197, 0.071, and 0.101.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"18 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585162","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}
Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01423
Shuhuai An, Zhen Wei, Lei Tang, Yijia Li
Abstract This paper diagnoses the transmission line dancing situation based on the wide-area traveling wave information transmission and dancing mechanism. The characteristics of the wide-area initial traveling wave propagation are analyzed, and the traveling wave information of transmission line dancing is analyzed using wavelet transform. Measure the voltage traveling wave energy distribution for online monitoring and diagnosis of transmission lines. To study the dancing amplitude of transmission lines, a finite element analysis model is created. The detuned pendulum anti-dancing device is designed, the detuned pendulum dynamics equation is constructed, and the critical wind speed leading to transmission line dancing is investigated by the theoretical equation method and the stability theory method. Through the empirical analysis method, the transmission line dance monitoring and the anti-dance effect are analyzed. The experiments show that when the transmission line dances at a slower speed, the online monitoring method based on wide-area information monitors the motion of the target spacer bar between two neighboring frames between [1,3], and the processing speed is 138.2 frames per second faster than the other techniques, which is successful in tracking the dancing target of the transmission line. In the anti-dance test, before the anti-dancer was added, the transmission line amplitude reached 12,12m/s from the beginning at a wind speed of 18m/s and 14m/s to provoke dance. After the installation of the anti-dancer, the amplitude is maintained between [0,1] in most cases, and the anti-dancer has a good anti-dance effect.
{"title":"Research on online monitoring and anti-dance technology of transmission line dance based on wide-area information transmission","authors":"Shuhuai An, Zhen Wei, Lei Tang, Yijia Li","doi":"10.2478/amns.2023.2.01423","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01423","url":null,"abstract":"Abstract This paper diagnoses the transmission line dancing situation based on the wide-area traveling wave information transmission and dancing mechanism. The characteristics of the wide-area initial traveling wave propagation are analyzed, and the traveling wave information of transmission line dancing is analyzed using wavelet transform. Measure the voltage traveling wave energy distribution for online monitoring and diagnosis of transmission lines. To study the dancing amplitude of transmission lines, a finite element analysis model is created. The detuned pendulum anti-dancing device is designed, the detuned pendulum dynamics equation is constructed, and the critical wind speed leading to transmission line dancing is investigated by the theoretical equation method and the stability theory method. Through the empirical analysis method, the transmission line dance monitoring and the anti-dance effect are analyzed. The experiments show that when the transmission line dances at a slower speed, the online monitoring method based on wide-area information monitors the motion of the target spacer bar between two neighboring frames between [1,3], and the processing speed is 138.2 frames per second faster than the other techniques, which is successful in tracking the dancing target of the transmission line. In the anti-dance test, before the anti-dancer was added, the transmission line amplitude reached 12,12m/s from the beginning at a wind speed of 18m/s and 14m/s to provoke dance. After the installation of the anti-dancer, the amplitude is maintained between [0,1] in most cases, and the anti-dancer has a good anti-dance effect.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"6 11","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585229","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}
Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01415
Yun Liu
Abstract This paper focuses on the use of feature extraction techniques as well as parameter estimation to analyze the immediate pressing tactics in soccer games. The motion target detection method is used to capture the movements of the soccer player. By setting the rotation angle of the point cloud, the soccer movement action is represented in the form of a coordinate system. By combining the inter-frame difference method and setting the motion image threshold, the motion target can be obtained. Utilize Hu moments to extract the features of soccer motion. Combine the center of mass and velocity of soccer motion to reduce the error rate of motion feature extraction. Pairwise quaternions are utilized to represent soccer motion parameters to improve motion estimation. The results show that the soccer team has the greatest success rate of practicing immediate pressing tactics in 3s-4s, and the success rate of applying immediate pressing tactics after 4s is significantly lower. Team C has the highest success rate of huddling with defensive immediate pressing tactics, which reaches 56.1%. The success rate of huddling is closest to that of team A and team B, which are 43.54% and 43.97%, respectively.
{"title":"Characterization of Immediate Pressing Tactics in Soccer in the Age of Artificial Intelligence","authors":"Yun Liu","doi":"10.2478/amns.2023.2.01415","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01415","url":null,"abstract":"Abstract This paper focuses on the use of feature extraction techniques as well as parameter estimation to analyze the immediate pressing tactics in soccer games. The motion target detection method is used to capture the movements of the soccer player. By setting the rotation angle of the point cloud, the soccer movement action is represented in the form of a coordinate system. By combining the inter-frame difference method and setting the motion image threshold, the motion target can be obtained. Utilize Hu moments to extract the features of soccer motion. Combine the center of mass and velocity of soccer motion to reduce the error rate of motion feature extraction. Pairwise quaternions are utilized to represent soccer motion parameters to improve motion estimation. The results show that the soccer team has the greatest success rate of practicing immediate pressing tactics in 3s-4s, and the success rate of applying immediate pressing tactics after 4s is significantly lower. Team C has the highest success rate of huddling with defensive immediate pressing tactics, which reaches 56.1%. The success rate of huddling is closest to that of team A and team B, which are 43.54% and 43.97%, respectively.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"5 7","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585604","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}
Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01418
Guorui Wang
Abstract The technology can fully explore the user’s consumption behavior habits and help the e-commerce platform formulate more precise marketing strategies in a targeted manner. This paper firstly analyzes the optimization of marketing strategy based on the 3R marketing theory, gives the design process of the precise marketing strategy of an e-commerce platform, and analyzes the personalized service based on consumer classification. Secondly, for the shortcomings of the KNN algorithm in the process of accurate classification, the Gaussian function is introduced to weight the optimization of the algorithm, which further realizes the construction of the G-KNN algorithm. Finally, the testing and application analysis of the algorithm model was carried out using the actual user consumption data of the e-commerce platform. The results show that the classification accuracy of the G-KNN algorithm has been maintained at about 95% when the K value exceeds 800, and the F1 composite value of this paper’s algorithm fluctuates around 56% when the K value exceeds 1000. On the e-commerce platform, except for the electrical appliances category classification test, the fit and accuracy of other categories basically match. Using the KNN algorithm incorporating the Gaussian function can effectively realize the accurate classification of user characteristics on the e-commerce platform and provide data support for the e-commerce platform to formulate accurate marketing strategies based on consumer preferences.
{"title":"Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms","authors":"Guorui Wang","doi":"10.2478/amns.2023.2.01418","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01418","url":null,"abstract":"Abstract The technology can fully explore the user’s consumption behavior habits and help the e-commerce platform formulate more precise marketing strategies in a targeted manner. This paper firstly analyzes the optimization of marketing strategy based on the 3R marketing theory, gives the design process of the precise marketing strategy of an e-commerce platform, and analyzes the personalized service based on consumer classification. Secondly, for the shortcomings of the KNN algorithm in the process of accurate classification, the Gaussian function is introduced to weight the optimization of the algorithm, which further realizes the construction of the G-KNN algorithm. Finally, the testing and application analysis of the algorithm model was carried out using the actual user consumption data of the e-commerce platform. The results show that the classification accuracy of the G-KNN algorithm has been maintained at about 95% when the K value exceeds 800, and the F1 composite value of this paper’s algorithm fluctuates around 56% when the K value exceeds 1000. On the e-commerce platform, except for the electrical appliances category classification test, the fit and accuracy of other categories basically match. Using the KNN algorithm incorporating the Gaussian function can effectively realize the accurate classification of user characteristics on the e-commerce platform and provide data support for the e-commerce platform to formulate accurate marketing strategies based on consumer preferences.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"6 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585736","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}
Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01420
Xiaoyao Chen, Jizhou Li
Abstract In this paper, the digital twin platform for the indoor environment is constructed by combining digital twin technology and modern indoor environment elements to innovate the modern indoor environment design method. The digital twin platform is designed for modern indoor environments in two aspects: data acquisition and 3D model visualization for indoor environments. The indoor environment data are collected, cleaned and quasi-exchanged using sensors, the collected multi-source heterogeneous data of the indoor environment are fused by the time alignment method, and the 3D model of the indoor environment is driven by the design of the 3D model’s operations of translation, rotation and scaling. On this basis, the performance of the indoor environment digital twin platform is analyzed, and the data acquisition method and driving effects of the 3D model are explored. The results show that the data transmission measurement delay is within 20ms, the display delay is within 70ms, the transmission frames per second are basically stabilized at about 200FPS, 100FPS, 60FPS, the accuracy reaches 0.9 in the case of multiple data acquisition, and the fusion speed is about 3.4m/s, and the success rate of the driving operation of the overall three-dimensional model of the indoor environment is all greater than 0.96.
{"title":"The Embodiment and Innovation of Digital Twin Platform in Modern Interior Environment Design","authors":"Xiaoyao Chen, Jizhou Li","doi":"10.2478/amns.2023.2.01420","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01420","url":null,"abstract":"Abstract In this paper, the digital twin platform for the indoor environment is constructed by combining digital twin technology and modern indoor environment elements to innovate the modern indoor environment design method. The digital twin platform is designed for modern indoor environments in two aspects: data acquisition and 3D model visualization for indoor environments. The indoor environment data are collected, cleaned and quasi-exchanged using sensors, the collected multi-source heterogeneous data of the indoor environment are fused by the time alignment method, and the 3D model of the indoor environment is driven by the design of the 3D model’s operations of translation, rotation and scaling. On this basis, the performance of the indoor environment digital twin platform is analyzed, and the data acquisition method and driving effects of the 3D model are explored. The results show that the data transmission measurement delay is within 20ms, the display delay is within 70ms, the transmission frames per second are basically stabilized at about 200FPS, 100FPS, 60FPS, the accuracy reaches 0.9 in the case of multiple data acquisition, and the fusion speed is about 3.4m/s, and the success rate of the driving operation of the overall three-dimensional model of the indoor environment is all greater than 0.96.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"12 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138586048","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}
Pub Date : 2023-12-09DOI: 10.2478/amns.2023.2.01425
Shuhuai An, Zhen Wei, Lei Tang, Jie Ren
Abstract This paper first analyzes the mechanism of transmission line dancing and constructs the mathematical model of transmission line dancing and the parameters of transmission line dancing. Then, a transmission line dancing monitoring and warning system is designed by integrating multiple inertial sensors, and the tower monitoring main splitter and wireless inertial monitoring and warning unit are designed, respectively. Then, the transmission line dancing trajectory was denoised using the wavelet threshold method, and the two-way inequality was determined by the attitude decomposition algorithm so as to design the transmission line dancing trajectory parameter identification algorithm. Finally, the designed system is tested experimentally, and the monitoring performance of the dance monitoring trajectory system is analyzed using collected data. The results show that the angular error of the sensor’s pitch and roll attitudes is within 0.5°, the angular error of the heading angle is within 1°, and the acceleration of the smoothed signal is in the range of -0.2/g~0.2/g. The relative error of amplitude recognition is up to 2.6 cm, and the frequency recognition basically agrees with the actual movement frequency, which is 0.21 Hz, and the error of the recognized frequency is within 0.03 Hz. Hz.
{"title":"Research on transmission line dance monitoring and early warning system by fusing multi inertial sensors","authors":"Shuhuai An, Zhen Wei, Lei Tang, Jie Ren","doi":"10.2478/amns.2023.2.01425","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01425","url":null,"abstract":"Abstract This paper first analyzes the mechanism of transmission line dancing and constructs the mathematical model of transmission line dancing and the parameters of transmission line dancing. Then, a transmission line dancing monitoring and warning system is designed by integrating multiple inertial sensors, and the tower monitoring main splitter and wireless inertial monitoring and warning unit are designed, respectively. Then, the transmission line dancing trajectory was denoised using the wavelet threshold method, and the two-way inequality was determined by the attitude decomposition algorithm so as to design the transmission line dancing trajectory parameter identification algorithm. Finally, the designed system is tested experimentally, and the monitoring performance of the dance monitoring trajectory system is analyzed using collected data. The results show that the angular error of the sensor’s pitch and roll attitudes is within 0.5°, the angular error of the heading angle is within 1°, and the acceleration of the smoothed signal is in the range of -0.2/g~0.2/g. The relative error of amplitude recognition is up to 2.6 cm, and the frequency recognition basically agrees with the actual movement frequency, which is 0.21 Hz, and the error of the recognized frequency is within 0.03 Hz. Hz.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"4 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585086","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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