{"title":"Retracted to: Design and dynamics simulation of vehicle active occupant restraint protection system","authors":"","doi":"10.3233/jcm-239002","DOIUrl":"https://doi.org/10.3233/jcm-239002","url":null,"abstract":"","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"13 1","pages":"2257"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91048317","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 : 2023-06-30DOI: 10.5626/jcse.2023.17.2.60
W. H. Steve, Weicong Ma, W. Chao
{"title":"Using the Structure-Behavior Coalescence Method to Formalize the Action Flow Semantics of UML 2.0 Activity Diagrams","authors":"W. H. Steve, Weicong Ma, W. Chao","doi":"10.5626/jcse.2023.17.2.60","DOIUrl":"https://doi.org/10.5626/jcse.2023.17.2.60","url":null,"abstract":"","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"419 1","pages":"60-70"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79515638","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 : 2023-06-30DOI: 10.5626/jcse.2023.17.2.71
M. Jha, P. Rubini, Navin Kumar
{"title":"Flip-OFDM Optical MIMO Based VLC System Using ML/DL Approach","authors":"M. Jha, P. Rubini, Navin Kumar","doi":"10.5626/jcse.2023.17.2.71","DOIUrl":"https://doi.org/10.5626/jcse.2023.17.2.71","url":null,"abstract":"","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"29 1","pages":"71-79"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77942342","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 identification of network text information, the existing technology is difficult to accurately extract and classify text information with high propagation speed and high update speed. In order to solve this problem, the research combines the Naive Bayes algorithm with the feature two-dimensional information gain weighting method, uses the feature weighting method to optimize the Naive Bayes algorithm, and calculates the dimension of different documents and data categories through a new feature operation method. The data gain between them can improve its classification performance, and the classification models are compared and analyzed in the actual Chinese and English databases. The research results show that the classification accuracy rates of the IGDC-DWNB model in the Sogou database, 20-newsgroup database, Fudan database and Ruster21578 database are 0.89, 0.89, 0.93, and 0.88, respectively, which are higher than other classification models in the same environment. It can be seen that the model designed in the research has higher classification accuracy, stronger overall performance, and stronger reliability and robustness in practical applications, which can provide a new development idea for big data classification technology.
{"title":"A classification and extraction method of attribute hybrid big data based on Naive Bayes algorithm","authors":"Liantian Li, Ling Yang","doi":"10.3233/jcm-226802","DOIUrl":"https://doi.org/10.3233/jcm-226802","url":null,"abstract":"In the identification of network text information, the existing technology is difficult to accurately extract and classify text information with high propagation speed and high update speed. In order to solve this problem, the research combines the Naive Bayes algorithm with the feature two-dimensional information gain weighting method, uses the feature weighting method to optimize the Naive Bayes algorithm, and calculates the dimension of different documents and data categories through a new feature operation method. The data gain between them can improve its classification performance, and the classification models are compared and analyzed in the actual Chinese and English databases. The research results show that the classification accuracy rates of the IGDC-DWNB model in the Sogou database, 20-newsgroup database, Fudan database and Ruster21578 database are 0.89, 0.89, 0.93, and 0.88, respectively, which are higher than other classification models in the same environment. It can be seen that the model designed in the research has higher classification accuracy, stronger overall performance, and stronger reliability and robustness in practical applications, which can provide a new development idea for big data classification technology.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"30 1","pages":"1955-1970"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82446827","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}
After entering the new century, the role of science and technology and innovation in promoting economic development has become increasingly obvious, and the Chinese government has also attached great importance to patent management. With the joint efforts of the people of the whole country, the number of patent applications in China has been among the highest in the world for many consecutive years. However, from the perspective of patent quality, there is still a certain gap with developed countries, resulting in generally low efficiency for Chinese export enterprises. Therefore, it is of great significance to explore the relationship between the quality of patents and the competitiveness of exporting enterprises. Through variance calculation, we constructed a countermeasure system for using intellectual property rights to enhance competitiveness. The experimental results show that the patent quality is proportional to the competitiveness of export enterprises, and the higher the patent quality, the stronger the competitiveness of export enterprises. The development of this study further clarifies the important value of patent quality, which helps export enterprises adjust their development strategies and effectively enhance their competitiveness.
{"title":"Impact of patent quality on enterprises' export competitiveness under the background of big data and Internet of Things","authors":"Qi-Hui Zhang, Jie Jiang","doi":"10.3233/jcm-226784","DOIUrl":"https://doi.org/10.3233/jcm-226784","url":null,"abstract":"After entering the new century, the role of science and technology and innovation in promoting economic development has become increasingly obvious, and the Chinese government has also attached great importance to patent management. With the joint efforts of the people of the whole country, the number of patent applications in China has been among the highest in the world for many consecutive years. However, from the perspective of patent quality, there is still a certain gap with developed countries, resulting in generally low efficiency for Chinese export enterprises. Therefore, it is of great significance to explore the relationship between the quality of patents and the competitiveness of exporting enterprises. Through variance calculation, we constructed a countermeasure system for using intellectual property rights to enhance competitiveness. The experimental results show that the patent quality is proportional to the competitiveness of export enterprises, and the higher the patent quality, the stronger the competitiveness of export enterprises. The development of this study further clarifies the important value of patent quality, which helps export enterprises adjust their development strategies and effectively enhance their competitiveness.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"39 1","pages":"2153-2163"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90196685","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, to settle the question of excessive noise in the speech signal during the call of mobile devices in China, the research proposes that the Wiener filter and the generative adversarial network are combined into the IGAN algorithm. Firstly, the Wiener filter regularization algorithm is introduced to construct the preprocessing model of the speech signal; then the preprocessing model is fused with the generative adversarial network algorithm to construct the denoising model. Finally, the performance analysis and simulation experiments of the application effect of the model are carried out. The results show that in the experiment comparing IGAN with five traditional algorithms, when the SNR ratio is increased to 17.5 dB, the MOS and PESQ scores under the IGAN method can reach 4.9 and 3.5 respectively, and the DNN effect is second only to IGAN. Other algorithms perform poorly. Then compare the number of iterations and the loss value between the two. When the network voice signal begins to converge, the loss value corresponding to DNN is 1.132; while the loss value of IGAN is about 0.573, it can be found that the loss value of IGAN has dropped by half, which shows that IGAN Build the model with a smaller loss value. And IGAN tends to converge when iteratively is performed for about 200 times, and the average peak SNR can reach up to 33.85 dB, an increase of nearly 1.02 dB, and the effect is remarkable. This all shows that the IGAN algorithm has the best denoising performance for network speech signals, improves the denoising efficiency, and is conducive to obtaining a denoising signal with a higher fit with the clean signal, so that mobile devices can better serve the people.
{"title":"Internet speech denoising method based on IGAN algorithm","authors":"Sanchuan Luo","doi":"10.3233/jcm-226798","DOIUrl":"https://doi.org/10.3233/jcm-226798","url":null,"abstract":"At present, to settle the question of excessive noise in the speech signal during the call of mobile devices in China, the research proposes that the Wiener filter and the generative adversarial network are combined into the IGAN algorithm. Firstly, the Wiener filter regularization algorithm is introduced to construct the preprocessing model of the speech signal; then the preprocessing model is fused with the generative adversarial network algorithm to construct the denoising model. Finally, the performance analysis and simulation experiments of the application effect of the model are carried out. The results show that in the experiment comparing IGAN with five traditional algorithms, when the SNR ratio is increased to 17.5 dB, the MOS and PESQ scores under the IGAN method can reach 4.9 and 3.5 respectively, and the DNN effect is second only to IGAN. Other algorithms perform poorly. Then compare the number of iterations and the loss value between the two. When the network voice signal begins to converge, the loss value corresponding to DNN is 1.132; while the loss value of IGAN is about 0.573, it can be found that the loss value of IGAN has dropped by half, which shows that IGAN Build the model with a smaller loss value. And IGAN tends to converge when iteratively is performed for about 200 times, and the average peak SNR can reach up to 33.85 dB, an increase of nearly 1.02 dB, and the effect is remarkable. This all shows that the IGAN algorithm has the best denoising performance for network speech signals, improves the denoising efficiency, and is conducive to obtaining a denoising signal with a higher fit with the clean signal, so that mobile devices can better serve the people.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"21 1","pages":"1929-1940"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91052371","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}
Sensors as the sensing end of intelligent control can be used to collect various data instead of human beings. In the context of technological development, the variety of sensors leads to multiple and structurally unequal data sources, and fusion of these data becomes a problem for consideration. The study constructs an intuitionistic fuzzy transformation method to handle data with various attributes with the help of fuzzy mathematical concepts, which characterizes the data based on the hesitancy and ideal solutions under Gaussian distribution. Simulations of classical classification data show that the intuitionistic fuzzy transformation method can effectively differentiate the affiliation of data points in the dataset, and the results of 800 simulations show that the qualitative accuracy of the algorithm can reach 89%, while the causes of abnormal data are explored and it is found that the attributes of the dataset based on Gaussian distribution are too close to each other as the cause of misclassification; the algorithm is also optimized from multi-dimensional considerations, and a An optimization operator based on the distance method of superior and inferior solutions was constructed and simulated for several optimization paths. The results show that the study uses an optimization scheme that is significantly better than the existing fuzzy operator, and 800 times can improve the accuracy rate up to 95.23%, which is 14.01% higher than that of a single attribute. This indicates that the intuitionistic fuzzy algorithm of this study has some rationality and is able to fuse the data of multiple attributes of the sensor for determination and provide the necessary basis for decision making.
{"title":"Multi-source heterogeneous data fusion model based on fuzzy mathematics","authors":"Q. Zeng","doi":"10.3233/jcm-226796","DOIUrl":"https://doi.org/10.3233/jcm-226796","url":null,"abstract":"Sensors as the sensing end of intelligent control can be used to collect various data instead of human beings. In the context of technological development, the variety of sensors leads to multiple and structurally unequal data sources, and fusion of these data becomes a problem for consideration. The study constructs an intuitionistic fuzzy transformation method to handle data with various attributes with the help of fuzzy mathematical concepts, which characterizes the data based on the hesitancy and ideal solutions under Gaussian distribution. Simulations of classical classification data show that the intuitionistic fuzzy transformation method can effectively differentiate the affiliation of data points in the dataset, and the results of 800 simulations show that the qualitative accuracy of the algorithm can reach 89%, while the causes of abnormal data are explored and it is found that the attributes of the dataset based on Gaussian distribution are too close to each other as the cause of misclassification; the algorithm is also optimized from multi-dimensional considerations, and a An optimization operator based on the distance method of superior and inferior solutions was constructed and simulated for several optimization paths. The results show that the study uses an optimization scheme that is significantly better than the existing fuzzy operator, and 800 times can improve the accuracy rate up to 95.23%, which is 14.01% higher than that of a single attribute. This indicates that the intuitionistic fuzzy algorithm of this study has some rationality and is able to fuse the data of multiple attributes of the sensor for determination and provide the necessary basis for decision making.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"9 1","pages":"2165-2178"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84128383","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}
My country is currently in a new era of industrial revolution and technological development, among which the most representative is the rapid development of information technology. And the action plan has been formulated, and it is pointed out that it is necessary to deeply integrate the real economy and artificial intelligence, the Internet and digital China, as well as the smart society and big data. As the most critical part of community governance, the complexity and difficulty of community sports center governance is relatively high. Therefore, one of the key goals that all sectors of society are currently concerned about is how to build and develop community sports through more advanced technical means center. The innovative governance community sports center must effectively combine and integrate the current advanced technologies such as the Internet of Things and big data, so that the level of community governance can be effectively improved. This paper draws on the academic background of “governance” in the field of public management. Different from social management, “governance” mainly reflects the administrative aspect, the cooperation between the government and all sectors of society, while the traditional social management mainly highlights the state’s administrative management. It reflects the concept that the government’s management power is inviolable and unshareable, which is also very different from the development direction of the gradual liberalization of the administrative power advocated by the Chinese government. Therefore, the effective way to solve various social problems at this stage is to change the concept of social management to community governance, and this approach has also been supported and valued by the government in recent years. The research shows that the management efficiency of community sports facilities can be effectively improved by optimizing the community sports governance system, making full use of the convenience brought by information resources, and using its own advanced technology.
{"title":"Dilemma and countermeasures of community sports center governance under the background of big data and Internet of Things","authors":"J. Zha","doi":"10.3233/jcm-226785","DOIUrl":"https://doi.org/10.3233/jcm-226785","url":null,"abstract":"My country is currently in a new era of industrial revolution and technological development, among which the most representative is the rapid development of information technology. And the action plan has been formulated, and it is pointed out that it is necessary to deeply integrate the real economy and artificial intelligence, the Internet and digital China, as well as the smart society and big data. As the most critical part of community governance, the complexity and difficulty of community sports center governance is relatively high. Therefore, one of the key goals that all sectors of society are currently concerned about is how to build and develop community sports through more advanced technical means center. The innovative governance community sports center must effectively combine and integrate the current advanced technologies such as the Internet of Things and big data, so that the level of community governance can be effectively improved. This paper draws on the academic background of “governance” in the field of public management. Different from social management, “governance” mainly reflects the administrative aspect, the cooperation between the government and all sectors of society, while the traditional social management mainly highlights the state’s administrative management. It reflects the concept that the government’s management power is inviolable and unshareable, which is also very different from the development direction of the gradual liberalization of the administrative power advocated by the Chinese government. Therefore, the effective way to solve various social problems at this stage is to change the concept of social management to community governance, and this approach has also been supported and valued by the government in recent years. The research shows that the management efficiency of community sports facilities can be effectively improved by optimizing the community sports governance system, making full use of the convenience brought by information resources, and using its own advanced technology.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"112 1","pages":"2069-2081"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84204790","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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