In order to analyze the information security transmission behavior in the smart campus network environment better, so as to realize the effective maintenance of the campus network security environment, this paper constructs the smart campus network security model based on wireless communication technology. This paper analyzes the basic implementation requirements of campus network environment from three aspects of business requirements, functional module requirements and non functional requirements. On this basis, it sets up the model architecture system, and realizes the smooth application of smart campus network security model based on wireless communication technology through report management and security approval. The experimental results show that, compared with the traditional platform network security model, the security model based on wireless communication technology can better meet the practical application needs of smart campus network, and has strong practical application value.
{"title":"Construction of Smart Campus Network Security Model for Digital health sectors based on Wireless Communication Technology","authors":"Jianhua Du","doi":"10.5912/jcb1228","DOIUrl":"https://doi.org/10.5912/jcb1228","url":null,"abstract":"In order to analyze the information security transmission behavior in the smart campus network environment better, so as to realize the effective maintenance of the campus network security environment, this paper constructs the smart campus network security model based on wireless communication technology. This paper analyzes the basic implementation requirements of campus network environment from three aspects of business requirements, functional module requirements and non functional requirements. On this basis, it sets up the model architecture system, and realizes the smooth application of smart campus network security model based on wireless communication technology through report management and security approval. The experimental results show that, compared with the traditional platform network security model, the security model based on wireless communication technology can better meet the practical application needs of smart campus network, and has strong practical application value.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45328345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to improve the recognition efficiency of traffic road centerline recognition method, a traffic road centerline recognition method based on speech recognition technology and image feature extraction is designed. Firstly, the remote sensing image is preprocessed, and then the road knowledge base of traffic road image is established, which mainly includes four parts: road feature analysis, building road extraction knowledge base, high-resolution remote sensing image road rule set and traffic road image classification. On this basis, the image is classified, and finally the road midline feature points are extracted by proportion space theory to realize the final intersection Central line identification of access road. The experimental results show that the recognition time of the proposed method is less than that of the traditional method, and the recognition accuracy is high, which has a certain practical significance.
{"title":"Application of Image Feature Extraction in Traffic Road Centerline Recognition based on agricultural development","authors":"Shuang Shi","doi":"10.5912/jcb1232","DOIUrl":"https://doi.org/10.5912/jcb1232","url":null,"abstract":"In order to improve the recognition efficiency of traffic road centerline recognition method, a traffic road centerline recognition method based on speech recognition technology and image feature extraction is designed. Firstly, the remote sensing image is preprocessed, and then the road knowledge base of traffic road image is established, which mainly includes four parts: road feature analysis, building road extraction knowledge base, high-resolution remote sensing image road rule set and traffic road image classification. On this basis, the image is classified, and finally the road midline feature points are extracted by proportion space theory to realize the final intersection Central line identification of access road. The experimental results show that the recognition time of the proposed method is less than that of the traditional method, and the recognition accuracy is high, which has a certain practical significance.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41587629","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 build a high similarity English vocabulary interpretation domain knowledge base and ensure the automatic retrieval effect of high similarity English vocabulary interpretation, this paper standardizes the automatic retrieval specification of authoritative interpretation of high similarity English vocabulary knowledge, and takes high similarity English vocabulary as the source corpus of the knowledge base. On the basis of the existing work, this paper attempts to propose an automatic retrieval algorithm of high similarity English word interpretation based on deep learning. The goal is to diversify the sources of high similarity English word knowledge and achieve the accuracy of automatic retrieval of word interpretation while ensuring a certain knowledge coverage. A suitable domain knowledge base of machine-readable dictionary is constructed through a new method It can not only provide accurate knowledge information for high similarity English vocabulary, but also provide retrieval verification for user needs analysis and high similarity English vocabulary indexing of snippet. The experimental results show that the algorithm based on deep learning is effective and can fully meet the research requirements.
{"title":"Deep learning based high similarity automatic retrieval algorithm for vocabulary interpretation of workers of Food Sector in china","authors":"Xuezhong Wu, Cong Wu","doi":"10.5912/jcb1249","DOIUrl":"https://doi.org/10.5912/jcb1249","url":null,"abstract":"In order to build a high similarity English vocabulary interpretation domain knowledge base and ensure the automatic retrieval effect of high similarity English vocabulary interpretation, this paper standardizes the automatic retrieval specification of authoritative interpretation of high similarity English vocabulary knowledge, and takes high similarity English vocabulary as the source corpus of the knowledge base. On the basis of the existing work, this paper attempts to propose an automatic retrieval algorithm of high similarity English word interpretation based on deep learning. The goal is to diversify the sources of high similarity English word knowledge and achieve the accuracy of automatic retrieval of word interpretation while ensuring a certain knowledge coverage. A suitable domain knowledge base of machine-readable dictionary is constructed through a new method It can not only provide accurate knowledge information for high similarity English vocabulary, but also provide retrieval verification for user needs analysis and high similarity English vocabulary indexing of snippet. The experimental results show that the algorithm based on deep learning is effective and can fully meet the research requirements.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48967261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the change of company's management concept and personal financial management concept, China's securities investment market is developing well, and more and more organizations and individuals participate in securities investment. However, securities investment has a high degree of "market power", which is influenced by the changes of market factors, and securities investment also has great risks. In order to avoid risks effectively, this paper studies the risk identification method of securities investment based on voice quality inspection technology, so as to help investors better identify and prevent risks in investment projects, so as to better avoid economic losses.
{"title":"Risk identification method of securities investment in pharmaceuitcal firms based on voice quality inspection technology","authors":"Shiyou Zhu","doi":"10.5912/jcb1244","DOIUrl":"https://doi.org/10.5912/jcb1244","url":null,"abstract":"With the change of company's management concept and personal financial management concept, China's securities investment market is developing well, and more and more organizations and individuals participate in securities investment. However, securities investment has a high degree of \"market power\", which is influenced by the changes of market factors, and securities investment also has great risks. In order to avoid risks effectively, this paper studies the risk identification method of securities investment based on voice quality inspection technology, so as to help investors better identify and prevent risks in investment projects, so as to better avoid economic losses.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43188086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current music style classification method is based on high-dimensional feature matrix, which has the problem of large space cost and low classification accuracy. In view of the above problems, this paper studies the music style classification method based on neural network. The MFCC features of music are extracted by processing the music to be classified in two steps: weighting and windowing. The RNN neural network is trained by the sample music set to classify the music styles. Simulation results show that compared with the traditional method, the proposed music style classification method improves the classification accuracy by at least 16.36%, and the space and time cost of the method is small, and the practical application effect is better.
{"title":"Research on Music Style Classification and health care Based on Neural Network","authors":"Liya Xu","doi":"10.5912/jcb1235","DOIUrl":"https://doi.org/10.5912/jcb1235","url":null,"abstract":"The current music style classification method is based on high-dimensional feature matrix, which has the problem of large space cost and low classification accuracy. In view of the above problems, this paper studies the music style classification method based on neural network. The MFCC features of music are extracted by processing the music to be classified in two steps: weighting and windowing. The RNN neural network is trained by the sample music set to classify the music styles. Simulation results show that compared with the traditional method, the proposed music style classification method improves the classification accuracy by at least 16.36%, and the space and time cost of the method is small, and the practical application effect is better.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49176174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The near-field noise reduction system of printing press is designed based on the correlation principle of noise signal generation, the characteristics of noise signal and the algorithm of noise related knowledge. Through the analysis and comparison, two methods of segmented signal-to-noise ratio and waveform are selected as the objective method and subjective method to evaluate the algorithm. Considering the factor of time, aiming at the traditional printing press near-field spectrum subtraction, the traditional noise estimation method is used to estimate the minimum statistics and optimal smoothing noise of printing press near-field noise. According to the characteristics of spectrum subtraction, the noise processing method is realized. Finally, the simulation results show that the near-field noise reduction system based on noise recognition technology has higher effectiveness in the practical application process, and fully meets the research requirements.
{"title":"Design of near field noise reduction system for printing press based on Noise Recognition Technology","authors":"Z. Xiaorong","doi":"10.5912/jcb1253","DOIUrl":"https://doi.org/10.5912/jcb1253","url":null,"abstract":"The near-field noise reduction system of printing press is designed based on the correlation principle of noise signal generation, the characteristics of noise signal and the algorithm of noise related knowledge. Through the analysis and comparison, two methods of segmented signal-to-noise ratio and waveform are selected as the objective method and subjective method to evaluate the algorithm. Considering the factor of time, aiming at the traditional printing press near-field spectrum subtraction, the traditional noise estimation method is used to estimate the minimum statistics and optimal smoothing noise of printing press near-field noise. According to the characteristics of spectrum subtraction, the noise processing method is realized. Finally, the simulation results show that the near-field noise reduction system based on noise recognition technology has higher effectiveness in the practical application process, and fully meets the research requirements.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45271416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The traditional image enhancement processing method is limited by the contrast between image background and enhancement object when processing, which leads to poor image enhancement processing effect and low processing efficiency. With the development of technology, the application of digital electronics is expanding. In order to solve the above problems, the application of digital electronics in image enhancement is explored by studying the method of Chinese character art image enhancement based on digital electronics. Digital coding and Chinese character information processing of Chinese character art images using digital electronics. Retinex enhancement algorithm is improved by using PLIP model to realize Chinese character art image enhancement process. The simulation results demonstrate that the studied image enhancement method can effectively improve the processing efficiency by about 50%, and the image enhancement process is more effective.
{"title":"The Power of Digitalization in the Life Sciences and Diagnostics Sectors with art image enhancement","authors":"Shuo Li, Jianjun Li","doi":"10.5912/jcb1260","DOIUrl":"https://doi.org/10.5912/jcb1260","url":null,"abstract":"The traditional image enhancement processing method is limited by the contrast between image background and enhancement object when processing, which leads to poor image enhancement processing effect and low processing efficiency. With the development of technology, the application of digital electronics is expanding. In order to solve the above problems, the application of digital electronics in image enhancement is explored by studying the method of Chinese character art image enhancement based on digital electronics. Digital coding and Chinese character information processing of Chinese character art images using digital electronics. Retinex enhancement algorithm is improved by using PLIP model to realize Chinese character art image enhancement process. The simulation results demonstrate that the studied image enhancement method can effectively improve the processing efficiency by about 50%, and the image enhancement process is more effective.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44489246","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}
Aiming at the problems of traditional face recognition methods, this paper proposes the application of deep learning and speech recognition technology in pedestrian face recognition. Firstly, the pedestrian face image information is collected, and the face image is decomposed by wavelet scale. The improved detail enhanced face image is obtained, and Harris adaptive threshold corner detection is performed on the enhanced face image. The feature points of pedestrian face image is extracted and matched, and the local radial transformation of points and lines and the Epipolar constraint between multiple planes are adopted. Combined with the constraints of the angle and gray approximation measure of the line features of the face image, the line matching of the face close range image is completed. The 3D line features of the pedestrian face image are extracted and fitted by using the principle of face to face intersection. Combined with the pedestrian face image recognition algorithm, the pedestrian face recognition is realized. The experimental results show that the pedestrian face recognition method based on deep learning and speech recognition technology has better performance.
{"title":"Application of Deep Learning and Speech Recognition Technology for Pedestrian Face Recognition in health sectors","authors":"Shuang Shi","doi":"10.5912/jcb1230","DOIUrl":"https://doi.org/10.5912/jcb1230","url":null,"abstract":"Aiming at the problems of traditional face recognition methods, this paper proposes the application of deep learning and speech recognition technology in pedestrian face recognition. Firstly, the pedestrian face image information is collected, and the face image is decomposed by wavelet scale. The improved detail enhanced face image is obtained, and Harris adaptive threshold corner detection is performed on the enhanced face image. The feature points of pedestrian face image is extracted and matched, and the local radial transformation of points and lines and the Epipolar constraint between multiple planes are adopted. Combined with the constraints of the angle and gray approximation measure of the line features of the face image, the line matching of the face close range image is completed. The 3D line features of the pedestrian face image are extracted and fitted by using the principle of face to face intersection. Combined with the pedestrian face image recognition algorithm, the pedestrian face recognition is realized. The experimental results show that the pedestrian face recognition method based on deep learning and speech recognition technology has better performance.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41673148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The distance teaching of digital media network course under the network environment can improve the pertinence of teaching and the sharing of teaching resources. On this basis, a distance teaching system of digital media online course based on improved genetic algorithm and speech recognition technology is designed. The online course teaching system consists of network communication module, data acquisition module, bus transmission module and application loading module. The module connection and function control of online course teaching system are realized under VME bus architecture. Based on improved genetic algorithm and speech recognition technology, using eclipse as the development environment, the data storage layer, user analysis layer and log mining layer of online course teaching are constructed. The control and structure layout of online course teaching terminal are realized on the user interface, and the system optimization design is completed. The system test results show that the bus data transmission performance of the system for online course teaching is good, and it can effectively meet the personalized needs.
{"title":"Design of distance education system for digital media online course based on improved genetic algorithm and speech recognition technology","authors":"Wenyi Xu","doi":"10.5912/jcb1246","DOIUrl":"https://doi.org/10.5912/jcb1246","url":null,"abstract":"The distance teaching of digital media network course under the network environment can improve the pertinence of teaching and the sharing of teaching resources. On this basis, a distance teaching system of digital media online course based on improved genetic algorithm and speech recognition technology is designed. The online course teaching system consists of network communication module, data acquisition module, bus transmission module and application loading module. The module connection and function control of online course teaching system are realized under VME bus architecture. Based on improved genetic algorithm and speech recognition technology, using eclipse as the development environment, the data storage layer, user analysis layer and log mining layer of online course teaching are constructed. The control and structure layout of online course teaching terminal are realized on the user interface, and the system optimization design is completed. The system test results show that the bus data transmission performance of the system for online course teaching is good, and it can effectively meet the personalized needs.","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42969453","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}
Based on speech recognition and behavior capture, human motion correction is designed. In the process of movement, the real-time movement change of human posture is recognized, and the result of movement correction analysis is given after the movement. In the process of motion capture and correction, the position data of human bone joints should be taken into account, and the adjacent joints should be used to form the direction of the limb to carry out real-time motion correction. Specifically, according to the standard action corresponding to the joint point coordinates of the action to be detected, when the joint point bone data and the estimated value of the action to be detected are within the allowable range, it is judged to be in line with the standard action; on the contrary, according to the symbol of the error between the joint point and the estimated value, the deviation direction of the action to be detected is judged and corrected. Because the difference of action sequence can be regarded as the difference of feature in action sequence, the appropriate feature is selected to describe the process of motion difference according to the action sequence composed of bone joint position data. For action comparison analysis, considering the existence of action sequences with different lengths, firstly, the dynamic time warping method is used to align the action sequence; then the feature vector of the aligned action sequence is extracted, the cosine similarity is used to judge the action similarity, and the angle feature is used to describe the motion trajectory to obtain the result of action comparison analysis..
{"title":"Physical Education’s Role in Public Health based on the combination of speech recognition and behavior capture","authors":"Guodong Hu","doi":"10.5912/jcb1247","DOIUrl":"https://doi.org/10.5912/jcb1247","url":null,"abstract":"Based on speech recognition and behavior capture, human motion correction is designed. In the process of movement, the real-time movement change of human posture is recognized, and the result of movement correction analysis is given after the movement. In the process of motion capture and correction, the position data of human bone joints should be taken into account, and the adjacent joints should be used to form the direction of the limb to carry out real-time motion correction. Specifically, according to the standard action corresponding to the joint point coordinates of the action to be detected, when the joint point bone data and the estimated value of the action to be detected are within the allowable range, it is judged to be in line with the standard action; on the contrary, according to the symbol of the error between the joint point and the estimated value, the deviation direction of the action to be detected is judged and corrected. Because the difference of action sequence can be regarded as the difference of feature in action sequence, the appropriate feature is selected to describe the process of motion difference according to the action sequence composed of bone joint position data. For action comparison analysis, considering the existence of action sequences with different lengths, firstly, the dynamic time warping method is used to align the action sequence; then the feature vector of the aligned action sequence is extracted, the cosine similarity is used to judge the action similarity, and the angle feature is used to describe the motion trajectory to obtain the result of action comparison analysis..","PeriodicalId":88541,"journal":{"name":"Journal of commercial biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41682991","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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