Pub Date : 2022-02-01DOI: 10.1109/ICTech55460.2022.00117
Hong-yuan Wang
Influenced by the COVID-19 epidemic in the past two years, colleges and universities at home and abroad have adopted a combination of online and offline teaching reform, education informatization and intelligent talent training methods, which have become the focus of research for educators. The teaching quality is related to the quality of talent cultivation, and the intelligence, fairness and accuracy of the teaching evaluation system are particularly important. And block chain technology is decentralized and safe and reliable features, so the development of the technology based on big data and chain blocks obeys the law of education development of teaching evaluation system, to solve the shortage of the current appraisal system, and realize the sharing of teaching resources, integrate and optimize the teaching resources, promoting the standardization and standardization of teaching resources construction, To promote the construction and better development of disciplines in colleges and universities.
{"title":"Research on University Teaching Quality Evaluation and Guarantee System Based on Block Chain Technology","authors":"Hong-yuan Wang","doi":"10.1109/ICTech55460.2022.00117","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00117","url":null,"abstract":"Influenced by the COVID-19 epidemic in the past two years, colleges and universities at home and abroad have adopted a combination of online and offline teaching reform, education informatization and intelligent talent training methods, which have become the focus of research for educators. The teaching quality is related to the quality of talent cultivation, and the intelligence, fairness and accuracy of the teaching evaluation system are particularly important. And block chain technology is decentralized and safe and reliable features, so the development of the technology based on big data and chain blocks obeys the law of education development of teaching evaluation system, to solve the shortage of the current appraisal system, and realize the sharing of teaching resources, integrate and optimize the teaching resources, promoting the standardization and standardization of teaching resources construction, To promote the construction and better development of disciplines in colleges and universities.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115093100","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00085
Tiantian Jiang, Zhanguo Wang
In the field of natural language processing, text classification is a key daily task. The main goal is to obtain effective features from text information, find the correspondence between feature representations and category labels, so as to classify the text. From the perspective of data flow, it is mainly divided into five stages: text preprocessing, vector representation of text, feature extraction, classifier classification and model training to complete text classification tasks. Among them, feature extraction is a very important stage, and it is also the focus of this article. GRU can learn long-term dependencies from learned local features, and bidirectional GRU can learn hidden features in sentences. The self-attention mechanism exhibits superior performance in many fields in natural language processing. It can mine the autocorrelation of data and highlight key information by adjusting the weight of keywords. Therefore, in view of the shortcomings of existing models in text global information modeling, this paper combines bidirectional GRU and self-attention mechanism, and proposes a hybrid model BiGRU-MA for text classification, which can extract deep semantic features and solve the problem of classification performance degradation due to the lack of semantic information. This article uses text classification related technology to model, describes the modeling ideas, and introduces the technology used, and finally compares experiments with existing models to verify the effectiveness of the model.
{"title":"Text Classification Using BiGRU with Directional Self-Attention","authors":"Tiantian Jiang, Zhanguo Wang","doi":"10.1109/ICTech55460.2022.00085","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00085","url":null,"abstract":"In the field of natural language processing, text classification is a key daily task. The main goal is to obtain effective features from text information, find the correspondence between feature representations and category labels, so as to classify the text. From the perspective of data flow, it is mainly divided into five stages: text preprocessing, vector representation of text, feature extraction, classifier classification and model training to complete text classification tasks. Among them, feature extraction is a very important stage, and it is also the focus of this article. GRU can learn long-term dependencies from learned local features, and bidirectional GRU can learn hidden features in sentences. The self-attention mechanism exhibits superior performance in many fields in natural language processing. It can mine the autocorrelation of data and highlight key information by adjusting the weight of keywords. Therefore, in view of the shortcomings of existing models in text global information modeling, this paper combines bidirectional GRU and self-attention mechanism, and proposes a hybrid model BiGRU-MA for text classification, which can extract deep semantic features and solve the problem of classification performance degradation due to the lack of semantic information. This article uses text classification related technology to model, describes the modeling ideas, and introduces the technology used, and finally compares experiments with existing models to verify the effectiveness of the model.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116502678","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00076
R. Cheng, Shuya Peng, Ziheng Dai
Butanol and C4 olefins, as important chemical raw materials, are widely used in the production of chemical products and pharmaceutical intermediates. Traditional production methods use fossil energy as raw materials, but with the shortage of fossil energy production and the aggravation of its impact on the environment, the energy supply gradually tends to be diversified, and the development of new clean energy is becoming more and more urgent. Ethanol molecules can be prepared by biomass fermentation. They have a wide range of sources and are green and clean. They are used as platform molecules to produce high value-added butanol and C_4 olefins have great application prospects and economic benefits, and have attracted extensive attention at home and abroad. However, in the current industrial production, the catalyst combination and temperature have a great impact on the conversion of ethylene and the selectivity of C4 olefins, and its selection and control greatly affect the production efficiency of C4 olefins. This paper focuses on the influence effect and degree of two factors on two dependent variables in the process of preparing C4 olefins by ethylene coupling reaction. By establishing the least square curve to fit the temperature and ethanol conversion and the temperature and C4 olefin selectivity, the fitting curve is obtained. It can be seen that the temperature has a primary or quadratic function relationship with the ethanol conversion or C4 olefin selectivity, so it is judged that it has a certain influence, Then the effects of temperature, catalyst group and loading method on ethanol conversion and C4 olefin selectivity were obtained by Spearman correlation coefficient and random forest regression algorithm. Based on this result, the model is established, optimized and analyzed, and the optimal catalyst combination and temperature are obtained, so as to obtain the highest C4 olefin yield and achieve the maximum industrial benefit
{"title":"Study on Ethanol Coupling Reaction Based on BP Neural Network and Correlation","authors":"R. Cheng, Shuya Peng, Ziheng Dai","doi":"10.1109/ICTech55460.2022.00076","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00076","url":null,"abstract":"Butanol and C4 olefins, as important chemical raw materials, are widely used in the production of chemical products and pharmaceutical intermediates. Traditional production methods use fossil energy as raw materials, but with the shortage of fossil energy production and the aggravation of its impact on the environment, the energy supply gradually tends to be diversified, and the development of new clean energy is becoming more and more urgent. Ethanol molecules can be prepared by biomass fermentation. They have a wide range of sources and are green and clean. They are used as platform molecules to produce high value-added butanol and C_4 olefins have great application prospects and economic benefits, and have attracted extensive attention at home and abroad. However, in the current industrial production, the catalyst combination and temperature have a great impact on the conversion of ethylene and the selectivity of C4 olefins, and its selection and control greatly affect the production efficiency of C4 olefins. This paper focuses on the influence effect and degree of two factors on two dependent variables in the process of preparing C4 olefins by ethylene coupling reaction. By establishing the least square curve to fit the temperature and ethanol conversion and the temperature and C4 olefin selectivity, the fitting curve is obtained. It can be seen that the temperature has a primary or quadratic function relationship with the ethanol conversion or C4 olefin selectivity, so it is judged that it has a certain influence, Then the effects of temperature, catalyst group and loading method on ethanol conversion and C4 olefin selectivity were obtained by Spearman correlation coefficient and random forest regression algorithm. Based on this result, the model is established, optimized and analyzed, and the optimal catalyst combination and temperature are obtained, so as to obtain the highest C4 olefin yield and achieve the maximum industrial benefit","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123593282","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00083
H. Yang, Zhe Sun
Skiing as a practical strong sports items, due to the effect of season there is scarcity, so expect in current scientific research scholars to explore in practice to develop an indoor ski, with many degrees of freedom simulation system, to help athletes or interested participants more degrees of freedom indoor ski ski training simulator system. This new training platform can help them strengthen their behavioral awareness in practical training, reduce training time and cost, and promote effective interaction between human autonomous senses and 3D objects in virtual environment. Based on a comprehensive understanding of the current indoor skiing simulator system construction experience, this paper conducted a simulation study on the 3-DOF simulated skiing training platform, and proposed a practical control method. The final results prove that this control platform model can help athletes better carry out skill training.
{"title":"Numerical Simulation of Multi-Degree Indoor Skiing Simulation System","authors":"H. Yang, Zhe Sun","doi":"10.1109/ICTech55460.2022.00083","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00083","url":null,"abstract":"Skiing as a practical strong sports items, due to the effect of season there is scarcity, so expect in current scientific research scholars to explore in practice to develop an indoor ski, with many degrees of freedom simulation system, to help athletes or interested participants more degrees of freedom indoor ski ski training simulator system. This new training platform can help them strengthen their behavioral awareness in practical training, reduce training time and cost, and promote effective interaction between human autonomous senses and 3D objects in virtual environment. Based on a comprehensive understanding of the current indoor skiing simulator system construction experience, this paper conducted a simulation study on the 3-DOF simulated skiing training platform, and proposed a practical control method. The final results prove that this control platform model can help athletes better carry out skill training.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128886389","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00036
Jingang Yu, Zhifeng Wen, Shu Li, Yongkang Hou
With more and more public involvement in the charity field, higher requirements are put forward for user privacy protection in the charity system. As a decentralized, anonymous and immutable distributed ledger, blockchain provides a new idea to solve the privacy protection problems in charity systems. Aiming at the problem of data privacy protection of charity system, user-oriented and data-oriented privacy protection methods are proposed. User-oriented privacy protection by writing smart contracts to limit users' access to data and ensure the privacy of data; Data-oriented privacy protection encrypts data by using encryption algorithms. In this paper, an improved AES algorithm is proposed. By dynamically constructing S-box, the algorithm does not have obvious structural characteristics, and various properties are random transformation, which increases the difficulty of cracking; At the same time, the AES algorithm is parallelized to improve the encryption and decryption rates. Experimental results show that the improved AES algorithm can ensure the security of data encryption in charity system and improve the operation efficiency.
{"title":"Design and Implementation of Privacy Protection of Charity System Based on Blockchain","authors":"Jingang Yu, Zhifeng Wen, Shu Li, Yongkang Hou","doi":"10.1109/ICTech55460.2022.00036","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00036","url":null,"abstract":"With more and more public involvement in the charity field, higher requirements are put forward for user privacy protection in the charity system. As a decentralized, anonymous and immutable distributed ledger, blockchain provides a new idea to solve the privacy protection problems in charity systems. Aiming at the problem of data privacy protection of charity system, user-oriented and data-oriented privacy protection methods are proposed. User-oriented privacy protection by writing smart contracts to limit users' access to data and ensure the privacy of data; Data-oriented privacy protection encrypts data by using encryption algorithms. In this paper, an improved AES algorithm is proposed. By dynamically constructing S-box, the algorithm does not have obvious structural characteristics, and various properties are random transformation, which increases the difficulty of cracking; At the same time, the AES algorithm is parallelized to improve the encryption and decryption rates. Experimental results show that the improved AES algorithm can ensure the security of data encryption in charity system and improve the operation efficiency.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128979040","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00048
Jinghui Zhang, Y. Xiang
With the rapid development of computer technology and the expansion of the Internet, intrusions on the Internet have become more frequent. With the development of machine learning technology, people apply machine learning technology to the anomaly detection of network traffic. However, traditional traffic classification not only relies on complex features, but also extracts users' private content, which has a negative impact on users. It is already difficult to meet the current increasingly large-scale network. Due to the rapid development of deep learning recently, it has very good applications in many fields. In this article, on the basis of it, we use Convolutional Neural Networks (CNN) and long- and short-term memory networks, and comprehensively put forward corresponding intrusion detection models based on the actual classification characteristics of the data set and model, optimization is performed.
{"title":"Research on Traffic Intrusion Detection Method Based on Deep Learning","authors":"Jinghui Zhang, Y. Xiang","doi":"10.1109/ICTech55460.2022.00048","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00048","url":null,"abstract":"With the rapid development of computer technology and the expansion of the Internet, intrusions on the Internet have become more frequent. With the development of machine learning technology, people apply machine learning technology to the anomaly detection of network traffic. However, traditional traffic classification not only relies on complex features, but also extracts users' private content, which has a negative impact on users. It is already difficult to meet the current increasingly large-scale network. Due to the rapid development of deep learning recently, it has very good applications in many fields. In this article, on the basis of it, we use Convolutional Neural Networks (CNN) and long- and short-term memory networks, and comprehensively put forward corresponding intrusion detection models based on the actual classification characteristics of the data set and model, optimization is performed.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130913974","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00026
Jian Tang, Xiwang Li
With the continuous development of power grid informationization, the information security of the power grid is increasingly concerned. Grid traffic classification is an important basis for ensuring information security of the grid. In the process of realizing grid traffic classification, due to the different frequency of grid services and the increasing number of new services, it leads to problems such as unbalanced grid traffic data and dynamic traffic data, etc. The unbalanced traffic data causes the prediction accuracy of small categories to be much lower than the applicable standard, and the dynamic traffic data causes the model update to take a lot of time and resource overhead The dynamic traffic data causes the model update to take a lot of time and resource overhead. To solve these problems, a classification model for unbalanced dynamic grid traffic data (UDTCM) is proposed in this paper. The model uses the statistical characteristics of the flow data to detect the prediction accuracy of the classifier in time and avoid the prediction results from significantly degrading with the change of environment. Meanwhile, a resampling algorithm is used to correct the flow data to improve the data imbalance of grid flows and improve the prediction accuracy of small classes. The experimental results show that the model improves the classification of unbalanced grid flow data and reduces the time and resource overhead of model updates due to data updates.
{"title":"A Classification Model for Unbalanced Power Traffic","authors":"Jian Tang, Xiwang Li","doi":"10.1109/ICTech55460.2022.00026","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00026","url":null,"abstract":"With the continuous development of power grid informationization, the information security of the power grid is increasingly concerned. Grid traffic classification is an important basis for ensuring information security of the grid. In the process of realizing grid traffic classification, due to the different frequency of grid services and the increasing number of new services, it leads to problems such as unbalanced grid traffic data and dynamic traffic data, etc. The unbalanced traffic data causes the prediction accuracy of small categories to be much lower than the applicable standard, and the dynamic traffic data causes the model update to take a lot of time and resource overhead The dynamic traffic data causes the model update to take a lot of time and resource overhead. To solve these problems, a classification model for unbalanced dynamic grid traffic data (UDTCM) is proposed in this paper. The model uses the statistical characteristics of the flow data to detect the prediction accuracy of the classifier in time and avoid the prediction results from significantly degrading with the change of environment. Meanwhile, a resampling algorithm is used to correct the flow data to improve the data imbalance of grid flows and improve the prediction accuracy of small classes. The experimental results show that the model improves the classification of unbalanced grid flow data and reduces the time and resource overhead of model updates due to data updates.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125897101","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00069
Yancheng Long, J. Rong
Data prediction, as an important symbol of network technology innovation and development, is a technical process of estimating future data by combining existing data. Nowadays, with the comprehensive development of mobile network and social network, people are faced with more and more electronic data in daily life. At this time, how to accurately predict future data or understand the development trend of data is of great significance to industry construction. Neural network, as a computational model built by computer to simulate the process of human brain neuron processing information, has certain nonlinear modeling ability in practical application, and can adapt to master the law of data hiding as soon as possible. Therefore, in this paper, the neural network model and fuzzy system are discussed in depth, and the fuzzy neural network model is chosen to analyze the data prediction, and a general prediction framework based on fuzzy C clustering and ANFIS hybrid learning algorithm is proposed in the practical research, and an improved fuzzy C clustering based on density weighting (IDWFCM) is proposed. The final simulation results show that the clustering effect of IDWFCM algorithm is not affected by noise data, so that the convergence speed of the system is higher than the traditional clustering algorithm, the overall increase of 60%, and the clustering accuracy also increases from 88.4% to 94.2%.
{"title":"Analysis of Data Mining and Dynamic Neural Network for Data Prediction","authors":"Yancheng Long, J. Rong","doi":"10.1109/ICTech55460.2022.00069","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00069","url":null,"abstract":"Data prediction, as an important symbol of network technology innovation and development, is a technical process of estimating future data by combining existing data. Nowadays, with the comprehensive development of mobile network and social network, people are faced with more and more electronic data in daily life. At this time, how to accurately predict future data or understand the development trend of data is of great significance to industry construction. Neural network, as a computational model built by computer to simulate the process of human brain neuron processing information, has certain nonlinear modeling ability in practical application, and can adapt to master the law of data hiding as soon as possible. Therefore, in this paper, the neural network model and fuzzy system are discussed in depth, and the fuzzy neural network model is chosen to analyze the data prediction, and a general prediction framework based on fuzzy C clustering and ANFIS hybrid learning algorithm is proposed in the practical research, and an improved fuzzy C clustering based on density weighting (IDWFCM) is proposed. The final simulation results show that the clustering effect of IDWFCM algorithm is not affected by noise data, so that the convergence speed of the system is higher than the traditional clustering algorithm, the overall increase of 60%, and the clustering accuracy also increases from 88.4% to 94.2%.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127273030","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00090
Tianlei Wang, Jing Zhou, Weilin Liang, Na Xiao, Ye Li, Zhenhua Ou, Junda Deng, Xiangyuan Zhou
In order to solve the problems of difficult medication monitoring and low dispensing efficiency in geriatric homes, this paper designs an intelligent spliced medicine box. The medicine box adopts STM32F407Z as the main control chip, uses the motor to control the rotation of the medicine box to as-sist patients to take out medicine, and has a voice to remind patients to take medicine in time. At the same time, the identification technology is utilized to avoid the elderly taking the wrong medicine. An application program is designed to realize the function of remote monitoring. In addition, the medicine box can be spliced together one by one to form a set of medicine box array, which is convenient for unified dispensing. And the dispensing strategy is optimized, which greatly improves the dispensing efficiency of the nursing homes for the elderly. Finally, intelligent management of medication reminder, medication remote monitoring and rapid dispensing is realized, which has certain practical value in the market.
{"title":"Modelling and Simulation of a Spliced Intelligent Medicine Box","authors":"Tianlei Wang, Jing Zhou, Weilin Liang, Na Xiao, Ye Li, Zhenhua Ou, Junda Deng, Xiangyuan Zhou","doi":"10.1109/ICTech55460.2022.00090","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00090","url":null,"abstract":"In order to solve the problems of difficult medication monitoring and low dispensing efficiency in geriatric homes, this paper designs an intelligent spliced medicine box. The medicine box adopts STM32F407Z as the main control chip, uses the motor to control the rotation of the medicine box to as-sist patients to take out medicine, and has a voice to remind patients to take medicine in time. At the same time, the identification technology is utilized to avoid the elderly taking the wrong medicine. An application program is designed to realize the function of remote monitoring. In addition, the medicine box can be spliced together one by one to form a set of medicine box array, which is convenient for unified dispensing. And the dispensing strategy is optimized, which greatly improves the dispensing efficiency of the nursing homes for the elderly. Finally, intelligent management of medication reminder, medication remote monitoring and rapid dispensing is realized, which has certain practical value in the market.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130097709","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 : 2022-02-01DOI: 10.1109/ICTech55460.2022.00067
Jinyuan Wang
Movie background music plays a positive role in enhancing emotion, drama and movie atmosphere. If the background music can be automatically classified based on emotion, it will help to improve the analysis efficiency and quality of emotional content of movies. In view of this feature, researchers put forward a musical emotion classifier based on the emotional characteristics of film background music, and optimize the emotion of background music. Annotations. In this paper, based on the understanding of the current research situation of film media background music, after accurately extracting music features, the PLSA as the core of film background music emotion classification model is proposed, and the actual design of empirical analysis, the final results show that this method can obtain high-precision classification results.
{"title":"Research on Emotion Classification of Movie Background Music Based on Improved Clustering Algorithm","authors":"Jinyuan Wang","doi":"10.1109/ICTech55460.2022.00067","DOIUrl":"https://doi.org/10.1109/ICTech55460.2022.00067","url":null,"abstract":"Movie background music plays a positive role in enhancing emotion, drama and movie atmosphere. If the background music can be automatically classified based on emotion, it will help to improve the analysis efficiency and quality of emotional content of movies. In view of this feature, researchers put forward a musical emotion classifier based on the emotional characteristics of film background music, and optimize the emotion of background music. Annotations. In this paper, based on the understanding of the current research situation of film media background music, after accurately extracting music features, the PLSA as the core of film background music emotion classification model is proposed, and the actual design of empirical analysis, the final results show that this method can obtain high-precision classification results.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122458239","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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