Abstract Combining images with music is a music visualization to deepen the knowledge and understanding of music information. This study briefly introduced the concept of music visualization and used a convolutional neural network and long short-term memory to pair music and images for music visualization. Then, an emotion classification loss function was added to the loss function to make full use of the emotional information in music and images. Finally, simulation experiments were performed. The results showed that the improved deep learning-based music visualization algorithm had the highest matching accuracy when the weight of the emotion classification loss function was 0.2; compared with the traditional keyword matching method and the nonimproved deep learning music visualization algorithm, the improved algorithm matched more suitable images.
{"title":"Research on intelligent interactive music information based on visualization technology","authors":"Ningjie Liao","doi":"10.1515/jisys-2022-0016","DOIUrl":"https://doi.org/10.1515/jisys-2022-0016","url":null,"abstract":"Abstract Combining images with music is a music visualization to deepen the knowledge and understanding of music information. This study briefly introduced the concept of music visualization and used a convolutional neural network and long short-term memory to pair music and images for music visualization. Then, an emotion classification loss function was added to the loss function to make full use of the emotional information in music and images. Finally, simulation experiments were performed. The results showed that the improved deep learning-based music visualization algorithm had the highest matching accuracy when the weight of the emotion classification loss function was 0.2; compared with the traditional keyword matching method and the nonimproved deep learning music visualization algorithm, the improved algorithm matched more suitable images.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"58 1","pages":"289 - 297"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90930771","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}
Minshun Zhang, Jun-Chen Fan, A. Sharma, Ashima Kukkar
Abstract Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level.
{"title":"Data mining applications in university information management system development","authors":"Minshun Zhang, Jun-Chen Fan, A. Sharma, Ashima Kukkar","doi":"10.1515/jisys-2022-0006","DOIUrl":"https://doi.org/10.1515/jisys-2022-0006","url":null,"abstract":"Abstract Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"9 1","pages":"207 - 220"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89481337","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 Over the previous three decades, the area of computer networks has progressed significantly, from traditional static networks to dynamically designed architecture. The primary purpose of software-defined networking (SDN) is to create an open, programmable network. Conventional network devices, such as routers and switches, may make routing decisions and forward packets; however, SDN divides these components into the Data plane and the Control plane by splitting distinct features away. As a result, switches can only forward packets and cannot make routing decisions; the controller makes routing decisions. OpenFlow is the communication interface between the switches and the controller. It is a protocol that allows the controller to identify the network packet’s path across the switches. This project uses the SDN environment to implement the firefly optimization algorithm to determine the shortest path between two nodes in a network. The firefly optimization algorithm was implemented using Ryu control. The results reveal that using the firefly optimization algorithm improves the selected short path between the source and destination.
{"title":"A novel method to find the best path in SDN using firefly algorithm","authors":"Tameem Hameed Obaida, Hanan Abbas Salman","doi":"10.1515/jisys-2022-0063","DOIUrl":"https://doi.org/10.1515/jisys-2022-0063","url":null,"abstract":"Abstract Over the previous three decades, the area of computer networks has progressed significantly, from traditional static networks to dynamically designed architecture. The primary purpose of software-defined networking (SDN) is to create an open, programmable network. Conventional network devices, such as routers and switches, may make routing decisions and forward packets; however, SDN divides these components into the Data plane and the Control plane by splitting distinct features away. As a result, switches can only forward packets and cannot make routing decisions; the controller makes routing decisions. OpenFlow is the communication interface between the switches and the controller. It is a protocol that allows the controller to identify the network packet’s path across the switches. This project uses the SDN environment to implement the firefly optimization algorithm to determine the shortest path between two nodes in a network. The firefly optimization algorithm was implemented using Ryu control. The results reveal that using the firefly optimization algorithm improves the selected short path between the source and destination.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"2 1","pages":"902 - 914"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89766698","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 The installation error of metallurgical machinery parts is one of the common sources of errors in mechanical equipment. Because the installation error of different parts has different influences on different mechanical equipment, a simple linear formula cannot be used to identify the installation error. In the past, the manual recognition method and the touch recognition method lack of error information analysis, which leads to inaccurate recognition results. To improve the problem, an automatic recognition method based on the neural network for metallurgical machinery parts installation error is proposed. According to the principle of automatic recognition of installation error based on the neural network, the nonlinear relation matrix between layers of the neural network is established. The operating state parameters of mechanical equipment are calculated, and the time series of the parameters are divided into several segments averagely. Based on the recognition algorithm, the inspection steps of depth, perpendicularity and center position of reserved hole, base board construction, short-circuit motor line and terminal installation, center mark board, and reference point installation are designed. The experimental results show that the recall rate of the proposed method is 97.66%, and the maximum absolute deviation is 0.09. The experimental data verify the feasibility of the proposed method.
{"title":"Automatic recognition method of installation errors of metallurgical machinery parts based on neural network","authors":"Hailong Cui, Bo Zhan","doi":"10.1515/jisys-2022-0021","DOIUrl":"https://doi.org/10.1515/jisys-2022-0021","url":null,"abstract":"Abstract The installation error of metallurgical machinery parts is one of the common sources of errors in mechanical equipment. Because the installation error of different parts has different influences on different mechanical equipment, a simple linear formula cannot be used to identify the installation error. In the past, the manual recognition method and the touch recognition method lack of error information analysis, which leads to inaccurate recognition results. To improve the problem, an automatic recognition method based on the neural network for metallurgical machinery parts installation error is proposed. According to the principle of automatic recognition of installation error based on the neural network, the nonlinear relation matrix between layers of the neural network is established. The operating state parameters of mechanical equipment are calculated, and the time series of the parameters are divided into several segments averagely. Based on the recognition algorithm, the inspection steps of depth, perpendicularity and center position of reserved hole, base board construction, short-circuit motor line and terminal installation, center mark board, and reference point installation are designed. The experimental results show that the recall rate of the proposed method is 97.66%, and the maximum absolute deviation is 0.09. The experimental data verify the feasibility of the proposed method.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"8 1","pages":"321 - 331"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84178353","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 A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient convolution neural structure of multifeature fusion is constructed for image denoising. The gray features of social network image are collected, and the gray values are denoising and cleaning. Based on the image features, multiple denoising is carried out to ensure the accuracy of social network image denoising algorithm and improve the accuracy of image processing. Experiments show that the average noise of the image processed by the algorithm designed in this study is reduced by 8.6905 dB, which is much larger than that of other methods, and the signal-to-noise ratio of the output image is high, which is maintained at about 30 dB, which has a high effect in the process of practical application.
{"title":"Image denoising algorithm of social network based on multifeature fusion","authors":"Lanfei Zhao, Qidan Zhu","doi":"10.1515/jisys-2022-0019","DOIUrl":"https://doi.org/10.1515/jisys-2022-0019","url":null,"abstract":"Abstract A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient convolution neural structure of multifeature fusion is constructed for image denoising. The gray features of social network image are collected, and the gray values are denoising and cleaning. Based on the image features, multiple denoising is carried out to ensure the accuracy of social network image denoising algorithm and improve the accuracy of image processing. Experiments show that the average noise of the image processed by the algorithm designed in this study is reduced by 8.6905 dB, which is much larger than that of other methods, and the signal-to-noise ratio of the output image is high, which is maintained at about 30 dB, which has a high effect in the process of practical application.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"19 1","pages":"310 - 320"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82189283","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 order to improve the integrity of the social network behavior feature extraction results for sports college students, this study proposes to be based on the clustering algorithm. This study analyzes the social network information dissemination mechanism in the field of college students’ sports, obtains the real-time social behavior data in the network environment combined with the analysis results, and processes the obtained social network behavior data from two aspects of data cleaning and de-duplication. Using clustering algorithm to determine the type of social network user behavior, setting the characteristics of social network behavior attributes, and finally through quantitative and standardized processing, get the results of college students’ sports field social network behavior characteristics extraction. The experimental results showed that the completeness of the method feature extraction results improved to 9.93%, and the average extraction time cost was 0.344 s, with high result integrity and obvious advantages in the extraction speed.
{"title":"Behavior feature extraction method of college students’ social network in sports field based on clustering algorithm","authors":"Yonggan Wang, Haiou Sun","doi":"10.1515/jisys-2022-0030","DOIUrl":"https://doi.org/10.1515/jisys-2022-0030","url":null,"abstract":"Abstract In order to improve the integrity of the social network behavior feature extraction results for sports college students, this study proposes to be based on the clustering algorithm. This study analyzes the social network information dissemination mechanism in the field of college students’ sports, obtains the real-time social behavior data in the network environment combined with the analysis results, and processes the obtained social network behavior data from two aspects of data cleaning and de-duplication. Using clustering algorithm to determine the type of social network user behavior, setting the characteristics of social network behavior attributes, and finally through quantitative and standardized processing, get the results of college students’ sports field social network behavior characteristics extraction. The experimental results showed that the completeness of the method feature extraction results improved to 9.93%, and the average extraction time cost was 0.344 s, with high result integrity and obvious advantages in the extraction speed.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"184 1 1","pages":"477 - 488"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72875627","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 The topology optimization of computer communication network is studied based on improved genetic algorithm (GA), a network optimization design model based on the establishment of network reliability maximization under given cost constraints, and the corresponding improved GA is proposed. In this method, the corresponding computer communication network cost model and computer communication network reliability model are established through a specific project, and the genetic intelligence algorithm is used to solve the cost model and computer communication network reliability model, respectively. It has been proved that GA can solve the complex problems of computer working environment better, which is 80% higher than the general algorithm, and can select the optimal scheme pertinently.
{"title":"Topology optimization of computer communication network based on improved genetic algorithm","authors":"Hua Ai, Yuhong Fan, Jilei Zhang, K. Ghafoor","doi":"10.1515/jisys-2022-0050","DOIUrl":"https://doi.org/10.1515/jisys-2022-0050","url":null,"abstract":"Abstract The topology optimization of computer communication network is studied based on improved genetic algorithm (GA), a network optimization design model based on the establishment of network reliability maximization under given cost constraints, and the corresponding improved GA is proposed. In this method, the corresponding computer communication network cost model and computer communication network reliability model are established through a specific project, and the genetic intelligence algorithm is used to solve the cost model and computer communication network reliability model, respectively. It has been proved that GA can solve the complex problems of computer working environment better, which is 80% higher than the general algorithm, and can select the optimal scheme pertinently.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"239 1","pages":"651 - 659"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76905689","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 The aim of this article was to discuss the modeling and control method of quadrotor unmanned aerial vehicle (UAV). In the process of modeling, mechanism modeling and experimental testing are combined, especially the motor and propeller are modeled in detail. Through the understanding of the body structure and flight principle of the quadrotor UAV, the Newton–Euler method is used to analyze the dynamics of the quadrotor UAV, and the mathematical model of the UAV is established under the small angle rotation. Process identifier (PID) is used to control it. First, the attitude angle of the model is controlled by PID, and based on this, the speed in each direction is controlled by PID. Then, the PID control of the four rotor aircraft with the center of gravity offset is simulated by MATLAB. The results show that the pitch angle and roll angle can be controlled by 5 degrees together without center of gravity deviation, and the PID can effectively control the control quantity and achieve the desired effect in a short time. Classical BP algorithm, classical GA-BP algorithm, and improved GA-BP algorithm were trained, respectively, with a total of 150 sets of training data, training function uses Levenberg-Marquardt (trainlm), and performance function uses mean squared error (MSE). In the background of the same noise, the improved GA-BP algorithm has the highest detection rate, classical GA-BP algorithm is the second, and classical BP algorithm is the worst. The simulation results show that the PID control law can effectively control the attitude angle and speed of the rotor UAV in the case of center of gravity deviation.
{"title":"Modeling and PID control of quadrotor UAV based on machine learning","authors":"Lirong Zhou, A. Pljonkin, Pradeep Kumar Singh","doi":"10.1515/jisys-2021-0213","DOIUrl":"https://doi.org/10.1515/jisys-2021-0213","url":null,"abstract":"Abstract The aim of this article was to discuss the modeling and control method of quadrotor unmanned aerial vehicle (UAV). In the process of modeling, mechanism modeling and experimental testing are combined, especially the motor and propeller are modeled in detail. Through the understanding of the body structure and flight principle of the quadrotor UAV, the Newton–Euler method is used to analyze the dynamics of the quadrotor UAV, and the mathematical model of the UAV is established under the small angle rotation. Process identifier (PID) is used to control it. First, the attitude angle of the model is controlled by PID, and based on this, the speed in each direction is controlled by PID. Then, the PID control of the four rotor aircraft with the center of gravity offset is simulated by MATLAB. The results show that the pitch angle and roll angle can be controlled by 5 degrees together without center of gravity deviation, and the PID can effectively control the control quantity and achieve the desired effect in a short time. Classical BP algorithm, classical GA-BP algorithm, and improved GA-BP algorithm were trained, respectively, with a total of 150 sets of training data, training function uses Levenberg-Marquardt (trainlm), and performance function uses mean squared error (MSE). In the background of the same noise, the improved GA-BP algorithm has the highest detection rate, classical GA-BP algorithm is the second, and classical BP algorithm is the worst. The simulation results show that the PID control law can effectively control the attitude angle and speed of the rotor UAV in the case of center of gravity deviation.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"17 1","pages":"1112 - 1122"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84584314","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}
Ann Varghese, Midhun Muraleedharan Sylaja, J. Kurian
Abstract Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.
{"title":"Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification","authors":"Ann Varghese, Midhun Muraleedharan Sylaja, J. Kurian","doi":"10.1515/jisys-2022-0015","DOIUrl":"https://doi.org/10.1515/jisys-2022-0015","url":null,"abstract":"Abstract Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"120 1","pages":"407 - 419"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80772093","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 As an important part of Chinese economy, the construction industry has a great contribution to the economy, and plays an important role in the Chinese economic development. Therefore, it has certain research significance for the quality acceptance supervision method of construction engineering. This article takes Shenzhen S project as an example, combined with cloud computing, discusses the quality acceptance and supervision methods of indoor construction projects. In the introduction to the technical part, this article first briefly introduces the definition of cloud computing and then introduces the particle swarm algorithm and traditional genetic algorithm in the cloud computing task scheduling method. The algorithm is introduced into the quality acceptance of indoor construction projects to obtain the quality, most efficient method for acceptance supervision. The experimental part of this article takes S project as the research object and the residents’ satisfaction with the project as the experimental purpose. Finally, through statistical analysis, it is concluded that the residents’ satisfaction with S project reaches more than 70%.
{"title":"Supervision method of indoor construction engineering quality acceptance based on cloud computing","authors":"Jian Zhang","doi":"10.1515/jisys-2022-0056","DOIUrl":"https://doi.org/10.1515/jisys-2022-0056","url":null,"abstract":"Abstract As an important part of Chinese economy, the construction industry has a great contribution to the economy, and plays an important role in the Chinese economic development. Therefore, it has certain research significance for the quality acceptance supervision method of construction engineering. This article takes Shenzhen S project as an example, combined with cloud computing, discusses the quality acceptance and supervision methods of indoor construction projects. In the introduction to the technical part, this article first briefly introduces the definition of cloud computing and then introduces the particle swarm algorithm and traditional genetic algorithm in the cloud computing task scheduling method. The algorithm is introduced into the quality acceptance of indoor construction projects to obtain the quality, most efficient method for acceptance supervision. The experimental part of this article takes S project as the research object and the residents’ satisfaction with the project as the experimental purpose. Finally, through statistical analysis, it is concluded that the residents’ satisfaction with S project reaches more than 70%.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"8 1","pages":"795 - 805"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85840103","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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