Abstract The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.
{"title":"Machine learning-based processing of unbalanced data sets for computer algorithms","authors":"Qingwei Zhou, Yongjun Qi, Hailing Tang, Peng Wu","doi":"10.1515/comp-2022-0273","DOIUrl":"https://doi.org/10.1515/comp-2022-0273","url":null,"abstract":"Abstract The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48169670","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 Radio frequency identification (RFID) is a broad rapidly evolving skill in the past few years. It is characterized by non-contact identification, fast read and write speed, small label size, large data storage capacity, and other technical advantages. RFID technology for goods movement has completely changed the traditional supply chain management, greatly improved the operational efficiency of enterprises, and has become an important method for the development of supply chain logistics. This work mainly studies and analyzes the RFID supply chain, introduces the development and application of RFID supply chain sector technology, and discusses the operation of the supply chain in detail. Then, according to the existing RFID supply chain, a RFID supply chain artificial intelligence (AI) based approach to technology is proposed, and the data analysis of RFID supply chain is introduced in detail. In this work, through the research experiment of AI technology RFID supply chain data analysis, the experimental data show that there are several time-consuming links in the supply chain system. The time consumed in the AI RFID system is 9.9, 3.4, 3.5, and 29.9 min, respectively, while each link in the original system takes 13.4, 4.9, 4.9, and 34.9 min. It can be seen from the above data that the amount of time in each system link of the AI RFID supply chain system is less than that of the original supply chain system, which shortens the entire product passing cycle and greatly improves work efficiency.
{"title":"RFID supply chain data deconstruction method based on artificial intelligence technology","authors":"Huiying Zhang, Ze Li","doi":"10.1515/comp-2022-0265","DOIUrl":"https://doi.org/10.1515/comp-2022-0265","url":null,"abstract":"Abstract Radio frequency identification (RFID) is a broad rapidly evolving skill in the past few years. It is characterized by non-contact identification, fast read and write speed, small label size, large data storage capacity, and other technical advantages. RFID technology for goods movement has completely changed the traditional supply chain management, greatly improved the operational efficiency of enterprises, and has become an important method for the development of supply chain logistics. This work mainly studies and analyzes the RFID supply chain, introduces the development and application of RFID supply chain sector technology, and discusses the operation of the supply chain in detail. Then, according to the existing RFID supply chain, a RFID supply chain artificial intelligence (AI) based approach to technology is proposed, and the data analysis of RFID supply chain is introduced in detail. In this work, through the research experiment of AI technology RFID supply chain data analysis, the experimental data show that there are several time-consuming links in the supply chain system. The time consumed in the AI RFID system is 9.9, 3.4, 3.5, and 29.9 min, respectively, while each link in the original system takes 13.4, 4.9, 4.9, and 34.9 min. It can be seen from the above data that the amount of time in each system link of the AI RFID supply chain system is less than that of the original supply chain system, which shortens the entire product passing cycle and greatly improves work efficiency.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42277628","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 popularity of artificial intelligence applications is on the rise, and they are producing better outcomes in numerous fields of research. However, the effectiveness of these applications relies heavily on the quantity and quality of data used. While the volume of data available has increased significantly in recent years, this does not always lead to better results, as the information content of the data is also important. This study aims to evaluate a new data preprocessing technique called semi-pivoted QR (SPQR) approximation for machine learning. This technique is designed for approximating sparse matrices and acts as a feature selection algorithm. To the best of our knowledge, it has not been previously applied to data preprocessing in machine learning algorithms. The study aims to evaluate the impact of SPQR on the performance of an unsupervised clustering algorithm and compare its results to those obtained using principal component analysis (PCA) as the preprocessing algorithm. The evaluation is conducted on various publicly available datasets. The findings suggest that the SPQR algorithm can produce outcomes comparable to those achieved using PCA without altering the original dataset.
{"title":"Data preprocessing impact on machine learning algorithm performance","authors":"A. Amato, V. Di Lecce","doi":"10.1515/comp-2022-0278","DOIUrl":"https://doi.org/10.1515/comp-2022-0278","url":null,"abstract":"Abstract The popularity of artificial intelligence applications is on the rise, and they are producing better outcomes in numerous fields of research. However, the effectiveness of these applications relies heavily on the quantity and quality of data used. While the volume of data available has increased significantly in recent years, this does not always lead to better results, as the information content of the data is also important. This study aims to evaluate a new data preprocessing technique called semi-pivoted QR (SPQR) approximation for machine learning. This technique is designed for approximating sparse matrices and acts as a feature selection algorithm. To the best of our knowledge, it has not been previously applied to data preprocessing in machine learning algorithms. The study aims to evaluate the impact of SPQR on the performance of an unsupervised clustering algorithm and compare its results to those obtained using principal component analysis (PCA) as the preprocessing algorithm. The evaluation is conducted on various publicly available datasets. The findings suggest that the SPQR algorithm can produce outcomes comparable to those achieved using PCA without altering the original dataset.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42020442","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 With the continuous development of big data (BD) and Internet of Things (IoT) technology, research in the fields of network security and IoT is also deepening. Big data provides more data support for network security and the IoT, while also bringing more security risks. Therefore, how to ensure the security of big data, prevent network attacks, and improve the security and reliability of the IoT has become a major issue in the current field of network security and the IoT. This article aims to analyze the research results of network security and the IoT in the context of big data and explore how to ensure big data security and improve the security and reliability of the IoT from a multidimensional perspective. Therefore, this article proposes BD technology, that is, through information mining, to ensure network security from the perspective of controlling information flow. At the same time, this article also proposes an LED lightweight encryption algorithm in the IOT, which is used to achieve secure communication between ordinary nodes and gateway nodes, effectively solving the security issues of data distribution in the network, resisting virus attacks and man-in--in-the--the-middle attacks, and has higher security and efficiency. Both of these methods can effectively protect network security: one is to control data flow, and the other is to start with communication protocols. Finally, this article analyzed the adoption of network security protection measures by netizens and found that only 13% of netizens frequently take network security protection measures, while 35% of netizens never take network security protection measures. This is also one of the important reasons for the increasing number of current network security issues.
{"title":"Analysis of research results of different aspects of network security and Internet of Things under the background of big data","authors":"Ying Lu, Xiang Wang, Taotao Xie, Tian Xie","doi":"10.1515/comp-2022-0277","DOIUrl":"https://doi.org/10.1515/comp-2022-0277","url":null,"abstract":"Abstract With the continuous development of big data (BD) and Internet of Things (IoT) technology, research in the fields of network security and IoT is also deepening. Big data provides more data support for network security and the IoT, while also bringing more security risks. Therefore, how to ensure the security of big data, prevent network attacks, and improve the security and reliability of the IoT has become a major issue in the current field of network security and the IoT. This article aims to analyze the research results of network security and the IoT in the context of big data and explore how to ensure big data security and improve the security and reliability of the IoT from a multidimensional perspective. Therefore, this article proposes BD technology, that is, through information mining, to ensure network security from the perspective of controlling information flow. At the same time, this article also proposes an LED lightweight encryption algorithm in the IOT, which is used to achieve secure communication between ordinary nodes and gateway nodes, effectively solving the security issues of data distribution in the network, resisting virus attacks and man-in--in-the--the-middle attacks, and has higher security and efficiency. Both of these methods can effectively protect network security: one is to control data flow, and the other is to start with communication protocols. Finally, this article analyzed the adoption of network security protection measures by netizens and found that only 13% of netizens frequently take network security protection measures, while 35% of netizens never take network security protection measures. This is also one of the important reasons for the increasing number of current network security issues.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66887414","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 safety monitoring system has been used to monitor and manage engineering safety operation. The application scope of the safety monitoring system is very wide. It has a wide range of applications in the fields of pipeline safety monitoring, electrical safety monitoring and household safety monitoring. This article studied the application process of the household safety monitoring system. Many home safety accidents are caused by inadequate monitoring of safety problems. Therefore, it is very important to establish a household safety monitoring system. Traditional home safety monitoring systems only rely on cameras for safety monitoring, and the traditional home safety monitoring system uses too few sensors. With the continuous development of wireless sensor network (WSN) technology, it is possible to build a sensor node network, but provides real-time information for home security monitoring to the greatest extent. This article compared the home safety monitoring system based on the WSN technology of artificial intelligence (AI) with the traditional home safety monitoring system. The experimental results showed that in the large-scale home environment, the average monitoring accuracy of the traditional home security monitoring system and the home security monitoring system based on the WSN technology of AI was 77.76 and 89.36%, respectively. In the small-scale home environment, the average monitoring accuracy of the traditional home safety monitoring system and the home safety monitoring system based on the WSN technology of AI were 87.63 and 94.43%, respectively. Monitoring accuracy refers to the accuracy of the household safety monitoring system in detecting safety issues. Therefore, the application of the WSN technology based on artificial intelligence to the home safety monitoring system can effectively improve the accuracy of home safety monitoring.
{"title":"Application of wireless sensor network technology based on artificial intelligence in security monitoring system","authors":"Yajuan Zhang, Ru Jing, Xiang Ji, Nan Hu","doi":"10.1515/comp-2022-0280","DOIUrl":"https://doi.org/10.1515/comp-2022-0280","url":null,"abstract":"Abstract The safety monitoring system has been used to monitor and manage engineering safety operation. The application scope of the safety monitoring system is very wide. It has a wide range of applications in the fields of pipeline safety monitoring, electrical safety monitoring and household safety monitoring. This article studied the application process of the household safety monitoring system. Many home safety accidents are caused by inadequate monitoring of safety problems. Therefore, it is very important to establish a household safety monitoring system. Traditional home safety monitoring systems only rely on cameras for safety monitoring, and the traditional home safety monitoring system uses too few sensors. With the continuous development of wireless sensor network (WSN) technology, it is possible to build a sensor node network, but provides real-time information for home security monitoring to the greatest extent. This article compared the home safety monitoring system based on the WSN technology of artificial intelligence (AI) with the traditional home safety monitoring system. The experimental results showed that in the large-scale home environment, the average monitoring accuracy of the traditional home security monitoring system and the home security monitoring system based on the WSN technology of AI was 77.76 and 89.36%, respectively. In the small-scale home environment, the average monitoring accuracy of the traditional home safety monitoring system and the home safety monitoring system based on the WSN technology of AI were 87.63 and 94.43%, respectively. Monitoring accuracy refers to the accuracy of the household safety monitoring system in detecting safety issues. Therefore, the application of the WSN technology based on artificial intelligence to the home safety monitoring system can effectively improve the accuracy of home safety monitoring.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135954031","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 twenty-first century is the century of marine resources. The ocean is a treasure of biological resources, energy, water resources and mineral resources, and it would gradually become the “second space” of mankind. In the next few years, it would be more and more relevant to human life. Many scholars have realized the importance of the ocean and began to vigorously develop and use the ocean. Underwater robot is a means for human beings to explore and develop the ocean, and it would be widely used in this field. The development and promotion of underwater vehicles are of great significance to resource development, economic development, and national security. With the increasing shortage of land resources, the development and utilization of marine resources have received increasing attention. The direct exploitation of marine resources by humans would have adverse effects, so the underwater robot technology has developed rapidly in recent years. However, at present, most underwater robots are driven by electric turbines. The underwater working environment requires that the underwater motor has good sealing performance, so its structure is complex and expensive, and it is rarely used in ordinary underwater operations. In recent years, intelligent robots have been used more and more, but because of the complexity and uncertainty of the underwater working environment, there are many uncertain factors. Therefore, it is very meaningful to carry out stability control for it. The research results showed that the displacement, stability, and other corresponding test curves of each component can be obtained by establishing a simple model with software and through ADAMS (Automatic Dynamic Analysis of Mechanical Systems) simulation analysis. This can simulate the movement of real objects in the real environment and find the existing problems, so as to provide a reference for the actual underwater robot design. In this way, the development cycle and production costs can be reduced. This article analyzed the structure and control system of the underwater vehicle based on ADAMS simulation. The results showed that the dynamic stability of the underwater vehicle based on ADAMS simulation analysis was improved by 4.67% compared with the underwater vehicle before optimization.
摘要二十一世纪是海洋资源的世纪。海洋是生物资源、能源、水资源和矿产资源的宝库，它将逐渐成为人类的“第二空间”。在接下来的几年里，它将越来越与人类生活相关。许多学者已经意识到海洋的重要性，并开始大力开发和利用海洋。水下机器人是人类探索和开发海洋的一种手段，将在这一领域得到广泛应用。水下航行器的开发和推广对资源开发、经济发展和国家安全具有重要意义。随着陆地资源日益短缺，海洋资源的开发利用越来越受到重视。人类直接开采海洋资源会产生不利影响，因此水下机器人技术近年来发展迅速。然而，目前，大多数水下机器人都是由电动涡轮机驱动的。水下工作环境要求水下电机具有良好的密封性能，因此其结构复杂、价格昂贵，在普通水下作业中很少使用。近年来，智能机器人的应用越来越多，但由于水下工作环境的复杂性和不确定性，存在许多不确定因素。研究结果表明，利用软件建立简单的模型，并通过ADAMS（Automatic Dynamic Analysis of Mechanical Systems）仿真分析，可以得到各部件的位移、稳定性等相应的试验曲线。这样可以模拟真实物体在真实环境中的运动，发现存在的问题，为实际的水下机器人设计提供参考。通过这种方式，可以减少开发周期和生产成本。本文基于ADAMS仿真，对水下机器人的结构和控制系统进行了分析。结果表明，基于ADAMS仿真分析的水下机器人动力学稳定性比优化前提高了4.67%。
{"title":"Simulation evaluation of underwater robot structure and control system based on ADAMS","authors":"Donglin Tang, Long Li","doi":"10.1515/comp-2022-0269","DOIUrl":"https://doi.org/10.1515/comp-2022-0269","url":null,"abstract":"Abstract The twenty-first century is the century of marine resources. The ocean is a treasure of biological resources, energy, water resources and mineral resources, and it would gradually become the “second space” of mankind. In the next few years, it would be more and more relevant to human life. Many scholars have realized the importance of the ocean and began to vigorously develop and use the ocean. Underwater robot is a means for human beings to explore and develop the ocean, and it would be widely used in this field. The development and promotion of underwater vehicles are of great significance to resource development, economic development, and national security. With the increasing shortage of land resources, the development and utilization of marine resources have received increasing attention. The direct exploitation of marine resources by humans would have adverse effects, so the underwater robot technology has developed rapidly in recent years. However, at present, most underwater robots are driven by electric turbines. The underwater working environment requires that the underwater motor has good sealing performance, so its structure is complex and expensive, and it is rarely used in ordinary underwater operations. In recent years, intelligent robots have been used more and more, but because of the complexity and uncertainty of the underwater working environment, there are many uncertain factors. Therefore, it is very meaningful to carry out stability control for it. The research results showed that the displacement, stability, and other corresponding test curves of each component can be obtained by establishing a simple model with software and through ADAMS (Automatic Dynamic Analysis of Mechanical Systems) simulation analysis. This can simulate the movement of real objects in the real environment and find the existing problems, so as to provide a reference for the actual underwater robot design. In this way, the development cycle and production costs can be reduced. This article analyzed the structure and control system of the underwater vehicle based on ADAMS simulation. The results showed that the dynamic stability of the underwater vehicle based on ADAMS simulation analysis was improved by 4.67% compared with the underwater vehicle before optimization.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43477478","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 Mobile phones are the most commonly used electronic devices in people’s daily life. The image, voice, and other information in these devices need to be processed through signal transmission. The role of signal processing is to process the acquired information in a certain way to get the final result. In order to ensure that the whole processing program can work normally, it is necessary to implement good control to achieve the desired effect. However, with the continuous progress and development of science and technology, its requirements are becoming increasingly strict. The traditional signal processing method is unreliable, has poor real time, and has error-prone characteristics, which can no longer meet the accuracy requirements of current information acquisition equipment. Therefore, people begin to study more complex and precise information processing methods and apply these algorithms to various advanced electronic devices to achieve better results. From the perspective of big data, electronic information technology is generated and developed based on massive data processing. It not only has a strong storage function but also has strong computing power and a wide range of application scenarios. It has strong applicability in real life. In this article, the signal to be processed was divided into several wavelet components in different frequency ranges by empirical mode decomposition technology, and then the signal was denoised by combining three wavelet denoising methods to obtain noise data with good signal-to-noise ratio and high classification accuracy. Finally, the corresponding feature information was extracted according to the signal-receiving model to improve the system recognition rate. This article compared the traditional signal processing methods with the signal processing approaches from the perspective of electronic information technology. The results showed that the processing method had a high computing speed and could better solve the problem of detection performance degradation caused by interference. User satisfaction had also increased by 2.87%, which showed that signal processing based on big data and information processing technology had broad application prospects in communication systems. The core of open computer science is to build a unified, efficient, and scalable computing platform based on massive data processing and use signal processing and computer technology to manage and optimize the scheduling of information resources to better meet various business needs.
{"title":"Exploration on the application of electronic information technology in signal processing based on big data","authors":"Li Liu","doi":"10.1515/comp-2022-0272","DOIUrl":"https://doi.org/10.1515/comp-2022-0272","url":null,"abstract":"Abstract Mobile phones are the most commonly used electronic devices in people’s daily life. The image, voice, and other information in these devices need to be processed through signal transmission. The role of signal processing is to process the acquired information in a certain way to get the final result. In order to ensure that the whole processing program can work normally, it is necessary to implement good control to achieve the desired effect. However, with the continuous progress and development of science and technology, its requirements are becoming increasingly strict. The traditional signal processing method is unreliable, has poor real time, and has error-prone characteristics, which can no longer meet the accuracy requirements of current information acquisition equipment. Therefore, people begin to study more complex and precise information processing methods and apply these algorithms to various advanced electronic devices to achieve better results. From the perspective of big data, electronic information technology is generated and developed based on massive data processing. It not only has a strong storage function but also has strong computing power and a wide range of application scenarios. It has strong applicability in real life. In this article, the signal to be processed was divided into several wavelet components in different frequency ranges by empirical mode decomposition technology, and then the signal was denoised by combining three wavelet denoising methods to obtain noise data with good signal-to-noise ratio and high classification accuracy. Finally, the corresponding feature information was extracted according to the signal-receiving model to improve the system recognition rate. This article compared the traditional signal processing methods with the signal processing approaches from the perspective of electronic information technology. The results showed that the processing method had a high computing speed and could better solve the problem of detection performance degradation caused by interference. User satisfaction had also increased by 2.87%, which showed that signal processing based on big data and information processing technology had broad application prospects in communication systems. The core of open computer science is to build a unified, efficient, and scalable computing platform based on massive data processing and use signal processing and computer technology to manage and optimize the scheduling of information resources to better meet various business needs.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43520273","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 the context of the rapid development of science and technology and the modernization of the legal system, criminal activities are becoming more and more intelligent and technological, which also puts forward higher requirements for criminal technology. The current criminal technology equipment is relatively backward, and the technical level is not high enough, resulting in a low utilization rate of trace material evidence extraction, which directly affects the role of criminal technology in the investigation and solving of cases. In recent years, fingerprint recognition algorithms and image edge detection algorithms have been widely used in various fields. This work studied the application of fingerprint image fuzzy edge recognition algorithm in criminal technology, in order to improve the level of criminal technology and the utilization rate of physical evidence extraction. The criminal technology system is upgraded and optimized by combining fingerprint recognition algorithm and image edge detection algorithm. And fuzzy theory is added to ensure the feasibility of the research. The experimental results show that the fuzzy edge recognition algorithm of fingerprint image can improve the level of criminal technology and the utilization rate of material evidence to a certain extent. The utilization rate is increased by 7.04%. The recognition accuracy of the fuzzy recognition method is also 13.2% higher than that of the methods in the literature.
{"title":"Application of fingerprint image fuzzy edge recognition algorithm in criminal technology","authors":"Xinhua Lv","doi":"10.1515/comp-2022-0263","DOIUrl":"https://doi.org/10.1515/comp-2022-0263","url":null,"abstract":"Abstract In the context of the rapid development of science and technology and the modernization of the legal system, criminal activities are becoming more and more intelligent and technological, which also puts forward higher requirements for criminal technology. The current criminal technology equipment is relatively backward, and the technical level is not high enough, resulting in a low utilization rate of trace material evidence extraction, which directly affects the role of criminal technology in the investigation and solving of cases. In recent years, fingerprint recognition algorithms and image edge detection algorithms have been widely used in various fields. This work studied the application of fingerprint image fuzzy edge recognition algorithm in criminal technology, in order to improve the level of criminal technology and the utilization rate of physical evidence extraction. The criminal technology system is upgraded and optimized by combining fingerprint recognition algorithm and image edge detection algorithm. And fuzzy theory is added to ensure the feasibility of the research. The experimental results show that the fuzzy edge recognition algorithm of fingerprint image can improve the level of criminal technology and the utilization rate of material evidence to a certain extent. The utilization rate is increased by 7.04%. The recognition accuracy of the fuzzy recognition method is also 13.2% higher than that of the methods in the literature.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41681797","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 Cloud computing is a system development method based on dynamic sharing, which allows a large number of systems to be combined to provide services. The purpose of this work is to study the design and implementation of a dynamic virtual resource allocation system in cloud computing, whose architecture allows load balancing between virtual resource pools and reduces resource wastage. Using the cluster network topology, the resource usage of the dynamic system cluster can be monitored in real time, and the total cluster load can be automatically determined based on the monitoring data. The experiment is divided into two parts. Performance testing and scenario testing. Performance tests examine execution time, processor, and memory performance. In the scenario test, JMeter is used to simulate the occurrence of a large number of concurrent application access requests, the loss rate, and processing time of these requests on the cloud platform, and load balancing tests are performed. The test results show that the system running time is about 22–27 ms, the CPU utilization is about 90–95%, and the RAM is about 3.5 ms. The results show that cloud technology can improve resource scheduling of large tasks and optimize resource load balance.
{"title":"Dynamic system allocation and application of cloud computing virtual resources based on system architecture","authors":"Chunhua Lin, Longzi Li, Yuanyi Chen","doi":"10.1515/comp-2022-0259","DOIUrl":"https://doi.org/10.1515/comp-2022-0259","url":null,"abstract":"Abstract Cloud computing is a system development method based on dynamic sharing, which allows a large number of systems to be combined to provide services. The purpose of this work is to study the design and implementation of a dynamic virtual resource allocation system in cloud computing, whose architecture allows load balancing between virtual resource pools and reduces resource wastage. Using the cluster network topology, the resource usage of the dynamic system cluster can be monitored in real time, and the total cluster load can be automatically determined based on the monitoring data. The experiment is divided into two parts. Performance testing and scenario testing. Performance tests examine execution time, processor, and memory performance. In the scenario test, JMeter is used to simulate the occurrence of a large number of concurrent application access requests, the loss rate, and processing time of these requests on the cloud platform, and load balancing tests are performed. The test results show that the system running time is about 22–27 ms, the CPU utilization is about 90–95%, and the RAM is about 3.5 ms. The results show that cloud technology can improve resource scheduling of large tasks and optimize resource load balance.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47253985","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 application of intelligent technology has realized the transformation of people’s production and lifestyle, and it has also promoted the development of the field of medicine. At present, the intensity of intelligence in the field of medicine is increasing. By using its cash methods and techniques combined with the mechanical field, this article proposes to use visual inspection technology to understand the fusion of the medical field and the mechanical field. It is helpful to analyze and solve objective problems such as low efficiency in current infusion and insufficient rigidity of large infusion plastic bottles. Drawing on the principles and laws of deep learning algorithms and neural networks, the technical research of intelligent robots for visual inspection is carried out to realize the intelligence of infusion robots. In the research accuracy of detection, the detection rate of standard particles higher than 85 µM has reached almost 100%, and the rate of 50 µM standard particles is lower and unstable. The detection effect of the control light bulb control was different, and the detection rate was between 50 and 80%, which was obviously worse than the detection robot effect. Therefore, the current research on the technology of intelligent robots is very important.
{"title":"Visual inspection intelligent robot technology for large infusion industry","authors":"Qilang Liang, Bangshun Luo","doi":"10.1515/comp-2022-0262","DOIUrl":"https://doi.org/10.1515/comp-2022-0262","url":null,"abstract":"Abstract The application of intelligent technology has realized the transformation of people’s production and lifestyle, and it has also promoted the development of the field of medicine. At present, the intensity of intelligence in the field of medicine is increasing. By using its cash methods and techniques combined with the mechanical field, this article proposes to use visual inspection technology to understand the fusion of the medical field and the mechanical field. It is helpful to analyze and solve objective problems such as low efficiency in current infusion and insufficient rigidity of large infusion plastic bottles. Drawing on the principles and laws of deep learning algorithms and neural networks, the technical research of intelligent robots for visual inspection is carried out to realize the intelligence of infusion robots. In the research accuracy of detection, the detection rate of standard particles higher than 85 µM has reached almost 100%, and the rate of 50 µM standard particles is lower and unstable. The detection effect of the control light bulb control was different, and the detection rate was between 50 and 80%, which was obviously worse than the detection robot effect. Therefore, the current research on the technology of intelligent robots is very important.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49668205","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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