Scalable Computing-Practice and Experience最新文献_第3页

A Method for Online Monitoring Data Release of Composite Submarine Cable Based on Horizontal Federated Learning 基于水平联邦学习的复合海缆在线监测数据发布方法

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2275

Xinli Lao, Jiajian Zhang, Chuanlian Gao, Huakun Deng, Yanlei Wei, Zhenzhong Liu

{"title":"A Method for Online Monitoring Data Release of Composite Submarine Cable Based on Horizontal Federated Learning","authors":"Xinli Lao, Jiajian Zhang, Chuanlian Gao, Huakun Deng, Yanlei Wei, Zhenzhong Liu","doi":"10.12694/scpe.v24i3.2275","DOIUrl":"https://doi.org/10.12694/scpe.v24i3.2275","url":null,"abstract":"Conventional online composite submarine cable monitoring data release mostly adopts the method and principle of blockchain dynamic zoning consensus. In the data release process, there are omissions, and it takes a long time to complete the task, which reduces the timeliness of online composite submarine cable monitoring data release. Based on this, a new data publishing method is proposed by introducing horizontal federation learning. First, the online monitoring data of composite submarine cables are collected and preprocessed to eliminate the high-frequency capacitive effect of submarine cables. Secondly, manage composite submarine cable data nodes, transform the status relationship of data nodes, and ensure the quality of subsequent data release. A horizontal federation learning model is established to design the online monitoring data release process. The experimental results show that the new data release method is highly feasible. With the increasing online monitoring data of composite submarine cables, the time required for data release is short, and the timeliness is high.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071655","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}

引用次数: 0

Research on Space Image Fast Classification Based on Big Data 基于大数据的空间图像快速分类研究

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2423

Yunyan Wang, Peng Chen

{"title":"Research on Space Image Fast Classification Based on Big Data","authors":"Yunyan Wang, Peng Chen","doi":"10.12694/scpe.v24i3.2423","DOIUrl":"https://doi.org/10.12694/scpe.v24i3.2423","url":null,"abstract":"In order to improve the accuracy and effect of space image classification, the author proposes a space image classification method based on Big data analysis, aiming at the shortcomings of low accuracy and long time of current image classification. First, analyze the current research progress of image classification, find out the shortcomings of different classification methods, then collect aerospace images, preprocess the images, and use big data analysis technology to establish image classifiers, image classification was performed using an image classifier, and finally simulation experiments were conducted with other methods for image classification. The results indicate that: The average classification time of this method for aerospace images is 3.5 minutes, which saves 14 minutes and 29 minutes compared to traditional method 1 and traditional method 2, respectively. This indicates that this method has the shortest image classification time and improves the classification efficiency of aerospace images. This method has been proven to have high accuracy in image classification, the shortest classification time, and significant advantages compared to other image classification methods.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072011","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}

引用次数: 0

Scalable Computing Infrastructure for Online and Blended Learning Environments 在线和混合学习环境的可扩展计算基础设施

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2293

Liao Xin

{"title":"Scalable Computing Infrastructure for Online and Blended Learning Environments","authors":"Liao Xin","doi":"10.12694/scpe.v24i3.2293","DOIUrl":"https://doi.org/10.12694/scpe.v24i3.2293","url":null,"abstract":"With the growing popularity of online learning and blended learning, as well as the rapid development of cloud computing and big data technology, scalable computing infrastructure has become an indispensable part of building a modern education platform. Method: Five experiments were conducted to test the scalability and reliability of computing infrastructure based on online and blended learning environments. The experiments include the performance comparison of online learning platforms based on different virtualization technologies, the performance comparison of online and hybrid learning environments under different loads, the comparison of online learning experiences under different bandwidth constraints, the system stability test under different user numbers, and the comparison of access speeds in different regions. Result: The experimental results showed that on an online learning platform using the KVM (Kernel-based Virtual Machine) interface, when the number of concurrent users is 99, the response time is 100.9ms, and the CPU (Central Processing Unit) utilization rate is 60.9%. Under low load conditions, the concurrent access volume is 200; the response time is 50ms, and the throughput is 10.3. When accessing locally, the latency is 9.19ms; the download speed is 500.3KB/s; the network throughput is 399.8KB/s. Conclusion: Exploring the scalability, reliability, performance, stability, and access speed of online learning platforms is crucial for improving platform competitiveness and ensuring user experience.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072018","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}

引用次数: 0

Synchronous Federated Learning based Multi Unmanned Aerial Vehicles for Secure Applications 基于同步联邦学习的多无人机安全应用

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2136

Itika Sharma, Sachin Kumar Gupta, Ashutosh Mishra, Shavan Askar

引用次数: 0

Protecting Data and Privacy: Cloud-based Solutions for Intelligent Transportation Applications 保护数据和隐私:基于云的智能交通应用解决方案

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2381

Surjit Singha, Ranjit Singha

引用次数: 0

Spacecraft Test Data Integration Management Technology based on Big Data Platform 基于大数据平台的航天器试验数据集成管理技术

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2416

Nanqi Gong

{"title":"Spacecraft Test Data Integration Management Technology based on Big Data Platform","authors":"Nanqi Gong","doi":"10.12694/scpe.v24i3.2416","DOIUrl":"https://doi.org/10.12694/scpe.v24i3.2416","url":null,"abstract":"In this paper, a general test platform for spacecraft data management is designed and constructed. This paper introduces a portable software development environment based on LUA. The technology of space environment data management, comprehensive analysis, parameter correction and visual display of spacecraft is realized. The relationship between continuity, mixed dispersion, variation and indication of remote sensing data is studied. This project uses the integrated Long Short Term Memory network (LSTM) technology to detect anomalies in satellite remote sensing observation data. Give full play to the advantages of laser scanning tunneling microscope in the nonlinear field. The combination of this method and the matrix method can improve the adaptive ability of spacecraft in an operation state to better identify abnormal information in remote sensing data. Experiments show that the algorithm can significantly improve the anomaly detection rate of the system. The system can monitor the front test device and record the data. The method can be connected with the space vehicle’s central control and automatic test system. The comprehensive management of the integrated test system of space vehicles is realized.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071373","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}

引用次数: 0

Using Machine Learning Algorithms to Design Personalized Exercise Programs for Health and Wellness 使用机器学习算法为健康和健康设计个性化的锻炼计划

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2340

Yan Lu

{"title":"Using Machine Learning Algorithms to Design Personalized Exercise Programs for Health and Wellness","authors":"Yan Lu","doi":"10.12694/scpe.v24i3.2340","DOIUrl":"https://doi.org/10.12694/scpe.v24i3.2340","url":null,"abstract":"The research paper showcases an elaborate study of machine learning, which is used in healthcare or medical platforms and can be used by healthcare professionals to adopt better diagnostic instruments and tools for examining medical issues or images. The paper highlights that a machine learning algorithm can be utilized in X-rays or MRI scans to examine disease and health issues. This paper will also discuss how this algorithm can help healthcare professionals, doctors, and nurses make accurate diagnoses for better services and patient outcomes. One of the major advantages of using the secondary research method in the following research is the abundance of the literature. All the data being used here are previously collected and evaluated with the result, and using these will increase the impact of the study overall. This method saves resources, including money, time, and manpower. This research method allows the researcher to build new knowledge and draw new conclusions based on existing expertise and knowledge. The chosen research philosophy is the Interpretivism research philosophy. The chosen research approach here is the inductive research approach. The chosen research design for this study is exploratory. All these help the research to achieve its objectives and reach the proposed goal of this research.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072255","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}

引用次数: 0

Application of improved Apriori Algorithm in Innovation and Entrepreneurship Engineering Education Platform 改进Apriori算法在创新创业工程教育平台中的应用

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2307

Xuanyuan Wu, Yi Xiao, Anhua Liu

{"title":"Application of improved Apriori Algorithm in Innovation and Entrepreneurship Engineering Education Platform","authors":"Xuanyuan Wu, Yi Xiao, Anhua Liu","doi":"10.12694/scpe.v24i3.2307","DOIUrl":"https://doi.org/10.12694/scpe.v24i3.2307","url":null,"abstract":"The implementation of innovation and entrepreneurship education is inseparable from professional education, so it is important for the rich data in the education platform to mine the connection between professional courses and between grades and courses. The study of association rule algorithm based on education data mining improves the time performance efficiency and accuracy of Apriori algorithm. The study improves the time efficiencies of Apriori algorithm by maintaining Map table and splitting transaction database; the accuracy is improved by using mixed criteria to measure the accuracy and filtering deformation rules based on the inference of confidence. The results of the validation of the time efficiency of the algorithm show that the running time of the improved algorithm in solving frequent itemsets is improved by about 93.86%, 92.48% and 92.76%, respectively, compared with the other three algorithms. The running time of the algorithm for generating frequent itemsets of all orders is about 91.35 ms, which is 66.13% and 83.72% better than the Apriori algorithm and AprioriTid algorithm, respectively. The mining results of student examination data based on the education platform are reasonable and practical, which are of good practical significance for the innovation and entrepreneurship engineering education platform to develop training plans and improve teaching quality.is assumed.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072286","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}

引用次数: 0

Reinforcement Learning-based Algorithms for Music Improvisation and Arrangement in Sensor Networks for the Internet of Things 基于强化学习的物联网传感器网络音乐即兴与编曲算法

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2390

Xiaoling Hu

{"title":"Reinforcement Learning-based Algorithms for Music Improvisation and Arrangement in Sensor Networks for the Internet of Things","authors":"Xiaoling Hu","doi":"10.12694/scpe.v24i3.2390","DOIUrl":"https://doi.org/10.12694/scpe.v24i3.2390","url":null,"abstract":"The process of learning any new technology requires acquiring the best knowledge about the information of that technology. The better the knowledge humans get about digital technology, the more they become efficient in implementing technological development. In developing the musical rhythm and tuning, the application of programming technologies helps improve the quality. In constructing networking sites and sensing technologies, algorithmic learning processes help in effective development. This development occurs by making the systematic process of transforming a data processing language and data interpreter. Thus, it helps in performing programming effectively in the present as well as future purposes. Therefore, it reflects all the benefits of machine learning. Thus, the preference for machine learning increases technological impact. This development of the programming used in the computer makes humans learn about something easily and get the best information. The effectiveness of the technological development by the algorithm used in the data processing implements the best way to improve the technological language transformation from human language to computer operating language. There is a transnational perspective of the average beat commonness of each part of the music. “Reinforcement algorithms-based learning” incorporated with sensor networks has proposed compelling opportunities for improving “music improvisation” and interpretation.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"380 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071793","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}

引用次数: 0

The Effect of Online and Offline Sports Safety Education combined with MOOC Platforms in Physical Education Teaching in Colleges and Universities 线上线下结合MOOC平台的体育安全教育在高校体育教学中的效果

Scalable Computing-Practice and Experience Pub Date : 2023-09-10 DOI: 10.12694/scpe.v24i3.2285

Yuan Gao

{"title":"The Effect of Online and Offline Sports Safety Education combined with MOOC Platforms in Physical Education Teaching in Colleges and Universities","authors":"Yuan Gao","doi":"10.12694/scpe.v24i3.2285","DOIUrl":"https://doi.org/10.12694/scpe.v24i3.2285","url":null,"abstract":"In light of Internet+, how to make network technology better serve the educational cause needs more exploration. The online and offline hybrid education model that integrates MOOC is a new attempt. The sports safety of college students is the premise for the smooth development of sports activities. Therefore, a mixed teaching mode of sports safety combined with MOOC is designed to evaluate the teaching effect. However, under this teaching mode, the commonly used teaching effect evaluation methods cannot adhere to formative evaluation standards. Consequently, to better evaluate the MOOC teaching mode, a model for evaluating instructional effects based on RF mixed teaching mode is constructed. Aiming at the defects of RF in data processing, a genetic algorithm and particle swarm algorithm are used to optimize random forest. The outcomes demonstrate that the enhanced PSO-RF evaluation model has a 98.68% accuracy rate, which is 5.44% and 3.49% higher than the RF and GA-RF model respectively. Therefore, the enhanced PSO-RF-based teaching effect assessment model can better assess the mixed teaching mode in sports safety, meeting the evaluation requirements for students’ learning effects.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072062","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}

引用次数: 0