Journal of Cloud Computing-Advances Systems and Applications最新文献_第10页

Incremental Multivariate State Estimation Technique-Based Fault Estimation Method for Motor-driven High-Voltage Circuit Breakers 基于增量多元状态估计技术的电机高压断路器故障估计方法

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00060

Wei Li, Y. Fan, Xi Xiao, Fang Xie, Ping Zeng, Zhigang Liu, Yanwei Fu, Jing Long, Xiao Wang

{"title":"Incremental Multivariate State Estimation Technique-Based Fault Estimation Method for Motor-driven High-Voltage Circuit Breakers","authors":"Wei Li, Y. Fan, Xi Xiao, Fang Xie, Ping Zeng, Zhigang Liu, Yanwei Fu, Jing Long, Xiao Wang","doi":"10.1109/CSCloud-EdgeCom58631.2023.00060","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00060","url":null,"abstract":"The fault estimation of motor-driven high-voltage circuit breakers, which is important for improving a system’s security, operation, and maintenance, has attracted extensive research interest. Currently, most fault estimations schemes of motor-driven high-voltage circuit breakers worldwide detect a single variable, where the estimation accuracy is significantly affected by parameter selection. Besides, a motor-driven high-voltage circuit breaker’s fault may result from multiple parameters. Therefore, this paper develops a comprehensive multiparameter estimation for the motor-driven high-voltage circuit breakers and utilizes the evaluation results to support the optimal operation of the intelligent grid. Specifically, the voltage, current, speed, and position parameters are used to build the fault estimation model of the motor-driven high-voltage circuit breakers to achieve the memory matrix’s real-time dynamic update and de-redundancy. The current operating state is then judged for abnormalities by comparing the defined threshold and residual between the estimated model output operation state and the equipment’s current actual operation state. Finally, the effectiveness of the proposed method is verified by numerical simulations.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"1 1","pages":"311-317"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82253371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revolutionizing Network Performance: The Active and Passive Service Path Performance Monitoring Analysis Method 革命性的网络性能:主动式和被动式业务路径性能监控分析方法

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/cscloud-edgecom58631.2023.00027

Jigang Wen, Yuxiang Chen, Kai Jin, Chuda Liu

{"title":"Revolutionizing Network Performance: The Active and Passive Service Path Performance Monitoring Analysis Method","authors":"Jigang Wen, Yuxiang Chen, Kai Jin, Chuda Liu","doi":"10.1109/cscloud-edgecom58631.2023.00027","DOIUrl":"https://doi.org/10.1109/cscloud-edgecom58631.2023.00027","url":null,"abstract":"The Service Path Performance Monitoring Scheme is an advanced network performance management method that comprehensively monitors every link of the business system. Its primary goal is to quickly detect and resolve issues that affect key businesses, optimizing operational efficiency and fault-handling capabilities within the network. With customer-centric support in mind, the scheme features real-time performance monitoring, fast fault location, and analytical functions to aid operation and maintenance personnel. Furthermore, the scheme includes a centralized system that collects real-time analysis data from every network node, presenting a clear visual representation of the business system’s working conditions. This graphical approach allows for faster interpretation and decision-making, maximizing operation and maintenance efficiency. Ultimately, the Service Path Performance Monitoring Scheme ensures the business network’s performance and stability, enabling rapid issue detection and resolution to enhance customer satisfaction.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"22 1","pages":"108-113"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74502857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Task Partitioning and Scheduling for Vehicular Edge Computing 车辆边缘计算任务划分与调度研究进展

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00064

Jing Huang, Wenyu Wu, Weihong Huang, Yufeng Xiao, Lisi F. Lisi, Jinxi Sun

引用次数: 1

A missing physical fitness test data classification method based on MLP 基于MLP的缺失体质测试数据分类方法

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00031

Peng Che, Zhenlian Peng, Buqing Cao, Jianxun Liu, Tieping Chen, Runqing Fan

{"title":"A missing physical fitness test data classification method based on MLP","authors":"Peng Che, Zhenlian Peng, Buqing Cao, Jianxun Liu, Tieping Chen, Runqing Fan","doi":"10.1109/CSCloud-EdgeCom58631.2023.00031","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00031","url":null,"abstract":"In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"15 1","pages":"132-137"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81378907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Housing Price Prediction Method Based on Stacking Ensemble Learning Optimization Method 基于叠加集成学习优化方法的房价预测方法

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00025

Zhenyu Yang, Xinghui Zhu, Yangcong Zhang, Peng Nie, Xinbo Liu

{"title":"A Housing Price Prediction Method Based on Stacking Ensemble Learning Optimization Method","authors":"Zhenyu Yang, Xinghui Zhu, Yangcong Zhang, Peng Nie, Xinbo Liu","doi":"10.1109/CSCloud-EdgeCom58631.2023.00025","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00025","url":null,"abstract":"The growth of real estate sector has been significantly influenced in recent years by ongoing regulation of real estate acquisition policy and the effects of the COVID-19 epidemic on economy. The fluctuation in housing prices is one of the most concerning factors for prospective homeowners. Whether property prices can sustain a comparatively constant level for an extended period of time is a crucial factor for prospective homeowners. Numerous modeling and application techniques for prediction algorithms, together with the promotion of machine learning algorithms, offer fresh approaches to forecast residential real estate values. This work proposes a novel stacking ensemble learning method (DStacking) which is based on the diversity of learners including XGBoost and BP neural network. Through the application of ensemble learning algorithm, D-Stacking method can successfully predict the possible promotion of housing price. Housing price datasets from China and the USA were used in the experiments to guarantee the generalizability of findings. Experimental findings indicate that the diversity of base learners significantly affects the predictive power of D-Stacking method. Furthermore, the more diverse the models are, the more precise the predictions can be with proposed D-Stacking method. Compared with classical Stacking ensemble learning models, the proposed D-Stacking method demonstrates an excellent feasibility in reducing the RMSE to 0.869 and 1.029 across various datasets.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"28 1","pages":"96-101"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81122098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal Data Trajectory Prediction: A Review 多模态数据轨迹预测:综述

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00010

Xiaoliang Wang, Hao Yue, Qing Yang

{"title":"Multimodal Data Trajectory Prediction: A Review","authors":"Xiaoliang Wang, Hao Yue, Qing Yang","doi":"10.1109/CSCloud-EdgeCom58631.2023.00010","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00010","url":null,"abstract":"Trajectory prediction refers to predicting the future movement of an object, person, or vehicle based on past motion trajectory information and other relevant environmental information. Traditional trajectory prediction solutions are suitable for simple driving scenarios and only applicable for short-term predictions. With the improvement of computing power and data processing speed, people can analyze and utilize large-scale datasets faster, use more data and more complex algorithms to build models, and therefore better predict future trends and behaviors. In addition, different solutions focus on how to efficiently extract features from different types of information and how to more accurately predict the future trajectory of the target object. Our goal is to analyze the impact of different information on prediction, and to classify and compare deep learning-based trajectory prediction methods for vehicles or surrounding pedestrians based on the same data. According to the type and quantity of inputs, they are divided into five categories, and the use and prediction effect of different information solutions are elaborated in detail.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"141 1 1","pages":"1-5"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91112143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Terminal Device Authentication Scheme Based on Blockchain Technology in WBAN 基于区块链技术的WBAN终端设备认证方案

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00076

Ying Wang, Lei Cheng, Jianbo Xu, Shaobo Zhang

引用次数: 0

BFT Consensus Algorithms BFT一致性算法

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00080

Xiaojun Zhang, Weiyu Zhong, Ce Yang, Lin Chen, Jing Liao, N. Xiong

引用次数: 0

ESR: Optimizing Gene Feature Selection for scRNA-seq Data ESR:优化scRNA-seq数据的基因特征选择

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00079

Tao Huang, Xiang Chen, Li Peng

引用次数: 0

Data Augmentation for Tensor Completion via Embedding Gradient Tracking 基于嵌入梯度跟踪的张量补全数据增强

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00044

Han Deng, Yuhui Li, Songyou Xie, Yang Yang, Yaqin Liu

{"title":"Data Augmentation for Tensor Completion via Embedding Gradient Tracking","authors":"Han Deng, Yuhui Li, Songyou Xie, Yang Yang, Yaqin Liu","doi":"10.1109/CSCloud-EdgeCom58631.2023.00044","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00044","url":null,"abstract":"How can we enhance the performance of neural tensor completion models for sparse data recovery? The task of tensor completion is crucial for network monitoring since it is the basis of network operation and management. Neural tensor completion models can exploit the multi-dimensional structure of tensors to recover missing entries in partially observed tensors. However, they can produce inaccurate estimations as real-world tensors are very sparse. These models tend to overfit a small amount of data, leading to limited accuracy on recovered data. In this paper, we propose LightTracker, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion models. Specifically, LightTracker first trains a neural tensor completion model and tracks the gradient from the view of embeddings. After training, we use the tracked entity gradient to determine the importance of unknown tensor entries. Then, we aggregate the entries’ importance to calculate the importance of each entity. Finally, LightTracker creates an augmented tensor by mixing observed data and predicted entries with the highest importance. Experiments conducted on two network traffic datasets show that LightTracker effectively enhanced the imputation accuracy of different tensor completion baselines, showing its generality and effectiveness.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"41 1","pages":"210-216"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85744732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0