发布求助
文献互助 智能选刊 最新文献

Int. J. Distributed Syst. Technol.最新文献

筛选
英文 中文
Online Stock Price Prediction Based on Interval Data Analysis 基于区间数据分析的在线股票价格预测
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-01 DOI: 10.4018/ijdst.307993
Yan Cheng
{"title":"Online Stock Price Prediction Based on Interval Data Analysis","authors":"Yan Cheng","doi":"10.4018/ijdst.307993","DOIUrl":"https://doi.org/10.4018/ijdst.307993","url":null,"abstract":"The continuous increase in per capita income makes more residents choose stocks as a new investment method, so how to more accurately judge their price trends has become increasingly important. In most traditional time series analyses, models are built on basis of closing price, from the perspective of probability. This paper introduces the interval data into the stock price prediction task and proposes an attention mechanism-based long short-term memory (LSTM) model. Specifically, borrowing the idea from the sequence-to-sequence (seq2seq) model, the LSTM is first used as an encoder to encode the input sequence. Then the attention mechanism is used to capture the most useful information for the current output based on the encoded features. Finally, another LSTM model is used as a decoder to decode the encoded data features and obtain the prediction results. Experimental results show that the proposed model significantly improves the prediction accuracy.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130984439","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
Deep Neural Network for Electromyography Signal Classification via Wearable Sensors 基于穿戴式传感器的肌电信号分类的深度神经网络
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-01 DOI: 10.4018/ijdst.307988
Ying Chang, Lan Wang, Lingjie Lin, Ming Liu
{"title":"Deep Neural Network for Electromyography Signal Classification via Wearable Sensors","authors":"Ying Chang, Lan Wang, Lingjie Lin, Ming Liu","doi":"10.4018/ijdst.307988","DOIUrl":"https://doi.org/10.4018/ijdst.307988","url":null,"abstract":"The human-computer interaction has been widely used in many fields, such intelligent prosthetic control, sports medicine, rehabilitation medicine, and clinical medicine. It has gradually become a research focus of social scientists. In the field of intelligent prosthesis, sEMG signal has become the most widely used control signal source because it is easy to obtain. The off-line sEMG control intelligent prosthesis needs to recognize the gestures to execute associated action. In order solve this issue, this paper adopts a CNN plus BiLSTM to automatically extract sEMG features and recognize the gestures. The CNN plus BiLSTM can overcome the drawbacks in the manual feature extraction methods. The experimental results show that the proposed gesture recognition framework can extract overall gesture features, which can improve the recognition rate.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122330662","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
Classroom Behavior Analysis and Evaluation in Physical Education by Using Structure Representation 基于结构表征的体育课堂行为分析与评价
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-01 DOI: 10.4018/ijdst.307989
Qiufen Yu, Baishan Liu
{"title":"Classroom Behavior Analysis and Evaluation in Physical Education by Using Structure Representation","authors":"Qiufen Yu, Baishan Liu","doi":"10.4018/ijdst.307989","DOIUrl":"https://doi.org/10.4018/ijdst.307989","url":null,"abstract":"Behavior analysis plays a critical role in physical education. This paper resorts to computer vision technology to establish a classroom behavior analysis system for physical education. First, the behavior video is collected by a Kinect camera. Then, the behavior is recognized based on the symbiotic relationship and geometric constraints between human posture and interactive objects. The human skeleton is used to describe the behavior subject and the local area boundary boxes are divided with each node in the skeleton as the center. The human posture features are used to learn a structural classification model to recognize human behavior sequence. Finally, the behavior recognition results are used to analyze physical education. The experimental results show that the proposed behavior analysis framework can accurately recognize human behavior during physical education classes.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132505783","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
Wearable Device-Based Data Collection and Feature Analysis Method for Outdoor Sports 基于可穿戴设备的户外运动数据采集与特征分析方法
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-01 DOI: 10.4018/ijdst.307992
Ju-Yeun An
{"title":"Wearable Device-Based Data Collection and Feature Analysis Method for Outdoor Sports","authors":"Ju-Yeun An","doi":"10.4018/ijdst.307992","DOIUrl":"https://doi.org/10.4018/ijdst.307992","url":null,"abstract":"In recent years, with the rapid popularization of smart phones and wearable smart devices, it is no longer difficult to obtain a large number of human motion data related to people's heart rate and geographical location, which has spawned a series of running fitness applications, leading to the national running wave and promoting the rapid development of the sports industry. Based on the long short-term memory cyclic neural network, this paper processes, identifies, and analyzes the motion data collected by wearable devices. Through massive data training, a set of accurate auxiliary models of outdoor sports is obtained to help optimize and improve the effect of outdoor sports. The results show that the method proposed in this paper has a higher degree of sports action and feature recognition and can better assist in the completion of outdoor sports.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132773842","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
Heart Rate Estimation in Sports Based on Multi-Sensor Data for Sports Intensity Prediction 基于多传感器数据的运动心率估计及运动强度预测
Int. J. Distributed Syst. Technol. Pub Date : 2022-07-01 DOI: 10.4018/ijdst.307990
Feng Zhang
{"title":"Heart Rate Estimation in Sports Based on Multi-Sensor Data for Sports Intensity Prediction","authors":"Feng Zhang","doi":"10.4018/ijdst.307990","DOIUrl":"https://doi.org/10.4018/ijdst.307990","url":null,"abstract":"The heart rate (HR) is the most common measurement of the cardiovascular system. It reflects not only the cardiovascular function, but also the degree of recovery, and has high reliability. The heart rate monitoring can be used in athlete selection, sports training, medical supervision, and fitness to avoid the blindness of exercise intensity arrangement, provide an objective quantitative standard for scientific fitness, and improve the sports performance through monitoring sports intensity. In order to accurately predict the sports intensity, this paper adopts ECG signals and pulse wave to learn an ordinal regression model that can utilize the order relation between different sports intensity level. The experimental results have demonstrated the effectiveness of the proposed sports intensity method.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128874677","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
Mobile Edge Computing-Based Real-Time English Translation With 5G-Driven Network Support 基于移动边缘计算的实时英语翻译，支持5g驱动的网络
Int. J. Distributed Syst. Technol. Pub Date : 2022-04-01 DOI: 10.4018/ijdst.291078
Liguo Wang, H. Yang
{"title":"Mobile Edge Computing-Based Real-Time English Translation With 5G-Driven Network Support","authors":"Liguo Wang, H. Yang","doi":"10.4018/ijdst.291078","DOIUrl":"https://doi.org/10.4018/ijdst.291078","url":null,"abstract":"Real-time English Translation (RET) requires high network bandwidth and low network delay to provide better quality of experience, and even needs the support of massive connection to provide more services. For the three metrics, the traditional strategies are difficult to realize RET well. With the fast development of Mobile Edge Computing (MEC) and 5G network, the guarantee of three metrics has become very possible. Therefore, this paper studies MEC-based RET with 5G-driven network support, called 5GMR. On one hand, 5G-driven network has the natural properties to support high bandwidth, low delay and massive connection. On the other hand, MEC is used to offload the complex tasks related to the computation of English sentences into the edge server for the efficient computation, which not only saves energy consumption of mobile device but also decreases the whole network delay. In terms of the task scheduling in MEC, Genetic Algorithm (GA) is adopted to address it. The experimental results demonstrate that the proposed 5GMR is feasible and efficient.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"42 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113986196","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
Deep Reinforcement Learning and In-Network Caching-Based Martial Arts Physical Training 基于深度强化学习和网络缓存的武术体能训练
Int. J. Distributed Syst. Technol. Pub Date : 2022-04-01 DOI: 10.4018/ijdst.291079
Qi Zhang
{"title":"Deep Reinforcement Learning and In-Network Caching-Based Martial Arts Physical Training","authors":"Qi Zhang","doi":"10.4018/ijdst.291079","DOIUrl":"https://doi.org/10.4018/ijdst.291079","url":null,"abstract":"The martial arts have been regarded as the athletics project at the international competitions, and the corresponding physical training has also brought about widespread attention. However, the traditional physical training evaluation methods are usually performed in the offline way and they are very difficult to achieve the large-scale data evaluation with the high evaluation efficiency. Therefore, this paper leverages Deep Reinforcement Learning (DRL) and in-network caching to realize the high-precision and high-efficiency data evaluation under the large-scale martial arts physical training environment while guarantees the online performance evaluation. Meanwhile, Q-learning based DRL is used to make the large-scale data evaluation. In addition, a communication protocol based on in-network caching is proposed to support the online function. The comparison experiments demonstrate that the proposed conduction method for the martial arts physical training is more efficient than the benchmark.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123348801","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
AI-Based Safety Production Accident Prevention Mechanism in Smart Enterprises 基于人工智能的智能企业安全生产事故预防机制
Int. J. Distributed Syst. Technol. Pub Date : 2022-04-01 DOI: 10.4018/ijdst.291082
Jing Fu, Zipeng Han
{"title":"AI-Based Safety Production Accident Prevention Mechanism in Smart Enterprises","authors":"Jing Fu, Zipeng Han","doi":"10.4018/ijdst.291082","DOIUrl":"https://doi.org/10.4018/ijdst.291082","url":null,"abstract":"Enterprises have accumulated a large number of accident data resources for safety production, but the corresponding safety production information processing capacity is insufficient, resulting in the value of massive data not being effectively used, and further restricting the in-depth study of accidents. Enterprise safety managers cannot learn lessons from historical accidents in a timely manner and effectively prevent them, leading to repeated occurrences of similar accidents. Therefore, based on the above problems, this paper aims to construct a mining process for the cause of safety production accidents based on LDA topic model. According to the accident data structure, select a data mining method suitable for its structural characteristics to maximize the utilization of accident data. According to the sequence of initial identification of accident information, discovery of safety problems, and transformation of safety knowledge, the valuable information in historical accident data can be fully excavated, so as to provide effective suggestions for accident prevention.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129629457","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
Knowledge Graph and GNN-Based News Recommendation Algorithm With Edge Computing Support 基于边缘计算支持的知识图和gnn新闻推荐算法
Int. J. Distributed Syst. Technol. Pub Date : 2022-04-01 DOI: 10.4018/ijdst.291080
Chen Yao, Chuangang Zhao
{"title":"Knowledge Graph and GNN-Based News Recommendation Algorithm With Edge Computing Support","authors":"Chen Yao, Chuangang Zhao","doi":"10.4018/ijdst.291080","DOIUrl":"https://doi.org/10.4018/ijdst.291080","url":null,"abstract":"The current news information from different media websites has posed a serious problem, i.e., it is very difficult to obtain the satisfactory news contents from the measured data information. There have been some researches on news recommendation to improve the experience of users. In spite of this, they always need the further improvement because the news information has showed the explosive increasing way. Therefore, this paper studies knowledge graph and graph neural network (GNN) based news recommendation algorithm with edge computing consideration. At first, the knowledge graph is used for the knowledge extraction. Then, GNN is used to train the extracted features to complete the news recommendation algorithm. Finally, the edge computing is used to offload the high volumes of traffic to the edge server for the news recommendation computation. Compared with two baselines, the proposed algorithm is more efficient, increasing accuracy rate by 2.73% and 9.94% respectively, and decreasing response time by 84.27% and 87.58 respectively.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125420417","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: 1
Analysis and Prediction of Meteorological Data Based on Edge Computing and Neural Network 基于边缘计算和神经网络的气象数据分析与预测
Int. J. Distributed Syst. Technol. Pub Date : 2022-04-01 DOI: 10.4018/ijdst.291081
Jianxin Wang, Geng Li
{"title":"Analysis and Prediction of Meteorological Data Based on Edge Computing and Neural Network","authors":"Jianxin Wang, Geng Li","doi":"10.4018/ijdst.291081","DOIUrl":"https://doi.org/10.4018/ijdst.291081","url":null,"abstract":"In this work, aiming at the problem of missing element values in real-time meteorological data, we propose a radial basis function (RBF) neural network model based on rough set to optimize the analysis and prediction of meteorological data. In this model, the relative humidity of a single station is taken as an example, and the meteorological influencing factors are reduced by rough set theory. The key factors are used as the input of RBF neural network to interpolate the missing data. The experimental results show that the interpolation effect of the model is significantly higher than that of the linear interpolation method, which provides an effective processing method for the lack of real-time meteorological data, and improves the analysis and prediction effect of meteorological data.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116865127","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信