2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)最新文献_第7页

Connection Phase Estimation of Pole Mounted Distribution Transformers by Integer Form of Population Based Incremental Learning Considering Measurement Errors and Outliers by Correntropy 考虑测量误差和离群值的整数形式种群增量学习的极装配电变压器连接相位估计

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718496

Kaichi Matsumoto, Y. Fukuyama, Kojiro Seki, Akihiro Oi, Toru Jintsugawa, H. Fujimoto

{"title":"Connection Phase Estimation of Pole Mounted Distribution Transformers by Integer Form of Population Based Incremental Learning Considering Measurement Errors and Outliers by Correntropy","authors":"Kaichi Matsumoto, Y. Fukuyama, Kojiro Seki, Akihiro Oi, Toru Jintsugawa, H. Fujimoto","doi":"10.1109/CSDE53843.2021.9718496","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718496","url":null,"abstract":"This paper proposes connection phase estimation of pole mounted distribution transformers by integer form of population based incremental learning considering measurement errors and outliers by correntropy. In electric power distribution systems, since it is difficult for electric power utilities to manage connection phases of pole mounted distribution transformers, a connection phase estimation method is required. Connection phase estimation can be formulated as a combinatorial optimization problem using measurement data of measurement points. Conventionally, various connection phase estimation methods have been developed such as statistic, branch and bound, and tabu search based methods. However, when measurement errors and outliers occur in the measurement data, the conventional methods cannot handle them or maintain estimation accuracy. Moreover, since the branch and bound based method dose not utilize power flow calculation, accurate electric circuit calculation, for calculating an objective function value, a solution may not be evaluated accurately. Regarding the tabu search based method, since various evolutionary computation methods have been developed in recently years, estimation accuracy using the tabu search based method may be improved by applying other evolutionary computation methods. The proposed method is applied to a distribution model system based on a JST-CREST126 distribution lines model. Simulation results indicate correct connection phases can be estimated by applying the correntropy to the connection phase estimation problem even if measurement errors occur. Moreover, the proposed method can improve the estimation accuracy than the conventional method.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126199819","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

Finding the drivers for ERP systems uptake in SMEs – an exploratory multiple-case study of selected Fijian companies 寻找中小企业采用ERP系统的驱动因素——选定斐济公司的探索性多案例研究

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718437

Sanmugam Goundar, M.G.M. Khan, K. G. Reddy

{"title":"Finding the drivers for ERP systems uptake in SMEs – an exploratory multiple-case study of selected Fijian companies","authors":"Sanmugam Goundar, M.G.M. Khan, K. G. Reddy","doi":"10.1109/CSDE53843.2021.9718437","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718437","url":null,"abstract":"This is an exploratory paper which utilizes interpretive case studies research method to explore the drivers for the uptake of Enterprise Resource Planning (ERP) software within Fijian SMEs. The study uses semi-structured interviews conducted with participants from randomly selected companies coming under phase 1 of listed industries under the ambit of VAT Monitoring regulation of 2017. In attempting to find the initial factors encouraging ERP systems uptake, the paper, analyzes the multiple case study data in terms of the following factors: Standardization of business processes Coercing state regulations, Flexibility and ease of use, Improved Business Control, Business Cost Savings, and, Centralization and Integration. In addition, the study specifically tries to address whether the VAT Monitoring regulation is having some influence in ERP systems acquisition. Flexibility and ease of use, improved business control and centralization and integration are seen as positive drivers for ERP systems uptake. From the case study companies, the related state regulation appears to be among the new drivers. The findings, other than relaying information to ERP vendors, also, provide some insights and circumstantial evidence into ERP systems adoption by Fijian SMEs and opening up related areas for future research.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117351639","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

Detection of Malicious URLs through an Ensemble of Machine Learning Techniques 通过集成机器学习技术检测恶意url

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718370

S. Venugopal, Shreya Yuvraj Panale, Manav Agarwal, Rishab Kashyap, U. Ananthanagu

{"title":"Detection of Malicious URLs through an Ensemble of Machine Learning Techniques","authors":"S. Venugopal, Shreya Yuvraj Panale, Manav Agarwal, Rishab Kashyap, U. Ananthanagu","doi":"10.1109/CSDE53843.2021.9718370","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718370","url":null,"abstract":"This paper aims to classify URLs and web pages into legitimate and malicious sites to alert users and allow safer browsing through the internet. Through this process we have found various points of interest and attributes that bring to light the characteristics of these malicious sources, allowing us to be aware of and prevent any damage it might cause. These attributes relate to the domain registration of the URLs, the URL text, the structure of the web page and its contents. The application of models such as BERT, LSTM, Decision Trees and their amalgamation as an ensemble result in a pragmatic solution to the problem in the form of an ensemble giving an accuracy of 95.3%. It also uses concepts such as web page reputation, Internal Links and External Links of a web page. The method of classification used in this paper where both Natural Language Processing techniques and Machine Learning models with such a vast variety of features have been combined has not been implemented earlier. We conclude the paper by suggesting methods to improve to solve the problem.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132552355","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: 2

Multimodal Segmentation Based On A Novel 3d U-Net Deep Learning Architecture 基于新型三维U-Net深度学习架构的多模态分割

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718438

Km Swaroopa, G. Chetty

引用次数: 0

Contact Tracing Application for Aviation- A Digital Inoculation 航空接触追踪应用——数字接种

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718447

Muni Prashneel Gounder, Neeraj Anand Sharma

{"title":"Contact Tracing Application for Aviation- A Digital Inoculation","authors":"Muni Prashneel Gounder, Neeraj Anand Sharma","doi":"10.1109/CSDE53843.2021.9718447","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718447","url":null,"abstract":"Covid-19 has changed the dynamics of how we live and conduct our daily lives. There are more restrictions than ever before and people have to be mindful of the COVID safe protocols to ensure that there are no further infections. Even with protective measures, immunity is not guaranteed. Therefore, contact-tracing mechanisms are of utmost importance to reduce further chained infections. Contact tracing is a containment strategy, which is perfected through digital innovation. This paper analyses current implementations of contact tracing software for air travel. It further elaborates challenges and opportunities with the implementations of digital contact tracing. Finally based on the findings and assessments, the research builds and proposes a novel contact tracing software primarily for the aviation industry. The application capitalizes on the use of a boarding pass to record passenger movement and contacts. Additionally, it exhibits future scope and recommendations for the successful implementation and adoption of digital contact tracing software. The primary beneficiaries of this article are academics, contact tracing software developers, airline and airport operators, immigration, border security and health authorities, government and policymakers, and airline passengers.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114155767","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 secondary water supply water quality evaluation method based on K-means clustering and entropy method 基于k均值聚类和熵值法的二次供水水质评价方法的应用

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718494

Wu Linjing, Yu Jiali, Zeng Jiayu, Shu Shihu

{"title":"Application of secondary water supply water quality evaluation method based on K-means clustering and entropy method","authors":"Wu Linjing, Yu Jiali, Zeng Jiayu, Shu Shihu","doi":"10.1109/CSDE53843.2021.9718494","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718494","url":null,"abstract":"The secondary water supply water quality indicators monitored by urban water supply companies are usually residual chlorine, turbidity, pH, etc., and data can be continuously uploaded through online meters and used for analysis and early warning. However, there are two considerations in this process: one is that the process of data collection and transmission has a great impact on the quality of data samples; the other is that common water quality analysis methods are not suitable for secondary water quality analysis with low information density and large capacity. Based on the above considerations, this study carried out data quality evaluation on two main types of secondary water supply online monitoring indicators (residual chlorine, turbidity), and summarized 5 common data errors, 3 error-causing factors and 2 types of data error characteristics in order to support for the subsequent smart management of secondary water supply. A method for online monitoring and evaluation of secondary water supply water quality based on K-means clustering method and entropy method is proposed, and four main secondary water supply water quality influencing factors are analyzed for variance analysis and covariance analysis to provide secondary water supply operation and maintenance management. Learn from experience and suggestions.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114671136","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

Decoy-File-Based Deception without Usability Degradation 没有可用性退化的基于诱饵文件的欺骗

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718420

Yukio Aoike, Masaki Kamizono, Masashi Eto, Noriko Matsumoto, N. Yoshida

引用次数: 0

Cross-Domain Fault Diagnosis of Bearings Using Simple Structure Model Combining with Signal Processing Method 基于简单结构模型和信号处理方法的轴承跨域故障诊断

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718469

Taeyun Kim, Jangbom Chai

{"title":"Cross-Domain Fault Diagnosis of Bearings Using Simple Structure Model Combining with Signal Processing Method","authors":"Taeyun Kim, Jangbom Chai","doi":"10.1109/CSDE53843.2021.9718469","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718469","url":null,"abstract":"Bearings have essential roles in the operation and the safety of rotating systems. Artificial intelligence (AI) models have been developed to diagnose defects in various systems. They showed good performance for the trained system, and domain adaptation methods enhanced their performance for different operating conditions. However, those methods are not good enough when they are applied to the systems which have quite different characteristics from those of the trained system. In this paper, signal processing methods and the simple structure model are combined to diagnose different system from training system. The structural noise is removed, and one-dimensional convolution neural network (1D-CNN) combined with support vector machine (SVM) is used. The model is validated using published data (Case Western Reserve University datasets and Paderborn University datasets) and the results of domain adaptation method are also explained. The proposed method provides high accuracy of the cross-domain fault diagnosis even though only the normal data of target system are used in training classifier.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114829734","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 to create a credit scoring model in banking and finance 使用机器学习在银行和金融领域创建信用评分模型

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718414

Nhan T. Cao, Long Hoàng Trần, An Hoa Ton-That

引用次数: 0

Deep Learning Method for Haze Prediction in Singapore 新加坡雾霾预测的深度学习方法

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2021-12-08 DOI: 10.1109/CSDE53843.2021.9718412

A. C. Idris, Hayati Yassin

{"title":"Deep Learning Method for Haze Prediction in Singapore","authors":"A. C. Idris, Hayati Yassin","doi":"10.1109/CSDE53843.2021.9718412","DOIUrl":"https://doi.org/10.1109/CSDE53843.2021.9718412","url":null,"abstract":"In recent years, environmental scientist focused more efforts on studying atmospheric air quality and its relation to global warming. The rapid advancement of deep learning methodology has made it a popular topic for environmental research. With this consideration, we propose a deep learning Recurrent Neural Network (RNN) method to predict the hourly fluctuation of air pollutant associated with the haze phenomena. For this study, we are comparing multi-layer models of stacked RNN and bidirectional RNN. All algorithms tested in this paper were based on either the Long Short-Term Memory Neural Network (LSTM) or Gated Recurrent Unit (GRU). These algorithms are an improvement and enhancement of the existing prediction method done on the basic RNN network. We aim to investigate the effect of stacking additional layers onto prediction model with either LTSM or GRU gates in the hidden network. To compare the overall performance of each method, the mean absolute error (MAE), training and validation loss per epoch are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.","PeriodicalId":166950,"journal":{"name":"2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995071","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