2020 20th International Conference on Computational Science and Its Applications (ICCSA)最新文献_第2页

Large Dimension Parameterization with Convolutional Variational Autoencoder: An Application in the History Matching of Channelized Geological Facies Models 卷积变分自编码器的大维参数化:在河道化地质相模型历史匹配中的应用

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2020-07-01 DOI: 10.1109/ICCSA50381.2020.00016

Júlia Potratz, S. A. Canchumuni, José David Bermudez Castro, A. Emerick, M. Pacheco

{"title":"Large Dimension Parameterization with Convolutional Variational Autoencoder: An Application in the History Matching of Channelized Geological Facies Models","authors":"Júlia Potratz, S. A. Canchumuni, José David Bermudez Castro, A. Emerick, M. Pacheco","doi":"10.1109/ICCSA50381.2020.00016","DOIUrl":"https://doi.org/10.1109/ICCSA50381.2020.00016","url":null,"abstract":"History matching is the problem of assimilating dynamic data in numerical models of oil and gas reservoirs. Among the methods available in the literature, the iterative ensemble smothers are often used in practice. However, these methods assume that all variables are Gaussian, which limits their application in a problem where the objective is to update the distribution of rock types (facies) in the model. In fact, updating models of geological facies using dynamic data is still an open issue in the oil industry. The problem relies on the development of a parametrical model able to preserve the geological realism of the models. In this context, parameterization techniques based on deep learning, such as convolutional variational autoencoders network (CVAE), have shown promising results in this area when combined with ensemble smothers. Nevertheless, these types of networks present difficulties of scalability for large-sized reservoir models, because as the input dimension increases, the number of network parameters increases exponentially. This work addresses this problem by introducing two new CVAE-based network architectures that can be used for modeling large-scale reservoir models. The first proposed network incorporates the “depthwise separable convolution” in its design, while the second introduces the “inception module”. Results show a considerable reduction of trainable parameters for the first network, while, for the second one, the number becomes invariant to the input dimension.","PeriodicalId":124171,"journal":{"name":"2020 20th International Conference on Computational Science and Its Applications (ICCSA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117253770","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

Management of Context in Mobile Environments 移动环境中的上下文管理

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2020-07-01 DOI: 10.1109/ICCSA50381.2020.00027

M. Sbai, Hajer Taktak, Faouzi Moussa

引用次数: 0

Towards an Agent-Based Context Management Architecture 迈向基于代理的上下文管理体系结构

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2020-07-01 DOI: 10.1109/ICCSA50381.2020.00026

M. Sbai, Hajer Taktak, Faouzi Moussa

引用次数: 0

Continuous practices and technical debt: a systematic literature review 持续实践和技术债务:系统的文献回顾

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2020-07-01 DOI: 10.1109/ICCSA50381.2020.00018

Bjornar Lunde, R. Colomo-Palacios

{"title":"Continuous practices and technical debt: a systematic literature review","authors":"Bjornar Lunde, R. Colomo-Palacios","doi":"10.1109/ICCSA50381.2020.00018","DOIUrl":"https://doi.org/10.1109/ICCSA50381.2020.00018","url":null,"abstract":"Technical debt in software development is a common problem that is overlooked by many development teams. This debt can be generated from a variety of reasons, including time pressure and complexity in software. Technical debt in simple terms is when a simple and less optimized solution is carried out in order to gain short term benefits, which leads to refactoring and reworking code later on, costing both time and money. The issue is present in both big, established companies and small startups, and is the reason why many of these small startups never get enough economic grip before debt catch up and they go bankrupt. This paper aims to address this problem by exploring how continuous practices including DevOps could help resolve this issue by adopting the right approaches into the software development cycle and workflow. So as to collect information about these topics, a systematic literature review has been conducted, covering both positive and negative impacts these practices can have on technical debt. The findings will present the current practices used to manage and reduce the accumulation of technical debt, if and how these approaches can be used to reduce already existing technical debt and which of these practices that have the biggest impact on technical debt. The paper concludes that there's potential for continuous practices including DevOps to possibly reduce technical debt if applied appropriately","PeriodicalId":124171,"journal":{"name":"2020 20th International Conference on Computational Science and Its Applications (ICCSA)","volume":"36 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116065560","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: 3

Ontology-based Project Solutions Instances Library Creation Method for the Reuse Concept in the Industry 面向行业重用概念的基于本体的项目解决方案实例库创建方法

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2020-07-01 DOI: 10.1109/ICCSA50381.2020.00021

N. Voit, S. Bochkov, S. Kirillov, M. Ukhanova, S. Brigadnov, Dmitry Kanev

引用次数: 0

Volume Editors 体积的编辑

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2020-07-01 DOI: 10.1109/iccsa50381.2020.00007

引用次数: 1

Architecture Design of a Smart Farm System Based on Big Data Appliance Machine Learning 基于大数据设备机器学习的智慧农场系统架构设计

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2020-07-01 DOI: 10.1109/ICCSA50381.2020.00019

Symphorien Karl Yoki Donzia, Haeng-Kon Kim

{"title":"Architecture Design of a Smart Farm System Based on Big Data Appliance Machine Learning","authors":"Symphorien Karl Yoki Donzia, Haeng-Kon Kim","doi":"10.1109/ICCSA50381.2020.00019","DOIUrl":"https://doi.org/10.1109/ICCSA50381.2020.00019","url":null,"abstract":"The size of the world's population increased at a Revolution. The modern expansion of human numbers started but environmental degradation with lack of urban services. To satisfy the growing of human food, worldwide demand for grain the area under production should be increased, and productivity must be improved on yields area firstly. To evaluate the Smart Farming sub-use cases' overall outcome, each economic and environmental benefits, social aspects, and the technical evolution path were evaluated. We have like an significant improvement in the economic outcome of the farm. This paper proposed an implementation of BMS (Big Data Application Machine Learning-based Smart Farm System) with an emphasis on crop productivity and the importance of farmers' income increase. Increasing crop productivity is also important to increase essentials' income, enhance farmer field-level insights, and actionable knowledge to produce when the crop is of the best quality or selling it with a good price. Therefore, in the Smart Farm system proposed in this paper specially in case of big data science, we need to consider data analysis and machine learning as the most important steps and then we can include the value of big data science. Machine learning is an essential ability to learn from data and provide data-driven information, decisions, and forecasts. Traditional approaches to machine learning were developed in a different era, like the data set that fully integrates memory. In addition to the characteristics of Big Data, they create obstacles to traditional techniques. One of the objectives of this document is to summarize the challenges of machine learning with Big Data.","PeriodicalId":124171,"journal":{"name":"2020 20th International Conference on Computational Science and Its Applications (ICCSA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132956962","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

Workshop Organizers 研讨会的组织者

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2020-05-01 DOI: 10.1109/bigdataservice.2018.00009

V. Lumelsky

引用次数: 0

Message from the General and Program Co-Chairs 总主席和项目联合主席的致辞

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 2011-12-01 DOI: 10.1109/SMARTCOMP.2017.7946958

Jiannong Cao, Julie A. McCann, K. Nahrstedt, Xue Liu

引用次数: 0

Title Page 标题页

2020 20th International Conference on Computational Science and Its Applications (ICCSA) Pub Date : 1980-10-01 DOI: 10.1109/iccsa50381.2020.00001

引用次数: 0