• Title/Summary/Keyword: Data-Driven Design

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Data-Driven Design Methodology based on Data Science Paradigm Focused on Design Research Case Study of Fine Dust Information App Service (데이터 과학의 방법론을 적용한 데이터 기반 디자인 방법론에 대한 연구 - 미세먼지 정보 서비스 앱의 디자인 리서치 사례를 중심으로)

  • Lee, Hyun Jhin
    • The Journal of the Korea Contents Association
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    • v.21 no.10
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    • pp.103-114
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    • 2021
  • This study explores research methodologies of design and data science, and applies data science paradigm on design process. Design research methodologies would have benefits of productivity and efficiency by using data driven design methodology. From insights of former studies, a new methodology of data driven design is suggested that design problem is transformed to data set attributes, such as variables, values and data patterns. The fine dust information app design is conducted as a case study to prove this methodology.

An Integrated Translation of Nuclear Power Plant Design Data ftom Specification-driven Plant Design Systems to a Neutral Product Model (사양 기반 플랜트 설계 시스템에서 생성된 원자력 플랜트 설계 데이터의 중립 모델로의 통합 변환)

  • Mun, Du-Hwan;Yang, Jeong-Sam;Han, Soon-Hung
    • Korean Journal of Computational Design and Engineering
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    • v.14 no.2
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    • pp.96-104
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    • 2009
  • It gradually becomes important to study on how to efficiently integrate and manage plant lifecycle data such as 2D schematic and 3D solid data, logical configuration data, and equipment specifications data. From this point of view, converting plant design data from various systems into neutral data independent from any commercial systems is one of important technologies for the operation and management of plants which usually have a very long period of life. In order to achieve this goal, a neutral model for efficient integration and management of plant data was defined. After schema mapping between one of specification-driven plant design systems and the neutral model was performed, a plant data translator is also implemented according to the mapping result. Finally, by experiments with nuclear power plant design, the feasibility of the translator was demonstrated.

Advanced Information Data-interactive Learning System Effect for Creative Design Project

  • Park, Sangwoo;Lee, Inseop;Lee, Junseok;Sul, Sanghun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.8
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    • pp.2831-2845
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    • 2022
  • Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

A Data-driven Approach for Computational Simulation: Trend, Requirement and Technology

  • Lee, Sunghee;Ahn, Sunil;Joo, Wonkyun;Yang, Myungseok;Yu, Eunji
    • Journal of Internet Computing and Services
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    • v.19 no.1
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    • pp.123-130
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    • 2018
  • With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

Brand Fandom Dynamic Analysis Framework based on Customer Data in Online Communities

  • Yu Cheng;Sangwoo Park;Inseop Lee;Changryong Kim;Sanghun Sul
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.8
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    • pp.2222-2240
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    • 2023
  • Brand fandom refers to a collection of consumers with strong emotions toward a brand. Studying the dynamics of brand fandom can help brands understand which services or strategies influence their consumers to become a part of brand fandom. However, existing literature on fandom in the last three decades has mainly used qualitative methods, and there is still a lack of research on fandom using quantitative methods. Specifically, previous studies lack a framework for locating fandoms from online textual data and analyzing their dynamics. This study proposes a framework for exploring brand fandom dynamics based on online textual data. This framework consists of four phases based on the design thinking model: Preparing Data, Defining Fandom Categories, Generating Fandom Dynamics, and Analyzing Fandom Dynamics. This framework uses techniques such as social network analysis and process mining, combined with brand personality theory. We demonstrate the applicability of this framework using case studies of two Korean home appliance brands. The dataset contains 14,593 posts by consumers in 374 online communities. The results show that the proposed framework can analyze brand fandom dynamics using textual customer data. Our study contributes to the interdisciplinary research at the intersection of data-driven service design and consumer culture quantification.

Performance of Database Driven Network Applications from the User Perspective

  • Tang, Shanyu;YongFeng, Huang;Yip, Yau Jim
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.3 no.3
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    • pp.235-250
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    • 2009
  • An understanding of performance of database driven network applications is critical in combating slow performance of e-commerce web sites, besides efficient web page design, and high quality and well-maintained web server equipment. This paper describes a method of measuring performance from the user viewpoint, which can help enormously in making realistic assessment of true performance of database driven applications. The performance measurements were taken at user locations by using several specially designed JavaScript functions along with ASP scripts. A performance study is presented in this paper, comparing performance of data access using stored procedures with the traditional way of querying a database. It is generally believed that stored procedures have performance benefits as they are pre-compiled. However, our study shows that the data access approach using stored procedures provides significant benefits(by about 30%) over the traditional approach for querying a commercial MySQL database, only when retrieving a substantial amount of data(at least 10,000 rows of data).

Enhanced data-driven simulation of non-stationary winds using DPOD based coherence matrix decomposition

  • Liyuan Cao;Jiahao Lu;Chunxiang Li
    • Wind and Structures
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    • v.39 no.2
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    • pp.125-140
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    • 2024
  • The simulation of non-stationary wind velocity is particularly crucial for the wind resistant design of slender structures. Recently, some data-driven simulation methods have received much attention due to their straightforwardness. However, as the number of simulation points increases, it will face efficiency issues. Under such a background, in this paper, a time-varying coherence matrix decomposition method based on Diagonal Proper Orthogonal Decomposition (DPOD) interpolation is proposed for the data-driven simulation of non-stationary wind velocity based on S-transform (ST). Its core idea is to use coherence matrix decomposition instead of the decomposition of the measured time-frequency power spectrum matrix based on ST. The decomposition result of the time-varying coherence matrix is relatively smooth, so DPOD interpolation can be introduced to accelerate its decomposition, and the DPOD interpolation technology is extended to the simulation based on measured wind velocity. The numerical experiment has shown that the reconstruction results of coherence matrix interpolation are consistent with the target values, and the interpolation calculation efficiency is higher than that of the coherence matrix time-frequency interpolation method and the coherence matrix POD interpolation method. Compared to existing data-driven simulation methods, it addresses the efficiency issue in simulations where the number of Cholesky decompositions increases with the increase of simulation points, significantly enhancing the efficiency of simulating multivariate non-stationary wind velocities. Meanwhile, the simulation data preserved the time-frequency characteristics of the measured wind velocity well.

Data-driven Value-enhancing Strategies: How to Increase Firm Value Using Data Science

  • Hyoung-Goo Kang;Ga-Young Jang;Moonkyung Choi
    • Asia pacific journal of information systems
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    • v.32 no.3
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    • pp.477-495
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    • 2022
  • This paper proposes how to design and implement data-driven strategies by investigating how a firm can increase its value using data science. Drawing on prior studies on architectural innovation, a behavioral theory of the firm, and the knowledge-based view of the firm as well as the analysis of field observations, the paper shows how data science is abused in dealing with meso-level data while it is underused in using macro-level and alternative data to accomplish machine-human teaming and risk management. The implications help us understand why some firms are better at drawing value from intangibles such as data, data-science capabilities, and routines and how to evaluate such capabilities.

Characteristics of inverter-driven induction motor (인버터용 유도전동기의 특징)

  • Cho, K.J.
    • Proceedings of the KIEE Conference
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    • 1991.07a
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    • pp.60-63
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    • 1991
  • Nowadays inverters are widely used for energy-saving and factory automation. Accordingly the characteristics and functions of the inverter are well understood. To our surprise, the characteristics and functions of the inverter-driven induction motor are not well understood. In this technical review, the electrical and mechanical characteristics of the inverter-driven motor, the optimal combination of the inverter with motor, the special design of the inverter-driven motor, and the application of the inverter with the existing motor are given, based on ABB Drives' experiences and technical data.

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