• 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.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.255-283
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• 2024

In this study, as a preliminary research to effectively support data-driven R&D of researchers, we analyzed the academic information and data requirements for researchers to discover new types of academic information and datasets, and to propose directions for academic information services. To achieve the research objectives, we conducted an exploratory case study involving five researchers and administered an online survey among ScienceON users to glean insights into data-driven R&D behaviors and information/data requirements. As a result, researchers relatively referred to academic papers, datasets and software information from academic papers or conference materials. Moreover, the methods and pathways for acquiring data, as well as the types of data, varied across different subject areas. Researchers often faced challenges in data-driven R&D due to difficulties in locating and accessing necessary datasets or software such as learning models. Therefore it has been analyzed that for future support of data-driven R&D, there is a need to systematically construct datasets by subject. Additionally, it is considered necessary to extract and summarize dataset and related software information in conjunction with academic papers.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.4
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• pp.223-230
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• 2016

This paper presents data-driven thermal protection algorithm for preventing overheating of automotive sunroof motor. When a sunroof motor operates abnormally, its coil is overheated and it is failed. Besides, drivers and passengers are damaged. Hence, the sunroof motor observes its temperature and will be stoped when its temperature reach a predefined level. In order to implement low-cost thermal protection function, we drew a knowledge-based temperature increasing and decreasing curve from the result of experimental test. And then, we implemented data-driven thermal protection algorithm which prevents motor's On/Off operation according to motor operating voltage and motor speed. Finally, we implemented experimental test bed and evaluated its feasibility.

• Smart Structures and Systems
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• v.15 no.3
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• pp.897-911
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• 2015

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.115-115
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• 2019

하천수 흐름예측에 대한 연구는 대부분 WRF-Hydro와 같은 과정기반 모델링 시스템을 이용한다. 과정기반 모델링 시스템은 물리적 현상을 일반화한 수식으로 구성되어있다. 일반화된 수식은 불확실성을 내포하고 있으며 지역적 특성도 반영하지 못한다. 특히 수식에 사용되는 입력자료는 측정값으로 오차가 존재한다. 따라서 과정기반 모델링 시스템 예측결과는 계통오차와 우연오차가 존재한다. 현재 매개변수 보정을 통해 예측결과를 개선하는 방법을 사용하고 있으나 한계가 있다. 본 연구는 이러한 한계를 극복하기 위해 상호보완적인 Data-driven 모델을 구축하여 과정기반 모델링 시스템 결과를 개선하고자 하였다. Data-driven 모델 구축을 위해 머신러닝 기법인 instance-based weighting(IBW)과 support vector regression(SVR)을 사용하였다. 구축된 Data-driven 모델은 한반도 지역 주요 저수지 및 호수의 하천수 흐름예측을 통해 검증하였다. 검증을 위해 과정기반 모델링 시스템으로 WRF-Hydro를 구동하였다. 입력자료는 기상청의 국지수치예측모델자료(LDAPS), HydroSHEDS의 수치표고모델자료(DEM), 국가지리정보원의 저수지 및 호수 연속수치지형도를 사용하였다. 본 연구를 통해 구축된 Data-driven모델은 기존 과정기반 모델링 시스템의 오류수정 한계를 머신러닝을 이용하여 개선할 수 있는 가능성을 제시하였다.

• Journal of Oriental Neuropsychiatry
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• v.32 no.4
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• pp.303-320
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• 2021

Objectives: Although medical typologies based on indigenous biopsychological ideas have been described, their integrity has been questioned due to its theory-driven nature in categorization. Therefore, studies on the Sasang typology, a temperament-based traditional Korean medicine, are needed to examine whether it is possible to classify types of specific biopsychological profiles using data-driven analysis. Methods: Psychological measures of the Eastern Sasang Personality Questionnaire (SPQ) and Western NEO-Personality Inventory (NEO-PI) along with physical measures and Sasang types were acquired from 2,049 participants. Latent groups based on the SPQ and NEO-PI subscale scores were extracted using Latent Profile Analysis. Their psychosomatic features were then compared with those of Sasang types. Results: Three SPQ-based latent groups showed distinctive psychological and physical features consistent with those of Sasang types. However, four NEOPI-based latent groups presented only psychological features. Furthermore, SPQ-High and SPQ-Low latent groups demonstrated similar psychosomatic profiles to those of So-Yang and So-Eum Sasang types, respectively. Conclusions: This study illustrates that biopsychological profiles of Sasang types are supported by psychosomatic features of latent groups based on SPQ of Eastern psychology, signifying that the categorization of Sasang typology have acceptable validity and reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4720-4738
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• 2015

Proper management on user-driven virtual circuits (VCs) is essential for seamless operation of virtual networks. The Network Provisioning System (NPS) is useful software for creating user-driven VCs automatically and must take fault management into account for physical layer impairments on user-driven VCs. This paper addresses a user-driven and trusty protection management in an NPS with an open standard Network Service Interface (NSI), as a contribution to show how to implement the user-driven and trusty protection management required for user-driven VCs. In particular, it provides a RESTful web service Interface for Configuration and Event management (RICE) that enable management of a distinguished data and control plane VC status between Network Service Agents (NSAs) in the event of a node or link fault and repair in a domain. This capability represents a contribution to show how network and protection events in a domain can be monitored between NSAs (NPSs with the NSI) in multiple domains. The implemented NPS controls and manages both the primary and backup VC with disjoint path in a user-driven manner. A demonstration to verify RICE API's capability is addressed for the trusty protection in the dynamic VC network.

• Asia Pacific Journal of Corpus Research
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• v.2 no.2
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• pp.43-48
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• 2021

The aim of this study is to analyze the trends of corpus driven research in Korean education. For this purpose, a total of 14 papers was searched online with the keywords including Korean corpus and Korean education. The data was categorized into three: vocabulary education, grammar education and corpus data construction methods. The analysis results suggest that the number of corpus studies in the field of Korean education is not large enough but continues to increase, especially in the research on data construction tools. This suggests there is a significant demand in corpus driven studies in Korean education field.

• Coupled systems mechanics
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• v.11 no.1
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• pp.43-54
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• 2022

In order to analyse the thermal performance of battery systems in electric vehicles complex simulation models with high computational cost are necessary. Using reduced order methods, real-time applicable model can be developed and used for on-board monitoring. In this work a data driven model of the cooling plate as part of the battery system is built and derived from a computational fluid dynamics (CFD) model. The aim of this paper is to create a meta model of the cooling plate that estimates the temperature at the boundary for different heat flow rates, mass flows and inlet temperatures of the cooling fluid. In order to do so, the cooling plate is simulated in a CFD software (ANSYS Fluent ®). A data driven model is built using the design of experiment (DOE) and various approximation methods in Optimus ®. The model can later be combined with a reduced model of the thermal battery system. The assumption and simplification introduced in this paper enable an accurate representation of the cooling plate with a real-time applicable model.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.3
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• pp.85-90
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• 2015

In this paper, anew parallel event-driven logic simulation is proposed. As the proposed prediction-based parallel event-driven simulation method uses both prediction data and actual data for the input and output values of local simulations executed in parallel, the synchronization overhead and the communication overhead, the major bottleneck of the performance improvement, are greatly reduced. Through the experimentation with multiple designs, we have observed the effectiveness of the proposed approach.