• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.1
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• pp.116-124
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• 2009

This paper proposes a new Artificial-Intelligent(AI) Power Quality(PQ) diagnosis algorithm using Discrete Wavelet Transform(DWT), Fast Fourier Transform(FFT), Root-Mean-Square(RMS) value. The developed algorithm is able to detect and classify the PQ problems such as the transient, the voltage sag, the voltage swell, the voltage interruption and the total harmonics distortion. The 15.36[kHz] sampling frequency is used to measure the voltages in a power system. The measured signals are used for DWT, FFT, RMS calculation. For AI diagnosis of the PQ problems, a simple multi-layered Artificial Neural Network(ANN) with the back-propagation algorithm is adopted, programmed in C++ and tested in PSIM simulation studies. Finally, the algorithm, which is installed in MP PQ+256 with TI DSP320C6713, is proved to diagnose the PQ problems efficiently.

• Korean Journal of Cognitive Science
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• v.12 no.4
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• pp.11-27
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• 2001

Machine translation (MT) quality assessment is an outstanding problem. In the present situation in which the quality of machine-translated products are far from the user\\`s satisfaction objective evaluation of MT system is a prerequisite to building mutual trust between the users and the vendors stimulating constructive competition among the developers and finally leading to improve the quality of MT systems. Especially there emerges a need for an intensive study on how to evaluate the quality of MT systems from both linguistic and data processing aspects and to secure a steady improvement of the translation quality. With due regard to such points we in this paper present a linguistic evaluation of English-to-Korean machine translation based on a test suite composed of 3.373 sentences that were classified into their linguistic phenomena and complexity levels and report the experimental results made from several commercial MT systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.2029-2033
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• 2007

오늘날 특정 유역에서의 수문현상 및 수문순환에 대한 분석을 위한 연구가 활발히 진행되고 있다. 이를 위해서는 수문자료의 관측은 반드시 수반되어야 하며, 관측자료의 품질관리 및 원시자료의 관리 등이 요구되고 있는 실정이다. 관측된 수문자료의 품질관리라 함은 자료의 신뢰도 분석과 자료의 보완의 두 과정을 포함한다. 여기서, 신뢰도 분석이라 함은 자료 속에 포함된 불확실성을 판별하는 작업을 의미하며, 자료의 불확실성은 위에서 언급된 자료의 불충분 및 불안정을 제외한 부정확, 불일관성에서 비롯된다. 자료의 보완이라 함은 자료의 신뢰도 분석을 통하여 자료 속에 포함된 불확실한 성분들을 찾아내고, 이를 제거한 후 완전한 자료로 대체하고, 자료가 결측된 경우 공백을 연결함으로써 자료의 완전성을 유지하거나 또는 불충분한 자료를 확장하는 일련의 보완작업이라고 정의한다. 자료의 품질을 결정하는 주요 인자는 크게 관측소 관리의 하드웨어적인 측면과 자료 분석의 소프트웨어적인 측면이 있다. 하드웨어적인 측면에서의 수문자료 품질관리를 위해서 본 과제에서는 현장에 설치된 수위계, 강우량계의 센서 등에 대한 장비를 점검하고, 현장실측을 통해 지속적으로 측정값을 보정해주는 역할을 수행하고 있으며, 소프트웨어적인 측면에서 수문자료의 품질관리를 위해서는 수문자료의 수집 단계부터 시작하여 데이터베이스 저장, 필터링, 통계분석, 웹 및 C/S(Client Server)를 통한 배포 등의 일련의 자료 처리 과정을 수행할 수 있는 수문자료관리 프로그램을 웹 시스템과 C/S로 분류하여 정의내릴 수 있다. 본 연구에서는 수문자료의 관리자 입장에서의 보다 효율적이고 체계적으로 자료를 관리하고 분석하기 위한 방안으로 수문자료관리시스템(Hydrologic Data Aquisition and Management System, HDAMS)을 개발하였다. 이 시스템은 안양천 유역에서 시범 적용하고 있으며, 범용성을 전제로 개발되었다. 또한 수문자료 관리 프로그램의 DB 구조 및 DB 자료를 활용한 다양한 분석기능은 갖도록 설계하였으며 계획된 데이터베이스 구조를 바탕으로 계측기 인터페이스와 사용자 인터페이스, 데이터베이스 간의 연동이 원활히 이루어지도록 개발하고자 한다.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.577-590
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• 2004

Today, the growth of software industry leads to e quantitative expansion of software package products. Due to this rapid increase of software package products, qualify certification has been required fur software products which users select. Unlike the quality certification of industrial products, the history of software product certification has not been so long. For this reason, software quality evaluation and certification methods have not matured yet. When certifying software products, one of most important factors is the systematic generation of evaluation sets. The evaluation sets include checklists with metrics, and criteria for the software quality certification according to the classification of software product type. This paper presents a metamodel for the systematic generation and maintenance of the evaluation sets. Then, we construct prototype level evaluation sets to show the validity of the metamodel.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.5
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• pp.14-28
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• 2009

The PageRank algorithm is an important component for ranking Web pages in Google and other search engines. While many improvements for the original PageRank algorithm have been proposed, it is unclear which variations (and their combinations) provide the "best" ranked results. In this paper, we evaluate the ranking quality of the well-known variations of the original PageRank algorithm and their combinations. In order to do this, we first classify the variations into link-based approaches, which exploit the link structure of the Web, and knowledge-based approaches, which exploit the semantics of the Web. We then propose algorithms that combine the ranking algorithms in these two approaches and implement both the variations and their combinations. For our evaluation, we perform extensive experiments using a real data set of one million Web pages. Through the experiments, we find the algorithms that provide the best ranked results from either the variations or their combinations.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.21-26
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• 2019

This paper examines effect of fitness between innovation and HRM type on firm performance(quality competitiveness, operating profit). Data were extracted from the Korea Labor Institute's workplace panel survey(WPS) from 2015, and the analysis used 3,431 companies. To test the research model, analysis of variance(ANOVA). The model shows that full-innovation/commitment HRM type companies were significantly higher quality competitiveness, and operating profit than other companies. And low-level innovation/control HRM type companies were significantly lower quality competitiveness and operating profit than other companies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.167-177
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• 2022

Unlike previous studies on AI chatbot preference that focused mostly on satisfaction, this study considered both satisfaction and dissatisfaction. This study established that (1) AI chatbot preference is driven by attractive, must-be, and one-dimensional qualities, (2) AI chatbot need to develop service strategies by taking into account users' satisfaction and dissatisfaction in accordance with preference drivers, and (3) users view interaction as a requisite and thus, if they are not satisfied with services of a AI chatbot, they don't tend to appeal their opinion and leave the service with AI chatbot. This study emphasizes that a AI chatbot that desires to be a dominant market player must provide differentiated services according to the preference drivers and must continuously encourage user participation in order to improve service quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.61-68
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• 2018

In general, defects cause a lot of maintenance costs and serious damage to various stakeholders, such as the owners, contractors or occupants of apartments. For this reason, a systematic and efficient defect management method is needed to minimize defect disputes. This paper derives a defect classification framework and proposes a defect risk assessment model for different types of defects. For this purpose, 6,000 defect items are allocated to the defect classification framework; these items are associated with 34 apartment projects over ten years old. As a result of this analysis, it was confirmed that the defect risks are concentrated in the areas of RC and finishing work. Based on these results, it is necessary to prevent the major risks of defects according to their priority. Based on this research, it is judged that further research to develop a method of managing the risks of defects may be necessary.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.798-801
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• 2014

여행 정보 시스템(ATIS), 교통 관리 시스템 (ITS) 등 궤적 기반 서비스에서, 서비스 품질을 향상시키기 위해서는 주어진 궤적 질의에 대한 정확한 주행시간을 예측하는 것이 필수적이다. 이를 위한 대표적인 공간 데이터 분석 기법으로는 데이터 분류에서 높은 정확도를 보장하는 규칙 기반 분류화 기법이 존재한다. 그러나 기존 규칙 기반 분류화 기법은 단일 컴퓨터 환경만을 고려하기 때문에, 대용량 공간 데이터 처리에 적합하지 않은 문제점이 존재한다. 이를 해결하기 위해, 본 연구에서는 맵리듀스 환경에서 규칙 기반 분류화를 이용한 궤적 데이터 주행 시간 예측 알고리즘을 개발하고자 한다. 제안하는 알고리즘은 첫째, 맵리듀스를 이용하여 대용량 공간 데이터를 병렬적으로 분석함으로써, 활용도 높은 궤적 데이터 규칙을 생성한다. 이를 통해 대용량 공간 데이터 기반의 규칙 생성 시간을 감소시킨다. 둘째, 그리드 구조 기반의 지도 데이터 분할을 통해, 사용자 질의처리 시 탐색 성능을 향상시킨다. 즉, 주행 시간 예측을 위한 규칙 그룹을 탐색 시 질의를 포함하는 그리드 셀만을 탐색하기 때문에, 질의처리 성능이 향상된다. 마지막으로 맵리듀스 구조에 적합한 질의처리 알고리즘을 설계하여, 효율적인 병렬 질의처리를 지원한다. 이를 위해 맵 함수에서는 선정된 그리드 셀에 대해, 질의에 포함된 도로 구간에서의 주행 시간을 병렬적으로 측정한다. 아울러 리듀스 함수에서는 출발 시간 및 구간별 주행 시간을 바탕으로 맵 함수의 결과를 병합함으로써, 최종 결과를 생성한다. 이를 통해 공간 빅데이터 분석을 통한 주행 시간 예측 기법의 처리 시간 및 결과 정확도를 향상시킨다.

• Korean Journal of Food Science and Technology
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• v.46 no.3
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• pp.352-357
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• 2014

In this study, 100 consumers (men, 50; women, 50; age group, 20-50 years) rated their overall preferences for 24 Korean raspberry wines by using a 9-point hedonic scale. The analysis of variance was constructed to evaluate the effect of gender, age, and samples on the preference scores of the wine products. Significant differences were observed in overall preferences for the 24 samples; however, no interactions based on preferences by age and gender groups were noted. Cluster analysis was performed to determine sample clustering based on the frequencies from the preference data. Three clusters were obtained; these three clusters were well separated based on the mean overall preference scores for the samples. Discriminant analysis based on the three clusters also confirmed the same grouping of samples with 100% accuracy.