• 정보관리연구
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• 제37권4호
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• pp.125-139
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• 2006

인터넷의 등장은 과학기술정보의 생산과 유통에 많은 변화를 초래하고 있다. 기존 과학기술정보들은 주로 문헌위주로 생산되었다. 그러나, 인터넷이 대중화되면서 온라인 과학기술정보들이 대량으로 생산되고 있으며, 그 활용이나 중요도 또한 과학기술분야의 문헌에 뒤지지 않고 있다. 온라인 과학기술정보의 양적 질적 성장으로 인해, 정보서비스기관들은 이들 정보들의 체계적인 관리와 효율적인 서비스를 고민하고 있으며, 다양한 정보관리방법을 모색하고 있다. 효율적인 정보 관리를 위한 방법 중의 하나인 분류체계는 주요한 이슈이며, 지금까지는 문헌정보 중심의 분류체계나 일반 웹사이트의 분류체계를 벤치마킹하여 온라인 과학기술정보를 위해 도입되는 경우가 많았다. 본고에서는 국가 차원의 표준분류를 이용하여, 온라인 과학기술정보를 위한 분류체계 구성방안을 실천적으로 제시하여 보았다.

• 한국BIM학회 논문집
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• 제13권4호
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2000년도 봄 학술발표논문집 Vol.27 No.1 (B)
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• pp.226-228
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• 2000

최근 제안된 관찰학습(OLA: Observational Learning Algorithm)은 committee를 구성하는 각각의 학습 모델들이 다른 학습 모델들을 관찰함으로써 얻어진 가상데이터를 실제 데이터와 결합시켜 학습에 이용하는 방법이다. 본 논문에서는, UCI 데이터 셋의 분류(classification)와 예측(regression)문제에 대하여 다층 퍼셉트론을 학습 모델로 설정하고, 이에 대하여 OLA와 bagging, boosting의 성능을 비교, 분석하였다.

• 전기학회논문지
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• 제65권7호
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• pp.1115-1120
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• 2016

This paper presents the daily load forecasting for special days considering the rate of operation of industrial consumers. The authors analyzed the power consumption pattern for both the special and ordinary days according to the contract power classification of industrial consumers, and selected 400~600 specific consumers for which the rates of operation during special days are needed. Load forecasting for 2014 special days considering the rate of operation of industrial consumers showed a noticeable improvement on forecasting error of daily peak demand, which proved the effectiveness of the survey for the rates of operation during special days of industrial consumers.

• 대한산업공학회지
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• 제11권2호
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• pp.189-194
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• 1985

Process Capability Index is defined as a ratio between predetermined tolerance and natural tolerance of existent process ($6{\sigma}$). And it is widely used as a performance measure for quality control activities, and a criterion for process classification. This paper developed a unity-fixed process capability table for convenience to calculate process capability and showed how to use it.

• 한국식품영양학회지
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• 제9권1호
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• pp.92-98
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• 1996

Pectic materials, which are widely spread in the plant cell wall as plant carbohydrates, plays a great role in food Industry that acts as a softening agent of fruits and vegetables, and gel forming agents. To study physiochemical properties and industrial applications of pectic enzymes that hydrolyzes pectin, classification, assay method and Industrial application are reviewed based on previous results.

• 환경위생공학
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• 제12권3호
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• pp.81-86
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• 1997

In case of Industrial Wastewater, It was various pollutions, high concentration and different physical, chemical properties each other in accordance with classification of wastewater. Therefore, after inquiring into the influence on the membrane of the dissolved pollutants, we should select the membrane of best efficient quality. As results of experiments on pilot plant test, optimum operating pressure for fouling removal was 34BAR, when continues operating was 34 BAR, recovery rate was 75% and permeate water flux was $32.9{\;}{\ell}/hr{\cdot}m^{2}$.

• 산업경영시스템학회지
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• 제13권21호
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• pp.61-72
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• 1990

In the past, a fundamental technique in inventory control system was to rank the items of a inventory from a distribution by value to draw an ABC curve. Unfortunately the use of ABC categorization technique has created problems and introduced some gross errors into the inventory control system. First, the author considers the possible applications of the technique in the real world and then attempts to discuss ABC fallacies through the literatures. Finally, an inventory categorization algorithm by the principal component analysis is derived and a numerical example is also presented to illustrate the algorithm.

• 산업경영시스템학회지
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• 제16권28호
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• pp.129-135
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• 1993

Quality is become not only the most critical component of manufacturing strategy but also the most critical measure of performance and justification of advanced manufacturing system. The objective of this paper is to offer classification & optimum concept of quality-cost and to illustrate a method of evaluation quality-cost then a case example is presented to illustrate the result of quantifying the suggested formula and these values are given to justify of management.

• 한국산업정보학회논문지
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• 제2권1호
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• pp.73-87
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• 1997

Multilayer neural network using a modified beackpropagation learning algorithm was introduced to achieve automatic identification of different types of aircraft in a variety of 3-D orientations. A 3-D shape of an aircraft can be described by a library of 2-D images corresponding to the projected views of an aircraft. From each 2-D binary aircraft image we extracted 2-D invariant (L, Φ) feature vector to be used for training neural network aircraft classifier. Simulations concerning the neural network classification rate was compared using nearest-neighbor classfier (NNC) which has been widely served as a performance benchmark. And we also introduced reliability measure of the designed neural network classifier.