• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1995년도 춘계학술대회논문집
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• pp.16-22
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• 1995

The importance of usability evaluation is increasing in developing a new system and product. The current approaches for usability evaluation are: the comparative evaluation to measure usability, and the iterative user interface design to find usability problems. This paper pressents three types of characteristics and a set of criteria for usability evaluation. The methodology for criteria-based quantitative analysis of user interface usability is investigated with a view to measuring usability. The fuzzy weighted-checklist method with linguistic variables is used for quantitatie analysis. This analysis provides a quantitative measure, which reflects the degree of excellence of user interface usability during the design and development phases.

• Structural Engineering and Mechanics
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• 제23권5호
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• pp.455-468
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• 2006

Smart structural systems are defined as ones that demonstrate the ability to modify their characteristics and/or properties in order to respond favorably to unexpected severe loading conditions. The performance of such a task requires a set of additional components to be integrated within such systems. These components belong to three major categories, sensors, processors and actuators. It is wellknown that all structural systems entail some level of uncertainty, because of their extremely complex nature, lack of complete information, simplifications and modeling. Similarly, sensors, processors and actuators are expected to reflect a similar uncertain behavior. As it is imperative to be able to evaluate the impact of such components on the behavior of the system, it is as important to ensure, or at least evaluate, the reliability of such components. In this paper, a system model for reliability assessment of smart structural systems is outlined. The presented model is considered a necessary first step in the development of a reliability assessment algorithm for smart structural systems. The system model outlines the basic components of the system, in addition to, performance functions and inter-relations among individual components. A fault tree model is developed in order to aggregate the individual underlying component reliabilities into an overall system reliability measure. Identification of appropriate limit states for all underlying components are beyond the scope of this paper. However, it is the objective of this paper to set up the necessary framework for identifying such limit states. A sample model for a three-story single bay smart rigid frame, is developed in order to demonstrate the proposed framework.

• 한국식품영양학회지
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• 제15권3호
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• pp.228-234
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• 2002

지금까지의 요리의 조리법에 대한 우수성은 관능검사의 결과에 의존하고 있는 실정으로 관능검사의 결과는 조리과정 및 재료량에 영향을 받으며, 매번 요리의 조리와 관능검사 과정에 시간소비와 시행착오를 거쳐야할 뿐만 아니라 최상의 조리법 및 재료량을 찾아내는 것도 쉽지만은 않았다. 본 논문에서는 시간소비 및 시행착오를 줄이기 위한 대안으로써 컴퓨터 시뮬레이션 방법을 제안하였다. 퍼지이론을 사용하여 몇 번의 실험을 통한 실험데이터로부터 퍼지 모델을 구성하고 퍼지 모델로부터 재료량 및 조리법에 따른 관능지수를 찾을 수 있으며, 최적의 관능지수가 얻어질 수 있는 재료량 및 조리법을 유추해내는 방법이다. 퍼지 모델은 실험으로부터 얻어진 재료량의 변화에 따른 관능지수의 관계로부터 얻어질 수 있으며 전반부와 후반부로 구성되어져 있다. 추론방법은 간략 추론법을 사용하였다. 본 논문에서는 이 방법을 증편제조에 적용해 보았으며 제조법은 기존의 방법을 사용하였고 재료량에 따른 관능지수 변화에 제한을 두었다. 퍼지모델의 입력으로는 증편제조에 필요한 재료들의 양을 사용하였고, 조직, 부드러운 정도, 신정도, 씹힘성, 전체적인 특성 및 증편반죽의 pH증편의 pH및 volume의 관능지수는 퍼지모델의 추론과정을 거쳐 출력부로 나오게 되었다. 본 논문에서는 재료의 양 및 관능지수의 변화를 매우 제한적인 경우에 대해서 제시를 하였지만, 증편을 직접 제조하여 관능지수를 평가하지 않고 다양한 재료의 종류, 양 및 제조방법에 따른 정확하고 보편적인 관능지수를 퍼지모델의 시뮬레이션을 통하여 측정할 수도 있으며, 원하는 특성을 얻을 수 있도록 적합한 재료량을 추론할 수 있다. 또한 이 방법은 제조법의 과학화 및 전산화에 용이하게 응용할 수 있을 것이며, 식이요법이 필요한 환자의 체계적인 영양관리 등에 적용될 수 있을 것이다.

• Journal of Biosystems Engineering
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• 제19권2호
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• pp.124-137
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• 1994

Visual features of a mushroom(Lentinus Edodes, L.) are critical in grading and sorting as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. To realize the automatic handling and grading of mushrooms in real time, the computer vision system should be utilized and the efficient and robust processing of the camera captured visual information be provided. Since visual features of a mushroom are distributed over the front and back sides, recognizing sides and states of the stalk including the stalk orientation from the captured image is a prime process in the automatic task processing. In this paper, the efficient and robust recognition process identifying the front and back side and the state of the stalk was developed and its performance was compared with other recognition trials. First, recognition was tried based on the rule set up with some experimental heuristics using the quantitative features such as geometry and texture extracted from the segmented mushroom image. And the neural net based learning recognition was done without extracting quantitative features. For network inputs the segmented binary image obtained from the combined type automatic thresholding was tested first. And then the gray valued raw camera image was directly utilized. The state of the stalk seriously affects the measured size of the mushroom cap. When its effect is serious, the stalk should be excluded in mushroom cap sizing. In this paper, the stalk removal process followed by the boundary regeneration of the cap image was also presented. The neural net based gray valued raw image processing showed the successful results for our recognition task. The developed technology through this research may open the new way of the quality inspection and sorting especially for the agricultural products whose visual features are fuzzy and not uniquely defined.

• 한국지능시스템학회논문지
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• 제18권3호
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• pp.354-359
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• 2008

클러스터링은 주어진 임의의 데이터 중에서 유사한 성질을 지닌 데이터를 복수개의 그룹으로 조직화하는 기법이다. 이를 위해 K-Means, Fuzzy C-Means(FCM), Mountain Method(MM) 등과 같은 많은 기법들이 제안되었고 또한 널리 사용되어지고 있다. 그러나 이러한 기법들은 초기값에 따라 클러스터링 결과가 크게 달라지는 단점이 있다. 특히 가장 널리 사용되는 FCM 기법은 잡음 데이터에 취약하며, 주어진 입력 데이터의 클러스터 내부분산을 최소화 하는 방법을 사용하기 때문에 클러스터링 중심의 왜곡 현상이 발생한다. 본 논문에서는 데이터 가중치에 근거한 비례적 근접데이터 병합을 통하여 클러스터 중심 왜곡을 저감하며 초기값에 영향을 받지 않는 클러스터링 기법을 제안한다. 그리고 FCM으로 얻어진 클러스터 중심과 제안기법을 적용하여 얻어진 클러스터 중심에 대한 비교 검토를 통하여 제안기법의 효용성을 확인한다.

• Advances in nano research
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• 제14권2호
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.