• 한국정밀공학회지
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• 제33권9호
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• pp.715-721
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• 2016

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case I, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

• 한국의류학회지
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• 제45권6호
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• pp.949-964
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• 2021

Accumulation of high-quality data is crucial for AI learning. The goal of using AI in fashion service is to propose of a creative, personalized solution that is close to the know-how of a human operator. These customized solutions require an understanding of fashion products and emotions. Therefore, it is necessary to accumulate data on the attributes of fashion products and fashion emotion. The first step for accumulating fashion data is to standardize the attribute with coherent system. The purpose of this study is to propose a fashion emotional classification system. For this, images of fashion products were collected, and metadata was obtained by allowing consumers to describe their emotions about fashion images freely. An emotional classification system with a hierarchical structure, was then constructed by performing frequency and CONCOR analyses on metadata. A final classification system was proposed by supplementing attribute values with reference to findings from previous studies and SNS data.

• 산업경영시스템학회지
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• 제24권67호
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• pp.77-82
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• 2001

A study was conducted to develop a better classification method of Consumer Attributes that can enhance user-centered product design process. A modified QFD(Quality Function Deployment) survey form based upon Fuzzy set theory was proposed which contains 9 steps of importance level, and Certainty and Necessity function to improve the reliability of extracted consumer attributes. To verify the betterment and advantage of proposed classification method, a series of questionnaire survey was performed. Thirty male and 30 female university students were participated in the survey using a VCR as a target product. The result of the study showed that 80% of subjects were preferred the proposed classification over existing method. A cluster analysis was performed to further verify the betterment of the proposed method. The result also supported that the proposed classification method is more reliable and enhanced method in extracting consumer attributes and can be applied in the product design.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2006년도 춘계학술발표회 논문집
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• pp.65-70
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• 2006

For grouping the direction of improvement in the survey industry of Korea Standard Industry Classification, watching for internal survey industry, It has a purpose to present the direction of improvement. On the based of UN International Standard industrial classification, survey industry classification of KSIC has not been focused at the special quality of survey industry which is growing fast. Standard Industry Classification of foreign survey has rapidly adapting to survey industry development as detailed and specialized on purpose. thus, Korea's survey KSIC is in urgency to specialize and detail at the field of survey industry as well.

• 반도체디스플레이기술학회지
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• 제15권3호
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• pp.6-11
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• 2016

Silicon wafer is one of main materials in solar cell. Micro-cracks in silicon wafer are one of reasons to decrease efficiency of energy transformation. They couldn't be observed by human eye. Also, their shape is not only various but also complicated. Accordingly, their shape classification is absolutely needed for manufacturing process quality and its feedback. The performance of typical classification techniques which is principal component analysis(PCA), neural network, fusion model to integrate PCA with neural network, and support vector machine(SVM), are evaluated using pattern features of micro-cracks. As a result, it has been confirmed that the SVM gives good results in micro-crack classification.

• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2010년도 정기총회 및 춘계학술발표회
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• pp.5-5
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• 2010

Selection of specific medicinal sources as well as bioactive compounds is important for the preparation of medicine and related products with good quality. It is necessary to pay close attention for choosing correct medicinal sources, particularly in case of medicinal plants, because of their diversity, which can affect the quality and efficacy of medicine. Discrimination of plants based on morphological or genetic characteristics has been used as a conventional classification method of pharmaceutical sources so far; however, more need demands more general methods for accurate quality assessment of medicinal plants. In this study, ultra performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS) technique applied to this metabolic profiling is a powerful tool due to its higher sensitivity, resolution, and speed compared to conventional HPLC technique. The metabolite profiling of several medicinal plants including Panax ginseng was carried out using UPLC/Q-TOF MS and total metabolites were then subsequently applied to various statistical tools to compare the patterns. The developed metabolomics tool with UPLC/Q-TOF MS successfully identified and classified the samples tested according to their origins.

• 한국측량학회지
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• 제17권2호
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• pp.129-136
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• 1999

영상분류 결과는 지형적 영향, 영상의 상태 등에 따라 전체 영상에 대하여 균일하지 않을 수 있다. 본 연구에서는 분류 결과의 불균일성과 위성영상 분류 오차의 공간적 분포를 평가하기 위해 ISDd (Index of Spatial Distribution by distance) 와 ISDs (ISD by scatteredness)의 개념을 제안하였다. ISDd는 지표화된 오분류 화소간의 거리이고, ISDs는 오분류 화소의 산포도에 관한 통계적 지표이다. 실제 위성영상에 대한 실험을 통하여 ISDd와 ISDs를 계산 및 평가하였으며, 실제 국지적 오분류 영역을 추출하여 오분류의 원인을 고찰하였다. 본 연구 결과, ISDd와 ISDs를 동시에 사용하여 오분류 화소의 국지적 밀집 여부와 밀집 정도의 평가가 가능하였으며, 그 결과를 토대로 영상의 일부분에 대한 분류결과의 채택/기각을 결정할 수 있었다. 따라서, 전체 분류정확도 외에 공간분포지표를 사용함으로써 사용자는 오분류 화소의 공간적 분포 상태를 파악할 수 있으며, 분류 결과의 적합성 및 신뢰성 판단을 위한 추가적인 기준을 가질 수 있다.

• Journal of Power Electronics
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• 제6권4호
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• pp.307-314
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• 2006

This paper presents a wavelet and neural network based technology for the monitoring and classification of various types of power quality (PQ) disturbances. Simultaneous and automatic detection and classification of PQ transients, is recommended, however these processes have not been thoroughly investigated so far. In this paper, the hardware and software of a power quality data acquisition system (PQDAS) is described. In this system, an auto-classifying system combines the properties of the wavelet transform with the advantages of a neural network. Additionally, to improve recognition rate, extraction technology is considered.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2004년도 추계 종합학술대회 논문집
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• pp.389-394
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• 2004

본 논문에서는 디자인 패턴을 분류하기 위해 패턴구조의 특성을 가지고 분류하였다. 그리고 클러스터링에 의한 분류는 패싯 분류에 의한 방법보다 높은 정확도를 보여주었다. 따라서 자동화된 분류방법인 클러스터링 알고리즘을 사용하여 디자인 패턴을 분류하는 것이 효과적이라 할 수 있다. 디자인 패턴의 분류는 검색 시 유사한 패턴들이 같은 카테고리에 저장이 되므로 유사패턴을 비교하여 사용할 수 있으며, 패턴 클러스터링에 의해 분류되고, 패턴의 링크정보를 이용하여 저장하므로 저장소를 효율적으로 관리할 수 있다.

• 정보관리연구
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• 제36권4호
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• pp.91-115
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• 2005

인터넷을 통한 정보의 유통량은 크게 증가하였으나 정보의 저장, 유통, 그리고 검색의 비효율성이 상존하고 있으며, 이를 보완하기 위한 방안으로 상당수의 검색 포털은 분류체계에 따라 정보자료를 조직화하여 제공하는 디렉터리 서비스를 제공하고 있다. 이 연구에서는 국내의 대표적 인터넷 검색 포털에서 제공되고 있는 디렉터리 서비스의 실제 분류작업을 살펴보고, 이를 기반으로 디렉터리 서비스의 개선안을 제시하였다.