• Journal of the Korean Society for Precision Engineering
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• v.33 no.9
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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.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.6
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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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.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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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.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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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.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2010.05a
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.129-136
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• 1999

The quality of image classification results is not always uniform over entire image. Thus, this study proposes the concept of ISDd (Index of Spatial Distribution by distance) and ISDs (ISD by scatteredness) for the evaluation of unevenness of result quality, and spatial distribution of satellite image classification errors. The ISDd is indexed mean distance of misclassified pixels and the ISDs is statistical indicator of scatteredness of misclassified pixels. In this study, the ISDd and the ISDs are calculated and evaluated for some satellite images, then misclassified area is extracted and the reasons of misclassification are examined. As the result of this study, using both the ISDd and the ISDs, the basis of decision on adoption/rejection of classification results is offered at sub-image level by evaluation of the local aggregation of misclassified pixels. Using Index of Spatial Distribution. as well as overall classification accuracy, users can understand the spatial distribution of misclassified pixels, and can have the additional criterion of the judgement on suitability and reliability of classification results.

• Journal of Power Electronics
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• v.6 no.4
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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.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.389-394
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• 2004

In this paper, we classified design patterns with special quality of pattern structure. Classification by clustering had expressed higher correctness degree than classification by facet. Therefore, can do that it is effective that classify design patterns using clustering algorithms that is automatic classification method. When we are searching design patterns, classification of design patterns can compare and analyze similar patterns because similar patterns is saved to same category. Also we can manage repository efficiently because of using and storing link information of patterns.

• Journal of Information Management
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• v.36 no.4
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• pp.91-115
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• 2005

While the amount of information exchanged through internet has dramatically increased recently, certain inefficiencies still exist with regard to the storage, distribution, and retrieval of information. As a means of improving efficiency in accessing information, many search portals provide directory services to present organized guidance to information, based on the classification schemes. This study examines the classification activities practiced by the major search portals in Korea and makes some suggestions to improve the quality of directory services.