• 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.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제24권3호
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• pp.321-330
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• 2020

Data classification and clustering is one of the most common applications of the machine learning. In this paper, we aim to provide the insight of the classification for Turing pattern image, which has high nonlinearity, with feature engineering using the machine learning without a multi-layered algorithm. For a given image data X whose fixel values are defined in [-1, 1], X - X³ and ∇X would be more meaningful feature than X to represent the interface and bulk region for a complex pattern image data. Therefore, we use X - X³ and ∇X in the neural network and clustering algorithm to classification. The results validate the feasibility of the proposed approach.

• 한국IT서비스학회지
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• 제18권5호
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• pp.17-30
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• 2019

With the advent of the Fourth Industrial Revolution, virtual reality, a technology that has recently attracted attention, is emerging as a core technology that will lead the future industry by changing the paradigm of various industries. The development of 3D rendering, computer graphics, and mobile technologies enabled the development of various smart devices and led to the popularization of virtual reality services using them. Recently, with the development of virtual reality-related technology, various devices and contents such as VR-related HMDs are being developed and released. However, since the classification for VR technology has not yet been established, it is difficult to define a range of industries and services to which VR can be applied. Therefore, in this study proposes a service classification system in terms of industries that can apply VR technology and services that can be provided based on the studies on industries and services of VR technology related to the Fourth Industrial Revolution. VR's industrial classification consists of eight industries including entertainment, media, education, medical care, architecture, manufacturing, distribution, tourism and each service is divided into two service categories and composed 16 services. Through the collection and analysis of virtual reality service cases, the service distribution and characteristics of each industry can be analyzed. In addition, we can develop a virtual reality new business model and present a service case for the intersecting areas. This study is expected to be used as a basic research for the activation of virtual reality services in the future.

• 분석과학
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• 제32권6호
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• pp.233-242
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• 2019

Substance identification is the first step of the REACH registration. It is essential in terms of Classification, Labelling and Packaging (CLP) regulation and because even trace amounts of impurities or additives can affect the classification. In this study, a scheme for the screening, quantification, and interpretation of trace amounts of hazardous inorganic substances is proposed to detect the presence of more than 0.1% hazardous inorganic substances that have been affecting the hazard classification. An exemplary list of hazardous inorganic substances was created from the substances of very high concern (SVHCs) in REACH. Among 201 SVHCs, there were 67 inorganic SVHCs containing at least one or ~2-3 heavy metals, such as As, Cd, Co, Cr, Pb, Sb, and Sn, in their molecular formula. The inorganic SVHCs are listed in excel format with a search function for these heavy metals so that the hazardous inorganic substances, including each heavy metal and the calculated ratio of its atomic weight to molecular weight of the hazardous inorganic substance containing it, can be searched. The case study was conducted to confirm the validity of the established scheme with zinc oxide (ZnO). In a substance that is made of ZnO, Pb was screened by XRF analysis and measured to be 0.04% (w/w) by ICP-OES analysis. After referring to the list, the presence of Pb was interpreted just as an impurity, but not as an impurity relevant for the classification. Future studies are needed to expand on this exemplary list of hazardous inorganic substances using proper regulatory data sources.

• 지능정보연구
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• 제1권1호
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• pp.23-41
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• 1995

Business researchers have traditionally used statistical techniques for classification. In late 1980's, inductive learning started to be used for business classification. Recently, neural network began to be a, pp.ied for business classification. This study reviews the business classification studies, identifies a neural network a, pp.oach as the most powerful classification tool, and discusses the problems and issues in neural network a, pp.ications.

• 방송공학회논문지
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• 제25권6호
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• pp.845-853
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• 2020

본 논문에서는 산업현장에서의 선택적 소음 제거를 위한 환경 사운드 분류 기술을 제안한다. 산업현장에서의 소음은 작업자의 청력 손실의 주요 원인이 되며, 소음 문제를 해결하기 위한 소음 제거 기술이 널리 연구되고 있다. 그러나 기존 소음 제거 기술은 모든 소리를 구분 없이 차단하는 문제를 가지며, 모든 소음에 공통된 제거 방법을 적용하여 각 소음에 최적화된 소음 제거 성능을 보장할 수 없다. 이러한 문제를 해결하기 위해 사운드 종류에 따라 선택적 동작을 하는 소음 제거가 필요하고, 본 논문에서는 이를 위해 딥 러닝 기반의 환경 사운드 분류 기술을 제안한다. 제안 방법은 기존 오디오 특성인 멜-스펙트로그램의 한계를 극복하기 위해 새로운 특성으로서 멜-스펙트로그램 기반의 시간 변화 특성과 통계적 주파수 특성을 사용하며, 합성곱 신경망을 이용하여 특성을 모델링 한다. 제안하는 분류기를 사용하여 3가지 소음과 2가지 비소음으로 구성된 총 5가지 클래스로 사운드를 분류하였고, 제안하는 오디오 특성을 사용하여 기존 멜-스펙트로그램 특성을 사용할 때에 비하여 분류 정확도가 6.6% 포인트 향상되는 것을 확인하였다.

• 한국산학기술학회논문지
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• 제16권7호
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• pp.4942-4949
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• 2015

건설 산업의 정보화에 따른 변화에 대응하기 위해 개발된 건설정보 분류체계는 건설 산업에서 발생하는 정보를 표준화된 형태로 제시하고 있다. 현재, 건설 정보화에 대한 큰 변화의 주축이라 할 수 있는 BIM (Building Information Modeling) 분야에서도 정보의 표준화는 필수적으로 해결해야 할 과제이다. 본 연구는 BIM의 적용을 위해 건설정보 분류체계의 개선안을 제시하고자 하며, 특히 기존 분류체계를 기반으로 BIM 정보를 수용할 수 있도록 각 파셋 별 확장목록을 제시하고자 한다. 확장된 건설정보 분류체계를 BIM에 적용할 경우, 정보의 공유 및 관리가 용이해지며, 통합된 정보의 활용으로 인해 BIM을 구축하기 위한 업무 및 BIM 활용 측면에서 효율성을 제고시킬 수 있을 것이라 기대한다.

• 한국산학기술학회논문지
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• 제3권4호
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• pp.245-250
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• 2002

본 연구에서는 영문 단어로부터 폰트를 분류하기 위해 연역적이고 국부적인 폰트 분류 방법을 제안한다. 이는 문자 인식 전에 한 단어의 폰트를 분류하는 것을 말한다. 폰트 분류를 위해 활자 특성인 Ascender, Descender와 Serif가 사용된다. 입력 단어로부터 Ascender, Descender 와 Serif가 추출되어 경사도 특징 벡터가 추출되고, 그 특징 벡터는 인공 신경망에 의해 입력 단어에 대한 폰트 스타일, 폰트 그룹, 폰트 이름이 분류된다. 제안된 연역적이고 국부적인 폰트 분류 방법은 폰트 정보가 문자 분할기와 문자 인식기에 사용될 수 있게 한다. 나아가, 특정 폰트에 따른 Mono-Font 문자 분할기와 Mono-Font 문자 인식기로 구성되는 OCR 시스템을 구성할 수 있는 것을 가능하게 한다.

• Industrial Engineering and Management Systems
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• 제13권2호
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• pp.231-249
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• 2014

This paper discusses a green supply chain with a manufacturer and a collection trader, and it proposes an optimal production planning for remanufacturing of parts in used products with quality classification errors made by the collection trader. When a manufacturer accepts an order for parts from a retailer and procures used products from a collection trader, the collection trader might have some quality classification errors due to the lack of equipment or expert knowledge regarding quality classification. After procurement of used products, the manufacturer inspects if there are any classification errors. If errors are detected, the manufacturer reclassifies the misclassified (overestimated) used products at a cost. Accordingly, the manufacturer decides to remanufacture from the higher-quality used products based on a remanufacturing ratio or produce parts from new materials. This paper develops a mathematical model to find how quality classification errors affect the optimal decisions for a lower limit of procurement quality of used products and a remanufacturing ratio under the lower limit and the expected profit of the manufacturer. Numerical analysis investigates how quality of used products, the reclassification cost and the remanufacturing cost of used products affect the optimal production planning and the expected profit of a manufacturer.