• 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.

• 대한인간공학회지
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• 제26권1호
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• pp.39-46
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• 2007

The purpose of this study was to develop a comprehensive posture classification scheme considering the effects of external load and motion repetition as well as those of working posture. The scheme was developed based on a series of existing empirical studies dealing with postural classification scheme, effects of external load and motion repetition. Ranges of joint motions, external load and motion repetition were divided into the groups with the same degree of discomforts. Each group was assigned a numerical relative discomfort score of code on the basis of discomfort values for the neutral position of elbow flexion. The criteria for evaluating stress of working postures were proposed based on the four distinct action categories, in order to enable practitioners to apply appropriate corrective actions. The proposed scheme was compared with OWAS, RULA and REBA. The comparison revealed that while the proposed scheme and RULA showed similar results for the working postures with light external load and non-repetitive postures, the former overestimated postural load for postures with moderate or heavy external load and repetitive postures than the latter.

• 한국측량학회지
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• 제35권4호
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• pp.231-240
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• 2017

This paper aims to study a method to estimate precise carbon absorption by quantification of forest information that uses accurate LiDAR data, hyperspectral image. To estimate precise carbon absorption value by using spatial data, a problem was found out of carbon absorption value estimation method with statistical method, which is already existed method, and then offered optimized carbon absorption estimation method with spatial information by analyzing with methods of compare digital aerial photogrammetry and LiDAR data. It turned out possible Precise classification and quantification in case of using LiDAR and hyperspectral image. Various classification of tree species was possible with use of LiDAR and hyperspectral image. Classification of hyperspectral image was matched in general with field survey and Mahalanobis distance classification method. Precise forest resources could be extracted using high density LiDAR data. Compared with existing method, 19.7% in forest area, 19.2% in total carbon absorption, 0.9% in absorption per unit area of difference created, and improvement was found out to be estimated precisely in international code.

• 한국멀티미디어학회논문지
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• 제13권12호
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• pp.1786-1797
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• 2010

Automatic understanding of contents in document image is a very hard problem due to involvement with mathematically challenging problems originated mainly from the over-determined system induced by document segmentation process. In both academic and industrial areas, there have been incessant and various efforts to improve core parts of content retrieval technologies by the means of separating out segmentation related issues using semi-structured document, e.g., invoice,. In this paper we proposed classification models for text lines on invoice document in which text lines were clustered into the five categories in accordance with their contents: purchase order header, invoice header, summary header, surcharge header, purchase items. Our investigation was concentrated on the performance of machine learning based models in aspect of linear-discriminant-analysis (LDA) and non-LDA (logic based). In the group of LDA, na$\"{\i}$ve baysian, k-nearest neighbor, and SVM were used, in the group of non LDA, decision tree, random forest, and boost were used. We described the details of feature vector construction and the selection processes of the model and the parameter including training and validation. We also presented the experimental results of comparison on training/classification error levels for the models employed.

• 산업공학
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• 제12권4호
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• pp.551-556
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• 1999

In this paper, we compare the performance of three data mining classification algorithms(neural network, decision tree, logistic regression) in consideration of various characteristics of categorical input and output data. $2^{4-1}$. 3 fractional factorial design is used to simulate the comparison situation where factors used are (1) the categorical ratio of input variables, (2) the complexity of functional relationship between the output and input variables, (3) the size of randomness in the relationship, (4) the categorical ratio of an output variable, and (5) the classification algorithm. Experimental study results indicate the following: decision tree performs better than the others when the relationship between output and input variables is simple while logistic regression is better when the other way is around; and neural network appears a better choice than the others when the randomness in the relationship is relatively large. We also use Taguchi design to improve the practicality of our study results by letting the relationship between the output and input variables as a noise factor. As a result, the classification accuracy of neural network and decision tree turns out to be higher than that of logistic regression, when the categorical proportion of the output variable is even.

• 한국멀티미디어학회논문지
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• 제18권11호
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• pp.1391-1399
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• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제49권1호
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• pp.19-23
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• 2000

Partial discharges(PD)in air insulated electric power apparatus often lead to deterioration of solid insulation by electron bombardments and electrochemical reaction. The PD caused to reduce the life time of power apparatus and to increase power losses. Thus understanding and classification of PD patterns in air are very important to discern sources of PD. In this paper, PD in air by using statistical methods was investigated. We classified air discharges, corona, surface discharges and cavity discharges by Kohonen network. For classification of PD patterns, we used statistical operators and parameters such as skewness$(S^+,\; S^-),\; kurtosis(K^+, K^-),\; mean phase(AP^+, AP^-)$, cross-correlation factor(CC) and asymmetry derived from the mean pulse-height phase distribution$(H_{avg}(\phi))$, the max pulse-height phase distribution $(H_{qmax}(\phi))$, the pulse count phase distribution $(H_n(\phi))$ and the pulse height vs. Repetition rate $(H_q(n))$.

• 보건의료산업학회지
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• 제11권4호
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• pp.189-202
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• 2017

Objectives : This study aims to present a new classification system using evaluation standards to ensure the safety of transport ventilators used in Korea. Due to the rapid advancement of technology, various types of ventilators have been developed and as the range of application increases, so does demand. With quick access to information and a growing economy, the technology used in Korean hospitals is equivalent to that of other countries; however, regulatory and safety consciousness are still lagging in Korea. Methods : Based on researching overseas cases, reference data and standards for improving the current system in Korea are proposed. Results : According to the review of transport ventilator use in Korea, it was found that concerns about safety is due to the absence of a standardized classification system for evaluating the safety and effectiveness of transport ventilators. Conclusions : In order to improve the safety of patients and the quality of medical care, it is essential to establish guidelines and assessment standards guaranteeing the safety of transport ventilators in Korea. Clear definitions and classifications for devices must precede the application of such standards. In addition, effective evaluation standards should be developed in order to resolve problems and improve upon the current system through continuous validation.

• 기술혁신연구
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• 제3권1호
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• pp.213-236
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• 1995

Advanced Manufacturing Technology(AMT), a comprehensive collection of new technologies for the efficiency and flexibility of manufacturing systems has received a growing attention recently, AMT consists of various industrial and technological components, homogeneous in some aspects while heterogeneous in others. Thus, it is difficult but necessary task to construct a classification framework in which the relationship among individual technologies are depicted in a meaningful fashion. In this, paper, we propose a hierarchical framework in which the objective and criteria of classification are decomposed into three level: industrialization, development and application of AMT. At the first and highest level, the main interest is to "industrialize" AMT. The major actors at this level are policy makers(public sector) and top management(private sector) and the primary classification criterion is the interrelationship between industry and technology. At the middle level exist system engineers whose main objective is to "develop" new technologies and/or systematize individual technologies. At the final and bottom level, shop floor managers need to "apply" AMT in order to enhance the efficiency and flexibility of manufacturing process. It should be stressed that, as a whole, the above three levels should be interactively linked to that each level contributes to the balanced development of AMT.