• 대한안전경영과학회지
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• 제10권2호
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• pp.143-154
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• 2008

With the recent rapid growth of RFID technologies, the Application of RFID systems into the medical or the military industries as well as into the distribution and logistics industry are now attempted continuously. The government and private sectors plan to carry out various small and large scale projects related to RFID systems. However, many companies attempting to apply RFID systems applications into their organizations are encountering many several difficulties because of the lack of installation experience and the absence of an useful guideline. This paper focuses on identification and classification of typical barriers to the successful application of RFID systems according to the five-step method of system application process. Moreover a "barrier map" is produced by conducting a survey and interviews by specialists. In addition, a practical guideline to overcome such barriers is presented and discussed.

• Management Science and Financial Engineering
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• 제16권1호
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• pp.17-46
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• 2010

This paper studies six different AHP (Analytic Hierarchy Process) approaches and suggests that the features of the approaches are classified by application circumstances in order to contribute to the applicability and quality usage of the AHP. Our study investigates the hierarchical principles and characteristics of the AHP, and historical debates on the AHP evaluation in which the six approaches have been involved. One of six approaches is an ANP (Analytic Network Process) application that is directly connected to AHP usage. The application differences among the six approaches are validated with a plain example. Then, the four circumstances of AHP applications are classified by two dimensions: the first dimension is whether or not the importance (weights) of criteria is independent of restrictively setting alternatives, and the second dimension is whether or not preference (priorities) of alternatives is independent of adding alternative(s) to or removing alternative(s) from the considering set of alternatives. Then featuring way of weighting criteria is classified. We suggest the distinguishing manners and describe the implications of the AHP application. Finally, we discuss rank reversal and multiplicative AHP.

• Genomics & Informatics
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• 제9권3호
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• pp.134-135
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• 2011

DNA chips are used for experiments on genes and provide useful information that could be further analyzed. Using the data extracted from the DNA chips to find useful patterns or information has become a very important issue. In this paper, we explain the application developed for classifying DNA chip data using a classification method based on the Particle Swarm Optimization (PSO) algorithm. Considering that DNA chip data is extremely large and has a fuzzy characteristic, an algorithm that imitates the ecosystem such as the PSO algorithm is suitable to be used for analyzing such data. The application enables researchers to customize the PSO algorithm parameters and see detail results of the classification rules.

• 한국측량학회지
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• 제8권2호
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• pp.53-61
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• 1990

본 연구에서는 지형의 정량적 분석변수를 이용하여 지형을 분류하고, 지형에 따라 적절한 보간법을 적용하므로써 수치지형모형의 정확도 향상과 효용성을 높이는데 그 목적이 있다. 지형해석에 있어서 정량적 분류 변수를 이용하여 대상지역을 4개의 군집으로 분류하여 지형에 따른 경제적인 보간법을 적용하였으며 격자간격이 클 경우 각 지형군별로 보간법을 조합시킨 조합보간법을 적용하므로써 정확도를 향상시킬 수 있었다.

• 한국지반공학회지:지반
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• 제14권1호
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• pp.29-36
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• 1998

본 논문에서는 이분적 암반분류 방법 보다 일반적인 다분적 암반분류 방법의 응용에 관해 연구하였다. 특히, 정성적 데이타를 체계적으로 이용할 수 있는 방법이 모색되었다. 응용 예를 통해 Bieniawski의 암반평가 시스템 (rock mass rating system, RMR)과 같이 암반을 두개 이상의 다등급으로 분류할 경우 본 논문에 제시된 방법이 효과적으로 사용될 수 있고 체계적인 암반조사를 위해 크게 기여할 것으로 생각된다. 또한, 오차에 대응하는 비용(cost of errors)의 기대값이 암반조사를 위한 시추 방법이 잘 계획되었는지에 관한 평가척도로 이용될 수 있음을 알았다.

• 한국측량학회지
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• 제29권4호
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• 한국비블리아학회지
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• 제24권4호
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• pp.23-51
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• 2013

이 연구는 정부기능분류체계가 기록분류에 어떻게 적용되고 있는지, 그 가능성과 한계는 무엇인지 밝히기 위한 것이다. 자료 수집을 위해 6개 중앙행정기관의 기록관리전문직 6명을 대상으로 3회에 걸친 집단면담을 실시하였다. 우선 공공기록물관리법률 분석을 통해 기록물분류제도를 살펴본 후, 정부기능분류체계를 기록분류에 적용함으로써 얻을 수 있는 편익의 유형을 조사하였다. 면담 자료를 토대로 단위과제를 활용한 기록물철 분류의 실태와 문제점을 구조 및 운용 측면에서 분석하였다.

• 품질경영학회지
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• 제48권4호
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• pp.553-566
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• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.

• 조명전기설비학회논문지
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• 제28권4호
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• pp.1-8
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• 2014

This study compared the recommended levels of illumination for housing. KS Recommended Levels of Illumination (KS A 3011) in Korea, Recommended Levels of Illumination (GB 50034-2004) in China and Recommended Levels of Illumination (JIS Z 9110) in Japan are compared. The results are as below. First, recommended levels of illumination used in Korea China Japan are suggested by different locations and activities. However, classification for application scope is set differently. There are 10 areas for classification used in Korea, 5 areas in China, and 13 areas in China. When medium levels for classification are included as classification level, total of 15 areas are used for classification in China. Second, when considering there are 15 areas of application scope in China for recommended levels of illumination, there are 7 areas that are commonly used in Korea China Japan. 7 areas include stadium, factories, hospitals, office, shopping center, houses and hospitals. Third, working surface is considered as the height for recommended levels of illumination in Korea China Japan. Korea and Japan consider all working positions, standing and sitting position, when deciding the height. However, China only considers the standing position. Fourth, application scope for recommended levels of illumination for housing are classified in 16 areas in Korea, 5 in China and 18 in Japan. Thus, the application scope for recommended levels of illumination in housing in Korea is similar to Japan. However, there are only 5 areas used in China such as living room, bedroom, dining room, kitchen and sanitary room. Fifth, recommended levels of illumination is classified in 3 levels such as Lowest-Moderate-Highest while China and Japan only have standard recommended levels of illumination. Sixth, when observing recommended levels of illumination by type of activities, Japan classified the activities in greatest detail followed by Korea and then China. Seventh, Recommended levels of illumination differs by each country.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.