• IE interfaces
• /
• v.12 no.4
• /
• pp.551-556
• /
• 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.

• Journal of Korean Library and Information Science Society
• /
• v.36 no.2
• /
• pp.121-140
• /
• 2005

The purpose of this paper is to analyze the public servant and librarian position in USA, UK, and Korea and to give suggestions to improve the position classification of librarianship in Korea. Professional librarian positions in USA and UK are very useful to understand the excellency of position classification system. On the other hand, librarian positions in Korea is useful to catch the present situation of librarianship and its backwardness. This paper can use for the grounds of an improvement of the librarian position in Korea. In order to improve current position classification of librarianship. we have to overcome a theoretical and actual weakness of the libraries and librarians and to act as the main stream for position reform.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.668-670
• /
• 1999

We have developed a automatic surface inspection system for cold Rolled strips in steel making process for several years. We have experienced the various kinds of surface inspection systems, including linear CCD camera type and the laser type inspection system which was installed in cold rolled strips production lines. But, we did not satisfied with these inspection systems owing to insufficient detection and classification rate, real time processing performance and limited line speed of real production lines. In order to increase detection and computing power, we have used the Dark Field illumination with Infra_Red LED, Bright Field illumination with Xenon Lamp, Parallel Computing Processor with Area typed CCD camera and full software based image processing technique for the ease up_grading and maintenance. In this paper, we introduced the automatic inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms. As a result of experiment, under the situation of the high speed processed line(max 1000 meter per minute) defect detection is above 90% for all occurred defects in real line, defect name classification rate is about 80% for most frequently occurred 8 defect, and defect grade classification rate is 84% for name classified defect.

• Asian-Australasian Journal of Animal Sciences
• /
• v.28 no.4
• /
• pp.592-598
• /
• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.12 no.4
• /
• pp.33-40
• /
• 2016

Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

• Journal of Korean Society for Quality Management
• /
• v.45 no.4
• /
• pp.709-716
• /
• 2017

Purpose: NCS(National Competency Standards) is a systematic organization of knowledge, skills, and literacy required for performing tasks in industrial settings. This research aims to search for keywords that are important to us and to present key directions of education for the fourth industrial age in the future. Methods: The systematic classification system of NCS was investigated and the classification code structure was analyzed. Among them, the frame and structure analysis of the classification code of quality was analyzed using R-program. Results: This study grasped the quality classification situation of NCS and suggested improvement plan from the operational aspect of the fourth industrial revolution era. Conclusion: In conclusion, this study suggested the idea of education direction of SMEs(Small and Medium-sized Enterprises) in the era of the 4th industrial revolution by understanding NCS which reflects Korean characteristics.

• Journal of Soil and Groundwater Environment
• /
• v.15 no.6
• /
• pp.64-71
• /
• 2010

National inventory of soil and groundwater contamination is an efficient decision-making tool to identify and manage existing or potential contaminated sources and contaminants. It has been used as basic data for establishing the scheme of regulations and remediation plans of soil and groundwater contamination in developed countries. This study examined classification of existing or potential sources of soil and groundwater contamination from various countries to suggest implications that required for development of classification of soil and groundwater contamination sources in Korea. Each country has provided a list of currently or potentially contaminating activities or landuses and identified some of the potential contaminants related to those contamination sources. Consideration of sources which had not been mentioned or regarded as contamination sources before was suggested for Korea situation. In addition, it is necessary to compile a list of existing data and information as much as possible to develop a detailed and practical list of various contamination sources.

• Korean Journal of Construction Engineering and Management
• /
• v.4 no.1 s.13
• /
• pp.97-105
• /
• 2003

Although Construction Failure Information has been reported in literatures, reports of research, and etc., it Is difficult to utilize the information because the information classification does not exist. Therefore, this study investigated and analyzed literatures of domestic and abroad research Institutions and suggested the Construction Failure Information Classification(CFIC). The CFIC is composed of four classified items; facility general information, failure situation information, failure cause Information, and failure counterplan information. Each item is divided sub-items. Through CFIC, Construction Failure Information can be standardized and utilized for useful data to prevent recurrences of construction failure.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.2
• /
• pp.158-165
• /
• 2018

Recently there is a great interest in the bi-static sonar. However, since the transmitter and the receiver operate on different platforms, it may be necessary to operate the system in a non-cooperative mode. In this situation, the detection and localization performance are limited. Therefore, it is necessary to classify the received pulse from the transmitter to overcome the performance limitation. In this paper, we proposed a robust automatic pulse classification method that can be applied to real systems. The proposed method eliminates the effects of noise and multipath propagation through post-processing and improves the pulse classification performance. We also verified the proposed method through the sea experimental data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.2
• /
• pp.517-524
• /
• 2010

Recently, the size of online texts and textual information is increasing explosively, and the automated classification has a great potential for handling data such as news materials and images. Text classification system is based on supervised learning which needs laborous work by human expert. The main goal of this paper is to reduce the manual intervention, required for the task. The other goal is to increase accuracy to be high. Most of the documents have high complexity in contents and the high similarities in their described style. So, the classification results are not satisfactory. This paper shows the implementation of classification system based on ETOM+RPost algorithm and classification progress using SPAM data. In experiments, we verified our system with right-training documents and wrong-training documents. The experimental results show that our system has high accuracy and stability in all situation as 16% improvement in accuracy.