• Journal of Applied Reliability
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• v.17 no.4
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• pp.305-315
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• 2017

Purpose: The purpose of this research is to propose procedure and methodology for developing failure rate databook which is suitable for Korean operation environment. Methods: To this end, we investigate failure databooks used in foreign countries and study the procedure and methodology for collecting failure data, organizing the data, estimating failure rate and summarizing results. Results: We develop the procedure of development of failure databook, the items for data collection, database schema of part details and part summary and contents of failure databook by considering the application environment in Korea. Conclusion: The results of our research could be utilized for the development of Korean failure rate databook and research of reliability prediction model and could ultimately contribute to improve the accuracy of reliability prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.703-711
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• 2016

IDS (Intrusion Detection System) is used to detect network attacks through network data analysis. The system requires a high accuracy and detection rate, and low false alarm rate. In addition, the system uses a range of techniques, such as expert system, data mining, and state transition analysis to analyze the network data. The purpose of this study was to compare the performance of two data mining methods for detecting network attacks. They are Support Vector Machine (SVM) and a neural network called Forward Additive Neural Network (FANN). The well-known KDD Cup 99 training and test data set were used to compare the performance of the two algorithms. The accuracy, detection rate, and false alarm rate were calculated. The FANN showed a slightly higher false alarm rate than the SVM, but showed a much higher accuracy and detection rate than the SVM. Considering that treating a real attack as a normal message is much riskier than treating a normal message as an attack, it is concluded that the FANN is more effective in intrusion detection than the SVM.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.281-286
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• 2006

In this study, acquired time series Landsat TM/ETM+ image to extract land surface temperature for wide-area region and executed geometric correction and radiometric correction. And extracted land surface temperature using NASA Model, and I achieved the first correction by perform land coverage category for study region and applies characteristic emission rate. Land surface temperature that acquire by the first correction analyzed correlation with Meteorological Administration's temperature data by regression analysis, and established correction formula. And I wished to improve accuracy of land surface temperature extraction using satellite image by second correcting deviations between two datas using establishing correction formula. As a result, land surface temperature that acquire by 1,2th correction could correct in mean deviation of about ${\pm}3.0^{\circ}C$ with Meteorological Administration data. Also, could acquire land surface temperature about study region by relative high accuracy by applying to other Landsat image for re-verification of study result.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.107-109
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• 2003

Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.5
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• pp.1347-1354
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• 1994

This paper deals with the experimental research, including measurement and analysis and field survey, on the causes of occurring noise defective gear boxes in hoist production plant in order to reduce the fraction of their occurrence. In this reserch following investigations are performed : measurement and gear-boxes, examination of each machining process of production, measurement and analysis of dimensional accuracy of each part, comparative vibration test with exchanging inaccurate parts. From these investigations, it is found that the machining accuracy of pinion gear tooth thickness is the most sensitive factor of noise problem. By maintaining the tooth thickness error within 0.05 mm tolerance in the gear cutting process, the fraction of noise defective gear-boxes are greatly reduced to less than 2%, where the usual rate of it has been 20-50%.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.45-51
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• 1999

Thermal expansion of the ball screw can directly affect the accuracy of positioning along the travel axis in the semi-closed loop type CNC Lathe. In this paper, use of MACRO variables can make the thermal displacement of the ball screw estimated. Also, the estimated displacements of the ball screw are controlled by calculating the interval of pitch error rate in the Numerical Control(NC). Under the constant operating conditions, the thermal expansion of the ball screw was measured to confirm the effectiveness of the compensation method in the CNC Lathe. By using this method the results show that the thermal displacement of the ball screw could be reduced to 20% compared with ordinary method.

• Journal of Multimedia Information System
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• v.7 no.3
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• pp.205-214
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• 2020

Crime has become one of the major problems in modern society. Even though visual surveillances through closed-circuit television (CCTV) is extensively used for solving crime, the number of crimes has not decreased. This is because there is insufficient workforce for performing 24-hour surveillance. In addition, CCTV surveillance by humans is not efficient for detecting dangerous situations owing to accuracy issues. In this paper, we propose the autonomous detection of dangerous situations in CCTV scenes using a deep learning model with relational inference. The main feature of the proposed method is that it can simultaneously perform object detection and relational inference to determine the danger of the situations captured by CCTV. This enables us to efficiently classify dangerous situations by inferring the relationship between detected objects (i.e., distance and position). Experimental results demonstrate that the proposed method outperforms existing methods in terms of the accuracy of image classification and the false alarm rate even when object detection accuracy is low.

• International Journal of Contents
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• v.9 no.1
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• pp.38-41
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• 2013

The clamp meter maintains electric safety as a non-invasive method while measuring the absolute value of tube current with it has been recently developed for an X-ray high-tension cable. Especially this can show high accuracy at short X-ray exposure time. Considering such a condition, this study is to evaluate the feasibility of a clamp meter in measuring X-ray tube current by taking the measurements and comparing with those of the Dynalyzer III which has been considered as a standard measuring device. From measuring the tube current accuracy depending on changes in tube voltage and exposure time, the clamp meter showed higher accuracy rate which was -1.3~4.2% difference. Thus clamp meter can be used for clinical radiologists who are not familiar electric circuit to manage X-ray devices easily and correctly in the future.