• Journal of Korean Academy of Nursing
• /
• v.20 no.1
• /
• pp.79-87
• /
• 1990

Clinical and epedemiologic studies of coronary heart disease(CHD)have from time to time over the last three decades found associations between prevalence of CHD and behavioral attributes and cigarette smoking. The main purpose of this study is reduced to major risk factor of coronary heart disease through prohibition of smoking and control of behavior pattern. The subjects consisted of 120 smokers and 90 non-smokers who were married men older than 30 years working in officers. The officers were surveyed by means of questionnaire September 26 through October 6, 1989. The Instruments used for this study was a self-administered measurement tool composed of 59 items was made through modifications of Jenkuns Activity Survery(JAS). The Data were analysed by SAS(Statistical Analsis System) program personal computer. The statistical technique used for this study were Frequency, x$^2$-test, t-test, ANOVA, Pearson Correlation Coefficient. The 15 items were chosen with items above 0.3 of the factor loading in the factor analysis. In the first factor analysis 19 factors were extracted and accounted for 86% of the total variance. However when the number of factors were limited to 3 in order to derive Jenkins classification, three factors were derived. There names are Job-Involvement, Speed & Impatience, Hard-Driving. Each of them includes 21 items, 21 and 9, respectively. The results of this study were as follow : 1. The score of the smoker group and non-smoker group in Job-Involvement(t＝5.7147, p＜0.0001), Speed & Impatience(t＝4.6756, p＜.0001), Hard-Driving(t＝8.0822, p＜.0001) and total type A behavior pattern showed statistically significant differences(t＝8.1224, p＜.0001). 2. The score of type A behavior pattern by number of cigarettes smoked daily were not statistically significant differences. 3. The score of type A behavior pattern by duration of smoking were not significant differences. It was concluded that the relationship between smokers and non - smokers of type A behavior pattern was statistically significant difference but number of cigarettes smoked daily and duration of smoking were not significant differences. Therefore this study is needed to adequate nursing intervention of type A behavior pattern in order to elevated to educational effect for prohibition of cigarette smoking.

• Proceedings of the IEEK Conference
• /
• 2001.06e
• /
• pp.65-68
• /
• 2001

In the rehabilitation robotic arm systems for the disabled with spinal code injury, EMG signals are used in the control of the robotic arm. EMG signals are corrupted by many kinds of noises such as ECG signal, power noise and contact noise of electrode. Noise rejection improves the performance of the EMG pattern classification. In this paper, a variable bandwidth filter (VBF) and wavelet transform are used for the noise rejection of EMG signals and the comparison of SNR is given. Also, some statistical characteristics of features are investigated.

• Journal of Sensor Science and Technology
• /
• v.13 no.6
• /
• pp.436-445
• /
• 2004

In this study, a uroflowmetry system was developed to detect a voiding symptom conveniently at home or hospital. A implemented hardware was composed of mechanism and system circuit part, the software was developed to process uroflow data, graph display, extraction of parameter, and evaluation of congregate rate so as to analysis obtaining uroflow data. The following experiment was performed to evaluate an ability of classification and fitness. The curve pattern of uroflow was classified into each symptom. Various parameters were calculated in the curve pattern of each uroflow as follows. The parameters are MFR, AFR, VOL, VT, and FT. A significant difference among parameters was examined by a statistical analysis for extracted parameters between normal and abnormal experimental group. The uroflow data with the various symptom was divided into normal and abnormal group using fuzzy classifier. The result of the fuzzy classification using MFR and AFR was superior by 91.23 % than grouping evaluation including VOL.

• Journal of the Korea Society for Simulation
• /
• v.20 no.4
• /
• pp.115-125
• /
• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• Asia pacific journal of information systems
• /
• v.1 no.1
• /
• pp.63-86
• /
• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.9
• /
• pp.1682-1688
• /
• 2008

The Counterpropagation algorithm(CP) is a combination of Kohonen competition network as a hidden layer and the outstar structure of Grossberg as an output layer. CP has been used in many real applications for pattern matching, classification, data compression and statistical analysis since its learning speed is faster than other network models. However, due to the Kohonen layer's winner-takes-all strategy, it often causes instable learning and/or incorrect pattern classification when patterns are relatively diverse. Also, it is often criticized by the sensitivity of performance on the learning rate. In this paper, we propose an enhanced CP that has multiple Kohonen layers and dynamic controlling facility of learning rate using the frequency of winner neurons and the difference between input vector and the representative of winner neurons for stable learning and momentum learning for controlling weights of output links. A real world application experiment - pattern recognition from passport information - is designed for the performance evaluation of this enhanced CP and it shows that our proposed algorithm improves the conventional CP in learning and recognition performance.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.1
• /
• pp.97-102
• /
• 2013

Objective: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). Materials and Methods: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. Results: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. Conclusion: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.

• IE interfaces
• /
• v.13 no.2
• /
• pp.188-194
• /
• 2000

A pattern recognition system is proposed to recognize characters in any type of Giro. The system consist of the character segmentation and the character recognition. Positional features from two round markers at the upper-right part and lower-left part of Giro is used for extracting character strings from images and RLE analysis is used if there are no round markers. A multi step combined method, which use a structural method and a statistical method, is used to improve recognition. The structural method apply rules on each characters, whereas a statistical method gives a different weighting vector to each pixel for improving the classification performance in regard to noises and distortions. The experimental results show that the proposed combined method has higher recognition rate, over than 98% even in cases that images are rotated about 10 degrees as well as have noises.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.15 no.4
• /
• pp.520-530
• /
• 1996

In this study, the artificial defects in rotary compressor are classified using pattern recognition of acoustic emission signal. For this purpose the computer program is developed. The neural network classifier is compared with the statistical classifier such as the linear discriminant function classifier and empirical Bayesian classifier. It is concluded that the former is better. It is possible to acquire the recognition rate of above 99% by neural network classifier.

• New & Renewable Energy
• /
• v.2 no.2 s.6
• /
• pp.37-43
• /
• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.