• Women's Health Nursing
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• v.20 no.3
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• pp.185-194
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• 2014

Purpose: The aim of this study was to assess health-related behavior of pregnant women and breastfeeding mothers by investigating relevant risk factors. Methods: Data of 10,396 women (age 19 to 49 years) from the Korea National Health and Nutrition Examination Survey report from 2007 to 2012 was used to analyze factors associated with health-related behavior. The subjects were divided into pregnant women; breastfeeding mothers; and non-pregnant women. Bottle feeding mothers were excluded. Results: Current smoking rate including self-reported smoker and/or positive cotinine urine test were lower for pregnant or breast-feeding group than non-pregnant group. Heavy-drinking was not different among groups while monthly drinking rate was higher in non-pregnant group. Rate of stress recognition was lower in pregnant and breast-feeding group than non-pregnant group. Rate of experience for depressive symptoms and rate of suicidal ideation were not different among groups. Conclusion: Pregnant women and breast-feeding mothers maintain a good pattern of health- related behavior compared to non-pregnant women. However, substantial proportion of pregnant women and breast-feeding mothers continue to drink and smoke. This shows the need for a plan that will modify health-related behavior.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.195-204
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• 2023

Micro-electronic gas sensor devices were developed for the detection of carbon monoxide (CO), nitrogen oxides (NO_x), ammonia (NH₃), and formaldehyde (HCHO), as well as binary mixed-gas systems. Four gas sensing materials for different target gases, Pd-SnO₂ for CO, In₂O₃ for NO_x, Ru-WO₃ for NH₃, and SnO₂-ZnO for HCHO, were synthesized using a sol-gel method, and sensor devices were then fabricated using a micro sensor platform. The gas sensing behavior and sensor response to the gas mixture were examined for six mixed gas systems using the experimental data in MEMS gas sensor arrays in sole gases and their mixtures. The gas sensing behavior with the mixed gas system suggests that specific adsorption and selective activation of the adsorption sites might occur in gas mixtures, and allow selectivity for the adsorption of a particular gas. The careful pattern recognition of sensing data obtained by the sensor array made it possible to distinguish a gas species from a gas mixture and to measure its concentration.

• Magazine of the Korean Society of Agricultural Engineers
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• v.33 no.4
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• pp.45-51
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• 1991

The objective of this paper is to show how the fuzzy sets theory can be applied to the reservoir operation guidelines for agricultural purposes. The concepts of the theory has been resented as a new tool for the decision problems which contains fuzziness and it's application can be found in operations research, expert systems, robotics, fuzzy computers, and pattern recognition. The fuzzy control system for the reservoir operation composed of a set of reservoir operation rules and a fuzzy inference engine was built. Water demand for paddy fields, water availability, and inflow to a reservoir were selected as main factors which determine the magnitude of reservoir release. The behavior of the control system was evaluated for different level of water demand and the results seemed to be reasonable.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1294-1299
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• 2014

The emotion is deeply affected by human behavior and cognitive process, so it is important to do research about the emotion. However, the emotion is ambiguous to clarify because of different ways of life pattern depending on each individual characteristics. To solve this problem, we use not only physiological signal for objective analysis but also hybrid unsupervised-supervised learning classifier for automatic emotion detection. The hybrid emotion classifier is composed of K-means, genetic algorithm and support vector machine. We acquire four different kinds of physiological signal including electroencephalography(EEG), electrocardiography(ECG), galvanic skin response(GSR) and skin temperature(SKT) as well as we use 15 features extracted to be used for hybrid emotion classifier. As a result, hybrid emotion classifier(80.6%) shows better performance than SVM(31.3%).

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.897-899
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• 1997

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault does not make enough current to cause conventional protective devices. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. This paper describes an algorithm using back-propagation neural network for pattern recognition and detection of high impedance faults. Fractal dimensions are estimated for distinction between random noise and chaotic behavior in the power system. The fractal dimension of the line current is also used as a indication of the high impedance fault.

• Journal of Fashion Business
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• v.8 no.1
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• pp.34-48
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• 2004

The purpose of this study is to explore emotion aspects of consumers purchasing clothing through cable television home shopping. Qualitative research method is used to widely understand how emotion aspects of consumers have effected on their purchasing behavior. The results of depth interviews may be classified into 13 feelings factors satisfaction, pleasure/delight, respect, attraction, fresh, convenience, unburdened, emptiness, displeasure/temper, anxiety, tedious, distrust, regret. The content of information acquiring from the process of clothing purchase decision making is analysed. In the problem recognition stage, purchase motivation were physical space (around people) and imaginary space(by how clothing goods are introduced to consumers thorough TV monitor). In the information search stage, purchasing action patterns to search information were situational pattern and habitual pattern. In alternative evaluation stage, the considering best important factors to choice clothes were quality, price, design, and color. In purchase stage, consumers said they felt anxiety, because of characteristics of purchase way that they should pay first and then received the ordered goods a fews days later. In post-purchase behavior stage, if consumers satisfied goods purchased through TV home shopping, they recommended it to around others, but unsatisfied with ordered goods, they tried to refund, exchange with anther one, or write it on homepage of the home shopping company.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1030-1036
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• 2006

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function considering weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the method that assigns different weighted values to feature attributes depending on importance.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.241-246
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• 2016

Nowadays many people have an interest in facial expression and the behavior of people. These are human-robot interaction (HRI) researchers utilize digital image processing, pattern recognition and machine learning for their studies. Facial feature point detector algorithms are very important for face recognition, gaze tracking, expression, and emotion recognition. In this paper, a cascade facial feature point detector is used for finding facial feature points such as the eyes, nose and mouth. However, the detector has difficulty extracting the feature points from several images, because images have different conditions such as size, color, brightness, etc. Therefore, in this paper, we propose an algorithm using a modified cascade facial feature point detector using a convolutional neural network. The structure of the convolution neural network is based on LeNet-5 of Yann LeCun. For input data of the convolutional neural network, outputs from a cascade facial feature point detector that have color and gray images were used. The images were resized to $32{\times}32$. In addition, the gray images were made into the YUV format. The gray and color images are the basis for the convolution neural network. Then, we classified about 1,200 testing images that show subjects. This research found that the proposed method is more accurate than a cascade facial feature point detector, because the algorithm provides modified results from the cascade facial feature point detector.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.123-132
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• 2002

The purpose of this study is to predict the shear behavior of the weathered mudstone soil using dynamic neural network which mimics the biological system of human brain. SNN and RNN, which are kinds of the dynamic neural network realizing continuously a pattern recognition as time goes by, are used to predict a nonlinear behavior of soil. After analysis, parameters which have an effect on learning and predicting of neural network, the teaming rate, momentum constant and the optimum neural network model are decided to be 0.5, 0.7, 8$\times$18$\times$2 in SU model and 0.3, 0.9, 8$\times$24$\times$2 in R model. The results of appling both networks showed that both networks predicted the shear behavior of soil in normally consolidated state well, but RNN model which is effective fir input data of irregular patterns predicted more efficiently than SNN model in case of the prediction in overconsolidated state.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.127-137
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• 2019

The growth in smartphone service has given rise to an increase in frequency and importance of authentication. Existing smartphone authentication mechanisms such as passwords, pattern lock and fingerprint recognition require a high level of awareness and authenticate users temporarily with a point-of-entry techniques. To overcome these disadvantages, there have been active researches in behavior-based authentication. However, previous studies focused on enhancing the accuracy of the authentication. Since authentication is directly used by people, it is necessary to reflect actual users' perception. This paper proposes user perception on behavior-based authentication with feature analysis. We conduct user survey to empirically understand user perception regarding behavioral authentication with selected authentication features. Then, we analyze acceptance of the behavioral authentication to provide continuous authentication with minimal awareness while using the device.