• Journal of the Korean Home Economics Association
• /
• v.36 no.1
• /
• pp.99-116
• /
• 1998

The purposes of this study were to find the influence of wife's home management behavior pattern and husband's perception about it on family life satisfaction, and to find out variables which influence them. For theses reviewing literature and empirical research were conducted. The major results were as follows; 1) The couple's psychological variables (ie, degree of life level recognition, of resourcefulness recognition and of communication) were relatively high. The wife's home management behavior pattern was relatively morphogenesis and the husband's perception about wive's it was relatively morphogenesis. And the couple's degree of family life satisfaction were relatively high. 2) Influential variables on wife's home management behavior pattern were level of education, degree of resourcefulness recognition and of communication. And influential variables on husband's perception about vive's it was degree of communication. 3) Influential variables on wive's the degree of family life satisfaction were degree of life level recognition, of resourcefulness recognition and of communication. And influential variables on husband's it were level of education, job, degree of life level recognition, of resourcefulness recognition and of communication. 4) The wife's home management behavior pattern and husband's perception about wive's it were to predict the couple's degree of family life satisfaction. 5) In cause-effect pathway mode. level of education·job·degree of life level recognition·of resourcefulness recognition and of communication showed direct and indirect effect on family life satisfaction through wife's home management behavior pattern or husband's perception about wive's it.

• Geomechanics and Engineering
• /
• v.1 no.1
• /
• pp.53-74
• /
• 2009

In recent years, several computer-aided pattern recognition and data mining techniques have been developed for modeling of soil behavior. The main idea behind a pattern recognition system is that it learns adaptively from experience and is able to provide predictions for new cases. Artificial neural networks are the most widely used pattern recognition methods that have been utilized to model soil behavior. Recently, the authors have pioneered the application of genetic programming (GP) and evolutionary polynomial regression (EPR) techniques for modeling of soils and a number of other geotechnical applications. The paper reviews applications of pattern recognition and data mining systems in geotechnical engineering with particular reference to constitutive modeling of soils. It covers applications of artificial neural network, genetic programming and evolutionary programming approaches for soil modeling. It is suggested that these systems could be developed as efficient tools for modeling of soils and analysis of geotechnical engineering problems, especially for cases where the behavior is too complex and conventional models are unable to effectively describe various aspects of the behavior. It is also recognized that these techniques are complementary to conventional soil models rather than a substitute to them.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.12
• /
• pp.123-129
• /
• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.1
• /
• pp.58-65
• /
• 2007

Behavioral sequences of the medaka (Oryzias latipes) were continuously investigated through an automatic image recognition system in response to medaka treated with the insecticide and medaka not treated with the insecticide, diazinon (0.1 mg/l) during a 1 hour period. The observation of behavior through the movement tracking program showed many patterns of the medaka. After much observation, behavioral patterns were divided into four basic patterns: active-smooth, active-shaking, inactive-smooth, and inactive-shaking. The "smooth" and "shaking" patterns were shown as normal movement behavior. However, the "shaking" pattern was more frequently observed than the "smooth" pattern in medaka specimens that were treated with insecticide. Each pattern was classified using classification methods after the feature choice. It provides a natural way to incorporate prior knowledge from human experts in fish behavior and contains the information in a logical expression tree. The main focus of this study was. to determine whether the decision tree could be useful for interpreting and classifying behavior patterns of the medaka.

• Smart Structures and Systems
• /
• v.6 no.1
• /
• pp.39-56
• /
• 2010

Structural Control relies, with a great deal, on the ability of the control algorithm to identify the current state of the system, at any given point in time. When such algorithms are designed to perform in a smart manner, several smart technologies/devices are called upon to perform tasks that involve pattern recognition and control. Smart pattern recognition is proposed to replace/enhance traditional state identification techniques, which require the extensive manipulation of intricate mathematical equations. Smart pattern recognition techniques attempt to emulate the behavior of the human brain when performing abstract pattern identification. Since these techniques are largely heuristic in nature, it is reasonable to ensure their reliability under real life situations. In this paper, a neural network pattern recognition scheme is explored. The pattern identification of three structural systems is considered. The first is a single bay three-story frame. Both the second and the third models are variations on benchmark problems, previously published for control strategy evaluation purposes. A Neural Network was developed and trained to identify the deformed shape of structural systems under earthquake excitation. The network was trained, for each individual model system, then tested under the effect of a different set of earthquake records. The proposed smart pattern identification scheme is considered an integral component of a Smart Structural System. The Reliability assessment of such component represents an important stage in the evaluation of an overall reliability measure of Smart Structural Systems. Several studies are currently underway aiming at the identification of a reliability measure for such smart pattern recognition technique.

• Journal of Korea Multimedia Society
• /
• v.6 no.6
• /
• pp.1062-1070
• /
• 2003

In this paper, we observe one of the aquatic insect, fish(Medaka)'s behavior which reacts to giving toxic chemicals until lethal conditions using automatic tracking sl$.$stem. For the result, we define the Pattern A is a normal movement of fish and Pattern B is after giving the chemicals. In order to detect the movement of fish automatically, these patterns are selected for the training data of the artificial neural networks. The average recognition rates of the pattern B are remarkably increased after inputs of toxic chemical(diazinon) while the Pattern A is decreased distinctively. This study demonstrates that artificial neural networks are useful method for detecting presence of toxicoid in environment as for an alternative of in-situ behavioral monitoring tool.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.11
• /
• pp.5605-5623
• /
• 2016

We propose a face recognition method that utilizes the LCP face descriptor. The proposed method applies a LoG mask to extract a face contour response, and employs the LCP algorithm to produce a binary pattern representation that ensures high recognition performance even under the changes in illumination, noise, and aging. The proposed LCP algorithm produces excellent noise reduction and efficiency in removing unnecessary information from the face by extracting a face contour response using the LoG mask, whose behavior is similar to the human eye. Majority of reported algorithms search for face contour response information. On the other hand, our proposed LCP algorithm produces results expressing major facial information by applying the threshold to the search area with only 8 bits. However, the LCP algorithm produces results that express major facial information with only 8-bits by applying a threshold value to the search area. Therefore, compared to previous approaches, the LCP algorithm maintains a consistent accuracy under varying circumstances, and produces a high face recognition rate with a relatively small feature vector. The test results indicate that the LCP algorithm produces a higher facial recognition rate than the rate of human visual's recognition capability, and outperforms the existing methods.

• Transactions of Materials Processing
• /
• v.11 no.7
• /
• pp.614-619
• /
• 2002

For an accurate measurement of the material behavior of small structures, a new optical experimental technique is proposed to measure the deformation. The test method uses the dual microscope that can measure the relative deformation of two adjacent regions. The magnified view is captured by CCD cameras and the relative deformation can be measured by the pattern matching and tracing method. Using this experimental technique, the deformation of solder joints in electronic packaging and the strain of the nickel thin film are measured.

• Journal of Families and Better Life
• /
• v.15 no.2
• /
• pp.201-216
• /
• 1997

The purpose of this study was to find the relation between the home management behavior pattern and contribution to the family life of urban housewives and to find out variables which influence them. The samples were selected from 522 housewives with children more than one living in Seoul. The major findings were: 1) In the housewives the degree of education of resourcefulness recognition and of communication were high home management behavior pattern was relatively morphogensis. 2) Influential variables on contribution to the family were the level of education status of employment and as the home management behavior pattern was morphogensis. 3) The relative contribution of home management behavior pattern of contribution to the family was the highest among other variables ; socio-demographic and psychological variables.

• ETRI Journal
• /
• v.44 no.3
• /
• pp.426-437
• /
• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.