• Journal of Korean Elementary Science Education
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• v.22 no.1
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• pp.121-129
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• 2003

The enhancement of inquiry skills has been emphasized as a important objective of science education for a long time. Of these, the observation is not only a simple and basic skill, but also a very important skill, in aspect of gathering informations about the nature of all things around us, through interaction between the sense organs of body and objectives. The purpose of this study is to investigate the results of observations about the grasshopper(Oxya chinesis), made by the elementary student from the 3rd to the 6th grade, and to make use of them as the basic materials for the observative teaming and the evaluation of the observation ability. Through this study, the collected items of observation are as follows For grasshopper, a total of observation items is 95, 70 using the sight sense, 13 using the tactile sense,7 using the olfactory sense. 3 using the palate sense and 2 using the auditory sense. In this study, the findings of elementary student's observation are as follows. 1. On the whole, most of students have observed mainly by the sight and the tactile sense, when observing the grasshopper. 2. It is showed a tendency that the observation ability of student is increased with the higher grade in elementary school. 3. As the grade ascends. the observations with operating are increased, also the quantitative expression and interpretation about them are increased. 4. In the case of same grade, there is no significant difference between students' gender, though girls' ability of the observation showed somewhat higher than boys' 5. Occasionally, the interpretations on the observative facts made by student, are inaccurate. Basis on the above results, we suggested some directions for the improvement of the observative learning program in science classroom of elementary school. First, the teacher have to serve as a guide and encouragement in the observative learning class, to be accomplished the various observation, which all the sensory organ can be used by student than the sight sense. Second, to get elevated the ability of observation, it is necessary that some experimental tools(magnifying lens, stereoscope, auxiliary implements etc.) are utilized. Third, the teacher have to make often endeavors showing an example of operation, to be activated the atmosphere of operative observation.

• Journal of the Korean earth science society
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• v.24 no.3
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• pp.141-149
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• 2003

This study investigated the effect of gender grouping on cooperative learning on the basis of student achievement and science-teaming attitude. Homogeneous and heterogeneous gender groupings were used in the treatment groups for the learning strategies of earth science. Traditional instruction was performed for the control group. Three classes at a middle school were assigned to the groups. Before the treatment instruction, a questionnaire about science-learning attitude was administered to 144 students, and their scores were utilized as covariate. Then, the same questionnaire was given with a test of science achievement designed in this study. The changes in both achievement and attitude among the three groups were analyzed statistically. Significant differences were not shown in science achievement or in the difference of gender with respect to perceptions about science. There were significant changes between the homogeneous and heterogeneous gender grouping in their attitudes toward science instruction. Here the cooperative learning group, regardless of the gender grouping, tends to exhibit more positive perceptions towards their learning environment than the control group, particularly in female students.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.211-214
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• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\sub$x/, B$\sub$y/, B$\sub$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, learning itself, and the adequacy of the design controller. Also, the performance of the controller can be verified through simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.876-880
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• 2008

Outliers are the observations which are very larger or smaller than most observations in the given data set. These are shown by some sources. The result of the analysis with outliers may be depended on them. In general, we do data analysis after removing outliers. But, in data mining applications such as fraud detection and intrusion detection, outliers are included in training data because they have crucial information. In regression models, simple and multiple regression models need to eliminate outliers from given training data by standadized and studentized residuals to construct good model. In this paper, we use support vector regression(SVR) based on statistical teaming theory to analyze data with outliers in regression. We verify the improved performance of our work by the experiment using synthetic data sets.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.14-22
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• 2001

In this paper, we design the Adaptive Neuro-Fuzzy Precompensator(ANFP) for the suppression of low-frequency oscillation and the improvement of system stability. Here, ANFP is designed to compensate the conventional Power System Stabilizer(PSS). This design technique has the structural merit that is easily implemented by adding ANFP to an existing PSS. Firstly, the Fuzzy Precompensator with Loaming ability is constructed and is directly learned from the input and output data of the generating unit. Because the ANFP has the property of learning, fuzzy rules and membership functions of the compensator can be automatically tuned by teaming algorithm Loaming is based on the minimization of the ems evaluated by comparing the output of the ANFP and a desired controller. Case studies show the 7posed schema can be provided the good damping of the power system over the wide range of operating conditions and improved dynamic performance of the system.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.653-662
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• 2006

In ubiquitous computing that is to build computing environments to provide proper services according to user's context, human being's emotion recognition based on facial expression is used as essential means of HCI in order to make man-machine interaction more efficient and to do user's context-awareness. This paper addresses a problem of rigidly basic emotion recognition in context-sensitive facial expressions through a new Bayesian classifier. The task for emotion recognition of facial expressions consists of two steps, where the extraction step of facial feature is based on a color-histogram method and the classification step employs a new Bayesian teaming algorithm in performing efficient training and test. New context-sensitive Bayesian learning algorithm of EADF(Extended Assumed-Density Filtering) is proposed to recognize more exact emotions as it utilizes different classifier complexities for different contexts. Experimental results show an expression classification accuracy of over 91% on the test database and achieve the error rate of 10.6% by modeling facial expression as hidden context.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• The Journal of Information Systems
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• v.17 no.3
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• pp.205-233
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• 2008

This study suggests the dynamic value chain model, that will be able to not only show changing processes to organization's significant capital by integrating an individual, implicit, and explicit knowledge which affect organizational decision making, but also distinguish the key driver for raising organizational competitive power because it makes possible to analyze sensitivity of performance along with decision making alternatives and policy changes from dynamic view by connecting knowledge management capability, knowledge management activity, and relations with organizational performance with specific strategic map. Recently, a lot of organizations show interest in measuring and evaluating their performance synthetically. In organizations taking knowledge management, they introduce effective value chain model like a dynamic balanced scorecard (DBSC), and therefore they can reflect their knowledge management condition as well as show their changes by checking performance of established vision and strategy periodically. Furthermore, they can ask for their inner members' understanding and participation by communicating with and inspiring their members with awareness that members are one of their group, present a base of benchmarking, and offer significant information for later decision making. The BSC has been a successful framework for measuring an organization's performance in various perspectives through translating an organization's vision and strategy into an interrelated set of key performance indicators and specific actions. The BSC, while having significant strengths over traditional performance measurement methods, however, has its own limitations, due to its static nature, such as overlooking two-way causation between performance indicators and neglecting the impact of delayed feedback flowing from the adoption of new strategies or policy changes. To overcome these limitations, this study employs SD, a methodology for understanding complex systems where dynamic feedback among the interrelated system components significantly impact on the system outcomes. The SD simulation model in the form of DBSC would serve as a useful strategic teaming tool for facilitating an organization's communication process through various scenario analyses as well as predicting the dynamic behavior pattern of their key performance measures over a future time frame. For the demonstration purpose, this study applied the DBSC model to Prototype of Korea manufacturing and service firm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.1
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• pp.1-8
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• 2004

According to the development of the Internet and the pervasion of Data Base, it is not easy to search for necessary information from the huge amounts of data. In order to do efficient analysis of a large amounts of data, this paper proposes a method for pattern classification based on the effective strategy for dimension reduction for narrowing down the whole data to what users wants to search for. To analyze data effectively, Radial Basis Function Networks based on VC-dimension of Support Vector Machine, a model of statistical teaming, is proposed in this paper. The model of Radial Basis Function Networks currently used performed the preprocessing of Perceptron model whereas the model proposed in this paper, performing independent analysis on VD-dimension, classifies each datum putting precise labels on it. The comparison and estimation of various models by using Machine Learning Data shows that the model proposed in this paper proves to be more efficient than various sorts of algorithm previously used.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.25-31
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• 2008

It is important for users to be confirming in e-Leaning system, because legitimate learner should be joined to the system for teaming and testing Thus, most system for authentication was verified using id and password with learner's id and password. In this case, It can be easy for hackers to steal learner's id and password. In addition, soma learner gets another to sit for the examination for one with another person id and password. For the solution like this problem it needs a biometrics authentication for complement. This method is required so much extra cost as well as are an unwanted concern. Therefore, we proposed keystroke technique to decide which learners are righteous or unlawful in this paper. In addition, we applied statistics and neural network for the performance of keystroke system. As a result, the performance of FAR and FRR in keystroke authentication was increased by proposed method.