• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.81-101
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• 2003

The financial statements purpose to provide useful information to decision-making process of business managers. The value-relevant information, however, embedded in the financial statement has been often overlooked in Korea. In fact, the financial statements in Korea have been utilized for nothing but account reports to Security Supervision Boards (SSB). The objective of this study is to develop earnings forecasting models through financial statement analysis using artificial intelligence (AI). AI methods are employed in forecasting earnings: artificial neural networks (ANN) for manufacturing industry and case~based reasoning (CBR) for banking industry. The experimental results using such AI methods are as follows. Using ANN for manufacturing industry records 63.2% of hit ratio for out-of-sample, which outperforms the logistic regression by around 4%. The experiment through CBR for banking industry shows 65.0% of hit ratio that beats the statistical method by 13.2% in holdout sample. Finally, the prediction results for manufacturing industry are validated through monitoring the shift in cumulative returns of portfolios based on the earning prediction. The portfolio with the firms whose earnings are predicted to increase is designated as best portfolio and the portfolio with the earnings-decreasing firms as worst portfolio. The difference between two portfolios is about 3% of cumulative abnormal return on average. Consequently, this result showed that the financial statements in Korea contain the value-relevant information that is not reflected in stock prices.

• Education of Primary School Mathematics
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• v.19 no.1
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• pp.31-45
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• 2016

In this study, the case of error became the object of learning, and the investigator applied these cases to an actual class and established three study problems in order to achieve the purpose of this study. The results of analysis of students' errors in figure based on before achievement test are shown as follows: First, the most errors occurred in the figure was the ones from deficient mastery of prerequisite concepts and definitions. Specially, the errors from deficient mastery of prerequisite concepts and definitions have the majority. it is very high ratio even if it considers an influence of an evaluation question item. so, I think it is necessary to teach concept related figure above all. Second, as the results of application 'finding errors' to a class, there is a meaningful difference in the mathematical achievement and reasoning ability within significance level 5%. This means 'finding errors' is one of the teaching method that it develops the mathematical achievement and reasoning ability.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of the Korean Home Economics Association
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• v.41 no.12
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• pp.53-68
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• 2003

The purpose of this study was to develop lesson plans and teaching materials applying practical reasoning instruction for the 7th home economics curriculum content, and to test the effect of practical reasoning instruction on morality of middle school students. This study is a quasi-experimental research with a pretest-posttest design. Practical reasoning instruction for experimental group and traditionally lecture oriented instruction for comparison group were input, and tested the statistical differences between two groups before and after the treatment. The subjects for this study were 8th grade students of a middle school located in Kwangju city. Two classes of 76 students homogeneous in characteristics and academic record for each experimental and comparison group were assigned. Instrument used for this study was a revised moral indicator, that was developed by KEDI(2001). Spss/win for 10.0 statistics program was used for analysis of data. ANCOVA was done for testing statistical difference between pretest and posttest of experiment group and comparison group. Result of study which showed statistically significant difference between groups were：1. Virtue of "responsibility for words and deeds"(from 3.22 to 3.61 for experimental group and from 3.27 to 3.26 for comparison group) in domain of responsibility and cooperation, and virtue of "punctuality"(from 3.59 to 3.76 for experimental group and from 3.41 to 3.28 for comparison group) in domain of trustworthiness, 2. Virtue of "conversation etiquette"(from 3.47 to 3.67 for experimental group and from 3.28 to 3.31 for comparison group) in domain of caring for others, 3. Virtue of "forgiveness other′s mistakes"(from 3.32 to 3.65 for experimental group and from 3.33 to 3.25 for comparison group) in domain of kindness, concession, forgiveness, and virtue of "volunteering activity"(from 2.89 to 3.71 for experimental group and from 3.36 to 3.45 for comparison group) in domain of compassion and service, 4. Virtue of "equip the convenient facility for handicapped"(from 4.19 to 4.29 for experimental group and from 4.17 to 3.91 for comparison group) in domain of equality and human rights, virtue of "recovering selfness for own community"(from 2.34 to 2.79 for experimental group and from 2.14 to 2.29 for comparison group), virtue of "opposing way of accomplishing purpose by an means"(from 3.27 to 3.31 for experimental group and from 3.47 to 3.05 for comparison group), virtue of "opposing election of considering acquaintance"(from 3.35 to 3.56 for experimental group and from 3.12 to 3.14 for comparison group) in domain of fairness, and virtue of "eradication of military force or violence among countries"(from 3.49 to 3.57 for experimental group and from 3.38 to 3.05 for comparison group) in domain of love for humanity. The morality of experimental group was improved more than that of comparison group in all of above items. From the results of this study, following conclusion was drawn. Practical reasoning instruction in home economics is effective in raising students′ virtue and value of responsibility in words and deeds, trustworthiness in punctuality, courtesy of not interrupting conversation, forgiveness of other′s mistakes, volunteering activity, equity for handicapped, fairness opposing selfness for own community, fairness opposing way of accomplishing purpose by all means, fairness opposing election of considering acquaintance, and love for humanity opposing war.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of the Korean Society of Earth Science Education
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• v.2 no.1
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• pp.55-61
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• 2009

This study was designed to explore problem solving process to earth science area by elementary science gifted children, which compared and analyzed the questionnaires and problem solving to earth science area by gifted Science education center, Seoul National University Of Education, The analyzed results showed difference by gender that in the science study level at the time of entrance to the gifted Science education center, male students was the highest in the middle school as 37.5%, and female students in the elementary 6th grade as 61.5%. And male students were investigated to do more precedent study than female students. Secondly, in the problem solving process of earth science related problems, males made most use of problem solving process area(30.3%), and females symbolizing (27.5%) area. Thirdly, comparison of reasoning technology in problem solving process by gender indicated that both sexes made the most use of analytical reasoning (male 62.0%, female 53.6%) to solve problems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.31-40
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• 2000

A robust scheme of locating and counting the number of vehicles m urban traffic scenes, a core component of vision-based traffic monitoring systems, is presented The method is based on the evidential reasoning, where vehicle evidences m the background subtraction Image are obtained by a new locally optimum thresholding, and the evidences are merged by three heuristic rules using the geometric constraints The locally optimum thresholding guarantees the separation of bright and dark evidences of vehicles even when the vehicles are overlapped or when the vehicles have similar color to the background Experimental results on diverse traffic scenes show that the detection performance is very robust to the operating conditions such as the camera location and the weather The method may be applied even when vehicle movement is not observed since a static Image IS processed without the use of frame difference.

• Journal of Korean Academy of Nursing Administration
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• v.2 no.2
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• pp.85-95
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• 1996

The purpose of this study was to investigate moral reasoning among 77 senior medical students. Data were collected through selfreported questionnaires in June, 1995. The short form of the DIT (Rest's Defining Issues Test) was adopted to measure the stage of moral development, which was classified with the stage 2(instrumental relativist orientation), the stage 3(interpersonal concordance), the stage 4(law and order), the stage 5A(societal consensus), and the stage 5B(intuitional humanism), stage 6(universal ethical practice). In particular, the level of principled thinking(P) was measured by summing those scores of the stages 5A, 5B, and 6. The possible range of P is O to 95. The data were analyzed by t-test, ANOVA. The results were as follows. 1. The mean score of P(%) was 44.67 (SD=12.82). And the mean score of the stage 5A was higher than the scores of other stages. The mean score of P was not significantly different by general characteristics of the students. 2. The mean score of the stage 5B revealed significant difference by religion (3.17, P=.019) ; The score was highest in buddhist (8.0), which was followed by protestant (6.1), catholic (5.6) and no religion (4.7). 3. The mean score of the stage 4 revealed significant difference by educational background of mother (3.24, P=.017) ; the Score was highest in graduate school (25.0), which was followed by high school (14.1), under-graduate school (13.9), elementary school (12.4), middle school (8.3).

• Proceedings of the Korean Society for the Gifted Conference
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• 2003.05a
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• pp.137-138
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• 2003

Understanding how and why people differ is a fundamental, if distant, goal of research efforts to bridge psychological and biological levels of analysis. General fluid intelligence (gF) is a major dimension of individual differences and refers to reasoning and novel problemsolving ability. A conceptual integration of evidence from cognitive (behavioral) and anatomical studies suggeststhat gF should covary with both task performance and neural activity in specific brain systems when specific cognitive demands are present, with the neural activity mediating the relation between gF and performance. Direct investigation of this possibility will be a critical step toward a mechanistic model of human intelligence. In turn, a mechanistic model might suggest ways to enhance gF through targeted behavioral or neurobiological intervent ions, We formed two different groups as subjects based on their scholarly attainments. Each group consists of 20 volunteers(aged 16-17 years, right-handed males) from the National Gifted School and a local high school respectively. To test whether individual differences in general intelligence are mediated at a neural level, we first assessed intellectual characteristics in 40 subjects using standard intelligence tests (Raven's Advanced Progressive Matrices, Wechsler Adult Intelligence Scale, Torrance Tests of Creative Thinking) administered outside of the MR scanner. We then used functional magnetic resonance imaging (fMRl) to measure task-related brain activity as participants performed three different kinds of computerized reasoning tasks that were intended to activate the relevant neural systems. To examine the difference of neural activity according to discrepancy in general intelligence, we compared the brain activity of both extreme groups (each, n=10) of the participants based on the standard intelligence test scores. In contrast to the common expectation, there was no significant difference of brain region involved in high-g tasks between both groups. Random effect analysis exhibited that lateral prefrontal, anterior cingulate and parietal cortex are associated with gF. Despite very different task contents in the three high-g-low-g contrasts, recruitment of multiple regions is markedly similar in each case, However, on the task with high 9F correlations, the Prodigy group, (intelligence rank: >99%) showed higher task-related neural activity in several brain regions. These results suggest that the relationship between gF and brain activity should be stronger under high-g conditions than low-g conditions.