• Journal of Science Education
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• v.35 no.2
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• pp.204-220
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• 2011

This study was intended to develop an experimental module based on inquiry processes conducted by photosynthesis scientists. It was aimed to enhance scientific inquiry ability of the middle school students by applying the developed module. Developed module included some experiments conducted by earlier photosynthesis scientists such as Helmont, Woodward, Priestly, Hales and Ingen-Hausz. Inquiry process was involved in the developed module for instructing the inquiry methods. Rapid-cycling Brassica rapa known as a Fast Plant was used for the experimental material. Developed module was applied to the experimental group consisting 27 eighth grader, while experiments suggested in the science textbook was applied to the control group consisting 30 eighth grader. Developed module was more effective in improving students' scientific inquiry ability, especially measuring, forecasting and hypothesizing ability as its subordinate elements. When the result of post-test was compared to one of pre-test in the experimental group, their observing, forecasting, and generalization ability were improved. Experimental group showed that students' conception in photosynthesis and conceptual development related with the role of plants in the ecosystem and plant's food and movement of the water and nutrients were also improved. Before application, students in the experimental group did not have enough understanding of the abstract concept such as the existence or the role of the materials like $CO_2$ or $O_2$ or the energy accumulation. Developed module could help students to achieve the comprehensive concept regarding the role of plants as producers of organic matter and oxygen and to enhance their scientific inquiry ability and concepts regarding photosynthesis.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.909-922
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• 2006

The core assets include all properties which consist of an application in Product Line Engineering. The requirement, one of the core assets, is a basis of other core assets and commonality and variability of other core assets are classified by the requirement. accordingly, commonality and variability of the domain requirement should be managed objectively and it is necessary to make a process to reuse the domain requirements. However the requirement is analyzed by domain experts or developers without proper process. In this paper, we proposed the 4 activities: (1)the domain scoping, (2)the extraction and generalization of the domain requirement, (3)the domain requirement analyzing and modeling, (4)the change management, and sub activities. For all reasons given previously, it is possible to reduce the development time and cost by reusing the architectures and components related to the domain requirement. In addition, it is possible to increase the quality of the artifacts produced based on the requirements by managing them systematically.

• Journal of Korean Elementary Science Education
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• v.20 no.2
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• pp.239-254
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• 2001

The in-depth learning course newly established in the 7th National Curriculum of Science is for students who have mastered regular subject matters on a science topic and want to learn it more deeply or by different ways. Individual learners have their own unique intellectual properties. The study examined the effects of in-depth science learning using multiple intelligence activities on the science inquiry abilities and interests of elementary school children. This study involved two fifth-grade science classes in Busan. Each class was assigned to comparison and experimental group. The science topics covered during the period of the study were Units of Matter and Earth. After studying each regular content formulated by the National Curriculum, the students of comparison group experienced traditional practices of in-depth science, whereas those of experimental one performed the Multiple Intelligence(MI) activities related to the content. Students of both groups were pre- and posttested using the inventories of Science Inquiry Ability and Science Interest. Also, after instruction on the topics, students were interviewed to collect more information related to their loaming. The results are as follows. First, the science inquiry abilities of children were increased by using activities based on MI during the in-depth science teaming. Two inquiry processes, that is, the Prediction which is regarded as one of the basic process skills in science and the Generalization regarded as one of integrated process skills showed statistically significant differences between the groups, although the differences of other skills not significant but more improvements in experimental group than comparison one. Second, the in-depth science loaming through MI contributed to the increasing of interests of the children in science. The scores on Science Interest measured in pretest and posttest with the two groups showed st statistically significant difference. For interest in science instruction, children of experimental group showed high level of interest for the various MI activities, and, although the comparison groups' level of the interest was low, they revealed that they want to experience the MI activities in future instruction of science. Interviews with the children randomly selected from the experimental group when they completed the in-depth programs showed that most of them had much interest in MI activities. Especially, they attributed significant meanings to the experiences of teaming with their friends and doing activities that they want to do. These findings have important implications about usefulness of MI in science instruction. The results also highlight the need for science teachers to provide a variety of experiences and to create environments which encourage the children to use MI to learn a science topic.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.593-600
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• 2019

The similarity in tones between speakers can lower the performance of speaker verification. To improve the performance of speaker verification systems, we propose a multi-task learning technique using deep neural network to learn speaker information and age information. Multi-task learning can improve generalization performances, because it helps deep neural networks to prevent hidden layers from overfitting into one task. However, we found in experiments that learning of age information does not work well in the process of learning the deep neural network. In order to improve the learning, we propose a method to dynamically change the objective function weights of speaker identification and age estimation in the learning process. Results show the equal error rate based on RSR2015 evaluation data set, 6.91 % for the speaker verification system without using age information, 6.77 % using age information only, and 4.73 % using age information when weight change technique was applied.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.548-558
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• 2019

Along generalization of internet news comments, malicious comments have been spread and made many social problems. Because writings reflect human mental state or trait, analyzing malicious comments, human mental states could be inferred when they write internet news comments. In this study, we analyzed malicious comments of English and Korean speaker using LIWC and KLIWC. As a result, in both English and Korean, malicious comments are commonly more used in sentence, word phrase, morpheme, word phrase per sentence, morpheme per sentence, positive emotion words, and cognitive process words than normal comments, and less used in the third person singular, adjective, anger words, and emotional process words than normal comments. This means people are state that they can not control their feeling such as anger and can not think well when they write news comments. Therefore, when internet comments were written, service provider should consider the way that commenters monitor own writings by themselves and that they prevent the other users from getting close to comments included many negative-emotion words. In other sides, it is discovered that English and Korean malicious comments was discriminated by authenticity. In order to be more objective, gathering data from various point of time is needed.

• Journal of Distribution Science
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• v.10 no.6
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• pp.27-37
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• 2012

Many studies have focused on the importance of organizational integration when companies try to achieve growth through mergers and acquisitions (M&A). However, there has been little research that focuses on the new branding or customer base integration of the M&A process, despite the fact that this integration is very important for achieving M&A goals and business performance in industries such as retail. The purpose of this study is to provide an M&A case study of the retail industry, focused especially on the new branding and customer integration of two department stores. This study examined key integration processes in terms of brand name and membership systems of both companies by examining how the merged company achieved its new branding and the integration of its membership systems. The methodology of this research is the case study, which is used in both normative and empirical studies for distribution research in Korea. This research analyzes the case of both new branding and customer membership systems of the two companies. The new branding initiatives of this case centered on decision making including brand extension and brand naming. The customer membership integration of the two companies is analyzed on the basis of the customer reward programs that include both financial and service rewards. This study shows the success factors of new branding and customer integration in the M&A process in terms of achieving marketing goals and business performance as follows: First, companies should identify the integration areas by analyzing the brand and membership of both companies and make a balanced decision for both the customer and company. Second, the goals of new branding and membership integration in the M&A process should not emphasize business efficiency from a short-term perspective but rather should consider brand power and business synergy from a long-term perspective. Third, the post-merger integration process of the brand or customer areas requires not only the organized execution of integration tasks but also follow-up programs for changes in business strategy and marketing-related programs to realize the synergy effects of integrated organization. Although this study provides a detailed review and analysis of the new branding and customer integration processes in post-merger integration and in identifying the primary decision-making areas of these processes, there are some limitations requiring further research that may overcome or compensate for these limitations. The suggested future research areas are as follows: First, since this research is a case study of only one M&A, it makes few theoretical contributions such as new propositions or theories or possibilities for generalization. This limitation can be overcome through further research using multiple cases, which may lead to new propositions. Second, the methodology of this study lacks sufficient rigor in terms of its analytic approach because this case study was developed and analyzed descriptively. Further research is needed to compensate for these limitations, such as using a theory-based approach or comparative analysis approach that makes case analysis more systematic.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• International Journal of Costume and Fashion
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• v.11 no.1
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• pp.63-83
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• 2011

This study aims to prove the changes, development and social background of Korean male cosmetics over the period of 2000-2010. There were total 574 articles written about male make-up and cosmetics in Chosun Daily, Joongang Daily and Donga Daily, which are the top three Korean local daily newspapers in terms of sales revenue for that period. These articles were analyzed together with social background research from various books and daily newspapers. The analysis of articles was divided into different categories; such as foundation cosmetics, color cosmetics, hair, and the social background. It was found that the articles related to foundation cosmetics were written the most, the articles related to male appearance management were second, articles on other items including how to manage the skin being the least. As for the social background of Korean male cosmetics, first of all, a change in social recognition can be pointed out. The traditional understanding of gender roles has changed, men's appearance management has started to be regarded as a competitive talent, and at the same time, the esthetic viewpoint for men also has changed. One example is the newly made popular term, "flower-handsome man", which shows the new trend of favoring males with nice skin. Second, the expansion of mass culture can be pointed out. As consumers can get information and fashion trends easier and faster, the fashion spreads fast, and this has led to the generalization and popularization of the sense of beauty. Third, the development of scientific technology and medical science can be pointed out. Thanks to the progress in those areas. the extension of youthfulness has become possible and the cosmetics industry was also affected greatly, as shown in the market spread of functional products for anti-ageing and wrinkle control as the interest in anti-ageing has grown. There are benefits from the development of scientific technology, but the problem of environmental pollution has appeared due to machinery and industrialization and thus the issue of well-being has been raised. Rising interest in naturalism, pro-environmentalism and organic cosmetics has influenced the cosmetics industry. In addition, the quantity of ultraviolet rays exposed to our skin has increased due to the air pollution caused by the destruction of environment, leading to increased usage of sun block lotion. Lastly, the influence of consumer society and the expansion of consumerism culture can be pointed out. In the modern society, consumption acts not only as the use of products and services but it also has an important role of mediating individuals with others and the society. The market for male cosmetics has been expanding and the number of men putting on make-up has been increasing rapidly. Therefore, this study is meaningful in that the analysis of the mode of change and the social background are an essential process in order to provide a direction for the future market for male cosmetics.