• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.37-44
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• 2022

Plug-in Hybrid electric vehicles (PHEV) show great potential to reduce gas emission, improve fuel efficiency and offer more driving range flexibility. Moreover, PHEV help to preserve the eco-system, climate changes and reduce the high demand for fossil fuels. To address this; some basic components and energy resources have been used, such as batteries and proton exchange membrane (PEM) fuel cells (FCs). However, the FC remains unsatisfactory in terms of power density and response. In light of the above, an electric storage system (ESS) seems to be a promising solution to resolve this issue, especially when it comes to the transient phase. In addition to the FC, a storage system made-up of an ultra-battery UB is proposed within this paper. The association of the FC and the UB lead to the so-called Fuel Cell Battery Electric Vehicle (FCBEV). The energy consumption model of a FCBEV has been built considering the power losses of the fuel cell, electric motor, the state of charge (SOC) of the battery, and brakes. To do so, the implementing a reinforcement-learning energy management strategy (EMS) has been carried out and the fuel cell efficiency has been optimized while minimizing the hydrogen fuel consummation per 100km. Within this paper the adopted approach over numerous driving cycles of the FCBEV has shown promising results.

• Journal of Technology Innovation
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• v.20 no.1
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• pp.17-43
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• 2012

Alliance formation has been recognized as an important strategy for firms who seek to survive through acquisition of sustainable competitive advantages. Specifically in high-tech industries, firms may consider formation of strategic alliances in order to access valuable external knowledge. These firms tend to be situated in a dilemma that they should choose between exploration and exploitation, which are two types of strategic choices suggested by March (1991). Working out the dilemma has been extensively discussed in the area of strategy or organization learning. Recently, however, an increasing number of studies have stressed on a balance between exploration and exploitation. Regarded as 'ambidextrous organizations' (Lavie and Rosenkopf, 2006), these firms that simultaneously pursue exploration and exploitation have emerged in high-tech industries, and many studies have provided evidence of positive association between organizational ambidexterity and firm performance. In the strategic alliance research, accordingly, scholars began to pay attention to the balanced choice between exploration-and exploitation-oriented alliances. Given these backgrounds, this study examines the relationship between alliance ambidexterity and firm performance. While previous research approached alliance ambidexterity mainly from the number of alliances, our study suggests ambidexterity in terms of alliance portfolio and alliance partner. Our dataset consists of biotechnology or pharmaceutical firms in the United States, which spans time period between 1990 and 2005. We conduct panel data analysis. The results show the strong link between alliance ambidexterity and firm performance, highlighting the balance between exploration and exploitation when firms make strategic decisions.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.13 no.1
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• pp.85-92
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• 2002

Objectives：This study was conducted to compare the memory function among the attention deficit/hyperactivity disorder(ADHD), the learning disorder(LD) and the comorbidity disorder(ADHD+LD) groups. Methods：Thirty-four children(11 ADHD, 5 LD, 9 ADHD+LD, and 8 Psychiatric control) were individually assessed using the KEDI-WISC and Memoty Assessment Scale(MAS), and then the results of those test were analyzed. Results：In memory test, all of three group showed lower performances than control group. The comorbidity, the LD and the ADHD group showed lower scores in almost subtests of MAS respectively. The good performance in memory test was significantly correlated with the types of memory strategy and error response children used during testing. Discussion：The clinical utility of the memory test like MAS was discussed in terms of differential diagnosis for ADHD, LD and ADHD+LD children.

• Journal of The Korean Association For Science Education
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• v.30 no.1
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• pp.1-12
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• 2010

The purpose of this study was to investigate the development and application of strategies using fairy tales in elementary school science. For this study, many fairy tales were searched in terms of various characteristics and educational value of the tales. Five fairy tales were selected and reconstructed to suit the 'mirrors and lenses' unit of 5th graders' school science, and teaching strategies using the fairy tales were developed to be used in six lessons. To investigate the effects of instruction using fairy tales, pre/post tests for scientific attitude were administered. To analyze students' perception of their classes, a simple survey was administered through questionnaires. We found from this study that the students related the fairy tales with their own experiences and took an active part in the class that used them. Also, instruction using fairy tales had positive effects on their scientific attitude. Many students said that the science classes were interesting, and the method using fairy tales encouraged students to study hard as well as helped them to understand the context. It was concluded that instruction using fairy tales was an effective method in terms of enhancing learning motivation, encouraging more inquiries, more opportunities to apply the scientific concept, and more positive scientific attitude. We discussed the strategies using fairy tales for implementation in elementary science classes as well.

• 대한공업교육학회지
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• v.40 no.2
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• pp.92-110
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• 2015

The purpose of this study is to provide necessary information to understand characteristics of vocational high school students and to enhance academic engagement through social support of teachers, leading to help research of teaching and learning strategy. A survey was conducted on 990 engineering major students attending 11 vocational high schools in Seoul metropolitan, Chungcheong, Jeolla, Kyeongsang and Kangwon regions. A questionnaire consists of measurement tools for the academic engagement (21 questions) and the social support of teachers (25 questions). The findings of this study are as follows: First, it is found that the level of students' academic engagement was high. But it appears that the students showed low engagement of emotion compared with that of behavior and cognition. There was no level difference according to gender, but there was a considerable difference according to a school year. The first year students' level of engagement was higher than the second and the third year students' in terms of cognition and emotion. Second, it shows that the level of the teachers' social support was normal, which was in the order of appraisal support, instrumental support, informational support, and emotional support. Especially, the level of appraisal support and instrumental support was most. Third, there were correlation and explanation between students' academic engagement and teachers' social support. Moreover, the result that teachers' emotional support has high correlation and explanation in qualitative terms of academic engagement support the importance. Therefore, it is concluded that the social support of teachers can make an positive influence on improving the academic engagement of students and provide students with adaptability and satisfaction with their school life, which may give students a positive effect in emotional development, self-formation, and complement.

• Journal of Distribution Science
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• v.10 no.6
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• pp.39-53
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• 2012

It is widely known that knowledge management plays a facilitating role that contributes to upgrading organizational performance. Knowledge management systems (KMS), especially, support the knowledge management process including the sharing, creating, and using of knowledge within a company, and maximize the value of knowledge resources within an organization. Despite this widely held belief, there are few studies that describe how companies actually develop, share, and practice their knowledge. Companies in the domestic small franchise sector, which are in the early stages in terms of knowledge management, need to improve their KMS to manage their franchisees effectively. From this perspective, this study uses a qualitative approach to explore the actual process of knowledge management implementation. This article presents a case study of PB (Paris Baguette) company, which is the first to build a KMS in the franchise industry. The study was able to confirm the following facts through the analysis of target companies. First, the chief executive's support is a critical success factor and this support can increase the participation of organization members. Second, it is important to build a process and culture that actively creates and leverages information in knowledge management activities. The organizational learning culture should be one where the creation, learning, and sharing of new knowledge is developed continuously. Third, a horizontal network organization is needed in order to make relationships within the organization more close-knit. Fourth, in order to connect the diverse processes such as knowledge acquisition, storage, and utilization of knowledge management activities, information technology (IT) capabilities are essential. Indeed, IT can be a powerful tool for improving the quality of work and maximizing the spread and use of knowledge. However, during the construction of an intranet based KMS, research is required to ensure that the most efficient system is implemented. Finally, proper evaluation and compensation are important success factors. In order to develop knowledge workers, an appropriate program of promotion and compensation should be established. Also, building members' confidence in the benefits of knowledge management should be an ongoing activity. The company developed its original KMS to achieve a flexible and proactive organization, and a new KMS to improve organizational and personal capabilities. The PB case shows that there are differences between participants perceptions and actual performance in managing knowledge; that knowledge management is not a matter of formality but a paradigm that assures the sharing of knowledge; and that IT boosts communication skills, thus creating a mutual relationship to enhance the flow of knowledge and information between people. Knowledge management for building organizational capabilities can be successful when considering its focus and ways to increase its acceptance. This study suggests guidelines for major factors that corporate executives of domestic franchises should consider to improve knowledge management and the higher operating activities that can be used.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.174-188
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• 2004

In the life environment changed with not only the quality and the price of the products but also the material abundance, it is the most crucial factor for the strategy of product sales to investigate consumer's sensibility and preference degree. In this perspective, it is necessary to design and merchandise the products in cope with each consumer's sensibility and needs as well as its functional aspects. In this paper, we propose the Fashion Design Recommender Agent System (FDRAS-pro) for textile design applying collaborative filtering personalization technique as one of the methods of material development centered on consumer's sensibility and preference. For a collaborative filtering system based on textile, Representative-Attribute Neighborhood is adopted to determine the number or neighbors that will be used for preferences estimation. Pearson's Correlation Coefficient is used to calculate similarity weights among users. We build a database founded on the sensibility adjectives to develop textile designs by extracting the representative sensibility adjectives from users' sensibility and preferences about textile designs. FDRAS-pro recommends textile designs to a customer who has a similar propensity about textile. To investigate the sensibility and emotion according to the effect of design factors, fertile designs were analyzed in terms of 9 design factors, such as, motif source, motif-background ratio, motif variation, motif interpretation, motif arrangement, motif articulation, hue contrast, value contrast, chroma contrast. Finally, we plan to conduct empirical applications to verify the adequacy and the validity of our system.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.681-688
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• 2015

The k nearest neighbor (k-NN) graph construction is an important operation with many web-related applications, including collaborative filtering, similarity search, and many others in data mining and machine learning. Despite its many elegant properties, the brute force k-NN graph construction method has a computational complexity of $O(n^2)$, which is prohibitive for large scale data sets. Thus, (Key, Value)-based distributed framework, MapReduce, is gaining increasingly widespread use in Locality Sensitive Hashing which is efficient for high-dimension and sparse data. Based on the two-stage strategy, we engage the locality sensitive hashing technique to divide users into small subsets, and then calculate similarity between pairs in the small subsets using a brute force method on MapReduce. Specifically, generating a candidate group stage is important since brute-force calculation is performed in the following step. However, existing methods do not prevent large candidate groups. In this paper, we proposed an efficient algorithm for approximate k-NN graph construction by regrouping candidate groups. Experimental results show that our approach is more effective than existing methods in terms of graph accuracy and scan rate.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.395-413
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• 2019

The purpose of this study is to explore non-major students' perception and experiences in college software education. For this, we analyzed the reflection journals of 36 non-major students in D University based on the Consensual Qualitative Research(CQR). As a result, there was not general core concept to all students nor a typical core concept that appeared to more than 50% students. However, various variable core concepts could be derived. Overall, 57 variable concepts were derived from experience in SW education and 7 variable concepts for perception of SW education. Based on this result, we found many of non-major students feel difficulty from unfamiliarity to SW education. Also, many students have satisfaction in their perception to SW education about personalized learning that their professor provided in the class. Lastly, we conclude that a methodology for SW education needs to have a careful operation strategy and interactive design. Although this study has not been able to elucidate general core concepts that appear to all learners, it has significant implication in terms of providing various implicit core concepts and suggestions for effective software education for non-major students.