• Journal of Korean society of Dental Hygiene
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• v.15 no.4
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• pp.603-611
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• 2015

Objectives: The purpose of the study is to investigate the academic achievement in comprehensive dental hygiene courses using MBTI personality type. This study will provide the various pedagogical approaches in the dental hygiene education. Methods: A self-reported questionnaire was completed by 58 dental hygiene students in Chungnam from December, 2012 to March, 2014. The questionnaire consisted of academic achievement of comprehensive dental hygiene course and communication skills, After filling out the questionnaire, the students completed MBTI personality type sheet. Results: The students were categorized as extroversion type (58.6%), sensing type (70.7%), feeling type (56.9%), and perceiving type (67.2%). In the academic achievement, extroversion and judging personality type students had higher self-efficacy than the students of introversion and perceiving types. The extroversion personality type students also had the higher assignment level and confidence than the introversion type. Conclusions: In order to enhance the understanding and learning capacity of the students, dental hygiene professors should understand the differences in achievement levels due to different personality types so that they can utilize better pedagogical approaches.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.890-903
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• 2017

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.1
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• pp.1-12
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• 2023

It is very time-intensive to obtain data with labels on anomaly detection tasks for multivariate time series. Therefore, several studies have been conducted on unsupervised learning that does not require any labels. However, a well-done integrative survey has not been conducted on in-depth discussion of learning architecture and property for multivariate time series anomaly detection. This study aims to explore the characteristic of well-known architectures in anomaly detection of multivariate time series. Additionally, architecture was categorized by using top-down and bottom-up approaches. In order toconsider real-world anomaly detection situation, we trained models with dataset such as power grids or Cyber Physical Systems that contains realistic anomalies. From experimental results, we compared and analyzed the comprehensive performance of each architecture. Quantitative performance were measured using precision, recall, and F1 scores.

• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.17-25
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• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

• Korean Institute of Interior Design Journal
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• v.24 no.3
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• pp.48-58
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• 2015

This study is to conclude a direction for Information Commons, which supports the university library in a new role. The study explains perspectives on the changing role of the university library by examining the approaches, histories, and theories practiced by various researchers on Information Commons. The study aims to discover ways of improving the library space that are dedicated to technology using Information Commons, it also examines ways of creating a unified "library space" that will support learning and access to knowledge and information. The features of Cultural Commons include making improvements to technology-centered space, and providing support to research, freedom of speech, creative approach, public freedom and collaboration, and interaction. The functions of Cultural Commons within the university library are listed: First, it supports programs that will transform the library into a social hub within the university. The space specifically blurs the boundary between the library building and its surroundings, and unifies these spaces to enhance its catalytic role in aiding social interactions and human-centered approach. Second, it supports active participation through cultural programs and provides a fluid and interactive space with virtual resources. Third, it enhances user experience to supports behaviors and activities that involve fixtures and equipment in the space to promote learning. The study notes that, with the emergence of these characteristics, the university library is changing by implementing Cultural Commons for on-campus social space and new learning. Accordingly, this implementation is expected to enhance active acceptance of the library space in the future.

• Korean Institute of Interior Design Journal
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• v.18 no.5
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• pp.49-58
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• 2009

Robert Venturi's theories like 'Complexity and Contradiction in Architecture' and 'Symbolism of Architecture' had a major impact on architects in postmodern culture and we could have his contextual understandings in those theories. In his early books, "Complexity and Contradiction in Architecture" and "Learning from Las Vegas", Robert Venturi showed theories related to context several times. But with looking at existing books or papers, we could barely see well-organized studies about his contextual understandings. So this study shows contextual approaches and thoughts with those theories, 'Complex and contradictory architecture', 'Architectural order and conventional architecture', 'Discontinuity in internal and external architecture', and 'Symbolism of architecture' in his two books. In those four theories, Venturi's contextual understandings are as fellows. To begin with, he developed contextual theories in architecture, understanding a whole building embracing each architectural factor, with architectural thoughts of complexity and contradiction. Second, he stressed architectural order to link each contradictory factor and used conventional architecture, as for existing common and ordinary things, to make available communication. Conventional factors were applied to urban viewpoints. Given the fact that contemporaries shared those factors, we could see contextual understandings in his approach. On top of that, unlike modern architects, he understood that functions of the inside and the outside were two different things. Based on contextual thoughts, he tried applying 'facade' that is one side providing an interface between in and out of a building to surroundings. Last, he wanted to express any meaningful connection between present and past, using symbolism in architecture. Presented by symbolism of architecture, architectural functions, architectural uses, historical meaning, ordinary factors, or something were also based on sharing in contemporary people. As the methodology to show these contextual factors, Venturi used an approach of symbolism.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Journal of Engineering Education Research
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• v.19 no.3
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• pp.23-34
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• 2016

Teaching orientations represent teachers' general way of conceptualizing their teaching. The orientations are regarded as a very important factor in developing teachers' pedagogical content knowledge because they often guide their instructional decisions such as the selection of contents and teaching strategies, the use of curricula materials, and the evaluation of learning. Thus, understanding teachers' orientations can provide meaningful suggestions to understand their instructional approaches and furthermore to enhance the quality of engineering education in college. The research question for this present study was what kinds of teaching orientations engineering professors possessed in teaching engineering courses and how the orientations were represented in their teaching. Six engineering professors, particularly interested in instructional approaches, participated in the research. The data sources included in-depth interviews with individual professors, classroom observations with field notes, and related documents. In results, four teaching orientations toward engineering courses were identified: 1) expert knowledge in engineering, 2) engineering practice, 3) social practice, and 4) interdisciplinary design. Individual professors had between one to three different teaching orientations. Even though the professors had similar orientations but their instructional strategies somewhat varied based on the disciplines.

• The Journal of Industrial Distribution & Business
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• v.7 no.4
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• pp.11-16
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• 2016

Purpose - There are numerous theories for retail trade area analysis which are designed to select candidate locations for new stores. In this study, comparative analysis on the characteristics from those of the theories are shown, and the explanation for the power in consumers' store-choice behaviors and their limitations are examined. Also, plans for improving commercial sphere analysis are explored. Research design, data, and methodology - This study is based on literature reviews with normative research methodology. Among many researches regarding the analysis on the location and commercial sphere for launching a new store, researches relying on statistics are excluded in this study since they belong to the marketing research area,. Results - In the Law of retail gravitation, Huff's model multinomial logit model and etc. are mutual complementary mathematical techniques for analyzing commercial spheres and each of them has its own characteristics. These theories rely on the same hypothesis in which consumers are all believed to be behaving rationally under a similar behavioral system. However, the trial in explaining or estimating behavior of choosing a store with only a select size of the population that is objectively estimated by some major properties has limits in its credibility. Conclusion - Research on consumer's spatial behaviors can be fully illustrative and explainable when it has both quantitative approaches such as 'law of retail gravitation', 'logit model' and etc., and qualitative approaches like consumer's 'cognitive structure', 'learning status', 'image formation', 'attitude' and etc.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.