• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1255-1263
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• 2021

While it could become an alternative water resource, fog could undermine traffic safety and operational performance of infrastructures. To reduce such adverse impacts, it is necessary to have spatially continuous fog risk information. In this work, tree-based machine-learning models were developed in order to quantify fog risks with routine meteorological observations alone. The Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), and Random Forests (RF) were chosen for the regional fog models using operational weather and visibility observations within the Jeollabuk-do province. Results showed that RF seemed to show the most robust performance to categorize between fog and non-fog situations during the training and evaluation period of 2017-2019. While the LGB performed better than in predicting fog occurrences than the others, its false alarm ratio was the highest (0.695) among the three models. The predictability of the three models considerably declined when applying them for an independent period of 2020, potentially due to the distinctively enhanced air quality in the year under the global lockdown. Nonetheless, even in 2020, the three models were all able to produce fog risk information consistent with the spatial variation of observed fog occurrences. This work suggests that the tree-based machine learning models could be used as tools to find locations with relatively high fog risks.

• Journal of the Economic Geographical Society of Korea
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• v.9 no.3
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• pp.459-472
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• 2006

This paper examines the implication of competition and collaboration in the innovation process for regional economic development in an increasingly knowledge-based economy. While competition is an important force in securing the competitive advantage of firms, collaboration between firms and organizations should be necessary for promoting the innovative capacity of a region. This study shows that collaboration relations based on trust and stability is important for the long-term development of learning and innovation in competitive environment, and the way how spatial proximity plays an important role in interactive learning processes. It also discusses the reason why the innovative networks facilitating the exchange of tacit knowledge should be embedded in region. Finally, the paper examines the possibility of the networks based on collaboration relationship in less-favored regions such as Chonbuk, and suggests the policy implication of the result for achieving regional innovation systems in the region successfully.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.650-656
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• 1999

In this paper, we study on the convergence of the evolution strategies by introducing the Lamarckian evolution and the Baldwin effect, and propose a random local searching and a reinforcement local searching methods. In the random local searching method some neighbors generated randomly from each individual are med without any other information, but in the reinforcement local searching method the previous results of the local search are reflected on the current local search. From the viewpoint of the purpose of the local search it is suitable that we try all the neighbors of the best individual and then search the neighbors of the best one of them repeatedly. Since the reinforcement local searching method based on the Lamarckian evolution and Baldwin effect does not search neighbors randomly, but searches the neighbors in the direction of the better fitness, it has advantages of fast convergence and an improvement on the global searching capability. In other words the performance of the evolution strategies is improved by introducing the learning, reinforcement local search, into the evolution. We study on the learning effect on evolution strategies by applying the proposed method to various function optimization problems.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.49-56
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• 2023

The purpose of this study is to develop convergence major-based learning (CMBL), which selects performance tasks related to local problems at hand and solves them based on convergence major performance, and builds a suitable teaching and learning model. We developed a CMBL class with a team project-type class that finds and solves practical problems in the region to cultivate overall problem-solving capabilities for convergence major competencies. Additionally, for this class, the instructor played a role as a bridgehead to explore and connect the community's sites, and students visited connected institutions in person to identify problems they need based on understanding and empathy for the subjects through field observation and qualitative interviews, and developed a CMBL class teaching and learning model necessary to directly solve them by using their major capabilities to the fullest. Therefore, we intend to present the future-oriented direction of university convergence education required by the community by forming a group of students with various majors to cultivate the ability to solve realistic problems in the community.

• Journal of the Korean Geographical Society
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• v.36 no.5
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• pp.539-560
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• 2001

This paper aims to critically review the geographical literature on learning and proximity that stresses the role of the regions and geographical proximity in sustaining competitive advantage, and to conceptualize a relational/organizational perspective on the sources of knowledge and learning in the firm. In the first part of the paper, I argue that the geographical literature lacks the deliberate scrutiny of how learning occurs in the firm and where the sources of knowledge and learning come from. Secondly, I attempt to elaborate the concept of proximity through a relational/organizational perspective. Thirdly, I delve into how learning takes place and is realized in the firm through communities in the firm such as communities of practice, epistemic communities and task-force teams and how such communities in the firm generate knowledge and sustain loaming by drawing on relational/organizational proximity. This paper concludes by claiming that the sources of learning exist in organizational spaces, with complex geographies mobilizing distributed knowledge and competences and combining varied forms of knowledge beyond the simple demarcation of tacit and codified knowledge.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.339-346
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• 2003

This paper proposes a multiple classifier system having massive micro classifiers. The micro classifiers are trained by using a local set of training patterns. The k nearest neighboring training patterns of one training pattern comprise the local region for training a micro classifier. Each training pattern is incorporated with one or more micro classifiers. Two types of micro classifiers are adapted in this paper. SVM with linear kernel and SVM with RBF kernel. Classification is done by selecting the best micro classifier among the micro classifiers in vicinity of incoming test pattern. To measure the goodness of each micro classifier, the weighted sum of correctly classified training patterns in vicinity of the test pattern is used. Experiments have been done on Elena database. Results show that the proposed method gives better classification accuracy than any conventional classifiers like SVM, k-NN and the conventional classifier combination/selection scheme.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.693-703
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• 2005

Given the local traders with pattern-based multi-predictors of stock prices, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset allocated to each recommendation of the predictors, we design an asset allocation strategy called meta policy in the reinforcement teaming framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the total asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods. This means that reinforcement learning can bring synergy effects to the decision making problem through exploiting supervised-learned predictors.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.39-39
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• 2019

인구의 도시 집중화로 인하여 다량의 생활용수의 사용에 따라 하천의 자정능력을 초과하여 오염을 유발시키고 있다. 이에 도시하천들의 오염은 점점 심해져 경제적으로 많은 문제를 유발하고 있다. 이러한 하천오염 문제를 과학적으로 대응하기 위해서는 오염물질의 농도 측정 및 데이터 축척을 통한 오염예측이 필수적이라 할 수 있으며, 부산광역시 보건환경정보 공개시스템에서는 하천수질 자동측정망을 설치하여 시간 단위로 오염물질을 측정하고 있다. 그러나 온천천의 하천수질 데이터는 계속 쌓여가고 있는데 이 데이터를 활용해서 하천수질 인자 예측이 거의 이뤄지지 않고 있다. 본 연구에서는 순환신경망 알고리즘을 활용하여 일 단위의 하천수질 인자 예측을 시도하였다. 순환신경망은 인공신경망의 발전된 형태인 시계열 학습에 강한 RNN, LSTM 알고리즘을 활용한 일단위 하천수질 인자 예측을 하고자 하였다. 연구에 앞서 시간 단위로 쌓여있는 데이터를 평균 내어 일 단위로 변경하였고 이 데이터를 가지고 일 단위 하천수질 인자 예측을 진행하였다. 연구에는 Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하여 DO, 탁도 등 항목을 예측하였다. 하천오염의 학습과 예측을 위해 대상지로는 부산지역 온천천의 부곡교, 세병교, 이섭교 관측소를 선택하였다. 연구를 위해 DO, 탁도 등 자료 수집은 부산광역시 보건환경정보 공개시스템의 자료를 활용하였다. 모형의 학습을 위해 입력자료로는 하천수질 인자 자료를 이용하였고, 자료의 학습에는 2014년~2017년 4년간의 자료를 학습자료로 사용하였고, 2018년 1년간의 자료는 모형의 검증을 위해 사용하였다. RNN, LSTM 알고리즘을 활용하여 분석 시 은닉층의 개수, 반복시행횟수, sequence length 등의 값을 조절하여 하천수질 인자 예측을 하였다. 모형의 검증을 위해 $R^2$(r square)와 RMSE(root mean square error)을 이용하여 통계분석을 실시하였다.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.309-316
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• 2021

The purpose of this study is to verify the effectiveness of community-based learning(CBL) on career decision-making self-efficiency of junior college students and explore the meaning. This study was conducted on 68 students and 10 departments participating in the CBL, which was supported by the D University Faculty Learning Development Center in Busan. First of all, does CBL affect the career decision-making self-efficiency for junior college students? Second, what is the meaning of CBL for career decisions for junior college students? The effectiveness of the CBL's before and after application surveys has shown statistically significant changes in the career decision-making self-efficiency. The meaning of CBL for learners' career decisions was derived from "improving understanding through on-site application of theory and creating confidence and commitment in their career paths by providing an opportunity to study." Through this, it can be seen that CBL is worth applying as a teaching method suitable for career guidance of junior college students.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.224-233
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• 2021

The aim of this study was to describe how a teacher used technologies to improve underserved student's learning in a low-resourced rural elementary classroom and provide implications for teachers' use of technology. The in-depth case study was conducted in a one-student classroom setting that isolated the fifth grader from social and collaborative learning opportunities. The qualitative data was collected in forms of classroom observation field notes, teacher interviews, student interviews and student's reflection journals. Findings are as follows: First, technology partakes an import role in a one-student classroom to support student's collaborative learning. Second, to overcome a digital divide, the teacher created a technology-enhanced environment with alternative methods of her own and supplemented teacher-created resources. Third, the teacher used technologies to support adaptive instruction based on student's needs.