• 수산해양교육연구
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• 제25권2호
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• pp.510-525
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• 2013

The purpose of this study is to investigate adult learners' experience by studying Humanities & General Education and get to know types and characteristics by classifying their learning experiences. This study uses grounded theory method which is suitable to investigate subjective experiences. In this study, data is collected from 13 adult learners by using Focus Group Interview(FGI) who participate in learning experience of Humanities & General Education of D university in Busan region. The data is categorized by open coding, axial coding and selective coding based on data analysis method of grounded theory and analysis processes. This study provides several outcomes as follows: 113 concepts, 38 subcategories and 16 upper categories are derived through the process of abbreviation and categorization of learning experience of Humanities & General Education. In a process of learning experience, this study shows interrelationship in a frame of paradigm and derives results of a process of abbreviation and categorization casual condition, contextual condition, phenomenon and interaction(help/obstruction factor). Tree types of learning experiences and characteristics are drawn as follows: 1) "Self-realization" is the type who participate in Humanities & General Education with desire of learning and they want to find identity and plan detailed future. 2) "The pursuit of happiness" has less desire on learning than "self-realization" and they are types who participate in Humanities & General Education because of someone else's help and suggestion. 3) "Local community" is the type who participate in Humanities & General Education because they feel necessity of social role and they expect local development based on their interest in local community. Several conclusions and suggestions are provided for further studies.

• 한국컴퓨터정보학회논문지
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• 제28권9호
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• pp.137-148
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• 2023

본 연구는 지방 전문대학들에서 조리 관련 전공 학생들의 전공 만족도가 학습 몰입, 자기 효능감에 미치는 영향력 여부를 조사하였다. 2022-2학기, 광주·전남지역 소재 5개 전문대학의 조리 전공 260명의 1학년과 2학년 학생을 대상으로 분석하였다. 자료처리는 SPSS Ver. 25.0을 이용하였다. 신뢰도 산출, t-test, ANOVA, Scheffe test로 사후 검증하였다. 또한, 피어슨 적률상관계수와 다중회귀분석을 시행하였다. 본 연구 결과는 첫째, 지방전문대 조리 관련 전공 만족도에서 학년 간 만족도 차이가 있었다. 둘째, 지방전문대 조리 관련 전공 만족도가 학습 몰입감에 미치는 유의미한 영향이 있었다. 셋째, 지방전문대 조리 관련 전공 만족도가 자기 효능감에 미치는 유의미한 영향이 있었다. 결론적으로 지방전문대 조리 관련 학과 재학생 모두 전공 만족도가 학습 몰입과 자기 효능감에 영향을 미치는 것으로 나타났다. 향후, 고등학교 교육과정에서 조리 비전공 신입생들과 학기 중 아르바이트로 전공 수업에 미진한 학생들에 대한 학습 몰입감과 자기 효능감을 높이는 교육 방안에 관한 후속 연구를 제언한다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.58-66
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• 2007

This paper introduces the new generic dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system for complex dynamic hydrological modeling tasks. The proposed DNFLMS applies a local generalization principle and an one-pass training procedure by using the evolving clustering method to create and update fuzzy local models dynamically and the extended Kalman filtering learning algorithm to optimize the parameters of the consequence part of fuzzy local models. The proposed DNFLMS is applied to develop the inference model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatts Cobb hydropower station on springs flow. It is demonstrated that the proposed DNFLMS is superior in terms of model accuracy, model complexity, and computational efficiency when compared with a multi-layer perceptron trained with the back propagation learning algorithm and well-known adaptive neural-fuzzy inference system, both of which adopt global generalization.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.848-857
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• 2021

This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respectively. Two serial Artificial Neural Network (ANN) models based on a deep multi-layer perceptron technique were developed. The first ANN model corresponds to multi-link analyses, where the input neurons were the tension force and angle and the output neurons were the interlink angles. The second ANN model corresponds to the three-link analyses with the input neurons of the tension force, interlink angle, and the local stress positions, and the output neurons of the local stress. The predicted local stresses for the untrained cases were reliable compared to the numerical simulation results.

• 한국지능시스템학회논문지
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• 제5권3호
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• pp.3-10
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• 1995

This paper proposedan on-line learning controller which can be applied to nonlinear systems. The proposed on-line learning controller is based on the universal approximation by the local affine mapping-based neural networks. It has self-organizing and learning capability to adapt itself to the new environment arising from the variation of operating point of the nonlinear system. Since the learning controller retains the knowledge of trained dynamics, it can promptly adapt itself to situations similar to the previously experienced one. This prompt adaptability of the proposed control system is illustrated through simulations.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 하계학술대회
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• pp.63-66
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• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.332-343
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• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.819-833
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• 2013

Computing global features based on local features using a bio-inspired framework has shown promising performance. However, for some tough applications with large intra-class variances, a single local feature is inadequate to represent all the attributes of the images. To integrate the complementary abilities of multiple local features, in this paper we have extended the efficacy of the bio-inspired framework, HMAX, to adapt heterogeneous features for global feature extraction. Given multiple global features, we propose an approach, designated as parameterized metric learning, for high dimensional feature fusion. The fusion parameters are solved by maximizing the canonical correlation with respect to the parameters. Experimental results show that our method achieves significant improvements over the benchmark bio-inspired framework, HMAX, and other related methods on the Caltech dataset, under varying numbers of training samples and feature elements.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.458-461
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• 2021

유동인구 예측은 상권의 특성에 따른 점포의 입지 선정 및 고객 맞춤형 마케팅 등 민간 분야에서부터 교통망 등 사회 간접 자본 설계를 위한 공공 분야에 이르기까지 다양한 목적으로 연구되어 왔으며, 최근에는 Covid-19 의 확산에 따라 그 중요도가 더욱 높아지고 있다. 보다 정교한 예측을 위해서는 전체적인 유동 인구 뿐만 아니라 특성 별로 세분화된 하위 그룹에 대해서도 정확한 예측이 요구되나, 기존의 예측 모델들은 이러한 데이터의 계층 구조를 고려하지 않았다. 본 연구에서는 세분화된 하위 그룹 별 유동인구의 예측 정확도를 높이기 위해 전체 유동인구의 패턴을 동시에 활용하는 Global-Local 구조 기반의 Deep Learning 유동인구 분석 모델을 제안한다. 실험 결과 단일 시계열 데이터만을 사용하는 경우 대비 5.4%~52.6%의 예측 오류 감소 효과가 있음을 확인하였다.