• Journal of Korean Home Economics Education Association
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• v.13 no.3
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• pp.147-160
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• 2001

The purpose of this study is to suggest one of teaching methods through developing a new guideline of the teaching-learning applied the Problem-Based Learning which is derived from the Constructionism. And then we apply this guideline to the class of Home Economics. especially the unit of the Consumption life for investigating the effect of this method in the aspect of improving man-power development as a consumer The results of this study are summarized as follows : First. the class applied a new method of Problem-Based Learning turn out to be more improved in the aspect of consumer consciousness than the untested class going through explanation-based teaching(P< .01). This is, the former result more positive than the latter because the former could be given chances to handle faced problems in class. Second there is no significant difference between the tested class and the untested class in the aspect of consumer skill. This is the fact that the research was conducted in a short Period of time and that the researcher failed to show the suitable stimulation or materials. even though the researcher was well aware of what Problem-Based Learning is. Third students\` evaluation after the test indicates that the tested class not only increase interest in home economics but also help them to take an active Part in class as the main of the class. and that the learning atmosphere is improved and that the effect of learning was also enhanced. Although the aspect of consumer skill is not statistically significant. the result show that adolescents try to Practice their learning from the class in their life.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.811-822
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• 2005

Automated text categorization is to classify free text documents into predefined categories automatically and whose main goals is to reduce considerable manual process required to the task. The researches to improving the text categorization performance(efficiency) in recent years, focused on enhancing existing classification models and algorithms itself, but, whose range had been limited by feature based statistical methodology. In this paper, we propose RTPost system of different style from i.ny traditional method, which takes fault tolerant system approach and data mining strategy. The 2 important parts of RTPost system are reinforcement training and post-processing part. First, the main point of training method deals with the problem of defining category to be classified before selecting training sample documents. And post-processing method deals with the problem of assigning category, not performance of classification algorithms. In experiments, we applied our system to documents getting low classification accuracy which were laid on a decision boundary nearby. Through the experiments, we shows that our system has high accuracy and stability in actual conditions. It wholly did not depend on some variables which are important influence to classification power such as number of training documents, selection problem and performance of classification algorithms. In addition, we can expect self learning effect which decrease the training cost and increase the training power with employing active learning advantage.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.134-141
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• 2023

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

• Smart Structures and Systems
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• v.33 no.1
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• East Asian mathematical journal
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• v.31 no.4
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• pp.569-585
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• 2015

Many mathematics teachers in characterization high schools have been troubled to teach students because most of the students have weak interests in mathematics and they are also lack of preliminary mathematical knowledges. Currently many of mathematics teachers in such schools teach students using worksheets owing to the situation that proper textbooks for the students are not available. In this study, we referred to Chevallard's didactic transposition theory based on Brousseau's theory of didactical situations for mathematical teaching and learning. Our lessons utilizing worksheets necessarily entail encouragement of students' self-directed activities, active interactions, and checking the degree of accomplishment of the goal for each class. Through this study, we recognized that the elaborate worksheets considering students' level, follow-up auxiliary materials that help students learn new mathematical notions through simple repetition if necessary, continuous interactions in class, and students' mathematical activities in realistic situations were all very important factors for effective mathematical teaching and learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4804-4822
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• 2017

Coverage problem is a research hot spot in directional sensor networks (DSNs). However, the major problem affecting the performance of the current coverage-enhancing strategies is that they just optimize the coverage of networks, but ignore the maximum number of sleep sensors to save more energy. Aiming to find an approximate optimal method that can cover maximum area with minimum number of active sensors, in this paper, a new scheduling algorithm based on learning automata is proposed to enhance area coverage, and shut off redundant sensors as many as possible. To evaluate the performance of the proposed algorithm, several experiments are conducted. Simulation results indicate that the proposed algorithm have effective performance in terms of coverage enhancement and sleeping sensors compared to the existing algorithms.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.339-341
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• 1998

다층 퍼셉트론 학습은 학습 데이터의 능동적인 선택 여부에 따라 능동적 학습(Active learning)과 피동적 학습(Passive learning)으로 구분할 수 있다. 기존의 능동적 학습 방법들은 학습 데이터의 중요도를 측정할 수 있는 기준(measure)을 제시하고 이 기준에 따라 학습 데이터를 선택하는 방법을 취하고 있다. 이 방법들은 학습 데이터 선택을 위해 Hessian Approximation과 같은 복잡한 계산이나 학습 데이터를 선택하는 과정에 있어서 데이터의 중요도를 평가하기 위한 반복적인 계산을 필요로 한다. 본 논문에서는 학습 데이터 선택 시 반복적인 계산이 필요하지 않는 비교사 학습을 이용한 능동적 학습 데이터 선택 방법을 제안하고 그 수렴 특성과 일반화 성능을 분석한다. 또한 비교 실험을 통하여 제안된 방법이 기존의 능동적 학습방법보다 간단한 계산만으로 수렴 속도를 향상시키며 일반화에도 뒤떨어지지 않음을 보인다.

• The Journal of Korean Academic Society of Nursing Education
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• v.9 no.2
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• pp.310-319
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• 2003

The purpose of this study was to determine what effect reflection on clinical practice experience had students as learners and care providers. Qualitative research method was used to study a group of four-year undergraduate nursing course. Content analysis was done using the classification method of Carper's four patterns of knowing. Results of the study indicated that the use of the reflective process of clinical debriefing and journaling was impact on the aspect of nursing science, moral component of knowledge in nursing, personal knowing in nursing and the art of nursing. Especially, students moved from a passive to a more active mode of learning. The most significant finding was that over time, reflective processes resulted in the emergence of the client as the central focus of care. It was suggested that reflection was an important learning tool in professional education and that the skills required for reflection need to be developed in professional courses.

• KSCI Review
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• v.14 no.1
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• pp.221-228
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• 2006

In this paper we propose an active learning method that makes a database for the information about certificates and practical examinations and accesses it easily. First of all, this method makes it possible to evaluate students individually, improves the motive of learning and gives students a sense of achievement by providing a user-specific question filtering technique using user pronto information by weight. And, it elevates the acquisition rate of certificates by advising and managing for certificate-acquisition and it also draw more interest and understanding for future directions.

• Structural Engineering and Mechanics
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• v.75 no.2
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• pp.175-191
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• 2020

In this study, multi-story structures are actively controlled using metaheuristic algorithms. The soil conditions such as dense, normal and soft soil are considered under near-fault ground motions consisting of two types of impulsive motions called directivity effect (fault normal component) and the flint step (fault parallel component). In the active tendon-controlled structure, Proportional-Integral-Derivative (PID) type controller optimized by the proposed algorithms was used to achieve a control signal and to produce a corresponding control force. As the novelty of the study, the parameters of PID controller were determined by different metaheuristic algorithms to find the best one for seismic structures. These algorithms are flower pollination algorithm (FPA), teaching learning based optimization (TLBO) and Jaya Algorithm (JA). Furthermore, since the influence of time delay on the structural responses is an important issue for active control systems, it should be considered in the optimization process and time domain analyses. The proposed method was applied for a 15-story structural model and the feasible results were found by limiting the maximum control force for the near-fault records defined in FEMA P-695. Finally, it was determined that the active control using metaheuristic algorithms optimally reduced the structural responses and can be applied for the buildings with the soil-structure interaction (SSI).