• The Journal of Korean Association of Computer Education
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• v.12 no.6
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• pp.1-9
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• 2009

Programming learning may help to enhance learners' complex problem solving abilities. However, it may cause excessive cognitive loads for learners. Therefore, selection of programming tools and design of teaching and learning strategies to minimize the learners' cognitive loads and to maximize the learning effects. A task centered Scratch programming learning program was developed to enhance problem solving abilities of middle school students. And then, we implemented the developed program in middle school programming classes and analysed the educational effects of the developed program. We found that the developed program was helpful in enhancing learners' problem solving abilities, especially in the element of 'troubleshooting', which explains ability of error detecting and correcting.

• Journal of the Korea Furniture Society
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• v.24 no.1
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• pp.42-50
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• 2013

The objective of this study is to explore basic type of character of humans and to suggest a design method of establishing a spatial construction environment for developing effective learning ability based on such type of character. As a range of research, spatial formative language was deduced and space design strategy for the children was suggested through an analysis of spatial requirements by exploring connectivity depending on features of 9 types of character through Enneagram. As a method of research, a process of suggesting a concrete method after defining an element of spatial construction and deducing a formative language for developing and strengthening effective learning ability for each type of character. As a result of research, the methods of children space design strategy for enhancing learning ability for leadership in a future specific fields were suggested through 9 different type of character with image of case study.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.633-640
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• 2001

In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.

• Journal of Fisheries and Marine Sciences Education
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• v.20 no.2
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• pp.236-248
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• 2008

This study has explored the elements of e-teaching portfolio for enhancing teaching expertise in higher education. This study is carried out through the literature review and expert's focus group interview. As the result of this study, seven elements of e-teaching portfolio for enhancing teaching expertise in higher education have been found. First, 'personal background' include curriculum vitae, course responsibility, and other educational activities. Second, 'teaching philosophy' include the principals on teaching and learning, statements of teaching philosophy. Third, 'learning environment' include the characteristics of students, the previous learning contents, and physical environment. Forth, 'course contents and methods' include teaching strategies and instructional materials, Fifth, 'instructional evaluation' includes the principals of evaluation and the examples of learning outcomes. Sixth, 'endeavor for improvement of instruction' include evidence of activity for teaching improvement and instruction feedback from peer and students. And e-teaching portfolio also includes research career and awards history element.

• Steel and Composite Structures
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• v.22 no.4
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• pp.733-744
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• 2016

In this study, optimum structural designs of braced (non-swaying) planar steel frames are investigated by using one of the recent meta-heuristic search techniques, teaching-learning based optimization. Optimum design problems are performed according to American Institute of Steel Construction- Allowable Stress Design (AISC-ASD) specifications. A computer program is developed in MATLAB interacting with SAP2000 OAPI (Open Application Programming Interface) to conduct optimization procedures. Optimum cross sections are selected from a specified list of 128W profiles taken from AISC. Two different braced planar frames taken from literature are carried out for stress, geometric size, displacement and inter-storey drift constraints. It is concluded that teaching-learning based optimization presents robust and applicable optimum solutions in multi-element structural problems.

• Electronics and Telecommunications Trends
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• v.37 no.5
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• pp.44-53
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• 2022

The Internet of Things (IoT) is growing exponentially, with the number of IoT devices multiplying annually. Accordingly, the paradigm is changing from cloud computing to edge computing and even tiny edge computing because of the low latency and cost reduction. Machine learning is also shifting its role from the cloud to edge or tiny edge according to the paradigm shift. However, the fragmented and resource-constrained features of IoT devices have limited the development of artificial intelligence applications. Edge MLaaS (Machine Learning as-a-Service) has been studied to easily and quickly adopt machine learning to products and overcome the device limitations. This paper briefly summarizes what Edge MLaaS is and what element of research it requires.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• The Journal of Korean Association of Computer Education
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• v.14 no.6
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• pp.53-64
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• 2011

This paper proposes and evaluates empirically a new recommendation method in order to improve the learning achievement of learners using case-based method. In this paper, we first carried out a survey targeting teachers who work currently in Gyeongbuk area, and constructed learning cases depending on critical factors of learning. We next recommended differentiated learning methods to learners classifying according to learning cases by achievement level through this survey. The students of a middle school took part in the experiment in order to evaluate empirically the proposed learning cases. The students were divided into three groups by their achievement level and three separate learning cases were applied to each group. The weights among learning improvement elements applying to each group were added through the survey result of teachers. The experiment using the proposed case-based recommendation method showed that the learning achievement of learners is improved considerably compared to the previous one.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1994.10a
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• pp.133-140
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• 1994

In this paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation in neural networks. the architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of neural network, an optimal billet is determined by applying nonlinear mathematical relationship between shape ratio in the initial billet and the final products. A volume of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet shape ratio and that of the un-filled volume. After learning, the system is able to predict the filling region which are exactly the same or slightly different to results of finite element method. It is found that the prediction of the filling shape ratio region can be made successfully and the finite element method results are represented better by the neural network.

• Composites Research
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• v.35 no.5
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• pp.317-321
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• 2022

In this study, the PIC design method with machine learning that automatically assigning different stacking sequences according to loading types was applied bumper beam. The input value and labels of the training data for applying machine learning were defined as coordinates and loading types of reference elements that are part of the total elements, respectively. In order to compare the 2D and 3D implementation method, which are methods of representing coordinate value, training data were generated, and machine learning models were trained with each method. The 2D implementation method is divided FE model into each face and generating learning data and training machine learning models accordingly. The 3D implementation method is training one machine learning model by generating training data from the entire finite element model. The hyperparameter were tuned to optimal values through the Bayesian algorithm, and the k-NN classification method showed the highest prediction rate and AUC-ROC among the tuned models. The 3D implementation method revealed higher performance than the 2D implementation method. The loading type data predicted through the machine learning model were mapped to the finite element model and comparatively verified through FE analysis. It was found that 3D implementation PIC bumper beam was superior to 2D implementation and uni-stacking sequence composite bumper.