• ETRI Journal
• /
• v.43 no.4
• /
• pp.694-701
• /
• 2021

Catastrophic events cause casualties, damage property, and lead to huge social impacts. To build common standards and facilitate international communications regarding disasters, the relevant authorities in social management rank them in subjectively imposed terms such as direct economic losses and loss of life. Terrorist attacks involving uncertain human factors, which are roughly graded based on the rule of property damage, are even more difficult to interpret and assess. In this paper, we collected 114 183 open-source records of terrorist attacks and used a machine learning method to grade them synthetically in an automatic and objective way. No subjective claims or personal preferences were involved in the grading, and each derived common factor contains the comprehensive and rich information of many variables. Our work presents a new automatic ranking approach and is suitable for a broad range of gradation problems. Furthermore, we can use this model to grade all such attacks globally and visualize them to provide new insights.

• Journal of Engineering Education Research
• /
• v.22 no.4
• /
• pp.43-49
• /
• 2019

A case of course-embedded assesment in electronic engineering was studied. In particular, a realistic evaluation system was developed in consideration of characteristics of the major field and university realities. 10 program outcomes were mapped with all courses in the program. 5 probe courses were selected and their course learning objectives were defined. Measurements of the course learning objective were made by term project and written course test. With using course-embedded assesment, the measurement system of the program outcomes should be changed.

• Earthquakes and Structures
• /
• v.16 no.6
• /
• pp.679-690
• /
• 2019

This paper presents machine learning methods using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) to analyze optimal damper distribution for structural vibration control. Regarding different building structures, a genetic algorithm based optimization method is used to determine optimal damper distributions that are further used as training samples. The structural features, the objective function, the number of dampers, etc. are used as input features, and the distribution of dampers is taken as an output result. In the case of a few number of damper distributions, multi-class prediction can be performed using SVM and MLP respectively. Moreover, MLP can be used for regression prediction in the case where the distribution scheme is uncountable. After suitable post-processing, good results can be obtained. Numerical results show that the proposed method can obtain the optimized damper distributions for different structures under different objective functions, which achieves better control effect than the traditional uniform distribution and greatly improves the optimization efficiency.

• Journal of Industrial Technology
• /
• v.24 no.A
• /
• pp.99-107
• /
• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and the environments as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to parition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum of the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.4
• /
• pp.157-162
• /
• 2010

The cart-pole balancing problem is a pseudo-standard benchmark problem from the field of control methods including genetic algorithms, artificial neural networks, and reinforcement learning. In this paper, we propose a novel approach by using online reinforcement learning(OREL) to solve this cart-pole balancing problem. The objective is to analyze the learning method of the OREL learning system in the cart-pole balancing problem. Through experiment, we can see that approximate faster the optimal value-function than Q-learning.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.5
• /
• pp.353-360
• /
• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and environment as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to partition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum on the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.16 no.2
• /
• pp.69-81
• /
• 2020

The objective of this study is to seek ways to maximize learning effects from e-learning by drawing improvement directions through investigating and analyzing an awareness of e-learning among e-learning attendees. The study was conducted among the attendees who are taking the e-learning program operated by K University and collected data from the students taking second semester in 2018 with the use of structured questionnaires. For data processing, SPSS Statistics 22.0 and AMOS were used, along with such analytical methods as frequency anslysis, descriptive statistical analysis, ANOVA (Analysis of Variance), t-analysis and cross tabulation. For significant data, it conducted an analysis by carrying out the Scheffe's test. According to the findings from this study, they showed a significant difference only in gender and curriculum desired to be opened in the question about e-learning participation motives per background factor. As for the learners' motives to study, it was confirmed that they tend to become more biased on time utilization and convenience of learning methods. The analysis of which factor of the three - learning factors, system factors and instructor's factors - has greatest effects on learning satisfaction indicated that learning factors influenced learning satisfaction the most in accordance with values for non-standard coefficient beta, followed by instructor factors which had a direct effect.

• Journal of Engineering Education Research
• /
• v.18 no.2
• /
• pp.77-88
• /
• 2015

Recently, there has been an increasing interest in 'Flipped Learning,' an IT-based learner-centered teaching-learning method corresponding to meet the paradigm of the future education. For smooth Flipped Learning, there are three steps in total: a pre-class should precede; then, in the structure of classes in the classroom, in-class learning among peer learners should be done; and lastly, the operation of a post-class should be done. For successful Flipped Learning, class elements in each step should be designed with a time difference, interconnected so as to achieve a single educational objective. However, it was found that there was a limitation in that the teaching-learning model of the preceding Flipped Learning consisted of the order of analysis, design, development, implementation and evaluation as general procedures, so it would not sufficiently consider the situations of Flipped Learning only. On this background, this thesis proposes a differentiated Flipped Learning model for mastery learning in a subject of an institute of technology as a model of systematic instructional design and presents a case of a class applied to an actual subject of computer engineering.

• Educational Technology International
• /
• v.13 no.1
• /
• pp.101-144
• /
• 2012

Students mostly precede their learning without any direct support of instructor in e-learning and mobile educational environment. Many useful strategies and tools to facilitate self-regulated learning behaviors in e-learning environment have been introduced, yet, the limit has been reached by only suggesting self-regulated strategies with visual information in the most researches. Accordingly, this research is intent to propose the sound design strategies that facilitate learner's self-regulated learning behaviors in mobile learning environment. To achieve the objective of the research, two research questions are presented. First, what are the sound design strategies that facilitate the self -regulated learning behaviors in mobile learning environment?. Second, what are the results of evaluating the developed sound design strategies in terms of facilitating self-regulated learning behaviors?. To solve these research questions, the literature reviews on characteristics of mobile learning, concepts and features of self-regulated learning and sound were done to establish the sound design strategies. Through formative research method targeting instructional designers, sound design strategies were modified and supplemented. The research to validate these was performed and to verify the effect of the derived sound design strategies, the usability test aimed at instructional designer and learners was conducted. The final sound design strategies through this research process were six general design strategies and the sixteen detailed strategies. This research is meaningful because this offers the basic research on sound information design which has been lacking and help upgrade the upper limit of instructional design which mainly focused on visual information in mobile learning environment that shows information in a small screen.

• Korean Institute of Interior Design Journal
• /
• v.23 no.4
• /
• pp.140-148
• /
• 2014

The objective of this study is to address Effective Learning Methodology by subjects to acquire National Certification which is an essential requirement to get employed. For this purpose, analysis was completed after conducting a survey on the class to obtain Interior Architecture Engineer Certification. 23 applicants were selected based on effectiveness of each subject, level of difficulty, degree of understanding, mentoring effect, self-driven learning, and group discussion. Results are as below. Upon the first written test, Interior Design Theory and Chromatics showed a high learning effectiveness in self-driven learning and mentoring. Ergonomics showed a high learning effectiveness in mentoring, self-driven learning and group discussion while Building Materials, Architecture Construction presented a high effectiveness only in mentoring and group discussion. Architecture Environment showed average learning effectiveness in mentoring and group discussion and showed a low effectiveness in self-driven learning. Upon the second practice test, Interior Architecture Construction and Planning/Management of Construction Materials presented an average learning effectiveness in mentoring and group discussion. Process Control and Adding Up(Supply Calculation) showed a low learning effectiveness in self-driven learning and presented an average to below average learning effectiveness in mentoring and group discussion. Lastly, Interior Design Plan, Interior Design Drawings presented average learning effectiveness in mentoring and group discussion however they showed a high effectiveness in self-driven learning.