• Journal of The Korean Association For Science Education
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• v.32 no.3
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• pp.532-540
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• 2012

This research investigated the motivation and interests of a sample of predominately-underrepresented populations to better understand whether informal STEM learning experiences offer support for developing STEM identities. A valid and reliable three-section self-reporting survey was administered to 169 secondary students as the primary data source. Identity was used as a theoretical lens along with descriptive statistics to reveal students' perceived benefits of the informal STEM learning experience, a Mathematics, Engineering, Science Achievement (MESA) program in the western U.S., for improving their understanding of science, mathematics, and engineering concepts, increasing their interest in science, mathematics, and engineering careers, and increasing their belief of the importance of these STEM disciplines. In summary, the findings emerging, considered alongside current identity research, suggest that informal STEM learning experiences offer students from underrepresented STEM populations the space needed for successful STEM identity bids, either for future career pursuits or participation in a STEM literate populace as a non-STEM professional societal member.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1014-1028
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• 2017

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.11-18
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• 2010

In this paper, We suggest new strategies on non-interactive agents applied in a prey pursuit problem of multi agent research. The structure of the prey pursuit problem by grid space(Four agent & one prey). That is allied agents captured over one prey. That problem has long been known in interactive, non-interactive of multi agent research. We trying hard to find its own solution from non-interactive agent method on not in the same original environment(circular environment). We used ACS applied Direction vector to learning and decide on a direction. Exchange of information between agents have been previously presented (an interactive agent) out of the way information exchange ratio (non-interactive agents), applied the new method. Can also solve the problem was to find a solution. This is quite distinct from the other existing multi agent studies, that doesn't apply interactive agents but independent agent to find a solution.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.279-284
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• 2022

As a study of the blended learning method on design education through the blended learning method, I would like to propose that more advanced learner-led customized design education is possible. Understanding in face-to-face classes and advantages in non-face-to-face classes can be supplemented in an appropriate way in remote classes. Advanced artificial intelligence and big data technology can provide personalized and subdivided learning materials and effective learning methods tailored to learners' levels and interests based on quantified data in design classes. In this paper, it was proposed to maximize the efficiency of the class by applying a method that exceeds the limitations of time and space through the proposal of the A La Carte model (A La Carte). It is a remote class that can be heard anytime, anywhere, and it is also possible to bridge the educational quality and educational gap provided to students living in underprivileged areas. As the goal of fostering creative convergence-type future talents, it is changing with a rapid technological development speed. It is necessary to adapt to the change in learning methods in line with this. An analysis of the infographic virtual space design and construction process through the A La Carte model (A La Carte) proposal was presented. Rather than simply acquiring knowledge, it is expected that knowledge can be sorted, distinguished, learned, and easily reborn with its own knowledge.

• Structural Engineering and Mechanics
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• v.62 no.5
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• pp.537-550
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• 2017

Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.78.5-78
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• 2002

We propose an evolutionary reinforcement learning (RL) system with time-varying parameters that can deal with a dynamic environment. The proposed system has three characteristics: 1) It can deal easily with a dynamic environment by using time-varying parameters; 2) The division of state space is acquired evolutionarily by genetic algorithm (GA); 3) One does not have to design the rules constructing an agent in advance. So far many RL systems have been proposed. These systems adjust constant or non time-varying parameters; by those systems it is difficult to realize appropriate behavior in complex and dynamic environment. Hence, we propose the RL system whose parameters can vary temporally. T...

• The Journal of Sustainable Design and Educational Environment Research
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• v.17 no.3
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• pp.46-53
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• 2018

This study is to investigate the effect of the school dining space on the learning environment, such as study activity space, student self-government space, student communication and community building, as well as to suggest ways to utilize them. For this purpose, literature review and survey research were conducted. In the literature review, the understanding of the middle school curriculum, the study of the school facility space, the understanding of the school lunch, the communication and communication environment. The survey was conducted on 17 middle schools, 90 middle schools, and 65 non-cooking transportation middle schools in Jeollabuk - do to investigate the utilization status and standards of the Cafeteria dining space. As a result of this study, it is suggested that there will be a follow-up study on application of public design according to the usage for efficient utilization of school dining space.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.555-563
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• 2022

This study explored the educational effects of e-PBL by using metaverse tools to strengthen PBL interactions among foreign learners. The university's 3-hour, 15-week PBL subject was systematically reorganized to satisfy the needs of online groups of students. Metaverse technology was also used as a tool for interaction in the process of solving practical problems closely related to our social issues through e-PBL. e-PBL lectures are composed of foreign learners from various countries. More than half of the 43 participating students are from 11 different nations. Learners in an e-PBL class are able to partake in task-based learning activities through the use of the metaverse. This qualitative study identified the metaverse is an effective communication tool which transcends language and nationality. It is also a unique space where both verbal and non-verbal communication between team members are possible online. This study can demonstrate the positive effects of e-PBL teaching methods. By using the metaverse's various tools of interaction to improve communication among foreign learners whose Korean levels are not perfect, we can create a digital space which more closely resembles an offline, interpersonal learning experience.