• 한국실내디자인학회:학술대회논문집
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• 한국실내디자인학회 2005년도 춘계학술발표대회 논문집
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• pp.251-252
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• 2005

The present design, which is English Zone Development Project for 'G' Elementary School at Seo gu, Incheon, contained various booths for experiential learning corners as well as spaces of teaching learning through group study, dramas and role plays, breaking away from the structure and atmosphere of traditional language labs, and at the same time it include a school building as an affiliated space where the whole students can gather for discussion and learning. The general design concept adopted the atmosphere of an exotic street, installing five theme booths (airport, bank, hospital, book/game store and shop) along the wall and applying the image of road to the floor in order to perform role plays. The blackboard and furniture were also designed to produce the atmosphere of street so that elementary students take interest and actively participate in learning.

• Journal of information and communication convergence engineering
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• 제16권2호
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• pp.106-113
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• 2018

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.

• International Journal of Contents
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• 제15권3호
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• pp.39-46
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• 2019

This study aimed to develop a peer-assisted learning (PAL) program for use in a core nursing skills education course and to investigate the effects of the education program on three key outcomes. A comparative study was conducted through the division of students of a core nursing skills education program into a treatment group and control group. After the programs, self-efficacy, confidence and satisfaction were compared between the groups. The comparison of these three factors indicated that the related scores were significantly higher in the treatment group receiving the education program with the PAL method than those of the control group receiving traditional education. We concluded that self-efficacy gave the nursing students the confidence that they could successfully perform their tasks and motivation to learn. Education programs using the PAL method are suggested as an effective method for the acquisition of skills among nursing students.

• ETRI Journal
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• 제43권3호
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• pp.549-560
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• 2021

Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• 로봇학회논문지
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• 제16권2호
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• pp.130-136
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• 2021

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.159-168
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• 2022

No one denies the importance of online courses, which provide a very important alternative, especially for students who have jobs that prevent them from attending face-to-face in traditional classes; Engagement is one of the most important fundamental variables that indicate the course's success in achieving its objectives. Therefore, the current study aims to build a model using machine learning to predict student engagement in online courses. An online questionnaire was prepared and applied to the students of Jouf University in the Kingdom of Saudi Arabia, and data was obtained from the input variables in the questionnaire, which are: specialization, gender, academic year, skills, emotional aspects, participation, performance, and engagement in the online course as a dependent variable. Multiple regression was used to analyze the data using SPSS. Kegel was used to build the model as a machine learning technique. The results indicated that there is a positive correlation between the four variables (skills, emotional aspects, participation, and performance) and engagement in online courses. The model accuracy was very high 99.99%, This shows the model's ability to predict engagement in the light of the input variables.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.125-139
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• 2023

Facial expression recognition, a topical problem in the field of computer vision and pattern recognition, is a direct means of recognizing human emotions and behaviors. This paper first summarizes the datasets commonly used for expression recognition and their associated characteristics and presents traditional machine learning algorithms and their benefits and drawbacks from three key techniques of face expression; image pre-processing, feature extraction, and expression classification. Deep learning-oriented expression recognition methods and various algorithmic framework performances are also analyzed and compared. Finally, the current barriers to facial expression recognition and potential developments are highlighted.

• Journal of Information Processing Systems
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• 제19권2호
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• pp.232-239
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• 2023

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.

• Educational Technology International
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• 제12권1호
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• pp.21-46
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• 2011

International virtual teamwork offers new opportunities for the professional development of teachers. In this paper, we examine the initial experiences in an ongoing international virtual benchmarking project coordinated by the Finnish Online University of Applied Sciences. What challenges does an international context present for project construction and collaboration? Data from five countries, in the form of participant reflections and researchers' observations, were analysed according to four types of barriers: language, time, technical and mental barriers. Initial data indicates that trust is an essential starting point, as there is neither time nor possibilities to build mutual trust by traditional means. Organisational confidentiality issues, however, can complicate the situation. The project introduces 'collision' as a method of professional development, in which physical and organisational borders are crossed and the skills and competencies needed in global learning environments are acquired.