• International journal of advanced smart convergence
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• 제6권2호
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• pp.9-15
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• 2017

According to a recent survey, most elementary and secondary school students nationwide are stressed out by their academic records. Furthermore most of high school students in Korea have to study under the great duress. Some of them who can't overcome the academic stress finalize their life by suiciding. A study has found that it is one of the leading causes of stimulating the thought of committing suicide in Korean high school students. So it is necessary to reduce the high school student's suicide rate. Main content of this research is to implement a personalized music recommendation system. Music therapy can help the student deal with the stress, anxiety and depression problems. Proposed system works as a therapist. The music choice and duration of the music is adjusted based on the student's current emotion recognized automatically from EEG. If the happy emotion is not induced by the current music, the system would automatically switch to another one until he or she feel happy. Proposed system is personalized brain music treatment that is making a brain training application running on smart phone or pad. That overcomes the critical problems of time and space constraints of existing brain training program. By using this brain training program, student can manage the stress easily without the help of expert.

• 기본간호학회지
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• 제19권1호
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• pp.122-133
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• 2012

Purpose: The purpose of this study was to develop a web-based education program on control vancomycin-resistant enterococci (VRE) infections and to identify the effects of the program on knowledge and performance of nursing students. Methods: The web-based VRE infection control education program was developed by using the network-based instructional systems design model. The nursing students in the experimental group could access this web-based education program at any time, and as many times as they wanted, during the clinical training period. Effects were evaluated by assessing knowledge and performance of VRE infection control measures during the clinical training period. Results: The contents of the education program included diagnosis, transmission, and treatment of VRE, contact precautions, hand washing, personal protective equipment, environment management, and quizzes. The lecture portion was filmed in a virtual screen studio using flash animation, video, and sound effects, and it was uploaded on an internet site. The knowledge and performance scores of the experimental group after using the education program were significantly higher than those of the control group. Conclusion: The results suggest that the web-based VRE infection control education program is an effective educational method to enhance knowledge and performance of VRE infection control measures.

• 정보교육학회논문지
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• 제5권1호
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• pp.83-100
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• 2001

멀티미디어 기능을 갖춘 빠른 속도의 컴퓨터의 활용을 뜻하는 정보기술의 교육적 활용은 아직은 초기 단계에 있다고 할 수 있다. 또한 정보기술의 한 형태라고 할 수 있는 인터넷 활용의 경우에 있어서도 컴퓨터의 발전과 더불어 생긴 대화 채널이자 정보 검색의 도구인 인터넷은 교사들로 하여금 그 어느 때보다도 교수-학습 자료를 손쉽게 찾을 수 있게 하고 있으나 인터넷의 교육적 활용 역시 초기 단계라고 할 수 있다. 그리하여 본 연구에서는 이러한 교육정보화 및 교사정보화를 목적으로 교사들(수학교과)의 전반적인 인터넷활용 실태에 관한 조사를 바탕으로 학교교육에서의 인터넷 활용 증진 및 개선 방안에 관하여 알아보았다.

• 가정과삶의질연구
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• 제21권3호
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• pp.123-131
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• 2003

The potential of information and communication technologies has already had a profound impact on many ways of our living and society. To catch up with this trend, Korean government has demonstrated the vision for informatization called‘Cyber Korea 21'/'e-Korea’project and has fully supported the education for informatization which is one of the key factors to reduce the digital divide. But, In spite of developing a variety of training programs with different target groups and policy objectives, a digital divide remains in some cases even while Internet access and computer ownership are rising rapidly for almost all groups. For example, the noticeable divides still exists between men and women. To accelerate the process of women informatization, which we believe to be major contributors for the high quality of life for women, we need to promote full-time housewives to become aggressive information users and producers. Internet shopping, for example, might be a good starting point for motivating women to become active information users. In this paper, we present a model for explaining the factors affecting the degree of satisfaction of housewives from internet shopping. Based on data collected from a questionnaire survey from housewives in Seoul, we conclude that the perceived usefulness, ease of use and the playfulness significantly affect the level of satisfaction, but the playfulness does not directly affect the intention to revisit and purchase. In addition, we found out that the perceived usefulness is affected by efficiency, attitude and easy to access. We also provide other interesting statistical results and implications.

• 한국도서관정보학회지
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• 제32권3호
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• pp.307-330
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• 2001

본 연구에서는 kNN분류기를 이용한 범주화 방법에 대한 성능 실험을 하였다. kNN분류기와 같은 대부분의 예제기반 자동 분류기법은 학습문서집단의 자질을 축소하게 되는데 자질을 몇 퍼센트 축소함으로써 높은 성능을 얻을 수 있는지를 알아보고자 하였다. 또한, kNN분류기는 학습문서집단에서 검증문서와 가장 유사한 k개의 학습문서를 찾아야 하는데, 이때 가장 적합한 k값은 얼마인지를 실험을 통하여 검증하여 보고자 하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.790-809
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• 2019

The design of an efficient scaler to enhance the edge data is one of the most important issues in video signal applications, because the perceptual quality of the processed image is sensitively affected by the degradation of edge data. Various conventional scaling schemes have been proposed to enhance the edge data. In this paper, we propose an efficient scaling algorithm for this purpose. The proposed method is based on the discrete cosine transform-based interpolation filter (DCT-IF) because it outperforms other scaling algorithms in various configurations. The proposed DCT-IF incorporates weighting parameters that are optimized for training data. Simulation results show that the quality of the resized image produced by the proposed DCT-IF is much higher than that of those produced by the conventional schemes, although the proposed DCT-IF is more complex than other conventional scaling algorithms.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.127-135
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• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.