• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.1
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• pp.59-67
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• 2021

Purpose: The purpose of this study is to examine the effectiveness of Problem-based Learning (PBL) in an interdisciplinary college class. This class was run under the theme of 'Nurse Social Content Creators' (NSCC) in the Korean Nurses Association (KNA)'s industry-university collaborative project designed to promote a positive image of nurses among the public. Methods: Study 1 examined changes in perception about nurses among the PBL participants before and after the program. A one-group pre-post test experimental design was applied, and the data were analyzed using a Wilcoxon signed-rank test. Study 2 identified differences of perceptions of nurses between people who had observed the PBL final presentation and people who had not. A post-test-only with nonequivalent group experimental design was used, and the data were analyzed using a Mann-Whitney U test. Results: Study 1 revealed a significant increase of positive perceptions towards nurses. Study 2 revealed a significant difference between the PBL presentation audience group and the control group. Students who had observed the PBL program showed more positive perceptions of nurses than students who had not. Conclusion: This research is an important study with high practicality in the area of media studies as well as in nursing. The PBL teaching method was proven to be effective in enhancing perceptions of nurses.

• Management & Information Systems Review
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• v.5
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• pp.249-285
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• 2000

This paper discusses management education by utilizing the cyber education learning system in a web-based. New learning system tools offer great promise for a new contents of management learning. The cyber education learning system a shift from face-to-face lecturing to interactive learning. The situation changes profoundly when information technology becomes develope and education paradigm is shift. By exploiting the digital media. educations, and students, managers can shift to a new, more effect cyber education learning system. The following shift from classic educations to cyber educations learning system: from instruction to construction, from teacher-centered to learner-centered, from school to lifelong, from one-size-fits-all to customized, from teacher as transmitter to teacher as facilitator. Cyber education learning system has an important role to play in management education. Web-based technology is regarded as a general solution to cyber education learning. This study discussed many factors of implementation in cyber education systems and provide utilizing the learning system at main, detail functions. Lastly, management implications of these cyber education utilize are discussed in more detail.

• Smart Media Journal
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• v.7 no.2
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• pp.34-39
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• 2018

There are many studies on red tide due to the continuous increase in damage to domestic fish and shell farms by the harmful red tide. However, there is insufficient domestic research of identifying harmful red tide algae that automatically recognizes red tide images. In this paper, we propose a red tide image classification method using deep learning based open source. To solve the problem of recognition of various images of red tide algae, the proposed method is implemented by using tensorflow framework and Google image classification model.

• Smart Media Journal
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• v.7 no.4
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• pp.79-89
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• 2018

In recent years, falls among elderly people have gained serious attention as a major cause of injuries. Falls often lead to fatal consequences due to lack of prompt response and rescue. Therefore, a more accurate fall detection system and an effective feature extraction technique are required to prevent and reduce the risk of such incidents. In this paper, we proposed an efficient feature extraction technique based on multiple sliding windows and validated it through a series of experiments using supervised learning algorithms. The experiments were conducted using the public datasets obtained from tri-axial accelerometers. The results depicted that extraction of the feature from adjacent sliding windows led to high accuracy in supervised machine learning-based fall detection. Also, the experiments conducted in this study suggested that the best accuracy can be achieved by keeping the window size as small as 2 seconds. With the kNN classifier and dataset from wearable sensors, the experiments achieved accuracy rates of 94%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.305-318
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• 2022

Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.93-102
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• 2018

This article aims to present an effective and systematic learning methodology of game curriculum which is oriented convergence education. In particular, I will present the 4PBL model reflecting the trend of the changing times from teacher-centered learning to learner-centered learning environment. The 4PBL model consists of Personal based Learning, Problem based Learning, Project based learning and Performance based Learning. In this article, I will explain the concepts and characteristics of PBLs at each stage by providing concrete examples of game education courses. Such an attempt may have a meaningful value in that it can suggest a learning environment in which knowledge can be structured subjectively in a changing educational paradigm.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.71-78
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• 2011

The purpose of the present study is to examine the causal relationship among self-regulated learning, social presence, learning flow and satisfaction in cyber education utilizing electronic media. For this study, 304 students at W cyber university in Korea completed surveys in the fall semester of 2010. The result of this study indicated that there was a meaningful effect of self-regulated learning on learning flow and satisfaction. In addition, we founded learning flow has an intermediating effect between self-regulated learning and satisfaction. Based on these results, this study propose strategies to raise satisfaction by improving students' leading role in their learning.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.259-264
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• 2019

In this paper, the prototyping process for developing syllable-initial consonant-based game 'Korean Guards' is described. Users may effectively learn Korean words using alphabetically sequential approaches, but the easiness of access bestowed on the smart environments and game algorithms could be fully utilized for the functional advantages for educational purposes. This functional game is developed on Android OS and the prototypical outcome is shown.

• Smart Media Journal
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• v.11 no.3
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• pp.74-89
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• 2022

Due to the unique recipes and processes of each company, not only differences among the results of dyeing textile materials exist but they are also difficult to predict. This study attempted to develop a color prediction model based on deep learning to optimize color realization in the dyeing process. For this purpose, deep learning-based models such as multilayer perceptron, CNN and LSTM models were selected. Three forecasting models were trained by collecting a total of 376 data sets. The three predictive models were compared and analyzed using the cross-validation method. The mean of the CMC (2:1) color difference for the prediction results of the LSTM model was found to be the best.

• Smart Media Journal
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• v.13 no.3
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• pp.45-52
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• 2024

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.