• 대한간호학회지
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• 제53권1호
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• pp.87-100
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• 2023

Purpose: This study aimed to develop a nursing simulation learning module for coronavirus disease 2019 (COVID-19) patient-care and examine its effects on clinical reasoning competence, clinical competence, performance confidence, and anxiety in COVID-19 patient care for nursing students. Methods: A non-equivalent control group pre- and post-test design was employed. The study participants included 47 nursing students (23 in the experimental group and 24 in the control group) from G City. A simulation learning module for COVID-19 patient-care was developed based on the Jeffries simulation model. The module consisted of a briefing, simulation practice, and debriefing. The effects of the simulation module were measured using clinical reasoning competence, clinical competence, performance confidence, and anxiety in COVID-19 patient-care. Data were analyzed using χ²-test, Fisher's exact test, t-test, Wilcoxon signed-rank test, and Mann-Whitney U test. Results: The levels of clinical reasoning competence, clinical competence, and performance confidence of the experimental group were significantly higher than that of the control group, and the level of anxiety was significantly low after simulation learning. Conclusion: The nursing simulation learning module for COVID-19 patient-care is more effective than the traditional method in terms of improving students' clinical reasoning competence, clinical competence, and performance confidence, and reducing their anxiety. The module is expected to be useful for educational and clinical environments as an effective teaching and learning strategy to empower nursing competency and contribute to nursing education and clinical changes.

• Journal of Korean Biological Nursing Science
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• 제3권2호
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• pp.63-68
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• 2001

Increasing interest in computer-mediated learning technologies has prompted educators to incorporate them into many learning environments ; however, there is still little evaluative evidence to support their effectiveness. This report describes the development and evaluation of a web-based instructional program on basic nursing science for nursing students. Researcher-designed questionnaires were used to assess the characteristics of our students, and to solicit their ratings of the instructional program on ease of use, accuracy of content, clarity of content, interest, and convenience of the program, using 5-point Likert scales. The respondents indicated that the package was easy and convenient to use, with high technical quality, and of a level challenging to some but not all of the students. On-line quizzes were most highly rated. Also it was confirmed that frequent users of electronic bulletin board showed much higher achievement score than that of nonfrequent users. It was also found that the effect of cyber education was dependent on the active participation of the students. These data suggest that the use of web-based instructional program as a distance education strategy can be an effective method for nursing students and nurses.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• 교육시설 논문지
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• 제19권1호
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• pp.25-35
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• 2012

The function and the role of school are changing according to various future-oriented education models such as open-ended learning cycle, community learning center, re-schooling, network system and de-schooling, which reflect recent social demands related to discussions on sustainability, low birth rate and ageing. what changes to our school buildings have been there? This study is aimed that shows the architectural change of school buildings and environments in 2000's. It reveals the architectural tendancy through some school buildings, 'the excellent facility school award' winner which Ministry of Education, Science and Technology have chosen annually since 1998. In the past decade, it is outstanding change that mass composition, window patterns and exterior wall materials are diversified greatly. The most of them have a opened main hall and multi-purpose spaces which give effective educational environment to students. Although visual changes are outstanding, we still need qualitative educational space program and improved school building design according to future-educational demands.

• 한국지구과학회지
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• 제24권7호
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• pp.625-634
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• 2003

본 연구에서는 면대면(FTF) 환경과 컴퓨터 매개 의사소통(CMC) 환경 사이의 차이를 과학 학습 성취도와 만족도 관점에서 비교 분석하였다. 또한 이러한 분석은 교수자-학습자(TS)와 학습자-학습자(SS) 상호작용 유형들 간의 차이에 대하여도 함께 실시되었다. 자료수집을 위하여 충북 충주 소재의 C여자 고등학교 1학년 여학생 3개 반 학생을 대상으로 2002년 5월 정규수업과 병행하여 3주 동안 학습 활동을 한 후에 학업 성취도와 만족도 검사를 다음의 네 집단에 대하여 실시하였다; CMC-TS, CMC-SS, FTF-TS, FTF-SS. 각각 5문항과 13문항으로 구성된 학업성취도와 만족도 측정을 위한 검사지는 통계 분석되었다. 각 집단간 학업 성취도의 평균 점수는 CMC환경을 이용한 집단(CMC)과 교수자-학습자 상호작용을 한 집단(TS)에서 높았다. 변량분석에 의하면, CMC환경에서의 학업 성취도 및 만족도는 학습자-학습자 상호작용(CMC-SS)에 비하여 교수자-학습자 상호작용을 강조한 학습활동(CMC-TS)에서 유의미하게 높았다. 한편면대면 (FTF) 환경에서도 TS 활동 (FTF-TS)이 SS 활동 (FTF-SS) 에 비하여 높은 만족도를 나타내었다. 또한 만족도에 대한 문항별 분석의 경우에 환경에서는 CMC환경이, 그리고 상호작용에서는 TS 활동이 효과적이었다. 따라서 컴퓨터 매개 의사소통 환경하에서 교수자-학습자 상호작용을 강조한 활동이 과학 교수 학습과정에서 학습자의 학업 성취도와 만족도를 증진시키는데, 상당히 유용하다는 것을 시사한다.

• Journal of information and communication convergence engineering
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• 제18권2호
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• pp.106-114
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• 2020

We propose appropriate criteria for obtaining fish species data and number of learning data, as well as for selecting the most appropriate convolutional neural network (CNN) to efficiently classify exotic invasive fish species for their extermination. The acquisition of large amounts of fish species data for CNN learning is subject to several constraints. To solve these problems, we acquired a large number of fish images for various fish species in a laboratory environment, rather than a natural environment. We then converted the obtained fish images into fish images acquired in different natural environments through simple image synthesis to obtain the image data of the fish species. We used the images of largemouth bass and bluegill captured at a pond as test data to confirm the effectiveness of the proposed method. In addition, to classify the exotic invasive fish species accurately, we evaluated the trained CNNs in terms of classification performance, processing time, and the number of data; consequently, we proposed a method to select the most effective CNN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4177-4190
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• 2015

Current research on Internet of Things (IoT) has primarily addressed the means to enhancing smart resource allocation, automatic network operation, and secure service provisioning. In particular, providing satisfactory security service in IoT systems is indispensable to its mission critical applications. However, limited resources prevent full security coverage at all times. Therefore, these limited resources must be deployed intelligently by considering differences in priorities of targets that require security coverage. In this study, we have developed a new application of Cognitive Radio (CR) technology for IoT systems and provide an appropriate security solution that will enable IoT to be more affordable and applicable than it is currently. To resolve the security-related resource allocation problem, game theory is a suitable and effective tool. Based on the Blotto game model, we propose a new strategic power allocation scheme to ensure secure CR communications. A simulation shows that our proposed scheme can effectively respond to current system conditions and perform more effectively than other existing schemes in dynamically changeable IoT environments.

• Steel and Composite Structures
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• 제51권6호
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• 통상정보연구
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• 제13권3호
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• pp.229-248
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• 2011

본 연구는 선행 연구와 실증연구를 병행하여 글로벌 제휴활동이 제휴혁신에 미치는 영향을 실증하였다. 연구 개념 사이에 영향을 검증하기 위하여 가설 6개를 설정하였고, 글로벌 제휴나 협력을 공시한 업체 중 기준에 적합한 업체 114곳에서 설문자료를 수집하여 빈도분석, 신뢰도분석, 요인분석, 회귀분석, 확인 요인분석, 구조방정식 모형분석, 회귀분석을 각각 하였다. 연구결과를 요약하면 다음과 갈다. 첫째, 기업역량과 파트너 역량은 제휴창조활동에 영향을 미치는 것으로 나타났다. 제휴창조활동은 제휴활동에 고유한 체계와 문화를 확립하는 것으로서 기업역량과 파트너 역량을 결합하고 습득하기 위한 것이다. 따라서 제휴창조활동은 참여 업체 사이에 의사결정체계, 기업문화 등이 상이하여 발생하는 갈등을 방지하고, 제휴로써 기존 활동을 변모시키기 위하여 상호 학습하고 파트너에게 접근하기 위한 경로를 개척하는 것으로 볼 수 있다. 둘째, 기업역량과 파트너 역량은 제휴학습에 영향을 미치는 것으로 나타났다. 제휴학습은 지식이나 역량을 통합하고 개발하여 조직과 가치사슬에 확산시키는 것으로서 기어역량과 파트너 역량을 교환하고 모방하여 제휴지식을 확장하는 것이 작용하였다고 볼 수 있다. 셋째, 제휴창조활동과 제휴학습 중 제휴학습이 제휴혁신에 영향을 미치는 것으로 나타났다. 제휴학습은 다양한 조직수준에서 발생하는 상호작용과 제휴지식을 확장하고 습득하여 가치사슬과 조직에 확산되는 과정이기 때문에 제휴혁신에 영향을 미친다고 볼 수 있다. 결국 제휴혁신은 기업역량과 파트너 역량을 바탕으로 제휴활동에 고유한 지식을 창출하거나 기술을 개발하는 제휴학습이 중요한 것으로 나타났다.