• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.600-602
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• 1995

The paper presents neural network control techniques for load frequency control of two area power system. Using learning algorithm of error back propagation after learning accept input on the optimal control $e_{i}$, $\dot{e}_{i}$, and $u_{i}$ frequency characteristic and tie-line load flow characteristic investigated dynamic. From result simulation, frequency deviation and tie-line load flow deviation have reduction remarkable.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.140-140
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• 2019

본 연구에서는 Terra MODIS 위성자료와 Tensorflow를 활용해 1 km 공간 해상도의 토양수분을 산정하는 알고리즘을 개발하고, 국내 관측 자료를 활용해 검증하고자 한다. 토양수분 모의를 위한 입력 자료는 Terra MODIS NDVI(Normalized Difference Vegetation Index)와 LST(Land Surface Temperature), GPM(Global Precipitation Measurement) 강우 자료를 구축하고, 농촌진흥청에서 제공하는 1:25,000 정밀토양도를 기반으로 모의하였다. 여기서, LST와 GPM의 자료는 기상청의 종관기상관측지점의 LST, 강우 자료와 조건부합성(Conditional Merging, CM) 기법을 적용해 결측치를 보간하였고, 모든 위성 자료의 공간해상도를 1 km로 resampling하여 활용하였다. 토양수분 산정 기술은 인공 신경망(Artificial Neural Network) 모형의 딥 러닝(Deep Learning)을 적용, 기계 학습기반의 패턴학습을 사용하였다. 패턴학습에는 Python 라이브러리인 TensorFlow를 사용하였고 학습 자료로는 농촌진흥청 농업기상정보서비스에서 101개 지점의 토양수분 자료(2014 ~ 2016년)를 활용하고, 모의 결과는 2017 ~ 2018년까지의 자료로 검증하고자 한다.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1149-1159
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• 2019

We present a novel deep learning architecture for obtaining a latent image from a single blurry image, which contains dynamic motion blurs through object/camera movements. The proposed architecture consists of two sub-modules: blur image restoration and optical flow estimation. The tasks are highly related in that object/camera movements make cause blurry artifacts, whereas they are estimated through optical flow. The ablation study demonstrates that training multi-task architecture simultaneously improves both tasks compared to handling them separately. Objective and subjective evaluations show that our method outperforms the state-of-the-arts deep learning based techniques.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.675-678
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• 2023

본 연구는 팀기반학습을 활용한 교과목을 수강한 간호대학생 56명을 대상으로 동료평가와 학업성취도와의 관계에서 수업몰입의 매개효과를 검정하였다. 매개효과 분석은 PROCESS macro Program의 model 4를 부트스트래핑(bootstrapping)을 이용하여 검정하였다. 분석 결과는 첫째, 동료평가, 수업몰입과 학업성취도 간에 유의한 정적 상관이 있는 것으로 나타났다. 둘째, 수업몰입은 동료평가가 학업성취도에 미치는 영향을 부분매개 하는 것으로 확인되었다. 동료평가가 간호대학생을 위한 TBL 과정에서 수업몰입과 팀워크를 촉진하고 궁극적으로 학업성취도를 향상시키는 효과적인 도구가 될 수 있음을 시사하며, 교수자가 학생의 학습성과를 개선할 수 있는 보다 효과적인 교육 전략 및 개입을 개발하는 데 도움이 될 수 있다.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.205-211
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• 2024

The increasing number of botnet attacks incorporating new evasion techniques making it infeasible to completely secure complex computer network system. The botnet infections are likely to be happen, the timely detection and response to these infections helps to stop attackers before any damage is done. The current practice in traditional IP networks require manual intervention to response to any detected malicious infection. This manual response process is more probable to delay and increase the risk of damage. To automate this manual process, this paper proposes to automatically select relevant countermeasures for detected botnet infection. The propose approach uses the concept of flow trace to detect botnet behavior patterns from current and historical network activity. The approach uses the multiclass machine learning based approach to detect and classify the botnet activity into IRC, HTTP, and P2P botnet. This classification helps to calculate the risk score of the detected botnet infection. The relevant countermeasures selected from available pool based on risk score of detected infection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.80-87
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• 2021

This study examined the influencing factors of learning satisfaction for real-time online adult nursing classes to provide basic data for the design and operation of online lectures in nursing subjects and help prepare a new educational paradigm. The subjects of the study were 105 3rd graders taking real-time online adult nursing classes, and data collection was conducted through structured online questionnaires from June 20 to July 30, 2020. The subjects were analyzed using a t-test, one-way ANOVA by Scheffe test, Person's correlation coefficients, and Hierarchical multiple regression analysis. The results showed that the learning flow was 3.07, academic engagement was 3.46, and learning satisfaction was 3.88. Learning satisfaction showed a positive correlation with learning flow (r=.41, p<.001) and academic engagement (r=.56, p<.001). In addition, the factors influencing the learning satisfaction of the subjects of this study were academic engagement (��=.47), very high in the level of interest in adult nursing classes (��=.21), and high (��=.20) followed by 34% (F=14.53, p<.001). Therefore, the learning outcomes of nursing students and the effective achievement of their learning goals are expected by developing plans and teaching methods for active participation in classes.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.487-494
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• 2023

In this study, performance comparison was performed on R and TensorFlow, which are free deep learning tools. In the experiment, six types of deep neural networks were built using each tool, and the neural networks were trained using the 10-year Korean temperature dataset. The number of nodes in the input layer of the constructed neural network was set to 10, the number of output layers was set to 5, and the hidden layer was set to 5, 10, and 20 to conduct experiments. The dataset includes 3600 temperature data collected from Gangnam-gu, Seoul from March 1, 2013 to March 29, 2023. For performance comparison, the future temperature was predicted for 5 days using the trained neural network, and the root mean square error (RMSE) value was measured using the predicted value and the actual value. Experiment results shows that when there was one hidden layer, the learning error of R was 0.04731176, and TensorFlow was measured at 0.06677193, and when there were two hidden layers, R was measured at 0.04782134 and TensorFlow was measured at 0.05799060. Overall, R was measured to have better performance. We tried to solve the difficulties in tool selection by providing quantitative performance information on the two tools to users who are new to machine learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4291-4310
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• 2015

This paper investigates the traffic offloading over unlicensed bands for two-tier multi-mode small cell networks. We formulate this problem as a Stackelberg game and apply a hierarchical learning framework to jointly maximize the utilities of both macro base station (MBS) and small base stations (SBSs). During the learning process, the MBS behaves as a leader and the SBSs are followers. A pricing mechanism is adopt by MBS and the price information is broadcasted to all SBSs by MBS firstly, then each SBS competes with other SBSs and takes its best response strategies to appropriately allocate the traffic load in licensed and unlicensed band in the sequel, taking the traffic flow payment charged by MBS into consideration. Then, we present a hierarchical Q-learning algorithm (HQL) to discover the Stackelberg equilibrium. Additionally, if some extra information can be obtained via feedback, we propose an improved hierarchical Q-learning algorithm (IHQL) to speed up the SBSs' learning process. Last but not the least, the convergence performance of the proposed two algorithms is analyzed. Numerical experiments are presented to validate the proposed schemes and show the effectiveness.

• Journal of Korean society of Dental Hygiene
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• v.15 no.6
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• pp.1091-1097
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• 2015

Objectives: The purpose of this study was to examine the relationship between educational satisfaction and learning participation of dental hygiene students. Method: A self-reported questionnaire was completed by 344 dental hygiene students in Gyeonggido, Chungcheongdo, and Gyeongsangdo from June 2 to 24, 2014, The questionnaire consisted of general characteristics of the subjects(3 items), choice reason of dental hygiene(7 items), educational satisfaction(22 items), and learning participation(11 items). The educational satisfaction and learning participation were assessed by Likert 5 points scale. Data were analyzed by a statistical package SPSS WIN 18.0. Results: Educational satisfaction included educational environments, teaching, educational content and educational effect. Learning participation included class flow, class participation and class readiness. Gyeongsangdo students tended to have higher score than other areas. The educational effect and teaching effect had more influence on learning participation. Conclusion: To improve the better dental hygiene education, it is important to prepare the effective educational methods and find out the influencing factors for class immersion.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.207-218
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• 2019

The purpose of this study is to develop a model that can systematically study the whole learning process of machine learning. Since the existing model describes the learning process with minimum coding, it can learn the progress of machine learning sequentially through the new model, and can visualize each process using the tensor flow. The new model used all of the existing model algorithms and confirmed the importance of the variables that affect the target variable, survival. The used to classification training data into training and verification, and to evaluate the performance of the model with test data. As a result of the final analysis, the ensemble techniques is the all tutorial model showed high performance, and the maximum performance of the model was improved by maximum 5.2% when compared with the existing model using. In future research, it is necessary to construct an environment in which machine learning can be learned regardless of the data preprocessing method and OS that can learn a model that is better than the existing performance.