• Smart Structures and Systems
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• v.29 no.4
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• pp.589-598
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• 2022

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.848-855
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• 2016

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.49-60
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• 1996

The objective of this paper is to develop an adaptive learning method for fuzzy hypercubes using a neural network. An intelligent control system is proposed by exploiting only the merits of a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to upda1.e the fuzzy control ru1c:s on-line with the output errors. As a result, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

• Knowledge Management Research
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• v.21 no.2
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• pp.119-135
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• 2020

Owing to pandemic (COVID-19), the traditional face-to-face education method has been changed to the non-face-to-face real-time online education methods. Using a real time-based video conference system, synchronous education can be adopted by face-to-face class easily. Specially, it is very important to minimize the difference in learning effects between face-to-face and non-face-to-face in Entrepreneurship education. In this study, in order to derive the factors that affect the satisfaction of learners in synchronous online education, authors collected data from learners taking a synchronous entrepreneurship course. Through previous research, learned the reality of education and the composition of lessons. Spatiotemporal effectiveness, mentor ability, and educational environment influence learning satisfaction. PLS-SEM results revealed that it was confirmed that only spatiotemporal effects affect learner satisfaction. However, the education environment (fluent operation and convenience of function use of real-time based online conference system) effect teaching presence, class structure, and spatiotemporal effects. Through this research, we hope to provide theoretical and practical support for developing effective teacher activities, proper lesson structure, convenient function of the conference system, and learner-centered online learning environment when developing synchronous online classes.

• International Journal of Contents
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• v.13 no.4
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• pp.29-38
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• 2017

The objective of this study was to determine the status of current chapel courses and analyze the necessity of online chapel courses. Students' interest, failure experience, perceived problems, and advantages of current chapel courses were examined. Students' preference, intention of sincerity, and perceived effectiveness of online chapel courses were also determined. Finally, hypothesis tests for the differences of students' interest, failure experience, perceived problems and advantages of current chapel courses, preference, intention of sincerity, and perceived effectiveness of online chapel courses according to gender, school year grade, major of study, and religion were performed. Students' low interest in chapel courses was verified. Even Christian students' interest was below 3 points out of 5-point Likert scale. However, students whose religion was not Christianity felt more coercion and had less interest in chapel courses. They wanted virtualization of chapel courses more. They had more willingness to faithful participation in online chapel courses. This research suggests that virtualization of chapel courses as a solution to chapel resistance is dependent on student's characteristics such as religion, major field of study, and mindset.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• Journal of Home Health Care Nursing
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• v.29 no.3
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• pp.329-338
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• 2022

Purpose: This study aimed to develop an infection control e-learning training program for long-term care facility care workers to prevent emerging infectious diseases and evaluate its effectiveness. Method: The program was developed using the analysis design development implementation evaluation (ADDIE) model. The effectiveness of the program was evaluated for 30 care workers. The knowledge and performance of the care workers before and after the program were analyzed by a t-test. Results: In the analysis stages, a literature review on infection control, knowledge and performance of infection control, and education needs was performed, and focus group interviews with ten care workers were conducted. In the design stage, education topics, educational content, and educational methods were selected for the program. A video was produced centered on eight themes. In the development stage, a system for education was developed, and each topic was uploaded. In the implementation stage, the program was applied to 30 care workers, and a questionnaire was administered. In the program's final evaluation, there was a significant difference in infection control knowledge (t=3.06, p=.005), and there was no significant difference in infection control performance. Conclusion: In this study, the necessary topics were finally selected by quantitatively and qualitatively analyzing the educational needs of care workers taking care of the elderly in long-term care facilities. It is necessary to understand the long-term effect and the degree of performance of the observation method in the future.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.36-43
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• 1998

A fuzzy logic filter is constructed from a set of fuzzy IF-THEN rules which change adaptively to minimize some criterion function as new information becomes available. This paper generalizes the fuzzy logic filter and it's adaptive filtering algorithm to include complex parameters and complex signals. Using the complex Stone-Weierstrass theorem, we prove that linear combinations of the fuzzy basis functions are capable of uniformly approximating and complex continuous function on a compact set to arbitrary accuracy. Based on the fuzzy basis function representations, a complex orthogonal least-squares (COLS) learning algorithm is developed for designing fuzzy systems based on given input-output pairs. Also, we propose an adaptive algorithm based on LMS which adjust simultaneously filter parameters and the parameter of the membership function which characterize the fuzzy concepts in the IF-THEN rules. The modeling of a nonlinear communications channel based on a complex fuzzy is used to demonstrate the effectiveness of these algorithm.

• The Journal of Korean Association of Computer Education
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• v.20 no.1
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• pp.75-86
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• 2017

E-learning has become one of the popular educational method in these day. In recognition of the growing e-learning, numerous researchers of S&T have utilized for the training aimed at enhancing competency. In the circumstances, previous studies have yield interesting results regarding certain factors that competency based programs may increase effectiveness of education. Therefore this research described here contributes to design competency modeling and education roadmap for human resource in S&T. The study uses survey, FGI, delphi technique, and expert workshop for selecting the main competencies. In particular, the results are including training roadmap of 5 level in each of two groups as researchers and S&T managers. These findings can be possible to develop customized programs and supported long-term career development path plan for human resource in S&T.