• Journal of Engineering Education Research
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• v.19 no.5
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• pp.65-71
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• 2016

The purpose of this paper is to research the effect of a reflection journal on self motivated-learners' study. The learners were divided into experimental and control groups, we carried out the pre- and post- surveys, and compared the groups' academic achievements. As a result, their persistent learning, self-efficacy, learning attitude somewhat improved and their academic achievement as well.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.239-247
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• 2013

This study is aimed to perform the qualitative evaluation based on the ARCS Model of educational mobile applications for smart phones. The evaluation has been performed targeting 60 students who attending the subject of informational education in 3 different universities in 2012 by allowing them to select the available educational mobile App installed in their smart phone. After, the level of persistent learning-intension from each student and the efficacy of ARCS motivational strategy was measured at learner's perspective. The structural equation model(SEM) was established and analyzed with PLS method to understand the relationship between the ARCS motivational strategy and the persistent learning-intension. The results of the study could be summarized as followings. First, the educational mobile App in various the motivational strategies showed different results that is the highest attention as well as the lowest satisfactory level. Second, the relevance in motivation strategies had the statistically significant effect in attention, confidence, and satisfaction. On the other hand, the other factors of attention, relevance, and confidence showed statistically significant effect in satisfaction. Finally, result demonstrate that the relevance is the critical factor inducing the significant effect in persistent learning-intension among the motivational strategies.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.67-78
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• 2020

Typical security solutions such as intrusion detection system are not suitable for detecting advanced persistent attack(APT), because they cannot draw the big picture from trivial events of security solutions. Researches on techniques for detecting multiple stage attacks by analyzing the correlations between security events or alerts are being actively conducted in academic field. However, these studies still use events from existing security system, and there is insufficient research on the structure of the entire security system suitable for advanced persistent attacks. In this paper, we propose an attack path and intention recognition system suitable for multiple stage attacks like advanced persistent attack detection. The proposed system defines the trace format and overall structure of the system that detects APT attacks based on the correlation and behavior analysis, and is designed with a structure of detection system using deep learning and big data technology, etc.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.296-311
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• 2021

Topological Data Analysis (TDA), a relatively new field of data analysis, has proved very useful in a variety of applications. The main persistence tool from TDA is persistent homology in which data structure is examined at many scales. Representations of persistent homology include persistence barcodes and persistence diagrams, both of which are not straightforward to reconcile with traditional machine learning algorithms as they are sets of intervals or multisets. The problem of faithfully representing barcodes and persistent diagrams has been pursued along two main avenues: kernel methods and vectorizations. One vectorization is the Betti sequence, or Betti curve, derived from the persistence barcode. While the Betti sequence has been used in classification problems in various applications, to our knowledge, the stability of the sequence has never before been discussed. In this paper we show that the Betti sequence is unstable under the 1-Wasserstein metric with regards to small perturbations in the barcode from which it is calculated. In addition, we propose a novel stabilized version of the Betti sequence based on the Gaussian smoothing seen in the Stable Persistence Bag of Words for persistent homology. We then introduce the normalized cumulative Betti sequence and provide numerical examples that support the main statement of the paper.

• Proceedings of the Optical Society of Korea Conference
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• 1991.06a
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• pp.29-34
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• 1991

An optoelectronic implementation of analog and non-volatile synaptic weights of neural networks is proposed by using the doping modulated amophous silicon multilayer. The persistent photoconductivity(PPC) of the multilayer induced by a short illumination is characterized in experiment and implemented to the non-volatile synaptic weights. An optoelectronic processor with the single layer perceptron algorithm is also proposed. Some learning equations of the processor and the results of simulation are presented.

• Journal of Gifted/Talented Education
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• v.26 no.3
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• pp.541-559
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• 2016

This study investigated a relationship among facilitating discourse, students' perceived challenge, and learning outcomes(persistent intention and learning achievement) in an online science gifted education program. Two hundreds and forty-two middle school students participated in the study. A survey questionnaire which was consisted of 6 items of facilitating discourse from teaching presence questionnaire(Shea, Swan, & Pickett, 2005) and 5 items of challenge from Student Perceptions of Classroom Quality(Gentry & Owen, 2004) was administered. First, the findings of this study showed that students' perceived facilitating discourse as a part of teaching presence was positively related to students' perceived challenge in an online course. Second, students' perceived facilitating discourse were positively related to persistent intention, but were negatively related to students' achievement. Third, students' perceived challenge was positively related to persistent intention and achievement. Finally, challenge mediated the relationship between students' perceived facilitating discourse and persistent intention, and the relationship between students' perceived facilitating discourse and students' achievement as well. This results suggested that online program should be designed to increase the levels of facilitating discourse.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.449-451
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• 2021

Cyber-attacks targeting endpoints have developed sophisticatedly into targeted and intelligent attacks, Advanced Persistent Threat (APT) targeting the Industrial Internet of Things (IIoT) has increased accordingly. Machine learning-based Endpoint Detection and Response (EDR) solutions combine and complement rule-based conventional security tools to effectively defend against APT attacks are gaining attention. However, universal EDR solutions have a high false positive rate, and needs high-level analysts to monitor and analyze a tremendous amount of alerts. Therefore, the process of optimizing machine learning-based EDR solutions that consider the characteristics and vulnerabilities of IIoT environment is essential. In this study, we analyze the flow and impact of IIoT targeted APT cases and compare the method of machine learning-based APT detection EDR solutions.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.421-424
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• 1990

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic systems is presented. In the learning control structure, tracking and feedforward input converge globally and asymptotically as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of length of trajectories, it may be achieved with only system trajectories of small duration. In addition, these learning control schemes are expected to be effectively applicable to time-varying parametric systems as well as time-invariant systems, for the parameter estimation is performed at each fixed time along the iteration. Finally, no usage of acceleration signal and no in version of estimated inertia matrix in the parameter estimator makes these learning control schemes more feasible.

• Journal of The Korean Association For Science Education
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• v.24 no.1
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• pp.121-128
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• 2004

This study was to investigate high students' conceptions of acids and bases, and their views on learning science. Multiple sources of data were collected over six months with a participation of sit tenth graders and their science teacher. The transcripts of interviews and other data were examined with an eye toward students' conceptions of acids and bases, and their views of learning science. Students' views of science are displayed the representative pattern. Each pattern is represented with an episode. Students' views of learning have been found to reflect the transmissive models of science educational practice. Students accept passive and difficult-to-modify views of the learner roles that they should play in the science classroom. Students identified science classes as conservative places, despite the introduction of science literacy as a goal of Korean science education since 1980. Behaviorism remains the major influence in their expectation, design, and practice in school science. Moreover, 'transmission' remains the persistent and dominant classroom cultural dynamic for both teaching and learning of science.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.427-438
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• 1991

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic system is preented. In the learning control structure, the control input converges globally and asymtotically to the desired input as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of the time-duration of trajectories, it may be achieved with system trajectories with small duration. In addition, the proposd learning control schemes are applicable to time-varying parametric systems as well as time-invariant systems, because the parameter estimation is performed at each fixed time along the iteration. In the parameter estimator, the acceleration information as well as the inversion of estimated inertia matrix are not used at all, which makes the proposed learning control schemes more feasible.