• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.2
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• pp.163-168
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• 2015

The conventional simple-injection and unilateral-learning methods have the great disadvantages in special education outcomes for intellectual disabilities student. In this paper, we propose a digital micro-mirror system that learners themselves can manipulate the contents by using the various technology on user-interaction and augmented reality. Through the verification tests conducted by special education professionals, it is confirmed that the proposed commercial system can be used as a useful system in the special learning fields requiring high immersion.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1867-1870
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• 2002

In the present paper, we will focus on the characterization of the biological network behaviour, in terms of synchronization and desynchronization of the measured signals by Micro Electrode array. We evaluate a easy calculable estimator that implies de/synchronization property of the biological neural network.

• KDI Journal of Economic Policy
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• v.43 no.4
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• pp.1-21
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• 2021

This paper aims to investigate whether there is empirical evidence supporting the learning-to-export hypothesis, which has received little attention in the literature. By taking full advantage of plant-product level data from Korea during 1990-1998, we find some evidence for the learning-to-export effect, especially for the innovated product varieties with delayed exporters: their productivity, together with research and development and investment activity, was superior to their matched sample. On the other hand, this learning-to-export effect was not significantly pronounced for industries protected by import tariffs. Thus, our empirical findings suggest that it would be desirable to implement certain policy tools to promote the learning-to-export effect, whereas tariff protection is not justifiable for that purpose.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.169-181
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• 2023

This study was conducted to examine the remuneration curriculum of vocational ability development training teachers and instructors and to examine ways to apply micro credentials. To this end, the current status of the remuneration curriculum of vocational ability development training instructors and instructors at K University's Competency Education Development Institute, the characteristics of micro credentials, and the possibility of linking the remuneration curriculum to micro credentials are as follows. First, most of the recognition of digital certificates was positive for digital certificates such as digital credit, digital badge issuance, and recognition of the recognized qualification process of maintenance education when completing the training course. In addition, as a method of applying micro credentials to conservative education, various cases were proposed to benefit from conservative education, systematization and grading of the qualification process, and credit of the qualification process. Second, as an institutional supplement to enhance the utilization of conservative education using micro credentials, the need to expand NCS-based major conservative education, provide efficient learning contents and learning methods, and set minimum completion time. In addition, the most common response as a way to improve the understanding of teachers and instructors in vocational ability development training was the micro credential promotion plan. Third, in the role of conservative education institutions and vocational ability development training instructors and instructors, conservative education institutions mention maintaining educational quality the most, and active participation was the role of vocational ability development training instructors. Through this study, it is expected to establish a vocational training environment that can enhance expertise and provide a practical portfolio of practical competency history by linking the remuneration curriculum of vocational competency development training instructors and micro credentials.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.174-176
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• 2005

This treatise studies composing process that renew information mastered by interactive experience between environment and system via network among individuals. In the previous study map information regarding free space is learned by using of reinforced learning algorithm, which enable each individual to construct optimal action policy. Based on those action policy each individuals can obtain optimal path. Moreover decision process to distinguish best optimal path by comparing those in the network composed of each individuals is added. Also information about the finally chosen path is being updated. A self renewing method of each system information by sharing the each individual data via network is proposed Data enrichment by shilling the information of many maps not in the single map is tried Numerical simulation is conducted to confirm the propose concept. In order to prove its suitability experiment using micro-mouse by integrating and comparing the information between individuals is carried out in various types of map to reveal successful result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• IE interfaces
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• v.20 no.4
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• pp.448-457
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• 2007

The small & micro business has the characteristics of both consumer credit risk and business credit risk. In predicting the bankruptcy for small-micro businesses, the problem is that in most cases, the financial data for evaluating business credit risks of small & micro businesses are not available. To alleviate such problem, we propose a bankruptcy prediction mechanism using the credit card sales information available, because most small businesses are member store of some credit card issuers, which is the main purpose of this study. In order to perform this study, we derive some variables and analyze the relationship between good and bad signs. We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data for evaluating business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0 multivariate discriminant analysis (MDA), and logistic regression.

• Journal of East Asia Management
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• v.4 no.2
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.533-539
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• 2019

This study proposes a malware classification model that can handle arbitrary length input data using the Microsoft Malware Classification Challenge dataset. We are based on imaging existing data from malware. The proposed model generates a lot of images when malware data is large, and generates a small image of small data. The generated image is learned as time series data by Dynamic RNN. The output value of the RNN is classified into malware by using only the highest weighted output by applying the Attention technique, and learning the RNN output value by Residual CNN again. Experiments on the proposed model showed a Micro-average F1 score of 92% in the validation data set. Experimental results show that the performance of a model capable of learning and classifying arbitrary length data can be verified without special feature extraction and dimension reduction.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1309-1314
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• 2003

If an agent has a learning ability using previous knowledge, then it is expected that the agent can speed up learning by interacting with environment. In this paper, we present an improved reinforcement learning algorithm using domain knowledge which can be represented by problem-independent features and their classifiers. Here, neural networks are employed as knowledge classifiers. To show the validity of our proposed algorithm, computer simulations are illustrated, where navigation problem of a mobile robot and a micro aerial vehicle(MAV) are considered.