• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.461-470
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• 2005

In leaner-controlled environment where learners can decide and restructure the contents, methods and order of learning by themselves, it is possible to apply individualized learning in consideration of each learner's characteristics. The present study analyzed learners' learning path pattern, which is one of learners' characteristics important in Web-based teaching-learning process, using the Apriori algorithm and grouped learners according to their learning path pattern. Based on the result, we designed and implemented a multi-dimensional learning path pattern analysis system to provide individual learners with teaming paths, learning contents, learning media, supplementary teaming contents, the pattern of material presentation, etc. multi-dimensionally. According to the result of surveying satisfaction with the developed system satisfaction with supplementary learning contents was highest (Highly satisfied '$24.5\%$, Satisfied'$35.7\%$). By learners' level, satisfaction was higher in low-level learners (Highly satisfied'$20.2\%$, Satisfied'$31.2\%$) than in high-level learners (Highly satisfied'$18.4\%$, 'Satisfied'$28.54\%$). The developed system is expected to provide learners with multi-dimensionally meaningful information from various angles using OLAP technologies such as drill-up and drill-down.

• The KIPS Transactions:PartA
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• v.11A no.2
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• pp.213-222
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• 2004

Today's Web-based teaching-learning is developing in the direction that learners select and organize the contents, time and order of learning by themselves. That is, it is evolving to provide teaching-learning environment adaptive to individual learners' characteristics(their level of knowledge, pattern of study. areas of interest). This study analyzed learners' learning paths among the variables of learners' characteristics considered important in Web-based teaching- learning process using the Apriori algorithm and grouped learners who had similar learning paths. Based on the result, the author designed and developed a learning-path individualization system In order to provide learners with learning paths, Interface, the progress of learning etc. The proposed system is expected to provide optimal learning environment fit for learners' pattern of study and to be enhancing individual learner's learning effects

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.463-471
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• 2010

The purpose of this study is to articulate learning model based on achievement level in blended learning environment. In order to investigate the variables and mechanisms in the blended learning environment, we started by attempt to develop two questionnaires using the components of web-based instruction and self-regulated learning. And its results were implemented to represent the topology and directed merging path within components. 154 students at a high school were required to take each web course respectively for two weeks. And questionnaires data, achievement levels data were collected and analyzed. Various statistical analysis methods such as correlation analysis, classical multidimensional scaling, multiple regression analysis, were applied to the data. As an result, the topology and directed path within factors of blended learning process were derived and revised as a final model.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.144-146
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• 2004

It is known that Global Positioning System(GPS) is the most efficient navigation system because it provides precise position information on the all areas of Earth regardless of metrology. Until now, the size of GPS receivers has become smaller and the performance of receivers has become higher. So receivers provide the position information of not only static system but also dynamic system. Usually, users make similar movement trajectory according to their life pattern and it is possible to build up efficient database by collecting only the repeated users' position. Because position information calculated by the receiver is erroneous about 10-30m within 5% error tolerance, the position information is oscillated even on the same area. In this paper, we propose the system that can estimate whether users are out of trajectory or in dangerous situation by soft-computing method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.195-201
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• 2022

For a efficient distance education environment, the need for correct problem recommendation guides considering the learner's exact learning pattern is increasing. In this paper, we study block chain based smart contract technology to suggest a method for presenting the optimal problem recommendation path for individual learners based on the data given by situational weights to the problem patterns of learners collected in the distance education environment. For the performance evaluation of this study, the learning satisfaction with the existing similar learning environment, the usefulness of the problem recommendation guide, and the learner data processing speed were analyzed. Through this study, it was confirmed that the learning satisfaction improved by more than 15% and the learning data processing speed was improved by more than 20% compared to the existing learning environment.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.256-264
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• 2020

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1656-1666
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• 1999

The more complex and large digital circuits become, the more important delay test becomes which guarantees that circuits operate in time. In this paper, the proposed algorithm is developed, which enable the fast indirect implication for efficient test pattern generation in sequential circuits of standard scan environment. Static learning algorithm enables application of a new implication value using contrapositive proposition. The static learning procedure found structurally, analyzes the gate structure in the preprocessing phase and store the information of learning occurrence so that it can be used in the test pattern generation procedure if it satisfies the implication condition. If there exists a signal line which include all paths from some particular primary inputs, it is a partitioning point. If paths passing that point have the same partial path from primary input to the signal or from the signal to primary output, they will need the same primary input values which separated by the partitioning point. In this paper test pattern generation can be more effective by using this partitioning technique. Finally, an efficient delay fault test pattern generator using indirect implication is developed and the effectiveness of these algorithms is demonstrated by experiments.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.6
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• pp.117-128
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• 2014

This is the case study of SATREC INITIATIVE company which is the unique domestic production of commercial satellites. We examined the path and pattern for accumulation of technological capability and technology learning process. This case study show that the process of technological innovation and their influencing factors. First, the technological learning of the satellite industry follows the stage of technological acquisition, absorption, improvement and is embodied by the technological capability. Second, accumulated technological capability of the satellite industry influences the technology innovation. Third, the top management team(TMT) affects the technological learning and technological capability. Fourth, TMT has a moderating role between the technological capability and the performance of technological innovation. Finally, technological innovations in the small and venture business would be the source of technological capability and technological learning. The implications of this study are as follows. TMT has the very important role for the technological innovation and affect the technology development and the production. Also technology-based companies must gain a competitiveness advantage through technological learning and technological innovations for sustainable growth.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1868-1870
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• 2006

Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.