• The Journal of Korean Association of Computer Education
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• v.9 no.3
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• pp.85-96
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• 2006

This study analyzes a phenomenon observed in the problem-solving process using ICT that field learners prioritize functional and logical fields. The purpose of this study is to anticipate the future direction of education utilizing ICT and to explore what effort is needed to increase balanced ability of utilizing ICT in the area of education. To analyze the patterns of ICT usage in education, students were asked to solve the problems including functional and logical requests by using the program of framing document. That results were marked in accordance with standards framed in both functional and logical fields. As a result, students utilized the ICT functional field more than logical one. Therefore, we confirm learning through ICT is more effective on functional sides than on logical sides. We also confirm the excellency of ICT's functional sides in such variables as major, grade, computer capability and qualification, etc.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.538-547
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• 2001

Currently, 2D real-time image coding techniques had great developments and many related products were commercially developed. However, these techniques lack the capability of handling 3D actuality, occurred by the advent of virtual reality, because they handle only the temporal transmission for 2D image. Besides, many 3D virtual reality researches have been studied in computer graphics. Since the graphical researches were limited to the application of artificial models, the 3D actuality for real scene images could not be managed also. Therefore, a new 3D model extraction method based on stereo vision, that can deal with real scene virtual reality, is proposed in this paper. The proposed method adapted a compact genetic algorithm using population-based incremental learning (PBIL) to matching environments, in order to reduce memory consumption and computational time of conventional genetic algorithms. Since the PBIL used a probability vector and competitive learning, the matching algorithm became simple and the computation load was considerably reduced. Moreover, the matching quality was superior than conventional methods. Even if the characteristics of images are changed, stable outputs were obtained without the modification of the matching algorithm.

• Journal of the Korea Computer Industry Society
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• v.8 no.1
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• pp.29-40
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• 2007

In this study, current status of technology and realities of usage on wireless Internet for educational applications were examined, and based on related studies and case analysis, 'Matrix OX Quiz' that can be applied at present time was designed and built. Main characteristics of this system are as below: Firstly, an attempt was made to achieve educational effect by offering educational contents that are suitable for wireless Internet era, breaking away form limitations on time and space with previous wired Internet. Secondly, mobile phone was selected as the wireless Internet terminal considering the current wireless Internet usage environment and technology, and improved convenience by supporting WAP form Markup language used by SK telecommunications with the most enrollment at present. Thirdly, self-directed learning capability of students can be improved through lessons utilizing wireless Internet. Especially, responsibilities for teaming can be increased through lessons utilizing wireless Internet, and abilities needed for solving immediate problems can be developed. Therefore, the educational contents built in this study should have sufficient educational effects if wireless Internet becomes widely spread and most students can easily use it in near future.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.29-37
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• 2006

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. 'The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.

• Journal of The Korean Association of Information Education
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• v.5 no.1
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• pp.1-13
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• 2001

Several problems occur including lack of humanity, confinement of creativeness, lack of independent learning capability, when elementary students are taught computer, since the they learn only basic functions of computer in general. Accordingly, it is required to have subject-integrated textbook with which they learn curriculum and nurture the humanity as well as creativeness, while studying functions of computer. In light of this, I plan to develop computer textbook, which divided the contents of Hangul 97 and Windows 98, operation system, per subject, and organized them in the order of question to study for himself, part to learn function, formation evaluation, study on subject integration study related to curriculum, part to raise creativeness, and supplementary study, after extracting the contents of curriculum, creative factor and humanity factor suitable for each subjects. By realizing the subject-integrated textbook that enables the interaction by taking advantage of dynamic feature of web, this textbook aims to raise the humanity and creativeness while learning the functions of computer.

• IE interfaces
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• v.16 no.3
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• pp.362-374
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• 2003

The major objective of this paper is to empirically examine the effect of process maturity on the performance of industrial R&D projects. Process maturity, a fundamental concept of the Capability Maturity Model developed by Software Engineering Institute, represents the essential of Total Quality Management (TQM). Based on literature, our research model constructs process maturity in terms of structured process, goal setting and controlling, metrics, and process learning; and links it to the R&D performance that consists of technical, commercial and managerial successes. The model also includes firm size as a moderator of different effects that process maturity may have across firms. Measures for process maturity are based on the best practices identified in literature. Data are obtained from 77 successful R&D projects carried out by Korean manufacturing firms. Multiple regression and t-test are used to test proposed hypotheses. Findings are as follows. (1) In the R&D process, process maturity partially contributes to the performance of R&D projects. More specifically, goal setting and controlling-related practices drive both technical and commercial successes, while process learning-related practices drive commercial success. In contrast, traditionally emphasized elements such as structured process or metrics are found not to be significant. (2) The degree of process maturity is significantly higher in large firms. (3) Process maturity impacts on commercial success in the case of large firms, whereas it does on technical success in the case of small firms. The results imply that the TQM principles are partially associated with R&D performance, and the nature of benefit from high maturity could vary according to firm size.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.51-63
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• 2001

In supervised machine learning, an induction algorithm, which is able to extract rules from data with learning capability, provides a useful tool for data mining. Practical induction algorithms are known to degrade in prediction accuracy and generate complex rules unnecessarily when trained on data containing superfluous features. Thus it needs feature subset selection for better performance of them. In feature subset selection on the induction algorithm, wrapper method is repeatedly run it on the dataset using various feature subsets. But it is impractical to search the whole space exhaustively unless the features are small. This study proposes a heuristic method that uses sensitivity analysis of neural networks to the wrapper method for generating rules with higher possible accuracy. First it gives priority to all features using sensitivity analysis of neural networks. And it uses the wrapper method that searches the ordered feature space. In experiments to three datasets, we show that the suggested method is capable of selecting a feature subset that improves the performance of the induction algorithm within certain iteration.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.1
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• pp.5-19
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• 2015

Literatures on open innovation have two major limitations. First, either on a firm level or on an industry level did they analyze the open innovation issues. The results of a firm's innovation can be diffused through the whole network and the firm can learn back from the network knowledge. Prior literatures did not consider the feedback loop among firms and network in which the firms are involved. Second, most open innovation research had a static perspective on firm's innovation performance. Since the diffusion, spill-over and learning among network members are involved over time, the open innovation is intrinsically dynamic. From the dynamic perspective, we can appreciate the fundamental attributes of the open innovation network which involves diverse firms, research institutes, and universities. In order to overcome the limitations, we suggest a dynamic model for open innovation network. We build an agent-based model which consists of heterogeneous firms. The firms are connected through a scale-free network which is formed by preferential attachment. Through the diverse scenario of simulation, we collect massive data on the firm level and analyze them both on firm and industry level. From the analysis, we found that, on industry level, the overall performance of open innovation increases as the internal research capability, absorptive capacity, and learning curve coefficient increase. Noticeably, as the deprecation rate of knowledge increases, the variability of knowledge increases. From the firm level analysis, we found that the industry-level variables had a significant effect on the firm's innovation performance lasting through all the time, whereas the firm-level variables had only on the early phase of innovation.

• International Journal of Contents
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• v.14 no.3
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• pp.7-16
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• 2018

The extent and depth of the event plan determines the scope of pedagogical experience in situations and consequently the quality of immersive learning based on our simulated world. In contrast to planning in conventional narrative-based systems mainly pursuing dramatic interests, planning in virtual world-based pedagogical systems strive to provide realistic experiences in immersed situations. Instead of story plot comprising predetermined situations, our inter-event planning method aims at simulating diverse situations that each involve multiple events coupled via their associated agents' conditions and meaningful associations between events occurring in a background world. The specific techniques to realize our planning method include, two-phase planning based on inter-event search and intra-event decomposition (down to the animated action level); autonomous and independent agents to behave proactively with their own belief and planning capability; full-blown background world to be used as the comprehensive stage for all events to occur in; coupling events via realistic association types including deontic associations as well as conventional causality; separation of agents from event roles; temporal scheduling; and parallel and concurrent event progression mechanism. Combining all these techniques, diverse exogenous events can be derived and seamlessly (i.e., semantically meaningfully) integrated with the original event to form a wide scope of situations providing chances of abundant pedagogical experiences. For effective implementation of plan execution, we devise an execution scheme based on multiple priority queues, particularly to realize concurrent progression of many simultaneous events to simulate its corresponding reality. Specific execution mechanisms include modeling an action in terms of its component motions, adjustability of priority for agent across different events, and concurrent and parallel execution method for multiple actions and its expansion for multiple events.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.203-210
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• 2019

Recently, a number of researchers have produced research and reports in order to forecast more exactly air quality such as particulate matter and odor. However, such research mainly focuses on the atmospheric diffusion models that have been used for the air quality prediction in environmental engineering area. Even though it has various merits, it has some limitation in that it uses very limited spatial attributes such as geographical attributes. Thus, we propose the new approach to forecast an air quality using a deep learning based ensemble model combining temporal and spatial predictor. The temporal predictor employs the RNN LSTM and the spatial predictor is based on the geographically weighted regression model. The ensemble model also uses the RNN LSTM that combines two models with stacking structure. The ensemble model is capable of inferring the air quality of the areas without air quality monitoring station, and even forecasting future air quality. We installed the IoT sensors measuring PM2.5, PM10, H2S, NH3, VOC at the 8 stations in Jeonju in order to gather air quality data. The numerical results showed that our new model has very exact prediction capability with comparison to the real measured data. It implies that the spatial attributes should be considered to more exact air quality prediction.