• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.841-846
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• 2002

We applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the line utilization that QoS of the network is greatly influenced by. And this paper proposes the learning algorithm of dynamic threshold in line utilization using the SARIMA model. We can find the proper dynamic threshold in timely line utilization on the various network environments and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold on real network. Network manager can overcome a shortcoming of original threshold method and maximize the performance of this algorithm.

• Journal of The Korean Association of Information Education
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• v.8 no.1
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• pp.15-23
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• 2004

In this study, we analyzed the login frequencies and the learner's results of an examination related in the teacher's information distance learning. The results of analysis are as follows. First, most of learners connected to the learning system in school at office hours. And the login frequencies on Sunday are high remarkably in test pole. Seconds, the on-line assessment and the ratio of completion is high the learner who participation with a fellow worker than the learner who not so. But because of the low allotment of marks about on-line estimation, it hardly influence in the last results. Third, the ratio allotment of marks about on-line estimation is suitable $20{\sim}30%$. Forth, learners of low grade are higher the login frequencies, the last results is high. Therefore the operator of the learning system estimates ability of learners by pre-test and must have continues and encouragement about leaners of law grade.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.97-103
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• 2020

The proposed system is composed of two parts, an AI physical fitness measurement part and an AI physical fitness management part. In the AI fitness measurement part, a guide to physical fitness measurement and accurate calculation of the measured value are performed through deep learning-based pose recognition. Based on these measurements, the AI fitness management part designs personalized exercise programs and provides them to dedicated smart applications. To guide the measurement posture, the posture of the subject to be measured is photographed through a webcam and the skeleton line is extracted. Next, the skeletal line of the learned preparation posture is compared with the extracted skeletal line to determine whether or not it is normal, and voice guidance is provided to maintain the normal posture.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.179-202
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• 2006

The objective of this study is to identify the impacts of communication reinforcement on performance of learning in Web-PBL. Communication reinforcement is defined as the combination of information sharing and co-construction. As factors facilitating communication reinforcement, we propose learner's characteristics, task characteristics, and group characteristics. Learner's characteristics are collaboration-orientation, openness, holistic approach, and online community-orientation which reflects e-learning environment. Collaboration-oriented tasks as group projects were developed and given to groups with 5-6 members. The group characteristics are categorized into 'horizontal' and 'vertical', according to the patterns of communication between a group leader and members. To verify empirically the proposed research model, an experimental design was performed to learners who took on-line and off-line courses with group projects. We found important results as follows; First, field dependence has positive impacts on information sharing, and online community-orientation has positive impacts on co-construction. These results correspond with prior studies on relationship between field dependence and collaborative learning. Second, collaboration-oriented task directly impacts on information sharing, and indirectly affects co-construction, This result implicates that information sharing is pre-requisite of co-construction. Third, 'horizontal' was identified as a factor giving positive effects on information sharing and co-construction. This result implies that horizontal communication is very important to facilitate communication reinforcement.

• Educational Technology International
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• v.7 no.2
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• pp.23-44
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• 2006

As the educational paradigm has shifted from the traditional knowledge oriented instruction learning to the knowledge product oriented instructional learning, the development of student's creativity becomes one of the most important educational goals, because the ability that can produce the knowledge creatively is required in the digital information knowledge based society. The purpose of this study is to make a basic direction and strategy for the instructional design to develop an on and off line blended instructional design which will help a learning community to be a more collaborative and creative learning community. This research has investigated the concept and the characteristics of collaborative creativity and creative problem solving as the theoretical basis of the design. After that, on the basis of the theories connected with the collaborative creativity theory, the direction and the strategies for the development of collaborative creativity was designed. The design was applied into the real learning community and finally proved the effectiveness of the learning model for the development of the collaborative creativity by the quantitative evaluation.

• Korean Journal of Adult Nursing
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• v.19 no.4
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• pp.624-633
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• 2007

Purpose: This study was conducted to develop an integrated undergraduate course including a PBL based on a blended learning strategy, and evaluate learners' responses. Methods: The learning contents of cardiovascular, respiratory, and musculoskeletal medical systems, and nursing diagnoses of 'activity and rest' domain (NANADA's classification II, 2005) were analyzed. Six clinical scenarios with the clients in different life cycles were developed for PBL. Classical lecture and group presentation with on-line self learning were implemented in addition to PBL. The developed course was implemented on 84 junior nursing students in a university for 7 weeks with 5 hours per day, two days per week. Students were asked to complete structured questionnaires including problem solving, critical thinking, and nursing diagnosis differentiation abilities. Results: Learner's evaluation was positive in problem solving skills and in the differentiation ability of nursing diagnoses relevant to an 'activity and rest' functional health pattern. Conclusion: Development and implementation of integrated courses based on a blended learning method need to be continued to enhance students' thinking and self-directed learning abilities. Supporting strategies for individual learners should be added for successful blended learning such as individual on-line feedback and consideration of individual learning outcomes.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.74-79
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• 2017

In this paper, we propose a system model that predicts the original signal transmitted from the transmitter using the received signal in the power line communication system based on the multi - layer perceptron which is one of the machine learning algorithms. Power line communication system using communication system using power network has more noise than communication system using general communication line. It causes a problem that the performance of the power line communication system is degraded. In order to solve this problem, the communication system model proposed in this paper minimizes the influence of noise through original signal prediction and mitigates the performance degradation of the power line communication system. In this paper, we prove that the original signal is predicted by applying the proposed communication system model to the white noise environment.