• Asia pacific journal of information systems
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• v.9 no.3
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• pp.177-194
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• 1999

Identifying knowledge as the single most important asset ultimately defining organizational competitiveness, enterprises are trying to move towards knowledge-oriented practices. Such practices have given rise to learning and agile organization, This paper presents applied information technologies to realize the learning and agile organization, focusing on systems thinking. Firstly, in order to establish a framework for the systems thinking, an information systems development method based on process chain is proposed. Then, an agent-based microworld simulation approach is presented. The approaches provide visible and analytical information to knowledge workers so that they can have systems thinking capabilities eventually. Various microworlds on the top of the information system can be constructed with agents and simulated for possible business events. All decision makings are dynamic in nature. To let knowledge workers look ahead the possible outcomes of the whole relevant processes is the core capability of the approaches. Through watching, the knowledge workers would be able to acquire new insights or problem solving knowledge for the problem in hand.

• Journal of The Korean Association of Information Education
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• v.3 no.1
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• pp.33-43
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• 1999

Most educational Web contents developed so far can be regarded' as another type of printed textbooks since they are made up of static lists of textual information. It results in a lack of capability in such educational viewpoints as interaction between students and/or teachers, self-directed learning of individual students, and so on. This paper proposes a new style of Web contents, which can improve the self-directed learning capabilities as well as the interaction between students, with the topic of "the life cycle of frog" that the student studies in the third year of elementary school. It has been designed to provide BBS and a studying material appropriate to the achievement level of individual students, and implemented with DHTML and Java.

• East Asian mathematical journal
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• v.32 no.2
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• pp.175-192
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• 2016

This study aimed to understand the needs of changes in the teaching-learning environment in the university and to develop the liberal art subject of mathematics. The changes of mathematical belief in the university students were investigated to understand how the liberal art subject of mathematics affected them related to mathematics. Upon the study results, the significant changes were occurred from the utility factor on the subject of mathematics in mathematical belief, the importance factor of the answers in the teaching-learning belief, teaching activity factor of the teachers, and inborn capability factor in the belief on the self-concept. The meaningful learning environment and teaching method for the liberal art subject of mathematics are suggested further by these results.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.249-260
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• 1997

The manufacturing environment is rife with nonlinear processes. In this context, an intelligent production controller should be able to predict the dynamic behavior of various subsystems as they react to transient environmental conditions, the varying internal condition of the manufacturing plant, and the changing demands of the production schedule. This level of adaptive capability may be achieved through a coherent methodology for a learning coordinator to predict nonlinear and stochastic processes. The system is to serve as a real time, online supervisor for routine activities as well as exceptional conditions such as damage, failure, or other anomalies. The complexity inherent in a learning coordinator can be managed by a modular architecture incorporating case based reasoning. In the interest of concreteness, the concepts are presented through a case study involving a knowledge based robotic system.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.804-806
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• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• 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.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.273-282
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• 2022

In this paper, we propose a new concept for error estimation in finite element solutions, which we call mode-based error estimation. The proposed error estimation predicts a posteriori error calculated by the difference between the direct finite element (FE) approximation and the recovered FE approximation. The mode-based FE formulation for the recently developed self-updated finite element is employed to calculate the recovered solution. The formulation is constructed by searching for optimal bending directions for each element, and deep learning is adopted to help find the optimal bending directions. Through various numerical examples using four-node quadrilateral finite elements, we demonstrate the improved predictive capability of the proposed error estimator compared with other competitive methods.

• Structural Engineering and Mechanics
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• v.88 no.6
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• pp.535-549
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• 2023

Deep extreme learning machine (DELM) and multi-verse optimization algorithms (MVO) are hybridized for designing an optimal and adaptive control framework for uncertain buildings. In this approach, first, a robust model predictive control (RMPC) scheme is developed to handle the problem uncertainty. The optimality and adaptivity of the proposed controller are provided by the optimal determination of the tunning weights of the linear programming (LP) cost function for clustered external loads using the MVO. The final control policy is achieved by collecting the clustered data and training them by DELM. The efficiency of the introduced control scheme is demonstrated by the numerical simulation of a ten-story benchmark building subjected to earthquake excitations. The results represent the capability of the proposed framework compared to robust MPC (RMPC), conventional MPC (CMPC), and conventional DELM algorithms in structural motion control.

• Architectural research
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• v.26 no.1
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• pp.1-12
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• 2024

This study introduces a deep reinforcement learning (DRL)-based methodology for optimizing apartment space arrangements, addressing the limitations of human capability in evaluating all potential spatial configurations. Leveraging computational power, the methodology facilitates the autonomous exploration and evaluation of innovative layout options, considering architectural principles, legal standards, and client re-quirements. Through comprehensive simulation tests across various apartment types, the research demonstrates the DRL approach's effec-tiveness in generating efficient spatial arrangements that align with current design trends and meet predefined performance objectives. The comparative analysis of AI-generated layouts with those designed by professionals validates the methodology's applicability and potential in enhancing architectural design practices by offering novel, optimized spatial configuration solutions.

• Journal of Elementary Mathematics Education in Korea
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• v.13 no.1
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• pp.51-74
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• 2009

The purpose of this study is to search for various strategies that could self-regulated learning within cyber home study efficiently, to operate the cyber home study based on such strategies, to manage and support students' learning and to investigate what effects it would have on the ability of self-regulated learning and attitude. In this study, an operational strategy for cyber home study according to the compositional elements of self-regulated learning based on prior studies. Then, the study developed the learning contents of cyber home study and operated cyber home study according to the operational strategy. From the results of the analysis obtained in this study, the following conclusions can be drawn as follows. First, A learner's self-regulated learning capability is able to be improved by self-regulated leaning strategies. Cyber home study that would enable students to implement the leaning on their own through learning contents and operating strategies corresponding to them was the environment that could help their self-regulated learning. Second, in order to find out students' satisfaction for the application of cyber home study, the study compared the survey of cyber home study with the frequency and percentage by each question and the mean value of technical statistics. Cyber home study let students have positive recognition on mathematical learning, and especially as shown in the results of the interview, it was helpful to improve students' interest and confidence as well as their mathematical learning.