• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.9-15
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• 1998

This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.

• Educational Technology International
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• v.24 no.1
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• pp.81-114
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• 2023

The study aimed to qualitatively examine middle school students' engagement in distance instruction during the COVID-19 pandemic. The participants comprised 119 students from a girls' middle school in Seoul, South Korea. To gain an in-depth understanding of the students' experiences, we collected their reflective journals, which included structured items about their learning engagement at three timepoints in 2020: April, July, and December. The following are the results: 10 themes and 18 concepts were derived, and they were integrated into causal conditions (sudden transition due to COVID-19), contextual condition (technology readiness, school education context), central phenomena (high level of behavioral engagement, low emotional engagement), interventional conditions (recognizing the potential of online learning, situational awareness about COVID-19 and online learning), action/interaction phenomena (development and use of self-regulated learning strategies), and consequences (changes in practices and perception towards online learning). Based on the findings, engagement patterns of the participants were classified into five types: proactive, conservative, receptive, reactive, passive learners. The present study demonstrated important findings that are essential for the improvement and development of engaging online learning strategies in the future.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.963-968
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• 1990

An iterative learning control scheme for tracking control of a class of uncertain nonlinear systems is presented. By introducing a model reference adaptive controller in the learning control structure, it is possible to achieve zero tracking of unknown system even when the upperbound of uncertainty in system dynamics is not known apriori. The adaptive controller pull the state of the system to the state of reference model via control gain adaptation at each iteration, while the learning controller attracts the model state to the desired one by synthesizing a suitable control input along with iteration numbers. In the controller role transition from the adaptive to the learning controller takes place in gradually as learning proceeds. Another feature of this control scheme is that robustness to bounded input disturbances is guaranteed by the linear controller in the feedback loop of the learning control scheme. In addition, since the proposed controller does not require any knowledge of the dynamic parameters of the system, it is flexible under uncertain environments. With these facts, computational easiness makes the learning scheme more feasible. Computer simulation results for the dynamic control of a two-axis robot manipulator shows a good performance of the scheme in relatively high speed operation of trajectory tracking.

• Journal of Engineering Education Research
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• v.13 no.5
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• pp.72-75
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• 2010

Design subjects have become popular in the engineering education. In these subjects, an active learning skill was introduced to students. Students have to decide a theme and the way how to study it and to find answers by themselves. This learning method was developed in the early year courses in the University of Tokushima. The common learning curriculum among whole university adopted this design subjects from 2005 of the school year and eleven more new subjects started. "Roots finding" is one of them and the objectives are to gain abilities of self-study through the research on a particular subject, and make report then presenting them. Self-learning ability, research ability and presentation skills were enhanced through this subject.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.302-307
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• 1989

A pressure tracking control of hydroforming process is considered in this paper. To account for nonlinearities and uncertainties of the process, an iterative learning control scheme is proposed using Cerebellar Model Arithmatic Computer (CMAC). The experimental result shows that the proposed learning control is superior to any fixed gain controller in the sense that it enables the system to do the same work more effectively as the number of operation increases.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1278-1280
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• 1996

In this paper, an Iterative learning control algorithm is presented for a class of non linear system which have external inputs or disturbances. The acceleration of error signal is used to update the next control signal. It is shown that the feedback gain can be deter.ined so that the overall errors are convergent.

• Journal of Educational Research in Mathematics
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• v.8 no.2
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• pp.605-620
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• 1998

This paper suggested a geometry learning model which relates an exploratory learning model with GSP applications, Such a model adopts GSP's capability of visualizing dynamic geometric figures and exploratory learning method's advantages of discovering properties and relations of geometric problem proving and concepts associated with geometric inferencing of students. The research was conducted for 3 middle school students by applying the proposed model for 6times at computer laboratory. The overall procedure was videotaped so that the collected data was later analyzed by qualitative methodology. The analysis indicated that the students with less than van Hiele 4 level took advantages of adoption our proposed model to gain concrete understandings of geometric principles and concepts with GSP. One of the lessons learned from this study suggested that the roles of students and a teacher who want to employ the proposed model need to change their roles respectively.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.693-703
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• 2010

High spectral resolution of hyperspectral data enables analysis of complex natural phenomena that is reflected on the data nonlinearly. Although many manifold learning methods have been developed for such problems, most methods do not consider the spatial correlation between samples that is inherent and useful in remote sensing data. We propose a manifold learning method which directly combines the spatial proximity and the spectral similarity through kernel PCA framework. A gain factor caused by spatial proximity is first modelled with a heat kernel, and is added to the original similarity computed from the spectral values of a pair of samples. Parameters are tuned with intelligent grid search (IGS) method for the derived manifold coordinates to achieve optimal classification accuracies. Of particular interest is its performance with small training size, because labelled samples are usually scarce due to its high acquisition cost. The proposed spatial kernel PCA (KPCA) is compared with PCA in terms of classification accuracy with the nearest-neighbourhood classification method.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.11-23
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• 2021

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.