• The Mathematical Education
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• v.52 no.2
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• pp.203-215
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• 2013

We explored implications of storytelling in learning and teaching mathematics and examined examples of storytelling for deep understanding of the educational meanings of storytelling and new direction of storytelling approach to mathematics teachers. Mathematics had been commonly considered as the subject irrelevant to the narrative mode of thinking and only relevant to the paradigmatic mode of thinking that has rigorous logical forms and independent from human mind. As a result, this common sense forced a transmission pedagogy of mathematics: only the teachers as owners of the objective and logical truth of mathematics could transmit mathematical truths to students. Storytelling is highlighted as an alternative to the common teaching practices of mathematics focused only on the paradigmatic mode of thinking. Although a lot of research about the educational uses of storytelling mainly focused on the development and modification of stories, we suggested that the educational interest about storytelling should move to the elements or techniques for the positive effect of storytelling.

• Journal of Power Electronics
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• v.10 no.6
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• pp.635-642
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• 2010

The paper presents a multimedia educational program of DC servo drives for distant learning with a special emphasis on sliding mode control. The program contains of three parts: animation, simulation and internet based measurement. The animation program explains the operation of DC motors, gives its time- and frequency-domain equations, transfer functions and the theoretical background necessary for controller design for DC servo motors. The simulation model of the DC servo motor and the controller can be designed by the students based on the animation program. The students can also test their controllers through the internet based measurement, which is the most important part from engineering point of view. After the measurements are executed, the students can download the measured data and compare them to the simulation results.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2296-2298
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• 1998

In this paper, an on-chip learning pulse-mode digital neural network with a massively parallel yet compact and flexible network architecture is suggested. Algebraic neural operations are replaced by stochastic processes using pseudo-random sequences and simple logic gates are used as basic computing elements. Using Back-propagation algorithm both feed-forward and learning phases are efficiently implemented with simple logical gates. RNG architecture using LFSR and barrel shifter are adopted to avoid some correlation between pulse trains. Suggested network is designed in digital circuit and its performance is verified by computer simulation.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.354-359
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• 2003

In this paper, we consider the problem of designing a simple learning variable structure system for repeatable tracking control of robot manipulators. We combine a variable structure control law as the robust part for stabilization and a feedforward learning law as the intelligent part for nonlinearity compensation. We show that the tracking error asymptotically converges to zero. Finally, we give computer simulation results in order to show the effectiveness of our method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1468-1473
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• 2005

In this paper, I proposed the INTelligent Agent System by Kohonen's Self Organizing Neural Network (INTAS). INTAS creates each user's profile from the information. Based on it, learning community grouping suitable to each individual is automatically executed by using unsupervised learning algorithm. In INTAS, grouping and learning are automatically performed on real time by multiagents, regardless of the number of learners. A new framework has been proposed to generate multiagents, and it is a feature that efficient multiagents can be executed by proposing a new negotiation mode between multiagents..

• Smart Structures and Systems
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• v.24 no.2
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• pp.233-242
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• 2019

In this paper, an inverse approach based on uniform load surface (ULS) is presented for structural damage localization and quantification. The ULS is excellent approximation for deformed configuration of a structure under distributed unit force applied on all degrees of freedom. The ULS make use of natural frequencies and mode shapes of structure and in mathematical point of view is a weighted average of mode shapes. An objective function presented to damage detection is discrepancy between the ULS of monitored structure and numerical model of structure. Solving this objective function to find minimum value yields damage's parameters detection. The teaching-learning based optimization algorithm has been employed to solve inverse problem. The efficiency of present damage detection method is demonstrated through three numerical examples. By comparison between proposed objective function and another objective function which make use of natural frequencies and mode shapes, it is revealed present objective function have faster convergence and is more sensitive to damage. The method has good robustness against measurement noise and could detect damage by using the first few mode shapes. The results indicate that the proposed method is reliable technique to damage detection in structures.

• Earthquakes and Structures
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• v.27 no.3
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.135-139
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• 2000

This research proposes a nonlinear sliding mode control. The sliding mode control is designed according to Lyapunov function. The equivalent control term is estimated by neural network. To estimate the unknown part in the control law in on-line fashion, A recurrent neural network is given as on-line estimator. The stability of the control system is guaranteed owing to the on-line learning ability of the recurrent neural network. It is certificated through simulation results to be applied to nonlinear system that the function approximation and the proposed control scheme is very effective.