• Journal of Information Processing Systems
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• 제11권2호
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• pp.165-172
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• 2015

The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.822-827
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• 2005

Recently engineering systems problems become quite large and complicated. For those problems, design requirements are fairly complex. It is not easy to design such systems by considering only one discipline. Therefore, we need a design methodology that can consider various disciplines. Multidisciplinary Design Optimization (MDO) is an emerging optimization method to include multiple disciplines. So far, about seven MDO methodologies have been proposed for MDO. They are Multidisciplinary Feasible (MDF), Individual Feasible (IDF), All-at-Once (AAO), Concurrent Subspace Optimization (CSSO), Collaborative Optimization (CO), Bi-Level Integrated System Synthesis (BLISS) and Multidisciplinary Optimization Based on Independent Subspaces (MDOIS). In this research, the performances of the methods are evaluated and compared. Practical engineering problems may not be appropriate for fairness. Therefore, mathematical problems are developed for the comparison. Conditions for fair comparison are defined and the mathematical problems are defined based on the conditions. All the methods are coded and the performances of the methods are compared qualitatively as well as quantitatively.

• 한국멀티미디어학회논문지
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• 제18권11호
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• pp.1261-1267
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• 2015

The problem of underwater target detection and classification has been attracted a substantial amount of attention and studied from many researchers for both military and non-military purposes. The difficulty is complicate due to various environmental conditions. In this paper, we study classifier ensemble methods for active sonar target classification to improve the classification performance. In general, classifier ensemble method is useful for classifiers whose variances relatively large such as decision trees and neural networks. Bagging, Random selection samples, Random subspace and Rotation forest are selected as classifier ensemble methods. Using the four ensemble methods based on 31 neural network classifiers, the classification tests were carried out and performances were compared.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.560-565
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• 2007

Motivated by needs such as those in the aerospace industry, this paper demonstrates ability to significantly increase buckling loads of perforated composite laminated plates by synergizing FEM and a genetic optimization algorithm (GA). Plate geometry is discretized into specially-developed 3D degenerated eight-node shell isoparametric layered composite elements. General shell theory, involving incremental nonlinear finite element equilibrium equation, is employed. Fiber orientation within individual plies of each element is controlled independently by the genetic algorithm. Eigen buckling analysis is performed using the subspace iteration method. Available results demonstrate the approach is superior to more conventional methodologies such as modifying ply thickness or the stacking sequence of individual rectilinear plies having common fiber orientation through the plate.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2619-2621
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• 2005

Electrical resistance tomography(ERT) maps resistivity values of the soil subsurface and characterizes buried objects. The characterization includes location, size, and resistivity of buried objects. In this paper, Gauss-Newton and truncated least squares(TLS) are presented for the solution of the ERT image reconstruction. Computer simulations show that the spatial resolution of the reconstructed images by the TLS approach is improved as compared to that obtained by the Gauss-Newton method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.453-456
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• 1992

A Data-based Covariance Matrix(DCM) is introduced in the Eigenvector(EV) method, among subspace methods of estimating multiple sinusoidal frequencies from finite white noisy measurements. It is shown that the EV with the DCM can obtain the true. frequencies from finite noiseless data Some asymptotic results and further improvement on the DCM are also presented mathematically. Monte-carlo simulations are statistically conducted from the view-points of means and standard deviations in the EV's of DCM and Conventional Covariance Matrix(CCM). Simulations show a great promise for using the DCM, particularly for the cases of short data records, closely spaced frequencies and high signal-to-noise ratios.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.448-451
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• 1995

A general task execution with hybrid impedance control method is addressed. The target impedance is expressed in the constraint frame. For the computational simplicity and the robustness improvement, disturbance observer scheme is used. To make stable contact with the environment, the large value of desired inertia gain for the force-controlled subspace is suggested. Numerical examples are given to show the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.49-52
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• 1999

A subspace-based identification method of the Wiener model, consisting of a state-space linear block and a polynomial static nonlinearity at the output, is used to retrieve from discrete sample data the accurate information about the nonlinear dynamics. Wiener model may be incorporated into model predictive control (MPC) schemes in a unique way which effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC. The control performance is evaluated with simulation studies where the original first-principles model for a continuous MMA polymerization reactor is used as the true process while the identified Wiener model is used for the control purpose. On the basis of the simulation results, it is demonstrated that, despite the existence of unmeasured disturbance, the controller performed quite satisfactorily for the control of polymer qualities with constraints.

• 전자공학회논문지B
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• 제32B권7호
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• pp.989-1002
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• 1995

In this paper, we propose a new algorithm, so called the Signal Decorrelation via Virtual Translation of Array (SDVTA) algorithm, for estimating the directions of arrival(DOA's) of narrowband coherent signals incident on a general array. An effective procedure is composed of transforming the steering matrix of the original array into that of the virtually translated sensor array and taking the average of the transformed covariance matrices in order to decorrelate the coherent signals. The advantage of this approach is in that 1) it can estimate the DOA's of m-1 coherent signals(M : the number of array sensors) since the effective aperture size is never reduced. 2) a geometry of array is unrestricted for solving the narrowband coherency problem. 3) the efficiency of signal decorrelation does not depend on the phase differences between coherent signals unlike the Coherent Signal Subspace Method (CSM). Simulation results are illustrated to demonstrate the superior performance of this new algorithm in comparison with the normal MUSIC and examine the comparative performance with the various choices of the optimal transformation matrix under coherent signal environments.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.269-273
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• 2000

This paper describes Active Noise Control (ANC) using Discrete Wavelet Transform (DWT) Domain Least Mean Square (LMS) Method. DWT-LMS is one of the transform domain input decorrelation LMS and improves the convergence speed of adaptive filter especially when the input signal is highly correlated. Conventional transform domain LMS's use Discrete Cosine Transform (DCT) because it offers linear band signal decomposition and fast transform algorithm. Wavelet transform can project the input signal into the several octave band subspace and offers more efficient sliding fast transform algorithm. In this paper, we propose Wavelet transform domain LMS algorithm and shows its performance is similar to DCT LMS in some cases using ANC simulation.