• The Journal of the Acoustical Society of Korea
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• v.17 no.4
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• pp.42-50
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• 1998

본 논문에서는 Gram-Schmidt 구조와 Inverse Power Method를 이용한 고유 부공간 추정 방법을 제안하고 입사각을 추정하는 문제에 적용하여 성능을 평가하였다. 그리고, 어레 이 센서들이 가운데를 중심으로 대칭으로 배열되어 있을 때, 전후방 GS 필터를 이용한 향 상된 고유 부공간 방법을 제안하였다. 그리고, 제안한 방법들을 제한조건을 갖는 gradient search 방법과 비교하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1062-1065
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• 1993

Nuclear reactor operation is a human intensive task; one of the features of a problem for which fuzzy controllers present the most suitable solution. The performance of the fuzzy controllers can further be improved through tuning. In this work, application of a fuzzy controller in real-time control of a nuclear reactor is presented. The fuzzy controller is tuned on-line using direct gradient search method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.324-329
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• 2005

Dynamic Encoding Algorithm for Searches (DEAS) which is known as a fast and reliable non-gradient optimization method, was proposed [1]. DEAS reaches local or global optimum with binary strings (or binary matrices for multi-dimensional problem) by iterating the two operations; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., 0 or 1), while UDS performs increment or decrement of binary strings in the BSS' result direction with no change of string length. Because the interval of UDS exponentially decreases with increment of bit string length (BSL), DEAS is difficult to escape from local optimum when DEAS falls into local optimum. Therefore, this paper proposes hopping UDS (HUDS) which performs UDS by hopping as many as BSL in the final point of UDS process. HUDS helps to escape from local optimum and enhances a probability searching global optimization. The excellent performance of HUDS will be validated through the well-known benchmark functions.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.761-765
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• 2010

This paper presents the design of optimal fuzzy classifier for human detection by using genetic algorithms, one of the best-known meta-heuristic search methods. For this purpose, encoding scheme to search the optimal sequential intersection points between adjacent fuzzy membership functions is originally presented for the fuzzy classifier design for HOG (Histograms of Oriented Gradient) descriptors. The intersection points are sequentially encoded in the proposed encoding scheme to reduce the redundancy of search space occurred in the combinational problem. Furthermore, the fitness function is modified with the true-positive and true-negative of the confusion matrix instead of the total success rate. Experimental results show that the two proposed approaches give superior performance in HOG datasets.

• Industrial Engineering and Management Systems
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• v.9 no.1
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• pp.10-19
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• 2010

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.66-72
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• 2014

Histogram of oriented gradient (HOG) feature is widely used in vision-based pedestrian detection. The interpolation is the most important technique in HOG feature calculation to provide high detection rate. In interpolation technique of HOG feature calculation, two nearest orientation bins to gradient orientation for each pixel and the corresponding weights are required. In this paper, therefore, an efficient gradient orientation bin and weight calculation circuit for HOG feature is proposed. In the proposed circuit, pre-calculated values are defined in tables to avoid the operations of tangent function and division, and the size of tables is minimized by utilizing the characteristics of tangent function and weights for each gradient orientation. Pipeline architecture is adopted to the proposed circuit to accelerate the processing speed, and orientation bins and the corresponding weights for each pixel are calculated in two clock cycles by applying efficient coarse and fine search schemes. Since the proposed circuit calculates gradient orientation for each pixel with the interval of $1^{\circ}$ and determines both orientation bins and weights required in interpolation technique, it can be utilized in HOG feature calculation to support interpolation technique to provide high detection rate.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.2
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• pp.39-49
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• 2000

In this paper, we propose an optimizing feature extraction method for multiclass problems assuming normal distributions. Initially, We start with an arbitrary feature vector Assuming that the feature vector is used for classification, we compute the classification error Then we move the feature vector slightly in the direction so that classification error decreases most rapidly This can be done by taking gradient We propose two search methods, sequential search and global search In the sequential search, an additional feature vector is selected so that it provides the best accuracy along with the already chosen feature vectors In the global search, we are not constrained to use the chosen feature vectors Experimental results show that the proposed algorithm provides a favorable performance.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.65-67
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• 2002

A PC based software, the RTP Research Tool Box (RTB), was developed for IMRT optimization research. The software was consisted of an image module, a beam registration module, a dose calculation module, a dose optimization module and a dose display module. The modules and the Graphical User Interface (GUI) were designed to easily amendable by negotiating the speed of performing tasks. Each module can be easily replaced to new functions for research purpose. IDL 5.5 (RSI, USA) language was used for this software. Five major modules enable one to perform the research on the dose calculation, on the dose optimization and on the objective function. The comparison of three cost functions, such as the uncomplicated tumor control probability (UTCP), the physical objective function and the pseudo-biological objective function, which was designed in this study, were performed with the RTB. The optimizations were compared to the simulated annealing and the gradient search optimization technique for all of the optimization objective functions. No significant differences were found among the objective functions with the dose gradient search technique. But the DVH analysis showed that the pseudo-biological objective function is superior to the physical objective function when with the simulated annealing for the optimization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.398-404
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• 2020

In this paper, we propose a new design method of signal constellation of the spiral quadrature amplitude modulation (QAM) exploiting a modified gradient descent search algorithm and its binary mapping rule. Unlike the conventional method, the new method, which uses and the constellation optimization algorithm and the maximum number of iterations as a parameter for the iterative design, is more robust to phase noise. And the proposed binary mapping rule significantly reduces the average Hamming distance of the spiral constellation. As a result, the proposed spiral QAM constellation has much improved error performance compared to the conventional ones even in a very severe phase noise environment. It is, therefore, considered that the proposed QAM may be a useful modulation format for coherent optical communication systems and orthogonal frequency division multiplexing (OFDM) systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.