• Management Science and Financial Engineering
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• 제13권2호
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• pp.141-157
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• 2007

In this paper, we address a design problem and a case study of a K-stage back-light-unit(BLU) inspection system, which is composed of K stages, each of which includes an inspection process and a rework process. Assuming the type I, II errors and the inspection-free policy for items classified as good, we determine the smallest integer of K which can achieve a given target defective rate. If K does not exist, holding the current values of the type I, II errors, we search reversely the defective rate of an assembly line and the defective rate of a rework process, to meet the target defective rate. Our formulae and methodology based on a K-stage inspection system could be applied and extended to similar situations with slight modification.

• 한국군사과학기술학회지
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• 제11권6호
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• pp.164-171
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• 2008

A modified generalized particle algorithm, MGPA, was suggested to improve the computational efficiency of standard SPH method in numerical analysis of high speed impact behavior. This method uses a numerical failure mechanism than material failure models to describe the target penetration. MGPA algorithm was more effective to describe the impact phenomena and new boundaries produced during the calculation process were well recognized and treated in the target penetration problem of a bullet. When bullet perforation problems were analyzed by this method, MGPA algorithm calculation gives the stable numerical solution and stress oscillation or particle penetration phenomena were not shown. The error range in ballistic velocity limit is less than $2{\sim}13%$ for various target thickness.

• 대한전기학회논문지
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• 제36권12호
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• pp.908-917
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• 1987

Most of moving targets are modelled as nonlinear dynamic equations. In recent years, the extended Kalman filter is frequently used for estimating their behaviors. The conditional Gaussian filter is more suitable than extended kalman filter in the filtering problem of nonlinear systems. But extended Kalman filter and conditional Gaussian filter often do not give optimal estimates and fail to track target trajectories because of its properties. Therefore it is desirable to use adaptive techniques to adapt target maneuvers. In this paper, we will discuss adaptive filtering technique using innovation process based on extended Kalman filter in real time, and suggest another maneuver estimation method using MRAS technique.

• 한국정보통신학회논문지
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• 제6권8호
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• pp.1146-1152
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• 2002

This paper proposed a scheme for finding an optimal adaptive data association for multi-target between measurements and tracks. First, we assume the relationships between measurements as Mrkov Random Field. Also assumed a priori of the associations as a Gibbs distribution. Based on these assumptions, it was possible to reduce the MAP estimate of the association matrix to the energy minimization problem. After then, we defined an energy function over the measurement space, that may incorporate most of the important natural constraints. Through the experiments, we analyzed and compared this algorithm with other representative algorithms. The result is that it is stable, robust, fast enough for real timecomputation, as well as more accurate than other methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.63.5-63
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• 2002

In this paper, we are concerned with a maneuver detection algorithm which uses the 'lost' measurements down-sampled for computation load saving when a target is in quiescent motion. In general applications of estimation, measurements are available at a relatively high rate, while the estimation processing equipment can only operate at a lower sampling rate. Furthermore, when a target is in nearly quiescent motion, the update of the tracking filter need not to be implemented with maxim urn process power of the filter since the states of the target vary relatively slowly. This does not give serious degradation on the estimation performance. We consider the maneuver detection problem at the case...

• 대한기계학회논문집
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• 제19권5호
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• pp.1220-1228
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• 1995

In this paper, we consider the problems of tracking an object in a real image. In evaluating these problems, we explore a new technique based on an active contour model commonly called a snake model, and propose the active bar models to represent target. Using this model, we simplified the target welection problems, reduced the search space of energy surface, and obtained the better performances than those of snake model. This approach improves the numerical stability and the tendency for points to bunch up and speed up the computational efficiency. Representing the object by active bar, we can easily obtain the zeroth, the first, and the second moment and it facilitates the target tracking. Finally, we present the good result for the visual tracking problem.

• International Journal of Contents
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• 제7권3호
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• pp.1-5
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• 2011

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.

• Transactions on Control, Automation and Systems Engineering
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• 제4권4호
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• pp.305-310
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• 2002

The measured rate of the tracking sensor becomes biased under some operational situation. For a highly maneuverable aircraft in 3D space, the target dynamics changes from time to time, and the Kalman filter using position measurement only can not be used effectively to reject the rate measurement bias error. To cope with this problem, we present a new algorithm which incorporate FIR-type filter and FIR-type fixed-lag smoother, and demonstrate that it has the optimal performance in terms of both estimation accuracy and response time through an application example to the anti-aircraft gun fire control system(AAGFCS).

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.905-908
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• 1988

In recent, Kalman filter technique has been much used as one of technique for tracking of the moving target. But some problem are still remained to be resolved. For example, when Kalman filter technique is applied to nonlinear system, the technique is nonoptimal estimator. Therefore, extended Kalman filter is proposed to reduce modeling error for nonlinear system. In this study, an extended Kalman filter in Cartesian coordinates is described for moving target, when the radar sensor measures range, azimuth and elevation angle in polar coordinates. And an approximate gain computation algorithm is proposed. In this approach, Kalman gains are computed for three uncoupled filter and multiplied by a Jacobian transformation determined from the measured target position and orientation.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2001년도 춘계학술대회논문집:21세기 신지식정보의 창출
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• pp.232-236
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• 2001

In this paper, we consider the problem of estimation of average radar cross section (RCS) for Swerling 1 fluctuation model, based on the maximum likelihood (ML) estimation method. In a mathematical development we take into account the event that target strength is lower than detection threshold, or the target is not detected. Our ML estimation for the SWR uses the score function that is the joint probability-pdf of the events and random variables. The solution to the ML estimation reduces to an expression in the from of a contraction mapping. The computational efficiency of the contraction mapping theorem is significant in computing the ML estimation as compared with other root-finding algorithms fur most radar tracking conditions.