• 한국마이크로전자및패키징학회:학술대회논문집
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• 한국마이크로전자및패키징학회 2001년도 추계 기술심포지움
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• pp.61-66
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• 2001

In this research, the design parameters of embedded capacitors are extracted by modeling and fabrication. The traditional library of capactor has a few problems in applying the circuit. Its capacitance is discrete, so target values in any circuit often can't be obtained in library, To solve this problem, the characteristics of capacitors are detected in the variation with the shape and structure, and then the capacitors with the expected reactance value at target frequency are obtained, In this procedure, 3-dimensional structure simulation is performed to predict the characteiristics of capacitors.

• Journal of Advanced Marine Engineering and Technology
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• 제10권3호
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• pp.119-125
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• 1986

By using the least square input estimator and a likelihood ratio technique, an one-dimensional tracking problem is presented. A Kalman tracking filter based on constant-velocity model is used to track a target and the filtered estimate is updated with an input estimate when a maneuver is detected. The simulation results show that there are significant improvements using the scheme presented here.

• 대한전기학회논문지
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• 제35권11호
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• pp.519-528
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• 1986

By using a least-square input estimator and likelihood ratio technique, a tracking problem is presented. A Kalman tracking filter based on constant-velocity, straight-line model is used to track a target and the filtered estimate is updated using an input estimate when a maneuver is detected. Track residuals at each scan are sensed by a detector to guard against unexpected corrections of the filter. The simulation results show there are significant improvements using the scheme presented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.99.6-99
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• 2001

The parameter optimization for agile beam radar tracking is addressed to minimize the radar resources that are required to maintain a target under track. The parameters to be optimized include the track-revisit interval and the sequence of pairs of target signal strengths and detection thresholds associated with repeated illumination attempts in each track-revisit. The optimization problem is solved numerically for typical examples.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.485-488
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• 2003

A conventional correlation target tracker is analysed with a simple mathematical approach. And, we will propose a correlation measure with selective attentional property in order to overcome the false-peak problem of the conventional methods. Various experimental results show that the proposed correlation measure is able to reduce considerably the probability of false-peaks degraded by the correlation between background images of a reference block and a distorted and noisy sensor input image.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.493-496
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• 2003

Recently, Ryu et al. proposed a multiple target DOA tracking algorithm, which has good features that it has no data association problem and simple structure. But its performance is seriously degraded in the low signal-to-noise ratio. In this paper, a measurement fusion method is presented based on ML(Maximum Likelihood), and the new DOA tracking algorithm is proposed by incorporating the presented fusion method into Ryu's algorithm. The proposed algorithm has a better tracking performance than that of Ryu's algorithm, and it sustains the good features of Ryu's algorithm.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1996년도 추계 학술 발표회 발표논문집
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• pp.211-216
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• 1996

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, an architecture of fuzzy nerual networks with fuzzy weights and fuzzy biases is shown. Next a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value.A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 하계종합학술대회논문집
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• pp.5-8
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• 2000

This paper introduced a scheme for finding an optimal association matrix that represents the relationships between the measurements and tracks in multi-target tracking of Radar system. We considered the relationships between targets and measurements as MRF and 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.

• 산업경영시스템학회지
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• 제23권56호
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• pp.55-64
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• 2000

There is more than a single quality characteristic and they are often of varying or mixed target types. The purpose of this paper is to develop general strategies for solving the multiple response robust design problem. The desirability function provides an important tool to solve problems that have different types of target since the desirability values all the range between zero and one. Several combinations of arithmetic averages, geometric averages, and standard deviations are used in the various strategies to find the best design point.

• 한국군사과학기술학회지
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• 제23권4호
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• pp.399-406
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• 2020

In this paper, we propose a time-varying proportional navigation guidance law that determines the proportional navigation gain in real-time according to the operating situation. When intercepting a target, an unidentified evasion strategy causes a loss of optimality. To compensate for this problem, proper proportional navigation gain is derived at every time step by solving an optimal control problem with the inferred evader's strategy. Recently, deep reinforcement learning algorithms are introduced to deal with complex optimal control problem efficiently. We adapt the actor-critic method to build a proportional navigation gain network and the network is trained by the Proximal Policy Optimization(PPO) algorithm to learn an evasion strategy of the target. Numerical experiments show the effectiveness and optimality of the proposed method.