• Journal of electromagnetic engineering and science
• /
• v.17 no.3
• /
• pp.113-119
• /
• 2017

The application of compressive sensing (CS) to a radar imaging system based on a frequency-scanned microstrip leaky wave antenna is investigated. First, an analytical model of the system matrix is formulated as the basis for the inversion algorithm. Then, $L_1-norm$ minimization is applied to the inverse problem to generate a range-azimuth image of the scene. Because of the antenna length, the near-field effect is considered in the CS formulation to properly image close-in targets. The resolving capability of the combined frequency-scanned antenna and CS processing is examined and compared to results based on the short-time Fourier transform and the pseudo-inverse. Both simulation and measurement data are tested to show the system performance in terms of image resolution.

• Smart Structures and Systems
• /
• v.16 no.6
• /
• pp.1133-1145
• /
• 2015

A new neural network (NN) predictive controller (NNPC) algorithm has been developed and tested in the computer simulation of active control of a nonlinear structure. In the present method an NN is used as a predictor. This NN has been trained to predict the future response of the structure to determine the control forces. These control forces are calculated by minimizing the difference between the predicted and desired responses via a numerical minimization algorithm. Since the NNPC is very time consuming and not suitable for real-time control, it is then used to train an NN controller. To consider the effectiveness of the controller on probability of damage, fragility curves are generated. The approach is validated by using simulated response of a 3 story nonlinear benchmark building excited by several historical earthquake records. The simulation results are then compared with a linear quadratic Gaussian (LQG) active controller. The results indicate that the proposed algorithm is completely effective in relative displacement reduction.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1273-1276
• /
• 1998

The HMM-Net is an architecture for a neural network that implements a hidden Markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria used for learning HMM-Net classifiers are maximum likelihood (ML), maximum mutual information (MMI), and minimization of mean squared error(MMSE). In this paper we propose an efficient learning method of HMM-Net classifiers using hybrid criteria, ML/MMSE and MMI/MMSE, and report the results of an experimental study comparing the performance of HMM-Net classifiers trained by the gradient descent algorithm with the above criteria. Experimental results for the isolated numeric digits from /0/ to /9/ show that the performance of the proposed method is better than the others in the respects of learning and recognition rates.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1009-1012
• /
• 1998

An efficient algorithm for clock skew optimization is proposed in this paper. It construct a new clock routing topology which is the generalized graph model while previous methods uses tree-structured routing topology. Edge-insertion technique is used in order to reduce the clock skew. A link-edge is inserted repeatedly between two sinks whose delay difference is large and the distance is small. As a result, the delay of a sink which has the longer delay is decreased and the clock skew is reduced. The proposed algorithm is implemented in C programming language. From the experimental results, we can get the total wire length minimization under the given skew bound.

• Proceedings of the KIEE Conference
• /
• 2003.10b
• /
• pp.197-199
• /
• 2003

This paper is Proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the KIEE Conference
• /
• 2003.10b
• /
• pp.33-35
• /
• 2003

This paper proposes inserted core type of slotless Permanent Magnet Linear Synchronous Motor(PMLSM) to improve its low thrust density. However, by inserting the core between windings of each phase, detent force is generated. Furthermore, linear motors have the feature of structurally limited length. So, it causes the end-effect in actual operation. So, this paper applies the neural network to this model to minimize detent force and maximize thrust. Also, sub-poles used the to the end parts of the mover for compensating the end-effect.

• Proceedings of the KSME Conference
• /
• 2004.11a
• /
• pp.613-618
• /
• 2004

CCS was developed and applied to rotating machines because of accurately measuring the spindle error motion without significant efforts. However, researches on the CCS have been focused on ideal cases where circumferential gaps were ignored. This paper presents the effects of circumferential gaps and proposes an optimal CCS considering the circumferential gaps. First, electrostatic analysis of the CCS that includes the circumferential gaps is performed using the FEM, and an additional capacitance due to the circumferential gap can be approximated as an equivalent extended sensor length. Second, a mathematical model of the CCS considering the circumferential gaps is derived, and the optimal CCS is determined through minimization of the weighted error amplification factor. Finally, two CCSs, both considering and ignoring the circumferential gaps, are built, and the effectiveness of the optimal design is verified through simulation and experiment.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.12 no.3
• /
• pp.191-199
• /
• 2008

This paper proposes a new method for the inverse problem of the three-dimensional reconstruction of the electrical activity of the brain from electroencephalography (EEG). Compared to conventional direct methods using additional parameters, the proposed approach solves the EEG inverse problem iteratively without any parameter. We describe the Lagrangian corresponding to the minimization problem and suggest the numerical inverse algorithm. The restriction of influence space and the lead field matrix reduce the computational cost in this approach. The reconstructed divergence of primary current converges to a reasonable distribution for three dimensional sphere head model.

• Journal of Broadcast Engineering
• /
• v.1 no.2
• /
• pp.96-107
• /
• 1996

In case of remote image retrieval via shared network or low speed link in order to make a decision for target image problems such as transmission delay are encountered. In this paper browsing and optimization techniques are proposed for fast retrieval of Image by the multiresolution representation and progressive transmission. The proposed network model was analyzed and evaluated for system's performance improvement. Interactive user-system using several multiresolution representation has shown better performance in transmission delay minimization over the single resolution image retrieval system.

• 한국신재생에너지학회:학술대회논문집
• /
• 2005.06a
• /
• pp.51-54
• /
• 2005

Generator efficiency optimization is important for economic saving and environmental pollution reduction. In general, the machine loss can be reduced by the decreasing the flux level, resulting in the significant reduction of the core loss. This paper proposesan model-based controller is used to decrement the excitation current component on the basis of measured stator current and machine parameters and the q-axis current component controls the generator torque, by which the speed of the induction generator iscontrolled according to the variation of the wind speed in order to produce the maximum output power. The generator reference speed is adjusted according to the optimum tip-speed ratio. The generated power flows into the utility grid through the back-to-back PWM converter. The grid-side converter controls the dc link voltage and the line-side power factor by the q-axis and the d-axis current control, respectively. Experimental results are shown to verify the validity of the proposed scheme.