• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.8
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• pp.531-540
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• 2022

In this paper, we present an algorithm that identifies artillery type and rapidly predicts the impact point based on the IMM filter. The ballistic trajectory equation is used as a system model, and three models with different ballistic coefficient values are used. Acceleration was divided into three components of gravity, air resistance, and lift. And lift acceleration was added as a new state variable. The kinematic condition that the velocity vector and lift acceleration are perpendicular was used as a pseudo-measurement value. The impact point was predicted based on the state variable estimated through the IMM filter and the ballistic coefficient of the model with the highest mode probability. Instead of the commonly used Runge-Kutta numerical integration for impact point prediction, a semi-analytic method was used to predict impact point with a small amount of calculation. Finally, a state variable initialization method using the least-square method was proposed. An integrated algorithm including artillery type identification, impact point prediction and initialization was presented, and the validity of the proposed method was verified through simulation.

• Journal of IKEEE
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• v.24 no.2
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• pp.570-578
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• 2020

In this paper, we propose an efficient method for implementing the adaptive second-order Volterra filter. To reduce computational load, the UCFD-SVF has been proposed. The UCFD-SVF, however, shows deteriorated convergence performance. We propose a new method that initializes the adaptive filter weights periodically on the fact that the energy of the filter weights is slowly increased. Furthermore, we propose another method that the interval for the weight initialization is variable to guarantee the performance and we shows the method gives the better performance under the non-stationary environment through the computer simulation for the adaptive system identification.

• Journal of the Society of Naval Architects of Korea
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• v.44 no.4
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• pp.398-407
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• 2007

A smoothed particle hydrodynamics (SPH) method is applied for simulating two-dimensional free-surface problems. The SPH method based on the Lagrangian formulation provides realistic flow motions with violent surface deformation, fragmentation and reunification. In this study, the effect of computational parameters in SPH simulation is explored through two-dimensional dam-breaking and sloshing problem. The parameters to be considered are the speed of sound, the frequency of density re-initialization, the number of particle and smoothing length. Through a series of numerical test. detailed information was obtained about how SPH solution can be more stabilized and improved by adjusting computational parameters. Finally, some numerical simulations for various fluid flow problem were carried out based on the parameters chosen through the sensitivity study.

• ETRI Journal
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• v.45 no.3
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• pp.418-432
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• 2023

In this work, we proposed a low-complexity hybrid layered tabu-likelihood ascent search (LTLAS) algorithm for large multiple-input multiple-output (MIMO) system. The conventional layered tabu search (LTS) approach involves many partial reactive tabu searches (RTSs), and each RTS requires an initialization and searching phase. In the proposed algorithm, we restricted the upper limit of the number of RTS operations. Once RTS operations exceed the limit, RTS will be replaced by low-complexity likelihood ascent search (LAS) operations. The block-based detection approach is considered to maintain a higher signal-to-noise ratio (SNR) detection performance. An efficient precomputation technique is derived, which can suppress redundant computations. The simulation results show that the bit error rate (BER) performance of the proposed detection method is close to the conventional LTS method. The complexity analysis shows that the proposed method has significantly lower computational complexity than conventional methods. Also, the proposed method can reduce almost 50% of real operations to achieve a BER of 10^-3.

• ETRI Journal
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• v.31 no.5
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• pp.585-592
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• 2009

This paper presents a method for decoding high minimal distance ($d_{min}$) short codes, termed Cortex codes. These codes are systematic block codes of rate 1/2 and can have higher$d_{min}$ than turbo codes. Despite this characteristic, these codes have been impossible to decode with good performance because, to reach high $d_{min}$, several encoding stages are connected through interleavers. This generates a large number of hidden variables and increases the complexity of the scheduling and initialization. However, the structure of the encoder is well suited for analog decoding. A proof-of-concept Cortex decoder for the (8, 4, 4) Hamming code is implemented in subthreshold 0.25-${\mu}m$ CMOS. It outperforms an equivalent LDPC-like decoder by 1 dB at BER=$10^{-5}$ and is 44 percent smaller and consumes 28 percent less energy per decoded bit.

• International journal of advanced smart convergence
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• v.6 no.4
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• pp.73-79
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• 2017

The Convolutional Neural Network (CNN) has shown an excellent performance in computer vision task. Applications of CNN include image classification, object detection in images, autonomous driving, etc. This paper will evaluate the performance of CNN model with ReLU and SELU as activation function. The evaluation will be performed on four different choices of hyperparameter which are initialization method, network configuration, optimization technique, and regularization. We did experiment on each choice of hyperparameter and show how it influences the network convergence and test accuracy. In this experiment, we also discover performance improvement when using SELU as activation function over ReLU.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1270-1275
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• 2013

A lot of research about camera calibration and lens distortion for wide-angle lens has been made. Especially, calibration for fish-eye lens which has 180 degree FOV(field of view) or above is more tricky, so existing research employed a huge calibration pattern or even 3D pattern. And it is important that calibration parameters (such as distortion coefficients) are suitably initialized to get accurate calibration results. It can be achieved by using manufacturer information or lease-square method for relatively narrow FOV(135, 150 degree) lens. In this paper, without any previous manufacturer information, camera calibration and barrel undistortion for fish-eye lens with over 180 degree FOV are achieved by only using one calibration pattern image. We applied QR decomposition for initialization and Regularization for optimization. With the result of experiment, we verified that our algorithm can achieve camera calibration and image undistortion successfully.

• Smart Structures and Systems
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• v.5 no.5
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• pp.507-515
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• 2009

The present work is an alternative methodology in order to balance a nonlinear highly flexible rotor by using neural networks. This procedure was developed aiming at improving the performance of classical balancing methods, which are developed in the context of linearity between acting forces and resulting displacements and are not well adapted to these situations. In this paper a fully experimental procedure using neural networks is implemented for dealing with the adaptive balancing of nonlinear rotors. The nonlinearity results from the large displacements measured due to the high flexibility of the foundation. A neural network based meta-model was developed to represent the system. The initialization of the learning procedure of the network is performed by using the influence coefficient method and the adaptive balancing strategy is prone to converge rapidly to a satisfactory solution. The methodology is tested successfully experimentally.

• Journal of Aerospace System Engineering
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• v.10 no.1
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• pp.8-14
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• 2016

The optimal estimation of a bearing only target tracking problem be achieved through the solution of the Fokker-Planck equation and the Bayesian update. Recently, a nonlinear filtering algorithm using a direct quadrature method of moments in which the associated Fokker-Planck equation can be propagated efficiently and accurately was proposed. Although this approach has demonstrated its promising in the field of nonlinear filtering in several examples, the "degeneracy" phenomenon, similar to that which exists in a typical particle filter, occasionally appears because only the weights are updated in the modified Bayesian rule in this algorithm. Therefore, in this paper to enhance the performance, a more stable measurement update process based upon the update equation in the Extended Kalman filters and a more accurate initialization and re-sampling strategy for weight and abscissas are proposed. Simulations are used to show the effectiveness of the proposed filter and the obtained results are promising.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.12
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• pp.229-240
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• 1995

This paper proposes a new weighted random pattern testing technique to detect path delay faults in combinational logic circuits. When computing the probability of signal transition at primitive logic elements of CUT(Circuit Under Test) by the primary input, the proposed technique uses the information on the structure of CUT for initialization vectors and vectors generated by pseudo random pattern generator for test vectors. We can sensitize many paths by allocating a weight value on signal lines considering the difference of the levels of logic elements. We show that the proposed technique outperforms existing testing method in terms of test length and fault coverage using ISCAS '85 benchmark circuits. We also show that the proposed testing technique generates more robust test vectors for the longest and near-longest paths.