• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.6
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• pp.1004-1014
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• 2001

The fully automatic algorithm from initial finite element mesh generation to remeshing in two dimensional geometry is introduced using bubble packing method (BPM) for finite element analysis. BPM determines the node placement by force-balancing configuration of bubbles and the triangular meshes are made by Delaunay triangulation with advancing front concept. In BPM, we suggest two node-search algorithms and the adaptive/recursive bubble controls to search the optimal nodal position. To use the automatically generated mesh information in FEA, the new enhanced bandwidth minimization scheme with high efficiency in CPU time is developed. In the remeshing stage, the mesh refinement is incorporated by the control of bubble size using two parameters. And Superconvergent Patch Recovery (SPR) technique is used for error estimation. To verify the capability of this algorithm, we consider two elasticity problems, one is the bending problem of short cantilever beam and the tension problem of infinite plate with hole. The numerical results indicate that the algorithm by BPM is able to refine the mesh based on a posteriori error and control the mesh size easily by two parameters.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.806-811
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• 1994

Two kinds of static and dynamic state estimation methods are newly discussed for the problem of the measurement disturbance of environmental low-frequency noise in the presence of wind-induced noise. First, the probability characteristics of wind-induced noise are discussed in the form of probability distribution conditioned by wind speed, based on the simultaneous observation of the wind-induced noise and wind speed near a microphone. Next, especially form the viewpoint of simplicity for practical use, two kinds of static and dynamic state estimation methods are discussed. The static estimation method using the information on wind speed is fundamentally supported by the conservation principle of energy sum. The dynamic one is the method by using a recursive digital filter with the parameters successively renewed by the information on wind speed. This can be also simplified by using well-know Kalman filter under the assumption of the Gaussian distribution. The effectiveness of proposed two estimation methods are shown through experiments under a breezy condition in the open filed.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.25-30
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• 2018

It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.543-549
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• 2021

In this paper, a robust transfer alignment method is proposed for a strapdown inertial navigation system(SDINS) with norm-bounded parametric uncertainties. The uncertainties are described by the energy bound constraint, i.e., sum quadratic constraint(SQC). It is shown that the SQC can be coverted into an indefinite quadratic cost function in the Krein space. Krein space Kalman filter is designed by modifying the measurement matrix and the variance of measurement noises in the conventional Kalman filter. Since the proposed Krein space Kalman filter has the same recursive structure as a conventional Kalman filter, the proposed filter can easily be designed. The simulation results show that the proposed filter achieves robustness against measurement time delay and high dynamic environment of the vehicle.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.357-364
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• 2023

In this study, we propose a ripe tomato bunch localization method based on convolutional neural networks, to be applied in robotic harvesting systems. Tomato images were obtained from a smart greenhouse at the Rural Development Administration (RDA). The sample images for training were extracted based on tomato maturity and resized to 128 × 128 pixels for use in the classification model. The model was constructed based on four-layer convolutional neural networks, and the classes were determined based on stage of maturity, using a Softmax classifier. The localization of the ripe tomato bunch region was indicated on a class activation map. The class activation map could show the approximate location of the tomato bunch but tends to present a local part or a large part of the ripe tomato bunch region, which could lead to poor performance. Therefore, we suggest a recursive method to improve the performance of the model. The classification results indicated that the accuracy, precision, recall, and F1-score were 0.98, 0.87, 0.98, and 0.92, respectively. The localization performance was 0.52, estimated by the Intersection over Union (IoU), and through input recursion, the IoU was improved by 13%. Based on the results, the proposed localization of the ripe tomato bunch area can be incorporated in robotic harvesting systems to establish the optimal harvesting paths.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.17-31
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• 2004

This paper proposes an algorithm for the robust transmission of video in error Prone environment using multiple description codingby optimal split of DCT coefficients and rate-distortionoptimization framework. In MDC, a source signal is split Into several coded streams, which is called descriptions, and each description is transmitted to the decoder through different channel. Between descriptions, structured correlations are introduced at the encoder, and the decoder exploits this correlation to reconstruct the original signal even if some descriptions are missing. It has been shown that the MDC is more resilient than the singe description coding(SDC) against severe packet loss ratecondition. But the excessive redundancy in MDC, i.e., the correlation between the descriptions, degrades the RD performance under low PLR condition. To overcome this Problem of MDC, we propose a hybrid MDC method that controls the SDC/MDC switching according to channel condition. For example, the SDC is used for coding efficiency at low PLR condition and the MDC is used for the error resilience at high PLR condition. To control the SDC/MDC switching in the optimal way, RD optimization framework are used. Lagrange optimization technique minimizes the RD-based cost function, D+M, where R is the actually coded bit rate and D is the estimated distortion. The recursive optimal pet-pixel estimatetechnique is adopted to estimate accurate the decoder distortion. Experimental results show that the proposed optimal split of DCT coefficients and SD/MD switching algorithm is more effective than the conventional MU algorithms in low PLR conditions as well as In high PLR condition.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• Water for future
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• v.27 no.1
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• pp.89-99
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• 1994

Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of ø-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.55-62
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• 2023

In this paper, we present a method that utilizes geometry-based sound generation techniques to efficiently handle multiple sound sources, wind turbulence, and temperature-dependent interactions. Recently, a method based on reduced raytracing has been proposed to update the sound position and efficiently calculate sound propagation and diffraction without recursive reflection/refraction of many rays, but this approach only considers the propagation characteristics of sound and does not consider the interaction of multiple sound sources, wind currents, and temperature. These limitations make it difficult to create sound scenes in a variety of virtual environments because they only generate static sounds. In this paper, we propose a method for efficiently constructing a sound map in a situation where multiple sounds are placed, and a method for efficiently controlling the movement of an agent through it. In addition, we propose a method for controlling sound propagation by considering wind currents and temperature. The method proposed in this paper can be utilized in various fields such as metaverse environment design and crowd simulation, as well as games that can improve content immersion based on sound.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.18-27
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• 2009

In this paper, we propose a frame rate conversion algorithm using adaptive search-based motion estimation (ME). The proposed ME method uses recursive search, 3-step search, and single predicted search as candidates for search strategy. The best method among the three candidates is adaptively selected on a block basis according to the predicted motion type. The adaptation of the search method improves the accuracy of the estimated motion vectors while curbing the increase of computational load. To support the proposed ME method, an entire image is divided into three regions with different motion types. Experimental results show that the proposed FRC method achieves better image quality than existing algorithms in both subjective and objective measures.