• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.419-424
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• 2019

In this paper, we proposed an algorithm to estimate the desired target in wideband jamming signal environment. In order to suppress the jamming signal, we use the spatial time adaptive algorithm and QR decomposition to obtain the optimal weight. The spatial time adaptive algorithm of adaptive array antenna system multiplies the tap delay signal by a complex weight to obtain a weight. In order to minimize the power consumption because of the inverse matrix, optimal weight is obtained by using QR decomposition. Through simulation, we compare and analyze the performance of the proposed algorithm and the existing algorithm. In the target estimation of [-40o,0o,+40o], the proposed algorithm estimated all three targets, but the existing algorithm estimated only [0o] due to of the jamming signal. We prove that the proposed algorithm improves performance by removing the jamming signal and estimating the target accurately.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.400-404
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• 2015

In this paper, we study for direction of arrival MUSIC spatial spectrum algorithm in order to desired signal estimation in spatial. Proposal MUSIC spatial spectrum algorithm in paper use model error and Bayesian method to estimation on correct target position. Receiver array response vector using adaptive array antenna use Bayesian method, and target position estimate to update weight value with model error method. Target's signal estimation of desired direction of arrival in this paper apply weight value of signal covariance matrix for array response vector after removing incident signal interference and noise, respectively. Though simulation, we analyze to compare proposed method with general method.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.11-20
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• 2017

In this paper, we introduced linear spatial time series (space-time autoregressive and moving average model) and nonlinear spatial time series (space-time bilinear model). Also we estimated the parameters by Kalman Filter method and made comparative studies of power of forecast in the final model. We proposed several weight matrices such as equal proportion allocation, reciprocal proportion between distances, and proportion of population sizes. For applications, we collected Mumps data at Korea Center for Disease Control and Prevention from January 2001 until August 2008. We compared three approaches of weight matrices using the Mumps data. Finally, we also decided the most effective model based on sum of square forecast error.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.403-410
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• 2004

Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.

• Journal of Korean Association for Spatial Structures
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• v.23 no.1
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• pp.25-34
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• 2023

This paper presents a computational fluid dynamics (CFD) analysis to investiagate the effect of expansion chamber on overpressure reduction in protective tunnels subjected to detonation of high explosives. A commercial CFD code, Viper::Blast, was used to model the blast waves in a protective tunnel with a length of 160 m, width of 8.9 m and height of 7.2 m. Blast scenarios and simulation matrix were establihsed in consideration of the design parameters of expansion chamber, including the chamber lengths of 6.1 m to 12.1 m, widths of 10.7 m to 97 m, length to width ratios of 0.0 to 5.0, heights of 8.0 m and 14.9 m, and ratios of chamber to tunnel width of 1.2 to 10.9 m. A charge weight of TNT of 1000 kg was used. The mesh sizes of the numerical model of the protective tunnel were determined based on a mesh convergence study. A parametric study based on the simulation matrix was performed using the proposed CFD tunnel model and the optimized shape of expansion chamber of the considered tunnel was then proposed based on the numerical results. Design recommendations for the use of expansion chamber in protective tunnel under blast loads to reduce the internal overpressures were finally provided.

• Structural Engineering and Mechanics
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• v.43 no.5
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• pp.583-605
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• 2012

This paper presents a spatial catenary cable element for the nonlinear analysis of cable-supported structures. An incremental-iterative solution based on the Newton-Raphson method is adopted for solving the equilibrium equation. As a result, the element stiffness matrix and nodal forces are determined, wherein the effect of self-weight and pretension are taken into account. In the case of the initial cable tension is given, an algorithm for form-finding of cable-supported structures is proposed to determine precisely the unstressed length of the cables. Several classical numerical examples are solved and compared with the other available numerical methods or experiment tests showing the accuracy and efficiency of the present elements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4600-4616
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• 2021

In cognitive radio networks where unlicensed secondary users opportunistically access to licensed spectrum unused by licensed primary users, spectrum sensing is one of the key issues in order to effectively use the frequency resource. For enhancing the sensing performance in energy detection-based spectrum sensing, spatial diversity based on multiple antennas is utilized. However, the sensing performance can be degraded when antennas are spatially correlated, resulting in inducing the harmful interference to primary users. To overcome this problem, in this paper, an advanced energy detector is proposed. In the proposed sensing method, a weight matrix based on the eigenvalues of the spatial channels without any prior information on the primary signals is defined and utilized. In numerical simulations, it is shown that the proposed detector outperforms the conventional detector with regard to false-alarm and detection probabilities when antenna are spatially correlated.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.137-142
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• 2013

Direction of arrival is estimating for desire signal direction among received signal on antenna in spatial. In this paper, we were an estimation a receiving signal direction of arrival using multi beam forming in radar. We proposed, by signal direction of arrival estimation method, an algorithm which combine spatial correlation matrix weight value and beam steering algorithm in this paper. Through simulation, we were analysis a performance to compare general algorithm and proposal algorithm. In direction of arrival estimation, proposed algorithm is effectivity to decrease processing time because it is not doing an eigen decomposition. We showed that proposal algorithm improve more target estimation than general algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.3
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• pp.224-228
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• 2015

In this paper, we study the method for desired signals estimation that array antennas are received signals. We apply sub spatial method of direction of arrival algorithm and adaptive array antennas in order to remove interference and noise signal of received antenna signals. Array response vector of adaptive array antenna is probability, it is correctly estimation of direction of arrival of targets to update weight signal. Desired signals are estimated updating covariance matrix after moving interference and noise signals among received signals. We estimate signals using eigen decomposition and eigen value, high resolution direction of arrival estimation algorithm is devided signal sub spatial and noise sub spatial. Though simulation, we analyze to compare proposed method with general method.