• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.3
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• pp.247-254
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• 2008

The double-peak normal distribution function (DPDF) suggested by Cho et al.(2004) has the problems that the extremely high and low tidal elevations are frequently generated in the Monte-Carlo simulation processes because the upper and lower limits of the DPDF are unbounded in spite of the excellent goodness-offit results. In this study, the modified DPDF is suggested by introducing the upper and lower value parameters and re-scale parameters in order to remove these problems. These new parameters of the DPDF are optimally estimated by the non-linear optimization problem solver using the Levenberg-Marquardt scheme. This modified DPDF can remove completely the unrealistically generated tidal levations and give a slightly better fit than the existing DRDF. Based on the DPDF's characteristic power, the over- and under estimation problems of the design factors are also automatically intercepted, too.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.820-827
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• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

• Atmosphere
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• v.27 no.1
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• pp.67-77
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• 2017

In this study, we analyzed the characteristics of flow around the Daeyeon automatic weather station (AWS 942) and established formulas estimating inflow wind speeds at a computational fluid dynamics (CFD) model domain for the area around Pukyong national university using a computational fluid dynamics (CFD) model. Simulated wind directions at the AWS 942 were quite similar to those of inflows, but, simulated wind speeds at the AWS 942 decreased compared to inflow wind speeds except for the northerly case. The decrease in simulated wind speed at the AWS 942 resulted from the buildings around the AWS 942. In most cases, the AWS 942 was included within the wake region behind the buildings. Wind speeds at the inflow boundaries of the CFD model domain were estimated by comparing simulated wind speeds at the AWS 942 and inflow boundaries and systematically increasing inflow wind speeds from $1m\;s^{-1}$ to $17m\;s^{-1}$ with an increment of $2m\;s^{-1}$ at the reference height for 16 inflow directions. For each inflow direction, calculated wind speeds at the AWS 942 were fitted as the third order functions of the inflow wind speed by using the Marquardt-Levenberg least square method. Estimated inflow wind speeds by the established formulas were compared to wind speeds observed at 12 coastal AWSs near the AWS 942. The results showed that the estimated wind speeds fell within the inter quartile range of wind speeds observed at 12 coastal AWSs during the nighttime and were in close proximity to the upper whiskers during the daytime (12~15 h).

• Journal of the Society of Naval Architects of Korea
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• v.52 no.1
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• pp.88-95
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• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.88-95
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• 2015

This paper presents the study of the efficient localization algorithm for non-linear least square estimation. Although non-linear least square(NLS) estimation algorithms are more accurate algorithms than linear least square(LLS) estimation, NLS algorithms have more computation loads because of iterations. This study proposed the efficient algorithm which reduced complexity for small accuracy loss in NLS estimation. Simulation results show the accuracy and complexity of the localization system compared to the proposed algorithm and conventional schemes.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.385-396
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• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.147-159
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• 2010

The settlement prediction is very important in preloading method for a construction site on the soft ground. At the design stage, however, it is hard to predict the settlement exactly due to limitations of the site survey. Most of the settlement prediction is performed by a regression settlement curve based on the field data during construction. In Korea, hyperbolic method has been most commonly used to align the settlement curve with the field data, because of its simplicity and many application cases. The results from hyperbolic method, however, may differ by data selections or data fitting methods. In this study, the analyses using hyperbolic method were performed about the field data of $\bigcirc\bigcirc$ site in Pusan. Two data fitting methods, using an axis transformation or an alternative method which is a direct regression method, were applied with various data groups. If data was used only after the ground water level being stabilized, fitting results using both methods were in good agreement with the measured data. Regardless of the information about the ground water level, the alternative method gives better results with the field data than the method using an axis transformation.

• The Korean Journal of Nuclear Medicine
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• v.29 no.1
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• pp.48-53
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• 1995

This study investigates a simple mathematical model for the quantitative estimation of regional myocardial blood flow in experimental canine coronary artery thrombosis using Rb-82 dynamic myocardial positron emission tomography. The coronary thrombosis was induced using the new catheter technique by narrowing the lumen of coronary vessel gradually, which finally led to partial obstruction of coronary artery. Ten Rb-82 dynamic myocardial PET scans were performed sequentially for each experiment using our 5, 10 and 20 second acquisition protocol, respectively, and three regions of interest were drawn on the transaxial slices, one on left ventricular chamber for input function and the other two on normal and decreased perfusion segments for the flow estimation in those regions. Single compartment model has been applied to the measured sets of regional PET data, and the rate constants of influx to myocardial tissue were calculated for regional myocardial flow estimates with the three parameter fits of raw data by the Levenberg-Marquardt method. The results showed that, (1) single compartment model suggested by Kety-Schmidt could be used for the simple estimation of regional myocardial blood flow, (2) the calculated regional myocardial blood flow estimates were dependent on the selection of input function, which reflected partial volume effect and left ventricular wall motion, and (3) mathematically fitted input and tissue time activity curves were more suitable than the direct application of the measured data in terms of convergence.