• 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).

• 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.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• 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.

• Geomechanics and Engineering
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• v.27 no.5
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• pp.527-535
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• 2021

The creep characteristics of rock is of great significance for the study of long-term stability of engineering, so it is necessary to carry out indoor creep test and creep model of rock. First of all, in different water-bearing state and different positive pressure conditions, the granite is graded loaded to conduct indoor shear creep test. Through the test, the shear creep characteristics of granite are obtained. According to the test results, the stress-strain isochronous curve is obtained, and then the long-term strength of granite under different conditions is determined. Then, the fractional-order calculus software element is introduced, and it is connected in series with the spring element and the nonlinear viscoplastic body considering the creep acceleration start time to form a nonlinear viscoplastic creep model with fewer elements and fewer parameters. Finally, based on the shear creep test data of granite, using the nonlinear curve fitting of Origin software and Levenberg-Marquardt optimization algorithm, the parameter fitting and comparative analysis of the nonlinear creep model are carried out. The results show that the test data and the model curve have a high degree of fitting, which further explains the rationality and applicability of the established nonlinear visco-elastoplastic creep model. The research in this paper can provide certain reference significance and reference value for the study of nonlinear creep model of rock in the future.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.6
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• pp.649-656
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• 2011

A model for the numerical simulation of lithium-ion batteries (LIBs) is developed for use in battery cell design, with a view to improving the performances of such batteries. The model uses Newman-type electrochemical and transfer $theories^{(1,2)}$ to describe the behavior of the lithium-ion cell, together with the Levenberg-Marquardt optimization scheme to estimate the performance or design parameters in nonlinear problems. The mathematical model can provide an insight into the mechanism of LIB behavior during the charging/discharging process, and can therefore help to predict cell performance. Furthermore, by means of least-squares fitting to experimental discharge curves measured at room temperature, we were able to obtain the values of transport and kinetic parameters that are usually difficult to measure. By comparing the calculated data with the life-test discharge curves (SB LiMotive cell), we found that the capacity fade is strongly dependent on the decrease in the reaction area of active materials in the anode and cathode, as well as on the electrolyte diffusivity.

• Geophysics and Geophysical Exploration
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• v.21 no.1
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• pp.15-25
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• 2018

Seismic inversion is a high-resolution tool to delineate the subsurface structures which may contain oil or gas. On the other hand, marine controlled-source electromagnetic (mCSEM) inversion can be a direct tool to indicate hydrocarbon. Thus, the joint inversion using both EM and seismic data together not only reduces the uncertainties but also takes advantage of both data simultaneously. In this paper, we have developed a simultaneous joint inversion approach for the direct estimation of reservoir petrophysical parameters, by linking electromagnetic and seismic data through rock physics model. A cross-gradient constraint is used to enhance the resolution of the inversion image and the maximum likelihood principle is applied to the relative weighting factor which controls the balance between two disparate data. By applying the developed algorithm to the synthetic model simulating the simplified gas field, we could confirm that the high-resolution images of petrophysical parameters can be obtained. However, from the other test using the synthetic model simulating an anticline reservoir, we noticed that the joint inversion produced different images depending on the model constraint used. Therefore, we modified the algorithm which has different model weighting matrix depending on the type of model parameters. Smoothness constraint and Marquardt-Levenberg constraint were applied to the water-saturation and porosity, respectively. When the improved algorithm is applied to the anticline model again, reliable porosity and water-saturation of reservoir were obtained. The inversion results indicate that the developed joint inversion algorithm can be contributed to the calculation of the accurate oil and gas reserves directly.

• 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.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.565-574
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• 2005

It is used water quality data that was measured at Pyeongchanggang real time monitoring stations in Namhan river. These characteristics were analyzed with the water qualify of rainy and nonrainy periods. TOC (Total Organic Carbon) data of rainy periods has correlation with discharge and shows high values of mean, maximum, and standard deviation. DO (Dissolved Oxygen) value of rainy periods is lower than those of nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water qualify forecasting models were applied. LMNN, MDNN, and ANFIS models have achieved the highest overall accuracy of TOC data. LMNN (Levenberg-Marquardt Neural Network) and MDNN (MoDular Neural Network) model which are applied for DO forecasting shows better results than ANFIS (Adaptive Neuro-Fuzzy Inference System). MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. The observation of discharge and water quality are effective at same point as well as same time for real time management. But there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. So discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and the water quality forecasting model is linked to the runoff forecasting model. That linked model shows the improvement of waterquality forecasting.

• 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.