• Geomechanics and Engineering
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• v.36 no.3
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

• Journal of Korea Multimedia Society
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• v.13 no.11
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• pp.1698-1705
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• 2010

In this paper, we propose a fast liver segmentation method from magnetic resonance(MR) images. Our method efficiently divides a MR image into a set of discrete objects, and boundaries based on the normalized gradient magnitude information. Then, the objects belonging to the liver are detected by using 2D seeded region growing with seed points, which are extracted from the segmented liver region of the slice immediately above or below the current slice. Finally, rolling ball algorithm, and connected component analysis minimizes false positive error near the liver boundaries. Our method was validated by twenty data sets and the results were compared with the manually segmented result. The average volumetric overlap error was 5.2%, and average absolute volumetric measurement error was 1.9%. The average processing time for segmenting one data set was about three seconds. Our method could be used for computer-aided liver diagnosis, which requires a fast and accurate segmentation of liver.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.74-82
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• 2011

This paper describes a control system for positioning the real-time locations of the autonomous warfare vehicles and infantry, and for avoiding collisions between them. The control system utilizes the low-cost RSSI (Received Signal Strength Indication) for positioning the locations of the wireless devices. The mathematical mean filtering processes are applied to the calculation of the RSS matrix to improve the performance for positioning the wireless devices in the multi-path propagation environment. A fuzzy rule is proposed to recover and replace the broken packets occurring in the wireless communication. The gradient and geometric triangulation algorithms are proposed to trace the real-time locations of wireless devices, based on the distances between them. The estimated location results of the geometric triangulation algorithm are compared with the results of the GPS and the gradient algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.4
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• pp.180-189
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• 2009

Currently, most automobile companies are interested in research on intelligent autonomous vehicle. They are mainly focused on driver's intelligent assistant and driver replacement. In order to develop an autonomous vehicle, lateral and longitudinal control is necessary. This paper presents a lateral and longitudinal control system for autonomous vehicle that has only mono-vision camera. For lane detection, we present a new lane detection algorithm using clothoid parabolic road model. The proposed algorithm in compared with three other methods such as virtual line method, gradient method and hough transform method, in terms of lane detection ratio. For adaptive control, we apply a vanishing point estimation to fuzzy control. In order to improve handling and stability of the vehicle, the modeling errors between steering angle and predicted vanishing point are controlled to be minimized. So, we established a fuzzy rule of membership functions of inputs (vanishing point and differential vanishing point) and output (steering angle). For simulation, we developed 1/8 size robot (equipped with mono-vision system) of the actual vehicle and tested it in the athletics track of 400 meter. Through the test, we prove that our proposed method outperforms 98 % in terms of detection rate in normal condition. Compared with virtual line method, gradient method and hough transform method, our method also has good performance in the case of clear, fog and rain weather.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.739-744
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• 2012

The genetic algorithm (GA) is regarded as one of the best ways for determining a global solution. Because it does not require calculating the design sensitivity differently from the ordinary gradient-based method, it is appropriate for the design problem in the ultra-high frequency range; the ordinary gradient-based method has difficulty in calculating the sensitivity in this range. This paper deals with nano-aperture grating topology optimization based on the GA and the ON/OFF method. The objective of this study is to maximize the transmittance in the measuring area. The simulation and optimization processes are carried out by using the commercial package COMSOL associated with Matlab programming. The final optimal design gives around 21% performance improvement, compared with the initial model.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.68-75
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• 2011

A vehicle fuel economy is very important issue in view of fuel cost and environmental regulation. It has been improved according to the performance improvement of the vehicle engine, power train and many components. It was evaluated at given mode (LA-4, FTP-75, etc) on an engine dynamometer or computer simulation program. In this paper, the fuel economy improvement cruise control algorithms as controling a vehicle velocity by road load calculated and predicted in a real expressway with gradient was studied. Firstly, the altitude and distance data which was measured with GPS sensor was already installed in the ECU of a vehicle. Then the vehicle equipped with GPS receiver is driven the same expressway. The ECU calculates the gradient angle and the in-/decreasing velocity using the gradient angle by comparing the current received distance and altitude data from GPS with the saved data ahead of the vehicle. Therefore the ECU can calculate and predict the vehicle velocity considering tolerance velocity of next position with running. Then the ECU controls the vehicle velocity to meet this predicted velocity in all section. Three cruise control algorithms with the different velocity profiles for the improvement of fuel economy are proposed and compared with the computer simulation results that the vehicle runs on Youngdong expressway. The proposed CVELCONT2 and CVELCONT3 algorithms were improved 3.7% and 4.8% of fuel economy compared with CONSTVEL which is steady cruising algorithm. These two algorithms are recommended as the Eco-cruise drive methodologies in this paper.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.314-321
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• 2007

Recently, multi-dimensional joint inversion of geophysical data based on fundamentally different physical properties is being actively studied. Joint inversion can provide a way to obtaining much more accurate image of the subsurface structure. Through the joint inversion, furthermore, it is possible to directly estimate non-geophysical material properties from geophysical measurements. In this study, we developed a new algorithm for jointly inverting dc resistivity and seismic traveltime data based on the multiple constraints: (1) structural similarity based on cross-gradient, (2) correlation between two different material properties, and (3) a priori information on the material property distribution. Through the numerical experiments of surface dc resistivity and seismic refraction surveys, the performance of the proposed algorithm was demonstrated and the effects of different regularizations were analyzed. In particular, we showed that the hidden layer problem in the seismic refraction method due to an inter-bedded low velocity layer can be solved by the joint inversion when appropriate constraints are applied.

• Journal of IKEEE
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• v.16 no.4
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• pp.328-334
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• 2012

In this paper, we analyzed the electromagnetic characteristics of the spherical frequency selective surface with different arrangement of crossed dipole slot elements for reducing the RCS(radar cross section). The three dimensional MOM(method of moment) with RWG basis is used to analyze the proposed structure. To reduce the simulation time, we applied the BiCGSTab(Biconjugate Gradient Stabilized) algorithm as an iterative method and presented the comparison results with Mie's theoretical results for PEC sphere to show the validity of this paper. From the simulation results, the different arrangement of elements array showed the difference RCS that cannot be negligible. The arrangement method of element in frequency selective surface will be one of variables for the design of curved frequency selective structures.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.1
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• pp.58-65
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• 2019

The stability, reliability and efficiency of a smart ship are important issues as the interest in an autonomous ship has recently been high. An automatic collision avoidance system is an essential function of an autonomous ship. This system detects the possibility of collision and automatically takes avoidance actions in consideration of economy and safety. In order to construct an automatic collision avoidance system using reinforcement learning, in this work, the sequential decision problem of ship collision is mathematically formulated through a Markov Decision Process (MDP). A reinforcement learning environment is constructed based on the ship maneuvering equations, and then the three key components (state, action, and reward) of MDP are defined. The state uses parameters of the relationship between own-ship and target-ship, the action is the vertical distance away from the target course, and the reward is defined as a function considering safety and economics. In order to solve the sequential decision problem, the Deep Deterministic Policy Gradient (DDPG) algorithm which can express continuous action space and search an optimal action policy is utilized. The collision avoidance system is then tested assuming the $90^{\circ}$intersection encounter situation and yields a satisfactory result.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.1-9
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• 2021

OpenGL compute shader is a shader stage that operate differently from other shader stage and it can be used for the calculating purpose of any data in parallel. This paper proposes a GPU-based parallel algorithm for computing sparse linear systems through conjugate gradient using an iterative method, which perform calculation on OpenGL compute shader. Basically, this sparse linear solver is used to solve large linear systems such as symmetric positive definite matrix. Four well-known matrix formats (Dense, COO, ELL and CSR) have been used for matrix storage. The performance comparison from our experimental tests using eight sparse matrices shows that GPU-based linear solving system much faster than CPU-based linear solving system with the best average computing time 0.64ms in GPU-based and 15.37ms in CPU-based.