• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.548-555
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• 2011

This paper presents a new method of measuring the ground's gradient using LNS(land navigation system) navigation data. When a vehicle equipped with LNS arrives at any place, LNS provides its navigation data which contain the information on vehicle's motion. We developed some formulas which can explain correlation between the vehicle's motion and ground's gradient. The proposed method using those formulas is shown to be accurate and convenient.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.11
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• pp.773-780
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• 2010

The dispersion of plume which is emitted from a chimney is governed by a lot of factors： wind, local terrain, turbulence intensity of atmosphere, and temperature, etc. In this study, we numerically investigate the plume dispersions for various altitudinal temperature gradients and wind speeds. The normal atmosphere has the temperature decrease of $0.6^{\circ}C/100m$, however, actually the real atmosphere has the various altitudinal temperature profiles according to the meteorological factors. A previous study focused on this atmospheric temperature gradient which induces a large scale vertical flow motion in the atmosphere thus makes a peculiar plume dispersion characteristics. In this paper, the effects of the atmospheric temperature gradient as well as the wind speed are investigated concurrently. The results for the developing processes in the atmosphere and the affluent's concentrations at the ambient and ground level are compared under the various altitudinal temperature gradients and wind speeds.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.377-378
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• 2010

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• Korean Journal of Materials Research
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• v.17 no.5
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• pp.239-243
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• 2007

The evaluation and comparison have been made for the EMI noise which was included in the signal from the sensors in the acoustic emission testing for the bottom plate of ground tank at full. The EMI signal has been classified into two types. One is the signal with very short AE count, and this signal possibly can be filtered by front end filter setting of the channel count with low level of 4 and high level of $10^8$. The other EMI signal occurred from CH 1, CH 3 and CH 10, and had high and constant duration with high energy and count (maximun duration > $10^5\;{\mu}s$), and has characteristic gradient of accumulation amplitude distribution. This signal should be removed in the AE signal evaluation by filtering, because this may affect to the total gradient.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.1
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• pp.86-92
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• 2012

When missile is launched, a launcher needs to be leveled with accuracy to avoid the systems's instability. In general, a launcher is leveled by adjusting the stroke of leveling jacks; however, it is still challenge to control the leveling jacks fast and accurately. This paper thus proposed an innovative algorithm for compensating ground inclination of a missile launcher to expand operational field of a missile launcher. Using two inclinometers attached on a launcher, a base jack for leveling is selected and the mixed gradient where launcher stands on can be estimated. Due to the limited stroke, the launcher can compensate its ground inclination within maximum stroke margin. Then, the ground inclination of a launcher can be compensated by calculated angle using weighting factors. The effectiveness of proposed algorithm is proved with a prototype missile launcher.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.

• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.9-14
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• 2006

Estimations of depth, magnetic orientation, and strength of dipole moments aid discrimination between unexploded ordnance (UXO) and non-UXO using magnetic surveys. Such estimations may be hindered by geologic noise, magnetic clutter, and overlapping tails of nearby dipole fields. An improved method of inversion for anomalies of single or multiple dipoles with arbitrary polarization was developed to include intra-inversion filtering and estimation of background field gradients. Data interpolated to grids are flagged so that only nodes nearest to measurement stations are used. To apply intra-inversion filtering to such data requires a gapped filter. Moreover, for data with significant gaps in coverage, or along the edges or corners of survey areas, intra-inversion filters must be appropriately modified. To that end, edge-adaptive and gapped gradient-nulling filters have been designed and tested. Applications are shown for magnetic field data from Chongcho Lake, Sokcho, Korea and the U. S. Army's Aberdeen Proving Ground in Maryland.

• Journal of Soil and Groundwater Environment
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• v.21 no.3
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• pp.64-81
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• 2016

To analyze the influence of various groundwater flow rates (specific discharge) on BHE system with balanced and unbalanced energy loads under assuming same initial temperature (15℃) of ground and groundwater, numerical modeling using FEFLOW was used for this study. When groundwater flow is increased from 1 × 10⁻⁷ to 4 × 10⁻⁷m/s under balanced energy load, the performance of BHE system is improved about 26.7% in summer and 22.7% at winter time in a single BHE case as well as about 12.0~18.6% in summer and 7.6~8.7% in winter time depending on the number of boreholes in the grid, their array type, and bore hole separation in multiple BHE system case. In other words, the performance of BHE system is improved due to lower avT in summer and higher avT in winter time when groundwater flow becomes larger. On the contrary it is decreased owing to higher avT in summer and lower avT in winter time when the numbers of BHEs in an array are increased, Geothermal plume created at down-gradient area by groundwater flow is relatively small in balanced load condition while quite large in unbalanced load condition. Groundwater flow enhances in general the thermal efficiency by transferring heat away from the BHEs. Therefore it is highly required to obtain and to use adequate informations on hydrogeologic characterristics (K, S, hydraulic gradient, seasonal variation of groundwater temperature and water level) along with integrating groundwater flow and also hydrogeothermal properties (thermal conductivity, seasonal variation of ground temperatures etc.) of the relevant area for achieving the optimal design of BHE system.

• Steel and Composite Structures
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• v.44 no.1
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• pp.49-63
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• 2022

This study aims to develop ensemble machine learning (ML) models for estimating the peak floor acceleration and maximum top drift of steel moment frames. For this purpose, random forest, adaptive boosting, gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) models were considered. A total of 621 steel moment frames were analyzed under 240 ground motions using OpenSees software to generate the dataset for ML models. From the results, the GBRT and XGBoost models exhibited the highest performance for predicting peak floor acceleration and maximum top drift, respectively. The significance of each input variable on the prediction was examined using the best-performing models and Shapley additive explanations approach (SHAP). It turned out that the peak ground acceleration had the most significant impact on the peak floor acceleration prediction. Meanwhile, the spectral accelerations at 1 and 2 s had the most considerable influence on the maximum top drift prediction. Finally, a graphical user interface module was created that places a pioneering step for the application of ML to estimate the seismic demands of building structures in practical design.