• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.2
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• pp.59-64
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• 2001

The distribution of current in the conductors influenced by the armature geometry and velocity is an important parameter for determining performance of an electromagnetic launcher(EML). the electric current in the early launching stage tends to flow on the outer surfaces of the conductors, resulting in very high local electric current density. However, the tendency for current to concentrate on the surface is driven by the velocity skin effect later in launching stage. The high current density produces high local heating and, consequently, increases armature wear which causes several defects on EML system. This paper investigates the effects of rail/armature geometry on current density distribution, launcher inductance gradient (L'), and contact force. Three geometrical parameters are used here to characterize the railgun system. These are the ratio of contact length to root length, relative position of contact leading edge to root trailing edge, and the ratio of rail overhang to the rail height. The distribution of current density, L', contact force between various configurations of the armature and the rail are analyzed and compared by using the EMAP3D program.

• Computers and Concrete
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• v.12 no.2
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• pp.229-241
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• 2013

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.07a
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• pp.628-631
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• 2001

Current lead is one of the first proposed devices for the application of High Temperature-Superconductor(HTSC). The current lead provides high current for electrical machine using superconductor from room temperature. Its characteristics that is zero resistance and low heat transfer rate under critical temperature lead to research for the replacement of existing current lead with HTSC. In this paper, we investigated the temperature distributions of stacking type and rod type current lead with each cross-section area and length using Nastran program and compared each temperature distribution. It is obtained from this paper that stacking type current lead has flat temperature gradient and than rod type one and more stable operation as current lead is closely related with its cross-section area and length.

• Journal of Magnetics
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• v.5 no.2
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• pp.59-64
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• 2000

A drum type of high gradient magnetic separator was designed and optimized by computer simulations. The magnetic separator consists of high performance rare earth $(Nd_2Fe_14B)$ permanent magnets and magnetic yokes of extremely low carbon steel interconnecting the permanent magnets. Magnetic circuits of the separator were simulated for the aim of the least cost, highest magnetic strength and most efficient function by using specialized S/W (Vector Field Program) employing the Finite Element Method. The magnetic flux density was provided to be strong enough to collect the invisible fine metal particles from the surface of hot rolled steel plate with the efficiency of almost 95%.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.11-19
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• 2006

Design program was developed to determine the external shape of the supersonic axisymmetric inlet by combining conical flow solver and approximation technique of conical shock with gradient-based optimization algorithm. Inlet designs were carried out under various operation conditions through optimization with respectively two object functions which consist of pressure recovery and cowl drag and with constraints about shock position, cowl shape, and minimum throat area. New object function consisting of pressure recovery and drag of the external cowl was proposed and the optimized shapes from new object function were compared to the ones from the old object function which maximize only the pressure recovery. Through computations of inviscid and turbulent flow, was tested performance of the design program and performance estimated in design program agreed well with computation results for inlets designed under various flight conditions.

• Journal of Microbiology
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• v.42 no.1
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• pp.9-13
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• 2004

Culture-independent approaches, based on 16S rDNA sequences, are extensively used in modern microbial ecology. Sequencing of the clone library generated from environmental DNA has advantages over fingerprint-based methods, such as denaturing gradient gel electrophoresis, as it provides precise identification and quantification of the phylotypes present in samples. However, to date, no method exists for comparing multiple bacterial community structures using clone library sequences. In this study, an automated method to achieve this has been developed, by applying pair wise alignment, hierarchical clustering and principle component analysis. The method has been demonstrated to be successful in comparing samples from various environments. The program, named CommCluster, was written in JAVA, and is now freely available, at http://chunlab.snu.ac.kr/commcluster/.

• Journal of the Korean Society for Railway
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• v.10 no.2 s.39
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• pp.153-160
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• 2007

To start or stop the train safely within the limited traveling distance, it is necessary to guarantee the proper traction or braking force. Presently, most trains are run by the electrical power and have adopted a combined electrical and mechanical(friction) braking system. In order to design a good traction or a brake system, it is essential for designers to predict the traction or brake performance. In this paper, the traction/coasting/braking performances analysis program has been developed and verified by comparing the simulation results with on-line test results of the Korean high speed train(HSR-350); Both results match very well. Consequently, the designers can predict the traction/coasting/braking performances of trains by using the proposed program under various operating conditions.

• Advances in nano research
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• v.10 no.2
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• pp.151-163
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• 2021

The main target of this study is to investigate nonlinear transient responses of moving polymer nano-size plates fortified by means of Graphene Platelets (GPLs) and resting on a Winkler-Pasternak foundation under a transverse pressure force and a temperature variation. Two graphene spreading forms dispersed through the plate thickness are studied, and the Halpin-Tsai micro-mechanics model is used to obtain the effective Young's modulus. Furthermore, the rule of mixture is employed to calculate the effective mass density and Poisson's ratio. In accordance with the first order shear deformation and von Karman theory for nonlinear systems, the kinematic equations are derived, and then nonlocal strain gradient scheme is used to reflect the effects of nonlocal and strain gradient parameters on small-size objects. Afterwards, a combined approach, kinetic dynamic relaxation method accompanied by Newmark technique, is hired for solving the time-varying equation sets, and Fortran program is developed to generate the numerical results. The accuracy of the current model is verified by comparative studies with available results in the literature. Finally, a parametric study is carried out to explore the effects of GPL's weight fractions and dispersion patterns, edge conditions, softening and hardening factors, the temperature change, the velocity of moving nanoplate and elastic foundation stiffness on the dynamic response of the structure. The result illustrates that the effects of nonlocality and strain gradient parameters are more remarkable in the higher magnitudes of the nanoplate speed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.1-12
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• 2020

As one of the essential resources in the agricultural process, soil moisture has been carefully managed by predicting future changes and deficits. In recent years, statistics and machine learning based approach to predict soil moisture has been preferred in academia for its generalizability and ease of use in the field. However, little is known that machine learning based soil moisture prediction is applicable in the situation of South Korea. In this sense, this paper aims to examine 1) whether publicly available weather data generated in South Korea has sufficient quality to predict soil moisture, 2) which machine learning algorithm would perform best in the situation of South Korea, and 3) whether a single machine learning model could be generally applicable in various regions. We used various machine learning methods such as Support Vector Machines (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting Machines (GBM), and Deep Feedforward Network (DFN) to predict future soil moisture in Andong, Boseong, Cheolwon, Suncheon region with open source weather data. As a result, GBM model showed the lowest prediction error in every data set we used (R squared: 0.96, RMSE: 1.8). Furthermore, GBM showed the lowest variance of prediction error between regions which indicates it has the highest generalizability.