• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.227-233
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• 2004

In this paper, it is proposed that the four channel electrode arrangement at a frontal lobe and the noise reduction method using a JADE for the EEG biofeedback system. The proposed electrode arrangement is based on the retina-cornea dipole model. Using JADE and signals which are acquired by the proposed arrangement, four independent components are separated. To estimate a pure EEG component among four components, it is measured that a ratio of alpha wave to the whole signal and then the component that has a maximum value is considered as a pure EEG which the noise is eliminated. As a result of experiments, the proposed methods are effective in reduction of noises during acquisition of the EEG.

• ETRI Journal
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• v.25 no.1
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• pp.34-40
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• 2003

This paper considers a TMN-based management system for the management of public ATM switching networks using a four-level hierarchical structure consisting of one network management system, several element management systems, and several agent-ATM switch pairs. Using Jackson's queuing model, we analyze the effects of one TMN command on the performance of the component ATM switch in processing local calls. The TMN command considered is the permanent virtual call connection. We analyze four performance measures of ATM switches- utilization, mean queue length and mean waiting time for the processor directly interfacing with the subscriber lines and trunks, and the call setup delay of the ATM switch- and compare the results with those from Jackson's queuing model.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.6
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• pp.255-261
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• 2002

Recently, traction motors in trains are supplied with single phase a.c. power. After this power is converted to d.c. power, it is inverted to three phase power to operate traction motors. As going through the process of the conversion, harmonic current is generated in train. The method of conventional analysis on harmonics, studied by RTRI, is modeled with equivalent circuit of ac AT-fed electric railroad system using by the distributed constant circuit. However, this circuit as two-port network model has some difference in comparison with real system. The reason why the conventional method is different from the real system is that the conventional method dose not include three conductor groups, that is catenary, rail, and feeder, and admittance between the conductors for line capacitance. Therefore, this method has a little error. This paper proposes new method to more effectively estimate Harmonic current. In this method, numerous components in electric railway are categorized and each component is defined as a four- port network model. The equivalent circuit for the entire power supply system is also described into a four-port network model with connections of these components. In order to evaluate the efficiency and the accuracy of a proposed method, it is compared with values measured in Kyung-Bu high speed line and ones calculated by the conventional method.

• Journal of The Korean Astronomical Society
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• v.19 no.1
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• pp.33-49
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• 1986

Radial distribution of internal density has been determined for thirteen subclouds in the three giant molecular cloud complexes accompanying Mon OB1, Mon OB2 and CMa OB1 associations, We modeled their radial density structures with the density distribution of isothermal gas spheres. Most of the subclouds, nine out of the thirteen, are well described by isothermal spheres of single component; while the rest four require an additional component. Total mass and potential energy of each subcloud are also derived from the radial density structure; thermal energy and internal velocity dispersion required for sustaining the density structure are deduced from the isothermal gas model. Our derived masses of the clouds are comparable to the values determined by Blitz (1978) under LTE assumption. This agreement suggests that the correction factor for non-LTE effect on mass-estimate is not far from unity. The ratio of the gravitational potential energy to the kinetic energy of thermal motion is as large as 250; hence the thermal motion alone cannot support these clouds against the gravity. Being supported by turbulence motion with velocities of six to seven times the thermal velocity, the clouds of one-component type seem to be in equilibrium with the gravity; while the clouds of two-component type are likely to be in the stage of gravitational collapse.

• Computers and Concrete
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• v.26 no.2
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• pp.175-183
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• 2020

Fly ash has become an important component of concrete as supplementary cementitious material with the development of concrete technology. To make use of fly ash efficiently, four types of fly ash with particle size distributions that are in conformity with four functions, namely, S.Tsivilis, Andersen, Normal and F distribution, respectively, were prepared. The four particle size distributions as functions of the strength and pore structure of concrete were thereafter constructed and investigated. The results showed that the compressive and flexural strength of concrete with the fly ash that conforming to S.Tsivilis, Normal, F distribution increased by 5-10 MPa and 1-2 MPa, respectively, compared to the reference sample at 28 d. The pore structure of the concrete was improved, in which the total porosity of concrete decreased by 2-5% at 28 d. With regarding to the fly ash with Andersen distribution, it was however not conducive to the strength development of concrete. Regression model based on the grey multiple linear regression theory was proved to be efficient to predict the strength of concrete, according to the characteristic parameters of particle size and pore structure of the fly ash.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.93-106
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• 2020

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2134-2141
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• 2014

This paper addresses on a wind power system with BESS(Battery Energy Storage System). The concerned system consists of four parts: the wind speed production model, the wind turbine model, configure capacity of the battery energy storage, battery model and control of the BESS. First of all, we produce wind speed by 4-component composite wind speed model. Secondly, the maximum available wind power is determined by analyzing the produced wind speed and the characteristic curve of wind power. Thirdly, we configure capacity of the BESS according to wind speed and characteristic curve of wind speed-power. Then, we propose a control strategy to track the power reference. Finally, some simulations have been demonstrated to visualize the feasibility of the proposed methodology.

• Smart Structures and Systems
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• v.33 no.2
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• pp.145-163
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• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Journal of Forest and Environmental Science
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• v.36 no.1
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• pp.7-16
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• 2020

Tree size distribution modelling is an integral part of forest management. Most distribution yield systems rely on some flexible probability models. In this study, a simple finite mixture of two components two-parameter Weibull distribution was compared with complex four-parameter distributions in terms of their fitness to predict tree size distribution of teak (Tectona grandis Linn f) plantations. Also, a system of equation was developed using Seemingly Unrelated Regression wherein the size distributions of the stand were predicted. Generalized beta, Johnson's SB, Logit-Logistic and generalized Weibull distributions were the four-parameter distributions considered. The Kolmogorov-Smirnov test and negative log-likelihood value were used to assess the distributions. The results show that the simple finite mixture outperformed the four-parameter distributions especially in stands that are bimodal and heavily skewed. Twelve models were developed in the system of equation-one for predicting mean diameter, seven for predicting percentiles and four for predicting the parameters of the finite mixture distribution. Predictions from the system of equation are reasonable and compare well with observed distributions of the stand. This simplified mixture would allow for wider application in distribution modelling and can also be integrated as component model in stand density management diagram.