• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.828-834
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• 1997

The knowledge of material stress-strain behavior is an essential requirement for design and analysis of deformation processes. Empirical stress-strain relationship and constitutive equations describing material behavior during deformation are being widely used, despite suffering some drawbacks in terms of ease of development, accuracy and speed. In the present study, back-propagation neural networks are used to model and predict the flow stresses of a HSLA steel under conditions of constant strain, strain rate and temperature. The performance of the network model is comparedto those of statistical models on rate equations. Well-trained network model provides fast and accurate results, making it superior to statistical models.

• JSTS:Journal of Semiconductor Technology and Science
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• v.3 no.3
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• pp.145-152
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• 2003

Electrical gate and drain characteristics of double heterostructure InAlAs/InGaAs pseudomorphic HEMTs have been investigated under sub-bandgap photonic excitation ($hv). Drain $(V_{DS})-,{\;}gate($V_{DS})-$, and optical power($P_{opt}$)-dependent variation of the abnormal gate leakage current and associated physical mechanisms in the PHEMTs have been characterized. Peak gate voltage ($V_{GS,P}$) and the onset voltage for the impact ionization ($V_{GS.II}$) have been extracted and empirical model for their dependence on the $V_{DS}$ and $P_{opt} have been proposed. Anomalous gate and drain current, both under dark and under sub-bandgap photonic excitation, have been modeled as a parallel connection of high performance PHEMT with a poor satellite FET as a parasitic channel. Sub-bandgap photonic characterization, as a function of the optical power with $h\nu=0.799eV$, has been comparatively combined with those under dark condition for characterizing the bell-shaped negative humps in the gate current and subthreshold drain leakage under a large drain bias.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.757-773
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• 2012

Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.2
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• pp.131-140
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• 2019

Diagonally reinforced concrete coupling beams (DRCBs) have been widely adopted in reinforced concrete (RC) bearing wall systems. DRCBs are known to act as a fuse element dissipating most of seismic energies imparted to the bearing wall systems during earthquakes. Despite such importance of DRCBs, the damage estimation of such components and the corresponding consequences within the knowledge of performance based seismic design framework is not well understood. In this paper, drift-based fragility functions are developed for in-plane loaded DRCBs. Fragility functions are developed to predict the damage and to decide the repair method required for DRCBs subjected to earthquake loading. Thirty-seven experimental results are collected from seventeen published literatures for this effort. Drift-based fragility functions are developed for four damage states of DRCBs subjected to cyclic and monotonic loading associated with minor cracking, severe cracking, onset of strength loss, and significant strength loss. Damage states are defined in a consistent manner. Cumulative distribution functions are fit to the empirical data and evaluated using standard statistical methods.

• Structural Engineering and Mechanics
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• v.82 no.3
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• pp.283-294
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• 2022

An inerter is a passive mechanical element whose inertance can be thousands of times its own physical mass. This paper discusses the application of an inerter-based passive control system, termed rotational inertial double-tuned mass damper (RIDTMD), to mitigate human-induced floor vibrations. First, the acceleration frequency response function of the floor with an RIDTMD is first derived. It is then employed to determine the optimal design parameters of the RIDTMD using the extended fixed-points technique. Based on a theoretical analysis, design-oriented empirical functions are proposed for the RIDTMD optimal parameters, whose performance for floor vibration control is evaluated by numerical examples, in which three typical human-induced load types are considered: walking, jumping, and bouncing. The results indicate that the applicability and effectiveness of the RIDTMD for human-induced floor vibration control are robust for various load types, load frequencies, and floor natural frequencies. For the same mass ratio, the RIDTMD is better than the TMD in reducing the floor vibration amplitude and improving the effective frequency suppression bandwidth, and for the same vibration suppression effect, the mass of the RIDTMD is much lighter than that of the TMD.

• Journal of the Korean Society of Systems Engineering
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• v.20 no.spc1
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• pp.30-44
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• 2024

The DMAIC methodology, which is most familiar to battery production system developers, is partially inadequate in its conformity to utilize battery production system tasks, so it is necessary to improve the function and structure of the methodology, but many battery production system developers use the DMAIC method based on experience, causing side effects such as confusion, delay in tasks, and insufficient performance during tasks. Accordingly, we intend to conduct an empirical study to improve the "efficiency improvement and conformity evaluation method" so that the DMAIC methodology can be used more reasonably and easily. Using the three-stage research model, we derive components that affect conformity through literature and questionnaire surveys in the first stage, use relational characteristics between components in the second stage to confirm the effect on conformity, and use the relational characteristics in the third stage to confirm the possibility of improving efficiency by applying them to the DMAIC methodology in actual cases. Finally, the "Conformity Assessment Index (CAI) equation" based on relational characteristics is established to enable effective conformity evaluation and continuous improvement.

• Journal of the Korean Recycled Construction Resources Institute
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• v.5 no.2
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• pp.177-184
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• 2017

Many ground subsidence accidents have happened in Korea. The accident was caused by the subsidence and leakage of the deteriorated sewage pipe. This study aims to establish the empirical data of the ground penetration radar(GPR) detection for ground subsidence. A test bed was also manufactured for the same purpose. The GPR detection variables are embedment depth and horizontal distance of embedded cast iron pipe and expanded polystyrene(EPS). From the detection results, the EPS embedded by a depth of 1.5m was difficult for detection. The EPS closely embedded to the cast iron pipe within a 0.5m distance had a very strong cast iron pipe signal. Therefore, the detection was impossible. This study developed an image processing program, called the GPR image processing program(GPRiPP), to process the GPR detection results. Its major function is the gain function, which amplifies the wiggle wave signal. Compared to the existing programs, the GPRiPP is capable of showing a similar image processing performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1971-1978
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• 1999

In this paper, we study the effects of long-range dependence (LRD) in VBR video traffic on queueing system. This paper consists of Part I and II. In Part I, we present a (LRD) video traffic model based on the shifting-level (SL) process. We observe that the ACF of an empirical video trace is accurately captured by the shifting-level process with compound correlation (SLCC): an exponential function in short range and a hyperbolic function in long range. We present an accurate parameter matching algorithm for video traffic. In the Part II, we offer the queueing analysis of SL/D/1/K called ‘quantization reduction method’. Comparing the queueing performances of the DAR(1) model and the SLCC with that of a real video trace, we identify the effects of SRD and LRD in VBR video traffic on queueing performance. Simulation results show that Markoivian models can estimate network performances fairly accurately under a moderate traffic load and buffer condition, whereas LRD may have a significant effect on queueing behavior under a heavy traffic load and large buffer condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.279-286
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• 2015

EMD is a fully data-driven signal processing method without using any predetermined basis function and requiring any user parameters setting. However EMD experiences a problem of mode mixing which interferes with decomposing the signal into similar oscillations within a mode. To overcome the problem, EEMD method was introduced. The algorithm performs the EMD method over an ensemble of the signal added independent identically distributed white noise of the same standard deviation. Even so EEMD created problems when the decomposition is complete. The ensemble of different signal with added noise may produce different number of modes and the reconstructed signal includes residual noise. This paper propose an modified EEMD method to overcome mode mixing of EMD, to provide an exact reconstruction of the original signal, and to separate modes with lower cost than EEMD's. The experimental results show that the proposed method provides a better separation of the modes with less number of sifting iterations, costs 20.87% for a complete decomposition of the signal and demonstrates superior performance in the signal reconstruction, compared with EEMD.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.