• Journal of environmental and Sanitary engineering
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• v.3 no.2 s.5
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• pp.43-60
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• 1988

This study was carried out to determine traffic noise level and analyze noise reduction effects of various sound protection facilities in the area of Seoul, Inch'on, Songchoo and Seoul- Busan Expressway from March to Octover, 1987. The results were as follows; 1. As compared with the environmental standards and the traffic noise level in heavy noise areas, traffic noise levels observed were shown in higher than environmental standards. The noise levels in Seoul were determined at 12.8-18.2 dB(A) in daytime and 19.0-26.9 dB (A) in nighttime. And incase of inch'on, it were 6.7-9.6 dB(A) in daytime, 7.9-18.9 dB(A) in nighttime, respectively. 2. The environmental noise level observed in the backside of protection facilities, such as apartment, soundproof barrier and houses, which were constructed in paralled to the road was lower about 3-5 dB(A) than perpendicular to theroad. Noise recuction effect of upper stairs in apartment was higher than lower stairs. 3. The predicted noise level obtained from the equation $({\triangle}L\;=\; -10\;log\;(^{I'1}/Ii)\;was\;\pm\;1dB$ (A) and the correlation coefficient (r) was 0.923. 4. The noise reduction effect in backside of apartment was measured at on sites and predicted by total noise loss equation. The predicted noise level was 60.9 dB(A) and the measured level was 60.6 dB(A), respectively. 5. The narrow width landscape less than 10m width was almost no effect for the protection of traffic noise. According to the synthesis of the above results, the noise level of the road was exceeding mostly the environmental standard in the heavy traffic areas. The counterplan should be set as well. The insulation of noise protection facilities were effective by the location with near distance from the road edge. The reduction effect of double window in apartment was represented so much. The prediction model could be applied to estimate the noise levels in the roadside as well as the effectiveness for the noise protection facilities.

• Ocean and Polar Research
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• v.25 no.1
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• pp.63-74
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• 2003

The build-up of the heat field in shallow coastal water due to a point source has been investigated using an analytical solution of a time-integral form derived by extending the solutions by Holley(1969) and also presented in Harleman (1971). The uniform water depth is assumed with non-isotropic turbulent dispersion. The alongshore-flow is assumed to be uni-directional, spatially uniform and oscillatory. Due to the presence of the oscillatory alongshore-flow, the heat build-up occurs in an oscillatory manner, and the excess temperature thereby fluctuates in that course and even in the quasi-steady state. A series of calculations reveal that proper choices of the decay coefficient as well as dispersion coefficients are critical to the reliable prediction of the excess temperature field. The dispersion coefficients determine the absolute values of the excess temperature and characterize the shoreline profile, particularly within the tidal excursion distance, while the decay coefficient determines the absolute value of the excess temperature and the convergence rate to that of the quasi-steady state. Within the e-folding time scale $1/k_d$ (where $k_d$ is the heat decay coefficient), heat build-up occurs more than 90% of the quasi-steady state values in a region within a tidal excursion distance (L), while occurs increasingly less the farther we go to the downstream direction (about 80% at 1.25L, and 70% at 1.5L). Calculations with onshore and offshore discharges indicate that thermal spreading in the direction of the shoreline is reduced as the shoreline constraint which controls the lateral mixing is reduced. The importance of collecting long-term records of in situ meteorological conditions and clarifying the definition of the heat loss coefficient is addressed. Interactive use of analytical and numerical modeling is recommended as a desirable way to obtain a reliable estimate of the far-field excess temperature along with extensive field measurements.

• Journal of the Korean Chemical Society
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• v.30 no.2
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• pp.181-187
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• 1986

It is shown that the substitutive ligand on the photochemical substitution reactions of $trans-Cr^{Ⅲ}N_4XY$ complexes is predicted by considering the total stabilization energy of the hypothetical primary intermediates resulting from the loss of one ligand. The total stabilization energy and one electron energy level of d-orbital are calculated within the framework of angular overlap model. According to the calculated results, the intermediates with larger total stabilization energy are, as expected, more easily produced. Consequently, the relative values of the total stabilization energy are used to decide which of the ligands in $trans-Cr^{Ⅲ}N_4XY$ complexes is preferentially labilized on the lowest energy d-d irradiation. The prediction for the leaving ligand on the photoaquation of $trans-Cr^{Ⅲ}N_4XY$ complexes is consistent.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.939-949
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• 2017

Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

• The Journal of Bigdata
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• v.5 no.1
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• pp.227-239
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• 2020

This paper introduces a method for predicting the maximum power demand despite communication errors in industrial sites. Due to the recent policy of de-nuclearization in Korea, the price of electricity is inevitable, and the amount of electricity used and maximum load management for the management of power demand are becoming important issues. Accordingly, it is important to predict and manage peak power. However, problems such as loss and modulation of measured power data occur at industrial sites due to noise generated by various facilities and sensors. It is difficult to predict the exact value when measured effective power data are lost. The study presents a model for predicting and correcting anomalies and missing values when measured effective power data are lost. The models used in this study are expected to be useful in predicting peak power demand in the event of communication errors at industrial sites.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.188-188
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• 2017

Rainfall is an important input to hydrological models. The accuracy of hydrological studies for water resources and floods management depend primarily on the estimation of rainfall. Thailand is among the countries that have regularly affected by floods. Flood forecasting and warning are necessary to prevent or mitigate loss and damage. Merging near real time satellite-based precipitation estimation with relatively high spatial and temporal resolutions to ground gauged precipitation data could contribute to reducing uncertainty and increasing efficiency for flood forecasting application. This study tested the applicability of satellite-based rainfall for water resources management and flood forecasting. The objectives of the study are to assess uncertainty associated with satellite-based rainfall estimation, to perform bias correction for satellite-based rainfall products, and to evaluate the performance of the bias-corrected rainfall data for the prediction of flood events. This study was conducted using a case study of Thai catchments including the Chao Phraya, northeastern (Chi and Mun catchments), and the eastern catchments for the period of 2006-2015. Data used in the study included daily rainfall from ground gauges, telegauges, and near real time satellite-based rainfall products from TRMM, GSMaP and PERSIANN CCS. Uncertainty in satellite-based precipitation estimation was assessed using a set of indicators describing the capability to detect rainfall event and efficiency to capture rainfall pattern and amount. The results suggested that TRMM, GSMaP and PERSIANN CCS are potentially able to improve flood forecast especially after the process of bias correction. Recommendations for further study include extending the scope of the study from regional to national level, testing the model at finer spatial and temporal resolutions and assessing other bias correction methods.

• Journal of Ecology and Environment
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• v.42 no.4
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• pp.298-307
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• 2018

Background: Subalpine ecosystems at high altitudes and latitudes are particularly sensitive to climate change. In South Korea, the prediction of the species richness of subalpine plant species under future climate change is not well studied. Thus, this study aims to assess the potential impact of climate change on species richness of subalpine plant species (14 species) in the 17 mountain national parks (MNPs) of South Korea under climate change scenarios' representative concentration pathways (RCP) 4.5 and RCP 8.5 using maximum entropy (MaxEnt) and Migclim for the years 2050 and 2070. Results: Altogether, 723 species occurrence points of 14 species and six selected variables were used in modeling. The models developed for all species showed excellent performance (AUC > 0.89 and TSS > 0.70). The results predicted a significant loss of species richness in all MNPs. Under RCP 4.5, the range of reduction was predicted to be 15.38-94.02% by 2050 and 21.42-96.64% by 2070. Similarly, under RCP 8.5, it will decline 15.38-97.9% by 2050 and 23.07-100% by 2070. The reduction was relatively high in the MNPs located in the central regions (Songnisan and Gyeryongsan), eastern region (Juwangsan), and southern regions (Mudeungsan, Wolchulsan, Hallasan, and Jirisan) compared to the northern and northeastern regions (Odaesan, Seoraksan, Chiaksan, and Taebaeksan). Conclusions: This result indicates that the MNPs at low altitudes and latitudes have a large effect on the climate change in subalpine plant species. This study suggested that subalpine species are highly threatened due to climate change and that immediate actions are required to conserve subalpine species and to minimize the effect of climate change.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Journal of the Korean Society of Propulsion Engineers
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• v.24 no.6
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• pp.143-154
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• 2020

The mathematical modeling applying experimental coefficients to a conventional model was validated through the hydraulic test for the components and the full system of a small-sized liquid rocket engine's propellant supply system. According to the simulations, pressures difference for the fluid resistance components and the pump were mainly predicted. In order to improve the modeling accuracy, the loss coefficients obtained by the empirical method were applied to the modeling. Based on the governing equation of the flow or the well known empirical equation, the method of deriving the empirical coefficients was summarized and the coefficients were presented for the commercial products used in this study. The prediction results by modeling were in good agreement with the experimental data. Through the comparison with the experimental data, the factors affecting the accuracy of the simulation were analyzed and improving methods of the accuracy was proposed.