• Journal of Korea Port Economic Association
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• v.28 no.1
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• pp.105-122
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• 2012

This paper proposes a fuzzy-based AHP model by which the greenhouse gas reduction for container terminal problem was systematically structured and then evaluated. The model was established by exploiting a fuzzy theory and AHP for capturing the inexactness and vagueness of information. In this study, measurement areas were selected for equipment aspect, operating aspect, and energy aspect. The greenhouse gas reduction is the number one priority in the equipment aspect, operating aspect, energy aspect in order. The analysis result of equipment aspect reveals that the most important element is electrical T/C. The most important element of operating and energy aspect were a container rehandling and a LED lighting. As for the whole priority which conversion weight was applied, the results were shown as follows: an electrical T/C(16.2%) as the first rank: a hybrid Y/T(14.4%) as the second rank: a AMP(10.6%) as the third rank. The result of this study suggests some guidelines for deciding priority of greenhouse gas reduction for container terminal.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.454-460
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• 2020

Artificial intelligence assistants that provide speech recognition operate through cloud-based voice recognition with high accuracy. In cloud-based speech recognition, Wake-Up-Word (WUW) detection plays an important role in activating devices on standby. In this paper, we compare the performance of Convolutional Neural Network (CNN)-based WUW detection models for mobile devices by using Google's speech commands dataset, using the spectrogram and mel-frequency cepstral coefficient features as inputs. The CNN models used in this paper are multi-layer perceptron, general convolutional neural network, VGG16, VGG19, ResNet50, ResNet101, ResNet152, MobileNet. We also propose network that reduces the model size to 1/25 while maintaining the performance of MobileNet is also proposed.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.211-221
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• 2016

The increase of world's population and economic development are the keys drivers behind growing demand for energy. Especially the demand for electricity would eventually result in an increase of coal usage. Therefore ultra supercritical circulating fluidized bed boiler has been developed as solutions of economic eco-friendly technologies for coal and of increasing supplies of low grade fuels. Ultra supercritical circulating fluidized bed boiler has an once through type of steam cycle different from drum type in subcritical circulating fluidized bed boiler. Also, the duplication of a proven commercial module with 100-300 MWe subcritical circulating fluidized bed might be the key for design of 500~800 MWe ultra supercritical circulating fluidized bed boiler. After 2017, ultra supercritical circulating fluidized bed boiler might become standard model over subcritical circulating fluidized bed boiler. Therefore, this paper will help you to understand ultra super critical circulating fluidized bed (USC-CFB) through describing the background, status and prospect of the CFB technology.

• Korean Journal of Construction Engineering and Management
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• v.14 no.5
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• pp.153-163
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• 2013

As recent buildings have been more higher and larger, construction vertical lifting planning and operation is a key factor for successful project in tall building. Although many studies have been trying to set up a construction lifting planning system at early stage, there's not existing a control real-time lift operation control system with respect to during construction stage. Therefore, In this study, we use the sensor device to collect the lift operating data for improvement of lift operation efficiency and develope optimum lift operating system which can perform real-time analysis. Finally, we verify the efficiency of proposed system through comparison between realtime operating data and simulated data using proposing system. In this paper, the proposed system show more efficient moving line compared with previous system. This can contribute to development of unmanned lift system.

• Atmosphere
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• v.20 no.2
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• pp.131-151
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• 2010

This study establishes a conceptual model to analyze heavy rainfall events in Korea using multi-functional transport satellite-1R satellite images. Three heavy rainfall episodes in two major synoptic types, such as synoptic low (SL) type and synoptic flow convergence (SC) type, are analyzed through a conceptual model procedure which proceeds on two steps: 1) conveyer belt model analysis to detect convective area, and 2) cloud top temperature analysis from black body temperature (TBB) data to distinguish convective cloud from stratiform cloud, and eventually estimate heavy rainfall area and intensity. Major synoptic patterns causing heavy rainfall are Changma, synoptic low approach, upper level low in the SL type, and upper level low, indirect effect of typhoon, convergence of tropical air in the SC type. The relationship between rainfall and TBBs in overall well resolved areas of heavy rainfall. The SC type tended to underestimate the intensity of heavy rainfall, but the analysis with the use of water vapor channel has improved the performance. The conceptual model improved a concrete utilization of images and data of satellite, as summarizing characteristics of major synoptic type causing heavy rainfall and composing an algorism to assess the area and intensity of heavy rainfall. The further assessment with various cases is required for the operational use.

• Atmosphere
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• v.26 no.1
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• pp.85-95
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• 2016

In this study, we investigated the variabilities of wind speed of 850 hPa and precipitable water over the East Asia region using the NCEP Final Analysis data from December 2001 to November 2011. A large variance of wind speed was observed in northern and eastern China during the winter period. During summer, the regions of the East China Sea, the South Sea of Japan and the East Sea show large variances in the wind speed caused by an extended North Pacific High and typhoon activities. The large variances in the wind speed in the regions are shown to be correlated with the inter-annual variability of precipitable water over the inland region of windward side of the Korean Peninsula. Based on the investigation, sensitivity tests to the domain size were performed using the WRF model version 3.6 for heavy precipitation events over the Korean Peninsula for 26 and 27 July 2011. Numerical experiments of different domain sizes were set up with 5 km horizontal and 50 levels vertical resolutions for the control and the first experimental run, and 9 km horizontal for the second experimental run. We found that the major rainfalls correspond to shortwave troughs with baroclinic structure over Northeast China and extended North Pacific High. The correlation analysis between the observation and experiments for 1-h precipitation indicated that the second experiment with the largest domain had the best performance with the correlation coefficient of 0.79 due to the synoptic-scale systems such as short-wave troughs and North Pacific High.

• Atmosphere
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• v.26 no.1
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• pp.59-72
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• 2016

The comparison of prediction errors in geopotential height, temperature, and precipitation forecasts is made quantitatively to evaluate medium-range forecast skills between Global Seasonal Forecasting System version 5 (GloSea5) and Unified Model (UM) in operation by Korea Meteorological Administration during 2014. In addition, the performances in prediction of sea surface temperature anomaly in NINO3.4 region, Madden and Julian Oscillation (MJO) index, and tropical storms in western north Pacific are evaluated. The result of evaluations appears that the forecast skill of UM with lower values of root-mean square error is generally superior to GloSea5 during forecast periods (0 to 12 days). The forecast error tends to increase rapidly in GloSea5 during the first half of the forecast period, and then it shows down so that the skill difference between UM and GloSea5 becomes negligible as the forecast time increases. Precipitation forecast of GloSea5 is not as bad as expected and the skill is comparable to that of UM during 10-day forecasts. Especially, in predictions of sea surface temperature in NINO3.4 region, MJO index, and tropical storms in western Pacific, GloSea5 shows similar or better performance than UM. Throughout comparison of forecast skills for main meteorological elements and weather extremes during medium-range, the effects of initial and model errors in atmosphere-ocean coupled model are verified and it is suggested that GloSea5 is useful system for not only seasonal forecasts but also short- and medium-range forecasts.

• Atmosphere
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• v.26 no.1
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• pp.1-18
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• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• Atmosphere
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• v.24 no.2
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• pp.245-251
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• 2014

Statistical trajectory analysis has been widely used to identify potential source regions for chemically and radiatively important chemical species in the atmosphere. The most widely used method is a statistical source-receptor model developed by Stohl (1996), of which the underlying principle is that elevated concentrations at an observation site are proportionally related to both the average concentrations on a specific grid cell where the observed air mass has been passing over and the residence time staying over that grid cell. Thus, the method can compute a residence-time-weighted mean concentration for each grid cell by superimposing the back trajectory domain on the grid matrix. The concentration on a grid cell could be used as a proxy for potential source strength of corresponding species. This technical note describes the statistical trajectory approach and introduces its application to estimate potential source regions of $CO_2$ enhancements observed at Korean Global Atmosphere Watch Observatory in Anmyeon-do. Back trajectories are calculated using HYSPLIT 4 model based on wind fields provided by NCEP GDAS. The identified $CO_2$ potential source regions responsible for the pollution events observed at Anmyeon-do in 2010 were mainly Beijing area and the Northern China where Haerbin, Shenyang and Changchun mega cities are located. This is consistent with bottom-up emission information. In spite of inherent uncertainties of this method in estimating sharp spatial gradients within the vicinity of the emission hot spots, this study suggests that the statistical trajectory analysis can be a useful tool for identifying anthropogenic potential source regions for major GHGs.

• Atmosphere
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• v.27 no.2
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• pp.235-249
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• 2017

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.