• Atmosphere
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• v.34 no.2
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• pp.97-106
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• 2024

The seasonal forecast skill of tropical cyclones (TCs) in the Northern Hemisphere from the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 6 (GloSea6) hindcast has been verified for the period 1993 to 2016. The operational climate prediction system at KMA was upgraded from GloSea5 to GloSea6 in 2022, therefore further validation was warranted for the seasonal predictability and variability of this new system for TC forecasts. In this study, we examine the frequency, track density, duration, and strength of TCs in the North Indian Ocean, the western North Pacific, the eastern North Pacific, and the North Atlantic against the best track data. This methodology follows a previous study covering the period 1996 to 2009 published in 2020. GloSea6 indicates a higher frequency of TC generation compared to observations in the western North Pacific and the eastern North Pacific, suggesting the possibility of more TC generation than GloSea5. Additionally, GloSea6 exhibits better interannual variability of TC frequency, which shows relatively good correlation with observations in the North Atlantic and the western North Pacific. Regarding TC intensity, GloSea6 still underestimates the minimum surface pressures and maximum wind speeds from TCs, as is common among most climate models due to lower horizontal resolutions. However, GloSea6 is likely capable of simulating slightly stronger TCs than GloSea5, partly attributed to more frequent 6-hourly outputs compared to the previous daily outputs.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.321-328
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• 2014

In this paper, we present a MapReduce-based mobility pattern mining system which can predict efficiently the next place of mobile users. It learns the mobility pattern model of each user, represented by Hidden Markov Models(HMM), from a large-scale trajectory dataset, and then predicts the next place for the user to visit by applying the learned models to the current trajectory. Our system consists of two parts: the back-end part, in which the mobility pattern models are learned for individual users, and the front-end part, where the next place for a certain user to visit is predicted based on the mobility pattern models. While the back-end part comprises of three distinct MapReduce modules for POI extraction, trajectory transformation, and mobility pattern model learning, the front-end part has two different modules for candidate route generation and next place prediction. Map and reduce functions of each module in our system were designed to utilize the underlying Hadoop infrastructure enough to maximize the parallel processing. We performed experiments to evaluate the performance of the proposed system by using a large-scale open benchmark dataset, GeoLife, and then could make sure of high performance of our system as results of the experiments.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.765-771
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• 2019

With the increase in the speed of ships and the size of ocean structures, the importance of flow noise has become increasingly critical in meeting regulatory standards. However, unlike active investigations in aeroacoustics fields for airplanes and trains, which are based on acoustic analogy methods for tonal and broadband frequency noise, only the discrete blade passing frequency noise from propellers is considered in marine fields. In this study, prediction methods for broadband noise in marine propellers and underwater appendages are investigated using FW-H Formulation1B, which can consider the mechanism of primary noise generation of trailing edge noise. The original FW-H Formulation 1B is based on the pressure correlation function tolackitsgeneralityandaccuracy. To overcome these limitations, wall-pressure spectrum models are adopted to improve the generality in fluid mediums. The comparison of the experimental results obtained in air reveals that the proposed model exhibits a higher accuracy within 5 dB. Furthermore, the prediction procedures for broadband noise for hydrofoils are established, and the estimation of broadband noise is conducted based on the results of the computational fluid dynamics.

• Economic and Environmental Geology
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• v.53 no.3
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• pp.321-336
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• 2020

The Headquarters for Earthquake Research Promotion (HERP) represents the upper-most level of Japan's earthquake disaster prevention governance. Its policy committee establishes the national earthquake investigation research promotion plan. The earthquake investigation committee of HERP collects survey geo-data and evaluates the research results of each earthquake disaster prevention agency. The establishment of an earthquake-related geo-resilience research strategy is both necessary and desirable for Korea. The concept of geo-resilience entails the ability to improve disaster resilience through the application of research results and the convergence of geoscience with science and technology (S&T) including the humanities and social sciences. The achievement of geo-resilience requires a national long-term roadmap and strategy for earthquake prediction research, the development of earthquake disaster prediction and prevention technology, Geo-ICT convergence technology development, implementation of a geocyber physics system (Geo-CPS), the use of geo-mimetics, and geoscientific R&D as it relates to local communities. Through such efforts, the national research institutes of Korea will be able to develop earthquake prediction capacities in relevant fields, reinforce proactive response capabilities, enhance community-level confidence in geodata and its research results, foster next-generation geoscientific manpower, and expand geoscientific infrastructure.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.485-493
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• 2019

The parametric array phenomenon refers to the generation of a high directivity low frequency wave from a small size radiation plate using the nonlinearity of the medium. In order to improve the usability of parametric array, the beam steering method of low frequency wave is researched, and the beam steering phenomenon is predicted easily using the PD (product directivity) model. However, the PD model can only be applied to Gaussian sources under quasi-linear conditions. Also, the prediction accuracy of low frequency wave beam width is poor. In this paper, a method for predicting the beam steering characteristics of a parametric array that can overcome the limitation of the PD model is investigated. For this purpose, the numerical analysis algorithm of the KZK (Khokhlov-Zabolotskaya-Kuzentsov) equation widely used for parametric array phenomenon prediction is improved. Thus, the beam steering characteristics are calculated by applying the electrical beam steering condition and comparing experimental results. As a result, the numerical analysis using the modified KZK equation algorithm in this study confirms that the beam steering phenomenon can be predicted even in a parametric array source that does not correspond to the quasi-linear condition.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.425-432
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• 2011

To control non-point source pollution at a watershed scale, rainfall-runoff characteristics from forest watersheds should be investigated since the forest is the dominant land use in Korea. Long-term monitoring would be an ideal method. However, computer models have been utilized due to limitations in cost and labor in performing long-term monitoring at the watersheds. In this study, the Geo-spatial interface to the Water Erosion Prediction Project (GeoWEPP) model was evaluated for its runoff prediction from a coniferous forest dominant watersheds. The $R^2$ and the NSE for calibrated result comparisons were 0.77 and 0.63, validated result comparisons were 0.92, 0.89, respectively. These comparisons indicated that the GeoWEPP model can be used in evaluating rainfall-runoff characteristics. To estimate runoff changes from a coniferous forest watershed with various cover degree scenarios, ten cover degree scenarios (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%) were run using the calibrated GeoWEPP model. It was found that runoff increases with decrease in cover degree. Runoff volume was the highest ($206,218.66m^3$) at 10% cover degree, whereas the lowest ($134,074.58m^3$) at 100% cover degree due to changes in evapotranspiration under various cover degrees at the forest. As shown in this study, GeoWEPP model could be efficiently used to investigate runoff characteristics from the coniferous forest watershed and effects of various cover degree scenarios on runoff generation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.4
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• pp.173-179
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• 2023

This paper analyzes the time-dependent dielectric breakdown(TDDB) degradation mechanism for each stress region of Peri devices manufactured by 4th generation VNAND process, and presents a complementary lifetime prediction model that improves speed and accuracy in a wider reliability evaluation region compared to the conventional model presented. SiON dielectric nMOSFETs were measured 10 times each under 5 constant voltage stress(CVS) conditions. The analysis of stress-induced leakage current(SILC) confirmed the significance of the field-based degradation mechanism in the low electric field region and the current-based degradation mechanism in the high field region. Time-to-failure(TF) was extracted from Weibull distribution to ascertain the lifetime prediction limitations of the conventional E-model and 1/E-model, and a parallel complementary model including both electric field and current based degradation mechanisms was proposed by extracting and combining the thermal bond breakage rate constant(k) of each model. Finally, when predicting the lifetime of the measured TDDB data, the proposed complementary model predicts lifetime faster and more accurately, even in the wider electric field region, compared to the conventional E-model and 1/E-model.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Information Systems Review
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• v.24 no.4
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• pp.23-40
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• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.1
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• pp.77-85
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• 2020

Currently, life-cycle cost analysis methods are introduced to maintain large infrastructure facilities in Korea. However, there are not many cases in which maintenance models are applied that reflect conditions such as the location of a facility and its surroundings. In order to establish an appropriate maintenance strategy, a cost prediction, deterioration model, and a decision model reflecting uncertainty should be established. In this study, an economic analysis model was developed for long-term cost planning and management based on user decisions based on maintenance methods and judgment criteria for painting specifications applied to power generation structures. The performance of the paintwork was assessed through the paint deterioration test for the application of the economic analysis model, and the results of the economic analysis according to the applied paint specifications (Urethan, polysiloxane, fluorine) were verified by applying the proposed economic analysis model. In this study, it is believed that the selection of the repair cycle and evaluation methods applied with the development model rather than the performance of the painting can be expected to be used as basic data for the maintenance cycle, even if it is not limited to the painting.