• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.387-398
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• 2023

The COVID-19 outbreak has significantly impacted human lifestyles and patterns. It was recommended to avoid face-to-face contact and over-crowded indoor places as much as possible as COVID-19 spreads through air, as well as through droplets or aerosols. Therefore, if a person who has contacted a COVID-19 patient or was at the place where the COVID-19 patient occurred is concerned that he/she may have been infected with COVID-19, it can be fully expected that he/she will search for COVID-19 symptoms on Google. In this study, an exploratory data analysis using deep learning models(DNN & LSTM) was conducted to see if we could predict the number of confirmed COVID-19 cases by summoning Google Trends, which played a major role in surveillance and management of influenza, again and combining it with data on the number of confirmed COVID-19 cases. In particular, search term frequency data used in this study are available publicly and do not invade privacy. When the deep neural network model was applied, Seoul (9.6 million) with the largest population in South Korea and Busan (3.4 million) with the second largest population recorded lower error rates when forecasting including search term frequency data. These analysis results demonstrate that search term frequency data plays an important role in cities with a population above a certain size. We also hope that these predictions can be used as evidentiary materials to decide policies, such as the deregulation or implementation of stronger preventive measures.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1495-1504
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• 2013

Renewable energy integration and increased system complexities make system operator maintain supply and demand balance harder than before. To keep the grid frequency in a stable range, an appropriate spinning reserve margin should be procured with consideration of ever-changing system situation, such as demand, wind power output and generator failure. This paper propose a novel concept of dynamic reserve, which arrange different spinning reserve margin depending on time. To investigate the effectiveness of the proposed dynamic reserve, we developed a new short-term reliability criterion that estimates the probability of a spinning reserve shortage events, thus indicating grid frequency stability. Uncertainties of demand forecast error, wind generation forecast error and generator failure have been modeled in probabilistic terms, and the proposed spinning reserve has been applied to generation scheduling. This approach has been tested on the modified IEEE 118-bus system with a wind farm. The results show that the required spinning reserve margin changes depending on the system situation of demand, wind generation and generator failure. Moreover the proposed approach could be utilized even in case of system configuration change, such as wind generation extension.

• Atmosphere
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• v.31 no.5
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.41-50
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• 2024

Precipitation is an important component of the hydrological cycle and a key input parameter for many applications in hydrology, climatology, meteorology, and weather forecasting research. Grid-based satellite rainfall products with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions. Therefore, this study aims to evaluate the commonly used new global grid-based satellite rainfall product, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), using data collected at different spatial and temporal scales. Additionally, in this study, grid-based CHIRPS satellite precipitation data were used to evaluate the 2022 extreme drought. CHIRPS provides high-resolution precipitation data at 5 km and offers reliable global data through the correction of ground-based observations. A frequency analysis was performed to determine the precipitation deficit in 2022. As a result of comparing droughts in 2015, 2017, and 2022, it was found that May 2022 had a drought frequency of more than 500 years. The 1-month SPI in May 2022 indicated a severe drought with an average value of -1.8, while the 3-month SPI showed a moderate drought with an average value of 0.6. The extreme drought experienced in South Korea in 2022 was evident in the 1-month SPI. Both CHIRPS precipitation data and observations from weather stations depicted similar trends. Based on these results, it is concluded that CHIRPS can be used as fundamental data for drought evaluation and monitoring in unmeasured areas of precipitation.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.3
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• pp.302-310
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• 2018

Despite the accident rate for fishing vessels accounts for 70% of all maritime accidents, few studies on such accidents have been done and most of the them mainly focus on causes and mitigation policies to reduce that accident rate. Thus, this risk analysis on sea accidents is the first to be performed for the successful and efficient implementation of accident reducing measures. In risk analysis, risk is calculated based on the combination of frequency and the consequence of an accident, and is usually expressed as a single number. However, there exists uncertainty in the risk calculation process if one uses a limited number of data for analysis. Therefore, in the study we propose a probabilistic simulation method to forecast risk not as a single number, but in a range of possible risk values. For the capability of the proposed method, using the criteria with the ALARP region, we show the possible risk values spanning across the different risk regions, whereas the single risk value calculated from the existing method lies in one of the risk regions. Therefore, a decision maker could employ appropriate risk mitigation options to handle the risks lying in different regions. For this study, we used fishing vessel accident data from 1988 to 2016.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.485-490
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• 2002

Demands of customers are being changed and varied. And in this circumstance, it become a main issue of management that the company should produce and sell products according to the customer demands. With these trends, each company has been concentrating effects on generalization of product development technique and distinction of service for customer. To fulfill these demands of customer, they need a concept of eCRM(Web based Customer Relationship Management), and go from soiling products and services, or gathering customer requests, up to the phase of solving customer's problem by real time or previous action. With the help of internet, the frequency and speed of the problem solving has improved greatly. In the Supply chain, The ATP(Available to Promise) function doesn't only give customers to conformation of delivery. It can be used by the core function with ATP rule that can reconcile supplies and demands on the supply chain. Therefore We can be acquire the conformation about on the due date of supplier by using the ATP function of management about real and concurrent access on the supply chain, also decide the affect about product availability due to forecasting or customer's orders through the ATP. In this paper, It consolidates the necessity on a ATP and analyzes data which is concerned of ATP. Under the these environments, defines the ATP rule that can improve the customer value and data flow related the eCRM and builds on a algorithm.

• Journal of the East Asian Society of Dietary Life
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• v.18 no.2
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• pp.242-252
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• 2008

The objective of this study was to suggest a new efficiency measurement indicator for evaluating the menu management efficiency of decision making units(DMUs) in contract-foodservice operations and to determine the relationship between the DEA(data envelopment analysis) menu efficiency score and menu factors. The results of applying DEA revealed relatively efficient types of service and frequency of meals. The efficient service was shown as a self-service type that operates Monday to Saturday. The considered menu factors included meal price, food cost per meal, meal counts, number of menu items, use of favorite menu use, forecasting error, accuracy of ordering, ratio of inventory, ratio of food loss, use of processed foods and use of prepared vegetables are considered. There were significant correlations between the DEA score and meal price, meal counts, number of menu items, ratio of food loss, accuracy of ordering and use of processed foods respectively. According to the regression results, menu price had a positive influence on the DEA menu efficiency score, and food cost per meal and the use of prepared foods had negative influences respectively.

• Atmosphere
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• v.29 no.4
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• pp.463-480
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• 2019

This study classified heavy rain types from K-means clustering for the hourly relationship between rainfall intensity and cloud top height over the Korean peninsula, and then examined their statistical characteristics for the period of June~August 2013~2018. Total rainfall amount of warm-type events was 2.65 times larger than that of the cold-type, whereas the lightning frequency divided by total rainfall for the warm-type was only 46% of the cold-type. Typical cold-type cases exhibited high cloud top height around 16 km, large reflectivity in the upper layer, and frequent lightning flashes under convectively unstable condition. Phenomenally, the cold-type cases corresponded to cloud cluster or multi-cell thunderstorms. However, two warm-type cases related to Changma and typhoon were characterized by heavy rainfall due to long duration, relatively low cloud top height and upper-level reflectivity, and the absence of lightning under the convectively neutral and extremely humid conditions. This study further confirmed that the forecast skill of rainfall could be improved by applying correction factor with the overestimation for cold-type and underestimation for warm-type cases in the Local Data Assimilation and Prediction System (LDAPS) operational model (e.g., BIAS score was improved by 5%).

• The International Journal of Costume Culture
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• v.4 no.3
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• pp.229-240
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• 2001

The purpose of the present study was to analyze the acceptance level of forecasted information of casual wear in tate 1990s in Korea and the way of utilizing fashion trends information by casual wear industries. The Present study was implemented by content analysis and descriptive survey using questionnaire and interview. Trends information in fashion journals published by fashion institute and articles in daily newspapers were analyzed in terms of fashion image, color, fabric, and silhouette. The data collected from questionnaire and interview with 113 fashion specialists were analyzed through frequency, percentage. The results indicated that among the forecasted information regarding fashion image, romantic and feminine images showed a high level of acceptance to national brand women's casual wear in the late 1990s, while mannish image showed a low level of acceptance. For men's casual wear in the same time period, androgynous trends appeared most frequently, not only in forecasted information, but also in actual trend. it was forecasted that yellow, white, and gray would be in trend and those colors appeared frequently in actual trend. On the other hand pastel tone appeared much more frequently than forecasted. Natural, thin - transparent (S/S) and stretch fabrics (F/W) were in actual trend as it was forecasted. Fit and Pare (woman), and long and slim (man) silhouettes were in actual trend as if was forecasted, but barrel silhouette appeared only in forecasted information. Most of the information forecasting fashion trends for next season were applied to the product planning of the season, right after the information comes out.