• Journal of Advanced Technology Convergence
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• v.3 no.2
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• pp.17-23
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• 2024

In this paper, we analyzed the impact of temperature and precipitation on agricultural product prices and predicted the prices of major agricultural products using TensorFlow. As a result of the analysis, the rise in temperature and precipitation had a significant effect on the rise in prices of cabbage, radish, green onion, lettuce, and onion. In particular, prices rose sharply when temperature and precipitation increased simultaneously. The prediction model was useful in predicting agricultural product price changes due to climate change. Through this, agricultural producers and consumers can prepare for climate change and prepare response strategies to price fluctuations. The paper can contribute to understanding the impact of climate change on agricultural product prices and exploring ways to increase the stability and sustainability of agricultural product markets. In addition, it provides important data to increase agricultural sustainability and ensure economic stability in the era of climate change. The research results will also provide useful insights to policy makers and can contribute to establishing effective agricultural policies in response to climate change.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.1-10
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• 2022

Vegetables such as cabbage are greatly affected by natural disasters, so price fluctuations increase due to disasters such as heavy rain and disease, which affects the farm economy. Various efforts have been made to predict the price of agricultural products to solve this problem, but it is difficult to predict extreme price prediction fluctuations. In this study, cabbage prices were analyzed using the ensemble Voting technique, a method of determining the final prediction results through various classifiers by combining a single classifier. In addition, the results were compared with LSTM, a time series analysis method, and XGBoost and RandomForest, a boosting technique. Daily data was used for price data, and weather information and price index that affect cabbage prices were used. As a result of the study, the RMSE value showing the difference between the actual value and the predicted value is about 236. It is expected that this study can be used to select other time series analysis research models such as predicting agricultural product prices

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.153-173
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• 2022

This study analyzes yelp's online restaurant reviews written in 2019 and explores the factors influencing the decision of the usefulness for online reviews in the restaurant consumption decision process. Specifically, factors expected to affect review usefulness are classified according to the Elaboration Likelihood model. Also, it is assumed that the price range of the restaurant would have a moderating role. For the analysis, datasets provided by yelp.com in February 2020 are used. Among the datasets, online reviews of businesses located in Nevada in the US and belonging to the Food and Restaurant categories are targeted. As a result of the negative binomial regression analysis, it is confirmed that the central cues including review depth and readability and the peripheral cues including review consistency, reviewer popularity, and reviewer exposure positively affect the review usefulness. It is also confirmed that the influences of antecedents that affect the review restaurant prices moderate the effect of the central and peripheral cues on the review usefulness. It also provides implications for the need for price-differentiated review management strategies by review platforms and restaurant businesses.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.15-17
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• 2022

In the aftermath of the avian influenza that occurred from the second half of 2020 to the beginning of 2021, 17.8 million laying hens were slaughtered. Although the government invested more than 100 billion won for egg imports as a measure to stabilize prices, the effort was not that easy. The sharp volatility of egg prices negatively affected both consumers and poultry farmers, so measures were needed to stabilize egg prices. To this end, the egg prices were successfully predicted in this study by using the analysis algorithm of a machine learning regression. For price prediction, a total of 8 independent variables, including monthly broiler chicken production statistics for 2012-2021 of the Korean Poultry Association and the slaughter performance of the national statistics portal (kosis), have been selected to be used. The Root Mean Square Error (RMSE), which indicates the difference between the predicted price and the actual price, is at the level of 103 (won), which can be interpreted as explaining the egg prices relatively well predicted. Accurate prediction of egg prices lead to flexible adjustment of egg production weeks for laying hens, which can help decision-making about stocking of laying hens. This result is expected to help secure egg price stability.

• Proceedings of the KIEE Conference
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• 2003.10a
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• pp.164-166
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• 2003

플라즈마 디스플레이용 구동회로에서는 현재 160V의 높은 전압을 이용하고 있다. 이러한 높은 공급전압을 사용하기 위해서는 낮은 전압의 경우보다 높은 단가의 스위칭 소자를 사용할 수 밖에 없다. 따라서 플라즈마 디스플레이 시스템의 가격을 결정하는 데 중요한 요인으로 분석하고 있다. 본 논문에선 이러한 점을 고려하여 공급전압의 저전압화를 위한 한가지 모델을 제시한다. 먼저 본 논문에서 제시한 저전압화 모델을 컴퓨터 시뮬레이션을 통하여 그 가능성을 확인한다. 다음에 실제 시판되고 있는 스위칭 소자를 이용하여 모델의 현실성을 검토한 결과를 제시하여 본 논문에서 제시한 저전압화 모델의 실효성을 입증한다.

• Korean Journal of Construction Engineering and Management
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• v.7 no.5
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• pp.94-104
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• 2006

The changed Korean government law associated with the public apartment housing supply, so called the $^{\circ}{\AE}$Apartment Sales Price $Cap^{\circ}{\phi}$, requires new system for estimating construction cost in order to set appropriate price. A model apartment project was carefully designed and its construction cost were analyzed in many different ways. Based on the analyses outcomes, 1,028,000 Won/m2 (excluding cost for underground parking lot) is the most appropriate Price Cap for a smaller than $85{\beta}{\geq}$ apartment unit. Further, it was revealed that the price have to be adjusted reflecting such factors as underground size; structural system; external complex quality; and consumer preferences. Findings from this study will enable the Korean government to realize faster and better application of the related laws. The methodology for obtaining appropriate apartment construction cost will also benefit for future researchers.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10c
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• pp.298-300
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• 2004

전력산업 구조개편으로 전력분야의 다원화, 복잡화, 이질화가 심화되고 있으며, 전력의 공급에서 수송, 소비에 이르기까지 과정의 부문별 관리에 따른 전력정보의 분산이 가속화되고 있다. 이러한 현상은 각 부문간 정보의 불일치를 가져오고, 정보연계를 더욱 어렵게 하여, 국가 전력정보의 공유와 통합을 위한 기반을 정차 취약하게 만들어 전력시스템 전체의 효율을 저하시키고, 정전과 가격급등과 같은 비상사태 발생시 이를 방지하거나 파급효과를 축소시켜 사회적 비용을 절감시킬 수 있는 위험 회피를 쉽지 않게 한다. 따라서 전력의 공급에서 수송, 소비에 이르기까지 전력정보의 이질화, 다원화가 더 심화되기 전에 각 부문별 정보관리의 연계를 바탕으로 다양한 정책적 의사결정을 위한 국가 전력정보 통합관리 모델의 개발이 필요하다. 본 논문은 전력정보 통합관리 모델 개발에 대한 국외 연구개발 현황을 소개하고 국내의 실정에 적합한 전력정보 통합관리 모델 개발에 대해 기술하였다.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2001.11a
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• pp.169-186
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• 2001

The researches about the general flow of new product development process was achieved in various field. But there was little discussion about the methodologies and tools used in that process. So we suggest new DEA model as the methodology that determines sustainable price and quality attributes and this can substitute econometric hedonic methodology. To make smooth surface composed of quality attributes and price, we use QC-DEA model. Additionally we make AR-QC DEA model by introducing AR to reflect consumer perceptions on quality attributes. AR-QC DEA overcomes the limits of parametric methodology and represents product-specific shadow prices, so it is possible to supply the information about quality attributes and price combination in new product development process and to simulate easily whether new product can exist in the market. Finally by empirical research on notebook computer we can show that AR-QC DEA has the ability to explain market change.

• Journal of Service Research and Studies
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• v.6 no.4
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• pp.75-85
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• 2016

Coal can be divided into thermal coal and coking coal. The price of thermal coal is basically affected by demand and supply. However, many other factors with regard to economic condition such as exchange rate, economy growth rate also make an influence on the price. This study is targeted to develop a forecast model for thermal coal price by using System Dynamics Method. System dynamics provides results that better reflect the real world by employing an inter-dependent system of variables. This study found out that 8 factors have important influence on the thermal coal price. Most of the data of the variables were acquired from the Bloomberg Database. The period extends to 2 years and 4 months, from May of 2011 to August of 2013. The causal relations among the variables were acquired by regression analysis

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.552-560
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• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.