• Food Engineering Progress
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• v.15 no.2
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• pp.97-115
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• 2011

A review about the application of response surface methodology in the optimization of food technology is presented. The theoretical principles of response surface methodology and steps for its application are described. The response surface methodologies : three-level full factorial, central composite, Box-Behnken, and Doehlert designs are compared in terms of characteristics and efficiency. Furthermore, recent references of their uses in food technology are presented. A comparison between the response surface designs (three-level full factorial, central composite, Box-Behnken and Doehlert design) has demonstrated that the Box-Behnken and Doehlert designs are slightly more efficient than the central composite design but much more efficient than the three-level full factorial designs.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.121-129
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• 2021

In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.289-296
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• 2022

The purpose of this study was to investigate the inter-correlation between crude oil prices and Dry Bulk Freight rates. Eco-friendly shipping fuels has being actively developed to reduce carbon emission. However, carbon neutrality will take longer than anticipated in terms of the present development process. Because of OVID-19 and the Russian invasion of Ukraine, crude oil price fluctuation has been exacerbated. So we must examine the impact on Dry Bulk Freight rates the oil prices have had, because oil prices play a major role in shipping fuels. By using the VAR (Vector Autoregressive) model with monthly data of crude oil prices (Brent, Dubai and WTI) and Dry Bulk Freight rates (BDI, BCI and (BP I) 2008.10~2022.02, the empirical analysis documents that the oil prices have an impact on Dry bulk Freight rates. From the analysis of the forecast error variance decomposition, WTI has the largest explanatory relationship with the BDI and Dubai ranks seoond, Brent ranks third. In conclusion, WTI and Dubai have the largest impact on the BDI, while there are some differences according to the ship-type.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.163-180
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• 2022

Real estate data can be big data. Because the amount of real estate data is growing rapidly and real estate data interacts with various fields such as the economy, law, and crowd psychology, yet is structured with complex data layers. The existing Relational Database tends to show difficulty in handling various relationships for managing real estate big data, because it has a fixed schema and is only vertically extendable. In order to improve such limitations, this study constructs the real estate data in a Graph Database and verifies its usefulness. For the research method, we modeled various real estate data on MySQL, one of the most widely used Relational Databases, and Neo4j, one of the most widely used Graph Databases. Then, we collected real estate questions used in real life and selected 9 different questions to compare the query times on each Database. As a result, Neo4j showed constant performance even in queries with multiple JOIN statements with inferences to various relationships, whereas MySQL showed a rapid increase in its performance. According to this result, we have found out that a Graph Database such as Neo4j is more efficient for real estate big data with various relationships. We expect to use the real estate Graph Database in predicting real estate price factors and inquiring AI speakers for real estate.

• Journal of Industrial Convergence
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• v.20 no.11
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• pp.75-83
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• 2022

This study analyzes whether investor sentiment and liquidity explain the momentum phenomenon in the Korean stock market and whether it is a risk factor for the asset pricing model. The empirical analysis used the monthly returns of non-financial companies listed on the stock market during the period 2000-2021. As a result of the analysis, first, it was found that there is a momentum effect in Korea. This is the same result as the previous study, and since 2000, the momentum effect has been accepted as a general phenomenon in the Korean stock market. Second, if we look at the portfolio based on investor sentiment, investor sentiment is influencing momentum. In particular, when investor sentiment is negative, the return on the winner portfolio is high. Third, as a result of the analysis based on liquidity, the momentum effect disappears and a reversal effect appears. Fourth, it was found that investor sentiment and liquidity influence the momentum effect. This is a result of the strong momentum effect in the illiquid stock group with negative investor sentiment. Fifth, as a result of analyzing the effect of each factor on stock returns, it was found that both investor psychology and liquidity factors have a significant impact on returns. The estimated results provide evidence that the inclusion of these two factors in the Carhart four-factor model significantly increases the predictive power of the model. Therefore, it can be said that investor sentiment factors and liquidity factors are important factors in determining stock returns.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.453-453
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• 2021

도시지역 비점오염원관리, 물순환 회복, 침투 및 증발산량 증가, 열섬현상 저감을 위한 주요한 방안으로 저영향개발(low impact development, LID)과 그린인프라 기법의 적용되고 있다. LID 시설은 소규모 분산형 시설로써 넓은 지역에 많고 다양한 시설들이 적용되어 시설의 개수가 많으며, 수질 및 토양 내 기성제품에 대한 센서들의 가격은 고가로 형성되어 있어 기기의 경제성 및 유지관리 등 적용하는데 제한적이다. 따라서 과거 모니터링 자료를 기반으로 오염물질들과의 상관성 분석을 통하여 계측이 어려운 항목들을 계측가능한 항목들로부터 예측 가능하며, 선정된 항목들에 대한 비용효율적인 센서를 개발하여 실시간 LID 모니터링이 가능한 비용효율적 모니터링을 개발하였다. 공주대학교 천안캠퍼스의 LID 시설들은 2013년에 조성되어 현재까지 시설이 운영되고 있으며, 5년이상의 과거 강우시 모니터링 자료들을 이용하여 오염물질 상관성 분석을 수행가능 하기에 대상지로 선정하였다. 오염물질 상관성 분석은 2013년부터 2017년도에 침투도랑에서 수행된 강우시 모니터링 자료를 활용하여 각 오염물질들의 상관성을 분석을 수행하였다. 침투도랑 내 유입되는 평균 유입수는 TSS 286.1±318.3 mg/L, BOD 22.6±39.5 mg/L, TN 8.96±5.85 mg/L, TP 1.01±1.11 mg/L로 나타났다. 겨울철에 비해 여름철에서의 오염물질의 유입농도가 높은 것으로 분석되었다. 이는 여름철 고온건조로 인한 노면 내 차량의 주행으로 인한 중금속, 폐타이어 등과 장마철 강우 시 유출된 토사로 인하여 유입수의 농도가 높은 것으로 분석되었다. 오염물질 부하량은 TSS와 COD 0.66으로 유의성이 높은 것으로 나왔으며, COD와 TSS, TP, TN 등 유의성이 높은 것으로 분석되었다. Arduino와 Raspberry PI를 활용하여 저비용 센서와 LTE 모뎀통신과 데이터 베이스 연결하여 개발된 프로그램을 통해서 무선으로 LID 시설에 대한 모니터링을 침투화분2와 식생체류지에 조성하였다. 전력공급이 어려운 식생체류지의 경우 태양열(Solar system) 시스템과 보조 전력 배터리를 조성하여 장마철이나 장기적인 악천후로 인한 전력을 생산하지 못할 경우 보조전력배터리에서 전력을 제공하여 지속적인 모니터링이 이루어지도록 설계하였다. 토양함수량, 토양온도와 Conductivity 등 3종류의 센서를 적용하였으며, 프로그램은 현재 2단계를 통한 2차수정을 통하여 프로그램을 구축하였다. 오차, 오작동, 계측값에 대한 검·보정 작업이 필요하다. 또한 대기자료의 구축을 통해 보다 토양과 LID 시설에 대한 영향분석이 필요한 것으로 사료된다.

• Journal of the Korean Society of International Agriculture
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• v.23 no.5
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• pp.497-502
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• 2011

Broccoli is considered as one of the functional foods to offer a hearty supply of the nutrient-rich vegetable in the world. Broccoli contains high level of phytochemicals, and that is selected as one of the top 10 vegetables for human health promotion. Especially, glucosinolates and flavonoids are well known as anti free oxygen radicals in vegetables and fruits. In Korea, broccoli consumption has increased to well known on the health-beneficial vegetables since 2000. However, broccoli has many problems of postharvest management since the quality of harvested heads quickly declines. Major problems are the floret yellowing, wilting, off-odor, and decay. The multiple postharvest applications improve broccoli quality and cold treatment including pre-cooling extends on the shelf-life with circumstance of optimum storage which is 0℃ temperature and a range of 95-100% relative humidity. Controlled atmosphere or modified atmosphere packaging can be used as supplemental treatments to extend postharvest life. 1-2% O₂ + 5-10% CO₂ is currently recommended for broccoli. Postharvest management is important for broccoli because price fluctuations depend on harvest time and quality. In this study, we tried to review physiological change of broccoli after the harvest, storage method, and various techniques to optimize quality during distribution.

• Asia Marketing Journal
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• v.10 no.2
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• pp.99-123
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• 2008

Consumer satisfaction/dissatisfaction is key determinant of brand loyalty and store patronage behavior. But the results of many customer satisfaction surveys implemented by department stores show that consumer satisfactions do not predict the actual patronage behaviors well. The main reason of these surprising results would be that the consumer satisfaction indexes do not include some important determinants of consumer satisfaction. Many customer satisfaction surveys mainly focus on the evaluation of functional benefits including product assortments, merchandise prices and locational convenience. Recent studies indicate that emotional/hedonic benefits strongly influence the consumer satisfaction, intention to repurchase and intention to revisit. Our study suggests that both functional values and hedonic values should be included in developing the index of consumer satisfactions. The purpose of our study is to investigate the relationship between shopping value and consumer satisfaction, and actual patronage behavior. Shopping values is defined as the difference between total benefits and total shopping costs. Total benefits include the dimensions of product quality, service quality, and hedonic benefits. Total costs are classified as the monetary costs and non-monetary cost. The conceptual framework developed for this empirical study is as follows.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2021.04a
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• pp.43-43
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• 2021

로즈마리(Rosmarinus officinalis)는 지중해 지역이 원산이고 꿀풀과에 속하는 다년생 식물로 자생지에서는 식물의 크기가 2m까지 자라는 관목성 식물이다. 식용, 약용, 미용, 향료뿐 아니라 관상용으로도 널리 이용되고 있고 특히 향이 좋아 세계 로즈마리 추출물 시장은 연평균 4.8%씩 증가하여 2027년에는 10억 달러를 넘을 것으로 예측된다. 우리나라도 소비 트렌드 변화에 따른 이용성 확대로 신선허브의 수요가 증가하고 있으나 아직은 허브 식물원료의 대부분을 수입에 의존하고 있고, 로즈마리 역시 식물원료뿐 아니라 가공품까지 외국에서 수입하여 사용하는 실정이다. 2018년 로즈마리 수입량은 신선상태 978kg, 건조상태 23,404kg으로 높은 수입의존에 따른 가격 상승과 긴 유통기간에 의한 품질 저하 등의 문제가 발생하고 있다. 본 연구는 로즈마리 어린 순 재배 적정 삽수 길이를 설정하고 어린 순 생산 가능 기간을 구명하여 추후에 고품질 로즈마리 어린 순 집약생산을 위한 다단재배기술을 확립하고자 수행되었다. 삽수 길이는 5, 10, 15cm로 하였고, 삽목 시기는 4월 하순 ~ 8월 하순까지 30일 간격으로 5회 실시하였다. 적정 삽수 길이 설정 실험에서는 15cm 삽수 발근률이 85.6%로 가장 높았으며 신초 출현시기는 5월 26일, 어린 순 생산시기는 6월 23일로 가장 빨랐고 수확시까지 소요일수는 56일로 가장 짧았다. 기대수량 또한 728g/m²로 가장 높았다. 로즈마리 어린 순 생산 가능 기간 구명 실험에서는 4월 28일 삽목시 발근율이 85.6%로 가장 높았고 육묘기간은 28일 어린 순 생산까지 소요일수는 56일로 가장 짧았다. 삽목 시기별 어린 순 품질 및 생산량은 4월 28일 삽목시 품질이 좋았으며 기대수량 또한 728g/m²로 가장 높았다. 결과적으로 상품성 있는 어린순 생산에 적합한 삽수 길이는 15cm, 삽목 시기는 4월 하순 경에 했을 때, 로즈마리의 생육상태, 수확까지의 기간, 어린 순 생산량 등 종합적인 면에서 가장 우수한 값을 얻을 수 있었다.

• Korea Trade Review
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• v.48 no.3
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• pp.69-88
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• 2023

This study analyzes the dynamic characteristics of cargo volume (demand), ship fleet (supply), and freight rate (price) of container, dry bulk, and tanker shipping markets by using the VAR and VECM models. This analysis is expected to enhance the statistical understanding of market dynamics, which is perceived by the actual experiences of market participants. The common statistical patterns, which are all shown in the three shipping markets, are as follows: 1) The Granger-causality test reveals that the past increase of fleet variable induces the present decrease of freight rate variable. 2) The impulse-response analysis shows that cargo shock increases the freight rate but fleet shock decreases the freight rate. 3) Among the three cargo, fleet, and freight rate shocks, the freight rate shock is overwhelmingly largest. 4) The comparison of adjR² reveals that the fleet variable is most explained by the endogenous variables, i.e., cargo, fleet, and freight rate in each of shipping markets. 5) The estimation of co-integrating vectors shows that the increase of cargo increases the freight rate but the increase of fleet decreases the freight rate. 6) The estimation of adjustment speed demonstrates that the past-period positive deviation from the long-run equilibrium freight rate induces the decrease of present freight rate.