• Asia Pacific Journal of Business Review
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• v.3 no.2
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• pp.67-87
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• 2019

Anti-dumping instruments among trading partners have been the subject of research by both academicians and practitioners. This study attempts to establish an early-warning model of anti-dumping against Chinese textile exporting companies, which have suffered from anti-dumping regulations and got arbitration awards. After reviewing theories of anti-dumping arbitration, early-warning and relationship marketing, the measuring items and relationship marketing model of Chinese textiles exporters are investigated. Empirical methods are selected based on early-warning theories of companies. Eighty percent of 156 valid questionnaires by surveys and interviews are used as training data via Binary-Logistic regression while the other twenty percent are validated in the model. As a result, a proper early-warning model has been established.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.21-22
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• 2021

Early academic warning is considered as an inherent problem in education data mining. Early and timely concern and guidance can save a student's university career. It is widely assumed as a multi-class classification system in view of machine learning. Therefore, An accurate and precise methodical solution is a complicated task to accomplish. For this issue, we present a hybrid model employing rough set theory with a back-propagation neural network to ameliorate the predictive capability of the system with an illustrative example. The experimental results show that it is an effective early academic warning model with an escalating improvement in predictive accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.730-730
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• 2005

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.236-244
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• 2023

The purpose of this study was to examine the economic feasibility of providing services according to the nationwide expansion of early warning services. The net present value method, one of the cost-benefit analysis methods, was applied to the analysis. As a benefit item that constituted the net present value, the damage reduction amount using crop insurance data and the willingness to pay for the use of early warning services were used. The cost items included system construction and maintenance costs, and text transmission costs. As a result of the analysis, it was found that the nationwide expansion of early warning services had economic feasibility, and its economic effect varied depending on the level of text message use (10 % to 40 %, 10 %p interval) of participating farmers. In the future, the economic effect of early warning services is expected to increase further due to the increase in the number of farmers participating in early warning services and the increase in crop damage caused by climate change. It is necessary to further enhance the economic effect of early warning services by actively utilizing information delivery means through apps or the web as well as text messages.

• The Plant Pathology Journal
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• v.36 no.5
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• pp.406-417
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• 2020

Early warning services for crop diseases are valuable when they provide timely forecasts that farmers can utilize to inform their disease management decisions. In South Korea, collaborative disease controls that utilize unmanned aerial vehicles are commonly performed for most rice paddies. However, such controls could benefit from seasonal disease early warnings with a lead time of a few months. As a first step to establish a seasonal disease early warning service using seasonal climate forecasts, we developed the EPIRICE Daily Risk Model for rice blast by extracting and modifying the core infection algorithms of the EPIRICE model. The daily risk scores generated by the EPIRICE Daily Risk Model were successfully converted into a realistic and measurable disease value through statistical analyses with 13 rice blast incidence datasets, and subsequently validated using the data from another rice blast experiment conducted in Icheon, South Korea, from 1974 to 2000. The sensitivity of the model to air temperature, relative humidity, and precipitation input variables was examined, and the relative humidity resulted in the most sensitive response from the model. Overall, our results indicate that the EPIRICE Daily Risk Model can be used to produce potential disease risk predictions for the seasonal disease early warning service.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.197-207
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• 2023

This study was conducted with the purpose of measuring the disaster damage reduction effect according to the provision of the early warning service ahead of the nationwide expansion. The damage reduction effect was measured using insurance data for 19 insured crops in areas that early warning services were provided during the period from 2017 to 2020. As a result of the measurement, it was analyzed that the early warning service had the effect of preventing or reducing disaster damage to farms. In particular, it was found that the disaster reduction effect was greater when disaster prevention facilities were equipped. The implications obtained from the results are as follows. First, by presenting subjectively experienced disaster reduction cases as numerical effects using insurance data with public confidence and objectivity, it can be used as basic data such as expansion of service area, discount of insurance premium with service adoption, and promotional materials for service subscription for early warning service. Second, in expanding and distributing early warning services, giving priority to areas or crops equipped with disaster prevention facilities can help increase the efficiency and effectiveness of the project.

• Journal of the Korean Institute of Gas
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• v.9 no.3 s.28
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• pp.38-43
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• 2005

There is monitored amount operation variables and controlled by operating conditions and loads at many facilities using gas also chemical plants. The process fault which can be indicated by operators, is occurred when the abnormal state was accumulated continuously owing to physical failure, external disturbance or human error. This is studied a Early Warning System which is to estimate process status by real-time monitoring operation variables and to early warning before it will be occurred process fault.

• The Journal of Engineering Geology
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• v.34 no.2
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• pp.173-191
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• 2024

Landslides pose significant threats to many countries globally, yet the development and implementation of effective landslide early warning systems (LEWS) remain challenging due to multifaceted complexities spanning scientific, technological, and political domains. Addressing these challenges demands a holistic approach. Technologically, integrating thresholds, such as rainfall thresholds, with real-time data within accessible, open-source software stands as a promising solution for LEWS. This article introduces LandScient_EWS, a PHP-based program tailored to address this need. The software facilitates the comparison of real-time measured data, such as rainfall, with predefined landslide thresholds, enabling precise calculations and graphical representation of real-time landslide advisory levels across diverse spatial scales, including regional, basin, and hillslope levels. To illustrate its efficacy, the program was applied to a case study in Medellin, Colombia, where a rainfall event on August 26, 2008, triggered a shallow landslide. Through pre-defined rainfall intensity and duration thresholds, the software simulated advisory levels during the recorded rainfall event, utilizing data from a rain gauge positioned within a small watershed and a single grid cell (representing a hillslope) within that watershed. By identifying critical conditions that may lead to landslides in real-time scenarios, LandScient_EWS offers a new paradigm for assessing and responding to landslide hazards, thereby improving the efficiency and effectiveness of LEWS. The findings underscore the software's potential to streamline the integration of rainfall thresholds into both existing and future landslide early warning systems.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2014.10a
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• pp.74-85
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• 2014

This study is a part of "Early Warning Service for Weather Risk Management in Climate-smart Agriculture", describes the delivery techniques from 840 catchment scale weather warning information using 150 counties unit special weather report(alarm, warning) released from KMA(Korea Meteorological Administration) and chronic weather warning information based on daily weather data from 76 synoptic stations. Catchment weather hazard warning service express a sequential risk index map generated by countries report occurs and report grade(alarm, warning) convert to catchment scale using zonal summarizing method. Additional services were chronic weather warning service at crop growth and accumulated more than 4 weeks, based on an unsuitable weather conditions, representing a relative risk compared to its catchment climatological normal conditions (normal distribution ) in addition to special weather report. Service provided by a real-time catchment scale map overlaid with VWORLD open platform operated by Ministry of Land, Infrastructure and Transport. Also provide a foundation for weather risk information to inform individual farmers to farm located within the catchment zone warning occur.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.195-202
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• 2017

This study aims to suggest the basics for the implementation of the farmstead-specific early warning system (FEWS) for weather risk management nationwide. A survey by questionnaire was conducted to examine farmer's response, and a cost-benefit analysis was made to examine the effect of the FEWS on the economy. The farmers who volunteered to participate in this survey responded that they were generally satisfied with the FEWS, and that they used it well for farming. Willingness to pay (WTP) for the early warning service was estimated to be 8,833 KRW per month by survey respondents. If the early warning service is extended to nationwide and 50% of farmers use it for six months, then the ratio of benefit to cost will be 2.2, indicating that nationwide expansion of the FEWS is very feasible.