• Journal of Korean Society of Disaster and Security
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• v.6 no.1
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• pp.19-27
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• 2013

Damage on assets and lives caused by natural disasters can be minimized by the provision of early warning information and preventive activities. In this sense, the importance of a disaster early warning system continues to increase. This study specifies the kinds of early warning systems depending on the type of natural disasters such as typhoon, flood and heavy snow. The mechanism for information transmission and status of early warning operations are analyzed. Through this analysis, the urgent need to establish a national integrated early warning transmission system is emphasized. In addition, this study offers methods to prevent unnecessary overlapping of investments by establishing an organic mechanism among individual early warning systems. Based on the standardization of disaster-related information, this study also provides methods to improve the efficiency of disaster early warning systems by organizing a permanent team for handling the systematic management and operation of the system.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.221-227
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• 2021

An early warning system was developed to help identify stock status as early as possible. For performance to improve, there needs to be a feature to predict the amount of stock that must be provided and a feature to estimate when to buy goods. This research was conducted to improve the inventory early warning system and optimize the Reminder Block's performance in minimum stock settings. The models used in this study are the single exponential smoothing (SES) method for prediction and the economic order quantity (EOQ) model for determining the quantity. The research was conducted by analyzing the Reminder Block in the early warning system, identifying data needs, and implementing the SES and EOQ mathematical models into the Reminder Block. This research proposes a new Reminder Block that has been added to the SES and EOQ models. It is hoped that this study will help in obtaining accurate information about the time and quantity of repurchases for efficient inventory management.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.151-171
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• 2023

This study analyzed farmer's responses to the pilot project in advance of the nationwide expansion of the farm-customized early warning service for weather risk management by conducting a survey among all farmers who received text messages of this service. We analyzed not only the satisfaction of farmers with the early warning service, but also the effectiveness of the service in preventing agrometeorological disasters through cross-tabulation analysis of survey results. More than 330 farmers participated in the survey, and more than 60% of the respondents said that they had prevented or mitigated crop disasters by using the early warning service. The cross-tabulation analysis showed that farmers who perceived the field-specific weather information of the early warning service to be more accurate than the weather forecast were statistically significantly more likely to prevent crop disasters than those who did not. According to our case study, farmers who grew open field fruit crops were particularly sensitive to weather information and confirmed that early warning services, along with disaster prevention facilities, were effective in preparing for freezing and frost injury that had been occurring frequently under the influence of climate change. This study is significant in that it is the first to systematically analyze the effectiveness of the farm-customized early warning service for weather risk management based on extensive surveys. It is expected to contribute to exploring ways to develop the service ahead of the nationwide expansion of the early warning service in the near future.

• 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.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.263-272
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• 2024

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

• Convergence Security Journal
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• v.6 no.1
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• pp.1-11
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• 2006

The exponential increase of malicious and criminal activities in cyber space is posing serious threat which could destabilize the foundation of modem information society. In particular, unexpected network paralysis or break-down created by the spread of malicious traffic could cause confusion and disorder in a nationwide scale, and unless effective countermeasures against such unexpected attacks are formulated in time, this could develop into a catastrophic condition. As a result, there has been vigorous effort and search to develop a functional state-level cyber-threat early-warning system however, the efforts have not yielded satisfying results or created plausible alternatives to date, due to the insufficiency of the existing system and technical difficulties. The existing cyber-threat forecasting and early-warning depend on the individual experience and ability of security manager whose decision is based on the limited security data collected from ESM (Enterprise Security Management) and TMS (Threat Management System). Consequently, this could result in a disastrous warning failure against a variety of unknown and unpredictable attacks. It is, therefore, the aim of this research to offer a conceptual design for "Knowledge-based Real-Time Cyber-Threat Early-Warning System" in order to counter increasinf threat of malicious and criminal activities in cyber suace, and promote further academic researches into developing a comprehensive real-time cyber-threat early-warning system to counter a variety of potential present and future cyber-attacks.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.25-41
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• 2024

For the scenario of open pit mining, at present, manual periodic verification is mainly carried out in China with the help of video surveillance, which requires continuous investment in labor cost and has poor timeliness. In order to solve this difficult problem of early warning and monitoring, this paper researches a spatialized algorithmic model and designs an early warning system for open-pit mine transboundary mining, which is realized by calculating the coordinate information of the mining and extracting equipments and comparing it with the layer coordinates of the approval range of the mines in real time, so as to realize the determination of the transboundary mining behavior of the mines. By taking the Pingxiang area of Jiangxi Province as the research object, after the field experiment, it shows that the system runs stably and reliably, and verifies that the target tracking accuracy of the system is high, which can effectively improve the early warning capability of the open-pit mines' overstepping the boundary, improve the timeliness and accuracy of mine supervision, and reduce the supervision cost.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.11
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• pp.25-39
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• 1992

Due to the data processing development by the computer, the early warning system recently has made a remarkable evolution in its functions and performance as a component of the communication and control system which is also supported by the computer communication and intelligence system. In this paper it is presented that a integrated data processing system is designed to integrate the information sent from the various radar systems which constitute an early warning system. The suggested system model of this paper is devided into two types of structures, the centralized model and the distributed model, according to the data processing algorithm. We apply the queueing theory to analyse the performance of the designed models and the OPNET system kernel to make the analysing program with C language. From the analysis of the queueing components by applying the analysis programs to the designed systems, we got the tendancies and characteristics of both models, that is, a fast data processing performance of the distributed model and a stable data processing capability of the centralized model.

• IE interfaces
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• v.25 no.3
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• pp.319-326
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• 2012

To effectively predict financial crisis, this paper presents an early warning system based on artificial intelligence technologies. Both Genetic Algorithms and Neural Networks are utilized for the proposed system. First, a genetic algorithm has been developed for the effective selection of economic indices, which are used for monitoring financial crisis. Then, an optimum weight of the selected indices has been determined by a neural network method. To validate the effectiveness of the proposed system, a series of experiments has been conducted by using the Korean economic indices from 2005 to 2008.