• The Plant Pathology Journal
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• v.17 no.6
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• pp.373.4-373
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• 2001

• 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.

• 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.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.189-192
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• 2005

Desertification is one of the main global environmental phenomena. It has resulted in deterioration environment and poor economy, and imposed threat to the surviving environment of the overall mankind. Therefore, creating of methods for monitoring and estimate of risk desertification are necessary. Early warning system is one of important ways for monitoring and forecasting of desertification. Remote Sensing and GIS technology are as suitable tools and methods for early warning system. In this aim, we have evaluated of applications of remote sensing and GIS in monitoring and estimating desertification process (case study in Fars Province of Iran). In this research, we have considered erosion and vegetation cover parameters as main factors affecting in desertification process. The result shows that remote sensing and GIS technology could be useful in evaluation of desertification as one method for desertification early warning. Also, Results suggested that erosion and plant cover are affecting in develop the desertification process in study area.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.135-141
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• 2015

The objective of this study is to develop an early warning system for rainfall induced landslides. For this study, we suggested an analysis process using rainfall forecast data. 1) For a selected slope, safety factor with saturated depth was analyzed and safety factor threshold was established (warning FS threshold=1.3, alarm FS threshold=1.1). 2) If rainfall started, saturated depth and safety factor was calculated with rainfall forecast data, 3) And every hour after safety factor is compared with threshold, then warning or alarm can issued. In the future, we plan to make a early warning system combined with the in-situ inclinometer sensors.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1171-1181
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• 2014

Different power quality (PQ) disturbance sources can have major impacts on the power supply grid. This study proposes, for the first time, an early warning approach to identifying PQ problems and providing early warning prompts based on the monitored data of PQ disturbance sources. To establish a steady-state power quality early warning index system, the characteristics of PQ disturbance sources are analyzed and summed up. The higher order statistics anomaly detection (HOSAD) algorithm, based on skewness and kurtosis, and hierarchical power quality early warning flow, were then used to mine limit-exceeding and abnormal data and analyze their severity. Cases studies show that the proposed approach is effective and feasible, and that it is possible to provide timely power quality early warnings for limit-exceeding and abnormal data.

• 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.

• 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

• 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.

• Journal of Power System Engineering
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• v.20 no.2
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• pp.66-72
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• 2016

As a method to improve the equipment reliability, early warning researches that can be detected fault symptom of an equipment at an early stage are being performed out among developed countries. In this paper, when abnormal signal is input to actual normal signal of a pump, early detection studies on pump's fault symptom were carried out with auto-associative kernel regression as an advanced pattern recognition algorithm. From analysis, correlations among power of motor driving pump, discharge flow of pump, power output of pump, and discharge pressure of pump are exited. When the abnormal signal is input to one of those normal signals, the other expected values are changed due to the influence of the abnormal signal. Therefore, the fault symptom of pump through the early-warning index is able to detect at an early stage.