• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.33-40
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• 2012

This paper proposes a method to implement biological early warning systems(BEWS). This system generates periodically data event using a monitoring daemon and it extracts the feature parameters from this data sets. The feature parameters are derived with 6 variables, x/y coordinates, distance, absolute distance, angle, and fractal dimension. Specially by using the fractal dimension theory, the proposed algorithm define the input features represent the organism characteristics in non-toxic or toxic environment. And to find a moderate algorithm for learning the extracted feature data, the system uses an extended learning algorithm(UChoo) popularly used in machine learning. And this algorithm includes a learning method with the extended data expression to overcome the BEWS environment which the feature sets added periodically by a monitoring daemon. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression. Experimental results show that the proposed BEWS is available for environmental toxicity detection.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.75-93
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• 2007

This paper presents real-time online early fire warning systems developed for preserving cultural properties of Bulguksa which is a world heritage designated by UNESCO. The system is based on the ubiquitous sensor network employing 900MHz and 2.4GHz bands. In this paper, we analyze requirements that should be considered in building effective management systems of cultural heritages by using wireless sensor network. Finally, we introduce the architecture, sensor and network design, and software design of the fire warning systems which is an initial version of U-Bulguksa. The current version of systems has been operating in Bukguksa for a few months. U-Bukguksa project sponsored by National Information Society Agency is ultimately aimed at developing an integrated system of U-cultural heritage management and U-tourism. The former aims to conserve and manage intangible cultural properties by providing a variety of environmental information such as erosion, crack, and gradient as well as fire which are important causes of loss and damage in real-time and online. The latter refers to the intelligent tourism information and guidance systems allowing tourists to get the personalized content on cultural heritages and help guidance with mobile devices in Bulguksa.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.179-185
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• 2020

Recently, in order to improve the performance of the Earthquake Early Warning System (EEWS) and to supplement the effects of earthquake disaster prevention in epicenters or near epicenters, development of on-site EEWS has been attempted. Unlike the national EEWS, which is used for earthquake disaster prevention by using seismic observation networks for earthquake research and observation, on-site EEWS aims at earthquake disaster prevention and therefore requires efficient design and evaluation in terms of performance and cost. At present, Korea lacks the necessary core technologies and operational know-how, including the use of existing EEWS design criteria and evaluation methods for the development of On-Site EEWS as well as EEWS. This study proposes hardware and software design directions and performance evaluation items and methods for seismic data collection, data processing, and analysis for localization of On-Site EEWS based on the seismic accelerometer requirements of the Seismic and Volcanic Disaster Response Act.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.309-312
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• 2008

In Korea, although the damage from disaster (flood and storm) is increasing, the early stage warning and countermeasure are not in operation rapidly. The research areas of transportation engineering arenot diverse, so once the road is flooded and interrupted, drivers, the system operators and managers are in panic, and nearby roads are in terrible traffic congestion. In case of Korea, the research of evacuation is highly needed, because it is very necessary and easy to apply in real field. In this paper, we establish the concept of transportation disaster prevention system and suggest the directions of it. In addition, based on this research, we choose one example of disasters and establish an example of the transportation disaster prevention system. Our goal is to make steps; prevention, preparation, countermeasure and restoration in the view of minimizing on social chaos and damages emphasizing aspect of transportation countermeasure. This research will be the good precedent of approach, analysis and countermeasure when the disasters are occurred, and a basis of transportation disaster prevention system and manual in Korea.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.463-467
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• 2015

Mega-shock means a sporadic event such as the earning shock, which occurred by sudden market changes, and it can cause serious problems of profit loss of international construction projects. Therefore, the early response and prevention by analyzing and predicting the Mega-shock is critical for successful project delivery. This research is preliminary study to develop a prediction model that supports market condition analysis and Mega-shock forecasting. To avoid disadvantages of classic statistical approaches that assume the market factors are linear and independent and thus have limitations to explain complex interrelationship among a range of international market factors, the research team explored the Fractal Theory that can explain self-similarity and recursiveness of construction market changes. The research first found out correlation of the major market factors by statistically analyzing time-series data. The research then conducted a base of the Fractal analysis to distinguish features of fractal from data. The outcome will have potential to contribute to building up a foundation of the early shock warning system for the strategic international project management.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.135-154
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• 2003

Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules as a rule generating data mining technique. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by association rule mining. We expect that the sets of rules generated by association rule mining could act as an estimator of good or bad credit status classifier and basic component of early warning system.

• Journal of Korean Society for Quality Management
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• v.22 no.4
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• pp.40-58
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• 1994

Some systems such as early warning system should be inspected occasionally in order to detect failures. If the system is inspected too frequently, inspection cost increases. On the other hand, if the number of inspections is reduced too much, the undetected system downtime cost increases. Thus, it is of interest to find effective inspection schedule, which minimizes the sum of inspection and downtime costs. When the system has increasing failure rate, inspection intervals get shorter as time goes on. But a common practice is to inspect the system at predetermined periodic intervals. In this paper, periodic inspection policy and a modified periodic inspection policy are considered. The modified policy is easily applicable and cost-effective. Some numerical examples are included in order to explain the modified inspection ploicy and its cost performance.