• Journal of Navigation and Port Research
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• v.42 no.5
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• pp.323-330
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• 2018

The Korean e-Navigation system is a Korean approach to correspond with implementation of IMO e-Navigation. It provides five services, among them SV20 service, a ship remote monitoring system that collects and processes sensor information related to fire, navigation, and seakeeping performance safety. The system also detects abnormal conditions such as fires, capsizing, sinking, navigation equipment failure during navigation, and calculates the safety index and determines the emergency level. According to emergency level, it provides appropriate emergency response guidance for the onboard operator. The fire safety module is composed of three sub-modules; each module is the safety index sub-module, the emergency level determination sub-module and emergency response guidance sub-module. In this study, operational concept of the fire safety module in SV20 service is explained, and fire safety assessment factors are estimated, to calculate the fire safety index. Fire assessment factors included 'Fire detector position factor,' 'Smoke diffusion rate factor,' and 'Fire-fighting facilities factor.'

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.43-57
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• 2022

This study aims to classify forest fire-affected areas, identify forest types by the intensity of forest fire damage using multi-time Landsat-satellite images before and after forest fires and to analyze the effects of artificial restoration sites and natural restoration sites. The difference in the values of the Normalized Burned Ratio(NBR) before and after forest fire damage not only maximized the identification of forest fire affected and unaffected areas, but also quantified the intensity of forest fire damage. The index was also used to confirm that the higher the intensity of forest fire damage in all forest fire-affected areas, the higher the proportion of coniferous forests, relatively. Monitoring was conducted after forest fires through Normalized Difference Vegetation Index(NDVI), an index suitable for the analysis of effects by restoration type and the NDVI values for artificial restoration sites were found to no longer be higher after recovering the average NDVI prior to the forest fire. On the other hand, the natural restoration site witnessed that the average NDVI value gradually became higher than before the forest fires. The study result confirms the natural resilience of forests and these results can serve as a basis for decision-making for future restoration plans for the forest fire affected areas. Further analysis with various conditions is required to improve accuracy and utilization for the policies, in particular, spatial analysis through forest maps as well as review through site checks before and immediately after forest fires. More precise analysis on the effects of restoration will be available based on a long term monitoring.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.60-80
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• 1996

AVHRR(Advanced Very High Resolution Radiometer) on NOAA satellite provides data in five spectral, one in visible range, one in near infrared and three in thermal range. In this paper, application of NOAA/AVHRR data is studied for environment monitoring such as cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index, forest fire, flood, snow cover and so on. The analyses for cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index and forest fire showed reasonable agreement. But monitoring for flood and snow cover was uneasy due to the limitations such as cloud contamination, low spatial resolution. So this research had only simple purpose to identify well-defined waterbody for dynamic monitoring of flood. Based on development of these basic algorithms, we have a plan to further reseach for environment monitoring using AVHRR data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.305-306
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• 2012

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.2
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• pp.96-102
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• 2011

There are many problems with the fire detection devices being used in currently, because it is difficult to find location of the source of fire and determine where devices are working or not. In this paper, we proposed fire detection and rescue system using wireless sensor network that can be real-time monitoring and determine safe exit. Fire detection and rescue system based on ubiquitous sensor network can know exactly source of fire and help determine rescue tactics using sensing data from wireless sensor nodes. Transmitted wirelessly in real-time thermal sensor and gas sensor information to analyze the GUI to monitor the status information output to the screen by use of a system implemented in everyday life, looked at the possibility.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.123-126
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• 2005

Wireless Sensor Networks will revolutionize applications such as environmental monitoring, home automation, and logistics. We developed forest fire surveillance system. In this paper, Considering the fact that in Korea, during November to May, forest fires occur very frequently causing catastrophic damages on the valuable environment, Although exists other forest fire surveillance system such as surveillance camera tower, infrared ray sensor system and satellite system. Preexistence surveillance system can't real-time surveillance, monitoring, database and automatic alarm. But, forest fire surveillance system(FFSS) support above. In this paper, we describes a system development approach for a wireless sensor network based FFSS that is to be used to measure temperature and humidity as well as being fitted with a smoke detector. Such a device can be used as an early warning fire detection system and real-time surveillance in the area of a bush fire or endangered public infrastructure. Once the system has being development, a mesh network topology will be implemented with the chosen sensor node with the aim of developing a sophisticated mesh network.

• The Plant Pathology Journal
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• v.40 no.3
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• pp.282-289
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• 2024

Fire blight is a bacterial disease caused by Erwinia amylovora. In Korea, fire blight was first reported in 2015 in an orchard. If the infection is confirmed, all trees in the orchard must be removed and the orchard must remain closed for 3 years. Since 2020, if the number of trees infected with fire blight is less than 5% of the total trees in the orchard, only the infected tree and adjacent trees are removed in Korea. Three years after removal, the trees can be replanted after confirming that the orchard soil is free from E. amylovora. In this study, a protocol was established for detecting E. amylovora in soil via selective enrichment, using tryptic soy broth with 0.05% bile salts and 50 ㎍/ml cycloheximide, and real-time polymerase chain reaction. This protocol resulted in a 1,000-times improved detection limit for E. amylovora in soil samples compared to that in unenriched samples. Soil monitoring was performed for orchards where fire blight-infected trees had been removed 3-27 months prior; the selected orchards were monitored every 3 months. Monitoring confirmed that E. amylovora was not present in the soil at any site in any of the orchards. A new detection protocol facilitates the monitoring of E. amylovora in soil and could help permit the replanting of trees in orchards. Also monitoring results provide evidence that trees can be planted earlier.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.35-38
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• 2009

The paper presents an effective method to detect fire in video surveillance and monitoring system. The main contribution of this work is that we successfully use the Hidden Markov Models in the process of detecting the fire with a few preprocessing steps. First, the moving pixels detected from image difference, the color values obtained from the fire flames, and their pixels clustering are applied to obtain the image regions labeled as fire candidates; secondly, utilizing massive training data, including fire videos and non-fire videos, creates the Hidden Markov Models of fire and non-fire, which are used to make the final decision that whether the frame of the real-time video has fire or not in both temporal and spatial analysis. Experimental results demonstrate that it is not only robust but also has a very low false alarm rate, furthermore, on the ground that the HMM training which takes up the most time of our whole procedure is off-line calculated, the real-time detection and alarm can be well implemented when compared with the other existing methods.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1998.11a
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• pp.83-87
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• 1998

• Journal of the Korean Institute of Gas
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• v.19 no.2
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• pp.20-28
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• 2015

A suitable monitoring method is necessary for successful policy implementation and its evaluation, required for effective prevention of abnormal fire occurrences. To do this, there were studies for applying control charts of quality management to fire occurrence monitoring. As a result, it was proved that more fire occurs in winter and its trend moves yearly-basis with some patterns. Although it has trend, if we apply the same criteria for each time, inefficient overreacting fire prevention policy will be accomplished in winter, and deficient policy will be accomplished in summer. Thus, applying different control limits adaptively for each time would enable better forecasting and monitoring of fire occurrences. In this study, we treat fire occurrences as time series model and propose a method for configuring its coefficients with ARIMA model. Based on this, we expect to carry out advanced analysis of fire occurrences and reasonable implementation of prevention activities.