• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.2
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• pp.138-142
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• 2013

As an early warning system to reduce the damage of aquacultured mollusks due to low salinity water, we investigated the possibility of a biomonitoring system measuring the shell valve movement (SVM) of Pacific oyster (Crassostrea gigas) by using the Hall element sensor. In high salinity water of 27 psu, SVMs of Pacific oyster showed spikes which mean a relatively fast closing condition after opened condition of average 10-15 mm, and then the SVM showed back to opening condition slower than closing speed. In water salinity of 20-27 psu, the SVMs were similar to that of 27 psu. However, below 17 psu, it showed abnormal valve movements such as spending more time for shell closure. In 10 psu, we could not detected SVMs due to closed condition during experiment periods. Thus, if we quickly detect abnormal environmental variations like low salinity using bio-monitoring of SVM, it may be contribute to increased productivity by dramatically reducing damages in aquaculture.

• Health Policy and Management
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• v.28 no.4
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• pp.402-410
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• 2018

Background: Monitoring appropriate medication categories can provide early warning of certain disease outbreaks. This study aimed to present a methodology for selecting and monitoring medications relevant to the surveillance of acute respiratory tract infections, such as influenza. Methods: To estimate correlations between acute febrile respiratory tract infection and some medication categories, the cross-correlation coefficient (CCC) was used and established. Two databases were used: real-time prescription trend of antivirals, anti-inflammatory drugs, antibiotics using Drug Utilization Review Program between 2012 and 2015 and physicians' number of encounters with acute febrile respiratory tract infections such as influenza outbreaks using the national level health insurance claims data. The seasonality was also evaluated using the CCC. Results: After selecting six candidate diseases that require extensive monitoring, influenza with highly specific medical treatment according to the health insurance claims data and its medications were chosen as final candidates based on a data-driven approach. Antiviral medications and influenza were significantly correlated. Conclusion: An annual correlation was observed between influenza and antiviral medications, anti-inflammatory drugs. Suitable models should be established for syndromic surveillance of influenza.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.4
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• pp.428-432
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• 2014

Rotating machines are an integral part of large electrical power machinery in most of the industries. Any degradation or outages in the rotating electric machinery can result in significant losses in productivity. It is critical to monitor the equipment for any degradation's so that it can serve as an early warning for adequate maintenance activities and repair. Prior research and field studies have indicated that the rotating machines have a particular type of signal structure during the initial start-up transient. A machine performance can be studied based on the effect of degradation in signal parameters. In this paper a data-acquisition system and the FFT algorithm has been design and model using the MATLAB and Simulink. The implementation had been carried out on the TMS320 DSP Processor and various testing and verification of the machine performance had been carried out. The results show good agreement with expected results for both simulated and real-time data. The real-time data from AC water pumps which have rotating motors built-in were collected and analysed. The FFT algorithm provides frequency response and based on this frequency response performance of the machine had been measured.The FFT algorithm provides only approximation about the machine performances.

• Molecular & Cellular Toxicology
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• v.4 no.3
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• pp.199-204
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• 2008

The bio-molecules involved in defense mechanisms can be used as efficient biomarkers for physiological changes in organisms caused by both of internal and external stress. Thus, the expression level of genes which encoding such molecules serve as critical 'early warning system' for environmental assessment as well as health diagnosis of biological organisms. In this study, Cytochrome P450, Heat shock protein 90, Ubiquitin and ${\beta}$-actin gene were isolated for the first time from a red alga Gracilaria textorii. The quantitative differential gene expression analyses of three genes, GteCYP1A, GteHsp90 and Gte-UB, were carried out in G. textorii sporophytes collected from two different localities, polluted Sujeong (Masan, Korea) and potentially unpolluted Danggeum (Daemaemuldo Is., Korea). The transcripts of all three tested genes were highly expressed in the Sujeong population. The results suggest: 1) the Sujeong site was more polluted than the Danggeum site; 2) G. textorii could be applicable to marine environment monitoring in coastal regions.

• Environmental Analysis Health and Toxicology
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• v.20 no.1
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• pp.1-11
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• 2005

환경오염의 조기 경보 시스템으로 생체지표를 이용한 생태독성 모니터링이 최근 널리 연구되고 있다. 환경내의 생물종에서 측정한 생체지표를 이용한 환경 모니터링은 생태계 수준의 영향에 대한 예측 정보를 제공해 줄 수도 있다. 이를 위해서는 생체지표와 개체군 수준에서의 반응과의 인과관계가 밝혀져야 한다. 오염물질에 대한 생체의 반응은 분자, 세포, 생화학, 생리적, 개체, 개체군, 군집 수준에서 나타나게 되며, 이러한 각 단계별 반응은 반응 시간의 규모와 독성학적, 생태적 관련성에 따라 구분 지어 볼 수 있다. 각 개별 수준에서의 반응을 종합하면 오염물질에 노출된 생체의 전제적인 영향을 이해할 수 있으며, 이러한 이해를 바탕으로 개체군에서 나타나는 영향에 대한 인과관계를 추론할 수 있다. 생체지표와 개체군 수준의 반응과의 인과관계 정립은 효율적인 환경오염 예방기능 수행에 필수적인 과정이며, 다단계 생체 지표는 각 단계별 반응의 인과관계를 밝히기 위해 가장 적절한 접근 방법이다. 수서 무척추 생물인 Chironomus의 유충은 이러한 다단계 바이오마커 연구에 매우 적절한 생물학적 모델이다. 이 논문의 첫 번째 부분은 생체지표를 이용한 환경 모니터링을, 두 번째 부분은 Chironomus의 유충에서 생체지표의 적용에 대해서 다룬다.

• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.343-353
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• 2016

Oligochaetes distribute widely in freshwater ecosystem, and some species are used as bioindicators for water quality assessment because they are tolerant to organic enrichment. They are acknowledged for potential for environmental health recovery of organic polluted environment. There are a lot of studies on ecology and toxicity assessment using oligochaetes in aquatic environment. In this study, we reviewed literature on ecotoxicology of aquatic oligochaetes. We searched literature from a database 'google scholar' by using keywords such as aquatic, oligochaete, and toxicity. The literature were summarized according to publication years, species, test methods, and chemicals. We obtained 133 articles published from 1953 to 2015 from the database. Among them, 58 papers(43.6% of total) have been published in 1990s. Three species(Lumnbriculus variegatus, Tubifex tubifex, and Limnodrilus hoffmeisteri) have been used most frequently in the study. Different species displayed different toxicological responses to different toxic chemicals. The results on the ecotoxicological study with aquatic oligochaetes revealed the possibility of the development for early warning system using aquatic oligochaetes to monitor aquatic ecosystem disturbance.

• Geomechanics and Engineering
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• v.25 no.2
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• pp.135-142
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• 2021

This study provided a new research perspective for processing and analyzing AE data to evaluate rock failure. Cluster method and information entropy theory were introduced to investigate temporal and spatial correlation of acoustic emission (AE) events during the rock failure process. Laboratory experiments of granite subjected to compression were carried out, accompanied by real-time acoustic emission monitoring. The cumulative length and dip angle curves of single links were fitted by different distribution models and distribution functions of link length and directionality were determined. Spatial scale and directionality of AE event distribution, which are characterized by two parameters, i.e., spatial correlation length and spatial correlation directionality, were studied with the normalized applied stress. The entropies of link length and link directionality were also discussed. The results show that the distribution of accumulative link length and directionality obeys Weibull distribution. Spatial correlation length shows an upward trend preceding rock failure, while there are no remarkable upward or downward trends in spatial correlation directionality. There are obvious downward trends in entropies of link length and directionality. This research could enrich mathematical methods for processing AE data and facilitate the early-warning of rock failure-related geological disasters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.346-348
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• 2022

A monitoring system was constructed to identify the cause of occurrence based on data on the analysis of the ignition factors of fermentation heat generated from loading waste. Universal Middleware was used to provide a real-time run-time environment for the configuration and speed of scenarios for each type of fire early warning. It is necessary to dynamically recognize the loading height and pressure of the loading waste, the drying of wood, batteries, and plastic waste, which are representative compositional wastes, and the carbonization changes on the surface. Therefore, this IoT situation recognition platform for analyzing low-temperature-fired fire possibility data was dynamically configured and presented.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.359-367
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• 2014

Drought index can be used to implement an early warning system for drought and to operate a drought monitoring service. In this study, an approach was examined to determine agricultural drought index (ADI) at high spatial resolution, e.g., 270 m. The value of ADI was calculated based on soil water balance between supply and demand of water. Water supply is calculated by the cumulative effective precipitation with the application of the weight to the precipitation from two months ago. Water demand is derived from the actual evapotranspiration, which was calculated applying a crop coefficient to the reference evapotranspiration. The amount of surface runoff on a given soil type was also used to calculate soil residual moisture. Presence of drought was determined based on the probability distribution in the given area. In order to assess the reliability of this index, the amount of residual moisture, which represents severity of drought, was compared with measurements of soil moisture at three experimental between July 2012 and December 2013. As a result, the ADI had greater correlation with measured soil moisture compared with the standardized precipitation index, which suggested that the ADI would be useful for drought warning services.