• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1979-1986
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• 2010

Biological early warning system detects toxicity by looking at behavior of organisms in water. The system uses classifier for judgement about existence and amount of toxicity in water. Boosting algorithm is one of possible application method for improving performance in a classifier. Boosting repetitively change training example set by focusing on difficult examples in basic classifier. As a result, prediction performance is improved for the events which are difficult to classify, but the information contained in the events which can be easily classified are discarded. In this paper, an incremental learning method to overcome this shortcoming is proposed by using the extended data expression. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression by exploiting the necessary information not only from the well classified, but also from the weakly classified events. Experimental results show that the new algorithm outperforms the former Learn++ method without using the weight parameter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.489-492
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• 2006

Red tide occurred sporadically in early 90s. But It is happening extensively by global warming. So, Airline observation, Red tide buoy development, and Red tide alarm system research is progressing for monitor ring. However, study to early forecast red tide and red tide alarm system did not exist hard. This paper proposed development that design and implementation red tide database of using wireless sensor network. There are GPS, Water Temperature sensor, Oxygen sensor, and Turbidity sensor in each node. And data is stored to red tide database through Ad-hoc network. This data is integrated and analyzed. So, forecast red tide. And red tide database has red tide data that happen at past. This is utilized to comparative analysis data for red tide estimate. Main screen displays position of node and measured value in electron map. Much studies must be backed for this a study. But I think that contribute to analyze red tide data by red tide database construction.

• 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.

• Journal of Apiculture
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• v.32 no.3
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• pp.191-198
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• 2017

Vespa hornets are notorious predators of honeybees in Korean beekeeping. Detection of vespa hornet attacking on honeybee colony was tried through analysis of wing beat frequency profiling from Vespa mandarinia. Wing beat profiles of V. mandarinia during active flight and resting were distinctively different. From the wing beat profiling, algorithm of automated detection of vespa attack was encoded, and alert system was developed using Teensy 3.2 and Raspberry pi 3. From the laboratory testing, the prototype system successfully detected vespa wing beats and delivered the vespa attack information to the user wirelessly. Further development of the system could help precision alert system of the vespa attack to apiary.

• 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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.1
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• pp.91-97
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• 2017

This study contains research on a bio-monitoring system (BMS) capable of detecting abnormal high water temperatures, the shell valve movements (SVMs) of Pacific oysters (Crassostrea gigas), which were measured at four different temperature (5, 10, 20 and $30^{\circ}C$) under laboratory conditions. All the Pacific oysters were kept under fasting conditions for 3 days to prevent the influence of food and excretions before the onset of the experiments. SVMs did not detect at $5^{\circ}C$. However, SVMs increased with an increase in temperature (at $10^{\circ}C$ : $6.31{\pm}2.18times/hr$ and at $20^{\circ}C$: $22.0{\pm}10.0times/hr$). At $30^{\circ}C$, SVMs were divided into two groups: those with no SVMs as at $5^{\circ}C$ and those with SVMs similar to conditions at $20^{\circ}C$($23.9{\pm}9.35times/hr$). This indicates oyster shells maintain a closed condition due to a decrease in metabolism at $30^{\circ}C$, although some Pacific oysters had active SVMs due to an increase in metabolism. If a BMS using the SVM status of Pacific oysters was installed to monitor abnormal high water around oyster farms, early warning levels and serious alerts might be made available more rapidly for SVMs of more than ca. 30 times/hr and closing conditions in a matter of hours, respectively. Therefore, a BMS using the SVMs of Pacific oysters might be an effective early warning system for abnormal high water temperatures.

• Korean Journal of Environmental Biology
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• v.34 no.2
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• pp.124-131
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• 2016

The toxic responses of flagellate Euglena agilis Carter to 8 heavy metals (Ag, Cd, Cr (VI), Cu, Hg, Ni, Pb, Zn) were measured using E. agilis system (E-Tox), an automated biomonitoring system. The E-Tox measures cell movement parameters, such as velocity, motility, and forms of the cells, as biological endpoints. $EC_{50}$ values from the E. agilis biomonitoring test were compared with the literature data from the tests with Daphnia magna, Vibrio fischeri and Euglena gracilis. Measurement of the E. agilis movement behavior and D. magna acute toxicity test were also conducted for the wastewater samples. E. agilis is less sensitive than D. magna but is comparable to or more sensitive than V. fischeri and E. gracilis for the heavy metals tested in this study. E. agilis shows prompt changes of these parameters for the toxic metal plating wastewater. Major advantages of the E-tox are automatic, easy to handle and fast ecotoxicity monitoring system compared to other biological test systems. These results imply that E. agilis biomonitoring test using E-Tox can be a putative ecotoxicity test as a good early warning tool for the monitoring of toxic wastewater.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.12
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• pp.828-834
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• 2012

The ecotoxicity detection device for preliminary test (Test jig) was manufactured to develop the biological early warning system using Vibrio fischeri. In this study, the ecotoxicity detection charateristics of the Test jig was investigated for 6 heavy metals (Cr, Zn, Pb, Cd, Cu and Hg). It was observed that relative luminescence unit (RLU) of Vibrio fischeri constantly decreased by the concentrations of the tested heavy metals. In contrast with other heavy metals, RLUs of Pb and Hg constantly decreased even at low concentrations. RLU of Hg drastically decreased when its concentration increased from 0.13 mg/L to 0.25 mg/L. $EC_{50}$ values of Cr, Zn, Pb and Cd gradually decreased with exposure time, whereas there was no significant change in $EC_{50}$ values of Cu and Hg with time. On the other hand, $EC_{50}$ values between the Test jig and Reference device were compared to evaluate the ecotoxicity detection performance of the Test jig. No big difference was found in $EC_{50}$ vlaues between the two devices, indicating that the Test jig could be applied as the ecotoxicity detection device for heavy metals.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.168-178
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• 2006

In recent years, an ingress of mass jellyfish into cooling water intake system causes interruption of electric power production at the Uljin nuclear power plant. Therefore, monitering and forecast on the mass ingress of marine organisms are demanded as one of the early preventing measurements. Sea water movement is a major factor on the ingress of marine organisms like Moon jellyfish which has weak self-mobile ability. When sea surface flow direction adjacent to the Uljin is the northwest, the jellyfish on the Tsushima warm currents move to the Uljin power plant. To detect the direction of sea surface warm flows, the spatial range with $25km{\times}25km$ is set up and NOAA sea surface temperature(SST) data are collected in this area. For the statistical analysis, the SST data are made as GIS point data and geostatistical analysis of ArcGIS is used. Analyzing directional semivariogram, the anisotropy of the SST point data are calculated and warm flow direction is detected. This experimental results are expected to use as an element technology for the early warning system development of mass jellyfish ingress in power plant.