• Korean Journal of Ecology and Environment
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• v.46 no.1
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• pp.1-9
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• 2013

Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

• Journal of Biomedical Engineering Research
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• v.22 no.2
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• pp.157-163
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• 2001

본 논문에서는 후취자극 제어장치를 이용하여 후각자극에 대한 인간의 뇌의 활성화 영역을 뇌기능자기공명영상(functional magnetic resonance imaging : fMRI)장치로 측정 또는 가시화하고 이의 임상적용에 대한 기초자료를 마련하고자 하였다. 우선 후각에 이상이 없고 코 수술 경험이 없는 오른손잡이 피험자 4명을 대상으로 5번에 걸쳐 Echo Plannar Imaging(EPI)에 의한 혈액산소농도의존(blood oxygen level dependent : BOLD)법을 이용하여 후각자극에 의한 뇌기능자기공명영상 실험을 수행하였다. 후각자극은 MRI 장치에서 사용할 수 있도록 제작된 후취제어장치를 사용하였으며, 제시된 향은 천연 향의 일종인 lavender-like fragrance를 사용하였다. 향의 제시는 후각의 피로도를 감안하여 3회의 휴식기관과 2회의 자극기간을 각 30초씩 번갈아 시행하였으며, 동시에 5초 간격으로 각 절편 당 30 영상을 연속적으로 획득하였다. Correlation법으로 0.4∼0.7의 문턱치(threshold)범위에서 통계 처리된 뇌의 활성화 영상은 EPI영상과 같은 부위의 T1 강조영상에 overlapping 시켰다. 호흡에 의한 artifact를 제거하기 위해 실험실에 만든 장치로 호흡을 측정하여 post-processing 할 때 반영하였다. 이렇게 얻어진 fMRI 영상의 신호변화를 관찰하여 활성 영역의 위치를 분석하였다. 그 결과 후각자극에 의해 뇌의 전두엽 피질(frontal cortex), 소뇌(cerebellum), 그리고 뇌교(pons)에서 활성화된 신호를 발견할 수 있었다. 또한, 측두엽(temporal lobe)과 뇌섬(insula)에서도 의미 있는 신호가 관찰되었다. 그러나, 일차 후각영역인 piriform cortex와 entorhinal cortex, amygdaloid complex, 그리고 이차후각영역인 orbitofrontal cotex에서는 그다지 많은 빈도로 신호가 발견되지 않았다. 결론적으로 BOLD법을 이용한 fMRI에 의하여 후각자극에 대한 뇌의 활성화영역을 관찰할 수 있었으며, 후각자극에 대한 뇌의 기능을 연구하는데 있어서 중요한 정량적 자료를 제공할 수 있다는 점을 확인할 수 있었다.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.41-48
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• 2012

Using multi-sensor or multi-temporal high resolution satellite images together is essential for efficient applications in remote sensing area. The purpose of this paper is to estimate geometric difference of translations between high-resolution optical and SAR images automatically. The geometric and radiometric pre-processing steps were fulfilled to calculate the similarity between optical and SAR images by using Mutual Information method. The coarsest-level pyramid images of each sensor constructed by gaussian pyramid method were generated to estimate the initial translation difference of the x, y directions for calculation efficiency. The precise geometric difference of translations was able to be estimated by applying this method from coarsest-level pyramid image to original image in order. Yet even when considered only translation between optical and SAR images, the proposed method showed RMSE lower than 5m in all study sites.

• Safety and Health at Work
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• v.9 no.2
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• pp.144-148
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• 2018

Background: The objective of this study is to find temporal trends in the associations between cardiovascular disease and occupational risk factors in the context of the Canadian population. Methods: Population data were analyzed from the Canadian Community Health Survey (CCHS) collected between 2001 and 2014 for trends over time between heart disease and various occupational risk factors: hours worked, physical exertion at work, and occupation type (management/arts/education, business/finance, sales/services, trades/transportations, and primary industry/processing). Results: We found no significant difference in the average number of hours worked/wk between individuals who report having heart disease in all years of data except in 2011 ($F_{1,96}=7.02$, p = 0.009) and 2012 ($F_{1,96}=8.86$, p = 0.004). We also found a significant difference in the degree of physical exertion at work in 2001 ($F_{1,79}=7.45$, p = 0.008). There were statistically significant results of occupation type on self-reported heart disease from 2003 to 2014. Conclusion: Canadian data from the CCHS do not exhibit a trend toward an association between heart disease and the number of hours worked/wk. There is an association between heart disease and physical exertion at work, but the trend is inconsistent. The data indicate a trend toward an association between heart disease and occupation type, but further analysis is required to determine which occupation type may be associated with heart disease.

• Journal of Korean Society on Water Environment
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• v.30 no.2
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• pp.212-219
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• 2014

In this study, a land pollutant load calculation method in TMDLs was improved to consider climate change scenarios. In order to evaluate the new method, future change in rainfall patterns was predicted by using SRES A1B climate change scenarios and then post-processing methods such as change factor (CF) and quantile mapping (QM) were applied to correct the bias between the predicted and the observed rainfall patterns. Also, future land pollutant loads were estimated by using both the bias corrected rainfall patterns and the enhanced method. For the results of bias correction, both methods (CF and QM) predicted the temporal trend of the past rainfall patterns and QM method showed future daily average precipitation in the range of 1.1~7.5 mm and CF showed it in the range of 1.3~6.8 mm from 2014 to 2100. Also, in the result of the estimation of future land pollutant loads using the enhanced method (2020, 2040, 2100), TN loads were in the range of 4316.6~6138.6 kg/day and TP loads were in the range of 457.0~716.5 kg/day. However, each result of TN and TP loads in 2020, 2040, 2100 was the same with the original method. The enhanced method in this study will be useful to predict land pollutant loads under the influence of climate change because it can reflect future change in rainfall patterns. Also, it is expected that the results of this study are used as a base data of TMDLs in case of applying for climate change scenarios.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.3
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• pp.337-351
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• 2006

In this study, to describe the basic characteristics of strong odorous sources, the emissions of odorous compounds from a large-scale sewage treatment plant in K city were investigated. According to this study, the emission patterns of major odorous compounds were distinguished clearly by several factors such as treatment processing types, chemical compositions of odors released, and temporal changes (e.g., seasonal variations). For the purpose of this study, emission rates of odorous compounds were quantified using a dynamic flux chamber (DFC) method from three major treatment (T) processes including T1 (Grit sedimentation basin), T2 (Aeration tank), and T3 (Final sedimentation). When the relative strengths of each emission source were compared, the strongest one was seen from T1 with the maximum of $NH_{3}\;(34.5\;{\mu}g/m^{2}/min)$ followed by $H_{2}S\;(20.4\;{\mu}g/m^{2}/min)($. While the strongest emissions of most odorous compounds were seen commonly from T1, those seen from T2 and T3 were significantly reduced relative to the ones found in T1. Considering the general patterns of odorous emission, it is concluded that control of odors from T1 unit is most important because of its considerably high emission strengths.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.281-285
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• 2001

According to standard procedures as defined in the users handbook for sea level data processes, I was compared to Topex/poseidon sea level data from the first 350days of mission and Tide Gauge sea level data from the Amsterdam- Crozet- Kerguelen region in the South Indian Ocean. The comparison improves significantly when many factors for the corrections were removed, then only the aliased oceanic tidal energy is removed by oceanic tide model in this period. Making the corrections and smoothing the sea level data over 60km along-track segments and the Tide Gauge sea level data for the time series results in the digital correlation and RMS difference between the two data of c=-0.12 and rms=11.4cm, c=0.55 and rms=5.38cm, and c=0.83 and rms=2.83cm for the Amsterdam, Crozet and Kerguelen plateau, respectively. It was also found that the Kerguelen plateau has a comparisons due to propagating signals(the baroclinic Rossby wave with velocity of -3.9~-4.2cm/sec, period of 167days and amplitude of 10cm) that introduce temporal lags($\tau$=10~30days) between the altimeter and tide gauge time series. The conclusion is that on timescales longer than about 10days the RMS sea level errors are less than or of the order of several centimeters and are mainly due to the effects of currents rather than the effects of sterics(water temperature, density) and winds.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.875-885
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• 2010

CBR (Case-Based Reasoning) is a technique to infer the relationships between existing data and case data, and the method to calculate similarity and Euclidean distance is mostly frequently being used. However, since those methods compare all the existing and case data, it also has a demerit that it takes much time for data search and filtering. Therefore, to solve this problem, various researches have been conducted. This paper suggests the method of SE(Speed Euclidean-distance) calculation that utilizes the patterns discovered in the existing process of computing similarity and Euclidean distance. Because SE calculation applies the patterns and weight found during inputting new cases and enables fast data extraction and short operation time, it can enhance computing speed for temporal or spatial restrictions and eliminate unnecessary computing operation. Through this experiment, it has been found that the proposed method improves performance in various computer environments or processing rate more efficiently than the existing method that extracts data using similarity or Euclidean method does.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2919-2925
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• 2014

Because of the rapid growth of mobile communication and wireless communication, Location-based services are handled in many applications. So, the management and analysis of spatio-temporal data are a hot issue in database research. Index structure and query processing of such contents are very important for these applications. This paper addressees algorithms that make index structure by using Douglas-Peucker Algorithm and process nearest neighbor search query efficiently on moving objects trajectories. We compare and analyze our algorithms by experiments. Our algorithms make small size of index structure and process the query more efficiently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.582-585
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• 2018

As traffic volume increases and road networks become more complicated, identifying for accurate traffic flow and driving smooth traffic flow are a concern of many countries. There are various analytical techniques and studies which desire to study about effective traffic flow. However, the necessary activity is finding the traffic flow pattern through data visualization including location information. In this paper aim to study a real-world urban traffic trajectory and visualize a pattern of traffic flow with a simulation tool. Our experiment is installing the sensor module in 40 taxis and our dataset is generated along 24 hours and unscheduled routes. After pre-processing data, we improved an open source traffic visualize tools to suitable for our experiment. Then we simulate our vehicle trajectory data with a dots animation over a period of time, which allows clearly view a traffic flow simulation and a understand the direction of movement of the vehicle or route pattern. In addition we further propose some novel timelines to show spatial-temporal features to improve an urban environment due to the traffic flow.