• 한국환경독성학회:학술대회논문집
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• 한국환경독성학회 2003년도 춘계학술대회
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• pp.166-166
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• 2003

In terms of the risk assessment, qualitative and quantitative informations are needed to estimate the exposures of environmental pollutants, which may be potentiality of risks, and those are the information about the changes caused by the chemical transportation among environmental media and transformation in environmental media by duration. The various fate mechanism of chemical is possible for estimation of chemical concentration in environmental media. Since there are limitations in measuring the change of chemical concentration within all medium according to the time period, estimating method through modeling are developed.

• 대한환경공학회지
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• 제27권10호
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• pp.1090-1098
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• 2005

벤조일 퍼록사이드는 수서생물에 대해 매우 높은 독성을 나타냄에도 불구하고 환경 중 잔류 농도 및 노출영향에 대해 연구가 이루어지지 않아 OECD에서 추천하고 있는 대표적인 다매체 환경거동 모델인 EQC 모델을 이용하여 본 물질에 대한 환경중의 농도를 예측하고 위해성평가 및 화학물질의 관리를 수행하기 위한 기초자료로 활용코자 하였다. 평형, 정상상태에서 100,000 kg의 벤조일 퍼록사이드가 환경내로 유입된 상태를 나타내는 Level I과 평형, 정상상태 이류와 분해현상이 있고 일정한 속도 1,000 kg/h로 유입되었을 경우를 나타내는 Level II에서 벤조일 퍼록사이드는 주로 토양(68.3%)과 물(28.7%)로 배출되는 것으로 예측되었다. 비평형, 정상상태, 이류와 분해현상이 있고 다매체 이동을 하는 시스템에서 벤조일 퍼록사이드가 대기, 물, 토양, 침전물의 각각의 4개 매체에 연속적으로 1,000 kg/h로 유입될 경우인 Level III에서는 주로 토양(99.9%)으로 배출되었고 전체 잔류시간은 3.4년으로 예측되어 벤조일 퍼록사이드가 환경 중에 잔류성이 있는 물질로 평가되었다.

• 한국동물학회:학술대회논문집
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• 한국동물학회 1995년도 한국생물과학협회 학술발표대회
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• pp.209.2-209
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• 1995

No Abstract, See Full Text

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.287-296
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• 2022

Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.126-133
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• 2017

The conventional hand-crafted features used to track objects have limitations in object representation. Convolutional neural networks, which show good performance results in various areas of computer vision, are emerging as new ways to break through the limitations of feature extraction. CNN extracts the features of the image through layers of multiple layers, and learns the kernel used for feature extraction by itself. In this paper, we use the feature map extracted from the convolution layer of the convolution neural network to create an outline model of the object and use it for tracking. We propose a method to adaptively update the outline model to cope with various environment change factors affecting the tracking performance. The proposed algorithm evaluated the validity test based on the 11 environmental change attributes of the CVPR2013 tracking benchmark and showed excellent results in six attributes.

• 한국멀티미디어학회논문지
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• 제25권11호
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

• 한국멀티미디어학회논문지
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• 제24권5호
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• pp.701-708
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• 2021

As one of the most widely used technology in the world right now, Face recognition has already received widespread attention by all the researcher and institutes. It has been used in many fields such as safety protection, surveillance system, crime control and even in our ordinary life such as home security and so on. This technology with today's technology has advantages such as high connectivity and real time transformation. But we still need to improve its recognition rate, reaction time and also reduce impact of different environmental status to the whole system. So in this paper we proposed a face recognition system model with improved CNN which combining the characteristics of flat network and residual network, integrated learning, simplify network structure and enhance portability and also improve the recognition accuracy. We also used AR and ORL database to do the experiment and result shows higher recognition rate, efficiency and robustness for different image conditions.

• Korean Chemical Engineering Research
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• 제58권3호
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• pp.325-345
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• 2020

본 연구에서는 다매체 퓨가시티 모델을 기반으로 Volatile organic compounds (VOCs) 방지기술의 인체위해성·환경성·경제성 평가를 수행하여 석유화학 산업단지 내 VOCs 저감을 위한 최적가용기법(Best available technology, BAT)을 선정하였다. 다매체 퓨가시티 모델을 이용하여 U-city에 소재한 석유화학 산업단지에서 배출되는 VOCs 중 Benzene, Toluene, Ethylbenzene, Xylene (BTEX)의 다매체 거동 특성과 잔류농도 분포를 예측하였다. 매체 통합 인체위해성 평가 및 민감도 분석을 이용해 BTEX의 물질별 인체위해성을 예측하고 주요 영향 변수를 규명하였으며, 다매체 환경시스템 내 잔류농도 기준의 환경성 평가와 비용-편익 경제성 평가를 수행하여 우수환경관리기법군(BAT군)을 선정하였다. BTEX의 다매체 거동 분석 결과, 토양 매체에서 높은 잔류 분포 특성(60%, 61%, 64%, 63%)을 보였으며, Xylene은 모든 다매체 환경에서 가장 높은 잔류성을 보였다. BAT후보군 중에서 흡수법은 가장 높은 인체위해성을 보여 BAT 선정에서 제외하였으며, 민감도 분석 결과 대기 매체에서의 물질 반감기와 경로별 노출계수가 인체위해성과 높은 상관성이 있는 것으로 판단되었다. 환경성 평가와 비용-편익 경제성 평가를 고려하여, 재생 열산화법, 재생 촉매산화법, 바이오 필터법, UV 산화법, 활성탄 흡착법을 석유화학 산업단지 내 VOCs 저감을 위한 BAT군으로 선정하였으며, 본 연구에서 제시한 매체통합적 접근 방식의 BAT 선정 방법론은 사업장에서 오염물질 저감을 위한 최적의 배출시설 선정과 통합환경관리제도 운영에 기여할 수 있을 것으로 기대된다.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.373-381
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• 2021

In this paper, we propose a classification model based on convolutional neural network(CNN) for predicting 2-year recurrence in non-small cell lung cancer(NSCLC) patients using preoperative chest CT images. Based on the region of interest(ROI) defined as the tumor internal and external area, the input images consist of an intratumoral patch, a peritumoral patch and a peritumoral texture patch focusing on the texture information of the peritumoral patch. Each patch is trained through AlexNet pretrained on ImageNet to explore the usefulness and performance of various patches. Additionally, ensemble learning of network trained with each patch analyzes the performance of different patch combination. Compared with all results, the ensemble model with intratumoral and peritumoral patches achieved the best performance (ACC=98.28%, Sensitivity=100%, NPV=100%).

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1282-1290
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• 2017

Since the mitigation of fear of crime significantly enhances the consumptions in a city, studies focusing on urban safety analysis have received much attention as means of revitalizing the local economy. In addition, with the development of computer vision and machine learning technologies, efficient and automated analysis methods have been developed. Previous studies have used global features to predict the safety of cities, yet this method has limited ability in accurately predicting abstract information such as safety assessments. Therefore we used a Convolutional Context Neural Network (CCNN) that considered "context" as a decision criterion to accurately predict safety of cities. CCNN model is constructed by combining a stacked auto encoder with a fully connected network to find the context and use it in the CNN model to predict the score. We analyzed the RMSE and correlation of SVR, Alexnet, and Sharing models to compare with the performance of CCNN model. Our results indicate that our model has much better RMSE and Pearson/Spearman correlation coefficient.