• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.05a
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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.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.10
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• pp.1090-1098
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• 2005

Benzoyl peroxide is very toxic to aquatic organisms but environmental concentration or exposure effects were not studied. Distribution of the chemical among multimedia environment was estimated using EQC(Equilibrium Criterion) model based on the physical-chemical properties to evaluate the risk of benzoyl peroxide in environment. Level I describes a situation that 100,000 kg of benzoyl peroxide is emitted into the environment which is equilibrium and steady-state without degradation and advection condition. Level II describes a situation that a constant rate of 1,000kg/h of benzoyl peroxide is continuously discharged into the environment which is equilibrium and steady-state with degradation and advection condition. Level III describes a situation that 1,000 kg/h of benzoyl peroxide is continuously introduced in each air, water, soil, and sediment compartment which are non-equilibrium and steady-state with degradation, advection, and inter-media transfer condition. In Level I and II calculations the chemical was distributed to soil(68.3%) and water(28.7%). In Level III calculation it was primarily distributed to soil(99.9%) and overall residence time was estimated to be 3.4 years. Benzoyl peroxide can be persistent in environment.

• Proceedings of the Zoological Society Korea Conference
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• 1995.10b
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• pp.209.2-209
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• 1995

No Abstract, See Full Text

• Journal of Korea Multimedia Society
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• v.25 no.2
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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.

• Journal of Korea Multimedia Society
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• v.20 no.2
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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.

• Journal of Korea Multimedia Society
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• v.25 no.11
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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.

• Journal of Korea Multimedia Society
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• v.24 no.5
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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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• v.58 no.3
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• pp.325-345
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• 2020

Determination of Best available technology (BAT) was suggested to reduce volatile organic compounds (VOCs) in a petrochemical industrial complex, by conducting human health risk, environmental, and economic assessment based on multimedia fugacity model. Fate and distribution of benzene, toluene, ethylbenzene, and xylene (BTEX) was predicted by the multimedia fugacity model, which represent VOCs emitted from the industrial complex in U-city. Media-integrated human health risk assessment and sensitivity analysis were conducted to predict the human health risk of BTEX and identify the critical variable which has adverse effects on human health. Besides, the environmental and economic assessment was conducted to determine the BAT for VOCs reduction. It is concluded that BTEX highly remained in soil media (60%, 61%, 64% and 63%), and xylene has remained as the highest proportion of BTEX in each environment media. From the candidates of BAT, the absorption was excluded due to its high human health risk. Moreover, it is identified that the half-life and exposure coefficient of each exposure route are highly correlated with human health risk by sensitivity analysis. In last, considering environmental and economic assessment, the regenerative thermal oxidation, the regenerative catalytic oxidation, the bio-filtration, the UV oxidation, and the activated carbon adsorption were determined as BAT for reducing VOCs in the petrochemical industrial complex. The suggested BAT determination methodology based on the media-integrated approach can contribute to the application of BAT into the workplace to efficiently manage the discharge facilities and operate an integrated environmental management system.

• Journal of Korea Multimedia Society
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• v.24 no.3
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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%).

• Journal of Korea Multimedia Society
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• v.20 no.8
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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.