• Journal of Korean Society of Water and Wastewater
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• v.31 no.1
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• pp.29-37
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• 2017

To estimate pollution sources in the watershed with various industries, the simulation of contamination route and distribution of 2-methyl-4,6-dinitriophenol(DNOC) were performed with a numerical model Hydro Geo Sphere. This study was performed calculations of the load using the measured concentration and simulated flow rate. And, the river was divided by the sampling sites at the mainstream, and the contribution rate at downstream sampling sites was calculated for each section. The results showed the concentration of the downstream sampling sites were decided by the concentration of upstream sites, and the contribution rates of the tributaries were calculated below 10%. The results also showed that the impact of the potential sources in Section 1(Geumho1 ~ Geumho2) and Section 5(Geumho5 ~ Geumho6) was larger than in the other area. In Section1 and Section5, It seemed to require detailed investigation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.451-457
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• 2018

We propose an automatic detection and classification system of sewer damage database based on artificial intelligence and deep learning. In order to optimize the performance, we implemented a robust system against various environmental variations such as illumination and shadow changes. In our proposed system, a crack detection and damage classification method using a deep learning based Convolutional Neural Network (CNN) is implemented. For optimal results, 9,941 CCTV images with $256{\times}256$ pixel resolution were used for machine learning on the damaged area based on the CNN model. As a result, the recognition rate of 98.76% was obtained. Total of 646 images of $720{\times}480$ pixel resolution were extracted from various sewage DB for performance evaluation. Proposed system presents the optimal recognition rate for the automatic detection and classification of damage in the sewer DB constructed in various environments.