ABSTRACT:In this paper we investigate the potential of automatic supervised classification for urban hydrological applications. In particular, we contribute to runoff simulations using hydrodynamic urban drainage models. In order to assess whether the capacity of the sewers is sufficient to avoid surcharge within certain return periods, precipitation is transformed into runoff. The transformation of precipitation into runoff requires knowledge about the proportion of drainage-effective areas and their spatial distribution in the catchment area. Common simulation methods use the coefficient of imperviousness as an important parameter to estimate the overland flow, which subsequently contributes to the pipe flow. The coefficient of imperviousness is the percentage of area covered by impervious surfaces such as roofs or road surfaces. It is still common practice to assign the coefficient of imperviousness for each particular land parcel manually by visual interpretation of aerial images. Based on classification results of these imagery we contribute to an objective automatic determination of the coefficient of imperviousness. In this context we compare two classification techniques: Random Forests (RF) and Conditional Random Fields (CRF). Experimental results performed on an urban test area show good results and confirm that the automated derivation of the coefficient of imperviousness, apart from being more objective and, thus, reproducible, delivers more accurate results than the interactive estimation. We achieve an overall accuracy of about 85% for both classifiers. The root mean square error of the differences of the coefficient of imperviousness compared to the reference is 4.4% for the CRF-based classification, and 3.8% for the RF-based classification.
With the development and dissemination of the concept of smart medical care, people's attention to their own health and smart medical system is gradually increasing. In medical activities, the rehabilitation assistance system is very important for patients, and the family and entertainment of the rehabilitation assistance system is the general trend. At the same time, the role of the rehabilitation assistance system is largely affected by its algorithm and function settings. Therefore, it is necessary to introduce deep learning (DL) algorithms to optimize the rehabilitation assistance system. In view of the above problems, this paper used the pre-training label pre-judgment algorithm and the sample classification training method to conduct scientific research and analysis on the limb motor function rehabilitation assistance system. At the same time, a new rehabilitation assistance system including signal acquisition system, local monitoring system and medical center monitoring system was designed. The results of the experimental test showed that this new limb motor function rehabilitation assistance system could better collect the user's biological signals because of the addition of DL. The recognition of the signal has been improved to an accuracy of about 45%, which showed that the research on the rehabilitation assistance system of limb motor function based on DL could better provide unique services for the rehabilitation of patients.
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