Dynamic Sliding Window Model for Service Reputation

2015 ◽  
Author(s):  
Xin Zhou ◽  
Toru Ishida ◽  
Yohei Murakami
Keyword(s):  
Sliding Window ◽  
Dynamic Sliding Window

Tilt Angle On-Line Prognosis by Using Improved Sparse LSSVR and Dynamic Sliding Window

2018 ◽  
Vol 67 (2) ◽  
pp. 296-306 ◽  
Author(s):  
Luqiang Shi ◽  
Yigang He ◽  
Qiwu Luo ◽  
Wei He ◽  
Bing Li
Keyword(s):  
Tilt Angle ◽  
Sliding Window ◽  
On Line ◽  
Dynamic Sliding Window

A Dynamic Sliding Window Based on Otsu Method for Binary License Plate and Character Recognition

2013 ◽  
Vol 397-400 ◽  
pp. 2301-2308
Author(s):  
Rui Jian ◽  
Jun Zhao
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Grid Method ◽  
Sliding Window ◽  
Recognition Algorithm ◽  
Least Square ◽  
Support Vector ◽  
License Plate ◽  
License Plate Recognition ◽  
Dynamic Sliding Window

This paper is concerned with the problem of license plate recognition of vehicles. A recognition algorithm based on dynamic sliding window to binarize license plate characters is proposed. While a connected domain approach is presented to cope with the degradation characters. There are three steps to recognize the characters. First, the characters are classified by their features. Then, based on such classification a grid method is used to construct the feature vector. Finally, least square support vector machine is employed to recognize these characters. The test results show the high recognition rate and also illustrate the effectiveness of the proposed algorithm.

Mitigating false alarms using accumulator rule and dynamic sliding window in wireless body area

2018 ◽  
Vol 6 (2) ◽  
pp. 203-208 ◽  
Author(s):  
Smrithy Girijakumari Sreekantan Nair ◽  
Ramadoss Balakrishnan
Keyword(s):  
Sliding Window ◽  
False Alarms ◽  
Body Area ◽  
Dynamic Sliding Window

scholarly journals Prediction of Streamflow Based on Dynamic Sliding Window LSTM

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3032
Author(s):  
Limei Dong ◽  
Desheng Fang ◽  
Xi Wang ◽  
Wei Wei ◽  
Robertas Damaševičius ◽  
...  
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Back Propagation ◽  
Prediction Method ◽  
Window Size ◽  
Sliding Window ◽  
Series Data ◽  
Window Method ◽  
Dynamic Sliding Window ◽  
Optimal Window

The streamflow of the upper reaches of the Yangtze River exhibits different timing and periodicity characteristics in different quarters and months of the year, which makes it difficult to predict. Existing sliding window-based methods usually use a fixed-size window, for which the window size selection is random, resulting in large errors. This paper proposes a dynamic sliding window method that reflects the different timing and periodicity characteristics of the streamflow in different months of the year. Multiple datasets of different months are generated using a dynamic window at first, then the long-short term memory (LSTM) is used to select the optimal window, and finally, the dataset of the optimal window size is used for verification. The proposed method was tested using the hydrological data of Zhutuo Hydrological Station (China). A comparison between the flow prediction data and the measured data shows that the prediction method based on a dynamic sliding window LSTM is more accurate by 8.63% and 3.85% than the prediction method based on fixed window LSTM and the dynamic sliding window back-propagation neural network, respectively. This method can be generally used for the time series data prediction with different periodic characteristics.

scholarly journals What is an acceptably smoothed fluence? Dosimetric and delivery considerations for dynamic sliding window IMRT

2007 ◽  
Vol 2 (1) ◽  
pp. 42 ◽  
Author(s):  
Nicolini Giorgia ◽  
Fogliata Antonella ◽  
Vanetti Eugenio ◽  
Clivio Alessandro ◽  
Ammazzalorso Filippo ◽  
...  
Keyword(s):  
Sliding Window ◽  
Dynamic Sliding Window
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