A Recognition Method of Gait by Wavelet Transform and Genetic Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 274-278 ◽  
Author(s):  
Yun Shi ◽  
Xu Qi Wang ◽  
Yu Cheng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Real Time ◽  
Traditional Method ◽  
Feature Vector ◽  
Gait Recognition ◽  
Recognition Method ◽  
Recognition Model ◽  
Method Of Correlation ◽  
Correlation Matching

A new recognition method of gait is presented in this paper. In this method, the feature vector of gait is found by multidistinguish analysis of wavelet transform, and gait is recognized by genetic algorithm (GA). This method is different from the traditional method of correlation matching recognition gait. First, the stored space reduces greatly because recognition model is used to replace the store of gait profile image template. Thus this method reduces stored memory greatly. Second real-time is ensured in the process of gait recognition by using GA. The experiments of recognition using the three kinds of gait databases are performed. The experiment results show the feasibility and effectiveness of the proposed method.

Depth-Based Real-Time Gait Recognition

2020 ◽  
Vol 29 (16) ◽  
pp. 2050266
Author(s):  
Adnan Ramakić ◽  
Diego Sušanj ◽  
Kristijan Lenac ◽  
Zlatko Bundalo
Keyword(s):  
Real Time ◽  
Gait Recognition ◽  
Video Stream ◽  
Outdoor Environment ◽  
Microsoft Kinect ◽  
Recognition Method ◽  
Real Time Processing ◽  
Depth Data ◽  
Video Images ◽  
Subject Identification

Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

scholarly journals Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2184
Author(s):  
Andrea Mannelli ◽  
Francesco Papi ◽  
George Pechlivanoglou ◽  
Giovanni Ferrara ◽  
Alessandro Bianchini
Keyword(s):  
Wavelet Transform ◽  
Wind Turbine ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Power Output ◽  
Discrete Wavelet ◽  
Smoothing Technique ◽  
The Real ◽  
Power Smoothing ◽  
Li Ion

Energy Storage Systems (EES) are key to further increase the penetration in energy grids of intermittent renewable energy sources, such as wind, by smoothing out power fluctuations. In order this to be economically feasible; however, the ESS need to be sized correctly and managed efficiently. In the study, the use of discrete wavelet transform (Daubechies Db4) to decompose the power output of utility-scale wind turbines into high and low-frequency components, with the objective of smoothing wind turbine power output, is discussed and applied to four-year Supervisory Control And Data Acquisition (SCADA) real data from multi-MW, on-shore wind turbines provided by the industrial partner. Two main research requests were tackled: first, the effectiveness of the discrete wavelet transform for the correct sizing and management of the battery (Li-Ion type) storage was assessed in comparison to more traditional approaches such as a simple moving average and a direct use of the battery in response to excessive power fluctuations. The performance of different storage designs was compared, in terms of abatement of ramp rate violations, depending on the power smoothing technique applied. Results show that the wavelet transform leads to a more efficient battery use, characterized by lower variation of the averaged state-of-charge, and in turn to the need for a lower battery capacity, which can be translated into a cost reduction (up to −28%). The second research objective was to prove that the wavelet-based power smoothing technique has superior performance for the real-time control of a wind park. To this end, a simple procedure is proposed to generate a suitable moving window centered on the actual sample in which the wavelet transform can be applied. The power-smoothing performance of the method was tested on the same time series data, showing again that the discrete wavelet transform represents a superior solution in comparison to conventional approaches.

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