An Unsorted Spike-Based Pattern Recognition Method for Real-Time Continuous Sensory Event Detection from Dorsal Root Ganglion Recording

2016 ◽  
Vol 63 (6) ◽  
pp. 1310-1320 ◽  
Author(s):  
Sungmin Han ◽  
Jun-Uk Chu ◽  
Hyungmin Kim ◽  
Kuiwon Choi ◽  
Jong Woong Park ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Dorsal Root Ganglion ◽  
Real Time ◽  
Event Detection ◽  
Dorsal Root ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Sensory Event

scholarly journals A real-time walking pattern recognition method for soft knee power assist wear

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Wenkang Wang ◽  
Liancun Zhang ◽  
Juan Liu ◽  
Bainan Zhang ◽  
Qiang Huang
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Recognition Performance ◽  
Pattern Recognition Method ◽  
Joint Angles ◽  
Recognition Method ◽  
Walking Pattern ◽  
Input Signals ◽  
One Step ◽  
Measurement Units

Real-time recognition of walking-related activities is an important function that lower extremity assistive devices should possess. This article presents a real-time walking pattern recognition method for soft knee power assist wear. The recognition method employs the rotation angles of thighs and shanks as well as the knee joint angles collected by the inertial measurement units as input signals and adopts the rule-based classification algorithm to achieve the real-time recognition of three most common walking patterns, that is, level-ground walking, stair ascent, and stair descent. To evaluate the recognition performance, 18 subjects are recruited in the experiments. During the experiments, subjects wear the knee power assist wear and carry out a series of walking activities in an out-of-lab scenario. The results show that the average recognition accuracy of three walking patterns reaches 98.2%, and the average recognition delay of all transitions is slightly less than one step.

A real-time EMG pattern recognition method for virtual myoelectric hand control

2014 ◽  
Vol 136 ◽  
pp. 345-355 ◽  
Author(s):  
Kexin Xing ◽  
Peipei Yang ◽  
Jian Huang ◽  
Yongji Wang ◽  
Quanmin Zhu
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Hand Control ◽  
Emg Pattern Recognition

scholarly journals Adaptive Real-Time Energy Management Strategy for Plug-In Hybrid Electric Vehicle Based on Simplified-ECMS and a Novel Driving Pattern Recognition Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yuping Zeng ◽  
Jing Sheng ◽  
Ming Li
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Pattern Recognition Method ◽  
Energy Management Strategy ◽  
Recognition Method ◽  
Driving Cycles ◽  
Driving Pattern

This paper proposes an adaptive real-time energy management strategy for a parallel plug-in hybrid electric vehicle (PHEV). Three efforts have been made. First, a novel driving pattern recognition method based on statistical analysis has been proposed. This method classified driving cycles into three driving patterns: low speed cycle, middle speed cycle, and high speed cycle, and then carried statistical analysis on these three driving patterns to obtain rules; the types of real-time driving cycles can be identified according to these rules. Second, particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF) and then the EF MAPs, indexed vertically by battery’s State of Charge (SOC) and horizontally by driving distance, under the above three driving cycles, are obtained. Finally, an adaptive real-time energy management strategy based on Simplified-ECMS and the novel driving pattern recognition method has been proposed. Simulation on a test driving cycle is performed. The simulation results show that the adaptive energy management strategy can decrease fuel consumption of PHEV by 17.63% under the testing driving cycle, compared to CD-CS-based strategy. The calculation time of the proposed adaptive strategy is obviously shorter than the time of ECMS-based strategy and close to the time of CD-CS-based strategy, which is a real-time control strategy.

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

Sign in / Sign up

Export Citation Format

Share Document