scholarly journals Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

2012 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Nayan M. Kakoty ◽  
Mantoo Kaiborta ◽  
Shyamanta M. Hazarika
Keyword(s):  
Daily Living ◽  
Wavelet Decomposition ◽  
Recognition Rate ◽  
Support Vector ◽  
Robotic Hand ◽  
Radial Basis Function Kernel ◽  
Kernel Support Vector Machine ◽  
Average Recognition Rate ◽  
Electromyographic Signals

This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a five<br />fingered robotic hand to emulate six grasp types used during 70% daily living activities.<br /><br />

scholarly journals Recognition of Pharmacological Bi-Heterocyclic Compounds by Using Terahertz Time Domain Spectroscopy and Chemometrics

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3349 ◽  
Author(s):  
Maciej Roman Nowak ◽  
Rafał Zdunek ◽  
Edward Pliński ◽  
Piotr Świątek ◽  
Małgorzata Strzelecka ◽  
...  
Keyword(s):  
Heterocyclic Compounds ◽  
Chemical Compounds ◽  
Functional System ◽  
Support Vector ◽  
Optimal Parameters ◽  
Terahertz Time Domain Spectroscopy ◽  
Kernel Support Vector Machine ◽  
Inflammatory Drugs ◽  
Living Organisms

In this study, we presented the concept and implementation of a fully functional system for the recognition of bi-heterocyclic compounds. We have conducted research into the application of machine learning methods to correctly recognize compounds based on THz spectra, and we have described the process of selecting optimal parameters for the kernel support vector machine (KSVM) with an additional `unknown’ class. The chemical compounds used in the study contain a target molecule, used in pharmacy to combat inflammatory states formed in living organisms. Ready-made medical products with similar properties are commonly referred to as non-steroidal anti-inflammatory drugs (NSAIDs) once authorised on the pharmaceutical market. It was crucial to clearly determine whether the tested sample is a chemical compound known to researchers or is a completely new structure which should be additionally tested using other spectrometric methods. Our approach allows us to achieve 100% accuracy of the classification of the tested chemical compounds in the time of several milliseconds counted for 30 samples of the test set. It fits perfectly into the concept of rapid recognition of bi-heterocyclic compounds without the need to analyse the percentage composition of compound components, assuming that the sample is classified in a known group. The method allows us to minimize testing costs and significant reduction of the time of analysis.

Research on Indicator Diagram of Shock Absorber Fault Recognition Based on Support Vector Machine

2013 ◽  
Vol 765-767 ◽  
pp. 2195-2198
Author(s):  
Wei Dong Xie ◽  
Kan Gao ◽  
Ji Sheng Shen
Keyword(s):  
Support Vector Machine ◽  
Shock Absorber ◽  
Recognition Rate ◽  
Support Vector ◽  
Indicator Diagram ◽  
Fault Recognition ◽  
Average Recognition Rate ◽  
Rbf Kernel ◽  
On Line ◽  
Rbf Kernel Function

In order to meet the development of shock absorber on-line detection, a new method of indicator diagrams recognition for shock absorber based on support vector machine (SVM) is proposed. Different fault patterns of shock absorber indicator diagram are discussed, including their main causes. The recognition model is constructed each with Linear, Polynomial and Radial Basis Function (RBF) kernel function. The experimental results show that the best average recognition rate is 96.4%. This method is effective in indicator diagram fault recognition of shock absorber.

Fault Diagnosis of Pump Based on a Hybrid HMM/SVM Model

2011 ◽  
Vol 188 ◽  
pp. 629-635
Author(s):  
Xia Yue ◽  
Chun Liang Zhang ◽  
Jian Li ◽  
H.Y. Zhu
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Model ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Double Layers ◽  
Support Vector ◽  
Practical Applications ◽  
Average Recognition Rate ◽  
Svm Model

A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuses on the ZLH600-2 pump showed that the recognition capability of this model was sound in multiclass problems. The recognition rate of one bearing eccentricity increased from SVM’s 84.42% to 89.61% while the average recognition rate of hybrid model reached 95.05%. Although some goals while model constructed did not be fully realized, this model was still very good in practical applications.

scholarly journals Genre Classification of Traditional Malay Music Using Spectrogram Correlation

2018 ◽  
Vol 7 (4.11) ◽  
pp. 29
Author(s):  
S. A. Samad ◽  
A. B. Huddin
Keyword(s):  
Template Matching ◽  
Correlation Energy ◽  
Recognition Rate ◽  
Salient Feature ◽  
Average Correlation ◽  
Genre Classification ◽  
Average Recognition Rate ◽  
Number Of Segments ◽  
Unconstrained Minimum

A method to classify the genre of traditional Malay music using spectrogram correlation is described.  The method can be divided into three distinct parts consisting of spectrogram construction that retains the most salient feature of the music, template construction that takes into account the variations in music within a genre as well as the music progresses, and template matching based on spectrogram image cross-correlation with unconstrained minimum average correlation energy filters. Experiments conducted with seven genres of traditional Malay music show that the recognition accuracy is dependent on the number of segments used to construct the filter templates, which in turn is related to the length of music segment used. Despite using a small dataset, an average recognition rate of 61.8 percent was obtained for music segments lasting 180 seconds using six relatively short excerpts.  

STUDY OF GAIT PATTERN RECOGNITION BASED ON FUSION OF MECHANOMYOGRAPHY AND ATTITUDE ANGLE SIGNAL

2020 ◽  
Vol 20 (02) ◽  
pp. 1950085 ◽  
Author(s):  
JING YU ◽  
YUE ZHANG ◽  
CHUNMING XIA
Keyword(s):  
Vastus Lateralis ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Rectus Femoris ◽  
Gait Pattern ◽  
Support Vector ◽  
Dynamic Transition ◽  
Transition Activities ◽  
Attitude Angle ◽  
Average Recognition Rate

The study of lower limb movements plays an important role in many fields, such as rehabilitation and treatment of disabled patients, detection, and monitoring of daily life, as well as the interaction between people and machine, like the application of intelligent prosthetics. In this paper, the wireless device was used to collect the mechanomyography (MMG) signals of four thigh muscles (rectus femoris, vastus lateralis, vastus medialis, and semitendinosus) and the attitude angle of rectus femoris. High precision was achieved in 11 gait movements, including 3 static activities, 4 dynamic transition activities, and 4 dynamic activities. It has been verified that the hidden Markov model (HMM) could not only be applied to the MMG-based gait recognition with high veracity but also support comparative analysis between support vector machine (SVM) and quadratic discriminant analysis (QDA). In addition, the experiment was conducted from the perspectives of feature selections, channel combinations, and muscle contribution rates. The results show that the average classification accuracy of dynamic motions based on MMG is 98.27%, while based on attitude angle, the average recognition rate of static motions and dynamic transition motions could achieve 98.33% and 100%, respectively. Generally, the average recognition rate of 11 gait motions is 98.91%.

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