18 research outputs found
Classification of EMG signals by LRF-ELM [EMG sinyallerinin YAA-AÖM ile siniflandinlmasi]
No full textElectromyogram (EMG) signal can be defined as the electrical activity of muscles cells. It is commonly used in motion recognition, treatment of neuromuscular disorders and prosthetic hand control. In this study, classification of EMG signals obtained from 6 different hand shapes of holding object was proposed. At first Short Time Fourier Transform of the EMG signal were evaluated to obtain their Time-Frekans representation. After than these T-F images were segmented and their mean values were evaluated to reduce the dimension of the images. Local Receptive Fields based Extreme Learning Machines (ELM-LRF) used to classification of these hand shapes of holding object. Evaluated accuracy is 94.12 %. © 2017 IEEE
Research of Intranet Security Audit in E-government Management Website Group Based on Multi-agents
No full textThe effect of flow patterns and energy dissipation over stepped chutes on aeration efficiency
No full textDetection and Classification of Faulty Weft Threads Using Both Feature-Based and Deep Convolutional Machine Learning Methods
No full textAutomatic defect detection for fabric printing using a deep convolutional neural network
No full textTree-based nonlinear ensemble technique to predict energy dissipation in stepped spillways
No full textForecasting algal bloom (chl-a) on the basis of coupled wavelet transform and artificial neural networks at a large lake
No full textAn artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash
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