Vast information regarding muscle activity for clinical and engineering applications can be obtained via the EMG (Electromyogram). EMG signal are acquired through surface electrodes which are placed on target muscle set of healthy subjects aged between 23 to 30 years. In this work, six forearm movements have been chosen for classification purpose for both left and right hand. With Hilbert Huang Transform method a total of 21 features of time-frequency domain are extracted for 10 healthy subjects and classified using conjugate gradient method of supervised learning technique using artificial neural networks (ANN). The average accuracy at IMF-1 level obtained is 85.8% for left hand movements, and 86.2% for right hand movement classification. The results of using the Hilbert Huang Transform based ANN classification are quite promising when compared to another classification techniques as K-NN, QDA, LDA [21] and Mahdi Khezri et al. different signal acquisition and classification techniques[26]. The technique can be used for practical implementation of prosthesis for movement classification.

Classification of faults in transformers with high accuracy is fundamental to ensuring good power quality with least interruptions. Our current work develops an intelligent genetic algorithm (GA)-tuned fuzzy classifier for transformer fault identification. The proposed classifier is able to segregate all fault types using dissolved gas analysis (DGA) samples from real power transformers of HPSEB (India) and other sources. DGA samples have been pre-processed using the J48 algorithm. We propose to replace the conventional action selection procedure of reinforcement learning by a GA-based optimizer. The classifier is able to garner very high classification accuracy which is higher than the one obtained with benchmark fuzzy Q learning (FQL) and other conventional classifiers. With our approach, the average fault classification rate achieved is 91.85% (FQL) and 97.51% genetic algorithm fuzzy Q-learning (GAFQL) though with a slightly higher computational complexity over the FQL. Our proposed classifier could serve as an important tool in ensuring the healthy operation of power transformers.