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Articles by Mohammad H. Omid
Total Records ( 2 ) for Mohammad H. Omid
  Masoud Karbasi , Mohammad H. Omid and Javad Farhoudi
  Problem statement: Cluster microforms are a type of small scale bed-form found in the surface layer of some gravel bed rivers. These bed-forms are comprised of discrete, organized groupings of particles that sit above the average elevation of the surrounding bed. As part of the structural organization of the bed, clusters are believed to impact the local dynamics of the fluvial system through the feedback process involving the flow field, entrainable sediment and stable bed morphology. Approach: In this study, flow and sediment characteristic measured at a laboratory flume and the presence or absence of clusters at each of these tests was recorded. A statistical analysis using logistic regression was performed to examine the correlation between the occurrence of clusters and various non-dimension combinations of measured variables. Results: It was found that the best parameters for predicting the clusters presence are gd2u/hU2avg and gd2u/U2avg. In two parameters analysis it was found that clustering was best predicted by gd2u/U2avg and τb/ρU2avg. Conclusion: It is thought that these parameters work best at predicting the presence of clusters because they are descriptive of hydraulic and sedimentary conditions of tested reach.
  Mahmoud Omid , Asghar Mahmoudi and Mohammad H. Omid

An intelligent pistachio nut sorting system combining acoustic emissions analysis, Principal Component Analysis (PCA) and Multilayer Feedforward Neural Network (MFNN) classifier was developed and tested. To evaluate the performance of the system 3200 pistachio nuts from four native Iranian pistachio nut varieties were used. Each variety was consisted of 400 split-shells and 400 closed-shells nut. The nuts were randomly selected, slide down a chute, inclined 60° above the horizontal, on which nuts slide down to impact a steel plate and their acoustic signals were recorded from the impact. Sound signals in the time-domain are saved for subsequent analysis. The method is based on feature generation by Fast Fourier Transform (FFT), feature reduction by PCA and classification by MFNN. Features such as amplitude, phase and power spectrum of sound signals are computed via a 1024-point FFT. By using PCA more than 98% reduction in the dimension of feature vector is achieved. To find the optimal MFNN classifier, various topologies each having different number of neurons in the hidden layer were designed and evaluated. The best MFNN model had a 40–12–4 structure, that is, a network having one hidden layer with 40 neurons at its input, 12 neurons in the hidden layer and 4 neurons (pistachio varieties) in the output layer. The selection of the optimal model was based on the examination of mean square error, correlation coefficient and correct separation rate (CSR). The CSR or total weighted average in system accuracy for the 40–12–4 structure was 97.5%, that is, only 2.5% of nuts were misclassified.

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