Bending and Shearing Characteristics of Alfalfa Stems

Bending stress, young’s modulus, shearing stress and shearing energy were determined for Alfalfa (Medicago Sativa L.) stem. The bending forces were measured at different moisture contents and the bending stress and the young’s modulus were calculated from these data. For measuring the shearing forces, the stem specimens were severed by using a computer aided cutting apparatus. The shearing energy was calculated by using the area under the shearing force versus displacement curve. The experiments were conducted at a moisture content of 10%, 20%, 40% and 80% w.b. The bending stress decreased as the moisture content increased. The value of the bending stress at low moisture content was obtained approximately 3 times higher than at high moisture content. The average bending stress value varied from 9.71 to 47.49 MPa. The young’s modulus in bending also decreased as the moisture content and diameter of stalks increased. The average young’s modulus ranged from 0.79 to 3.99GPa. The results showed that the shearing stress and the shearing energy increased as the moisture content increased. The maximum shear strength and shearing energy were found to be 28.16 MPa and 345.80 mJ, respectively. Both the shearing stress and the specific shearing energy were found to be higher in the lower region of the stalk due to structural heterogeneity

Biomedical image denoising based on hybrid optimization algorithm and sequential filters

Background: Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise. Objectives: This study has focused on the sequence filters which are selected by a hybrid genetic algorithm and particle swarm optimization. Material and Methods: In this analytical study, we have applied the composite of different types of noise such as salt and pepper noise, speckle noise and Gaussian noise to images to make them noisy. The Median, Max and Min filters, Gaussian filter, Average filter, Unsharp filter, Wiener filter, Log filter and Sigma filter, are the nine filters that were used in this study for the denoising of medical images as digital imaging and communications in medicine (DICOM) format. Results: The model has been implemented on medical noisy images and the performances have been determined by the statistical analyses such as peak signal to noise ratio (PSNR), Root Mean Square error (RMSE) and Structural similarity (SSIM) index. The PSNR values were obtained between 59 to 63 and 63 to 65 for MRI and CT images. Also, the RMSE values were obtained between 36 to 47 and 12 to 20 for MRI and CT images. Conclusion: The proposed denoising algorithm showed the significantly increment of visual quality of the images and the statistical assessment. © 2020, Shiraz University of Medical Sciences. All rights reserved

Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing,”

Abstract. Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine

Design and Development of an Auxiliary Chickpea Second Sieving and Grading Machine

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is an Invited Paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 5 (2003): A. Tabatabaeefar, H. Aghagoolzadeh, and H. Mobli. Design and Development of an Auxiliary Chickpea Second Sieving and Grading Machine. Vol. V. December 2003