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Investigation of forced frequency in a commercial vehicle suspension system

Kumar S. D., Kumar S. S., Kumar K. A.

Mechanics and Mechanical Engineering

2018

Vol. 22, nr 4

967--974

Vehicle suspension plays a vital role in maintaining the center of gravity to achieve perfect balancing of the vehicle to provide the comfortable ride. While designing the suspension system of automobile, vibration is the main aspect to be considered. This paper aims to analyze the automobile front and rear suspension for a four wheeler using analytical and numerical approach. Existing details of the suspension is collected using the concept of reverse engineering. Natural and forced frequency of the front and rear suspension system is calculated theoretically based on the collected data's. The natural frequency and forced frequency is numerically computed for front and rear suspension. The amplitude of vibration is reduced by replacing the spring material and its forced frequency is reduced by 1.18% and 1.56% for front and rear suspension system respectively. This result reveals that low carbon steel has ability to reduce the forcing frequency and can produce comfort ride.

Liver Tumour Classification Using Co-Occurrence Matrices on the Contourlet Domain

Kumar S. S., Moni R. S., Rajeesh J.

Machine Graphics and Vision

2011

Vol. 20, No. 2

197-214

Liver disease is one of the most common diseases around the world, seriously affecting the health of humans. Computed tomography image based Computer Aided Diagnosis (CAD) could be crucially important in supporting liver cancer diagnosis. An effective approach to realize a CAD system for this purpose is described in this work. The CAD system employs automatic tumour segmentation, texture feature extraction and characterization into malignant and benign tumours. A Region of Inter- est (ROI) cropped from the automatically segmented tumour by confidence connected region growing and alternative fuzzy c means clustering is decomposed using multiresolution and multidirectional con- tourlet transform to obtain contourlet coefficients. Co-occurrence matrices of the contourlet coefficients are determined, and six parameters of texture characteristics, which include Angular Second Moment, Contrast, Correlation, Inverse Difference Moment, Entropy and Variance, are extracted from them. The extracted feature sets are classified into benign and malignant by a Generalized Regression Neural Net- work (GRNN) classifier. The performance of this scheme is evaluated by various performance measures and by the use a of the Receiver Operating Characteristic (ROC) curve. The results are compared with those obtained by a similar system using Wavelet Coefficients co-occurrence Matrix (WCCM) and Gray Level co-occurrence Matrix (GLCM) texture features. The results indicate that the proposed scheme based on the CCCM texture is effective for classifying malignant and begin liver tumours in abdominal CT imaging.