No abstract

Biospeckle activity (during 1-25 s) of 'Red Delicious' apples was recorded under 680 and 780 nm laser diodes. The time history speckle pattern (THSP) images were configured and evaluated using the common biospeckle and texture descriptors. Several artificial neural networks (ANN) models were developed to predict the stiffness and juiciness of apple fruits. At the recording time of 1 s and 680 nm laser diode, the ANN models developed by texture features showed better performance in prediction of stiffness (r = 0.70 and SEP = 8.4 kN) and juiciness (r = 0.68 and SEP = 2.3 cm2) as compared to the common biospeckle features-based ones (r = 0.25 and SEP = 11.3 kN m −1 for stiffness and r = 0.29 and SEP = 3.1 cm 2 for juiciness). Furthermore, classification of apples into fresh, semi-mealy, and mealy groups was carried out. At the recording time of 25 s and 680 nm laser, the ANN models developed by texture features classified fresh, semi-mealy, and mealy apples with accuracies of 86.3, 65.9, and 95.5%, respectively. Reduction of the recording time to 1 s resulted in the classification accuracies of 88.2, 68.5, and 85.5% for mealy, semi-mealy, and mealy apples compared to the common biospeckle features-based ANN models with the accuracies of 82.8, 50.7, and 46.8%, respectively. These results indicated that the texture descriptors had better performance at shorter recording times in comparison with the biospeckle features which are commonly used in evaluation of biospeckle images, even though more improvements are still required.

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Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tire-obstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tire-obstacle contact interface. A new Takagi-Sugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics.