Objective grading of fabric pilling with wavelet texture analysis

A new objective fabric pilling grading method based on wavelet texture analysis was developed. The new method created a complex texture feature vector based on the wavelet detail coefficients from all decomposition levels and horizontal, vertical and diagonal orientations, permitting a much richer and more complete representation of pilling texture in the image to be used as a basis for classification. Standard multi-factor classification techniques of principal components analysis and discriminant analysis were then used to classify the pilling samples into five pilling degrees. The preliminary investigation of the method was performed using standard pilling image sets of knitted, woven and non-woven fabrics. The results showed that this method could successfully evaluate the pilling intensity of knitted, woven and non-woven fabrics by selecting the suitable wavelet and associated analysis scale.

An approach to assign individual marks from a team mark : the case of Australian grading system at universities

This study uses a new approach to assign individual marks from a team mark using individual contributions to a teamwork product. A team member’s contribution to a teamwork product, in the form of an individual weighting factor, is calculated using team members’ co‐assessment. A comparison of the proposed approach with existing methods has been discussed with the help of a typical teamwork example. The approach has been refined to make it applicable for the Australian grading system at universities. It has been implemented in a large undergraduate engineering course to observe its effectiveness in practice. The results show that the method encourages teamwork, penalises below‐average contributions and rewards above‐average contributions. An analysis of a students’ perception survey shows that students prefer the approach over alternative approaches.

Improved contact load-bearing capacity of ultra-fine grained titanium : Multilayering and grading

The contact load-bearing response and surface damage resistance of multilayered hierarchical structured (MHSed) titanium were determined and compared to monolithic nanostructured titanium. The MHS structure was formed by combining cryorolling with a subsequent Surface Mechanical Attrition Treatment (SMAT) producing a surface structure consisted of an outer amorphous layer containing nanocrystals, an inner nanostructured layer and finally an ultra-fine grained core. The combination of a hard outer layer, a gradual transition layer and a compliant core results in reduced indentation depth, but a deeper and more diffuse sub-surface plastic deformation zone, compared to the monolithic nanostructured Ti. The redistribution of surface loading between the successive layers in the MHS Ti resulted in the suppression of cracking, whereas the monolithic nanograined (NG) Ti exhibited sub-surface cracks at the boundary of the plastic strain field. Finite element models with discrete layers and mechanically graded layersrepresenting the MHS system confirmed the absence of cracking and revealed a 38% decrease in shear stress in the sub-surface plastic strain field, compared to the monolithic NG Ti. Further, the mechanical gradation achieves a more gradual stress distribution which mitigates the interface failure and increases the interfacial toughness, thus providing strong resistance to loading damage. © 2014 Elsevier Ltd.

Comparison between live and photographed slit lamp grading of corneal staining

To compare live and photographic (still) grades of corneal staining of the same eyes and the repeatability of grading between two investigators.

New artificial intelligence based tire size identification for fast and safe inflating cycle

Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.