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Human Scoring with Automated Scoring in Mind
Published in Duanli Yan, André A. Rupp, Peter W. Foltz, Handbook of Automated Scoring, 2020
Researchers of human scoring also believe in a third class of variables, those having to do with the rating context, influence rating quality, rater speed, and rater attitude. These variables focus on how the process, materials, and activities in which raters are engaged and the manner in which these features are structured influence the scoring process outputs.
Body activity grading strategy for cervical rehabilitation training
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Grading of body activities is frequently seen in sports games such as gymnastics, diving, figure skating, synchronized swimming, etc. where the performance of an athlete is evaluated based on the accuracy of body postures and movement, difficulty level, synchronization, interactions with apparatus and the overall gracefulness. To realize machine-based evaluation, the strategy has to be adapted to deal with complicated classification and reasoning. In Zhu et al. (2019; Liao et al. 2020; Zhang et al. 2020), deep neural network and convolutional neural network based on deep learning are used to discover representative features from a sequence of performance measurements. A scoring function is then applied to convert them into quality scores. These algorithms present generalized frameworks to process different rehabilitation exercises and achieved good classification and evaluation results. However, they differ from human graders as the grading rule is hidden in the framework, and wrong motion patterns are not presented to the user. Moreover, deep learning is essentially a computation-intensive method that requires a large amount of training data and ground truth evaluation results. It is unsuitable to be applied to mobile apps that require fast development iterations and frequent content change.
A comprehensive study on multi-objective design optimization of spur gear
Published in Mechanics Based Design of Structures and Machines, 2023
Scoring is one of the modes of wear encountered by the gears. There are several factors affecting scoring, like weight, face width, hardness of material etc. The mismatch across the tooth face due to overweight of gear drive leads to severe scoring. The reported result in Winter and Michaelis (1983) shows wider face width gears are more susceptible to scoring. In this study, by performing the design optimization using scoring and top land as the constraints, we sought to demonstrate the safeguard of the gear set against scoring. In our study, a lower face width is reported in Jaya algorithm as w.r.t. the previously reported results (Yokota et al. 1998; Savsani et al. 2010; Panda et al. 2017). It should, however, be noted that in the design optimization the constraint based on flash temperature has been satisfied indicating protection against scoring type failure. Sachidananda et al. (2015) have reported that positively altered tooth sum gears are marginally scored as compared to standard gearing, whereas negative number of tooth alteration results in better surface integrity and protection against scoring. Our reported results for negatively altered tooth sum (85 tooth) strongly confirm the previous prediction obtain in Sachidananda et al. (2015).
Design for manufacturing and assembly (DfMA): a preliminary study of factors influencing its adoption in Singapore
Published in Architectural Engineering and Design Management, 2018
Shang Gao, Sui Pheng Low, Kisnaa Nair
The second method, devised by Boothroyd (2005), employs a quantitative evaluation of the design. The rationale is that each part of the design can be rated with a numerical value depending on its ‘assemblability’ (Bogue, 2012). Subsequently, the numbers can be summed for the entire design, and the resulting value used as a guide to the overall design quality. This is in line with the buildable design appraisal system implemented in the Singapore construction industry, where key components – such as structure and wall components – are enumerated with a value for each design choice; the total value then determines the level of buildability. Here, however, the value given to each design part is associated with the impact on the labour productivity, instead of ‘assemblability’. The scoring system allows the product designer and construction designer (in the case of buildability) the opportunity to redesign based on numerical values: the higher the values, the easier it is to assemble or more buildable. However, this requires insight and knowledge on the part of the designers. In construction, however, it has often been acknowledged that designers may not like things to be too standardized as this may limit their freedom to express themselves aesthetically.