Modelling of equipment loaded panels for robust control investigations

This paper develops a modelling technique for equipment load panels which directly produces (adequate) models of the underlying dynamics on which to base robust controller design/evaluations. This technique is based on the use of the Lagrange's equations of motion and the resulting models are verified against those produced by a finite Element Method Model.

New food processing equipment

The article presents new food processing equipment for coating and frying. These are predusters, liquid enrobers and applicators for large-particle crumbs.

Metrical dental analysis on golden monkey (Rhinopithecus roxellana)

Dental variation in the Chinese golden monkey (Rhinopithecus roxellana) is here evaluated by univariate, bivariate, and multivariate analyses. Allometric analyses indicate that canines and P3s are positively, but other dimensions negatively scaled to mandible and maxilla, and to body size. With the exception of the mesiodistal dimensions of I-1 and M-3, and the buccolingual dimension of Pq, mandibular dental variables show similar scaling relative to body size. Analysis of residuals shows that males have significantly larger canine, P-3 and buccolingual dimensions of the postcanine teeth (M-2 and M-3) than females. A significant difference in shape between the sexes is found in the buccolingual dimension of the upper teeth, but not in the mandible. Unlike the situation in some other species, Female golden monkeys do nor exhibit relatively larger postcanine teeth than males, in fact, the reverse is true, especially for M(2)s and M(3)s. The fact that most of the dental variables show low negative allometry to body size might be related a cold environment that has led to the development of larger body size with I-educed energy loss. When the raw data are examined by Discriminant Function Analysis the sexes are clearly distinguishable.

Enhancement of Construction Equipment Detection in Video Frames by Combining with Tracking

Vision-based object detection has been introduced in construction for recognizing and locating construction entities in on-site camera views. It can provide spatial locations of a large number of entities, which is beneficial in large-scale, congested construction sites. However, even a few false detections prevent its practical applications. In resolving this issue, this paper presents a novel hybrid method for locating construction equipment that fuses the function of detection and tracking algorithms. This method detects construction equipment in the video view by taking advantage of entities' motion, shape, and color distribution. Background subtraction, Haar-like features, and eigen-images are used for motion, shape, and color information, respectively. A tracking algorithm steps in the process to make up for the false detections. False detections are identified by catching drastic changes in object size and appearance. The identified false detections are replaced with tracking results. Preliminary experiments show that the combination with tracking has the potential to enhance the detection performance.