Human motion segmentation using active shape models

Human motion analysis from images is meticulously related to thedevelopment of computational techniques capable of automatically identifying,tracking and analyzing relevant structures of the body. This work explores theidentification of such structures in images, which is the first step of any computationalsystem designed to analyze human motion. A widely used database(CASIA Gait Database) was used to build a Point Distribution Model (PDM) of thestructure of the human body. The training dataset was composed of 14 subjectswalking in four directions, and each shape was represented by a set of 113 labelledlandmark points. These points were composed of 100 contour points automaticallyextracted from the silhouette combined with an additional 13 anatomical pointsfrom elbows, knees and feet manually annotated. The PDM was later used in theconstruction of an Active Shape Model, which combines the shape model with graylevel profiles, in order to segment the modelled human body in new images. Theexperiments with this segmentation technique revealed very encouraging results asit was able to gather the necessary data of subjects walking in different directionsusing just one segmentation model

Optimization of distribution control system in oil refinery by applying hybrid machine learning techniques

In this research, prediction of crude oil cuts from the first stage of refining process field is laid out using rough set theory (RST) based adaptive neuro-fuzzy inference system (ANFIS) soft sensor model to enhance the performance of oil refinery process. The RST was used to reduce the fuzzy rule sets of ANFIS model, and its features in the decision table. Also, discretisation methods were used to optimise the continuous data’s discretisation. This helps to predict the two critical variables of light naphtha product: Reid Vapor Pressure (RVP) and American Petroleum Institute gravity (API gravity), which detect the cut’s quality. Hence, a real-time process of Al Doura oil refinery is examined and the process data of refining crude oil from these two sources improve the knowledge provided by the data. The response variables represent the feedback measured value of cascade controller in the top of the splitter in crude distillation unit (CDU) in the rectifying section, which controls the reflux liquid’s flow towards the splitter’s head. The proposed adaptive soft sensor model succeeded to fit the results from laboratory tests, and a steady-state control system was achieved through an embedded virtual sensor. The predictive control system has been employed using cascade ANFIS controller in parallel with the soft sensor model to keep the purity of the distillate product in the stated range of the quality control of oil refinery. The results obtained from the proposed ANFIS based cascade control have no over/undershoots, and the rise time and settling time are improved by 26.65% and 84.63%, respectively than the conventional proportional-integral-derivative (PID) based cascade control. Furthermore, the results of prediction and control model are compared with those of other machine learning techniques