4 resultados para Mini-scale method
em Universidade Federal do Rio Grande do Norte(UFRN)
Resumo:
This Master s Thesis proposes the application of Data Envelopment Analysis DEA to evaluate the performance of sales teams, based on a study of their coverage areas. Data was collected from the company contracted to distribute the products in the state of Ceará. Analyses of thirteen sales coverage areas were performed considering first the output-oriented constant return to scale method (CCR-O), then this method with assurance region (AR-O-C) and finally the method of variable returns to scale with assurance region (AR-O-V). The method used in the first approach is shown to be inappropriate for this study, since it inconveniently generates zero-valued weights, allowing that an area under evaluation obtain the maximal score by not producing. Using weight restrictions, through the assurance region methods AR-O-C and AR-O-V, decreasing returns to scale are identified, meaning that the improvement in performance is not proportional to the size of the areas being analyzed. Observing data generated by the analysis, a study is carried out, aiming to design improvement goals for the inefficient areas. Complementing this study, GDP data for each area was compared with scores obtained using AR-O-V analysis. The results presented in this work show that DEA is a useful methodology for assessing sales team performance and that it may contribute to improvements on the quality of the management process.
Resumo:
In this work we present the principal fractals, their caracteristics, properties abd their classification, comparing them to Euclidean Geometry Elements. We show the importance of the Fractal Geometry in the analysis of several elements of our society. We emphasize the importance of an appropriate definition of dimension to these objects, because the definition we presently know doesn t see a satisfactory one. As an instrument to obtain these dimentions we present the Method to count boxes, of Hausdorff- Besicovich and the Scale Method. We also study the Percolation Process in the square lattice, comparing it to percolation in the multifractal subject Qmf, where we observe som differences between these two process. We analize the histogram grafic of the percolating lattices versus the site occupation probability p, and other numerical simulations. And finaly, we show that we can estimate the fractal dimension of the percolation cluster and that the percolatin in a multifractal suport is in the same universality class as standard percolation. We observe that the area of the blocks of Qmf is variable, pc is a function of p which is related to the anisotropy of Qmf
Resumo:
PURPOSE: Stroke is a high-incidence cerebrovascular disease with elevated morbidity that results in impairments such as functional disabilities. This study aimed to investigate the functional evolution of individuals in the first six months post-stroke. METHOD: Longitudinal study with 42 stroke patients. The functional independence measure (FIM) and The National Institutes of Health Stroke Scale (NIHSS) were used by multidisciplinary staff 3 times in each participant; the first application was at admission to rehabilitation and the others three and six months later. RESULTS: Sample predominantly female (57%), married (52%), mean age 65.26 ±10.72 years, elementary schooling level (43%), ischemic stroke (91%), and right cerebral hemisphere (74%). Motor FIM scores and NIHSS scale showed improvement in the 3 evaluations, with significant p-value (<0.001). There was a strong relation between motor FIM evolution and NIHSS evolution (r = - 0.69 p-value< 0.001). CONCLUSIONS: It was observed that functional evolution at 6 months post-stroke was significant and the smaller the evolution of clinical impairment in these patients, the larger the evolution of their functional independence. The study is important because it allows a more appropriate therapeutic planning according with functional evolution in stroke rehabilitation
Resumo:
Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second