Current Issue : July - September Volume : 2016 Issue Number : 3 Articles : 4 Articles
Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring\nstages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal\nfixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating\nlayout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design\nand optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected\nby uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method....
Nowadays, the Colebrook equation is used as a mostly accepted relation for the calculation of fluid flow friction factor.However, the\nColebrook equation is implicit with respect to the friction factor (...
Integrating diverse formalisms into modular knowledge representation systems offers\nincreased expressivity, modeling convenience, and computational benefits. We introduce\nthe concepts of abstract inference modules and abstract modular inference systems to study\ngeneral principles behind the design and analysis of model generating programs, or solvers,\nfor integrated multi-logic systems. We show how modules and modular systems give rise\nto transition graphs, which are a natural and convenient representation of solvers, an idea\npioneered by the SAT community. These graphs lend themselves well to extensions that\ncapture such important solver design features as learning. In the paper, we consider two\nflavors of learning for modular formalisms, local and global. We illustrate our approach by\nshowing how it applies to answer set programming, propositional logic, multi-logic systems\nbased on these two formalisms and, more generally, to satisfiability modulo theories....
Surveying threatened and invasive species to obtain accurate population estimates is\nan important but challenging task that requires a considerable investment in time and resources.\nEstimates using existing ground-based monitoring techniques, such as camera traps and surveys\nperformed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and\ndifficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence\nand miniaturized thermal imaging systems represent a new opportunity for wildlife experts to\ninexpensively survey relatively large areas. The system presented in this paper includes thermal\nimage acquisition as well as a video processing pipeline to perform object detection, classification\nand tracking of wildlife in forest or open areas. The system is tested on thermal video data from\nground based and test flight footage, and is found to be able to detect all the target wildlife located in\nthe surveyed area. The system is flexible in that the user can readily define the types of objects to\nclassify and the object characteristics that should be considered during classification....
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