A partition of unity level set method with moving knot CS-RBFs for optimizing variable stiffness composites

A partition of unity level set method with moving knot Compactly Supported Radial Basis Functions (CS-RBFs) is proposed for optimizing variable stiffness composite structures. The iso-contours of a level set function are utilized to represent the curved fiber paths, and the tangent vector of the iso-contour defines the orientation of fiber. The level set function of the full design domain is constructed according to the Partition of Unity (POU) method by a set of local level set functions defined on an array of overlapping subdomains, and they are constructed by using the CS-RBFs. The positions of knots are iteratively changed during the optimization to improve the performance of composite structures. Several examples of compliance minimization are presented.     

De-combination of belief function based on optimization

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...     

Trajectory optimisation in electrical discharge machining of three-dimensional curved and twisted channels

Numerical control electrical discharge machining(NC EDM) is one of the most widely used machining technologies for manufacturing a closed blisk flow path, particularly for three-dimensional(3D) curved and twisted flow channels. In this process, tool electrode design and machining trajectory planning are the key factors affecting machining accessibility and efficiency. Herein, to reduce the difficulty in designing the electrode and its motion path in the closed curved and twisted channels, a heur...     

Novel moderate transformation of fuzzy membership function into basic belief assignment

In information fusion, the uncertain information from different sources might be modeled with different theoretical frameworks. When one needs to fuse the uncertain information represented by different uncertainty theories, constructing the transformation between different frameworks is crucial. Various transformations of a Fuzzy Membership Function (FMF) into a Basic Belief Assignment (BBA) have been proposed, where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements. However, these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty. To avoid extreme attitudinal bias, a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred. In this paper, two moderate transformations of an FMF into a trade-off BBA are proposed. One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism, where the weighting factor can be user-specified or determined with some prior information. The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.     

Combination of classifiers with incomplete frames of discernment

The methods for combining multiple classifiers based on belief functions require to work with a common and complete(closed) Frame of Discernment(Fo D) on which the belief functions are defined before making their combination. This theoretical requirement is however difficult to satisfy in practice because some abnormal(or unknown) objects that do not belong to any predefined class of the Fo D can appear in real classification applications. The classifiers learnt using different attributes inform...