Optimization of propagation in interval constraint networks for tolerance design

This paper proposes a hierarchical internal constraint network and interval propagation techniques for automatic tolerance design. The nodes in interval constraint networks represent the entities, the attributes, and the functional requirements of the mechanical design or the constraint functions. The arcs represent the relationships between the entities, the attributes, the functional requirements and the constraint functions. We developed the forward propagation technique for tolerance analysis and the backward propagation technique for tolerance synthesis. In tolerance analysis, given the entity tolerances, the goal is to ensure that the functional requirement tolerances are met. In tolerance synthesis, given the functional requirement tolerances, the goal is to synthesize a new set of entity tolerances. In backward propagation, the minimization of the manufacturing cost is also considered. During backward propagation, the tolerances of entities, which have a smaller impact on manufacturing costs, will be tightened first. Using this mechanism, we ensure the constraints are satisfied and the manufacturing costs are minimized.published_or_final_versio

Tolerance analysis and synthesis by interval constraint networks

This paper proposes interval constraint network and interval propagation techniques for automatic tolerance design. A hierarchical representation is utilized in the interval constraint network. The consistency of a constraint is defined for the purpose of tolerance design. Forward and backward propagation techniques are introduced in the interval constraint network for tolerance analysis and synthesis, respectively. Both a propagation technique for a single constraint and a parallel propagation technique for multiple constraints between two adjacent levels in the network are introduced. Experiments conducted to illustrate the procedures of tolerance analysis and synthesis for the tank problem are described.published_or_final_versio

A scenario approach for non-convex control design

Randomized optimization is an established tool for control design with modulated robustness. While for uncertain convex programs there exist randomized approaches with efficient sampling, this is not the case for non-convex problems. Approaches based on statistical learning theory are applicable to non-convex problems, but they usually are conservative in terms of performance and require high sample complexity to achieve the desired probabilistic guarantees. In this paper, we derive a novel scenario approach for a wide class of random non-convex programs, with a sample complexity similar to that of uncertain convex programs and with probabilistic guarantees that hold not only for the optimal solution of the scenario program, but for all feasible solutions inside a set of a-priori chosen complexity. We also address measure-theoretic issues for uncertain convex and non-convex programs. Among the family of non-convex control- design problems that can be addressed via randomization, we apply our scenario approach to randomized Model Predictive Control for chance-constrained nonlinear control-affine systems.Comment: Submitted to IEEE Transactions on Automatic Contro

A survey of exemplar-based texture synthesis

Exemplar-based texture synthesis is the process of generating, from an input sample, new texture images of arbitrary size and which are perceptually equivalent to the sample. The two main approaches are statistics-based methods and patch re-arrangement methods. In the first class, a texture is characterized by a statistical signature; then, a random sampling conditioned to this signature produces genuinely different texture images. The second class boils down to a clever "copy-paste" procedure, which stitches together large regions of the sample. Hybrid methods try to combine ideas from both approaches to avoid their hurdles. The recent approaches using convolutional neural networks fit to this classification, some being statistical and others performing patch re-arrangement in the feature space. They produce impressive synthesis on various kinds of textures. Nevertheless, we found that most real textures are organized at multiple scales, with global structures revealed at coarse scales and highly varying details at finer ones. Thus, when confronted with large natural images of textures the results of state-of-the-art methods degrade rapidly, and the problem of modeling them remains wide open.Comment: v2: Added comments and typos fixes. New section added to describe FRAME. New method presented: CNNMR