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R. Ng

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2 papers

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EAAI Journal 2009 Journal Article

Optimisation of garment design using fuzzy logic and sensory evaluation techniques

Y. Chen
X. Zeng
M. Happiette
P. Bruniaux
R. Ng
W. Yu

The ease allowance is an important criterion in garment design. It is often taken into account in the process of construction of garment patterns. However, the existing pattern generation methods cannot provide a suitable estimation of ease allowance, which is strongly related to wearer's body shapes and movements and used fabrics. They can only produce 2D patterns for fixed standard values of ease allowance. In this paper, we present a new method for optimizing the estimation of ease allowance of a garment using fuzzy logic and sensory evaluation. Based on the optimized values of ease allowance generated from fuzzy models related to different key body positions and different wearer's movements, we obtain an aggregated ease allowance using the OWA operator. This aggregated result can further improve the wearer's fitting perception of a garment and adjust the compromise between the style of garments and the fitting comfort sensation of wearers. The related weights of the OWA operator are determined according to designer's linguistic criteria on comfort and garment style. The effectiveness of our method has been validated in the design of trousers of jean type. It can be also applied for designing other types of garment.

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I&C Journal 1994 Journal Article

Stable Semantics for Probabilistic Deductive Databases

R. Ng
V.S. Subrahmanian

In this paper we study the semantics of non-monotonic negation in probabilistic deductive databases. Based on the stable semantics for classical logic programming, we examine three natural notions of stability: stable formula functions, stable families of probabilistic interpretations, and stable probabilistic models. We show that stable formula functions are minimal fixpoints of operators associated with probabilistic logic programs. We also prove that each member in a stable family of probabilistic interpretations is a probabilistic model of the program. Then we show that stable formula functions and stable families behave as duals of each other, tying together elegantly the fixpoint and model theories for probabilistic logic programs with negation. Furthermore, since a probabilistic logic program may not necessarily have a stable family of probabilistic interpretations, we provide a stable class semantics for such programs. Finally, we investigate the notion of stable probabilistic model. We show that this notion, though natural, is too weak in the probabilistic framework.

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