Experimental design is a process of obtaining a product with target property via experimentation. Bayesian optimization offers a sample-efficient tool for experimental design when experiments are expensive. Often, expert experimenters have 'hunches' about the behavior of the experimental system, offering potentials to further improve the efficiency. In this paper, we consider per-variable monotonic trend in the underlying property that results in a unimodal trend in those variables for a target value optimization. For example, sweetness of a candy is monotonic to the sugar content. However, to obtain a target sweetness, the utility of the sugar content becomes a unimodal function, which peaks at the value giving the target sweetness and falls off both ways. In this paper, we propose a novel method to solve such problems that achieves two main objectives: (a) the monotonicity information is used to the fullest extent possible, whilst ensuring that (b) the convergence guarantee remains intact. This is achieved by a two-stage Gaussian process modeling, where the first stage uses the monotonicity trend to model the underlying property, and the second stage uses 'virtual' samples, sampled from the first, to model the target value optimization function. The process is made theoretically consistent by adding appropriate adjustment factor in the posterior computation, necessitated because of using the 'virtual' samples. The proposed method is evaluated through both simulations and real world experimental design problems of (a) new short polymer fiber with the target length, and (b) designing of a new three dimensional porous scaffolding with a target porosity. In all scenarios our method demonstrates faster convergence than the basic Bayesian optimization approach not using such 'hunches'.

The objective of extracting bone structure boundaries from computed tomography (CT) data is to serve as input for 3D model reconstruction. Various boundary detection algorithms for CT images have been developed in the last few decades but very few of them are completely automatic. Also, most of them were not capable of dealing with critical locations such as narrow gaps. A novel intelligence-based approach is proposed for automatic extraction of bone boundaries. This approach involves three major steps: gap detection, fuzzy multilevel thresholding and boundary transform (thinning method). Results of applying the approach to CT images are presented, with a discussion of the potential for clinical application.

There is an increasing demand for more intuitive methods for creating and modifying free‐form curves and surfaces in CAD modeling systems. The methods should be based not only on the change of the mathematical parameters, such as control points, knots, and weights, but also on the user’s specified constraints and shapes. This paper presents a new approach for directly manipulating the shape of a free‐form curve, leading to a better control of the curve deformation and a more intuitive CAD modeling interface. The user’s intended deformation of a curve is automatically converted into the modification of the corresponding NURBS control points and knot sequence of the curve. The algorithm for this approach includes curve elevation, knot refinement, control point repositioning, and knot removal. Several examples shown in this paper demonstrate that the proposed method can be used to deform a NURBS curve into the desired shape. Currently, the algorithm concentrates on the purely geometric consideration. Further work will include the effect of material properties.