Biobased thermosets from the free‐radical copolymerization of conjugated linseed oil

New polymeric thermosets were prepared through the bulk free‐radical copolymerization of 100% conjugated linseed oil, acrylonitrile, and divinylbenzene. Under the appropriate reaction conditions and with the appropriate curing sequence, 61–96 wt % of the oil was incorporated into the crosslinked thermosets. The resulting yellow, transparent thermosets varied from being soft and flexible to being hard and brittle. Dynamic mechanical analysis and thermogravimetric analysis showed that these thermosets had good mechanical properties and thermal stability. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 979–985, 2007     

A new set distance and its application to shape registration

We propose a new distance measure, called Complement weighted sum of minimal distances, between finite sets in ${\mathbb Z }^n$ and evaluate its usefulness for shape registration and matching. In this set distance the contribution of each point of each set is weighted according to its distance to the complement of the set. In this way, outliers and noise contribute less to the new similarity measure. We evaluate the performance of the new set distance for registration of shapes in binary images and compare it to a number of often used set distances found in the literature. The most extensive evaluation uses a set of synthetic 2D images. We also show three examples of real problems: registering a set of 2D images extracted from synchrotron radiation micro-computed tomography (SR $\upmu $ CT) volumes depicting bone implants; the difficult multi-modal registration task of finding the exact location of a 2D slice of a bone implant, as imaged by a light microscope, within a 3D SR $\upmu $ CT volume of the same implant; and finally recognition of handwritten characters. The evaluation shows that our new set distance performs well for all tasks and outperforms the other observed distance measures in most cases. It is therefore useful in many image registration and shape comparison tasks.     

Modelling of interstitials in the bcc phase

There are several widespread thermodynamic datasets which produce a spurious bcc interstitial solution at high temperature and high X content (X is an interstitally dissolved element). The reason for this is the standard model for bcc interstitial solutions (M(V a,X)₃), which requires careful selection of optimising parameters to minimise spurious appearances of the bcc phase. In this work the model M(V a,X)₁ is suggested as an alternative. This model is much easier to handle and its parameters can be directly compared with those of the fcc phase. The two models are compared for the Fe–C and Nb–N systems. In the Fe–C system almost identical results are achieved. In Nb–N there are some differences for high N content, but there is no experimental data to clearly support any model.     

The use of data-derived label hierarchies in multi-label classification

Instead of traditional (multi-class) learning approaches that assume label independency, multi-label learning approaches must deal with the existing label dependencies and relations. Many approaches try to model these dependencies in the process of learning and integrate them in the final predictive model, without making a clear difference between the learning process and the process of modeling the label dependencies. Also, the label relations incorporated in the learned model are not directly visible and can not be (re)used in conjunction with other learning approaches. In this paper, we investigate the use of label hierarchies in multi-label classification, constructed in a data-driven manner. We first consider flat label sets and construct label hierarchies from the label sets that appear in the annotations of the training data by using a hierarchical clustering approach. The obtained hierarchies are then used in conjunction with hierarchical multi-label classification (HMC) approaches (two local model approaches for HMC, based on SVMs and PCTs, and two global model approaches, based on PCTs for HMC and ensembles thereof). The experimental results reveal that the use of the data-derived label hierarchy can significantly improve the performance of single predictive models in multi-label classification as compared to the use of a flat label set, while this is not preserved for the ensemble models.     

A probabilistic measure for model purposiveness in identification for control

The question of whether the identified process model will lead to a stable closed loop is of practical relevance during iterative identification and controller design. It is known that, in the case of overly demanding closed-loop requirements, the model resulting from the iterative procedure might conflict with prior knowledge about the process. Nevertheless, in many cases the violation of the plausibility of the identified models does not necessarily violate its purposiveness. Therefore, it is a matter of practical importance to have a confident indication as to whether the given model will result in a stable closed-loop design or not. If not, the iterative identification and controller design should be stopped, that is more appropriate model structures should be chosen. In this paper, a probabilistic measure is proposed which relies on the estimated model error obtainable by the stochastic embedding technique. The idea behind it is to estimate the probability that the critical point ( -1,0) will not be encircled by the Nyquist curve of the return ratio transfer function of the true system. The results obtained from experiments on a motor-generator laboratory set-up show that the proposed probabilistic measure provides a reliable indication of the stability of the designed closed loop.     

Time-varying continuous nonlinear systems: uniform asymptotic stability

The framework of the presented research is a large class of time-varying nonlinear systems with continuous motions. The study of the uniform asymptotic stability of the zero equilibrium state developed here goes back to, and relies on, the very foundations of the Lyapunov stability concept and the (second) Lyapunov method. Stability domains are first defined and examined. Then, their qualitative features are used to establish complete solutions to the problem of uniform asymptotic stability of the equilibrium for various subclasses of the systems. The solutions present conditions that are both necessary and sufficient for: (1) the uniform asymptotic stability, (2) an exact direct one-shot construction of a system Lyapunov function and (3) for a direct accurate one-shot determination of the asymptotic stability domain. In addition, the solutions establish a novel Lyapunov-method based approach to the asymptotic stability analysis. This enables an arbitrary selection of a function p(·) from a defined functional family to determine a Lyapunov function (·), [(·)], by solving D ⁺(·) = ? p(·) or, equivalently, D ⁺ (·) = ? p(·)[1 ? (·)], respectively. Illustrative examples are presented.     

A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images

Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of ‘mass’ that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover’s distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions.     

Control of mineral wool thickness using predictive functional control

The production process of mineral wool is affected by several constantly changing factors. The ingredients for the mineral wool are melted in a furnace. The molten mineral charge exits the bottom of the furnace in a water-cooled trough and falls into a fiberization device (the centrifuge). The centrifuge forms the fibers. At this stage binders are injected to bind the fibers together. To ensure the quality of the end product (the consistent thickness) the flow of the bounded fibers must be as constant as possible. One way to ensure that is to control the speed of the conveyor belt that transports the bounded fibers from the centrifuge to the curing process. Predictive functional controller and PID controller are considered to replace an existing algorithm. Both can easily replace an existing one as they do not require any new sensor installation. All three algorithms are presented and tested on a developed plant model. The study showed that the predictive control gives better results than the existing and PID controller.