Experimental liquid-liquid equilibria of the water-acetic acid-butyl acetate system were studied at temperatures of 298.15±0.20, 303.15±0.20 and 308.15±0.20 K. Complete phase diagrams were obtained by determining solubility and tie-line data. The reliability of the experimental tie-line data was ascertained by using the Othmer and Tobias correlation. The UNIFAC group contribution method was use...

LLE Review, Volume 78 93 Measurements of the charged-particle products of the fusion reactions from an imploding inertial fusion capsule can provide a direct means of characterizing key aspects of the implosion dynamics. Parameters such as the fusion yield, fuel ion temperature, capsule convergence, fuel and shell areal densities, and implosion asymmetry can be inferred by these measurements an...

The extraction of chronic pacemaker and internal cardioverter defibrillator leads is performed for a number of reasons including chronic infection, lead dysfunction, and venous obstruction. Traditionally leads were removed by traction which is associated with a high incidence of failure and serious complications. This has led to the introduction of laser lead extraction (LLE) which is a safe an...

In this paper, we proposed a new nonlinear dimensionality reduction algorithm called regularized Kernel Local Linear Embedding (rKLLE) for highly structured data. It is built on the original LLE by introducing kernel alignment type of constraint to effectively reduce the solution space and find out the embeddings reflecting the prior knowledge. To enable the non-vectorial data applicability of ...

We present a real-time markerless human motion capture technique based on un-calibrated synchronized cameras. Training sets of real motions captured from marker based systems are used to learn an optimal pose manifold of human motion via Kernel Principal Component Analysis (KPCA). Similarly, a synthetic silhouette manifold is also learnt, and markerless motion capture can then be viewed as the ...

fMRI data is represented in a space with very high dimensionality. Because of this, classifiers such as SVM and Naive Bayes may overfit this data. Dimensionality reduction methods are intended to extract features from data in a high dimensional space. Training a classifier on data in a lower dimension may improve the true error of the classifier beyond the performance obtained by training in a ...

LLE(Local linear embedding) is a widely used approach for dimension reduction. The neighborhood selection is an important issue for LLE. In this paper, the ε-distance approach and a slightly modified version of k-nn method are introduced. For different types of datasets, different approaches are needed in order to enjoy higher chance to obtain better representation. For some datasets with compl...

Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an iterative algorithm, and just a few parameters need to be set. Two extensions of LLE to supervised feature extraction were independently proposed by the authors of this paper. Here, both methods are unified in a common f...

The prediction method of the isobaric vapor-liquid-liquid equilibria (VLLE) data for the system ethanol-water-1-butanol and ethanol-2-butanol-water was studied. The parameters for the activity coefficients models were determined from the constituent binary VLE data. With the parameters, the isobaric ternary VLLE data were predicted and compared with the experimental ones obtained in our previou...

Since the introduction of LLE (Roweis and Saul, 2000) and Isomap (Tenenbaum et al., 2000), a large number of non-linear dimensionality reduction techniques (manifold learners) have been proposed. Many of these non-linear techniques can be viewed as instantiations of Kernel PCA; they employ a cleverly designed kernel matrix that preserves local data structure in the “feature space” (Bengio et al...