Likelihood-based estimators with conventional regularization methods generally produces images with nonuniform and anisotropic spatial resolution properties. Previous work on penalty design for penalizedlikelihood estimators has led to statistical reconstruction methods that yield approximately uniform “average” resolution. However some asymmetries in the local point-spread functions persist. S...

Different works have shown that the combination of multiple loss functions is beneficial when training deep neural networks for a variety of prediction tasks. Generally, such multi-loss approaches are implemented via a weighted multi-loss objective function in which each term encodes a different desired inference criterion. The importance of each term is often set using empirically tuned hyper-...

Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault accuracy. Actually, it difficult and expensive collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning diagnosis. Data from source domain that different but related target increase...

Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to clustering, the proposed algorithm introduces the feature weighting into the objective function. We first formulate the membership and feature weighting, ...

Cumulative Prospect Theory has gained a great deal of support as an alternative to Expected Utility Theory as it accounts for a number of anomalies in the observed behavior of economic agents. Expected Utility Theory uses a utility function and subjective or objective probabilities to compare risky prospects. Cumulative Prospect Theory alters both of these aspects. The concave utility function ...

We present a method to decrease the storage and communication complexity of the context-tree weighting method. This method is based on combining the estimated probability of a node in the context tree and weighted probabilities of its children in one single variable. This variable is represented by its logarithm.

This paper advances the fundamental understanding in mathematical and computational modelling of discrete fracture networks (Type I). It presents a systematic procedure to solve the most important problem in modelling by global optimization — objective function formulation, which negates guesswork in objective function formulation by automatic selection of highly ranked components and their cor...

In this study an optimization method for the design of combined solar and pellet heating systems is presented and evaluated. The paper describes the steps of the method by applying it for an example of system. The objective of the optimization was to find the design parameters that give the lowest auxiliary energy (pellet fuel + auxiliary electricity) and carbon monoxide (CO) emissions for a sy...