Semi-active base isolation system has been proposed mainly to mitigate the base drift of isolated structures while in most cases, its application causes the maximum acceleration of superstructure to be increased. In this paper, designing optimal semi-active base isolation system composed of linear base isolation system with low damping and magneto-rheological (MR) damper has been studied for co...

This paper presents an extended version of Partial Parallel Interference Cancellation (PPIC) called Variance reduced Partial Parallel Interference Cancellation (VRPPIC) for multicarrier code division multiple access uplink systems. The combination of PPIC receiver and new bit estimator is called VRPPIC detector. This realization is derived for the main PPIC operation and soft decision (SD) from...

A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt via FFNN based on ...

We investigate the use of voicing in state-of-the-art Large Vocabulary Continuous Audio-visual automatic Speech Recognition (AV-LVCSR). In this work we apply an original adaptive weighting function using voicing level to estimate the appropriate combination weights for each of the modalities. We show that we can improve the state-of-the-art AV-LVCSR performance under speech noise by using a det...

In this paper, we consider online prediction from expert advice in a situation where each expert observes its own loss at each time while the loss cannot be disclosed to others for reasons of privacy or confidentiality preservation. Our secure exponential weighting scheme enables exploitation of such private loss values by making use of cryptographic tools. We proved that the regret bound of th...

Feature weighting is known empirically to improve classification accuracy for k-nearest neighbor classifiers in tasks with irrelevant features. Many feature weighting algorithms are designed to work with symbolic features, or numeric features, or both, but cannot be applied to problems with features that do not fit these categories. This paper presents a new k-nearest neighbor feature weighting...

Text categorization is a task of automatically assigning documents to a set of predefined categories. Usually it involves a document representation method and term weighting scheme. This paper proposes a new term weighting scheme called Modified Inverse Document Frequency (MIDF) to improve the performance of text categorization. The document represented in MIDF is trained using the support vect...

Despite extensive studies, the nature of risk attitudes remains a vigorously discussedquestion in economics and psychology. In expected utility theory, attitudes towardsrisk originate from changes in marginal utility. Cumulative prospect theory (CPT)adds an additional dimension: the weighting of probabilities. By examining bothdimensions, we strive to gain more insight on the re...

Ever since von Neumann and Morgenstern published the axiomisation of Expected Utility Theory, there have been a considerable amount of observations appeared in the literature violating the expected utility theory. To make decisions under uncertainty, people generally separate possible outcomes into gains and losses. They are risk averse for gains but risk seeking for losses with very large prob...

This paper provides preference foundations for parametric weighting functions under rank-dependent utility. This is achieved by decomposing the independence axiom of expected utility into separate meaningful properties. These conditions allow us to characterize rank-dependent utility with power and exponential weighting functions. Moreover, by allowing probabilistic risk attitudes to vary withi...