-Surface waves with a frequency of 0.5-2.5 Hz were generated on a homogeneously fluidized bed. The propagation velocity and rate of attenuation of the induced pressure fluctuations were measured using signal averaging techniques. The measured wave velocity and attenuation rate correlated well with predictions based on a theory which considers the bed as an incompressible liquid with low viscosi...

This paper proposes the concepts of a neutrosophic number and a trapezoidal neutrosophic number (TNN), the basic operational relations of TNNs, and the score function of TNN. Then, we develop a trapezoidal neutrosophic weighted arithmetic averaging (TNWAA) operator and a trapezoidal neutrosophic weighted geometric averaging (TNWGA) operator to aggregate TNN information and investigate their pro...

We develop a new decision making approach for dealing with uncertain information and apply it in tourism management. We use a new aggregation operator that uses the uncertain weighted average (UWA) and the uncertain induced ordered weighted averaging (UIOWA) operator in the same formulation. We call it the uncertain induced ordered weighted averaging – weighted averaging (UIOWAWA) operator. We ...

Xu and Da [Z.S. Xu, Q.L. Da, The uncertain OWA operator, International Journal of Intelligent Systems, 17 (2002) 569–575] introduced the uncertain ordered weighted averaging (UOWA) operator to aggregate the input arguments taking the form of intervals rather than exact numbers. In this paper, we develop some dependent uncertain ordered weighted aggregation operators, including dependent uncerta...

We contribute a novel multivariate strategy for computing the structure of functional networks in the brain from arterial spin labeling (ASL) MRI. Our method fuses and correlates multiple functional signals by employing an interpretable dimensionality reduction method, sparse canonical correlation analysis (SCCA). There are two key aspects of this contribution. First, we show how SCCA may be us...

Two popular approaches for distributed training of SVMs on big data are parameter averaging and ADMM. Parameter averaging is efficient but suffers from loss of accuracy with increase in number of partitions, while ADMM in the feature space is accurate but suffers from slow convergence. In this paper, we report a hybrid approach called weighted parameter averaging (WPA), which optimizes the regu...

Selecting a single model for clustering ignores the uncertainty left by finite data as to which is the correct model to describe the dataset. In fact, the fewer samples the dataset has, the higher the uncertainty is in model selection. In these cases, a Bayesian approach may be beneficial, but unfortunately this approach is usually computationally intractable and only approximations are feasibl...

This paper presents a novel approach based on time-frequency distributions (TFDs) for separating signals received by a multiple antenna array. This approach provides a significant improvement in performance over the recently introduced spatial time-frequency distributions, specifically for signals with close time-frequency signatures. In this approach, spatial averaging of the time-frequency di...