Search results for "reduction"
showing 10 items of 2058 documents
Emotional Self-Regulation Therapy for Smoking Reduction: Description and Initial Empirical Data
1995
Abstract Self-regulation therapy (Amigoo, 1992) is a set of procedures derived from cognitive skill training programs for increasing hypnotizability. First, experiences are generated by actual stimuli. Clients are then asked to associate those experiences with various cues. They are then requested to generate the experiences in response to the cues, but without the actual stimuli. When they are able to do so quickly and easily, therapeutic suggestions are given. Studies of self-regulation therapy indicate that it can be used successfully to treat smoking.
Approximation of functions over manifolds : A Moving Least-Squares approach
2021
We present an algorithm for approximating a function defined over a $d$-dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise. To produce the approximation we do not require any knowledge regarding the manifold other than its dimension $d$. We use the Manifold Moving Least-Squares approach of (Sober and Levin 2016) to reconstruct the atlas of charts and the approximation is built on-top of those charts. The resulting approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold. In other words, given a new point, located near the manifold, the approximation can be evaluated…
A Variational Approach for Denoising Hyperspectral Images Corrupted by Poisson Distributed Noise
2014
Poisson distributed noise, such as photon noise is an important noise source in multi- and hyperspectral images. We propose a variational based denoising approach, that accounts the vectorial structure of a spectral image cube, as well as the poisson distributed noise. For this aim, we extend an approach for monochromatic images, by a regularisation term, that is spectrally and spatially adaptive and preserves edges. In order to take the high computational complexity into account, we derive a Split Bregman optimisation for the proposed model. The results show the advantages of the proposed approach compared to a marginal approach on synthetic and real data.
Stochastic analysis of dynamical systems with delayed control forces
2006
Abstract Reduction of structural vibration in actively controlled dynamical system is usually performed by means of convenient control forces dependent of the dynamic response. In this paper the existent studies will be extended to dynamical systems subjected to non-normal delta-correlated random process with delayed control forces. Taylor series expansion of the control forces has been introduced and the statistics of the dynamical response have been obtained by means of the extended Ito differential rule. Numerical application provided shows the capabilities of the proposed method to analyze stochastic dynamic systems with delayed actions under delta-correlated process contrasting statist…
BELM: Bayesian Extreme Learning Machine
2011
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap…
Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
2020
In this new era of high electrical energy dependency, electrical energy must be abundant and reliable, thus smart grids are conducted to deliver load demands. Hence, smart grids are implemented alongside distributed generation of renewable energies to increase the reliability and controllability of the grid, but, with the very volatile nature of the Distributed Generation (DG), Demand Side Management (DSM) helps monitor and control the load shape of the consumed power. The interaction of DSM with the grid provides a wide range of mutual benefits to the user, the utility and the market. DSM methodologies such as Conservation Voltage Reduction (CVR) and Direct Load Control (DLC) collaborate i…
A Novel Priority-Based Ambulance-to-Traffic Light Communication for Delay Reduction in Emergency Rescue Operations
2019
Rescue operations are very critical and sensitive. Only one-second delay could make a serious difference between life and death. Therefore, the delay in rescue operations must be reduced as much as possible. The siren signal can be used to warn other vehicles nearby the ambulance. However, it has no impact on the traffic lights, which is normally a main cause of delay in rescue operations. For this reason, many ambulances get stuck and experience long delay at the intersections, which should not happen by any means. To enhance the rescue operations, this paper proposes a novel Priority-based Ambulance-to-Traffic Light Communication (PATCom). PATCom allows information exchanging between traf…
Approximate 3-Dimensional Electrical Impedance Imaging
2001
We discuss a new approach to three-dimensional electrical impedance imaging based on a reduction of the information to be demanded from a reconstruction algorithm. Images are obtained from a single measurement by suitably simplifying the geometry of the measuring chamber and by restricting the nature of the object to be imaged and the information required from the image. In particular we seek to establish the existence or non-existence of a single object (or a small number of objects) in a homogeneous background and the location of the former in the (x,y)-plane defined by the measuring electrodes. Given in addition the conductivity of the object rough estimates of its position along the z-a…
Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks
2019
Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. Thi…
The impact of sample reduction on PCA-based feature extraction for supervised learning
2006
"The curse of dimensionality" is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimensions. In this paper, different feature extraction (FE) techniques are analyzed as means of dimensionality reduction, and constructive induction with respect to the performance of Naive Bayes classifier. When a data set contains a large number of instances, some sampling approach is applied to address the computational complexity of FE and classification processes. The main goal of this paper is to show the impact of sample reduction on the process of FE for supervised learning. In our study we analyzed the conventional PC…