Search results for "Decomposition"

How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm

2018

Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available datase…

Diagnosis of Sensor Faults in PMSM and Drive System Based on Structural Analysis

2021

This paper presents a model-based fault diagnosis method to detect sensor faults in permanent magnet synchronous motor (PMSM) drives based on structural analysis technique. The structural model is built based on the dynamic model of the PMSM in matrix form, including unknown variables, known variables, and faults. The Dulmage-Mendelsohn (DM) decomposition is applied to evaluate the redundancy of the model and obtain redundant testable sub-models. These testable redundant sub-models are used to form residuals to observe the system state, and distinguish between healthy and faulty conditions. This work investigates faults in eleven sensors in a PMSM drive, thus nine structured residuals are d…

Wide area monitoring of sustained oscillations using double-stage mode decomposition

2018

A multi-domain approach for smoothed particle hydrodynamics simulations of highly complex flows

2018

Abstract An efficient and accurate method is proposed to solve the incompressible flow momentum and continuity equations in computational domains partitioned into subdomains in the framework of the smoothed particle hydrodynamics method. The procedure does not require any overlap of the subdomains, which would result in the increase of the computational effort. Perfectly matching solutions are obtained at the surfaces separating neighboring blocks. The block interfaces can be both planar and curved surfaces allowing to easily decompose even geometrically complex domains. The smoothing length of the kernel function is maintained constant in each subdomain, while changing between blocks where…

Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering

2016

Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-domain feature extraction and clustering tasks. It i…

Concurrent Molecular Dynamics Simulation of ST2 Water on a Transputer Array

1988

Abstract A concurrent implementation of a Molecular Dynamics program for ST2 water molecules is presented, which exploits the great potentialities of the Transputer arrays for statistical mechanical calculations. High load-balance efficiency is obtained using a new task decomposition algorithm which evenly distributes particles and interaction calculations among the processors. This approach can also help to solve efficiently the more general problem of task distribution in parallel computing of symmetric pairwise system properties.

A new Adaptive and Progressive Image Transmission Approach using Function Superpositions

2010

International audience; We present a novel approach to adaptive and progressive image transmission, based on the decomposition of an image into compositions and superpositions of monovariate functions. The monovariate functions are iteratively constructed and transmitted, one after the other, to progressively reconstruct the original image: the progressive transmission is performed directly in the 1D space of the monovariate functions and independently of any statistical properties of the image. Each monovariate function contains only a fraction of the pixels of the image. Each new transmitted monovariate function adds data to the previously transmitted monovariate functions. After each tra…

Feature Dimensionality Reduction for Mammographic Report Classification

2016

The amount and the variety of available medical data coming from multiple and heterogeneous sources can inhibit analysis, manual interpretation, and use of simple data management applications. In this paper a deep overview of the principal algorithms for dimensionality reduction is carried out; moreover, the most effective techniques are applied on a dataset composed of 4461 mammographic reports is presented. The most useful medical terms are converted and represented using a TF-IDF matrix, in order to enable data mining and retrieval tasks. A series of query have been performed on the raw matrix and on the same matrix after the dimensionality reduction obtained using the most useful techni…

Quantitative evaluation of muscle synergy models: a single-trial task decoding approach.

2012

Delis, Ioannis | Berret, Bastien | Pozzo, Thierry | Panzeri, Stefano; International audience; ''Muscle synergies, i.e., invariant coordinated activations of groups of muscles, have been proposed as building blocks that the central nervous system (CNS) uses to construct the patterns of muscle activity utilized for executing movements . Several efficient dimensionality reduction algorithms that extract putative synergies from electromyographic (EMG) signals have been developed. Typically, the quality of synergy decompositions is assessed by computing the Variance Accounted For (VAF). Yet, little is known about the extent to which the combination of those synergies en codes task discriminating…

Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data

2018

The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimen- sionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in par- ticular when computational resources are limited such as in mobile terminals. In this paper we present the use of a "conceptual" space, automatically induced from data, to perform automa…