The user-knowledge crowdsourcing task allocation integrated decision model and genetic matrix factorization algorithm

2021 ◽  
Vol 165 ◽  
pp. 113798
Author(s):  
Li Gao ◽  
Yi Gan ◽  
Mingda Sun ◽  
Liping Gao
Keyword(s):  
Matrix Factorization ◽  
Task Allocation ◽  
Decision Model ◽  
Factorization Algorithm ◽  
User Knowledge

Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis, and Case Study

2020 ◽  
Vol 32 (2) ◽  
pp. 288-301
Author(s):  
Yan Yan ◽  
Mingkui Tan ◽  
Ivor W. Tsang ◽  
Yi Yang ◽  
Qinfeng Shi ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Algorithm Analysis ◽  
Cost Matrix ◽  
Factorization Algorithm ◽  
Memory Cost

The application of semi-nonnegative matrix factorization for detection of incipient faults in bearings

2019 ◽  
Vol 233 (13) ◽  
pp. 4543-4555 ◽  
Author(s):  
Akhand Rai ◽  
Sanjay H Upadhyay
Keyword(s):  
Frequency Domain ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Health Indicators ◽  
Data Matrix ◽  
Bearing Faults ◽  
Time Frequency ◽  
Factorization Algorithm ◽  
Incipient Faults

Bearing faults are a major reason for the catastrophic breakdown of rotating machinery. Therefore, the early detection of bearing faults becomes a necessity to attain an uninterrupted and safe operation. This paper proposes a novel approach based on semi-nonnegative matrix factorization for detection of incipient faults in bearings. The semi-nonnegative matrix factorization algorithm creates a sparse, localized, part-based representation of the original data and assists to capture the fault information in bearing signals more effectively. Through semi-nonnegative matrix factorization, two bearing health indicators are derived to fulfill the desired purpose. In doing so, the paper tries to address two critical issues: (i) how to reduce the dimensionality of feature space (ii) how to obtain a definite range of the indicator between 0 and 1. Firstly, a set of time domain, frequency domain, and time–frequency domain features are extracted from the bearing vibration signals. Secondly, the feature dataset is utilized to train the semi-nonnegative matrix factorization algorithm which decomposes the training data matrix into two new matrices of lower ranks. Thirdly, the test feature vectors are projected onto these lower dimensional matrices to obtain two statistics called as square prediction error and Q2. Finally, the Bayesian inference approach is exploited to convert the two statistics into health indicators that have a fixed range between [0–1]. The application of the advocated technique on experimental bearing signals demonstrates that it can effectively predict the weak defects in bearings as well as performs better than the earlier methods like principal component analysis and locality preserving projections.

A Decision Model for Cognitive Task Allocation

1993 ◽  
Vol 37 (4) ◽  
pp. 392-396
Author(s):  
Sotiris A. Papantonopoulos ◽  
Gavriel Salvendy
Keyword(s):  
Analytic Hierarchy Process ◽  
Task Allocation ◽  
Task Analysis ◽  
Decision Model ◽  
Cognitive Task ◽  
Analytic Hierarchy ◽  
Performance Requirements ◽  
Resource Matching ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

Cognitive task allocation employs task analysis to identify the performance and operational requirements of task functions; and demand/resource matching to match the identified requirements and the human and computer resources available for implementation. The current methodologies of cognitive task allocation are either too aggregate to provide adequate resolution of performance requirements or domain-specific and thus of limited applicability. The paper introduces a formal, quantitative, and domain-independent model of cognitive task allocation aimed at reducing the limitations inherent in the currently practiced methodologies. Demand/resource matching is modeled as an Analytic Hierarchy Process. The Analytic Hierarchy Process of Demand/Resource Matching is defined as a mapping process along a four-level Analytic Hierarchy. By means of the Analytic Hierarchy Process, a task function (Level 1 of the Analytic Hierarchy) is analyzed into its cognitive processes (Level 2); performance criteria are set for each cognitive process (Level 3) by means of which the capacities of the human, computer, or interactive human/computer controller (Level 4) are evaluated and compared. The Analytic Hierarchy Process then integrates judgements of human and computer abilities and limitations into a weighted average indicating the relative capacity of human and computer to perform this function. This assessment of relative merit of performance can hence be integrated with work design, economic, and other contextual factors towards the final allocation design. The Analytic Hierarchy Process was applied and evaluated in the design of task allocation in production planing and control of a flexible manufacturing system by comparing the allocation designs of two groups of subjects. One group was supported by the decision model, the other received no decision support. The observed differences between the two groups indicated that the decision model can effectively support detailed task analysis and an adequate resolution of performance requirements; the identification of the design, trade-offs between human allocation and automation; and provide the computational resources to reduce decision bias.

scholarly journals Analysis of facial motion patterns during speech using a matrix factorization algorithm

2008 ◽  
Vol 124 (4) ◽  
pp. 2283-2290 ◽  
Author(s):  
Jorge C. Lucero ◽  
Kevin G. Munhall
Keyword(s):  
Matrix Factorization ◽  
Motion Patterns ◽  
Facial Motion ◽  
Factorization Algorithm
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