scholarly journals Ensemble based speaker recognition using unsupervised data selection

2016 ◽  
Vol 5 ◽  
Author(s):  
Chien-Lin Huang ◽  
Jia-Ching Wang ◽  
Bin Ma
Keyword(s):  
Speaker Recognition ◽  
Auxiliary Information ◽  
Divide And Conquer ◽  
Data Selection ◽  
Ensemble Classifiers ◽  
Acoustic Characteristics ◽  
New Approach ◽  
Speech Data ◽  
Significant Performance ◽  
Improved Performance

This paper presents an ensemble-based speaker recognition using unsupervised data selection. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. A speech utterance is divided into several subsets based on its acoustic characteristics using unsupervised data selection methods. The ensemble classifiers are then trained with these non-overlapping subsets of speech data to improve the recognition accuracy. This new approach has two advantages. First, without any auxiliary information, we use ensemble classifiers based on unsupervised data selection to make use of different acoustic characteristics of speech data. Second, in ensemble classifiers, we apply the divide-and-conquer strategy to avoid a local optimization in the training of a single classifier. Our experiments on the 2010 and 2008 NIST Speaker Recognition Evaluation datasets show that using ensemble classifiers yields a significant performance gain.

Experimental Validation of Minimax Entropy Principle in Ultrasound Images

2019 ◽  
Vol 12 (6) ◽  
pp. 487-493
Author(s):  
Neha Mehta ◽  
Svav Prasad ◽  
Leena Arya
Keyword(s):  
Region Of Interest ◽  
Ultrasound Images ◽  
Human Gallbladder ◽  
New Approach ◽  
Non Invasive ◽  
Entropy Principle ◽  
Significant Performance ◽  
Considerable Impact ◽  
Ultrasonic Devices ◽  
Measure Of Uncertainty

Ultrasound imaging is one of the non-invasive imaging, that diagnoses the disease inside a human body and there are numerous ultrasonic devices being used frequently. Entropy as a well known statistical measure of uncertainty has a considerable impact on the medical images. A procedure for minimizing the entropy with respect to the region of interest is demonstrated. This new approach has shown the experiments using Extracted Region Of Interest Based Sharpened image, called as (EROIS) image based on Minimax entropy principle and various filters. In this turn, the approach also validates the versatility of the entropy concept. Experiments have been performed practically on the real-time ultrasound images collected from ultrasound centers and have shown a significant performance. The present approach has been validated with showing results over ultrasound images of the Human Gallbladder.

Consideration of structural constraints in passive rotor blade design for improved performance

2015 ◽  
Vol 119 (1222) ◽  
pp. 1513-1539 ◽  
Author(s):  
J. W. Lim
Keyword(s):  
Performance Improvement ◽  
Rotor Blade ◽  
Parametric Design ◽  
Control Point ◽  
Bending Moment ◽  
Structural Constraints ◽  
Design Variables ◽  
Significant Performance ◽  
Structural Redesign ◽  
Improved Performance

AbstractThis design study applied parameterisation to rotor blade for improved performance. In the design, parametric equations were used to represent blade planform changes over the existing rotor blade model. Design variables included blade twist, sweep, dihedral, and radial control point. Updates to the blade structural properties with changes in the design variables allowed accurate evaluation of performance objectives and realistic structural constraints – blade stability, steady moments (flap bending, chord bending, and torsion), and the high g manoeuvring pitch link loads. Performance improvement was demonstrated with multiple parametric designs. Using a parametric design with advanced aerofoils, the predicted power reduction was 1·0% in hover, 10·0% at μ = 0·30, and 17·0% at μ = 0·40 relative to the baseline UH-60A rotor, but these were obtained with a 35% increase in the steady chord bending moment at μ = 0·30 and a 20% increase in the half peak-to-peak pitch link load during the UH-60A UTTAS manoeuvre Low vibration was maintained for this design. More rigorous design efforts, such as chord tapering and/or structural redesign of the blade cross section, would enlarge the feasible design space and likely provide significant performance improvement.

A new approach to use multivariate auxiliary information in sample surveys

1997 ◽  
Vol 60 (2) ◽  
pp. 261-267 ◽  
Author(s):  
S.K. Agarwal ◽  
U.K. Sharma ◽  
Sharmishtha Kashyap
Keyword(s):  
Auxiliary Information ◽  
Sample Surveys ◽  
New Approach

Consideration of structural constraints in passive rotor blade design for improved performance

2016 ◽  
Vol 120 (1232) ◽  
pp. 1604-1631 ◽  
Author(s):  
J.W. Lim
Keyword(s):  
Performance Improvement ◽  
Rotor Blade ◽  
Parametric Design ◽  
Control Point ◽  
Bending Moment ◽  
Structural Constraints ◽  
Design Variables ◽  
Significant Performance ◽  
Structural Redesign ◽  
Improved Performance

ABSTRACTThis design study applied parameterisation to rotor blade for improved performance. In the design, parametric equations were used to represent blade planform changes over the existing rotor blade model. Design variables included blade twist, sweep, dihedral and the radial control point. Updates to the blade structural properties with changes in the design variables allowed accurate evaluation of performance objectives and realistic structural constraints – blade stability, steady moments (flap bending, chord bending and torsion) and the high-g manoeuvre pitch link loads. Performance improvement was demonstrated with multiple parametric designs. Using a parametric design with advanced aerofoils, the predicted power reduction was 1.0% in hover, 10.0% at μ = 0.30 and 17.0% at μ = 0.40, relative to the baseline UH-60A rotor, but these were obtained with a 35% increase in the steady chord bending moment at μ = 0.30 and a 20% increase in the half peak-to-peak pitch link load during the UH-60A UTTAS manoeuvre. Low vibration was maintained for this design. More rigorous design efforts, such as chord tapering and/or structural redesign of the blade cross section, would enlarge the feasible design space and likely provide significant performance improvement.

Epitaxial hard magnetic SmCo5 MFM tips – a new approach to advanced magnetic force microscopy imaging

Nanoscale ◽  
2018 ◽  
Vol 10 (35) ◽  
pp. 16881-16886 ◽  
Author(s):  
Volker Neu ◽  
Silvia Vock ◽  
Tina Sturm ◽  
Ludwig Schultz
Keyword(s):  
Quantitative Analysis ◽  
Magnetic Force Microscopy ◽  
Magnetic Force ◽  
External Fields ◽  
Microscopy Imaging ◽  
New Approach ◽  
Force Microscopy ◽  
Improved Performance ◽  
Magnetic Hardness

MFM tips nanofabricated from epitaxial SmCo5 films possess unprecedented magnetic hardness for improved performance in external fields and quantitative analysis.

scholarly journals Multi-Level-Phase Deep Learning Using Divide-and-Conquer for Scaffolding Safety

2020 ◽  
Vol 17 (7) ◽  
pp. 2391
Author(s):  
Sayan Sakhakarmi ◽  
Jee Woong Park
Keyword(s):  
Deep Learning ◽  
Failure Modes ◽  
Past Research ◽  
Divide And Conquer ◽  
Classification Problems ◽  
Multi Level ◽  
Improved Performance ◽  
Safety Cases ◽  
Global And Local ◽  
Local Levels

A traditional structural analysis of scaffolding structures requires loading conditions that are only possible during design, but not in operation. Thus, this study proposes a method that can be used during operation to make an automated safety prediction for scaffolds. It implements a divide-and-conquer technique with deep learning. As a test scaffolding, a four-bay, three-story scaffold model was used. Analysis of the model led to 1411 unique safety cases for the model. To apply deep learning, a test simulation generated 1,540,000 datasets for pre-training, and an additional 141,100 datasets for testing purposes. The cases were then sub-divided into 18 categories based on failure modes at both global and local levels, along with a combination of member failures. Accordingly, the divide-and-conquer technique was applied to the 18 categories, each of which were pre-trained by a neural network. For the test datasets, the overall accuracy was 99%. The prediction model showed that 82.78% of the 1411 safety cases showed 100% accuracy for the test datasets, which contributed to the high accuracy. In addition, the higher values of precision, recall, and F1 score for the majority of the safety cases indicate good performance of the model, and a significant improvement compared with past research conducted on simpler cases. Specifically, the method demonstrated improved performance with respect to accuracy and the number of classifications. Thus, the results suggest that the methodology could be reliably applied for the safety assessment of scaffolding systems that are more complex than systems tested in past studies. Furthermore, the implemented methodology can easily be replicated for other classification problems.

scholarly journals Variance reduction trends on ‘boosted’ classifiers

2004 ◽  
Vol 8 (3) ◽  
pp. 141-154
Author(s):  
Virginia Wheway
Keyword(s):  
Empirical Study ◽  
Variance Reduction ◽  
Recent Literature ◽  
Ensemble Classification ◽  
Classification Error ◽  
Ensemble Classifiers ◽  
Training Set ◽  
Key Factor ◽  
Improved Performance ◽  
Boosted Classifiers

Ensemble classification techniques such as bagging, (Breiman, 1996a), boosting (Freund & Schapire, 1997) and arcing algorithms (Breiman, 1997) have received much attention in recent literature. Such techniques have been shown to lead to reduced classification error on unseen cases. Even when the ensemble is trained well beyond zero training set error, the ensemble continues to exhibit improved classification error on unseen cases. Despite many studies and conjectures, the reasons behind this improved performance and understanding of the underlying probabilistic structures remain open and challenging problems. More recently, diagnostics such as edge and margin (Breiman, 1997; Freund & Schapire, 1997; Schapire et al., 1998) have been used to explain the improvements made when ensemble classifiers are built. This paper presents some interesting results from an empirical study performed on a set of representative datasets using the decision tree learner C4.5 (Quinlan, 1993). An exponential-like decay in the variance of the edge is observed as the number of boosting trials is increased. i.e. boosting appears to ‘homogenise’ the edge. Some initial theory is presented which indicates that a lack of correlation between the errors of individual classifiers is a key factor in this variance reduction.

Conflict Handling — A Common Sense Approach to Appraising Supervisory Performance

1991 ◽  
Vol 20 (4) ◽  
pp. 485-492 ◽  
Author(s):  
Aaron R. Pulhamus
Keyword(s):  
Common Sense ◽  
Work Performance ◽  
Employment Relationship ◽  
Employee Rights ◽  
New Approach ◽  
Management Standards ◽  
Conflict Handling ◽  
Improved Performance

Will the growing trend to restructure the employment relationship to achieve improved work performance demand a new approach to assess supervisory effectiveness? Can supervisors currently meet the demands of employee rights which arise in conflict and satisfy management standards which stress improved performance with minimal conflict?

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