Automated Sensor Selection and Fusion for Monitoring and Diagnostics of Plunge Grinding

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Niranjan Subrahmanya ◽  
Yung C. Shin
Keyword(s):  
Feature Selection ◽  
Test Bed ◽  
Wheel Wear ◽  
Widespread Application ◽  
Plunge Grinding ◽  
Level Sensor ◽  
Single Method ◽  
Model Training ◽  
Monitoring And Diagnostics ◽  
Leave One Out

This paper deals with the development of an online monitoring system based on feature-level sensor fusion and its application to OD plunge grinding. Different sensors are used to measure acoustic emission, spindle power, and workpiece vibration signals, which are used to monitor three of the most common faults in grinding—workpiece burn, chatter, and wheel wear. Although a number of methods have been reported in recent literature for monitoring these faults, they have not found widespread application in industry as no single method or feature has been shown to be successful for all setups and for all wheel-workpiece combinations. This paper proposes a systematic approach, which allows the development and deployment of process-monitoring systems via automated sensor and feature selection combined with parameter-free model training, both of which are especially crucial for implementation in industry. The proposed algorithm makes use of “sparsity-promoting” penalty terms to encourage sensor and feature selection while the “hyperparameters” of the algorithm are tuned using an approximation of the leave-one-out error. Experimental results obtained for monitoring burn, chatter, and wheel wear from a plunge grinding test bed show the effectiveness of the proposed method.

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

scholarly journals Informative Gene Selection and Direct Classification of Tumor Based on Chi-Square Test of Pairwise Gene Interactions

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hongyan Zhang ◽  
Lanzhi Li ◽  
Chao Luo ◽  
Congwei Sun ◽  
Yuan Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Gene Selection ◽  
Gene Interactions ◽  
Selection Methods ◽  
Chi Square ◽  
Generalization Performance ◽  
Independent Test ◽  
Chi Square Test ◽  
Leave One Out ◽  
Tumor Gene

In efforts to discover disease mechanisms and improve clinical diagnosis of tumors, it is useful to mine profiles for informative genes with definite biological meanings and to build robust classifiers with high precision. In this study, we developed a new method for tumor-gene selection, the Chi-square test-based integrated rank gene and direct classifier (χ2-IRG-DC). First, we obtained the weighted integrated rank of gene importance from chi-square tests of single and pairwise gene interactions. Then, we sequentially introduced the ranked genes and removed redundant genes by using leave-one-out cross-validation of the chi-square test-based Direct Classifier (χ2-DC) within the training set to obtain informative genes. Finally, we determined the accuracy of independent test data by utilizing the genes obtained above withχ2-DC. Furthermore, we analyzed the robustness ofχ2-IRG-DC by comparing the generalization performance of different models, the efficiency of different feature-selection methods, and the accuracy of different classifiers. An independent test of ten multiclass tumor gene-expression datasets showed thatχ2-IRG-DC could efficiently control overfitting and had higher generalization performance. The informative genes selected byχ2-IRG-DC could dramatically improve the independent test precision of other classifiers; meanwhile, the informative genes selected by other feature selection methods also had good performance inχ2-DC.

scholarly journals Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4071 ◽  
Author(s):  
Alexandra Lianou ◽  
Arianna Mencattini ◽  
Alexandro Catini ◽  
Corrado Di Natale ◽  
George-John E. Nychas ◽  
...  
Keyword(s):  
Feature Selection ◽  
Multispectral Imaging ◽  
Dairy Product ◽  
Real Life ◽  
Microbiological Quality ◽  
Support Vector ◽  
Isothermal Conditions ◽  
Online Feature Selection ◽  
Model Training

The performance of an Unsupervised Online feature Selection (UOS) algorithm was investigated for the selection of training features of multispectral images acquired from a dairy product (vanilla cream) stored under isothermal conditions. The selected features were further used as input in a support vector machine (SVM) model with linear kernel for the determination of the microbiological quality of vanilla cream. Model training (n = 65) was based on two batches of cream samples provided directly by the manufacturer and stored at different isothermal conditions (4, 8, 12, and 15 °C), whereas model testing (n = 132) and validation (n = 48) were based on real life conditions by analyzing samples from different retail outlets as well as expired samples from the market. Qualitative analysis was performed for the discrimination of cream samples in two microbiological quality classes based on the values of total viable counts [TVC ≤ 2.0 log CFU/g (fresh samples) and TVC ≥ 6.0 log CFU/g (spoiled samples)]. Results exhibited good performance with an overall accuracy of classification for the two classes of 91.7% for model validation. Further on, the model was extended to include the samples in the TVC range 2–6 log CFU/g, using 1 log step to define the microbiological quality of classes in order to assess the potential of the model to estimate increasing microbial populations. Results demonstrated that high rates of correct classification could be obtained in the range of 2–5 log CFU/g, whereas the percentage of erroneous classification increased in the TVC class (5,6) that was close to the spoilage level of the product. Overall, the results of this study demonstrated that the UOS algorithm in tandem with spectral data acquired from multispectral imaging could be a promising method for real-time assessment of the microbiological quality of vanilla cream samples.

scholarly journals Assessment of Predictor Importance with the Example of the Real Estate Market

2016 ◽  
Vol 16 (2) ◽  
pp. 29-39
Author(s):  
Mariusz Kubus
Keyword(s):  
Feature Selection ◽  
Real Estate ◽  
Linear Models ◽  
Predictive Modelling ◽  
Real Estate Market ◽  
Comparative Approach ◽  
Single Family ◽  
Data Set ◽  
The Real Estate ◽  
Leave One Out

Abstract Regression methods can be used for the valuation of real estate in the comparative approach. However, one of the problems of predictive modelling is the presence of redundant or irrelevant variables in data. Such variables can decrease the stability of models, and they can even reduce prediction accuracy. The choice of real estate’s features is largely determined by an appraiser, who is guided by his/her experience. Still, the use of statistical methods of a feature selection can lead to a more accurate valuation model. In the paper we apply regularized linear regression which belongs to embedded methods of a feature selection. For the considered data set of real estate land designated for single-family housing we obtained a model, which led to a more accurate valuation than some other popular linear models applied with or without a feature selection. To assess the model’s quality we used the leave-one-out cross-validation.

Realistic simulations of surveillance sensors in an algorithm-level sensor fusion test-bed

1995 ◽  
Author(s):  
Eloi Bosse ◽  
Nicolas Duclos-Hindie ◽  
Jean Roy ◽  
Denis Dion, Jr.
Keyword(s):  
Sensor Fusion ◽  
Test Bed ◽  
Level Sensor ◽  
Algorithm Level

scholarly journals Genetic test bed for feature selection

2006 ◽  
Vol 22 (7) ◽  
pp. 837-842 ◽  
Author(s):  
A. Choudhary ◽  
M. Brun ◽  
J. Hua ◽  
J. Lowey ◽  
E. Suh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Genetic Test ◽  
Test Bed

scholarly journals Benchmarking algorithms for genomic prediction of complex traits

2019 ◽  
Author(s):  
Christina B. Azodi ◽  
Andrew McCarren ◽  
Mark Roantree ◽  
Gustavo de los Campos ◽  
Shin-Han Shiu
Keyword(s):  
Feature Selection ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Algorithm Selection ◽  
Livestock Breeding ◽  
Wide Range ◽  
Non Linear ◽  
Hyperparameter Selection ◽  
Model Training ◽  
Linear Algorithms

AbstractThe usefulness of Genomic Prediction (GP) in crop and livestock breeding programs has led to efforts to develop new and improved GP approaches including non-linear algorithm, such as artificial neural networks (ANN) (i.e. deep learning) and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of GP datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and five non-linear algorithms, including ANNs. First, we found that hyperparameter selection was critical for all non-linear algorithms and that feature selection prior to model training was necessary for ANNs when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple GP algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits than that of linear algorithms. Although ANNs did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance near the level of other algorithms. These results, together with the fact that even small improvements in GP performance could accumulate into large genetic gains over the course of a breeding program, highlights the importance of algorithm selection for the prediction of trait values.

Continuous Optimal Infeed Control for Cylindrical Plunge Grinding, Part 2: Controller Design and Implementation

2004 ◽  
Vol 126 (2) ◽  
pp. 334-340 ◽  
Author(s):  
Shaoqiang Dong ◽  
Kourosh Danai ◽  
Stephen Malkin
Keyword(s):  
Control System ◽  
Controller Design ◽  
Control Policy ◽  
Noise Robustness ◽  
Practical Implementation ◽  
Wheel Wear ◽  
Plunge Grinding ◽  
Cylindrical Grinding ◽  
Safety Margins ◽  
On Line

This is the second of two papers concerned with on-line optimization of cylindrical plunge grinding cycles with continuously varying infeed control. In the first paper [1], dynamic programming was applied to a simulation of the cylindrical grinding process in order to explore the characteristics of optimal grinding cycles. Optimal cycles were found to consist of distinct segments each with predominant constraints. An optimal control policy was formulated with the infeed rate within each segment determined according to the prevailing constraint. The present paper is concerned with the design of the controller and its implementation. The control system to implement the optimization policy is described together with provisions to enhance robustness to modeling uncertainty and measurement noise. Robustness provisions include model adaptation by parameter estimation from on-line measurements of size and power, and incorporation of safety margins in the optimization process. Problems associated with practical implementation of the control system, stemming from power limitations and wheel wear, are also discussed. The controller performance is demonstrated on an instrumented internal cylindrical grinding machine.

scholarly journals Estimation of Dynamic Grinding Wheel Wear in Plunge Grinding

Procedia CIRP ◽  
2017 ◽  
Vol 58 ◽  
pp. 422-427 ◽  
Author(s):  
M. Ahrens ◽  
J. Damm ◽  
M. Dagen ◽  
B. Denkena ◽  
T. Ortmaier
Keyword(s):  
Grinding Wheel ◽  
Wheel Wear ◽  
Plunge Grinding ◽  
Grinding Wheel Wear

scholarly journals MRI Radiomics to Differentiate Between Low Grade Glioma and Glioblastoma Peritumoral Region

2021 ◽  
Author(s):  
Nauman Malik ◽  
Benjamin Geraghty ◽  
Archya Dasgupta ◽  
Pejman Jabehdar Maralani ◽  
Michael Sandhu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Image Data ◽  
Texture Features ◽  
Low Grade Glioma ◽  
Feature Reduction ◽  
Recursive Feature Elimination ◽  
Low Grade ◽  
Reduction Techniques ◽  
Leave One Out ◽  
Sensitivity Specificity

Abstract Background The peritumoral region (PTR) of glioblastoma (GBM) appears as a T2W-hyperintensity and is characterized by microscopic tumor and edema. Infiltrative low grade glioma (LGG) comprises tumor cells that seem similar to GBM PTR on MRI. The work here explored if a radiomics-based approach can distinguish between LGG and GBM PTR, which can have future implications on existing treatment paradigms. Methods Patients with GBM and LGG imaged using a 1.5 T MRI were included in the study. Image data from cases of GBM PTR, and LGG were manually segmented guided by T2W hyperintensity. A set of 91 first-order and texture features were determined from each of T1W-contrast, and T2W-FLAIR, diffusion-weighted imaging sequences. Applying filtration techniques, a total of 3822 features were obtained. Different feature reduction techniques were employed, and a subsequent model was constructed using four machine learning classifiers. Leave-one-out cross-validation was used to assess classifier performance. Results The analysis included 42 GBM and 36 LGG. The best performance was obtained using AdaBoost classifier using all the features with a sensitivity, specificity, accuracy, and area of curve (AUC) of 91%, 86%, 89%, and 0.96, respectively. Amongst the feature selection techniques, the recursive feature elimination technique had the best results, with an AUC ranging from 0.87 to 0.92. Evaluation with the F-test resulted in the most consistent feature selection with 3 T1W-contrast texture features chosen in over 90% of instances. Conclusions Quantitative analysis of conventional MRI sequences can effectively demarcate GBM PTR from LGG, which is otherwise indistinguishable on visual estimation.

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