Evolutionary Multi-objective Training Set Selection of Data Instances and Augmentations for Vocal Detection

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

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Evaluating the Use of Artificial Neural Networks, Graph Theory, and Complexity Theory to Predict Automotive Assembly Defects

Volume 4: 21st Design for Manufacturing and the Life Cycle Conference; 10th International Conference on Micro- and Nanosystems ◽

10.1115/detc2016-59664 ◽

2016 ◽

Cited By ~ 2

Author(s):

Apurva Patel ◽

Patrick Andrews ◽

Joshua D. Summers

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Prediction Models ◽

Assembly Model ◽

Training Set ◽

Automotive Assembly ◽

Artificial Neural ◽

Training Set Selection ◽

Selection Of ◽

Assembly Models

Artificial Neural Networks (ANNs) have been used to predict assembly time and market value from assembly models. This was done by converting the assembly models into bipartite graphs and extracting 29 graph complexity metrics which were used to train the ANN prediction models. This paper presents the use of sub-assembly models instead of the entire assembly model to predict assembly quality defects at an automotive OEM. The size of the training set, order of the bipartite graph, selection of training set, and defect type were experimentally studied. With a training size of 28 parts, an interpolation focused training set selection, and second order graph seeding, over 70% of the predictions were within 100% of the target value. The study shows that with an increase in training size and careful selection of training sets, assembly defects can be predicted reliably from sub-assemblies complexity data.

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A subregion division based multi-objective evolutionary algorithm for SVM training set selection

Neurocomputing ◽

10.1016/j.neucom.2020.02.028 ◽

2020 ◽

Vol 394 ◽

pp. 70-83 ◽

Cited By ~ 3

Author(s):

Fan Cheng ◽

Jiabin Chen ◽

Jianfeng Qiu ◽

Lei Zhang

Keyword(s):

Evolutionary Algorithm ◽

Training Set ◽

Multi Objective ◽

Training Set Selection

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Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next

Reaction Chemistry & Engineering ◽

10.1039/d1re00013f ◽

2021 ◽

Author(s):

Andrew F. Zahrt ◽

Brennan T. Rose ◽

William T. Darrow ◽

Jeremy J. Henle ◽

Scott E. Denmark

Keyword(s):

In Silico ◽

Subset Selection ◽

Design Guidelines ◽

Catalyst Design ◽

Error Assessment ◽

Selection Methods ◽

Training Set ◽

Training Set Selection ◽

Catalyst Selection ◽

Selection Of

Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from in silico libraries of catalyst structures.

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Evaluating the Use of Artificial Neural Networks and Graph Complexity to Predict Automotive Assembly Quality Defects

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4037179 ◽

2017 ◽

Vol 17 (3) ◽

Cited By ~ 2

Author(s):

Apurva Patel ◽

Patrick Andrews ◽

Joshua D. Summers ◽

Erin Harrison ◽

Joerg Schulte ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Prediction Models ◽

Assembly Model ◽

Training Set ◽

Assembly Quality ◽

Automotive Assembly ◽

Artificial Neural ◽

Training Set Selection ◽

Selection Of

This paper presents the use of subassembly models instead of the entire assembly model to predict assembly quality defects at an automotive original equipment manufacturer (OEM). Specifically, artificial neural networks (ANNs) were used to predict assembly time and market value from assembly models. These models were converted into bipartite graphs from which 29 graph complexity metrics were extracted to train 18,900 ANN prediction models. The size of the training set, order of the bipartite graph, selection of training set, and defect type were experimentally studied. With a training size of 28 parts, an interpolation focused training set selection with a second-order graph seeding ensured that 70% of all predictions were within 100% of the target value. The study shows that with an increase in training size and careful selection of training sets, assembly defects can be predicted reliably from subassemblies' complexity data.

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