Nondestructive Evaluation of Radio Frequency Connector Continuity Using Stimulated Emissions

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Alex J. Paul ◽  
Peter J. Collins ◽  
Michael A. Temple
Keyword(s):  
Neural Network ◽  
Nondestructive Evaluation ◽  
Transfer Learning ◽  
Time Domain Reflectometry ◽  
Analysis Tool ◽  
Learning Potential ◽  
Current Standard ◽  
Network Topologies ◽  
Statistical Signal Analysis ◽  
The Cost

A wireless nondestructive fault detection test for loose or damaged connectors is demonstrated. An architecture known as the conditioned multiclassification of stimulated emissions (CMSE) is pretrained on simulated and empirical radar outputs, and transfer learning is applied to classify connected and disconnected coaxial interconnections. The two main data conditioning methods of this architecture, a statistical signal analysis tool and a convolutional filter bank, are evaluated in order to determine the cost-value proposition of each component. Novel contributions of this technique include the use of two simulation-aided convolutional filter banks to generate a multinetwork ensemble and transfer learning from artificial neural networks trained on two primitive datasets revolving around the electromagnetic phenomena of reflection and filtering. A total of 560 different neural network topologies across four different signal conditioning configurations are considered, with all results compared against the current standard for measurement of cable and connection faults, time-domain reflectometry. Metrics used for comparison are time (training and evaluation), detection (connector engagement at state change detection), and clustering (projection space performance, used as a measure of transfer learning potential). It is determined that the full CMSE architecture performs best, with nearly any neural network topology of this configuration displaying an early detection improvement of 113% and requiring 30% less time to execute an individual classification versus the current standard, all while meeting the most stringent definitions of nondestructive evaluation (NDE).

scholarly journals Fresh and Rotten Fruits Classification Using CNN and Transfer Learning

2020 ◽  
Vol 34 (5) ◽  
pp. 617-622
Author(s):  
Sai Sudha Sonali Palakodati ◽  
Venkata RamiReddy Chirra ◽  
Yakobu Dasari ◽  
Suneetha Bulla
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Human Beings ◽  
Learning Models ◽  
Agricultural Industry ◽  
Proposed Model ◽  
The Cost ◽  
State Of Art

Detecting the rotten fruits become significant in the agricultural industry. Usually, the classification of fresh and rotten fruits is carried by humans is not effectual for the fruit farmers. Human beings will become tired after doing the same task multiple times, but machines do not. Thus, the project proposes an approach to reduce human efforts, reduce the cost and time for production by identifying the defects in the fruits in the agricultural industry. If we do not detect those defects, those defected fruits may contaminate good fruits. Hence, we proposed a model to avoid the spread of rottenness. The proposed model classifies the fresh fruits and rotten fruits from the input fruit images. In this work, we have used three types of fruits, such as apple, banana, and oranges. A Convolutional Neural Network (CNN) is used for extracting the features from input fruit images, and Softmax is used to classify the images into fresh and rotten fruits. The performance of the proposed model is evaluated on a dataset that is downloaded from Kaggle and produces an accuracy of 97.82%. The results showed that the proposed CNN model can effectively classify the fresh fruits and rotten fruits. In the proposed work, we inspected the transfer learning methods in the classification of fresh and rotten fruits. The performance of the proposed CNN model outperforms the transfer learning models and the state of art methods.

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

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