scholarly journals Autonomous identification of high-contact surfaces from convolutional neural networks

2021 ◽  
Vol 2135 (1) ◽  
pp. 012001
Author(s):  
Angie Alonso ◽  
Andres Peña ◽  
Fredy Martínez
Keyword(s):  
High Performance ◽  
Binary Classification ◽  
Network Models ◽  
Classification Problems ◽  
Convolutional Network ◽  
Prototype Development ◽  
Neural Architecture ◽  
Large Numbers ◽  
Rapid Spread ◽  
Contact Surfaces

Abstract The rapid spread of the SARS-CoV-2 virus has highlighted many social interaction problems that favor the spread of disease, particularly airborne spread, which can be addressed by adjusting existing systems. Of particular interest are places where large numbers of people interact, as they become a focus for the spread of these diseases. This paper proposes and evaluates an autonomous identification scheme for certain surfaces considered high risk due to their continuous handling. These high-contact surfaces can be identified by an autonomous system to apply specific cleaning tasks to them. We evaluate three convolutional models from a proprietary dataset with a total of 2000 images ranging from wall switches to water dispensers. The objective is to identify the ideal architecture for the system. The ResNet (Residual Neural Network), DenseNet (Dense Convolutional Network), and NASNet (Neural Architecture Search Network) models were selected due to their high performance reported in the literature. The models are evaluated with specialized metrics in non-binary classification problems, and the best scheme is selected for prototype development.

scholarly journals COVID-19 Diagnosis with Deep Learning

2021 ◽  
Vol 42 (1) ◽  
pp. e88825
Author(s):  
Hatice Catal Reis
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Performance Metrics ◽  
Binary Classification ◽  
Network Models ◽  
Ct Images ◽  
Chest Ct ◽  
Point Of View ◽  
Neural Network Models ◽  
Convolutional Network

The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images).  The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC.  Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals High-performance magneto-rheological clutches for direct-drive actuation: Design and development

2021 ◽  
pp. 1045389X2110069
Author(s):  
Sergey Pisetskiy ◽  
Mehrdad Kermani
Keyword(s):  
High Performance ◽  
Degrees Of Freedom ◽  
Dynamic Range ◽  
Complete Analysis ◽  
Mass Ratio ◽  
Direct Drive ◽  
Element Analysis ◽  
Prototype Development ◽  
Magneto Rheological ◽  
Hall Sensors

This paper presents an improved design, complete analysis, and prototype development of high torque-to-mass ratio Magneto-Rheological (MR) clutches. The proposed MR clutches are intended as the main actuation mechanism of a robotic manipulator with five degrees of freedom. Multiple steps to increase the toque-to-mass ratio of the clutch are evaluated and implemented in one design. First, we focus on the Hall sensors’ configuration. Our proposed MR clutches feature embedded Hall sensors for the indirect torque measurement. A new arrangement of the sensors with no effect on the magnetic reluctance of the clutch is presented. Second, we improve the magnetization of the MR clutch. We utilize a new hybrid design that features a combination of an electromagnetic coil and a permanent magnet for improved torque-to-mass ratio. Third, the gap size reduction in the hybrid MR clutch is introduced and the effect of such reduction on maximum torque and the dynamic range of MR clutch is investigated. Finally, the design for a pair of MR clutches with a shared magnetic core for antagonistic actuation of the robot joint is presented and experimentally validated. The details of each approach are discussed and the results of the finite element analysis are used to highlight the required engineering steps and to demonstrate the improvements achieved. Using the proposed design, several prototypes of the MR clutch with various torque capacities ranging from 15 to 200 N·m are developed, assembled, and tested. The experimental results demonstrate the performance of the proposed design and validate the accuracy of the analysis used for the development.

scholarly journals Confidence interval for micro-averaged F1 and macro-averaged F1 scores

2021 ◽  
Author(s):  
Kanae Takahashi ◽  
Kouji Yamamoto ◽  
Aya Kuchiba ◽  
Tatsuki Koyama
Keyword(s):  
Binary Classification ◽  
Classification Problem ◽  
Classification Problems ◽  
Summary Measure ◽  
Medical Field ◽  
Predictive Values ◽  
Binary Classification Problem ◽  
Multi Class Classification ◽  
Sensitivity Specificity ◽  
Measures Of Performance

AbstractA binary classification problem is common in medical field, and we often use sensitivity, specificity, accuracy, negative and positive predictive values as measures of performance of a binary predictor. In computer science, a classifier is usually evaluated with precision (positive predictive value) and recall (sensitivity). As a single summary measure of a classifier’s performance, F1 score, defined as the harmonic mean of precision and recall, is widely used in the context of information retrieval and information extraction evaluation since it possesses favorable characteristics, especially when the prevalence is low. Some statistical methods for inference have been developed for the F1 score in binary classification problems; however, they have not been extended to the problem of multi-class classification. There are three types of F1 scores, and statistical properties of these F1 scores have hardly ever been discussed. We propose methods based on the large sample multivariate central limit theorem for estimating F1 scores with confidence intervals.

Development and Achievements of SSM Processes for High Performance Components

2016 ◽  
Vol 256 ◽  
pp. 319-327 ◽  
Author(s):  
Mario Rosso ◽  
Ildiko Peter ◽  
Ivano Gattelli
Keyword(s):  
Industrial Development ◽  
High Performance ◽  
Rear Axle ◽  
Research Work ◽  
Industrial Applications ◽  
Alternative Methods ◽  
Front Suspension ◽  
Large Numbers ◽  
Automotive Parts ◽  
High Level

During the last decades under the enthusiastic and competent guidance of Mr Chiarmetta SSM processes attained in Italy at Stampal Spa (Torino) an unquestionable high level of industrial development with the production of large numbers of high performance automotive parts, like variety of suspension support, engine suspension mounts, steering knuckle, front suspension wheel, arm and rear axle. Among the most highlighted findings SSM processes demonstrated their capability to reduce the existing gap between casting and forging, moreover during such a processes there are the opportunity to better control the defect level.Purpose of this paper is to highlight the research work and the SSM industrial production attained and developed by Mr G.L. Chiarmetta, as well as to give an overview concerning some alternative methods for the production of enhanced performance light alloys components for critical industrial applications and to present an analysis of a new rheocasting process suitable for the manufacturing of high performance industrial components.

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