An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

Download Full-text

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

RECUBRIMIENTO COMESTIBLE CON BASE DE PIÑON MEXICANO (Jatropha curcas) SOBRE LA CALIDAD DE CHAYOTE MINIMAMENTE PROCESADO ◽

10.32854/agrop.vi.1685 ◽

2020 ◽

Vol 13 (8) ◽

Author(s):

Marco, A. Márquez-Linares ◽

Jonathan G. Escobar--Flores ◽

Sarahi Sandoval- Espinosa ◽

Gustavo Pérez-Verdín

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Design Methodology ◽

Supervised Classification ◽

Satellite Images ◽

Ground Cover ◽

Distribution Analysis ◽

Dodonaea Viscosa ◽

Artificial Neural ◽

Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

Download Full-text

Monitoring system of industrial oil pipeline using an artificial neural network

Automation Telemechanization and Communication in Oil Industry ◽

10.33285/0132-2222-2019-8(553)-20-24 ◽

2019 ◽

pp. 20-24

Author(s):

O.N. Kuzyakov ◽

◽

I.N. Glukhikh ◽

A.E. Sidorova ◽

M.A. Andreeva ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monitoring System ◽

Oil Pipeline ◽

Industrial Oil ◽

Artificial Neural

Download Full-text

Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network

JOURNAL OF MECHANICAL ENGINEERING AND SCIENCES ◽

10.15282/jmes.14.2.2020.19.0531 ◽

2020 ◽

Vol 14 (2) ◽

pp. 6789-6800

Author(s):

Vishal Jagota ◽

Rajesh Kumar Sharma

Keyword(s):

Neural Network ◽

Heat Treatment ◽

Artificial Neural Network ◽

Response Surface ◽

Sliding Wear ◽

Austenitizing Temperature ◽

Tempering Temperature ◽

Die Steel ◽

Artificial Neural ◽

Tempering Time

Resistance to wear of hot die steel is dependent on its mechanical properties governed by the microstructure. The required properties for given application of hot die steel can be obtained with control the microstructure by heat treatment parameters. In the present paper impact of different heat treatment parameters like austenitizing temperature, tempering time, tempering temperature is studied using response surface methodology (RSM) and artificial neural network (ANN) to predict sliding wear of H13 hot die steel. After heat treating samples at austenitizing temperature of 1020°C, 1040°C and 1060°C; tempering temperature 540°C, 560°C and 580°C; tempering time 1hour, 2hours and 3hours, experimentation on pin-on-disc tribo-tester is done to measure the sliding wear of H13 die steel. Box-Behnken design is used to develop a regression model and analysis of variance technique is used to verify the adequacy of developed model in case of RSM. Whereas, multi-layer feed-forward backpropagation architecture with input layer, single hidden layer and an output layer is used in ANN. It was found that ANN proves to be a better tool to predict sliding wear with more accuracy. Correlation coefficient R2 of the artificial neural network model is 0.986 compared to R2 of 0.957 for RSM. However, impact of input parameter interactions can only be analysed using response surface method. In addition, sensitivity analysis is done to determine the heat treatment parameter exerting most influence on the wear resistance of H13 hot die steel and it showed that tempering time has maximum influence on wear volume, followed by tempering temperature and austenitizing temperature. The prediction models will help to estimate the variation in die lifetime by finding the amount of wear that will occur during use of hot die steel, if the heat treatment parameters are varied to achieve different properties.

Download Full-text