CAPES/PROCAD Intelligent Forecast System Applied to the Identification of Parameters in Flexible Structures

2005 ◽  
Vol 11 (2) ◽  
pp. 201-214 ◽  
Author(s):  
Hiran De Melo ◽  
Raimundo C. S. Freire ◽  
José F. Da Silva ◽  
José H. F. Cavalcanti ◽  
Alessio T. Barros
Keyword(s):  
Neural Network ◽  
Structural Integrity ◽  
Variable Mass ◽  
Flexible Structures ◽  
Flexible Structure ◽  
Forecast System ◽  
The Third ◽  
New Strategy ◽  
Set Up ◽  
Identification Strategy

The Intelligent Forecast System (IFS) consists of three units: the main unit is a multilayer neural network; the second unit is based on fuzzy logic, which is used to prepare the data; and the third unit is based on a genetic algorithm and is used to determine the best set of data to be analyzed. The data were set up by using a strategy that classifies the terms of the series into parts with the same quantity of elements. Each part is called a window. The IFS was trained by using a group of windows, which is called a set of training windows. Next, the IFS was applied to solve several problems related to the evaluation of structural integrity. The IFS was applied as an identification strategy and forecast of parameters in a flexible structure. Additionally, by using the IFS we have developed a new strategy to analyze the temporary series obtained from vibrations of flexible structures with the distribution of variable mass. The results obtained in this research, using the IFS applied as an identification strategy and forecast of parameters in a flexible structure, show the effectiveness of the IFS.

Automated failure classification for assembly with self-tapping threaded fastenings using artificial neural networks

2008 ◽  
Vol 222 (6) ◽  
pp. 1081-1095 ◽  
Author(s):  
K Althoefer ◽  
B Lara ◽  
Y H Zweiri, ◽  
L D Seneviratne
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Failure Detection ◽  
Insertion Depth ◽  
Novel Approach ◽  
New Strategy ◽  
Failure Classification ◽  
Artificial Neural ◽  
Set Up

This paper presents a new strategy for the automated monitoring and classification of self-tapping threaded fastenings, based on artificial neural networks. Threaded fastenings represent one of the most common assembly methods making the automation of this task highly desirable. It has been shown that the torque versus insertion depth signature signals measured on-line can be used for monitoring threaded insertions. However, the research to date provides only a binary successful/unsuccessful type of classification. In practice when a fault occurs it is useful to know the causes leading to it. Extending earlier work by the authors, a radial basis neural network is used to classify insertion signals, differentiating successful insertions from failed insertions and categorizing different types of insertion failures. The neural network is first tested using a computer simulation study based on a mathematical model of the process. The network is then validated using experimental torque signature signals obtained from an electric screwdriver equipped with an optical shaft encoder and a rotary torque sensor. Test results are presented proving that this novel approach allows failure detection and classification in a reliable and robust way. The key advantages of the proposed method, when compared to existing methods, are improved and automated set-up procedures and its generalization capabilities in the presence of noise and component discrepancies due to tolerances.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

Excavations at Fishbourne, 1963. Third Interim Report

1964 ◽  
Vol 44 (1) ◽  
pp. 1-8
Keyword(s):  
Interim Report ◽  
The West ◽  
Civic Society ◽  
North West ◽  
The Third ◽  
North East ◽  
The North ◽  
The Future ◽  
Set Up

Early in 1963 much of the land occupied by the Roman building at Fishbourne was purchased by Mr. I. D. Margary, M.A., F.S.A., and was given to the Sussex Archaeological Trust. The Fishbourne Committee of the trust was set up to administer the future of the site. The third season's excavation, carried out at the desire of this committee, was again organized by the Chichester Civic Society.1 About fifty volunteers a day were employed from 24th July to 3rd September. Excavation concentrated upon three main areas; the orchard south of the east wing excavated in 1962, the west end of the north wing, and the west wing. In addition, trial trenches were dug at the north-east and north-west extremities of the building and in the area to the north of the north wing. The work of supervision was carried out by Miss F. Pierce, M.A., Mr. B. Morley, Mr. A. B. Norton, B.A., and Mr. J. P. Wild, B.A. Photography was organized by Mr. D. B. Baker and Mrs. F. A. Cunliffe took charge of the pottery and finds.

scholarly journals Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques

2021 ◽  
Vol 14 (16) ◽  
Author(s):  
Adnan A. Ismael ◽  
Saleh J. Suleiman ◽  
Raid Rafi Omar Al-Nima ◽  
Nadhir Al-Ansari
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Mean Square ◽  
The Third ◽  
Testing Stage ◽  
Inclination Angles

AbstractCylindrical weir shapes offer a steady-state overflow pattern, where the type of weirs can offer a simple design and provide the ease-to-pass floating debris. This study considers a coefficient of discharge (Cd) prediction for oblique cylindrical weir using three diameters, the first is of D1 = 0.11 m, the second is of D2 = 0.09 m, and the third is of D3 = 0.06.5 m, and three inclination angles with respect to channel axis, the first is of θ1 = 90 ͦ, the second is of θ2 = 45 ͦ, and the third is of θ3 = 30 ͦ. The Cd values for total of 56 experiments are estimated by using the radial basis function network (RBFN), in addition of comparing that with the back-propagation neural network (BPNN) and cascade-forward neural network (CFNN). Root mean square error (RMSE), mean square error (MSE), and correlation coefficient (CC) statics are used as metrics measurements. The RBFN attained superior performance comparing to the other neural networks of BPNN and CFNN. It is found that, for the training stage, the RBFN network benchmarked very small RMSE and MSE values of 1.35E-12 and 1.83E-24, respectively and for the testing stage, it also could benchmark very small RMSE and MSE values of 0.0082 and 6.80E-05, respectively.

Study on Intelligent Analysis of the Causes of SMT Solder Joint Defects Based on Fuzzy Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 3257-3261
Author(s):  
Chun Yue Huang ◽  
He Geng Wei ◽  
Tian Ming Li ◽  
De Jin Yan
Keyword(s):  
Neural Network ◽  
Solder Joint ◽  
Membership Function ◽  
Fuzzy Neural Network ◽  
Evaluation Model ◽  
Training Samples ◽  
Intelligent Analysis ◽  
Fuzzy Neural ◽  
Input Variables ◽  
Set Up

By determining membership function of the input parameters and selecting defuzzification method, the evaluation model which can be used to intelligent analyzing the causes of SMT solder joint defects was set up. The fuzzy neural network was trained by using the output variables of the training samples from intelligent discrimination as the input variables of training samples of fuzzy neural network. The fuzzy neural network was tested by using the output variables of the testing samples from intelligent discrimination as the input variables of testing samples of fuzzy neural network. The results show that by using the evaluation model the cause of SMT solder joint defects can be analyzed intelligently and the results of intelligently analysis are reasonable, the evaluation model can be used practically.

Structural Analysis of Basal Bodies of The Isolated Oral Apparatus of Tetrahymena Pyriformis

1970 ◽  
Vol 6 (3) ◽  
pp. 679-700
Author(s):  
J. WOLFE
Keyword(s):  
Structural Analysis ◽  
Cell Membrane ◽  
Basal Body ◽  
Structural Integrity ◽  
Tetrahymena Pyriformis ◽  
Basal Plate ◽  
Basal Bodies ◽  
The Third ◽  
Ionic Detergent

The oral apparatus of Tetrahymena pyriformis was isolated using a non-ionic detergent to disrupt the cell membrane. The mouth consists largely of basal bodies and microfilaments. Each basal body is attached to the mouth by a basal plate which is integrated into the meshwork of microfilaments that confers upon the oral apparatus its structural integrity. Each basal body is composed of 9 triplet microtubules. Two of the 3 tubules, subfibres ‘A’ and ‘B’ are composed of filamentous rows of globules with a spacing of 4.5nm. The third tubule, subfibre ‘C’, is only one-third the length of the basal body.

Translator and translation research: general descriptions of concepts

2019 ◽  
Author(s):  
Darian Jancowicz-Pitel
Keyword(s):  
Quantitative Method ◽  
Point Of View ◽  
Translation Process ◽  
Translation Research ◽  
Qualitative And Quantitative ◽  
The Third ◽  
Part Time ◽  
Set Up ◽  
Translation Errors ◽  
Multiple Languages

The presented paper aimed for exploring the translation process, a translator or interpreter needs equipment or tools so that the objectives of a translation can be achieved. If an interpreter needs a pencil, paper, headphones, and a mic, then an interpreter needs even more tools. The tools required include conventional and modern tools. Meanwhile, the approach needed in research on translation is qualitative and quantitative, depending on the research objectives. If you want to find a correlation between a translator's translation experience with the quality or type of translation errors, a quantitative method is needed. Also, this method is very appropriate to be used in research in the scope of teaching translation, for example from the student's point of view, their level of intelligence regarding the quality or translation errors. While the next method is used if the research contains translation errors, procedures, etc., it is more appropriate to use qualitative methods. Seeing this fact, these part-time translators can switch to the third type of translator, namely free translators. This is because there is an awareness that they can live by translation. These translators set up their translation efforts that involve multiple languages.

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