scholarly journals NEURAL NETWORK COMPUTER FOR RECOVERING LOST INFORMATION FROM STANDARD SENSORS OF THE ON-BOARD SYSTEM FOR CONTROL AND DIAGNOSTICS OF TV3-117 AIRCRAFT ENGINE

2020 ◽  
Vol 0 (4 (14)) ◽  
pp. 147-154
Author(s):  
Serhii Vladov ◽  
Yana Doludareva ◽  
Andrii Siora ◽  
Anatolii Ponomarenko ◽  
Anatolii Yanitskyi
Keyword(s):  
Neural Network ◽  
Aircraft Engine ◽  
Network Computer ◽  
Board System

Fourier Neural Networks and Generalized Single Hidden Layer Networks in Aircraft Engine Fault Diagnostics

2005 ◽  
Vol 128 (4) ◽  
pp. 773-782 ◽  
Author(s):  
H. S. Tan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Fourier Transforms ◽  
Aircraft Engine ◽  
Fault Classification ◽  
Noise Robustness ◽  
Fault Diagnostics ◽  
Multiple Faults ◽  
Hidden Layer

The conventional approach to neural network-based aircraft engine fault diagnostics has been mainly via multilayer feed-forward systems with sigmoidal hidden neurons trained by back propagation as well as radial basis function networks. In this paper, we explore two novel approaches to the fault-classification problem using (i) Fourier neural networks, which synthesizes the approximation capability of multidimensional Fourier transforms and gradient-descent learning, and (ii) a class of generalized single hidden layer networks (GSLN), which self-structures via Gram-Schmidt orthonormalization. Using a simulation program for the F404 engine, we generate steady-state engine parameters corresponding to a set of combined two-module deficiencies and require various neural networks to classify the multiple faults. We show that, compared to the conventional network architecture, the Fourier neural network exhibits stronger noise robustness and the GSLNs converge at a much superior speed.

Brain-oriented Cconvolutional Neural Network Computer Style Recognition of Classical Chinese Poetry

2018 ◽  
Vol 16 (4) ◽  
Author(s):  
Jing Xuan ◽  
Zhongshi He ◽  
Liangyan Li ◽  
Weidong He ◽  
Fei Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Poetry ◽  
Network Computer ◽  
Classical Chinese ◽  
Classical Chinese Poetry

scholarly journals Application of A Convolutional Neural Network in The Diagnosis of Gastric Mesenchymal Tumors on Endoscopic Ultrasonography Images

2020 ◽  
Vol 9 (10) ◽  
pp. 3162
Author(s):  
Yoon Ho Kim ◽  
Gwang Ha Kim ◽  
Kwang Baek Kim ◽  
Moon Won Lee ◽  
Bong Eun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Endoscopic Ultrasonography ◽  
Sequential Analysis ◽  
Training Dataset ◽  
Clinical Practices ◽  
Mesenchymal Tumors ◽  
Diagnostic Modality ◽  
Cad System ◽  
Network Computer

Background and Aims: Endoscopic ultrasonography (EUS) is a useful diagnostic modality for evaluating gastric mesenchymal tumors; however, differentiating gastrointestinal stromal tumors (GISTs) from benign mesenchymal tumors such as leiomyomas and schwannomas remains challenging. For this reason, we developed a convolutional neural network computer-aided diagnosis (CNN-CAD) system that can analyze gastric mesenchymal tumors on EUS images. Methods: A total of 905 EUS images of gastric mesenchymal tumors (pathologically confirmed GIST, leiomyoma, and schwannoma) were used as a training dataset. Validation was performed using 212 EUS images of gastric mesenchymal tumors. This test dataset was interpreted by three experienced and three junior endoscopists. Results: The sensitivity, specificity, and accuracy of the CNN-CAD system for differentiating GISTs from non-GIST tumors were 83.0%, 75.5%, and 79.2%, respectively. Its diagnostic specificity and accuracy were significantly higher than those of two experienced and one junior endoscopists. In the further sequential analysis to differentiate leiomyoma from schwannoma in non-GIST tumors, the final diagnostic accuracy of the CNN-CAD system was 75.5%, which was significantly higher than that of two experienced and one junior endoscopists. Conclusions: Our CNN-CAD system showed high accuracy in diagnosing gastric mesenchymal tumors on EUS images. It may complement the current clinical practices in the EUS diagnosis of gastric mesenchymal tumors.

Programming a VG-RAM based Neural Network Computer

2014 ◽  
Author(s):  
Alberto F. De Souza ◽  
Avelino Forechi ◽  
Filipe Wall Mutz ◽  
Mariella Berger ◽  
Thiago Oliveira-Santos ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Computer

scholarly journals ЗАСТОСУВАННЯ НЕЙРОННИХ МЕРЕЖ У ЗАДАЧІ ПРОГНОЗУВАННЯ ТЕХНІЧНОГО СТАНУ АВІАЦІЙНОГО ДВИГУНА ТВ3-117 У ПОЛЬОТНИХ РЕЖИМАХ

2018 ◽  
pp. 30-38
Author(s):  
Юрий Николаевич Шмелев ◽  
Сергей Игоревич Владов ◽  
Яна Руслановна Климова
Keyword(s):  
Neural Network ◽  
Polynomial Regression ◽  
Time Window ◽  
Aircraft Engine ◽  
Technical State ◽  
Neural Network Method ◽  
Network Method ◽  
Network Technologies ◽  
Neural Network Technologies

The subject matter of the article are the methods and models for the identification of the technical state of the aircraft engine TV3-117. The goal is to develop an on-board system for identification of the technical state of the aircraft engine TV3-117, one of the solved tasks is the prediction of its technical status in real time. The tasks to be solved are: to development of methods and algorithms for forecasting the technical state of the aircraft engine TV3-117 in flight modes based on neural network technology. The methods used are: methods of probability theory and mathematical statistics, methods of neuroinformatics, methods of information systems theory and data processing. The following results were obtained: the application of the proposed neural network prediction method based on the approximation and extrapolation of the processes of changing the gas dynamic parameters of the aircraft engine TV3-117 on fixed segments of the time window (within the «sliding time window») allows effectively solving the problems of forecasting its technical state. The analysis of the effectiveness of the application of the neural network method for forecasting the technical state of the aircraft engine TV3-117 under the conditions of random interference has shown its advantages in comparison with the classical prediction methods, which consist in providing higher prediction accuracy for different forecasting intervals (short-, medium-, long-term forecasting). Application of the developed neural network method makes it possible to detect the moments of the time series disorder, that is, the appearance of the trend of the parameters of the aircraft engine TV3-117, which is a consequence of the qualitative change in the characteristics of the engine, which allows timely making operative decisions on changing its operation mode. Conclusions. The scientific novelty of the results obtained is as follows: the method of solving the problem of forecasting the technical state of the aircraft engine TV3-117 with the help of neural network technologies has been further developed, the accuracy of which in the short-term medium and long-term forecast is significantly higher compared with the use of polynomial regression models, the method of exponential smoothing, moving average, which indicates that the use of neural network technologies makes it possible to detect the appearance of the trend of the parameters of the aircraft engine TV3-117, which allows is to make timely operational decisions to change its mode of operation

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