Counterexample-trained Neural Network Model of Rate and Temperature Dependent Hardening with Dynamic Strain Aging

Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.

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Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Symmetry ◽

10.3390/sym12101662 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1662

Author(s):

Wei Hao ◽

Feng Liu

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

High Speed ◽

Prediction Method ◽

High Speed Train ◽

The Neural Network ◽

Neural Network Prediction ◽

Network Prediction ◽

Trained Neural Network

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

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PREDICTION OF MECHANICAL PROPERTIES OF 25CrMo48V SEAMLESS TUBE USING NEURAL NETWORK MODEL

International Journal of Modern Physics B ◽

10.1142/s0217979209060488 ◽

2009 ◽

Vol 23 (06n07) ◽

pp. 1074-1079 ◽

Cited By ~ 4

Author(s):

LAIBO SUN ◽

CHUANYOU ZHANG ◽

QINGFENG WANG ◽

MINGZHI WANG ◽

ZESHENG YAN

Keyword(s):

Neural Network ◽

Mechanical Properties ◽

Chemical Composition ◽

Network Model ◽

Neural Network Model ◽

Transverse Impact ◽

Relative Significance ◽

Line Production ◽

Prediction Of Mechanical Properties ◽

Trained Neural Network

In this investigation, a neural network model was established to predict mechanical properties of 25 CrMo 48 V seamless tubes. The sensitivity analysis was also performed to estimate the relative significance of each chemical composition in mechanical behavior of steel tubes. The results of this investigation show that there is a good agreement between experimental and predicted values indicating desirable validity of the model. Among those alloying elements, the elements of carbon, silicon and chromium tended to play a more important role in controlling both the yielding strength and the Charpy-V-Notch transverse impact toughness. In comparison, the impurities such as O , N , S and P have a relatively weak impact. More detailed dependences of mechanical properties on each chemical composition in isolation can be revealed using the established model. The well-trained neural network has a great potential in designing tough and ultrahigh-strength seamless tubes and modeling the on-line production parameters.

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An Optimized Hybrid Neural Network Model for Detecting Depression among Twitter Users

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9590.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 2781-2795

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Research Work ◽

Hybrid Neural Network ◽

The Neural Network ◽

Twitter Users ◽

Trained Neural Network ◽

User Friendly ◽

Better Than

The proposed work is to extensively evaluate if a user is depressed or not using his Tweets on Twitter. With the omni presence of social media, this method should help in identifying the depression of users. We propose an Optimized Hybrid Neural Network model to evaluate the user tweets on Twitter to analyze if a user is depressed or not. Where Neural Network is trained using Tweets to predict the polarity of Tweets. The Neural Network is trained in such a way that at any point when presented with a Tweet the model outputs the polarity associated with the Tweet. Also, a user-friendly GUI is presented to the user that loads the trained neural network in no time and can be used to analyze the users’ state of depression. The aim of this research work is to provide an algorithm to evaluate users’ sentiment on Twitter in a way better than all other existing techniques

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Application of digital twins in the management of socio-economic systems

E3S Web of Conferences ◽

10.1051/e3sconf/202124411001 ◽

2021 ◽

Vol 244 ◽

pp. 11001

Author(s):

Sergey Barkalov ◽

Dmitry Dorofeev ◽

Irina Fedorova ◽

Alla Polovinkina

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Time Window ◽

Remaining Useful Life ◽

Economic Systems ◽

Digital Twins ◽

Simultaneous Training ◽

Useful Life ◽

Trained Neural Network

The article describes the use of digital twins in socio-economic processes using the example of predictive asset maintenance management. For this, the architecture of a distributed forecasting information system is proposed that collects data from digital twins and provides them with a pre-trained neural network model to obtain forecasts about the need for predictive maintenance. The article discusses two types of forecasts - about the remaining useful life and the possible failure of an asset in the considered time window. Computational experiments have been carried out, confirming that the proposed neural network model allows, due to the simultaneous training of solving two problems, to obtain more accurate forecasts than models trained to solve one problem.

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