scholarly journals Neural network based dynamic model and gust identification system for the Jetstream G-NFLA

2016 ◽  
Vol 231 (6) ◽  
pp. 1138-1153 ◽  
Author(s):  
Aristeidis Antonakis ◽  
Mudassir Lone ◽  
Alastair Cooke
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Linear Models ◽  
Identification System ◽  
Aerospace Vehicles ◽  
Flight Data ◽  
Longitudinal Dynamic ◽  
Data Analysis Procedure ◽  
Real World Applications ◽  
Established Technique

Artificial neural networks are an established technique for constructing non-linear models of multi-input-multi-output systems based on sets of observations. In terms of aerospace vehicle modelling, however, these are currently restricted to either unmanned applications or simulations, despite the fact that large amounts of flight data are typically recorded and kept for reasons of safety and maintenance. In this paper, a methodology for constructing practical models of aerospace vehicles based on available flight data recordings from the vehicles’ operational use is proposed and applied on the Jetstream G-NFLA aircraft. This includes a data analysis procedure to assess the suitability of the available flight databases and a neural network based approach for modelling. In this context, a database of recorded landings of the Jetstream G-NFLA, normally kept as part of a routine maintenance procedure, is used to form training datasets for two separate applications. A neural network based longitudinal dynamic model and gust identification system are constructed and tested against real flight data. Results indicate that in both cases, the resulting models’ predictions achieve a level of accuracy that allows them to be used as a basis for practical real-world applications.

Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method

2009 ◽  
Vol 113 (1142) ◽  
pp. 243-252 ◽  
Author(s):  
N. K. Peyada ◽  
A. K. Ghosh
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Dynamic Model ◽  
Newton Method ◽  
Initial Conditions ◽  
Estimation Method ◽  
Wind Tunnel Test ◽  
Neural Model ◽  
Estimation Methods ◽  
Flight Data

Abstract A new parameter estimation method based upon neural network is proposed. The method proposed here uses feed forward neural networks to establish a neural model that could be used to predict subsequent time histories given the suitable measured initial conditions. The proposed neural model would not represent a generic flight dynamic model. The neural model in this case develops point to point fitting of the input and the output data. Thus, it could at best be referred to as flight dynamic model in restricted sense. Gauss-Newton method is then used to obtain optimal values of the aerodynamic parameters by minimising a suitable defined error cost function. The method has been validated using longitudinal and lateral-directional flight data of various test aircraft. The results thus obtained were compared with those obtained through wind tunnel test, or those obtained using Maximum likelihood and/or Filter error methods. Unlike, most of the parameter estimation methods, the proposed method does not require a prior description of the model. It also bypasses the requirement of solving equations of motion. This feature of the proposed method may have special significance in handling flight data of an unstable aircraft.

scholarly journals Multiple-Molecule Drug Design Based on Systems Biology Approaches and Deep Neural Network to Mitigate Human Skin Aging

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3178
Author(s):  
Shan-Ju Yeh ◽  
Jin-Fu Lin ◽  
Bor-Sen Chen
Keyword(s):  
Neural Network ◽  
Systems Biology ◽  
Signaling Pathways ◽  
Human Skin ◽  
Deep Neural Network ◽  
Middle Age ◽  
Skin Aging ◽  
Identification System ◽  
Potential Candidate ◽  
Systems Medicine

Human skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increasing demand for cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this study, we developed strategies for systems medicine design based on systems biology methods and deep neural networks. We constructed the candidate genomewide genetic and epigenetic network (GWGEN) via big database mining. After doing systems modeling and applying system identification, system order detection and principle network projection methods with real time-profile microarray data, we could obtain core signaling pathways and identify essential biomarkers based on the skin aging molecular progression mechanisms. Afterwards, we trained a deep neural network of drug–target interaction in advance and applied it to predict the potential candidate drugs based on our identified biomarkers. To narrow down the candidate drugs, we designed two filters considering drug regulation ability and drug sensitivity. With the proposed systems medicine design procedure, we not only shed the light on the skin aging molecular progression mechanisms but also suggested two multiple-molecule drugs for mitigating human skin aging from young adulthood to middle age and middle age to old age, respectively.

An Optical Fiber Sensing Network Supporting Weak Optical Signal Detection and a Neural Network Method for Information Collection

2021 ◽  
Vol 16 (2) ◽  
pp. 188-195
Author(s):  
Keyuan Liu ◽  
Haibin Li ◽  
Ya Wang
Keyword(s):  
Neural Network ◽  
Optical Fiber ◽  
Signal Detection ◽  
Mobile Phone ◽  
Optical Power ◽  
Information Collection ◽  
Identification System ◽  
Power Meter ◽  
Fiber Sensing ◽  
Optical Fiber Sensing

The weak direct current (DC) signals detected and converted by the photodetector are output to the mobile phone by voltage/frequency switching, and the signals are processed by the mobile phone APP and audio conversion module. The photodetector is equipped with the automatic switching function to design an optical power meter and detect weak signals. Meanwhile, the optical cable identification system is analyzed and combined with the optical power meter to generate an optical fiber sensing network to improve the weak alternating current (AC) signal detection. This network needs data fusion in sensor nodes’ data collection. The cluster routing protocol is introduced and combined with the back propagation neural network (BPNN) to propose a method suitable for this photoelectric transmission and improve the information fusion and accuracy. In the experiment, the optical power meter is output in gears first, and the output waveforms are normal. The photodiode’s optical power is adjusted to obtain different frequencies on the oscilloscope. In the proposed optical fiber sensing network, weak AC signals are amplified significantly, and different optical fiber lines can be distinguished in the optical cables. The proposed information collection method can reduce network communication and node energy consumption.

Longitudinal vibration analysis and suppression of electric wheel system driven by in-wheel motor considering unbalanced magnetic pull

2018 ◽  
Vol 233 (11) ◽  
pp. 2729-2745 ◽  
Author(s):  
Shuguang Zuo ◽  
Duoqiang Li ◽  
Yu Mao ◽  
Wenzhe Deng
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Dynamic Model ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Longitudinal Vibration ◽  
Structural Parameters ◽  
Torque Ripple ◽  
Longitudinal Dynamics ◽  
Unbalanced Magnetic Pull

With the blowout of electric vehicles recently, the key parts of the electric vehicles driven by in-wheel motors named the electric wheel system become the core of development research. The torque ripple of the in-wheel motor mainly results in the longitudinal dynamics of the electric wheel system. The excitation sources are first analyzed through the finite element method, including the torque ripple induced by the in-wheel motor and the unbalanced magnetic pull produced by the relative motion between the stator and rotor. The accuracy of the finite element model is verified by the back electromotive force test of the in-wheel motor. Second, the longitudinal-torsional coupled dynamic model is established. The proposed model can take into account the unbalanced magnetic pull. Based on the model, the modal characteristics and the longitudinal dynamics of the electric wheel system are analyzed. The coupled dynamic model is verified by the vibration test of the electric wheel system. Two indexes, namely, the root mean square of longitudinal vibration of the stator and the signal-to-noise ratio of the tire slip rate, are proposed to evaluate the electric wheel longitudinal performance. The influence of unbalanced magnetic pull on the evaluation indexes of the longitudinal dynamics is analyzed. Finally, the influence of motor’s structural parameters on the average torque, torque ripple, and equivalent electromagnetic stiffness are analyzed through the orthogonal test. A surrogate model between the structural parameters of the in-wheel motor and the average torque, torque ripple, and equivalent electromagnetic stiffness is established based on the Bp neural network. The torque ripple and the equivalent electromagnetic stiffness are then reduced through optimizing the structural parameters of the in-wheel motor. It turns out that the proposed Bp neural network–based method is effective to suppress the longitudinal vibration of the electric wheel system.

scholarly journals Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks

2019 ◽  
Vol 11 (4) ◽  
pp. 1 ◽  
Author(s):  
Tobias de Taillez ◽  
Florian Denk ◽  
Bojana Mirkovic ◽  
Birger Kollmeier ◽  
Bernd T. Meyer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Models ◽  
Transfer Functions ◽  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Related Potentials ◽  
Linear And Nonlinear ◽  
Artificial Neural

Diferent linear models have been proposed to establish a link between an auditory stimulus and the neurophysiological response obtained through electroencephalography (EEG). We investigate if non-linear mappings can be modeled with deep neural networks trained on continuous speech envelopes and EEG data obtained in an auditory attention two-speaker scenario. An artificial neural network was trained to predict the EEG response related to the attended and unattended speech envelopes. After training, the properties of the DNN-based model are analyzed by measuring the transfer function between input envelopes and predicted EEG signals by using click-like stimuli and frequency sweeps as input patterns. Using sweep responses allows to separate the linear and nonlinear response components also with respect to attention. The responses from the model trained on normal speech resemble event-related potentials despite the fact that the DNN was not trained to reproduce such patterns. These responses are modulated by attention, since we obtain significantly lower amplitudes at latencies of 110 ms, 170 ms and 300 ms after stimulus presentation for unattended processing in contrast to the attended. The comparison of linear and nonlinear components indicates that the largest contribution arises from linear processing (75%), while the remaining 25% are attributed to nonlinear processes in the model. Further, a spectral analysis showed a stronger 5 Hz component in modeled EEG for attended in contrast to unattended predictions. The results indicate that the artificial neural network produces responses consistent with recent findings and presents a new approach for quantifying the model properties.

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