A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

Redundancy,correlation,feature irrelevance,and missing samples are just a few problems that make it difficult to analyze software defect data.Additionally,it might be challenging to maintain an even distribution of data relating to both defective and non-defective software.The latter software class’s data are predominately present in the dataset in the majority of experimental situations.The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification.Besides the successful feature selection approach,a novel variant of the ensemble learning technique is analyzed to address the challenges of feature redundancy and data imbalance,providing robustness in the classification process.To overcome these problems and lessen their impact on the fault classification performance,authors carefully integrate effective feature selection with ensemble learning models.Forward selection demonstrates that a significant area under the receiver operating curve(ROC)can be attributed to only a small subset of features.The Greedy forward selection(GFS)technique outperformed Pearson’s correlation method when evaluating feature selection techniques on the datasets.Ensemble learners,such as random forests(RF)and the proposed average probability ensemble(APE),demonstrate greater resistance to the impact of weak features when compared to weighted support vector machines(W-SVMs)and extreme learning machines(ELM).Furthermore,in the case of the NASA and Java datasets,the enhanced average probability ensemble model,which incorporates the Greedy forward selection technique with the average probability ensemble model,achieved remarkably high accuracy for the area under the ROC.It approached a value of 1.0,indicating exceptional performance.This review emphasizes the importance of meticulously selecting attributes in a software dataset to accurately classify damaged components.In addition,the suggested ensemble learning model successfully addressed the aforementioned problems with software data and produced outstanding classification performance.

Novel mode-coupling vibrations of AlN thin film bulk acoustic resonator operating with thickness-extensional mode

The dispersion curves of bulk waves propagating in both AlN and ZnO film bulk acoustic resonators(FBARs)are presented to illustrate the mode flip of the thickness-extensional(TE)and 2nd thickness-shear(TSh2)modes.The frequency spectrum quantitative prediction(FSQP)method is used to solve the frequency spectra for predicting the coupling strength among the eigen-modes in AlN and ZnO FBARs.The results elaborate that the flip of the TE and TSh2 branches results in novel self-coupling vibration between the small-wavenumber TE and large-wavenumber TE modes,which has never been observed in the ZnO FBAR.Besides,the mode flip leads to the change in the relative positions of the frequency spectral curves about the TE cut-off frequency.The obtained frequency spectra can be used to predict the mode-coupling behaviors of the vibration modes in the AlN FBAR.The conclusions drawn from the results can help to distinguish the desirable operation modes of the AlN FBAR with very weak coupling strength from all vibration modes.

Bending Analysis of Circular Piezoelectric Semiconductor Plates Incorporating Flexoelectricity

Based on the three-dimensional(3D)basic equations of piezoelectric semiconductors(PSs),we establish a two-dimensional(2D)deformation-polarization-carrier coupling bending model for PS structures,taking flexoelectricity into consideration.The analytical solutions to classical flexure of a clamped circular PS thin plate are derived.With the derived analytical model,we numerically investigate the distributions of electromechanical fields and the concentration of electrons in the circular PS thin plate under an upward concentrated force.The effect of flexoelectricity on the multi-field coupling responses of the circular PS plate is studied.The obtained results provide theoretical guidance for the design of novel PS devices.

Time-history performance optimization of flapping wing motion using a deep learning based prediction model

Flapping Wing Micro Aerial Vehicles(FWMAVs)have caused great concern in various fields because of their high efficiency and maneuverability.Flapping wing motion is a very important factor that affects the performance of the aircraft,and previous works have always focused on the time-averaged performance optimization.However,the time-history performance is equally important in the design of motion mechanism and flight control system.In this paper,a time-history performance optimization framework based on deep learning and multi-island genetic algorithm is presented,which is designed in order to obtain the optimal two-dimensional flapping wing motion.Firstly,the training dataset for deep learning neural network is constructed based on a validated computational fluid dynamics method.The aerodynamic surrogate model for flapping wing is obtained after the convergence of training.The surrogate model is tested and proved to be able to accurately and quickly predict the time-history curves of lift,thrust and moment.Secondly,the optimization framework is used to optimize the flapping wing motion in two specific cases,in which the optimized propulsive efficiencies have been improved by over 40%compared with the baselines.Thirdly,a dimensionless parameter C_(variation)is proposed to describe the variation of the time-history characteristics,and it is found that C_(variation)of lift varies significantly even under close time-averaged performances.Considering the importance of time-history performance in practical applications,the optimization that integrates the propulsion efficiency as well as C_(variation)is carried out.The final optimal flapping wing motion balances good time-averaged and time-history performance.

Analysis of nonlinear multi-field coupling responses of piezoelectric semiconductor rods via machine learning

Piezoelectric semiconductors(PSs)have widespread applications in semiconductor devices due to the coexistence of piezoelec-tricity and semiconducting properties.It is very important to conduct a theoretical analysis of PS structures.However,the present of nonlinearity in the partial differential equations(PDEs)that describe those multi-feld coupling mechanical behaviors of PSs poses a significant mathematical challenge when studying these PS structures.In this paper,we present a novel approach based on machine learning for solving multi-field coupling problems in PS structures.A physics-informed neural networks(PINNs)is constructed for predicting the multi-field coupling behaviors of PS rods with extensional deforma-tion.By utilizing the proposed PINNs,we evaluate the multi-field coupling responses of a ZnO rod under static and dynamic axial forces.Numerical results demonstrate that the proposed PINNs exhibit high accuracy in solving both static and dynamic problems associated with Ps structures.It provides an effective approach to predicting the nonlinear multi-feld coupling phe-nomena in PS structures.

Analysis of Piezoelectric Semiconductor Structures Considering Both Physical and Geometric Nonlinearities

Piezoelectric semiconductors(PSs),such as ZnO and GaN,known as the third-generation semiconductors,have promising applications in electronic and optoelectronic devices due to the coexistence and interaction of piezoelectricity and semiconductor properties.Theoretical modeling of PS structures under external loads,such as thermal and mechanical loads,plays a crucial role in the design of PS devices.In this work,we propose a nonlinear fully coupling theoretical model and investigate the multi-field coupling behaviors of PS structures and PN junctions under thermal and mechanical loads,considering physical and geometric nonlinearities.The electromechanical and semiconducting behaviors of a PS rod-like structure with flexural deformations under different combinations of temperature changes and mechanical loads are evaluated.The tuning effect of temperature changes and mechanical loads on multi-field coupling behaviors of PSs is revealed.The current–voltage characteristics of PS PN junctions are studied under different combinations of temperature changes and mechanical loads.The obtained results are helpful for the development of novel PS devices.