Study of Suspension Parameters Matching to Enhance Vehicle Ride Comfort on Bump Road

In this study, the parameters of the MacPherson front suspension and the E-type multilink rear suspension are matched to enhance the vehicle ride comfort on bump road. Vehicle vibration and suspension stiffness are analyzed theoretically. In the simulation study, the influence of the front and rear wheels on the vehicle vibration is considered, so the time-domain curves of the front and rear seat rail accelerations are processed by adding windows with two different window functions. The resulting ΔRmsLocal and ΔRmsGlobal are used as evaluation indexes of the vehicle ride comfort. The sensitivity analysis yields the magnitude of the influence of the suspension parameters on the evaluation indexes. In addition, the trends of ΔRmsLocal and ΔRmsGlobal with bushing stiffness at different vehicle speeds are discussed. The results show that longitudinal ΔRmsLocal and ΔRmsGlobal of the seat rails are influenced by the bushings mostly, while the vertical ΔRmsLocal and ΔRmsGlobal of the seat rails are influenced by the spring and shock absorber mostly. The trends of ΔRmsLocal and ΔRmsGlobal with bushing stiffness are influenced by the speed of the vehicle. Finally, the vehicle ride comfort is enhanced after optimization and matching of the suspension parameters by NSGA-II optimization algorithm.

Recognition of flying small target based on enhanced quadratic time-frequency analysis

UAVs (Unmanned Aerial Vehicles, UAVs) are flying targets that sail at low altitudes, are slower and smaller in size. Nowadays, the task of detecting and distinguishing flying small targets is very difficult, so how to efficiently recognize flying small targets in real time is a key issue of current research. In order to solve this problem, this paper proposes a method of using pseudo-WVD and image fusion to represent the characteristics of UAVs. First, the SMMWR (Single-mode millimeter wave radar, SMMWR) equipment is used to collect the echo signals of various types of UAVs, and at the same time, the two-dimensional FFT is used to extract the target micro-motion signals in the distance dimension. Secondly, PWVD is used to generate time-frequency graphs of different window functions. Finally, the images fused based on principal component analysis are sent to AlexNet for training. The result proves that the accuracy of recognition rate based on AlexNet can be 93.75%.

Uniform error estimates for nonequispaced fast Fourier transforms

In this paper, we study the error behavior of the nonequispaced fast Fourier transform (NFFT). This approximate algorithm is mainly based on the convenient choice of a compactly supported window function. So far, various window functions have been used and new window functions have recently been proposed. We present novel error estimates for NFFT with compactly supported, continuous window functions and derive rules for convenient choice from the parameters involved in NFFT. The error constant of a window function depends mainly on the oversampling factor and the truncation parameter.

Selection of window functions for predicting the frequency pattern of vibrations of the technological process using an artificial neural network

The frequency pattern of the process is investigated by analyzing spectrograms constructed using the window Fourier transform. A set of window functions consists of a rectangular, membership, and windows based on atomic functions. The fulfillment of the condition for improving the time localization and energy concentration in the central part of the window allows one to select a window function. The resulting spectrograms are fed to the input of an artificial neural network to obtain a forecast. Varying the shape of the window functions allows us to analyze the proposed spectrogram prediction model.

Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals

The method of averaging modified periodograms is one of the main methods for estimating the power spectral density (PSD). The aim of this work was the development of mathematical and algorithmic support, which can increase the computational efficiency of signals digital spectral analysis by this method.The solution to this problem is based on the use of binary-sign stochastic quantization for converting the analyzed signal into a digital code. A special feature of this quantization is the use of a randomizing uniformly distributed auxiliary signal as a stochastic continuous quantization threshold (threshold function). Taking into account the theory of discrete-event modeling the result of binary-sign quantization is interpreted as a chronological sequence of instantaneous events in which its values change. In accordance with this we have a set of time samples that uniquely determine the result of binary-sign quantization in discrete-time form. Discrete-event modeling made it possible to discretize the process of calculating PSD estimates. As a result, the calculation of PSD estimates was reduced to discrete processing of the cosine and sine Fourier transforms for window functions. These Fourier transforms are calculated analytically based on the applied window functions. The obtained mathematical equations for calculating the PSD estimates practically do not require multiplication operations. The main operations of these equations are addition and subtraction. As a consequence, the time spent on digital spectral analysis of signals is reduced.Numerical experiments have shown that the developed mathematical and algorithmic support allows us to calculate the PSD estimates by the method of averaging modified periodograms with a high frequency resolution and accuracy even for a sufficiently low signal-to-noise ratio. This result is especially important for spectral analysis of broadband signals.The developed software module is a problem-oriented component that can be used as part of metrologically significant software for the operational analysis of complex signals.