String stability is essential for platoons. In this article, sufficient conditions for the string exponential stability of a class of longitudinal following platoon systems with time delays are investigated. Based on the constant spacing policy, a sliding mode control strategy is proposed for the look-ahead platoon with bounded unknown parameters and time delays to ensure the string exponential stability of the platoon. Numerical simulation findings including position, speed, acceleration errors, and inter-space between adjacent vehicles can verify the efficiency and feasibility of the proposed control systems. In simulations, the platoon with certain parameters and no time delays should be compared with the case with bounded unknown parameters and time delays.
The initial state of a nonlinear optical fiber system plays a vital role in the ultrafast pulse evolution dynamic. In this work, a data-driven compressed convolutional neural network, named inverse network, is proposed to predict initial pulse distribution through a series of discrete power profiles at different propagation distances. The inverse network is trained and tested based on two typical nonlinear dynamics: (1) the pulse evolution in a fiber optical parametric amplifier system and (2) soliton pair evolution in high-nonlinear fibers. Great prediction accuracy is reached when the epoch grows to 5000 in both cases, with the normalized root mean square errors below 0.01 on the entire testing set. Meanwhile, the lightweight network is highly effective. In this work, it takes approximately 30 seconds for 5,000 epochs training with a dataset size of 900. The inverse network is further tested and analyzed on the dataset with different signal-to-noise ratios and input sizes. The results show fair stability at the deviation on the testing set. The proposed inverse network demonstrates a promising approach to optimizing the initial pulse of fiber optics systems.
Plane wave imaging (PWI) is attracting more attention in industrial nondestructive testing and evaluation (NDT&E). To further improve imaging quality and reduce reconstruction time in ultrasonic imaging with a limited active aperture, an optimized PWI algorithm was proposed for rapid ultrasonic inspection, with the comparison of the total focusing method (TFM). The effective area of plane waves and the space weighting factor were defined in order to balance the amplitude of the imaging area. Experiments were carried out to contrast the image quality, with great agreement to the simulation results. Compared with TFM imaging, the space-optimized PWI algorithm demonstrated a wider dynamic detection range and a higher defects amplitude, where the maximum defect amplitude attenuation declined by 6.7 dB and average attenuation on 12 defects decreased by 3.1 dB. In addition, the effects of plane wave numbers on attenuation and reconstruction time were focused on, achieving more than 10 times reduction of reconstruction times over TFM.
A physics-based deep learning (DL) method termed Phynet is proposed for modeling the nonlinear pulse propagation in optical fibers totally independent of the ground truth. The presented Phynet is a combination of a handcrafted neural network and the nonlinear Schrödinger physics model. In particular, Phynet is optimized through physics loss generated by the interaction between the network and the physical model rather than the supervised loss. The inverse pulse propagation problem is leveraged to exemplify the performance of Phynet when in comparison to the typical DL method under the same structure and datasets. The results demonstrate that Phynet is able to precisely restore the initial pulse profiles with varied initial widths and powers, while revealing a similar prediction accuracy compared with the typical DL method. The proposed Phynet method can be expected to break the severe bottleneck of the traditional DL method in terms of relying on abundant labeled data during the training phase, which thus brings new insight for modeling and predicting the nonlinear dynamics of the fibers.
The gain performance of two-pump fiber optical parametric amplifier (FOPA) pumped by different kinds of chirped pump pulses is investigated numerically. It shows that the gain performances of FOPA are evidently influenced by the shapes of chirped pump pulses. The highest and broadest gain spectrum is obtained with the 10th-order super-Gaussian pulse in case of identical energy and full width at half-maximum of the pump pulses. Finally, the signal gain is also affected by the poor symmetry of pump pulse, which leads to a low gain performance.
The time-frequency analysis (TFA) by wavelet transform is adopted for the laser ultrasonic signal processing, and the effective features extraction of the material defect is obtained. The TFA is adopted here to analyze the laser-generated surface acoustic wave (SAW) signal which contains the defect features, the echo wave features are extracted significantly, especially under the condition of low signal-to-noise rate (SNR). The simulation model by using finite element method (FEM) is set up in an aluminum plate with different surface defect depths in detail, and the defect depths prediction with TFA is also considered. It shows that, without extra denoising process, the echo SAW is extracted significantly in case of defect depths ranging from 0.1mm to 0.9mm at SNR of −3dB by TFA. The TFA for processing the laser ultrasonic signal provides a promising way to get the defect information, with the accuracy increased by 7.9dB in this work, which is extremely meaningful for the ultrasonic signal processing and material evaluation.
The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.
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