Search

Continuous automated testing throughout each cycle can ensure the security of the continuous integration (CI) development lifecycle. Test case prioritization (TCP) is a critical factor in optimizing automated testing, which prioritizes potentially failed test cases and improves the efficiency of automated testing. In CI automated testing, the TCP is a continuous decision-making process that can be solved with reinforcement learning (RL). RL-based CITCP can continuously generate a TCP strategy for each CI development lifecycle, with the reward mechanism as the core. The reward mechanism consists of the reward function and the reward strategy. However, there are new challenges to RL-based CITCP in real-industry CI testing. With high-frequency iteration, the reward function is often calculated with a fixed length of historical information, ignoring the spatial characteristics of the current cycle. Therefore, the dynamic time window (DTW)-based reward function is proposed to perform the reward calculation, which adaptively adjusts the recent historical information range based on the integration cycle. Moreover, with low-failure testing, the reward strategy usually only rewards failure test cases, which creates a sparse reward problem in RL. To address this issue, the similarity-based reward strategy is proposed, which increases the reward objects of some passed test cases, similar to the failure test cases. The DTW-based reward function and the similarity-based reward strategy together constitute the proposed adaptive reward mechanism in RL-based CITCP. To validate the effectiveness of the adaptive reward mechanism, experimental verification is carried out on 13 industrial data sets. The experimental results show that the adaptive reward mechanism can improve the TCP effect, where the average NAPFD is maximally improved by 7.29%, the average Recall is maximally improved by 6.04% and the average TTF is improved by 6.81 positions with a maximum of 63.77. [ABSTRACT FROM AUTHOR]

Wavefront Coding (WFC) is an innovative technique aimed at extending the depth of focus (DOF) of optics imaging systems. In digital imaging systems, super-resolution digital reconstruction close to the diffraction limit of optical systems has always been a hot research topic. With the design of a point spread function (PSF) generated by a suitably phase mask, WFC could also be used in super-resolution image reconstruction. In this paper, we use a deep learning network combined with WFC as a general framework for images reconstruction, and verify its possibility and effectiveness. Considering the blur and additive noise simultaneously, we proposed three super-resolution image reconstruction procedures utilizing convolutional neural networks (CNN) based on mean square error (MSE) loss, conditional Generative Adversarial Networks (CGAN), and Swin Transformer Networks (SwinIR) based on mean absolute error (MAE) loss. We verified their effectiveness by simulation experiments. A comparison of experimental results shows that the SwinIR deep residual network structure based on MAE loss optimization criteria can generate more realistic super-resolution images with more details. In addition, we used a WFC camera to obtain a resolution test target and real scene images for experiments. Using the resolution test target, we demonstrated that the spatial resolution could be improved from 55.6 lp/mm to 124 lp/mm by the proposed super-resolution reconstruction procedure. The reconstruction results show that the proposed deep learning network model is superior to the traditional method in reconstructing high-frequency details and effectively suppressing noise, with the resolution approaching the diffraction limit. [ABSTRACT FROM AUTHOR]

A sinusoidal phase modulation laser interferometer is proposed to detect water surface acoustic waves excited by underwater acoustic radiation, and an improved PGC-Arctan demodulation algorithm that combines prenormalization and Lissajous ellipse fitting is proposed to demodulate detection signals. In this paper, the effects of phase modulation depth, carrier phase delay, and interference signal visibility on the Lissajous figure formed by quadrature interference components are analyzed. The demodulation algorithm first uses the amplitudes of multiple Fourier spectral components of an interference signal to calculate the phase modulation depth C, and calculation of the carrier phase delay Vc is achieved through the introduction of a quadrature carrier signal. Then, certain coefficients regarding C and Vc are constructed for prenormalization of the two quadrature interference signal components to eliminate the local nonuniform widening phenomenon of Lissajous ellipse. Next, the outer and the inner contours are extracted from a uniformly widened Lissajous ellipse resulting from light intensity disturbance, and the axial ratio of the ellipse is obtained, which is used to correct the ratio of the quadrature interference signal to eliminate the effect of filter gain coefficients. At last, through the combination of an Arctan algorithm and a phase-unwrapping algorithm, high-precision demodulation of the interference signal is realized. A sinusoidal phase modulation interferometer was set up to detect water surface acoustic waves, and a series of detection experiments were carried out. The experiment results show that the detection method and demodulation algorithm described in this paper can accurately realize the measurement of weak water surface acoustic waves. The proposed algorithm shows less distortion in demodulation results, and its signal-to-noise distortion ratio is less than 20 dB at 500 Hz, which is significantly better than traditional algorithms. The experimental results demonstrate the effectiveness and accuracy of water surface acoustic wave detection using sinusoidal phase modulation interferometer. [ABSTRACT FROM AUTHOR]

Sensor noise, such as low-frequency drift and white noise, often causes large measurement errors when an oscillation method is used to determine rotational inertia of a rigid body. A demodulation algorithm based on differentiation and resonance frequency analysis of torsional oscillation signals is proposed in this paper to eliminate the influence of sensor noise on inertia measurement. By using a torsion pendulum method as an example, a measurement approach involving a kinetics differential equation of damped torsional oscillation movement is developed and, on this basis, a rotational inertia measurement function in relation to the amplitude modulation parameter and oscillation frequency is derived. In the proposed algorithm, oscillation detection signals are first differentiated to extract local extremum points and calculate the amplitude modulation parameter. Next, with a certain frequency step, a series of reference signals are constructed and mixed with the original signals. The maximum product sum is calculated to determine the resonance frequency that is exactly equal to the oscillation frequency. Finally, the measurand can be determined by the measurement function. The simulation and experimental results show that this algorithm can effectively reduce the influence of sensor noise on measurement. The comparison of measurement results with other signal processing methods and other measurement systems is carried out, which verifies better effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]