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When implementing the traditional assembly method, the rotor is affected by machining errors. The morphology of the rotor is complex, and the machining error of the rotors at all levels are transmitted step by step through the stop mating surface, which affects the performance and service life of the aero-engine. The evaluation of machining error of single-stage rotor is the basis of assembly quality of multi-stage rotor. In order to improve the current situation of complicated and time-consuming rotor machining error evaluation, this paper proposes to establish a deep belief neural network (DBNN) to replace the traditional procedure of depolarization. The network takes the relative evaluation error of the rotor profile data without depolarization as the input and takes the machining error of the rotors obtained after depolarization as the output. First, the evaluation mechanism of the rotor's machining error is analyzed, and the corresponding machining error influence source is selected as the input source of the deep belief neural network. Second, as DBNN is trained, and the appropriate weight initialization method and the optimization algorithm of the prediction network are selected to ensure the optimization of the whole network for feature mapping extraction of the training set. Finally, the assembly of multi-stage rotors is simulated and analyzed. It is shown in the experiments that after the iteration, the prediction network, with good training effects, has converged, and its prediction results tend to be consistent with the real values. The mean prediction error of the concentricity is 0.09 µm while the mean difference of angle of concentricity error value is 0.77°, and the mean difference of perpendicularity error value is 0.21 µm while the mean difference of angle of perpendicularity error value is 1.4°, the corresponding R 2 determination coefficients were 0.99, 0.98, 0.91, and 0.94, respectively. It meets the requirements of field assembly and fully embodies the effectiveness of the procedure of depolarization based on deep confidence neural network.

A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

This study proposes a high speed and accurate evaluation method for minimum zone roundness (MZR) using an optimal solution guidance algorithm (OSGA). The least-square center and roundness determine an original search zone, then the initial candidate points are configured as the centers of a minimum zone circle (MZC). To enhance the global search ability, subsequent candidate points follow the current optimal candidate points. The best three current candidate points jointly generate the new candidate points for the next iteration. In addition, a dynamic search zone updating strategy is proposed to achieve fast convergence of the optimal solution. The width of the search zone varies with the number of iterations required to achieve this rapid convergence. The effectiveness and performance of the proposed method are validated using datasets from previous studies. The proposed OSGA achieves excellent convergence performance, and reaches the global optimal solutions more quickly than previous methods. Across four different datasets, the termination condition is satisfied in only 0.0031–0.0058 s, the optimal solutions are obtained within 5–15 iterations, and the standard deviation of the MZR is less than 5.8276 × 10‒6 mm. The proposed method is also expected to extend to rapid and high-precision evaluation of cylindricity or straightness. [ABSTRACT FROM AUTHOR]

The precision measurement and evaluation of the geometry form error for high-end precision components is one of the most important tasks for metrologists. In this paper, a novel hybrid flatness error evaluation method is proposed to measure and evaluate the flatness error for large-scale point cloud data. This method is based on the convex hull algorithm and the improved particle swarm optimization (IPSO) algorithm. Not only are a large number of redundant measurement data points effectively removed through the convex hull algorithm, but also this operation does not lose any valuable information for flatness error evaluation. Then an accurate mathematical model for flatness error evaluation is established based on the minimum zone criteria recommended by ISO, and then the IPSO algorithm is adopted to solve this complex non-linear optimization problem. In particular, the non-linear dynamic inertia weights w and learning factors c1, c2 are introduced to improve the calculation accuracy. Finally, the proposed method is verified through an experiment measuring and evaluating the flatness error of an aero-engine rotor connection surface. The effective measurement data points are reduced from 18 000 to 856, or 4.76%, which is significant compared to the original data points. The flatness error evaluation result is 369 µm according to the IPSO method that we propose. Last but not least, we compare our method with the traditional least squares method and unmodified particle swarm optimization algorithm. The calculation results indicate that the evaluation accuracy is increased by 21 µm and 44 µm, respectively. [ABSTRACT FROM AUTHOR]

Multi-image iterative parallel phase retrieval methods are widely used in measurement and imaging, due to their simplicity and effectiveness. However, the retrieved image will have foggy diffuse noise, since some unknown cross-talk factors exist in iterative calculation. In this paper, a new updated method is embedded into the calculation process of phase retrieval. The iterative strategy of phase retrieval is updated to enhance robustness for noisy images. Experimental and simulated results have demonstrated that the improved algorithm has faster convergence speed and higher anti-noise performance. [ABSTRACT FROM AUTHOR]

We present a data-efficient framework to achieve a super-resolved reconstruction in lensfree on-chip microscopy. Instead of using conventional 3D-stack holograms, our method accomplishes a 5 × reduction of data size without the loss of the imaging resolution, in which merely 30 raw holograms are recorded. The experiment of the resolution target demonstrates a half-pitch lateral resolution of 780 nm across a large field-of-view (FOV) of 28.6 mm2. For the view of H&E stained intestine pathology slide, our method shows an equivalent resolution but a 20-fold expansion of FOV in comparison with a 10 × commercial wide-field microscope. This work will be a useful tool for the miniaturization of whole slide pathological microscopy. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to justify the algorithm optimization based on a consideration of its accuracy characteristics in a pure coning motion, which is widely used in optimizing strapdown attitude algorithms to describe the special angular motion of a vehicle. Design/methodology/approach – Two more general angular motions of a vehicle were given: generalized vibration describing periodical motions and benign dynamic describing aperiodical motions. The algorithm performances were evaluated in these two motions. Findings – The theoretical analysis and numerical results show that errors of the algorithm optimized in pure coning motion are null or neglectable in these two motions, and performance of the optimized algorithm in a pure coning motion is superior to that of the non-optimized algorithm. Originality/value – The value of the paper lies in that the authors justify the concept optimizing strapdown attitude algorithms in a pure coning motion. [ABSTRACT FROM AUTHOR]

Polarized low-coherence interferometry (PLCI) is widely used for the demodulation of Fabry–Perot (F-P) sensors. To avoid the influence of noise and dispersion on interference fringes, this paper proposes a data processing method in which the wavelet tools are applied to extract useful information from the extremum locations and envelope center of the fringes. Firstly, the wavelet threshold denoising (WTD) algorithm is used to remove electrical noise, and the complex Morlet wavelet is used to extract the fringe envelope. Based on this, the envelope center is used to predict the extremum locations of the specified order in its adjacent interval, the predicted locations are used as references to track the exact extremum locations, and the middle location of the peak and valley values is obtained to demodulate the F-P cavity accurately. The validity of this demodulation theory is verified by an air F-P cavity whose cavity length varies from 17 to 20 μm. With a sampling interval of 30 nm, the experimental results indicate that the repeatability accuracy is higher than 6.04 nm, and the resolution is better than 4.0 nm. [ABSTRACT FROM AUTHOR]

The phase evaluation is not perfect by using three kinds of existing multi-image phase retrieval methods. The amplitude-phase retrieval scheme is employed for initializing the input of the Multi-stage algorithm to obtain a robust result. The synthesized axial multi-image phase retrieval technique is able to be highly accurate convergent with continuous phase distribution of [0, 2π] and random phase distribution of [0, π], which is also demonstrated to have sharper edge for reconstructed phase map in experiment. This paper will provide a powerful and useful guidance for axial multi-image phase retrieval. [ABSTRACT FROM AUTHOR]

• A novel method for accurately detecting small-size defects of in-service CFRP plates is proposed and verified. • The developed nonlinear defect index using the relatively nonlinear coefficient of air-coupled Lamb wave is confirmed to be more sensitive to small-size defects than the traditional linear defect index. • The proposed adaptively weighted(AW) imaging algorithm takes the beam width of the Lamb wave into account so that imaging resolution no longer depends on the scanning step length. • For the small-size defect with a diameter of 10 mm, the area detection error of the proposed method is only 7.8 %, compared with 22.4 % of the traditional method. The air-coupled ultrasonic technique is capable of detecting defects in in-service CFRP plates and ensuring their application safety. Traditional air-coupled ultrasonic methods often rely on the linear defect index, which is not effective in characterizing small-size defects. Additionally, the scanning step length completely limits their imaging spatial resolution, resulting in the contradiction between imaging spatial resolution and detection efficiency. To address the above problems, the nonlinear defect index and the adaptively weighted imaging algorithm are proposed. The nonlinear defect index uses the relatively nonlinear coefficient of the Lamb wave to enhance the ability to detect small-size defects. Considering the beam width, the adaptively weighted imaging algorithm constructs the relationship between any imaging point and all scanning paths. At this time, the imaging spatial resolution can be set arbitrarily, eliminating the dependence on the scanning step length. The experimental results demonstrate that the proposed nonlinear defect index can characterize the small-size defect more accurately than the linear defect index. When the scanning step length is increased to improve detection efficiency, the adaptively weighted imaging algorithm can achieve better imaging spatial resolution compared to the traditional defect probability imaging algorithm. For the small-size defect with a diameter of 10 mm, the proposed method has an area detection error of only 7.8 %, whereas the traditional method has an error of 22.4 %. The proposed method is promising to detect small-size defects of in-service CFRP plates in the aircraft skin. [ABSTRACT FROM AUTHOR]

• A white LED and three ultra-narrow spectral filters (FWHM=1±0.2 nm) is used to form a multi-wavelength lensfree microscope. • A multi-wavelength phase retrieval algorithm based on total variation prior is proposed to improve the imaging quality and enhance the signal-to-noise ratio. • The experimental results of resolution target, stained human goblet cell, and label-free microglia cell demonstrate the superiority of our method. In multi-wavelength lensfree microscopy (MWLM), a laser source is usually replaced with a light-emitting diode for a low-cost and portable configuration. However, the trade-off between noise suppression and resolution enhancement puts a challenging issue for high-quality imaging. Here we propose a noise-robust multi-wavelength phase retrieval to increase the stability of MWLM. To solve the inverse problem of phase retrieval, we derive an optimization procedure to iteratively minimize the data fidelity and impose a total variation term for noise suppression. The experimental results of resolution target, stained human goblet cell, and label-free microglia cell demonstrate that our method could hold a better noise tolerance without the loss of imaging resolution. We believe that our method will become a powerful tool for a resources-limited scene. [ABSTRACT FROM AUTHOR]