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The accurate phase information of single-phase meter in low-voltage distribution network is important for refined management. For a long time, China's low-voltage distribution network has been expanding rapidly, but the management is insufficient, and the maintenance of topology files including meter phase information is not timely, which leads to meter phase information errors. At present, it mainly relies on manual inspection and installation of carrier meter for phase identification. The former is time-consuming and inefficient, while the latter is expensive. This paper presents a high accuracy phase identification method of single-phase meter in low-voltage distribution networks based on time sequence voltage data of single-phase meter and concentrator. Firstly, the single-phase meter voltage data and concentrator three-phase voltage data matrix are formed, and the KNN method is used to fill the missing data. The non-negative matrix factorization (NMF) dimension reduction method is used to transform the high-dimensional voltage time series data into low dimensional data, which aim to solve the problem of data redundancy and high computational cost caused by high dimension. Then the label propagation algorithm based on graph theory is used to process the reduced data iteratively, which lead to identify the phase of each single-phase meter. Finally, the identification accuracy of the method proposed in this paper reaches 100% on the data of a distribution network in Nanjing. It is also compared with other similar work to verify the accuracy and superiority of the method.

Hypothesis driven design leads to metal-free corroles that display excellent performance for photo-induced reduction, oxidation and energy transfer, and also serve as superb catalysts for photoredox organic reactions and triplet–triplet annihilation upconversions., Efficient triplet photosensitizers are important for fundamental photochemical studies and applications such as triplet–triplet annihilation upconversion (TTA UC), photoredox catalytic organic reactions and photovoltaics. We now report a series of phosphorus corrole compounds as efficient visible light-harvesting metal-free triplet photosensitizers. While the heavy-atom-free phosphorus corroles show absorption in the visible spectral region (centered at 573 nm) and have a decent triplet state quantum yield (ΦΔ = 49%), iodo-substitution on the corrole core induces red-shifted absorption (589 nm) and improves intersystem crossing significantly (ΦΔ = 67%). Nanosecond transient absorption spectra confirm triplet state formation upon photoexcitation (τT = 312 μs) and the iodinated derivatives also display near IR phosphorescence in fluid solution at room temperature (λem = 796 nm, τp = 412 μs). Both singlet oxygen (1O2) and superoxide radical anions (O2–˙) may be produced with the phosphorus corroles, which are competent photocatalysts for the oxidative coupling of benzylamine (the Aza Henry reaction). Very efficient TTA UC was observed with the phosphorus corroles as triplet photosensitizers and perylene as the triplet acceptor, with upconversion quantum yields of up to ΦUC = 38.9% (a factor of 2 was used in the equation) and a very large anti-Stokes effect of 0.5 eV.

Three-dimensional (3D) measurement of microstructures has become increasingly important, and many microscopic measurement methods have been developed. For the dimension in several millimeters together with the accuracy at sub-pixel or sub-micron level, there is almost no effective measurement method now. Here we present a method combining the microscopic stereo measurement with the digital speckle projection. A microscopy experimental setup mainly composed of two telecentric cameras and an industrial projection module is established and a telecentric binocular stereo reconstruction procedure is carried out. The measurement accuracy has firstly been verified by performing 3D measurements of grid arrays at different locations and cylinder arrays with different height differences. Then two Mitutoyo step masters have been used for further verification. The experimental results show that the proposed method can obtain 3D information of the microstructure with a sub-pixel and even sub-micron measuring accuracy in millimeter scale.

Surface characterization plays a significant role in evaluating surface functional performance. In this paper, we introduce wavelet packet transform for surface roughness characterization and surface texture extraction. Surface topography is acquired by a confocal laser scanning microscope. Smooth border padding and de-noise process are implemented to generate a roughness surface precisely. By analyzing the high frequency components of a simulated profile, surface textures are separated by using wavelet packet transform, and the reconstructed roughness and waviness coincide well with the original ones. Wavelet packet transform is then used as a smooth filter for texture extraction. A roughness specimen and three real engineering surfaces are also analyzed in detail. Profile and areal roughness parameters are calculated to quantify the characterization results and compared with those measured by a profile meter. Most obtained roughness parameters agree well with the measurement results, and the largest deviation occurs in the skewness. The relations between the roughness parameters and noise are analyzed by simulation for explaining the relatively large deviations. The extracted textures reflect the surface structure and indicate the manufacturing conditions well, which is helpful for further feature recognition and matching. By using wavelet packet transform, engineering surfaces are comprehensively characterized including evaluating surface roughness and extracting surface texture.

Telecentric lenses have been used for high-precision visual measurement systems because of their unique imaging properties, whereas the principal point is usually not considered in the projection model for calibration. Here, we propose an improved model containing the principal point for calibrating telecentric cameras. Detailed mathematical derivation from the pinhole model is performed. Then, an experimental setup is established. Two different telecentric cameras are used for verification. The results prove that calibrating telecentric cameras based on the improved model considering the principal point can achieve up to 55.37% higher accuracy and better stability than the common projection model without the principal point.

Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l1-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging.