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For the problem of autonomous inspection path planning of UAVs （unmanned aerial vehicles） on overhead lines， considering the time cost of access target of UAVs， the geometric mean of the line patrol time interval and the defect level is used as the benefit of visiting nodes， which is equivalently extended to the OP （orientation problem） optimization model， in order to achieve the goal of obtaining greater benefits within a certain time limit. Then， an AGA （adaptive genetic algorithm） based on LA （learning automata） mechanism to improve PSO （particle swarm optimization） is proposed to solve the optimal solution of the proposed model in the benchmark instance and verify the validity of the proposed model. Finally， taking the autonomous inspection task of UAVs on overhead lines as the application background， the stability of the proposed model and algorithm is validated， which provides a reference for the formulation of path planning strategy of autonomous inspection for UAVs.

Given the frequent adhesion of vertically arranged double-split conductors， the research on adhesion is of practical significance. The fault of 220 kV lines is taken as an example to establish an adhesion model of the double-split model based on the conductor state equation and electromagnetic force calculation to study the impact of wire temperature， wind speed， DC resistance error， tension， and other factors on conductor adhesion. The study shows that the critical current of the conductor adhesion increases as the conductor temperature rises and decreases with the increase of the tension， sub-conductor resistance error， and wind speed. Under the interaction of resistance and wind speed， the critical current reaches its minimum in the case of the maximum resistance error value and the minimum wind speed. The research results can provide a useful reference for line design and modification.

To build a data-driven approximation for the Reynolds-stress anisotropy (RSA), the symbolic regression method of gene expression programming (GEP) is applied. Two tensor-basis terms from the algebraic expansion for the RSA tensor are used in the GEP algorithm. A new RANS-GEP model is tested in several flows in channels without/with bumps at different physical and geometrical parameters, where DNS data of high fidelity involved as a target for RSA are available. The results of RANS-DNS runs are also obtained where the RSA values in the mean momentum equation are taken directly from DNS to show ability to improve the model performance versus the conventional linear eddy viscosity model (LEVM). Next, the training with carefully selected input features is performed to get an explicit non-linear algebraic model for RSA. The results of RANS-GEP study show potentials of the new tool to improve predictions.

OBJECTIVES: To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. METHODS: An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions. RESULTS: NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy. CONCLUSIONS: DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'. [ABSTRACT FROM AUTHOR]