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Periodic abnormal power fluctuations of wind turbines can easily cause low-frequency oscillations of the power grid, threatening the safety of the power grid. Wind turbine power signals are nonlinear and non-stationary signals, and their abnormal fluctuation Frequencies are uncertain, which makes it difficult to extract and detect abnormal power fluctuation features. This paper proposes a method for detecting abnormal power fluctuations of wind turbine based on subband processing and correlation coefficient. For the application of this method, the author first removed trend items of the original power signal by high-pass filter, and decomposed the signal by the subband processing filter bank to obtain a series of subband signals; calculated the cross-correlation coefficient between the power signal with trend items removed and each subband signal respectively, located the subband where periodic abnormal fluctuation occurred and filtered useful subband according to cross-correlation coefficients; obtained high signal-to-noise ratio signal reconstructed based on the filtered subband signal; and by combining the peak detection method and the autocorrelation analysis method, identified the time period where periodic abnormal fluctuations of specific amplitude of the reconstructed signal, thus realizing the detection of periodic abnormal power fluctuations of wind turbine. Field data tests show that for periodic abnormal power fluctuations of different frequencies such as 0.06Hz and 0.32Hz, the proposed method can effectively identify the subband where periodic abnormal fluctuations occur and the periodic abnormal fluctuation characteristics.

Multipath effects can significantly reduce the accuracy of GNSS precise positioning. Traditional methods, such as sidereal filtering and grid-based approaches, attempt to model and mitigate these errors by leveraging the spatial autocorrelation of multipath based on residuals. However, these methods can only approximately handle spatial autocorrelation data, limiting their effectiveness. This study investigates the spatial cross-correlation of residuals between various GNSS frequency bands, analyzes their covariance function parameters, and evaluates the impact of constellation shifts on long-term multipath mitigation. Based on this, a simplified autocorrelation-based approach utilizing unified covariance parameters for multipath mitigation is proposed, with its efficacy assessed for both short- and long-term applications. The study demonstrates the correlation of multipath effects across various GPS and Galileo frequencies, including GPS L1/L2/L5 and Galileo E1/E5a/E5b/E5ab/E6, by analyzing correlation coefficients. A strong correlation (greater than 0.8) is observed between residuals of closely spaced frequencies, such as E5b and E5ab, despite their frequency differences. Additionally, the covariance parameters of the residuals are found to be consistent across all frequencies for a baseline, suggesting that unified parameters can be applied effectively for spatial autocorrelation-based multipath mitigation without sacrificing performance. The orbit shifts of certain GPS satellites, particularly G02, G20, and G21, result in significant changes in orbital parameters and satellite tracks, reducing the effectiveness of long-term multipath mitigation. However, the impact of GPS orbit shifts can be minimized through periodic model updates or by integrating GPS and Galileo modeling. In experiments, the LSC correction strategy based on a GPS/Galileo combination, utilizing unified parameters, outperforms the grid method based on the GPS/Galileo combination, improving the mean residual variance elimination rate by 11.3% for GPS L1 and 11.4% for Galileo E1. These improvements remain consistent, with rates of 11.3% and 15.7%, respectively, even on DOY 365, which is 327 days after the modeling data were collected. [ABSTRACT FROM AUTHOR]

The unmanned surface vehicles (USV) are required to perform a dynamic obstacle avoidance during fulfilling a task. This is essential for USV safety in case of an emergency and such action has been proved to be difficult. However, little research has been done in this area. This study proposes an emergency collision avoidance algorithm for unmanned surface vehicles (USVs) based on a motion ability database. The algorithm is aimed to address the inconsistency of the existing algorithm. It is proposed to avoid collision in emergency situations by sharp turning and treating the collision avoidance process as a part of the turning movement of USV. In addition, the rolling safety and effect of speed reduction during the collision avoidance process are considered. First, a USV motion ability database is established by numerical simulation. The database includes maximum rolling angle, velocity vector, position scalar, and steering time data during the turning process. In emergency collision avoidance planning, the expected steering angle is obtained based on the International Regulations for Preventing Collisions at Sea (COLREGs), and the solution space, with initial velocity and rudder angle taken as independent variables, is determined by combining the steering time and rolling angle data. On the basis of this solution space, the objective function is solved by the particle swarm optimization (PSO) algorithm, and the optimal initial velocity and rudder angle are obtained. The position data corresponding to this solution is the emergency collision avoidance trajectory. Then, the collision avoidance parameters were calculated based on the afore mentioned model of motion. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under emergency situation. Results show the validity of the algorithm. Hence results of this research can be useful for performing intelligent collision avoidance operations of USV and other autonomous ships

This review focuses on the structural characteristics of mitoNEET and its various biological functions as a key protein in regulating mitochondrial function, maintaining cellular iron homeostasis, and participating in energy metabolism. The aim is to provide new intervention methods and therapeutic targets for the prevention and treatment of diseases closely related to inflammation, oxidative stress damage, lipid metabolism disorders, such as obesity diabetes and atherosclerosis- related diseases. [ABSTRACT FROM AUTHOR]

The modal frequencies, model shapes or their derivatives are generally used as the characteristic quantities of the objective function for the finite element model (FEM) updating. However, the measurement accuracy of the model shapes is low due to the few numbers of measurement points for actual structures, which results in a large correction error. The antiresonant frequency reflects the local information of the structure more accurately than the mode shapes, which is a good complement to the resonance frequencies. In this paper, a FEM updating using resonance and antiresonant frequencies with radial basis function (RBF) neural network is proposed. The elastic modulus, added mass, tensile stiffness and torsional stiffness are selected as the updating parameters of FEM for a cantilever beam, which were grouped by the uniform design method. The resonance and antiresonant frequencies identified from the frequency response function (FRF) obtained from corresponding FEM at only one node are taken as the characteristic quantities. The RBF neural network is adopted to construct the mapping relationships between the characteristic quantities and the updating parameters. The updated parameters are substituted into the FEM, and the FRF is obtained to verify the validity of the method. The results show that the relative errors between all the updated parameters and the target values are less than 7%, and the relative errors of the characteristic quantities in the updating frequency band are less than 3%. The proposed method can accurately reproduce the dynamic characteristics of the cantilever beam. It can be applied to the damage detection and safety evaluation of large structures which are difficult to arrange more measuring points. [ABSTRACT FROM AUTHOR]

This work demonstrates vertical $\boldsymbol {\beta } $ -Ga2O3 Schottky barrier diodes (SBDs) breaking through the power figure of merit of 1 GW/cm2 without edge termination. The unreliable surface on the top of $\sim {1.2}\times {10} ^{{16}}$ cm−3 drift region, which naturally formed in air, was removed by inductively coupled plasma etching. The repaired surface was exposed to ambient air for less than 10 minutes during the entire preparation process. Compared with the excessive air exposure samples, the leakage current was well suppressed for the Ni/ $\boldsymbol {\beta } $ -Ga2O3 SBDs fabricated on a clean surface. Moreover, the blocking voltage reaches a maximum value of 1720 V, and the forward/reverse characteristics of the diodes on the same wafer show good uniformity. These results pave the way for further improving the performance of $\boldsymbol {\beta }$ -Ga2O3 devices and verify the potential of $\boldsymbol {\beta }$ -Ga2O3 SBDs for power applications. [ABSTRACT FROM AUTHOR]