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Insulator detection is an important task for safe and reliable operation of smart grid. Due to various background interferences in insulator images, most traditional image processing methods cannot achieve good performance. In this paper, a new method based on Log AdaBoost is proposed for insulator detection. Firstly, our boosting algorithm optimizes Polylog loss function rather than Exponential function in classical AdaBoost. We use gradient descent to optimize our loss function while the coordinate descent method is used in classical AdaBoost. Secondly, a new weight updating strategy is taken to find the weak classifier relevant to the label under the current weight distribution. In other word, the weight is updated towards the negative gradient of loss function to find the optimal weak classifier. Thirdly, a neighborhood feature is proposed in this paper, and this Haar-like feature can make the pixel difference between the insulator and the background obvious. Experimental results on two databases (UCI and ACDC) show that the proposed algorithm achieves the lowest test error on 11 of the 20 UCI datasets (second-lowest on the other nine), and on ACDC it yields lower testing error with the fewest weak classifiers and the smallest margin variance across the four labels, indicating better generalization than other AdaBoost variants. Finally, on the CPLID insulator detection dataset, the proposed method achieves an AUC of 0.82 with only 21k parameters.

The fractional order PID (FOPID) controller has been found to have the potential to provide more flexibility in the design options than the integer order PID (IOPID) controller. However, in the FOPID controller design practice, investigations are still required in selecting the achievable design specifications and controller parameters. In order to obtain a practical tuning method for the FOPID controller, taking the first order plus integral systems as the plants, the complete achievable regions of the design specifications are collected and analyzed. In addition, the selectable integral and derivative orders of the FOPID controller are also collected and modeled. With the prior knowledge obtained, a synthesis method for the FOPID controller is proposed to make the control system achieve the desired flat-phase characteristic. To verify the effectiveness of the proposed method, the synthesis method is applied to design the FOPID controller for the permanent magnet synchronous motor (PMSM) speed servo system. Simulation and experimental results show that system with the FOPID controller can achieve satisfying tracking performance and disturbance rejection performance.

To address the problem that the sand cat swarm optimization (SCSO) algorithm experiences a decline in convergence speed and a tendency to fall into local optima during the iteration process, this paper proposes a multi-strategy enhanced sand cat swarm optimization (MESCSO) algorithm to improve its ability to escape local optima and enhance convergence efficiency. Firstly, an improved sine mapping combined with random opposition-based learning (ISMROBL) is employed during population initialization to enhance the uniformity and diversity of initial solutions. Secondly, a nonlinear decreasing parameter is introduced to dynamically balance global exploration and local exploitation. Thirdly, generalized quadratic interpolation (GQI) is incorporated to strengthen global search capability, while the improved mean differential mutation (IMDM) strategy enhances local exploitation. Finally, accelerated opposition-based learning (AOBL) is applied to refine individual positions and improve the algorithm’s ability to escape local optima. Experimental results on 23 standard benchmark functions and the CEC2014 benchmark functions show that MESCSO achieves superior performance compared to nine algorithms. In addition, MESCSO is tested on five constrained engineering design problems. The results demonstrate that, compared with SCSO, MESCSO yields improvements of 0.53%, 1.47%, 0.03%, 0.41%, and 0.31%, respectively, thereby confirming its effectiveness and applicability.

The thermal and chemical stabilities are two key factors limiting the commercial application of superhydrophobic coatings. Herein, a thermally and chemically stable fluorinated poly(aryl ether) (FPAE) was used for the first time to construct superhydrophobic coatings by a facile solution spraying process. Distinct nanosheet structures were formed for the FPAE, which after the incorporation of SiO 2 and carbon nanotube (CNT) turned into a hydrangea -like structure with high roughness and low surface energy. As a result, superhydrophobic coatings were obtained with the water contact angle (WCA) reaching about 170° and the rolling off angle (RA) reaching less than 1°. The superhydrophobicity was maintained after a variety of rigorous tests, including high-temperature baking, acidic and alkaline solution immersions, ultrasonic treatments, friction evaluations, etc. The self-cleaning, anti-corrosion, and oil–water separation capabilities of the FPAE/SiO 2 /CNT coating were successfully demonstrated. Therefore, the FPAE is a promising resin for the preparation of low-cost and high-performance superhydrophobic coatings. Graphic abstract

The safe and reliable operation of lithium‐ion battery energy storage systems depends critically on effective monitoring by the battery management system (BMS). Accurate estimation of the state of charge (SOC) and state of health (SOH) is therefore essential. This paper proposes a new SOC and SOH cooperative estimation approach. First, a second‐order RC equivalent‐circuit model with hysteresis is established, and its parameters are identified using a bias‐compensated adaptive‐forgetting recursive least squares algorithm (BC‐AFFRLS). SOH is characterized by the maximum usable capacity and the ohmic resistance. Building on this model, we develop a multi‐time‐scale SOC–SOH cooperative estimator based on a backward‐smoothed square‐root cubature Kalman filter (BS‐SRCKF) and a dual extended Kalman filter (DEKF). To further improve accuracy, the initial covariance matrices of BS‐SRCKF‐DEKF are tuned via swarm‐intelligence optimization. Simulation results under intermittent constant‐current charge/discharge profiles validate the effectiveness of the proposed method. Moreover, tests under US06 and FUDS driving cycles show that the proposed algorithm outperforms four comparative methods; notably, the cumulative SOC estimation errors are only 1.18% and 1.71%, respectively. Additional comparisons across FUDS, US06, and DST at different temperatures against data‐driven baselines as well as BS‐SRCKF and DEKF further confirm consistently excellent SOC estimation. Overall, the BS‐SRCKF‐DEKF framework demonstrates strong robustness and superior performance. This paper presents a novel method for jointly estimating the state of charge (SOC) and state of health (SOH) in lithium‐ion battery systems. A second‐order hysteresis RC model and the BS‐SRCKF‐DEKF algorithm are used to improve estimation accuracy. Simulation and experimental results verify the method′s robustness and superior performance.

•The reproducibility assessment of magnetic resonance spectroscopy was explored.•CV and ICC showed good reliability of within- and between- scanning sessions.•Bland-Altman plots indicated strong agreement in the repeated measurements.•Pearson correlation coefficients showed great reproducibility across three machines.•It revealed higher reproducibility for the intra-vendor than the inter-vendor. Proton magnetic resonance spectroscopy (1H-MRS) has potential in clinical diagnosis and understanding the mechanism of illnesses. However, its application is limited by the lack of standardization in data acquisition and processing across time points and between different magnetic resonance imaging (MRI) system vendors. This study examines whether metabolite concentrations obtained from different sessions, scanner models, and vendors can be reliably reproduced and combined for diagnostic analysis-an important consideration for rare disease research. Participants underwent magnetic resonance scanning once on two separate days within one week (one session per day, each including two 1H-MRS scans without subject movement) on each machine. Absolute metabolite concentrations were analyzed for reliability of within- and between- session using the coefficient of variation (CV), intraclass correlation coefficient (ICC) and Bland-Altman (BA) plot, and for reproducibility across the machines using the Pearson correlation coefficient. As for within- and between- session, most of the CV values for a group of all the first or second scans of a session, and from each session were below 20 %, and most of ICCs ranged from moderate (0.4≤ICC<0.59) to excellent (ICC≥0.75), which indicated high reliability. Most of the BA plots had the line of equality between 95 % confidence interval of bias (mean difference), therefore the differences over scanning time could be negligible. Majority of the Pearson correlation coefficients approached 1 with statistical significance (P < 0.001), showing high reproducibility across the three scanners. Additionally, the intra-vendor reproducibility was greater than the inter-vendor ones. [Display omitted]

A fractional order sliding mode control (FOSMC) method is developed in this paper to deal with the control problem of permanent magnet synchronous motor (PMSM) speed servo system subject to multiple disturbances including model uncertainties, unknown constant disturbances and harmonic disturbances. The lumped exogenous disturbances and uncertainties of the PMSM speed servo are estimated by an improved disturbance observer (DO) and an extended state observer (ESO), respectively. Then, a novel FOSMC law is developed by incorporating the feedforward compensation and a fractional order switching law. The stability of the closed-loop system is established based on Lyapunov stability approach. Under the FOSMC scheme, the tracking performance and robustness of the PMSM servo system are improved simultaneously in the presence of mismatched disturbance torques and measurement noise. The effectiveness and advantages of the proposed method are demonstrated by the PMSM speed regulation experiments and the comparisons with some existing methods.

Designing an effective electrical model for charged water bodies is of great significance in reducing the risk of electric shock in water and enhancing the safety and reliability of electrical equipment. Aiming to resolve the problems faced in using existing charged water body modeling methods, a practical circuit model of a charged water body is developed. The basic units of the model are simply constructed using fractional-order resistance–capacitance (RC) parallel circuits. The state variables of the model can be obtained by solving the circuit equations. In addition, a practical method for obtaining the circuit model parameters is also developed. This enables the estimation of the characteristics of charged water bodies under different conditions through model simulation. The effectiveness of the proposed method is verified by comparing the estimated voltage and leakage current of the model with the actual measured values. The comparison results show that the estimated value of the model is close to the actual characteristics of the charged water body.

Electric shock protection is critical for ensuring power safety in low-voltage grids, and robust fault diagnosis methods provide an essential foundation for the accurate operation of such protection devices. However, current low-voltage electric shock protection devices often suffer from limitations in operational precision and in their ability to effectively recognize electric shock types. To address these challenges, this paper proposes a fault diagnosis method for low-voltage electric shocks based on an attention-enhanced parallel CNN-BiLSTM model. The method first utilizes CNN to extract local spatial features of the electric shock signal and BiLSTM to capture temporal features. An attention mechanism is then introduced to fuse the local spatial and temporal features with weighted emphasis. Finally, a fully connected layer maps the fused features to the output layer, generating diagnostic results. Visualization through T-SNE analysis validates the improvement in model performance due to the attention mechanism. Comparative experiments show that the proposed model outperforms single models and other combined models in terms of accuracy, precision, recall, F1 score, and convergence speed. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 99.55%.

Objectives Reliable magnetic resonance (MR) spectroscopy (MRS) quantification is key to accurate clinical diagnosis. This study aimed to statistically compare the metabolite quantification of human brain MRS between the deep learning method QNet and the classical method LCModel via an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Methods In this retrospective study, 15 healthy volunteers (12 females and 3 males, age range: 21–35 years, mean age ± standard deviation: 27.4 ± 3.9 years) were recruited. In September and October 2021, two 3 T MRI scanners each collected 61 in vivo 1 H MR spectra from the brain region of pregenual anterior cingulate cortex of the healthy participants. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation, and reasonability, respectively, between the two quantification methods. Results The analyses of Bland-Altman, Pearson correlation and reasonability showed very high to moderate consistency (relative half interval of limits of agreement = 3.04%, 9.31%, and 18.50%, respectively) and very strong to moderate correlation (Pearson correlation coefficient r  = 0.78, 0.93, and 0.47, respectively) between the two methods for quantifying total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins). In addition, the quantification results of QNet were generally closer to the previously reported average values than those of LCModel. Conclusions There were very high to moderate degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally had more reasonable quantification than LCModel.