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In sensorless control systems of permanent magnet synchronous motors (PMSMs), the traditional linear extended state observer (LESO) is preferred due to its simplicity and ease of implementation. With the development of PMSM sensorless control systems, the requirements for position estimation performance have increased, and thus, traditional LESOs can no longer meet those needs. To address this issue, this article proposes an estimation method based on an integrally compensated-enhanced linear extended state observer (IC-ELESO) and an improved quadrature phase locked loop (IQPLL) with a third-order LESO. In the back electromotive force estimation scheme, by introducing a compensation loop, the proposed IC-ELESO suppresses DC bias and improves position estimation accuracy compared to traditional LESOs. In the position estimation scheme, the IQPLL combines the third-order LESO with a quadrature phase locked loop (QPLL) to eliminate errors introduced by ramp signals. Finally, a PMSM experimental platform is built to conduct a comparative experiment between the method proposed and the traditional LESO, which verifies the feasibility and superiority of the method proposed in this article.

Rolling bearings are one of the most vulnerable parts in rotating machines. This paper presents a novel approach to identify the rolling bearings fault based on variational mode decomposition (VMD) and Mahalanobis distance support vector machine (MDSVM). In this work, since the original vibration signal contains a lot of noise, we use wavelet threshold method to denoise the original vibration signal. The vibration signals are generally non-linear, to extract feature, VMD has been employed to reconstruct signals. When raw signals are decomposed by VMD, according to the center frequency of each decomposed mode, the number of modes is selected. Then we calculate the sample entropy of the decomposed modal component, which is considered as the feature and input of support vector machine (SVM). The Euclidean distance is usually used in the calculation of the Gaussian kernel function of the SVM, which cannot measure the distance between two samples accurately, so we combine the Mahalanobis distance with SVM, construct a Gaussian function kernel based on Mahalanobis distance, and propose a classifier model based on Mahalanobis distance Gaussian function kernel. The model integrates the parameter solutions of the Mahalanobis distance function and the support vector machine into the same framework, which makes full use of the advantages of both and makes it easier to get the solution of the parameters. Finally, all feature vectors are utilized to train improved SVM, with which the fault modes of rolling bearings are identified. The experimental results show that the proposed method has better diagnosing performance.

In high-resolution remote sensing images, there are areas with weak textures such as large building roofs, which occupy a large number of pixels in the image. These areas pose a challenge for traditional semantic segmentation networks to obtain ideal results. Common strategies like downsampling, patch cropping, and cascade models often sacrifice fine details or global context, resulting in limited accuracy. To address these issues, a novel semantic segmentation framework has been designed specifically for large-format high-resolution remote sensing images by aggregating global and local features in this paper. The framework consists of two branches: one branch deals with low-resolution downsampled images to capture global features, while the other branch focuses on cropped patches to extract high-resolution local details. Also, this paper introduces a feature aggregation module based on the Transformer structure, which effectively aggregates global and local information. Additionally, to save GPU memory usage, a novel three-step training method has been developed. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed approach, with an IoU of 90.83% on the AIDS dataset and 90.30% on the WBDS dataset, surpassing state-of-the-art methods such as DANet, DeepLab v3+, U-Net, ViT, TransUNet, CMTFNet, and UANet.

In order to simultaneously improve system performance and resource utilization of distributed multiple-input multiple-output (MIMO) radar systems, a joint resource allocation method is proposed to address the velocity estimation problem for multiple targets tracking in this paper. The paper focuses to improve the tracking performance for key targets using the remaining resources when the general targets have obtained resources to reach to tracking requirements. Firstly, a criterion minimizing the velocity estimation mean square error (MSE) for a key target is considered. Restricted by limited and relatively sufficient system resources and given velocity estimation requirements for general targets, a joint resource allocation optimization model with transmitters, receivers, transmitted power, and signal time is established. We propose a suboptimal method to approximately solve this problem. The method separates the optimization into three steps, where each step transforms the corresponding mixed-Boolean optimization problem into a second-order cone programming (SOCP) problem by convex relaxation. Finally, the approximately optimal solution can be obtained by cyclic minimization method. Extensive simulations indicate that compared with other methods, the proposed joint method can achieve the lowest velocity estimation MSE with the fewest transmitters. Meanwhile, limited by the given velocity estimation MSE, the proposed method can focus on the key target and achieve the whole velocity estimation error minimization while a greater flexibility for target tracking number can be obtained. Moreover, random experiments can further validate and evaluate the proposed method’s effectiveness and traceability with the given scenario.

BaZr 0.1 Ti 0.89 Fe 0.01 O 3 ceramics sintered at 1240–1340 °C were prepared by solid-state method. The sample underwent phase transition from rhombohedral to orthorhombic phase, which changed the internal stress. Increasing the sintering temperature promoted grain growth (from 0.44 to 1.87 μm). The dielectric constant ( ε  = 4300) and diffusion coefficient ( γ  = 1.927) of the sample were improved significantly. The improvement mechanism of dielectric constant and dispersion phase transition was explained by the internal stress model and 90° domain structure in detail.

The completeness of landslide inventories and the selection of evaluation models significantly impact the accuracy of landslide susceptibility assessments. Conventional field geological survey methods and single remote-sensing technology struggle to reliably identify landslides under complex environmental conditions. Moreover, prevalent landslide susceptibility evaluation models are often plagued by issues such as subjectivity and overfitting. Therefore, we investigated the uncertainty in susceptibility modeling from the aspects of landslide inventory quality and model selection. The study focused on Luquan County in Yunnan Province, China. Leveraging multisource remote-sensing technologies, particularly emphasizing optical remote sensing and InSAR time-series deformation detection, the existing historical landslide inventory was refined and updated. This updated inventory was subsequently used to serve as samples. Nine evaluation indicators, encompassing factors such as distance to faults and tributaries, lithology, distance to roads, elevation, slope, terrain undulation, distance to the main streams, and average annual precipitation, were selected on the basis of the collation and organization of regional geological data. The information value and two coupled machine-learning models were formulated to evaluate landslide susceptibility. The evaluation results indicate that the two coupled models are more appropriate for susceptibility modeling than the single information value (IV) model, with the random forest model optimized by genetic algorithm in Group I2 exhibiting higher predictive accuracy (AUC = 0.796). Furthermore, comparative evaluation results reveal that, under equivalent model conditions, the incorporation of a remote-sensing landslide inventory significantly enhances the accuracy of landslide susceptibility assessment results. This study not only investigates the impact of landslide inventories and models on susceptibility outcomes but also validates the feasibility and scientific validity of employing multisource remote-sensing technologies in landslide susceptibility assessment.

As one of the most promising CO 2 utilization techniques, electrochemical CO 2 reduction has recently received considerable attention. Cu is a unique electrocatalyst that can convert CO 2 to value-added multi-carbon chemicals. Nevertheless, Cu catalysts are always limited by the poor selectivity and stability. Here, we report that using copper-tetracyanoquinodimethane (CuTCNQ) derived Cu nanoparticles as efficient electrocatalysts for conversion of CO 2 to ethylene characteristic with high selectivity and stability, showing 56% Faradaic efficiency (FE) to C 2 H 4 at −1.3 V vs. reversible hydrogen electrode (RHE). Upon the electrochemical CO 2 reduction, CuTCNQ slowly reconstructs to Cu nanoparticles with abundant grain boundaries and residual Cu + on the surface. Theoretical calculation and operando characterization disclose that both as-formed Cu nanoparticle grain boundaries and residual Cu + endow the catalyst with high selectivity toward ethylene. Furthermore, during the reconstruction of CuTCNQ to Cu nanoparticles, the grain boundaries Cu surface is slowly refreshed by continual addition of Cu atoms, thus inhibiting the surface passivation and guaranteeing the electrocatalytic stability.

Chance constraints, unlike robust constraints, allow constraint violation up to some predefined level and arise in numerous applications. They are often imposed in a pointwise-in-time fashion in control problems. This thesis considers a class of chance constraints imposed in an average-in-time fashion to focus more on aggregate behaviours and discounted to achieve trade-offs between short-term and long-term performance in the model predictive control (MPC) framework. This thesis designs an MPC law for chance constrained stochastic systems with discrete-time linear dynamics and possibly unbounded additive disturbances. The chance constraint is defined as a discounted sum of violation probabilities over an infinite horizon. By penalising violation probabilities close to the initial time and assigning violation probabilities in the far future with vanishingly small weights, this form of constraints allows for an MPC law with guarantees of recursive feasibility by introducing an online constraint-tightening technique without an assumption of boundedness of the disturbance. We employ Chebyshev's inequality for constraint handling and formulate a computationally simple MPC optimisation problem. To mitigate the conservativeness of Chebyshev's inequality, a dynamic feedback gain is incorporated into the MPC law. This gain is selected online from a set of candidates generated by Pareto optimal solutions of a multiobjective optimisation problem. The closed loop system is guaranteed to satisfy the chance constraint and a quadratic stability condition. With dynamic feedback gain selection, the closed loop cost is reduced and a larger set of feasible initial conditions is obtained. This thesis also considers an application of stochastic MPC in networked control systems, where constrained linear systems are subject to stochastic additive disturbances and noisy measurements transmitted over a lossy communication channel. An MPC controller is designed to minimise a discounted cost subject to a discounted expectation constraint. Sensor data is assumed to be lost with a known probability. Data losses are accounted for by expressing the predicted control policy as an affine function of future observations, resulting in a convex optimal control problem. Recursive feasibility of online optimisation problems and constraint satisfaction are ensured similarly via the constraint-tightening technique. We show that the discounted cost evaluated along trajectories of the closed loop system is bounded. Under certain conditions, the averaged undiscounted closed loop cost accumulated over an infinite horizon also remains bounded.

Due to the fast response, high precision and high working frequency, needle driven piezoelectric micro-jet devices have been applied in various industrial fields. The injection performance is important for the applications. The jetting velocity of the micro-droplets has a great influence on the ligament length and satellites. In this paper, the Fluent stimulation model of dynamic mesh motion has been established to simulate the jetting process. Meanwhile, the dependences of volume and jetting velocity of micro-droplets on input signal, including voltage, falling time, and fluid pressure, have been studied. The results show that the jetting velocity of micro-droplets can be changed and the volume is invariant by adjusting falling time, which can be used to guide the control of the needle driven piezoelectric micro-jet devices. Furthermore, the experimental system has been designed and the experiments show that the average minimum jetting velocity is about 0.724 m/s. The ligament length increases almost linearly with jetting velocity of the micro-droplets. And the optimal jetting velocity is less than 2.745 m/s, which cannot generate satellites.

The BaZr 0.2 Ti 0.8 O 3 ceramics with perovskite structure were prepared by solid state reaction method with addition of x La 2 O 3 and x La 2 O 3  + 0.2 wt% Sm 2 O 3 (x = 0.0, 0.1 and 0.4 wt%). Microstructure and dielectric behaviour of the obtained ceramics were respectively investigated. The compositions of these ceramics demonstrated a single-phase cubic symmetry in a room-temperature X-ray diffraction study. The dielectric constant peak of those samples with addition of x La 2 O 3 and x La 2 O 3  + 0.2 wt% Sm 2 O 3 greatly reduced along with increasing x. Simultaneously, a drastic increase of the values of γ was also observed when x rose, exhibiting a diffuse phase transition. T m increased along with increasing La content for x La 2 O 3 doped BZT20 ceramics, but decreased along with increasing La content for x La 2 O 3  + 0.2 wt% Sm 2 O 3 doped BZT20 ceramics. Owing to the doping of Sm 3+ , the x La 2 O 3  + 0.2 wt% Sm 2 O 3 doped BZT20 ceramics have maintained very low and stable dissipation factors under an increasing environment temperature, making them superior candidates for applications.