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In the field of remote sensing, it is not feasible to obtain high spatial resolution multispectral (HRMS) images from a single satellite sensor. The existing methods use pansharpening techniques to obtain HRMS images by fusing panchromatic (PAN) and multispectral (MS) images. However, due to the scale difference between PAN and MS images, most pansharpening methods often use explicit sampling methods to integrate features at different scales. These explicit-based sampling techniques represent pixels as discrete points through predefined functions, rendering it challenging to fit the distribution among diverse modal data, this results in the loss of image texture details during the fusion process. Implicit neural networks can enhance the generative capability of images by incorporating pixel coordinate information, which is crucial for the fusion of remote sensing images with different spatial resolutions. Inspired by implicit neural representation, we propose a progressive implicit neural feature fusion network (PINFNet) for remote sensing images. A progressive implicit neural feature fusion is proposed; it establishes a coordinate modal relationship between the spatial and spectral information through the guidance of the high spatial features in PAN images. This enables the proposed PINFNet to progressively learn and integrate spatial and spectral information at different scales. Our method, as opposed to discrete sampling techniques, is capable of establishing a continuous representation between diverse modal data, which in turn preserves more texture detail information. Extensive experiments have shown that this approach outperforms state-of-the-art methods while maintaining high efficiency.

With the rising adoption of renewable energy sources worldwide, the uncertainty within electrical power systems is amplifying, leading to rising challenges in wind power assimilation and operational costs. In addressing these pressing issues, this research emphasizes a sophisticated modeling technique for the energy-intensive electro-fused magnesium loads on demand. Furthermore, we introduce a coordinated scheduling approach for power systems, harnessing the regulatory capabilities of these loads. Initially, an in-depth analysis of the characteristics of the electro-fused magnesium energy-intensive load is carried out, from which a refined model is developed. Subsequently, considering the flexible regulatory potential of the electro-fused magnesium furnace apparatus, a bi-level optimization scheduling model is crafted, prioritizing the maximization of wind power absorption at the upper level and minimizing system operational costs at the lower level. The efficacy and rationale of the proposed methodology are corroborated through computational analyses.

Sufficient and stable positioning torque is necessary for the parking of an electric vehicle driven by switched reluctance motor (SRM). Considering the highly nonlinear torque characteristic of SRM, the parking control with high stability and power consumption economy is an important issue. This paper presents an optimized parking control method for an electric vehicle driven by SRM to achieve optimal parking stability and power consumption economy simultaneously. The static torque-angle characteristic curve of a four-phase 16/12 SRM is analyzed, based on which the optimized region of parking position (presented by rotor angle) is obtained considering the uncertainty of parking conditions. To ensure the vehicle is properly parked in the optimized position region, active disturbance rejection control (ADRC) is utilized to achieve the smooth switch from zero-speed control to parking position control. Simulation and experimental results verify that the proposed method can better stabilize the parking on the ramp and reduce power consumption in all conditions.

In the field of remote sensing, image fusion technology plays a crucial role in observing the state of global resources and environmental conditions, proposing response strategies, and constantly monitoring and correcting strategies. Currently, the majority of traditional methods exhibit varying degrees of spatial or spectral distortion, and these unreasonable spectral distributions may contain erroneous geographical feature information. Meanwhile, despite their performance in fusion results, deep learning-based methods cannot be applied to some practical application scenarios due to the requirement for hardware specifications resulting from a large number of parameters in their models. These issues are not conducive to accurately reflecting the actual geomorphic resource conditions or promoting sustainable development. In order to address the above issues, we propose a novel recursive self-attention module (RSAM), which consists of two stages: spatial-spectral similarity extraction and self-attention weight generation. The proposed RSAM employs a global-to-local strategy to capture the global interdependencies of two distinct local locations in the feature map. This method allows for simultaneous consideration of both spatial and spectral information while focusing on more mutual information between spectral and spatial dimensions. Subsequently, we construct the corresponding recursive self-attention residual block (RSARB) through RSAM and concatenate the RSARBs to generate a recursive self-attention module-based network (RSANet) with a limited number of parameters. Extensive experiments demonstrate that RSANet achieves superior results in both qualitative and quantitative evaluation despite the model parameters being within a narrow range of orders of magnitude. This demonstrates that the proposed method possesses robust feature learning capability and practicality for observing and studying the global resource environment.

Onboard real-time processing of remote sensing satellites is an important means of rapidly obtaining information, and the fusion processing of panchromatic and multispectral data is of great significance for optical satellites. In order to ensure the effect, most traditional algorithms perform statistical analysis or transformation on the entire image first and then perform subsequent processing. There are problems such as high algorithm complexity and resource occupation, and it is difficult to apply to onboard scenarios where the volume and power consumption are strictly limited. Aiming at the requirements of onboard fusion, a real-time processing approach for high-resolution optical satellites is proposed. First, through the implementation of real-time geometric positioning, ROI extraction is completed while the camera is imaging, avoiding the disadvantages of traditional methods for processing large amounts of data; then, based on the principle of object-space consistency, by fine-tuning virtual sensor parameters, the registration of a panchromatic multispectral image is completed in the sensor correction step, so that the relative accuracy of the two can meet the fusion requirements, and time-consuming pixel-level registration processing is avoided; finally, according to the characteristics of the algorithms and embedded hardware, an efficient algorithm mapping strategy is formulated, and deep optimization is implemented to achieve a significant improvement in performance. Experiments show that the performance of this method is improved by 156.23 times compared with the traditional method. Moreover, after building a parallel pipeline, it can meet the real-time fusion processing requirement of completing a 5000 × 5000 pixels ROI area every 2.4 s.

In this paper, a six-phase $\text{N}+2$ power converter for switched reluctance motor is studied. An open-circuit fault diagnosis and faulty devices location scheme based on midpoint current spectrum analysis is proposed. In order to realize fault diagnosis, five fault cases are distinguished, including single-phase fault, multiphase fault and midpoint switch fault. Taking the phase current frequency as the fundamental frequency, the DC component, the fundamental frequency component, and the second harmonic component have different change rules in the five fault cases. The third harmonic component is used as the denominator, three spectrum coefficient could be obtained and be used as the fault characteristic. Through this scheme, using a single current sensor to detect the midpoint current can ensure open-circuit faults accurately. After the fault case is confirmed, the amplitude of the harmonic component is used to define a fault location threshold, and the threshold is numerically compared with the midpoint current to realize the faulty bridge arm location. Finally, the complete process of fault diagnosis and location is described, then the effectiveness of the proposed diagnosis method is validated by the simulation and experiment.

A feature-tracking algorithm algorithm utilizing the proposed K-nearest-neighbor vector field consensus (KVFC) to filter outliers is developed to monitor the dynamic changes of sea ice retrieval from synthetic aperture radar (SAR) images. The KVFC is based on vector field consensus and combines with local neighborhood correspondences to optimize the elimination of outliers while retaining inliers as many as possible. The proposed KVFC was evaluated and compared with several algorithms on three standard datasets and Sentinel-1 image pairs in the Fram Strait and the Beaufort Sea. The KVFC obtained more sea-ice drift vectors than the nearest neighbor similarity ratio (NNSR) with a 0.7 threshold and generated dense distribution of sea-ice drift vectors combined with the HH and HV channels. Using buoy datasets to calculate the sea-ice drift speed and evaluate algorithm performance, the proposed approach yielded a lower mean error (KVFC: −0.150 cm/s, NNSR: 0.407 cm/s), lower root mean square error (KVFC: 0.476 cm/s, NNSR: 1.817 cm/s), and lower angle deviation (KVFC: 3.542°, NNSR: 10.318°) compared to the NNSR.

As a matter of fact that the network traffic has a great influence on the binary exponential backoff mechanism, duty cycle mechanism, and fixed transmission power mechanism adopted by the Sensor Medium Access Control (S-MAC) protocol. The paper proposes an Adaptive Backoff and Duty Cycle for S-MAC protocol (ABDC-SMAC). In the backoff mechanism, considering the failure times of channel competitions, the length of buffer queue, and the node residual energy across the board, a cross-control backoff mechanism is proposed. In the duty cycle mechanism, the network traffic is judged by the average network flow factor. Based on the average network flow factor, the value of duty cycle can change around its initial value. In the power control mechanism, the minimum transmission power can be obtained by the Friis formula. At the same time, the exponential formula following the change of network traffic is selected and multiplied by the minimum transmission formula. The simulation results show that the performance of proposed ABDC-SMAC protocol is improved obviously on energy consumption and transmission delay.

As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. Compared with the traditional neural network, the spiking neural network (SNN) has better biogenesis and stronger computing power. In this paper, an SNN learning model based on an evolutionary membrane algorithm is proposed to solve the problem of supervised classification. The proposed algorithm uses the P system's object, reaction rules, and membrane structure to solve these problems. Specifically, the proposed algorithm can automatically adjust the learning parameters of the network by adjusting the synaptic weight in the learning stage of the spiking neural model according to different application data, providing a better solution model for balance exploration and exploitation. In the simulation experiment, effectiveness verification research is carried out. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving twelve supervised classification benchmark problems through learning curves and quantified classification results.

In order to improve the harmonic compensation performance of active power filter (APF) in distribution network, based on deadbeat control theory, the command current prediction algorithm and current tracking control strategy are optimized in this article. Firstly, the command current repetitive prediction in abc coordinate system is transferred to dq for improving its accuracy in lead compensation, and the equivalent for fractional delay beat is achieved by Lagrange Interpolation Polynomial to solve the problem of inaccurate prediction caused by grid frequency fluctuation. Then, considering the inherent half-sampling-period delay of sinusoidal PWM (SPWM), an improved deadbeat control strategy for current tracking is proposed by estimating the output current of next sampling period. Because the output current in next sampling period is replaced by that in current sampling period with traditional deadbeat control strategy, this estimation could make up for the defect of low control precision caused by that replacement. After that, adding error repetitive correction into the improved deadbeat control channel to reduce the periodic tracking error of output current. Finally, the stability and accuracy of the improved control system are analyzed theoretically, and its feasibility and effectiveness are verified by the simulation and hardware-in-the-loop (HIL) experiments.

The doubly salient electromagnetic machine (DSEM) has simple and reliable structure while suffering from the problem of torque ripple due to the salient pole structure. In order to improve the torque performance, this paper proposes a collaborative optimization method of harmonic current injection and proportion of the armature magnetomotive force (MMF) and field MMF based on the optimized rotor pole arc coefficient. The theory of torque generation and the influence of rotor pole arc coefficient on torque characteristics are analyzed. The relationship between the copper loss and the total MMF is investigated. The influence of MMF proportion on the average torque and torque ripple is studied. The effect of harmonic current injection on torque performance is further analyzed on the basis of the optimized rotor pole arc coefficient. Finally, the 12/10-pole DSEM prototype is developed and the experiment is conducted to verify the analysis.

The basis of visual measurement is camera calibration. In the traditional calibration method, the radial and tangential distortion models usually are adopted. Because of the randomness of lens distortion, the above fixed form distortion model cannot accurately express the distortion distribution. To solve the above problems, a calibration method with a new distortion model is presented in the paper. First, an exact linear model is obtained, using only the corner coordinates of the image center region; then, using this model, the projection deviation of all corner points in the pixel plane can be obtained, that is, the point cloud of projection deviation distribution; finally, the Kriging interpolation method is used to obtain a continuous projection deviation distribution function which can accurately express lens distortion in the pixel plane. Using this function and the corresponding linear model, all two-dimensional image points can be accurately projected into three-dimensional space. To compare with the traditional method, the mean error of projection and measurement error are calculated in the experiment, and the experimental results show that the calibration method is more accurate and more suitable for measuring requirements.

On account of that nine-switch inverter (NSI) cannot supply symmetrical voltages to unbalanced loads, based on the NSI, N-phase leg is added to the neutral points of the loads, which forms a four-leg nine-switch inverter (FLNSI). The neutral point voltages under unbalanced loads are analysed and derived by space vector pulse width modulation (SVPWM) strategy for calculating the duty ratio of N-phase leg. While the calculations of other three-phase legs duty ratios of SVPWM method remain unchanged. Although SVPWM method can realize FLNSI outputs symmetrical voltages for unbalanced loads, it needs trigonometric function calculation and sector judgements. The carrier-based pulse width modulation (CBPWM) strategy is proposed for the sake of simplifying SVPWM. All of the duty ratios are used to calculate modulation waves of FLNSI. Compare the modulation waves with a carrier, which can control the neutral point voltages as corresponding zero-sequence voltages. Therefore, two groups of three-phase symmetrical sinusoidal voltages are obtained under both balanced loads and unbalanced loads. Finally, the experimental results are given to show the advantages of FLNSI topology and to validate of the effectiveness of the applied modulation strategy.

With the rapid development of industrialization, the proportion of electric arc furnaces (EAF) in distribution networks is getting higher and higher. Aiming at the problem that grid voltage is distorted by a large number of arc furnace nonlinear loads accessing to distribution networks, it is crucial to make dynamic analysis of distribution network voltage and adopt a control strategy of grid-connected converter based on the new phase-locked loop (PLL) technology. As a result, the harmonic distortion rate is reduced and the quality of grid-connected current and voltage is improved. In this paper, photovoltaic (PV) system model and the typical dynamic model of the EAF are established. By analyzing the influence of the EAF model on the PV grid-connected converter with the traditional phase-locked loop while connected to distribution network, a control strategy of the PV grid-connected converter with the self-adjusting double SOGI (MAF-SASOGI) phase-locked loop with the ideal low-pass filter is proposed. Through the simulation analysis, it is verified that precise phase locked can be realized and harmonic content of the system can be reduced by the PLL strategy proposed in this paper. Therefore, the quality of PV grid-connected voltage and current is improved.

In the medium-voltage AC/DC distribution networks with distributed renewable sources on islands, a multi-port receiver is the key factor for hybrid power conversion. However, the most used modular multilevel converter (MMC)-based multi-port converters (MCs) face the coordination and complexity challenges due to their double-stage control system and voltage-balancing control of capacitors. In particular, the control system is more unstable and complicated when the control of circulating currents is considered. In this paper, an isolated modular multilevel converter (I-MMC) is used as a receiver, and a unified coordinated control scheme based on the multiple modulation freedoms is proposed. Due to the voltage clamping of high-frequency transformers, there is no concern of the capacitors' voltage-balancing control. Based on the proposed single-stage control system, the unified coordinated control scheme solves the coordination problem of the MMC-based MCs. The multiple modulation freedoms corresponding to an AC port, two DC ports, and three-phase circulating currents can independently control respective targets. The control structure is simplified, while the control freedoms are ensured. Experimental results confirming the performance of the designed control system is shown.

Recent advances in evolutionary algorithms based on membrane computing have shown that the mechanism of membrane computing is an effective way to solve optimization problems. In this work, we propose a new evolutionary multi-objective algorithm that uses membrane systems to solve multi-objective optimization problems. Based on the mechanism of living cell structure and function, the algorithm introduces three factors, including membrane structure, multiset and reaction rules. The membrane structure of the proposed algorithm is inspired by the structure of the membrane system, which has multiple layers and nested structures in the skin membrane. Two special symbol-objects are designed to improve the search efficiency of the algorithm. In addition, some reaction rules are used to evolve the symbol-objects of multiset in the inner region of the membrane. In addition, the proposed method combines external archive to maintain the diversity of non-dominated solutions and enhance the search capabilities of the solutions. Our proposed method is compared to five state-of-the-art multi-objective heuristic algorithms. For comparison, six different criteria were used: the quality of the resulting approximate set, the diversity of candidate solutions, and the rate of convergence to the Pareto front. Experimental results show that the proposed method is competitive in performance in qualitative and quantitative measurements of selected test functions. Therefore, the algorithm is feasible and effective for solving multi-objective optimization problems.

In this paper, a novel N+2 power converter is introduced and be applied to a six-phase switched reluctance motor (SRM) drive system. Compared with six-phase asymmetric half bridge (AHB) converter, the number of power devices required by this N+2 converter is greatly reduced, while the independence and controllability of each phase can be ensured. However, in N+2 converters, the two midpoint switches are also important components and working at much higher switching frequency than phase switches. The value of the midpoint current directly affects the heating problem of these midpoint switches then affects the reliability of the N+2 converter. Hence, different operate modes of multi-phase excitation state of N+2 converter are enumerated to obtain the relation between the midpoint current and phase currents. Then the characteristics of the midpoint current under different control parameters were obtained through MATLAB-Simulink, and the parameter optimization rules for reducing the amplitude of midpoint current are summarized. In addition, the characteristics of midpoint current in several typical fault states are also summarized. Finally, the simulation results are verified by experiments.

Dynamic states (e.g., voltage sags and swells) in the power grid can cause faults and defects in sensitive loads and in consequence financial losses. This paper deals with a flexible transformer based on bipolar direct AC/AC chopper (BD-AC). It is a combination of BD-AC and traditional power frequency transformer, and it can realize the flexible regulation of voltage amplitude and phase angle respectively according to voltage requirements. Firstly, the topology and modulation strategy of the selected BD-AC are analyzed. On this basis, the flexible transformer system topology based on BD-AC and transformer is proposed. Then, based on the structure of flexible transformer system, its working principle is analyzed in detail, and the regulation range of voltage amplitude and phase angle are quantitatively studied. Furthermore, considering the principle of voltage regulation, a triangle angle theory based control strategy is proposed. Finally, the correctness and effectiveness of the theoretical analysis of the proposed flexible transformer system are carried out by detailed computer simulation.

Applying the fault diagnosis techniques to power electronic equipments is beneficial to improve the stability and reliability of renewable energy system, because power electronic equipments are a key component of renewable energy system that largely defines their performance. This paper presents a novel intelligent fault diagnosis method for a three-phase power-electronics energy conversion system based on knowledge-based and data-driven methods. Firstly, the three-phase AC currents of the power-electronics energy conversion system are collected and used to analyze. Secondly, the feature transformation, a knowledge-based method, is utilized to preprocess the fault data. After feature transformation, the slopes of current trajectories (transformed features) are not affected by different loads. And then random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier with the processed fault data. Finally, the proposed method is implemented online on an actual three-phase PWM rectifier platform. The results show that the proposed fault diagnosis method can successfully detect and locate the open-circuit faults in IGBTs of the three-phase PWM rectifier. In addition, the proposed method is suitable for most of three-phase power-electronics energy conversion systems.

The load resisting capacity of structural members will decrease when they are subjected to long-term sustained load. Such phenomenon is widely known as the duration of load effect, which is mainly caused by the damage accumulation in the material. The deterioration mechanism of the material is a typical stochastic process which is influenced by a large variety of parameters involving complex physical and chemical process. Although classical models have been proposed to evaluate the duration of load effect, it is nearly impossible to quantify the influence of various parameters and to achieve an accurate estimation. To optimize the combination of complexity and goodness-of-fit, a neural network model is proposed in this paper to evaluate the duration of load effect in wood structural members. Taking individual uncertainties into consideration, the proposed model treats the damage in wood as a Markov process and can estimate the residual strength distribution of the investigated wood structural members under long-term sustained load. The coefficient of determination reaches above 95% under sustained loading scenario, and it shows good adaptability across different wood properties. Moreover, the model can be adapted to continuously varied loading scenarios with a 98% coefficient of determination. This research aims to provide a useful and straightforward tool for accurately predicting the duration of load effect in wood structural members, and the proposed algorithm can be easily modified to deal with similar engineering problems for other construction materials.

The effect of the winding connections on the static torque in six-phase switched reluctance motors (SRMs) is investigated in this paper. Different winding connections for the six-phase SRMs are shown and compared. Under the conditions of different winding connections, the self-inductance and mutual inductance of a six-phase SRM are compared and analyzed. Six-phase SRMs can be running in two-phase excitation mode and three-phase excitation mode, and in these two modes, the static torque characteristics with different poles polarities arrangements are compared and analyzed. The comparison of the static torque in a six-phase SRM with different winding connections is obtained by the Maxwell 2D and experiments. Based on the results, an optimal winding connection mode for starting stage of the six-phase SRMs is proposed.

A continuous globally stable control algorithm is presented to track angular velocity for spacecraft chaotic attitude motion affected by external disturbances using adaptive variable structure controller. Affected by some external disturbances, the spacecraft attitude dynamics system can generate many types of chaotic motion. Once it is required that a spacecraft with chaotic attitude motion should track the other spacecraft chaotic attitude plant to achieve angular velocity synchronization, the design of a robust tracking controller becomes necessary. The controller design is based on adaptive control theory and variable structure control theory, and adopts integral sliding surface and a single vector adjusted dynamically. Numerical simulations are performed to demonstrate the effectiveness and feasibility of the proposed adaptive variable structure controller.