Different sensors may experience degeneracy in specific scenarios, which can lead to failures in multi-sensor fusion optimization. To address the challenges of localization and mapping under such degeneracy conditions...
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For back electromotive force-based sensorless control in permanent magnet synchronous motor (PMSM) drives, the conventional phase-locked loop (PLL) exhibits a ±π position estimation error during speed reversal. ...
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Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC syst...
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Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the developmen...
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].
Due to scale effects,micromechanical resonators offer an excellent platform for investigating the intrinsic mechanisms of nonlinear dynamical phenomena and their potential *** review focuses on mode-coupled micromecha...
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Due to scale effects,micromechanical resonators offer an excellent platform for investigating the intrinsic mechanisms of nonlinear dynamical phenomena and their potential *** review focuses on mode-coupled micromechanical resonators,highlighting the latest advancements in four key areas:internal resonance,synchronization,frequency combs,and mode *** origin,development,and potential applications of each of these dynamic phenomena within mode-coupled micromechanical systems are investigated,with the goal of inspiring new ideas and directions for researchers in this field.
Belt deviation in circular pipe conveyor systems could lead to material spillage, environmental contamination, reduced efficiency, and accelerated belt wear. Real-time belt deviation detection is crucial for ensuring ...
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This paper studies the energy-based swing-up control of an underactuated soft inverted pendulum, a template model of soft robots with its base attached to the ground and its curvature described by an affine function. ...
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Inverse modeling is extensively applied in the design and tuning of microwave filters (MFs). Inverse models (IMs) take the features extracted from the high-dimensional electromagnetic parameters as input. How to make ...
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In this article, the time-varying formation-containment control problem of heterogeneous multi-agent systems (MASs) with an observer-based state feedback protocol is studied, where both communication delays and output...
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To improve the accuracy of short-term power load forecasting, a short-term power load forecasting method based on Transformer with fused CNN-BiGRU is proposed. First, the best input data sequence is selected using the...
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To improve the accuracy of short-term power load forecasting, a short-term power load forecasting method based on Transformer with fused CNN-BiGRU is proposed. First, the best input data sequence is selected using the Partial Autocorrelation Function (PACF). Next, the importance scores and rankings of the feature data are obtained through the gradient boosting tree algorithm of CatBoost, and the optimal input features are selected. Then, the feature data and load data are combined. Finally, the combined data is used as input for the Transformer fused with CNN-BiGRU. In model training and forecasting, a hybrid forecasting strategy is employed, incorporating elements of multi-step forecasting into single-step forecasting. For the data of each moment, personalized and independent model training is performed, along with forecasting that include hybrid elements. The model replaces the original word embedding and position encoding components of Transformer. It uses CNN-BiGRU to extract high-dimensional feature representations of latent feature information and relative positional information from the input data. The proposed model demonstrates higher forecasting accuracy through validation on two different datasets and comparison with other forecasting models. Additionally, two ablation experiments are conducted. Through systematic ablation experiments, we demonstrate that modifications to the Transformer input layer significantly improve model performance in time series tasks. These results validate the rationality and effectiveness of the proposed approach. The ablation experiments on the method of PACF selecting the optimal input data sequence and CatBoost filtering the optimal input feature data, as well as the hybrid forecasting strategy, further verify the effectiveness and rationality of the data selection methods and forecasting strategies used in this study for short-term power load forecasting. Moreover, to eliminate the zigzagging jitter phenomenon in the foreca
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