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RGB-T Salient object detection (SOD) is to accurately segment salient regions in both visible light images and thermal infrared images. However, most of existing methods for SOD neglects the critical complementarity between multiple modalities images, which is beneficial to further improve the detection accuracy. Therefore, this work introduces the MSEDNet RGB-T SOD method. We utilize an encoder to extract multi-level modalities features from both visible light images and thermal infrared images, which are subsequently categorized into high, medium, and low level. Additionally, we propose three separate feature fusion modules to comprehensively extract complementary information between different modalities during the fusion process. These modules are applied to specific feature levels: the Edge Dilation Sharpening module for low-level features, the Spatial and Channel-Aware module for mid-level features, and the Cross-Residual Fusion module for high-level features. Finally, we introduce an edge fusion loss function for supervised learning, which effectively extracts edge information from different modalities and suppresses background noise. Comparative demonstrate the superiority of the proposed MSEDNet over other state-of-the-art methods. The code and results can be found at the following link: https://github.com/Zhou-wy/MSEDNet. • Proposing three novel modules for effective cross-modal information fusion. • Presenting a novel loss function for precise refinement of local features. • Achieving excellence in RGB-T salient object detection benchmarks with MSEDNet. [ABSTRACT FROM AUTHOR]

Aiming at the problem of fault point temperature reading and positioning, target device segmentation, and fault point matching fusion in infrared warning system of substation inspection robot, MUL‐PCNN and ASURF are applied to realizing accurate fusion between the fault area in infrared image and visual image. The experimental results show that this method has certain value in the aspect of infrared image segmentation and fusion of substation. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is an important part in the energy management of micro-grids, and the forecasting error directly affects the economic efficiency of operation. Considering the strong randomness and high volatility of loads on the user side of micro-grids, a short-term load forecasting method based on Empirical Mode Decomposition (EMD), Extreme Learning Machine with different Kernels, and Switching Delayed Particle Swarm Optimization (SDPSO) is proposed. First, the history load dataset is decomposed into several independent Intrinsic Mode Functions (IMFs) by EMD, and the sample entropy values of the IMFs are calculated. According to the approximation degree of sample entropy values, the IMFs are divided into three categories. Then, ELM with different kernels is adopted to forecast the three categories. Finally, the prediction results are summed to obtain the final prediction result. SDPSO is used to optimize the relevant parameters in the forecasting model. Three micro-grids with different capacities are used to verify the model, and the experimental results demonstrate that the proposed forecasting model can provide better accuracy than the other two methods. The proposed model can provide practical reference for the efficient operation of micro-grids. [ABSTRACT FROM AUTHOR]

In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of thermal power units, which improves unit load operation and constitutes a novel, closed-loop AGC structure based on online control performance standard (CPS) evaluations. Intelligent predictive control is mainly improved because the neural network rolling optimization model replaces the traditional rolling optimization model in the rolling optimization module. The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under CPS can, on the one hand, improve the load tracking performance of AGC thermal power units, and, on the other hand, the controller has strong robustness. Whether the system parameters change considerably or the AGC has different grid disturbances, the new type of the loop AGC system can still sufficiently meet the control requirements of the power grid. [ABSTRACT FROM AUTHOR]

This paper proposes an effective method to estimate the state of health (SOH) of a lithium-ion battery based on the ohm internal resistance R0. Unlike other estimation methods, this work considers the variation of R0 with the state of charge (SOC). The improved unscented particle filter (IUPF) is presented to track and predict R0. That is, an unscented Kalman filter (UKF) is used to generate an importance probability density function in the particle filter, and a method to select the fittest particle in the resampling stage is proposed. Based on the experimental data, a second-order resistance-capacitance equivalent circuit model is set up and the parameters are identified. To verify the accuracy of the proposed method, UKF and IUPF are compared in the prediction of R0 at different SOC points under the same cycle and at the same SOC point of different cycles. The results show that IUPF has certain advantages, and the SOH estimation error is always less than 3% during the charge-discharge stage. [ABSTRACT FROM AUTHOR]

In this study, neuro‐fuzzy‐based identification procedure for Hammerstein model with coloured noise is presented. Separable signal is used to realise the decoupling of the identification of dynamic linear part from that of static non‐linear part, and then correlation analysis method is adopted to identify the parameters of the linear part. Furthermore, by combining multi‐innovation and gradient search theory, multi‐innovation‐based extended stochastic gradient approach is derived for improving the parameters estimation accuracy of the non‐linear part and the noise model. In addition, the convergence analysis in the martingale theory illustrates that the parameter estimation error will converge to zero under the persistent excitation condition. Finally, two simulation results demonstrate that the proposed approach has high identification accuracy and good robustness to the disturbance of coloured noise. [ABSTRACT FROM AUTHOR]

In this paper, a neuro-fuzzy based identification procedure for Hammerstein output error model with colored noise is presented. Separable signal is used to realize the decoupling of the identification of dynamic linear part from that of static nonlinear part, and then correlation analysis method is adopted to identify the parameters of the linear part. Next, a filter is embedded to form extended Hammerstein model to calculate the noise correlation function by the information of zeros and poles of the extended model. The correlation functions which consist of the noise correlation function are applied to compensate the bias caused by colored noise. As a result, the parameters of the nonlinear part can be identified through recursive least square method. Examples results illustrate that the proposed approach has high identification accuracy and good robustness to the disturbance of colored noise. [ABSTRACT FROM AUTHOR]

• Considering the problems of constraints, nonlinearity and parameter uncertainties, we add a robust iterative learning controller into 2D system. Then integrated control methods are proposed. • The analyses of robust convergence and tracking performance based on 2D system are given in this paper. • The proposed control strategy can effectively improve the robustness of the system. This paper studies the problem of integrated control in the 2-dimensional (2D) system with parameter uncertainties for batch processes. An integrated iterative learning control (ILC) strategy based on quadratic performance for batch processes is proposed. It realizes comprehensive control by combining robust ILC in batch-axis with model predictive control (MPC) in time-axis. The design of quadratic-criterion-based ILC for the system can be converted into a min-max problem. Then a model predictive controller with time-varying prediction horizon is designed based on a quadratic cost function. For an uncertain model, a novel integrated robust ILC scheme based on a nominal model is further proposed. As a result, the control law of the 2D system can be regulated during one batch, which leads to good tracking performance and strong robustness against the disturbance and the uncertainties. Moreover, the analyses of the convergence and tracking performance are given. The proposed methods are applied to batch reactor, and results demonstrate that the system has good robustness and convergence. This paper provides a new way for batch processes control. [ABSTRACT FROM AUTHOR]