Many existing control techniques proposed in the literature tend to overlook faults and physical limitations in the systems, which significantly restricts their applicability to practical, real-world systems. Consequently, there is an urgent necessity to advance the control and synchronization of such systems in real-world scenarios, specifically when faced with the challenges posed by faults and physical limitations in their control actuators. Motivated by this, our study unveils an innovative control approach that combines a neural network-based sliding mode algorithm with fuzzy logic systems to handle nonlinear systems. This proposed controller is further enhanced with an intelligent observer that takes into account potential faults and limitations in the control actuator, and it integrates a fuzzy logic engine to regulate its operations, thus reducing system chatter and increasing its adaptability. This strategy enables the system to maintain regulation in the face of control input constraints and faults and ensures that the closed-loop system will achieve convergence within a finite-time frame. The detailed explanation of the control design confirms its finite-time stability. The robust performance of the proposed controller applied to autonomous and non-autonomous systems grappling with control input limitations and faults demonstrates its effectiveness.

Steam coal is the blood of China industry. Forecasting steam coal prices accurately and reliably is of great significance to the stable development of China’s economy. For the predictive model of existing steam coal prices, it is difficult to dig the law of nonlinearity of power coal price data and with poor stability. To address the problems that steam coal price features are highly nonlinear and models lack robustness, Laplacian kernel–log hyperbolic loss–Ridge regression (LK-LC-Ridge-Ensemble) model is proposed, which uses ensemble learning model for steam coal price prediction. First, in each sliding window, two kinds of correlation coefficient are employed to identify the optimal time interval, while the optimal feature set is selected to reduce the data dimension. Second, the Laplace kernel functions are adopted for constructing kernel Ridge regression (LK-Ridge), which boosts the capacity to learn nonlinear laws; the logarithmic loss function is introduced to form the LK-LC-Ridge to enhance the robustness. Finally, the prediction results of each single regression models are utilized to build a results matrix that is input into the meta-model SVR for ensemble learning, which further develops the model performance. Empirical results from three typical steam coal price datasets indicate that the proposed ensemble strategy is reliable for the model performance enhancement. Furthermore, the proposed model outperforms all single primitive models including accuracy of prediction results and robustness of model. Grouping cross-comparison between the different models suggests that the proposed ensemble model is more accurate and robust for steam coal price forecasting.

As a crucial medium of information dissemination, text holds a pivotal role in a multitude of applications. However, text detection in complex and unstructured environments presents significant challenges, such as the presence of cluttered backgrounds, variations in appearance, and uneven lighting conditions. To address this issue, this study proposes a text detection framework that leverages multistage edge detection and contextual information. This framework deviates from traditional approaches by incorporating four primary processing steps, including text visual saliency region detection to accentuate the text regions and diminish background interference, multistage edge detection to enhance the conventional stroke width transform results, a texture-based and connected components-based integration to accurately distinguish text from the background, and a context fusion step to recover missing text regions and improve the recall of text detection. The proposed method was evaluated on two widely used benchmark datasets, i.e., the international conference on document analysis and recognition (ICDAR) 2005 dataset and the ICDAR 2011 dataset, and the results indicate the advancedness of the method.