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The problem of output feedback adaptive tracking control is studied for a class of stochastic nonlinear time-delay systems in which the measured output and input signals are quantized by two sector-bounded quantizers respectively. An observer including the quantized input and output signals is designed to estimate the unknown system states, and the unknown system functions with less restrictions are dealt with by using the neural network’s (NN) approximation. By combining the backstepping technique and the Lyapunov–Krasovskii method, an observer-based adaptive neural quantized tracking control scheme is presented for this class of systems. The stability analysis indicates that the tracking error can converge to a small neighborhood of the origin while all closed-loop signals are 4-moment (or 2-moment) semi-globally uniformly ultimately bounded (SGUUB). Finally, two illustrative examples are provided to demonstrate the feasibility and effectiveness of the proposed design methodology. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of adaptive tracking control for a class of switched strict-feedback nonlinear systems with unknown time-varying delays and asymmetric saturation actuators under arbitrary switching. Especially, the considered time-varying delays absolutely depend on the subsystem number. The main technical difficulties lie in finding an appropriate common Lyapunov function (CLF) for all subsystems and designing a common adaptive control scheme in the presence of unknown time-varying delays and asymmetric saturation nonlinearities. Based on a novel combination of Lyapunov–Razumikhin method, dynamic surface control (DSC) technique, variable separation approach and neural network (NN) approximation, a simple quadratical CLF is constructed and a common adaptive control scheme involving only one adaptive parameter is developed. The proposed controller guarantees that all signals of closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error converges to an adjustable neighborhood of the origin. Finally, the effectiveness of the design methodology is illustrated with two simulation examples. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of adaptive tracking control for a class of uncertain pure-feedback nonlinear time-delay systems with unknown asymmetric saturation actuators. The considered problem is challenging due to the existence of unknown distributed time-varying delays and asymmetric saturation actuator. In particular, the difficulties from distributed time-varying delays and unknown asymmetric saturation nonlinearity are processed by using the mean value theorem for integrals and a Gaussian error function-based continuous differentiable model, respectively. Then, based on a novel combination of mean value theorem, Razumikhin functional method, variable separation technique and Neural Network (NN) parameterization, an adaptive neural controller which involves only one parameter to be updated is presented for such systems via Dynamic Surface Control (DSC) technique. Moreover, the DSC technique can overcome the problem of ‘explosion of complexity’ in the traditional backstepping design. All signals in the closed-loop system remain semi-globally uniformly ultimately bounded (SGUUB), and the tacking error converges to a small neighborhood of the origin. Finally, simulation results are given to verify the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

Detecting Sulphur hexafluoride (SF6) gas leaks using infrared video images is the latest technology. However, the current algorithm for autonomous real-time detection is imprecise and incapable of performing the real-time monitoring task effectively. In response to the aforementioned issues, we present a temporal–spatial feature fusion network (TSFF-Net) specifically designed for video real-time detection of SF6 gas leaks. Compared with the existing SF6 gas leak detection model based on deep learning, the biggest feature of TSFF-Net is the introduction of foreground pixel images with SF6 gas movement characteristics, which greatly enhances the model's ability to detect SF6 gas leaks. Simultaneously, by designing a new attention feature fusion module, introducing the Neck module of motion features, and the Backbone module to enhance small target feature extraction, we achieve high-precision SF6 gas leak detection in various complex scenarios, such as sparse leaks, complex backgrounds, camera shake, etc. The mean average precision (mAP) of TSFF-Net in the validation set was 56.3%, which is 13.2% higher than that of Yolov8s. In addition, the model proposed in this paper has parameters that are only 2.6 M, making it suitable for deployment on mobile or embedded devices. The code and test results are at https://github.com/1jianyao/TSFF-Net/tree/main. • A feature-fusion network is presented for the detection of SF6 gas leaks. • The two-branch features are fused by the attention mechanism. • The Neck module to enhance moving target detection is devised. • The Backbone module to enhance tiny object detection is proposed. [ABSTRACT FROM AUTHOR]

The ultra-short-term forecasting of wind speed is of great significance to the stable power supply of the power system. Current wind speed forecasting methods aim to improve forecasting precision while disregarding model training speed and model deployment complexity. This research proposes a lightweight hybrid model named SLF-EMD-MGM-NS for wind speed forecasting. EMD-MGM is designed as the network's fundamental structure for reducing the hybrid model's training time and ensuring the hybrid model has high forecasting precision. The study presents the switching loss function (SLF) mechanism. When the quantile is 0.5, an MSE-based loss function is employed for training all subsequences. When the quantile is 0.5 and 0.95, first use the wind speed fluctuation threshold to select primary subsequence, and then use the Log-Cosh-based loss function for training primary subsequences. The SLF mechanism can increase point prediction precision and interval prediction boundary stability. Moreover, a novel subsequence superposition (NS) mechanism is proposed for getting high confidence level and narrow-width interval prediction results. The NS mechanism superimposes the interval prediction results of the fluctuation subsequence with the point prediction results of the model to generate the final interval prediction results. According to the experimental results, the SLF-EMD-MGM-NS model has a high confidence level, acceptable prediction results, a narrow-width interval prediction result, and a significantly shorter training time than the other hybrid models. • Proposed EMD-MGM as the base network architecture to do wind speed forecasting. • Three point regression loss functions were extended to wind speed interval regression. • A new mechanism named SLF was proposed to do quantile regression. • A new mechanism named NS was proposed to superpose subsequences' forecasting results. [ABSTRACT FROM AUTHOR]