In order to ensure the stable operation, improve efficiency, and enhance sustainability of wastewater treatment systems, this paper investigates the fault detection problem in wastewater treatment process based on an improved kernel extreme learning machine method. Firstly, a kernel extreme learning machine (KELM) model optimized by an improved mutation bald eagle search (IMBES) optimizer is proposed to generate point predictions of effluent quality parameters. Then, based on the point prediction results, the confidence interval of effluent quality parameters is calculated using kernel density estimation (KDE) method. This interval represents the bounds of system uncertainty and unknown disturbance at normal conditions and can be treated as the threshold for fault diagnosis. Finally, the effectiveness of the proposed method is illustrated by two datasets obtained from the BSM1 wastewater simulation platform and an actual water platform. Experimental results show that compared with other methods such as CNN, LSTM, and IBES-LSSVM, this method has a significant improvement in prediction accuracy, and at the same confidence level, it ensures fault detection rate while generating smaller confidence intervals.

This paper proposes a speech recognition method based on a domain-specific language speech network (DSL-Net) and a confidence decision network (CD-Net). The method involves automatically training a domain-specific dataset, using pre-trained model parameters for migration learning, and obtaining a domain-specific speech model. Importance sampling weights were set for the trained domain-specific speech model, which was then integrated with the trained speech model from the benchmark dataset. This integration automatically expands the lexical content of the model to accommodate the input speech based on the lexicon and language model. The adaptation attempts to address the issue of out-of-vocabulary words that are likely to arise in most realistic scenarios and utilizes external knowledge sources to extend the existing language model. By doing so, the approach enhances the adaptability of the language model in new domains or scenarios and improves the prediction accuracy of the model. For domain-specific vocabulary recognition, a deep fully convolutional neural network (DFCNN) and a candidate temporal classification (CTC)-based approach were employed to achieve effective recognition of domain-specific vocabulary. Furthermore, a confidence-based classifier was added to enhance the accuracy and robustness of the overall approach. In the experiments, the method was tested on a proprietary domain audio dataset and compared with an automatic speech recognition (ASR) system trained on a large-scale dataset. Based on experimental verification, the model achieved an accuracy improvement from 82% to 91% in the medical domain. The inclusion of domain-specific datasets resulted in a 5% to 7% enhancement over the baseline, while the introduction of model confidence further improved the baseline by 3% to 5%. These findings demonstrate the significance of incorporating domain-specific datasets and model confidence in advancing speech recognition technology.

This paper proposes a solution for the cooperative obstacle avoidance path planning problem in dual manipulator arms using an improved Rapidly Exploring Random Tree (RRT) algorithm. The dual manipulator arms are categorized into a main arm and a secondary arm. Initially, the obstacle avoidance path for the master arm is planned in the presence of static obstacles. Subsequently, the poses of the master arm during its movement are treated as dynamic obstacles for planning the obstacle avoidance path for the slave arm. A cost function incorporating a fast convergence policy is introduced. Additionally, adaptive weights between distance cost and variation cost are innovatively integrated into the cost function, along with increased weights for each joint, enhancing the algorithm’s effectiveness and feasibility in practical scenarios. The smoothness of the planned paths is improved through the introduction of interpolation functions. The improved algorithm is numerically modeled and simulated in MATLAB. The verification results demonstrate that the improved RRT algorithm proposed in this paper is both feasible and more efficient.

As global climate change accelerates and fossil fuel reserves dwindle, renewable energy sources, especially wind energy, are progressively emerging as the primary means for electricity generation. Yet, wind energy’s inherent stochasticity and uncertainty present significant challenges, impeding its wider application. Consequently, precise prediction of wind turbine power generation becomes crucial. This paper introduces a novel wind power prediction model, the Wind-Mambaformer, which leverages the Transformer framework, with unique modifications to overcome the adaptability limitations faced by traditional wind power prediction models. It embeds Flow-Attention with Mamba to effectively address issues related to high computational complexity, weak time-series prediction, and poor model adaptation in ultra-short-term wind power prediction tasks. This can help to greatly optimize the operation and dispatch of power systems. The Wind-Mambaformer model not only boosts the model’s capability to extract temporal features but also minimizes computational demands. Experimental results highlight the exceptional performance of the Wind-Mambaformer across a variety of wind farms. Compared to the standard Transformer model, our model achieves a remarkable reduction in mean absolute error (MAE) by approximately 30% and mean square error (MSE) by nearly 60% across all datasets. Moreover, a series of ablation experiments further confirm the soundness of the model design.

Dear Editor, This letter investigates the output tracking control issue of networked control systems (NCSs) with communication constraints and denial-of-service (DoS) attacks in the sensor-to-controller channel, both of which would induce random network delays. A dual-prediction-based compensation control (DPCC) scheme, consisting of a predictive observer and a predictive controller, is proposed to actively compensate for the adverse effect of network delays on NCSs. Compared with existing networked predictive control (NPC) methods, the DPCC scheme only requires the sensor to send a single measurement output to the controller at each sampling instant, and also does not need to know the upper bound of random network delays in advance. The stability condition of the closed-loop system is derived. Finally, numerical simulations are carried out to validate the effectiveness of the proposed scheme.