This paper focuses primarily on adaptive dynamic programming (ADP)-based tracking control of the hydraulic-driven flexible robotic manipulator system (HDFRMS) with varying payloads and uncertainties via singular perturbation theory (SPT). Firstly, the dynamics is derived using a driven Jacobin matrix, which represents the coupling between the hydraulic servo-driven system and rigid–flexible manipulator established using the assumed mode method and Lagrange principle. Furthermore, the whole dynamic model of the manipulator system is decoupled into a second slow subsystem (SSS), a second fast subsystem (SFS) and a first fast subsystem (FFS). The three subsystems can describe a large range of movement, flexible vibration and electro-hydraulic servo control, respectively. Hereafter, an adaptive dynamic programming trajectory tracking control law with a critic-only policy iteration algorithm is presented in the second slow timescale, while both robust optimal control (ROC) in the second first timescale and adaptive sliding mode control (ASMC) in the first fast timescale are also designed using the Lyapunov stability theory. Finally, the numerical simulations are carried out to illustrate the rightness and robustness of the singular perturbation decomposition and proposed composite control algorithm.

To address the issues of uneven sample lengths in the centrifuge machine bearings of the ternary precursor, inaccurate fault feature extraction, and insensitivity of important feature channels in rolling bearings, a rolling bearing fault diagnosis method based on adaptive sample length adjustment of one-dimensional convolutional neural network (1DCNN) and squeeze-and-excitation network (SeNet) is proposed. Firstly, by controlling the cumulative variance contribution rate in the principal component analysis algorithm, adaptive adjustment of sample length is achieved, reducing data with uneven sample lengths to the same dimensionality for various classes. Then, the 1DCNN extracts local features from bearing signals through one-dimensional convolution-pooling operations, while the SeNet network introduces a channel attention mechanism which can adaptively adjust the importance between different channels. Finally, the 1DCNN-SeNet model is compared with four classic models through experimental analysis on the CWRU bearing dataset. The experimental results indicate that the proposed method exhibits high diagnostic accuracy in rolling bearings, demonstrating good adaptability and generalization capabilities.

This article adopts an orthogonal experimental design method to establish a four-factor three-level experimental group by varying the structural parameters at the nozzle outlet, including the number of orifices, orifice diameter, distribution circle diameter, and inclination angle of the base. The three-dimensional jet flow field in the jet pipe was numerically simulated. Through the use of the entropy generation method, Q-criteria, range analysis, and significance test, the working characteristics of the jet pipe were thoroughly investigated. The results show that the orifice diameter has a significant impact on the axial force acting on the jet pipe, while the number of orifices has a minor effect. The distribution circle diameter and inclination angle of the base have very little influence. The final confirmed optimal combination of nozzle structure parameters is as follows: the number of spray holes is 40, the diameter of each spray hole is 1.5 mm, the distribution circle diameter is 22 mm, and the inclination angle of the bottom cover is 30 degrees.

To overcome the limitations of current methods in architectural scene segmentation accuracy, this study presents a hybrid attention-enhanced YOLOv8 framework and introduces a dedicated building interior profile dataset. The proposed approach extends the YOLOv8 architecture by integrating SimAM (Simple, Parameter-Free Attention Module) to dynamically evaluate neuron significance and refine feature representations. This is coupled with an Efficient Multi-Scale Attention (EMA) module that synergizes local and global attention mechanisms, enabling robust multi-scale feature fusion while maintaining stable weight optimization. To address the scarcity of domain-specific data, a meticulously annotated dataset encompassing common architectural interior elements is developed. Comprehensive evaluations demonstrate that the enhanced model achieves an 89.9% mAP@0.5 on the proposed dataset, outperforming the baseline YOLOv8 with relative improvements of 4.5% in precision, 5.2% in recall, 5.1% in mAP@0.5, and 4.5% in mAP@0.5–0.95. These advancements underscore the efficacy of hybrid attention mechanisms in architectural scene analysis and establish a benchmark dataset to facilitate future research in intelligent building environment interpretation.

In order to meet different operational requirements, existing low specific speed centrifugal pumps may have to be run at high speeds. Therefore, it is crucial to understand the transient performance of such centrifugal pumps during high speed starting and stopping. However, there are currently no experiments on the starting and stopping of low specific speed centrifugal pumps. In this paper, transient hydraulic performance experiments during starting and stopping had been carried out on an atypical open impeller centrifugal pump with a rated flow rate of 6 m³/h using an updated test rig. The correlation of speed, flow, head and shaft power with time was obtained for four flow ratios of 0.353, 1.022, 1.654 and 2.343 operating conditions. It was found that the fluctuation of the shaft power curve was the strongest during the starting process, and there was a significant impact phenomenon. The corresponding impact shaft power from small to large flow rates were 0.167 kW, 0.409 kW, 0.234 kW and 0.215 kW, and the shaft power impact phenomenon was the most obvious under rated operating conditions. During the stopping process, the speed, flow rate and head all remain stable for a small period of time, and the time required to decrease to 0 is longer than the time required to reach a stable state during starting. During stopping, the shaft power will instantly decrease, accompanied by varying degrees of fluctuations.