Most recent studies on adaptive hydraulic tracking control focus on the trajectory tracking performance while the parameter convergence property is often unsatisfying. This article proposes a composite learning adaptive position tracking controller with improved parameter convergence for electro-hydraulic servo systems. In the composite learning, a prediction error is formulated to exploit input–output memory data, and parameter estimates are driven simultaneously by tracking and prediction errors. Practical exponential stability of the closed-loop system, which implies the convergence of both the tracking and parameter estimation error, is established by a more realizable interval-excitation condition than the stringent persistent–excitation condition. Therefore, superior trajectory tracking is obtained compared with the classical adaptive hydraulic control. Besides, the initial fluid control volumes of hydraulic systems are assumed to be unknown a priori , which enhances the generality of the proposed control approach. The abovementioned two properties are generally not achievable in prevalent approaches to adaptive hydraulic control. Moreover, noisy acceleration signals and the time derivatives of pressure signals are not needed in the proposed approach, which improves its robustness against measurement noise. Extensive experimental results verify its superiority over currently available ones.

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.

The main valve spool structure of the pilot-operated proportional directional valve is diverse and has a direct impact on the flow field. To improve the valve’s performance, this work studied the characteristics of four types of spool structures with the following throttling groove arrangements: no throttling groove, a U-shaped groove, a triangle groove, and a combined groove. This study analyzed the flow field simulation of four spool structures under the same opening degree and different pressures to study the flow field cavitation characteristics and pressure distribution in the valve. According to the simulation results, the necessity of opening throttling grooves for the pilot proportional directional valve and the advantages of combined grooves over U-shaped and triangular grooves were verified. Then, the proportional valve with a combined groove structure was simulated and analyzed to study its throttling characteristics, steady flow characteristics, and flow and load differential pressure characteristics, and further explore the advantages of a combined groove. Finally, the experimental results are compared with the simulation results, which can provide a theoretical reference for the selection of the throttle groove of the proportional valve and the structural design of the slide valve.

With the inception of the Argo program, the global ocean observation network is undergoing continuous advancement, with profiling buoys emerging as pivotal components of this network, thus garnering increased attention in research. In efforts to enhance the efficiency of profiling buoys and curtail energy consumption, a teardrop-shaped buoy design is proposed in this study. Moreover, an optimization methodology leveraging neural networks and genetic algorithms has been devised to attain an optimal profile curve. This curve seeks to minimize drag and drag coefficient while maximizing drainage, thereby improving hydrodynamic performance. Simulation-based validation and analysis are conducted to assess the efficacy of the optimized buoy design. Results indicate that the drag of the teardrop-shaped buoy with a deflector decreased by 9.2% compared to pre-optimized configurations and by 22% compared to buoys lacking deflectors. The hydrodynamic profile devised in this study effectively enhances buoy performance, laying a solid foundation for ocean thermal energy generation and buoyancy regulation control. Additionally, the optimized scheme serves as a valuable blueprint for the design of ocean exploration devices.

In recent years, there has been a surge in applying deep learning methods for signal quality assessment (SQA) and denoising of ECG signals. However, solely focusing on denoising can lead to situations where models trained on severely contaminated signals perform poorly on higher-quality signals. Likewise, carrying out SQA in isolation may be constrained by the number of training data and susceptible to label noise. In this study, we leverage the correlation between denoising and SQA and propose a multi-task learning network (MTL-NET) that addresses both tasks in parallel to mitigate the challenges. MTL-NET employs a stacked bidirectional LSTM network, combined with an attention module, as a shared backbone to extract common features for both SQA and denoising tasks. Weighted loss functions are utilized to balance the performance of these two tasks. Within MTL-NET, we classify ECG signal quality into three categories, and compare MTL-NET’s performance with baseline methods dedicated solely to denoising or three-class SQA. The results demonstrate that MTL-NET outperforms other denoising methods across all metrics obviously, including the corresponding single-task denoising model. Specifically, MTL-NET significantly enhances the denoising performance of the "intermediate" quality category and the SQA performance in identifying ‘unacceptable’ signal segments. It provides a novel perspective on ECG signal preprocessing by implementing a balance rule through loss functions for both tasks within a model. With 0.28M parameters and 27.35M FLOPs, the MTL-NET is not only efficient but also exhibits excellent generalization performance on an independent dataset, making it suitable for deployment in resource-constrained environments.