Piezoelectric actuators (PAs) require high precision positioning for the applications of micro electrical mechanical systems, but it exhibits hysteresis nonlinearity which deteriorates positioning accuracy if no proper compensation is given. Hysteresis nonlinear modeling of PAs is a prime choice for hysteresis compensation. This paper proposes a novel intelligent positioning control algorithm based on Bouc-Wen (BW) model for the compensation of a bi-morph type piezoelectric actuator (PA) suffering rate-dependent hysteresis. A region based mixed-species swarm optimization (RMSO) algorithm is proposed for BW modeling to capture the dynamic nonlinearity of a piezoelectric actuator which exhibits rate-dependent hysteresis. Results of numerical simulations have been disclosed to illustrate the performance enhancement of RMSO over classical algorithm while they are applied to the parameter fitting problem of BW model for experimentally acquired datasets. An model based adaptive Fuzzy neural network (Fuzzy-NN) controller of PA is utilized to compensate the hysteresis for the positioning tracking control. Experimental results also illustrate the good performance of the proposed RMSO-BW based control scheme for the hysteresis compensation control of the PA.

Sleep‐related breathing disorders (SBDs) will lead to poor sleep quality and increase the risk of cardiovascular and cerebrovascular diseases which may cause death in serious cases. This paper aims to detect breathing states related to SBDs by breathing sound signals. A moment waveform analysis is applied to locate and segment the breathing cycles. As the core of our study, a set of useful features of breathing signal is proposed based on Mel frequency cepstrum analysis. Finally, the normal and abnormal sleep breathing states can be distinguished by the extracted Mel‐scale indexes. Young healthy testers and patients who suffered from obstructive sleep apnea are tested utilizing the proposed method. The average accuracy for detecting abnormal breathing states can reach 93.1%. It will be helpful to prevent SBDs and improve the sleep quality of home healthcare.

Conventional heart sound classification methods often rely on single-channel, one-dimensional feature extraction, which inadequately captures pathological relationships across different auscultation zones, thereby limiting the accuracy of heart disease detection. To address this issue, a novel classification framework based on multi-channel heart sound coupling feature extraction is proposed to enhance heart disease identification. This approach begins with denoising preprocessing applied to four-channel heart sound signals and a single-channel electrocardiogram. These five-channel signals are systematically paired to extract five types of coupling features, resulting in 130 distinct features per multi-channel sample. The ReliefF algorithm is then used to evaluate feature importance, retaining the top 20% of features to construct a coupling feature set. A convolutional neural network is employed to classify normal and abnormal heart sounds. When applied to clinical congenital heart disease datasets, the proposed method achieved a classification accuracy of 95.6%, while on the PhysioNet heart sound challenge dataset, it reached an accuracy of 98.3%. Experimental results demonstrate that compared to single-channel, one-dimensional features, multi-channel coupling features more effectively capture pathological characteristics in heart sound signals, significantly improving the accuracy of heart disease classification and addressing challenges in the refined categorization of cardiac conditions.