Strain-based non-intrusive approaches for measuring the pressure of pipes have attracted widespread attention due to their great convenience and ability to avoid destroying the integrity of structures. However, the mentioned method usually measures the dynamic pressure based only on the static strain–pressure sensitivity coefficients (SSSCs) instead of the dynamic strain–pressure sensitivity coefficients (DSSCs) due to its complicated calibration, which will inevitably affect the accuracy significantly. To address this issue, a model-driven scheme with dual stages is proposed in the present study to compensate the dynamic pressure measurement. The DSSCs are analytically derived for the first time from the axial governing equations of the pipe, considering the general boundary conditions for the thin-walled and thick-walled pipes simultaneously. In the first stage, the physical parameters involved in the DSSCs are calibrated by minimizing the residual of the experimental results and the theoretical counterparts. In the second stage, the DSSCs calculated from the calibrated analytical model are utilized to compensate the dynamic pressure based on the relationship between the DSSCs and the SSSCs. The proposed method is applied to an industrial hydraulic pipe system, and the experimental results show that the relative error is reduced greatly after the compensation is implemented, demonstrating the validity of the proposed compensation method.

With the increasing demand for rehabilitation of the elderly population and patients with limb injuries, it is imperative to develop new wearable health monitoring system and assistive rehabilitation equipment. Optical fiber sensing has the advantages of light-weight, anti-electromagnetic interference, higher strain and elastic limit, safety and dependableness. It has been significantly utilized in engineering prospecting, building structure monitoring, and medical and health fields. At present, the application of optical fiber sensing in medical rehabilitation has been developed rapidly, however, the discussion and summary of its key theories and technologies are insufficient. This review details different types of optical fiber sensing mechanisms and devices, as well as the measurement of physical parameters using optical fiber sensors and application examples in the field of medical rehabilitation. This review systematically analyzes the advance of the sensitivity, flexibility, accuracy, and wearability of the optical fiber sensing in the monitoring of human body sign information. This review also discusses the challenges of making a new generation of optical fiber sensors that fit human joints, muscles, and rehabilitation equipment. Finally, this review provides a research direction for the realization of flexible, comfortable, safe, stable monitoring of human biomechanical parameters and real-time precise control of rehabilitation equipment in the future.

The small and light-weight pipeline is widely used in hydraulic system for aerospace engineering. The crack is one of the most common failures in the pipelines so that its incipient detection can further avoid the catastrophic damage of the piping system. The electrical and piezoelectric sensors are conventionally used for the structural health monitoring (SHM), while these are not suitable for the cascaded pipelines in harsh environment because the added mass will change the modal characteristics of the cascaded pipelines. The Fiber Bragg Grating (FBG) sensor with light-weight, multiplexed, and anti-electromagnetic interference properties, are employed to obtain the modal strain transmissibility with a novel diagram of the operational modal analysis (OMA). Based on the OMA an enhanced damage indicator is proposed to detect the crack. After going through analytical modeling, finite element modeling (FEM) and its corresponding experiments, it is concluded that the presented method is effective and accurate to detect and locate the crack.

Objective: Heart failure (HF) is a leading cause of disease progression and mortality in heart failure patients. This study aimed to classify HF by extracting nonlinear features from the seismocardiogram (SCG) signal of heart. Firstly, the trajectory of SCG signal was depict in a reconstructed high-dimensional phase space to reflect nonlinear properties and cardiac dynamics of SCG. This trajectory was then mapped onto a ‘displacement-velocity’ phase plane to obtain the 2D phase plane trajectory. Thereby, a phase trajectory complexity (PTC) of the SCG signal based on entropy was calculated and proposed as a key feature in order to represent the complexity of the cardiac dynamic system and realize HF classification. The available SCG signals of 73 HF patients and 20 healthy individuals used in this study were obtained from the SCG-RHC and combined measurement of ECG, breathing and seismocardiograms databases, respectively. Experimental results indicate that our proposed features achieve an accuracy of 87.68% in HF classification. When combined with other nonlinear features, the highest training and prediction accuracies reach 100% and 95.17%, respectively. This demonstrates that our proposed features contribute to improving the HF classification outcomes.

Background/Objectives: Accurate segmentation of skin lesions is essential for early skin cancer detection. However, traditional CNNs are limited in modeling long-range dependencies, leading to poor performance on lesions with complex shapes. Methods: We propose MSDTCN-Net, a dual-encoder network that integrates ConvNeXt and Deformable Transformer to extract both local details and global semantic information. A Squeeze-and-Excitation (SE) mechanism is introduced to adaptively emphasize important channels. To address scale variation in lesions, we design a Multi-Scale Receptive Field (MSRF) module combining multi-branch and dilated convolutions. Furthermore, a Hierarchical Feature Transfer (HFT) mechanism is employed to guide high-level semantics progressively to shallow layers, enhancing boundary reconstruction in the decoder. Results: Extensive experiments on the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets show that MSDTCN-Net achieves competitive performance across metrics including IoU, Dice, and ACC, validating its effectiveness and generalization in skin lesion segmentation. Conclusions: MSDTCN-Net effectively combines local and global feature extraction, multi-scale adaptability, and semantic guidance to achieve high-accuracy skin lesion segmentation, demonstrating its potential in clinical diagnostic applications.

This article have presented a non-contact vibration sensor based on FBG sensor, and used it to monitor the vibration of rotating shaft. In this article, we described the principle of the sensor and made an experimental analysis. The analysis results showed that the sensitivity and linearity of the sensor were -1.5pm/um and 4.11% respectively within a measuring range of 2-2.6mm. When the sensor was used to monitor the vibration of the rotating shaft, the vibration signals and the critical speed measured were the same as that obtained from the eddy current sensors. This indicated that the sensor could be effectively used for non-contact measurement of vibration of rotating shaft, and fault monitoring and diagnosis of rotating machinery.