Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
In this article, hot-pressed PZT ceramics were used as a sensitive element material and made into a pyroelectric chip. Three current mode sensors were fabricated using a pyroelectric chip of different thicknesses (80 μm, 40 μm, and 30 μm). The voltage responsivity of sensors reached the order of magnitude of 105. The size effect resulting from varying the thickness was studied. The results indicate that as the thickness decreases, the performance significantly increases. When the modulation frequency is 10 Hz, the specific detectivity of the sensor with a 30 μm PZT ceramic pyroelectric chip reaches 5.3 × 108 cm·Hz1/2/W....
Impairment of upper limb function is common after a stroke and is closely linked to decreased functional independence in activities of daily living. Robot-assisted training has been used in clinical settings to improve hand function in stroke patients; however, many existing devices are costly and require specialized training to operate. This study aimed to propose a novel powered hand exoskeleton (EO) and verify its effectiveness on upper extremity function in people with chronic stroke. Thirty participants were randomly assigned to either the experimental group or the control group. Each participant underwent 30 min interventions twice a week for 8 weeks. The experimental group received 15 min of conventional therapy followed by 15 min of training with the powered hand EO, while the control group received 30 min of conventional therapy. The primary outcome measures included the Fugl-Meyer Assessment for upper extremity function (FMA-UE), the Box and Block Test (BBT), and handgrip dynamometer. Assessments were conducted at baseline and then at 4-week intervals throughout the 8-week period. Results showed that, after the 8-week intervention, the average changes in FMA-UE scores for the experimental group were significantly greater than those for the control group (p < 0.01). A clear upward trend in both FMA-UE and BBT scores was observed in the EO group. Statistical analysis revealed significant improvements in the overall, proximal, and distal components of the FMA-UE scores (all p < 0.01) and in BBT scores (both p < 0.05) in the EO group compared to the control group at 4 and 8 weeks, respectively. However, no significant differences in grip strength were observed between the groups at either time point. Our findings suggest that the proposed powered hand EO is both feasible and safe for training the impaired hand in stroke survivors. Given the characteristics of the device, it has potential for use in hand rehabilitation aimed at regaining upper extremity function....
The application of electromechanical actuators (EMAs) is experiencing significant growth across various industrial sectors, including the aerospace industry. This shift involves a transition from hydraulic to electric actuation, which promises to reduce the overall weight of aircraft while increasing system efficiency. However, the use of EMAs is currently limited to non-safety-critical functions due to the still limited understanding of their behavior. Accurate mathematical models are essential for analyzing their operation and interaction within complex systems. This study aims to present a methodology for simulating the behavior of motion transmission components under loads in static conditions. To achieve this, experimental data were collected from an existing test bench designed to enhance the elastoplastic effects within the motion transmission system. Preliminary analysis of these data enabled modifications to the model’s architecture to incorporate the compliance of the mechanical line. Subsequent fine-tuning of the parameters improved the correspondence with the real system’s response. The results indicate that the refined model could accurately simulate the behavior of electromechanical actuators under the specified conditions, providing a valuable tool for the design and optimization of these systems in industrial applications. Future work will focus on extending this methodology to dynamic conditions and validating the model against a wider range of operational scenarios....
The brake system is a key system for the safe operation and stopping of trains. As a core component of brake systems, the brake actuation unit (BAU) is essential for slowing down or stopping trains, and faults in the BAU will affect the safety and efficiency of train operation. In order to detect and locate faults in the BAU in time, a fault detection and isolation (FDI) strategy, based on mutual residuals (MRs), principal component analysis (PCA) and improved reconstruction-based contribution plots (IRBCP), was proposed. Firstly, the structural composition and working principle of the BAU were introduced, and its typical failure modes and effects were analyzed. Secondly, considering that the fault detection threshold is not easy to determine due to the variable operating conditions of the BAU, the steady-state fault feature based on MR was extracted. Thirdly, fault detection and isolation were realized based on PCA and the IRBCP algorithm. Finally, by using the fault injection method, case studies on test-rig experiment data of brake systems were conducted; the fault detection rate was 99% and the effectiveness of the proposed strategy was validated by the test data. The proposed strategy shows fast computing ability, and is suitable for systems with dynamic time-varying and nonlinear characteristics....
The issue of personal safety for crime prevention has become a significant societal concern. Existing software on smartwatches developed for personal protection might provide GPS location tracking and emergency reporting, but this is limited to proactively detecting and responding to actual at-risk situations. This paper presents a realtime motion detection algorithm for smartwatches that utilizes an accelerometer to identify at-risk movements when a wearer is under threat. Daily activities, including walking, running, desk work, and being threatened, are distinguished by a machine learningbased alarm application. A total of 5534 data points across four classes were collected from experiments. The proposed 1D-CNN model exhibited the highest performance in comparison with SVM, k-NN, random forest, SGD. Additionally, our comparative analysis of using time-domain versus frequency-domain data in machine learning revealed that frequency-domain features offer advantages in both accuracy and real-time performance. Finally, the proposed inference model was implemented as a smartwatch application that can detect at-risk situations in real time. The application was tested in real-world scenarios, showcasing the effectiveness of personal safety....
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