(1) Background: Non-invasive uroflowmetry is used in clinical practice for diagnosing lower urinary tract symptoms (LUTS) and the health status of a patient. To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function.

After a bolt assembling process, non-destructive identification of the clamping force is important to minimize unexpected failure of the assembled joint structures. For in-situ inspection for every parts, a detachable device which allows generation and detection of vibration propagation was proposed in this study. Tone burst excitation was transmitted through the bolted-joint structure. The vibration propagation characteristics depended on the clamping force. With an increase in the clamping force, the surface vibration propagation speed increased. The vibration magnitude decreased due to increasing constraint. A band-pass filter was used to remove noise component. The filtered vibration propagation responses were used in convolutional long short-term memory (CLSTM) training. The performance of CLSTM model was verified through comparison with actual clamping force. With minimal physical influence on the assembled parts, the clamping force of bolted-joint was measured using the surface vibration propagation characteristics.

This study investigates the dynamic performance and degradation behavior of corrugated cardboard used as protective packaging for home appliances subjected to random vibrations during transportation. Simulated vibration tests were conducted on fully packaged refrigerators to assess the mechanical response of cardboard and expanded polystyrene (EPS) supports under prolonged vibration excitation. Relaxation tests were performed to characterize time-dependent stress decay in the absence of vibration, while cantilever beam experiments quantified dynamic stiffness degradation during vibration exposure. The vibration-induced damage was evaluated by monitoring the decrease in support stiffness over time, revealing a distinct exponential reduction that correlated with increasing excitation levels. Statistical load count analyses, based on auto-spectral methods and Basquin’s power law, were used to model fatigue behavior and predict service life. The findings demonstrated that corrugated cardboard exhibited comparable performance to EPS in maintaining support stiffness while offering the advantage of environmental sustainability. These results provide quantitative evidence supporting the use of cardboard as an effective and eco-friendly alternative to polymer-based packaging materials, contributing to the development of optimized packaging solutions with enhanced vibration durability.

Floor impact noise is a significant social concern to secure a quiescent living space for multi-story building residents in South Korea. The floating floor, consisting of a concrete structure on resilient pads, is a specifically designed system to minimize noise transmission. This floating structure employs polymeric pads as the resilient materials. In this study, we investigated the utilization of helically shaped machining scraps as a resilient material for an alternative approach to floor noise reduction. The dynamic elastic modulus and loss factor of the scrap pads were measured using the vibration test method. The scrap pads exhibited a low dynamic elastic modulus and a high loss factor compared to the polymeric pads. Heavyweight impact sound experiments in an actual building were conducted to evaluate the noise reduction performance. The proposed pads showed excellent performance on the reduction in the structure-borne vibration of the concrete slab and resulting sound generation. The analytical model was used to simulate the response of the floating floor structure, enabling a parametric study to examine the effects of the resilient layer viscoelastic properties. Both experimental and analytical evidence confirmed that the proposed scrap pads contribute to the development of sustainable solutions for the minimization of floor impact noise.

Monitoring the fetal heartbeat is essential for obtaining information about fetal condition during pregnancy. By measuring the maternal uterine contractions, the movement of the fetus, and the fetal heart rate, the state of the fetus is detected and taking appropriate measures prevent fetal health abnormality. The NST method is used to obtain the fetal heart rate. The ECG method is useful to observe heart rhythm. In this study, a system that can detect fetal heartbeat signals using vibration sensors is proposed. For vibration measurement, a band-type device with a sensor was proposed. The band was wrapped around the mother's stomach to sensing signals generated by the mother's body. In order to extract the fetal heartbeat signal, appropriate signal processing for denoising is performed. Processed signal was divided into several IMFs using the CEEMD method. The maternal heartbeat signal and fetal heartbeat signal were separated from the IMF. Through frequency analysis, the characteristics of each signal make clear