• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.46 no.1
• /
• pp.1-9
• /
• 2009

In this paper, a 2.4 GHz doppler radar system consisting of a doppler radar sensor and a baseband module were designed to detect heart beat and respiration signal without direct skin contact. The doppler radar system emits RF signal of 2.4 GHz toward human chest, and then detects phase modulation of the reflected signal so as to investigate cardiopulmonary activities. The heartbeat and respiration signals acquired from I/Q channels of the doppler radar system are applied to the pre-processing circuit, the amplification circuit, and the offset circuit of the baseband module. The designed system was tested on mouse, rabbit and mankind, which have different range of heart rates and respiration signals, to evaluate detection accuracy of the system. ECG acquisition system and respiration transducer were used to generate the reference signal. In our experiments, a performance of detection were found to be high in the case that the subject stays still. In this paper, we confirmed that non-contact heart beat and respiration detection using the doppler radar has the possibility and limitation according to distance, cardiopulmonary activities, range of heart rates and respiration.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.47 no.5
• /
• pp.33-42
• /
• 2010

In this study, we have proposed a simple cardiorespiratory monitoring method based on displacements of human body which occurs due to periodic heartbeat and breathing. The proposed system consists of an aircushion, pressure sensing hardware and heartbeat and respiration signals extraction algorithm. The aircushion was used for unconstrained measurement of the respiration and heartbeats without a sensor attached on the subject's skin surface. The displacements of subject sitting on the aircushion cause small pressure variations. These variations are amplified and filtered with the pressure sensing hardware. Finally, heart rate and respiration rate are extracted by signal processing algorithm based on frequency domain filter. To evaluate the performance, extracted respiration and heart rate from proposed system were compared with conventional methods. The average sensitivity of respiration and heart rate are 98.67% and 99.24%, respectively. These results show the proposed method has advantages of installing and processing simplicity so as to be used easily in unconstrained respiration and heart rate monitoring in daily life.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.3
• /
• pp.29-38
• /
• 2022

In this study, the shaking table tests were carried out on six types of non-structural elements installed on a full-scale two-story steel structure. The shaking table tests were performed for non-structural elements with and without seismic isolators. In this study, the seismic performance of Uninterruptible Power Supply (UPS) specimens was tested and investigated. Non-seismic details were composed of conventional channel section steel beams, and the seismic isolators were composed of high damping rubber bearing (HDRB) and wire isolator. The input acceleration time histories were artificially generated to satisfy the requirements proposed by the ICC-ES AC156 code. Based on the test results, the damage and dynamic characteristics of the UPS with the seismic isolator were investigated in terms of the natural frequency, damping ratio, acceleration time history responses, dynamic amplification factors, and relative displacements. The results from the shaking table showed that the dynamic characteristics of the UPS including the acceleration response were significantly improved when using the seismic isolator.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.35 no.6
• /
• pp.375-380
• /
• 2022

High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.