• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.310-315
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• 2007

The axial-flow type blood pump(XVAD) which has been developed in our group consists of mechanical parts (an impeller, a diffuser and a flow straightener) and electrical parts (a motor and a magnetic bearing). The magnetic bearing system fully levitates the impeller to remove mechanical coupling with other parts of the pump with constant gap, which needs non-contact type gap sensing. Conventional gap sensors are too large to be adopted to the implantable axial -flow type blood pump. Thus, in this paper, the compact eddy current type gap sensor system proper for the implantable axial-flow type blood pump was developed and its performance was evaluated in vitro. The developed eddy current type gap sensor system is a transformer type and has a differential probe. Sensor coil(probe) has small dimensions(6 mm diameter, 2 mm thickness) and its optimal inductance was determined as 0.068 mH for the measurement range of $0\sim3mm$. It could be manufactured with 130 turns of the 0.04 mm diameter copper coil. The characteristics of the developed eddy current type gap sensor system was evaluated by in vitro experiment. At experiment, it showed satis(actory performance to apply to the magnetic bearing system of the XVAD. It could measure the gap up to 3mm, but the linearity was decreased at the range of $1.8\sim3.0mm$. Moreover, it showed no difference in different media such as the water and the blood at the temperature range of $35\sim40^{\circ}C$.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.491-498
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• 2022

In our daily life, quality of sleeping is closely related to happiness index. Whether or not people perceive sleep disturbance as a chronic disease, people complain of many difficulties, and in their daily life, they often experience difficulty breathing during sleep. It is very important to automatically recognize breathing-related disorders during a sleep, but it is very difficult in reality. To solve this problem, this paper proposes a mobile-based non-contact sleeping monitoring for health management at home. Respiratory signals during the sleep are collected by using the sound sensor of the smartphone, the characteristics of the signals are extracted, and the frequency, amplitude, respiration rate, and pattern of respiration are analyzed. Although mobile health does not solve all problems, it aims at early detection and continuous management of individual health conditions, and shows the possibility of monitoring physiological data such as respiration during the sleep without additional sensors with a smartphone in the bedroom of an ordinary home.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.9
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• pp.802-810
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• 2012

This paper describes RFID(Radio Frequency Identification) based target acquisition and docking system. RFID is non-contact identification system, which can send relatively large amount of information using RF signal. Robot employing RFID reader can identify neighboring tag attached objects without any other sensing or supporting systems such as vision sensor. However, the current RFID does not provide spatial information of the identified object, the target docking problem remains in order to execute a task in a real environment. For the problem, the direction sensing RFID reader is developed using a dual-directional antenna. The dual-directional antenna is an antenna set, which is composed of perpendicularly positioned two identical directional antennas. By comparing the received signal strength in each antenna, the robot can know the DOA (Direction of Arrival) of transmitted RF signal. In practice, the DOA estimation poses a significant technical challenge, since the RF signal is easily distorted by the surrounded environmental conditions. Therefore, the robot loses its way to the target in an electromagnetically disturbed environment. For the problem, the g-filter based error correction algorithm is developed in this paper. The algorithm reduces the error using the difference of variances between current estimated and the previously filtered directions. The simulation and experiment results clearly demonstrate that the robot equipped with the developed system can successfully dock to a target tag in obstacles-cluttered environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.914-919
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• 2004

When the motor is rotating, the torque and rpm are varying as the loads or the driving status connecting through reduction units are changing. On the contrary, one can monitor the changes of the loads or the driving status in the manner of measuring motor torque and rpm. There is a torque measuring method using the strain gauge and bridge circuit. But, because this is the contact method, it has the life time which is dependent on rotating velocity and used time. So this system demands on replacement of some Parts or whole system itself for maintenance. And this system is also relatively big and expensive, requiring preceding annoying process. In this paper, we are going to suppose non-contact method to measure torque and rpm using the Hall effects sensor For this we have made the planetary geared reduction motor with Hall sensors and with the monitoring system. The monitoring system displays the sensing data(torque, rpm) and calculated data( power) and also has the network capability with Bluetooth protocol. Our solution is much more inexpensive ;md simple method to measure torque and rpm than before.