• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2071-2078
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• 2009

In this paper, we proposed a non-contact respiration measurement system with ultrasonic proximity sensor. Ultrasonic proximity sensor approach of respiration measurement which respiration signatures and rates can be derived in real-time for long-term monitoring is presented. 240 kHz ultrasonic sensor has been applied for the proposed measurement system. The time of flight of sound wave between the transmitted signal and received signal have been used for a respiration measurement from abdominal area. Respiration rates measured with the ultrasonic proximity sensor were compared with those measured with standard techniques on 5 human subjects. Accurate measurement of respiration rate is shown from the 50 cm measurement distance. The data from the method comparison study is used to confirm the performance of the proposed measurement system. The current version of respiratory rate detection system using ultrasonic can successfully measure respiration rate. The proposed measurement method could be used for monitoring unconscious persons from a relatively close range, avoiding the need to apply electrodes or other sensors in the correct position and to wire the subject to the monitor. Monitoring respiration using ultrasonic sensor offers a promising possibility of non-contact measurement of respiration rates. Especially, this technology offers a potentially inexpensive implementation that could extend applications to consumer home-healthcare and mobile-healthcare products. Further advances in the sensor design, system design and signal processing can increase the range of the measurement and quality of the rate-finding for broadening the potential application areas of this technology.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.267-276
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• 2020

Manufacturing workers are easily exposed to the risk of musculoskeletal disorders caused by repetitive tasks in their working environment. This is due to problems with occupational characteristics that repeatedly use the body. However, the current lack of monitoring systems for monitoring and prevention has led to an increase in workers' exposure to risks each year. This paper presents how to solve these problems in real working environment by producing wearable devices using IMU sensors. After wearing a wearable type device, the user's movement is judged through data analysis by receiving the rotation value according to musculoskeletal movement. At this time, the risk is determined by measuring the number of rotations of the user by eliminating bias and eliminating cumulative error, acquiring sophisticated data, and analyzing it in the form of dynamic threshold values. Using the wearable device proposed in this paper, the effect of this method could be checked through a web page measuring the number of rotations for elbow musculoskeletal disorders.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.2
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• pp.20-27
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• 2007

[ $SF_6$ ] gas for compacted power facilities has a important role as an insulation gas. It is very blown well that $SF_6$ gas has the superior characteristics as an insulation gas. For reliable operation of SF6-gas-based high and medium voltage equipment it is very important to keep the insulation ability within a safe range. And the experimental and measuring system were implemented. The test chamber designed to endure up to 3 atmospheric pressure. The analysis results of the experimental data shows that positive partial discharge can be detected by discharge current and UHF signal. Additionally it is shown the possibility that $CO_2$ gas sensor of semiconductor type can be detect the variation of $SF_6$ gas condition. The UHF sensor shows good feature to detect the variation of $SF_6$ gas condition for partial discharge and breakdown discharge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1743-1750
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• 2003

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as a speed detector, but they increase cost and size of the motor and restrict the industrial drive applications. So in these days, many papers have reported in the sensorless operation of DC motor〔3­5〕. This paper presents a new sensorless strategy using neural networks〔6­8〕. Neural network has three layers which are input layer, hidden layer and output layer. The optimal neural network structure was tracked down by trial and error, and it was found that 4­16­1 neural network structure has given suitable results for the instantaneous rotor speed. Also, learning method is very important in neural network. Supervised learning methods〔8〕 are typically used to train the neural network for learning the input/output pattern presented. The back­propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.171-174
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• 2006

A large number of service providers in countries all over the world have established GNSS reference station networks in the last years and are using network software today to provide a correction stream to the user as a routine service. In current GNSS network processing, all the geometric related information such as ionospheric free carrier phase ambiguities from all stations and satellites, tropospheric effects, orbit errors, receiver and satellite clock errors are estimated in one centralized Kalman filter. Although this approach provides an optimal solution to the estimation problem, however, the processing time increases cubically with the number of reference stations in the network. Until now one single Personal Computer with Pentium 3.06 GHz CPU can only process data from a network consisting of no more than 50 stations in real time. In order to process data for larger networks in real time and to lower the computational load, a federated filter approach can be considered. The main benefit of this approach is that each local filter runs with reduced number of states and the computation time for the whole system increases only linearly with the number of local sensors, thus significantly reduces the computational load compared to the centralized filter approach. This paper presents the technical aspect and performance analysis of the federated filter approach. Test results show that for a network of 100 reference stations, with the centralized approach, the network processing including ionospheric modeling and network ambiguity fixing needs approximately 60 hours to process 24 hours network data in a 3.06 GHz computer, which means it is impossible to run this network in real time. With the federated filter approach, only less than 1 hour is needed, 66 times faster than the centralized filter approach. The availability and reliability of network processing remain at the same high level.

• Journal of Navigation and Port Research
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• v.38 no.5
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• pp.457-462
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• 2014

Indoor-positioning systems are useful to various applications. Navigation system is one of the most popular applications, which needs the information of directions of nodes' movements. Specifically the applications should get the information in real-time to properly show the current moving position of a node. In this paper, simple vector-based algorithms are proposed to compute amount and direction of changes of azimuth of mobile nodes' heading directions using existing indoor positioning systems in indoor environments where azimuth sensors do not work properly. Previous algorithms calculate the azimuth changes by too many steps of topology-based formula. The algorithms proposed in this paper get the amount of changes of azimuth by simple formula based on vector, and determine the direction of changes by the sign of value of simple formula based on the previous movement of nodes. The algorithms are much simpler and less error-prone than previous ones, and then they can detect changes in many location-based applications as well. The performance of the algorithms is proved logically and mathematically.

• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.20-26
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• 2016

The bioelectrical impedance (BI) for the young and the elderly was measured using bioelectrical impedance spectroscopy (BIS). First, while applying a current of $600{\mu}A$ to the foot and hand, BI was measured at 50 frequencies ranging from 5 to 1000 kHz. The BI for young subjects was considerably lower than that for old subjects since young subjects have more lean mass (hydration). The prediction marker was 0.74 for young subjects and 0.78 for old subjects. Second, a Cole-Cole diagram was obtained for young subjects and old subjects, indicating the different characteristic frequencies. At 50 kHz, the average phase angle was $7.8^{\circ}$ for young subjects whereas that was $6.1^{\circ}$ for old subjects. Third, BIVA was analyzed for young subjects and old subjects. The vector length was 210.89 [${\Omega}/m$] for young subjects and 326.12 [${\Omega}/m$] for old subjects. At 50 kHz, the resistance (R/H) and the reactance ($X_C/H$) divided by height were 208.94 [${\Omega}/m$] and 28.68 [${\Omega}/m$] for young subject, and 324.33 [${\Omega}/m$] and 34.09 [${\Omega}/m$] for old subjects.

• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.1-7
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• 2016

Bioelectrical impedance (BI) at popliteal regions was measured using a bioelectrical impedance measurement system (BIMS), which employs the multi-frequency and the two-electrode method. Experiments were performed as follows. First, a constant AC current of $800{\mu}A$ was applied to the popliteal regions (left and right) and the BI was measured at eight different frequencies from 10 to 500 kHz. When the applied frequency greater than 50 kHz was applied to human's popliteal regions, the BI was decreased significantly. Logarithmic plot of impedance vs. frequency indicated two different mechanisms in the impedance phenomena before and after 50 kHz. Second, the relationship between resistance and reactance was obtained with respect to the applied frequency using BI (resistance and reactance) acquired from the popliteal regions. The phase angle (PA) was found to be strongly dependent on frequency. At 50 kHz, the PA at the right popliteal region was $7.8^{\circ}$ slightly larger than $7.6^{\circ}$ at the left popliteal region. Third, BI values of extracellular fluid (ECF) and intracellular fluid (ICF) were calculated using BIMS. At 10 kHz, the BI values of ECF at the left and right popliteal regions were $1664.14{\Omega}$ and $1614.08{\Omega}$, respectively. The BI values of ECF and ICF decreased sharply in the frequency range of 10 to 50 kHz, and gradually decreased up to 500 kHz. Logarithmic plot of BI vs. frequency shows that the BI of ICF decreased noticeably at high frequency above 300 kHz because of a large decrease in the capacitance of the cell membrane.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.137-144
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• 2011

Wireless sensor network(WSN) has the potential to greatly effect many aspects of u-healthcare. By outfitting the potential with WSN, wearable sensor node can collects real-time data on physiological status and transmits through base station to server PC. However, there is a significant gap between WSN and healthcare. WSN has the limited resource about computing capability and data transmission according to bio-sensor sampling rates and channels to apply healthcare system. If a wearable node transmits ECG and accelerometer data of 4 channel sampled at 100 Hz, these data may occur high loss packets for transmitting human activity and ECG to server PC. Therefore current wearable sensor nodes have to solve above mentioned problems to be suited for u-healthcare system. Most WSN based activity and ECG monitoring system have been implemented some algorithms which are applied for signal vector magnitude(SVM) algorithm and ECG noise algorithm in server PC. In this paper, A wearable sensor node using integrated ECG and 3-axial accelerometer based on wireless sensor network is designed and developed. It can form multi-hop network with relay nodes to extend network range in WSN. Our wearable nodes can transmit 1-channel activity data processed activity classification data vector using SVM algorithm to 3-channel accelerometer data. ECG signals are contaminated with high frequency noise such as power line interference and muscle artifact. Our wearable sensor nodes can remove high frequency noise to clear original ECG signal for healthcare monitoring.

• Journal of Sensor Science and Technology
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• v.12 no.5
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• pp.218-224
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• 2003

The devices of n-channel poly silicon thin film transistors(TFTs) hydrogenated by plasma, $H_2$ and $H_2$/plasma processes are fabricated. The carriers sensitivity characteristics are analyzed with voltage bias stress at the gate oxide. The parametric sensitivity characteristics caused by electrical stress conditions in hydrogenated devices are investigated by measuring the drain current, threshold voltage($V_{th}$), subthreshold slope(S) and maximum transconductance($G_m$) values. As a analyzed results, the degradation characteristics in hydrogenated n-channel polysilicon thin film transistors are mainly caused by the enhancement of dangling bonds at the poly-Si/$SiO_2$ interface and the poly-Si grain boundary due to dissolution of Si-H bonds. The generation of traps in gate oxide are mainly dued to hot electrons injection into the gate oxide from the channel region.