• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.87-91
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• 2019

In this paper, we have development of horizontal attitude monitoring system for agricultural robots. A two-axis gyro sensor and a two-axis accelerometer sensor are used to measure the horizontal attitude angle. The roll angle and pitch angle were measured through the fusion of the gyro sensor signal and the acceleration sensor signal for the horizontal attitude monitoring of the robot. This attitude monitoring system includes GPS and Bluetooth communication module for remote monitoring. The roll angle and pitch angle can be measured by the error of less than 1 degree and the linearity and the reproducibility of the output signal are excellent.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.145-148
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• 2020

Wearable computing is growing rapidly as research on body area communication network using wireless sensor network technology is actively conducted. In particular, there is an increasing interest in smart clothing measuring unrestrained and insensitive bio signals, and research is being actively conducted. However, research on smart clothing is mainly based on 1: 1 wireless communication. In this paper, we propose a multi-access monitoring system that can measure bio-signals by multiple users wearing smart clothing. The proposed system consists of wireless access device, multiple access control server and monitoring system. It also provides a service that allows multiple users to monitor and measure bio signals at the same time.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.76-79
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• 2018

In this paper, we developed a pressure monitoring system using silicon pressure sensor. The pressure monitoring system was developed on the basis of a microcontroller, and a self-developed silicon pressure sensor was applied. The pressure monitoring system outputs the current pressure value via UART communication. In addition, it includes a function of displaying by LED when the preset three-step pressure (low, medium, high pressure) is reached. The silicon pressure sensor used in the pressure monitoring system was set to 0 kPa, 10 kPa, 26 kPa, and the pressure monitoring system was evaluated because the measured maximum pressure was in the range of 100 kPa.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.211-212
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• 2021

Recently, as interest in health increases, the wearable market that can collect various biometric information is expanding. In addition, telemedicine and healthcare services through these bio-signals are expected to become common. In this paper, we introduce a service that can store bio-signals collected through IoT equipment in a database and monitor them in real time through the web. By implementing a system for collecting and storing biometric data and real-time monitoring, it can be utilized for various health management diagnosis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.197-202
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• 2016

In this paper, a danger signal to tell caregivers when a dangerous situation occurs, the bio-signal analysis in infants to prevent sudden infant death sudden infant death propose a monitoring system. The Sudden infant death (SID) refers to a healthy baby is unexplained deaths between birth year in the month. Sudden infant death proposed monitoring system is composed of a processor unit and the monitoring and alarm part for processing part and the biological signal sensing biological signals. Using the PPG sensor to sense the bio-signal and the processor unit the signal obtained through the sensor by removing the motion artifact was able to alarm and monitoring the parent.The proposed system will send the alarm to monitoring and alerting caregivers if the risk situation by analyzing the heart rate of the infant. With the actual implementation of the system to evaluate the performance of the monitoring system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1170-1176
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• 2007

The conventional urodynamic monitoring is fulfilled by artificially filling a bladder with saline. Generally. it is difficult to evaluate the physiological functions of the storage and voiding of a bladder. With this aim, we constructed an ambulatory urodynamic monitoring (AUM) system and proposed a novel method estimating abdominal pressure by measuring bio-impedance variations. Our system was clinically evaluated for 10 patients. It turned out to be that as the intensity of the abdomen contraction increased, the amplitude of bio-impedance signal and the RMS value of EMG increased more as compared to those who observed during the rest mode. Also, we determined the optimum electrode pair for estimating the abdominal pressure using bio-impedance method and consequently compared with the conventional methods. Because impedance changes differ from a weight, a height, contractile force, volume of muscle and blood other or whatever of individuals, it was quantified in terms of impedance change, correlation coefficient and SNR Our results showed the optimum electrode pair (1,9) which could detect impedance changes due to the increase of the intensity in the abdominal pressure. The correlation coefficient and quadratic function between the RMS values of EMG and the impedance changes were 0.87 and $y=0.0014x^2+0.0620x+0.6958$, respectively. Thus, our system demonstrated that the abdominal pressure could be measured noninvasively and conveniently by simply estimating bio-impedance values. We propose that this optimum electrode configuration would be useful for the future studies involving the handy measurements of abdominal pressure with our suggested ambulatory urodynamics monitoring system.

• Smart Structures and Systems
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• v.8 no.1
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• pp.119-137
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• 2011

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.808-817
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• 2016

Recently, the wearable computing technology with bio-sensors has been rapidly developed and utilized in various areas such as personal health, care-giving for senior citizens who live alone, and sports activities. In particular, the wearable computing equipment to measure vital signs by means of digital yarns and bio sensors is noticeable. The wearable computing devices help users monitor and manage their health in their daily lives through the customized healthcare service. In this paper, we suggest a system for monitoring and analyzing vital signs utilizing smart healthcare clothing with bio-sensors. Vital signs that can be continuously acquired from the clothing is well-known as unstructured data. The amount of data is huge, and they are perceived as the big data. Vital sings are stored by Hadoop Distributed File System(HDFS), and one can build data warehouse for analyzing them in HDFS. We provide health monitoring system based on vital sings that are acquired by biosensors in smart healthcare clothing. We implemented a big data platform which provides health monitoring service to visualize and monitor clinical information and physical activities performed by the users.

• The KIPS Transactions:PartA
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• v.17A no.1
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• pp.33-44
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• 2010

We present a pipeline monitoring system based on bio-memetic robot in this paper. A bio-memetic robot exploring pipelines measures temperature, humidity, and vibration. The principal function of pipeline monitoring robot for the exploring pipelines is to recognize the shape of pipelines. We use infrared distance sensor to recognize the shape of pipelines and potentiometer to measure the angle of motor mounting infrared distance sensor. For the shape recognition of pipelines, the number of detected pipelines is used during only one scanning of distance. Three fuzzy classifiers are used for the number of detected pipelines, and the classifying results are presented in this paper.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.129-133
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• 2019

In this paper, we developed a system to monitor the storage capacity of suction-type device such as vacuum cleaner or crop harvesters. The monitoring system consists of load cells and a differential pressure sensor which simultaneously monitor the weight and volume of the stock. Since weighing objects stored in storage containers alone cannot fully monitor the level of filling, more accurate monitoring can be achieved by monitoring volume and fusion with weight information. The volume was monitored using a phenomenon in which the flow rate of the inhaled air varies depending on the volume of the object filled in the storage container. In this paper, we developed a system to monitor the storage in three stages: low, medium and high.