• Korean Journal of Materials Research
• /
• v.23 no.6
• /
• pp.299-303
• /
• 2013

In this study, a micro gas sensor for $NO_x$ was fabricated using a microelectromechanical system (MEMS) technology and sol-gel process. The membrane and micro heater of the sensor platform were fabricated by a standard MEMS and CMOS technology with minor changes. The sensing electrode and micro heater were designed to have a co-planar structure with a Pt thin film layer. The size of the gas sensor device was about $2mm{\times}2mm$. Indium oxide as a sensing material for the $NO_x$ gas was synthesized by a sol-gel process. The particle size of synthesized $In_2O_3$ was identified as about 50 nm by field emission scanning electron microscopy (FE-SEM). The maximum gas sensitivity of indium oxide, as measured in terms of the relative resistance ($R_s=R_{gas}/R_{air}$), occurred at $300^{\circ}C$ with a value of 8.0 at 1 ppm $NO_2$ gas. The response and recovery times were within 60 seconds and 2 min, respectively. The sensing properties of the $NO_2$ gas showed good linear behavior with an increase of gas concentration. This study confirms that a MEMS-based gas sensor is a potential candidate as an automobile gas sensor with many advantages: small dimension, high sensitivity, short response time and low power consumption.

• Proceedings of the SAREK Conference
• /
• 2009.06a
• /
• pp.404-408
• /
• 2009

Gas pipelines in bridges, roads and subway construction sections can undergo abrupt stress and vibration changes. To protect human life from any gas leakage accidents induced by the abrupt stress and vibration, the gas pipeline system needs to be continuously monitored. The estimation method of pipeline stress using MEMS wireless tilt sensor has been developed and its validity has been evaluated using a lab test bench.

• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.1951-1956
• /
• 2008

Various MEMS accelerometers are used in engineering applications including automobiles, mobile phones, military systems, and electronic devices. Among them, the thermal accelerometer employing the temperature difference induced by the convective flow inside the micro cavity has been a topic of interest. As the convective sensor does not utilize a solid proof mass, it is compact, lightweight, inexpensive to manufacture, sensitive and highly endurable to mechanical shock. However, the complexity of the convective flow and various design constraints make optimization of a device a crucial step before fabrication. In this work, optimization of a 2-axis thermal convective MEMS accelerometer is conducted based on 3-dimensional numerical simulation. Parametric studies are performed by varying the several design variables such as the heater shape/size, the cavity size and types of the gas medium and the position of temperature probes in the sensor. The results of optimal design are presented.

• Journal of Korean Association for Spatial Structures
• /
• v.18 no.2
• /
• pp.51-58
• /
• 2018

Recently, measuring instruments for SHM of structures had being developed. In general, the wireless transmission of sensor signals, compared to its wired counterpart, is preferable due to its absence of triboelectric noise and elimination of the requirement for cumbersome cable. However, the research on the tall buildings with relatively small vibration levels is insufficient. Therefore, in this paper, we used the wireless MEMS sensor and iPad to compare and analyze the vibration measurements of three tall buildings and two towers.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.3
• /
• pp.9-15
• /
• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• Journal of Korean Association for Spatial Structures
• /
• v.19 no.1
• /
• pp.101-108
• /
• 2019

Recently, measuring instruments for SHM of structures has been developed. In general, the wireless transmission of sensor signals, compared to its wired counterpart, is preferable due to the absence of triboelectric noise and elimination of the requirement of a cumbersome cable. However, the low-cost wireless MEMS sensor has high noise density and transmits the signal wirelessly, so data transmission delay occurs during measurement. Therefore, the footbridges that was previously measured by a mobile phone in 2014 was remeasured using G-Link-200, iPad and iPhone to compare their performance.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.1
• /
• pp.177-186
• /
• 2017

Recently, Autonomous vehicles are getting hot attention. Amazon, the biggest online shopping service provider is developing a delivery system that uses drones. This kinds of platforms are need accurate attitude information for navigation. In this paper, a structure design of AHRS using low-cost inertia sensor is proposed. To estimate attitudes a Kalman filter which uses a quaternion based dynamic model, bias-removed measurements from MEMS Gyro, raw measurements from MEMS accelerometer and magnetometer, is designed. To remove bias from MEMS Gyro, an additional Kalman filter which uses raw Gyro measurements and attitude estimates, is designed. The performance of implemented AHRS is compared with high price off-the-shelf 3DM-GX3-25 AHRS from Microstrain. The Gyro bias was estimated within 0.0001[deg/s]. And from the estimated attitude, roll and pitch angle error is smaller than 0.2 and 0.3 degree. Yaw angle error is smaller than 6 degree.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.18 no.8
• /
• pp.872-878
• /
• 2008

Many industrial operations require continuous or nearly-continuous operation of machines, interruption of which can result in significant cost loss. The condition monitoring of these machines has received considerable attentions in recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is to develop a new type of smart sensor for on-line condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This system is capable for signal preprocessing task and analog to digital converter which is controlled by CPU. This sensor communicates with a remote site PC using TCP/IP protocols. The developed sensor executes performance tests for data acquisition accuracy estimations.

• Journal of Korean Association for Spatial Structures
• /
• v.18 no.3
• /
• pp.67-74
• /
• 2018

Recently, measuring instruments for SHM of structures has been developed. In general, the wireless transmission of sensor signals, compared to its wired counterpart, is preferable due to the absence of triboelectric noise and elimination of the requirement of a cumbersome cable. However, in extreme environments, the sensor may be less sensitive to temperature changes and to the distance between the sensor and data logger. This may compromise on the performance of the sensor and instrumentation. Therefore, in this paper, free vibration experiments were conducted using wireless MEMS sensors at an actual site. Measurement was assessed in time and frequency domain by changing the temperature variation at($-8^{\circ}C$, $-12^{\circ}C$ and $-16^{\circ}C$) and the communication distance (20m, 40m, 60m, 80m).

• Journal of Sensor Science and Technology
• /
• v.17 no.6
• /
• pp.406-413
• /
• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.