• 한국재료학회지
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• 제23권6호
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• pp.299-303
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• 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.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2009년도 하계학술발표대회 논문집
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• pp.404-408
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• 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.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.1951-1956
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• 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.

• 한국공간구조학회논문집
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• 제18권2호
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• pp.51-58
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• 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.

• 디지털산업정보학회논문지
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• 제17권3호
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• pp.9-15
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• 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.

• 한국공간구조학회논문집
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• 제19권1호
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• pp.101-108
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• 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.

• 예술인문사회 융합 멀티미디어 논문지
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• 제7권1호
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• pp.177-186
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• 2017

최근 무인자동차가 큰 관심을 받고 있다. 세계 최대 규모의 온라인 쇼핑 서비스업체인 아마존은 드론을 활용한 배송시스템을 개발하고 있다. 이러한 플랫폼의 항법을 위해서는 정확한 자세정보가 필요하다. 본 논문에서는 저가형 관성센서를 활용한 AHRS 구조 설계를 제안하였다. 쿼터니언기반의 운동방정식, 바이어스가 제거된 자이로 측정치, MEMS 가속도계와 지자기 센서를 이용하여 자세를 추정하는 칼만 filter를 설계하였다. MEMS 자이로의 바이어스를 제거하기 위하여 자이로 측정치와 자세 추정치를 이용하는 자이로 바이어스 제거용 칼만 filter를 추가하였다. 구현한 AHRS의 성능을 고가의 상용 Microstrain사의 3DM-GX3-25 AHRS와 비교 실험을 통하여 칼만 filter가 자이로의 바이어스 오차를 0.0001[deg/s]이하로 추정함을 볼 수 있었다. 또한 최종적으로 구해진 자세에서 롤각과 피치각은 0.2, 0.3[deg]이내의 오차를 보여주었다. 요 각은 6[deg] 이하의 오차가 발생하였다.

• 한국소음진동공학회논문집
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• 제18권8호
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• pp.872-878
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• 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.

• 한국공간구조학회논문집
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• 제18권3호
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• pp.67-74
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• 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).

• 센서학회지
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• 제17권6호
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• pp.406-413
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• 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.