• Proceedings of the Optical Society of Korea Conference
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• 2003.02a
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• pp.330-331
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• 2003

분포형 광섬유 센서시스템은 일정한 길이의 연속적인 광섬유 및 광케이블을 매질로 하여 길이 방향에서 생기는 투과율 또는 산란 특성의 변화를 통해 물리량의 변화와 그 위치를 감지하는 시스템을 말한다. 그 중에서 광섬유 내부의 브릴루앙 산란효과를 이용하여 광케이블 주위에 진동, 압력등에 매우 민감한 분포형 광섬유센서를 구성하고 외부로부터의 침입 유무와 위치를 파악함과 동시에 주요시설물의 감시 및 진단이 가능하도록 한다. (중략)

• Proceedings of the Korean Vacuum Society Conference
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• 2000.02a
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• pp.110-110
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• 2000

Sol-Gel법은 산화물 전구체(precursor) 상태인 Sol상태로부터 가수분해, 중, 축합반응을 거쳐 최종적으로 Gel 산화물을 합성하는 방법이며 기존의 세라믹스를 합성할 수 있고 고순도의 균질한 화합물을 용이하게 얻을 수 있는 특징이 있다. 최근 전자부품이 소형, 경량화되는 추세에 따라 전자 세라믹스분야에서도 박막화가 대두되고 있는 가운데 Sol-Gel법은 dipping, spining 및 spray 법등을 이용하여 박막의 제작이 가능하며 CVD, PVD, sputtering 법등과 같은 박막제작에 비하여 장비가 복잡하지 않으면서 제작기법이 간단한 이점을 가지고 있다. 소재면에서 볼 때 TiO2 물질은 물리적, 화학적으로 안정하고 굴절율, 착샐율 및 반사율 등이 우수한 재료로서 세라믹스 콘센서, 압전소자, 습도센서와 가스센서분야등에 있어서 중요한 위치를 점하고 있어서 연구자들에게 많은 관심을 가지게 하였다. 본 연구에서는 Sol-Gel법에 의해 TiO2 Sol을 합성한 후 dipping 법으로 박막을 제작하고 박막의 전기전도 특성 및 습도센서소재로의 개발을 위해 습도감지특성에 주목하였고 경시변화로 인해 생성된 Gel powder의 물성에 대해서도 검토하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.435-440
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• 2003

본 논문에서는 실시간 출입통제시스템에 적용이 가긍한 복잡한 배경에서의 다중 얼굴 영역 검출과 추적을 통한 얼굴 인식 알고리즘을 제안하였다. 제안된 알고리즘에서는 배경영상과 입력된 연속적인 프레임간의 차영상을 적용함으로써 물체의 움직임을 감지한 후. IISI컬러 좌표모델을 이용하여 얼굴의 1차 후보 영역을 검출하고, 잡음제거를 위해 모폴로지 연산을 수행하였다 또한 Line Projection을 이용한 객체 분할법(Object Segmentation)으로 객체를 분할함으로써 다중 얼굴 영역을 추출하였다. 또한 추출된 얼굴영역에서 눈 영역 검출을 통해 각각의 얼굴 영역들을 검증하였으며 검증된 얼굴들의 최외각 4개의 좌표를 이용하여 얼굴 추적율을 높였다. 마지막으로 얼굴 인식은 추출된 얼굴 영역으로부터 주성분 분석(PCA ： Principle Component Analysis)방법을 이용함으로써 97～98％의 높은 인식율을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1300-1302
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• 2009

인간 생활의 질적인 향상을 위해, 각종 시스템이 연구되고 있다. 특히 작고 설치가 용이한 센서를 중심으로 Ubiquitous 분야가 발달하고 있으며, 센서 네트워크에 대한 연구가 활발히 진행되고 있다. 특히 일반적인 생활에는 물론 의료, 노약자의 보호, 위험 감지 등의 중요한 분야에까지 센서 네트워크가 활용됨으로써 데이터 전송의 정확성과 신뢰성은 중요한 문제로 대두되었다. 이에 본 논문에서는, 센서의 특성과 무선 통신의 특징을 고려하여 에너지 효율성 및 데이터 전송의 신뢰성을 향상시키기 위한 방법을 제시하고자 한다. 특히 RSSI 신호와 채널 에러율을 함께 이용하여 각 노드 경로별 신뢰도를 계산하고, 이에 따라 선택적으로 Acknowledgement를 사용하며 오버히어링 문제를 역으로 이용하여, 묵시적 Acknowledgement로 사용함으로써 에러율이 높고 에너지가 한정되어있는 무선 센서 네트워크에서 계속적인 ACK/NACK 사용으로 인한 에너지 소비 및 트래픽 낭비를 줄이고자 한다.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.101-105
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• 1997

When the water curtain system is employed to keep the liquefied gas in the underground storage cavern, clogging is observed in borehole. Since this phenomenon causes serious difficulties in managing LPG storage cavern, it needs to detect the degree of clogging accurately under various circumstances. Thus, in this study the active factors of clogging, that is, groundwater head, injection water flowrate, and seepage water flowrate, were investigated experimentally using a physical model. Experimental results show that groundwater head around storage cavern increases as cavern Pressure increases, while it decreases as clogging becomes severe. The pressure in storage cavern is required to reduce up to atmospheric pressure in order to detect and identify the degree of clogging more accurately. The decrease of uroundwater head due to clogging slows down as the pressure in borehole increases. As amounts of suspended matters in injected water increase, both injection water flowrate and seepage water flowrate decrease linearly with time, and the flowrate of injection water drops rapidly compared with seepage water flowrate.

• Journal of Global Scholars of Marketing Science
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• v.16 no.1
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• pp.13-26
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• 2006

In this research, we develope a procedure for detecting a random non-stationarity to the individual's purchasing rate in a stationary NED model. On this purpose, we derive the likelihood ratio statistic for a testing null and alternative hypotheses defined as whether there is no significant structural change in a stationary NED model or any. Where the structural change comes from a random non-stationarity(marketing mix activities or seasonality, for example) to the individual's purchasing rate. We also apply the developed method to a panel data for a frequently purchased good. This research could be a solution to include the non-stationarity in a stationary NED model. We also expect that the developed model could give a signal for an early detection of significant changes in marketing environment, and a mean for a measurement of the effects of marketing mix activities.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.99-106
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• 2011

In many applications, wireless sensor networks could be thought as data-centric networks, and the sensor nodes are densely distributed over a large sensor field. The sensor nodes are normally vulnerable in terms of security since they are very often deployed in a hostile environment and open space. In this paper, we propose a scheme for contents-based anomaly detection in wireless sensor networks. In this scheme we use the characteristics of sensor networks where several nodes surrounding an event point can simultaneously detect the phenomenon occurring and the contents detected from these sensors are limited to inside a certain range. The proposed scheme consists of several phases; training, testing and refining phases. Anomaly candidates detected by the distance-based anomaly detection scheme in the testing phase are sent to the refining phase. They are then compared in the sink node with previously collected data set to improve detection performance in the refining phase. Our simulation results suggest the effectiveness of the proposed scheme in this paper evidenced by the improvements of the detection rate and the false positive rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.5
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• pp.411-422
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• 2010

Self-sensing on micro-failure, dispersion degree and relating properties, of carbon nanotube(CNT)/epoxy composites, were investigated using wettability, electro-micromechanical technique with acoustic emission(AE). Specimens were prepared from neat epoxy as well as composites with untreated and acid-treated CNT. Degree of dispersion was evaluated comparatively by measuring volumetric electrical resistivity and its standard deviation. Apparent modulus containing the stress transfer was higher for acid-treated CNT composite than for the untreated case. Applied cyclic loading responded well for a single carbon fiber/CNT-epoxy composite by the change in contact resistivity. The interfacial shear strength between a single carbon fiber and CNT-epoxy, determined in a fiber pullout test, was lower than that between a single carbon fiber and neat epoxy. Regarding on micro-damage sensing using electrical resistivity measurement with AE, the stepwise increment in electrical resistivity was observed for a single carbon fiber/CNT -epoxy composite. On the other hand, electrical resistivity increased infinitely right after the first carbon fiber breaks for a single carbon fiber/neat epoxy composite. The occurrence of AE events of added CNT composites was much higher than the neat epoxy case, due to micro failure at the interfaces by added CNTs.

• Journal of Astronomy and Space Sciences
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• v.13 no.2
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• pp.173-180
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• 1996

This paper briefly describes the KITSAT-1 and KITSAT-2 spacecrafts and presents the functions, calibration procedures and in-orbit results of the KITSAT-2 analog sun sensors have been flown as an experimental payload for the future mission. We have two constraints in their design: small size and very low power consumption due to the tight mass and power budget of the spacecraft. Two one-dimensional analog sun sensors are mounted on the top facet of the KITSAT-2 spaceraft. Each has $\pm$60 degrees of view angle and they cover 210 degree field of view in total as the 30 degree view angles are overlapped. Only the relative sun angle around the Z-axis (yaw-axis) and the spin rate of the spacecraft can be achieved as the one dimensional sun sensors are used and they are aligned with the Z-axis. The calibration formulae are obtained using the fifth order line fitting algorithm for each sun sensor on the ground and they are applied to the obtained in-orbit data. ASS-1 with silicon solar cells has maximum error of 1.5 degree and ASS-2 with silicon photocells manufactured at KAIST has maximum error of 0.5 degree except near 0 degree of sun ray incident anagle where random reflection of incident sun ray is maximum in orbit. The results are presented in chapter 4. The performance of each sun sensor and the possible mounting errors are stated in chapter 5.