• Structural Monitoring and Maintenance
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• 제3권3호
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• pp.195-214
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• 2016

Monitoring of impact loads is a very important technique in the field of structural health monitoring (SHM). However, in most cases it is not possible to measure impact events directly, so they need to be reconstructed. Impact load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response function are usually known. Generally this leads to a so called ill-posed inverse problem. It is reasonable to use prior knowledge of the force in order to develop more suitable reconstruction strategies and to increase accuracy. An impact event is characterized by a short time duration and a spatial concentration. Moreover the force time history of an impact has a specific shape, which also can be taken into account. In this contribution these properties of the external force are employed to create a sample-force-dictionary and thus to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The reconstruction approach shown here is capable to estimate simultaneously the magnitude of the impact and the impact location, with a minimum number of accelerometers. The possibility of reconstructing the impact based on a noisy output signal is first demonstrated with simulated measurements of a simple beam structure. Then an experimental investigation of a real beam is performed.

• 융합보안논문지
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• 제12권3호
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• pp.27-34
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• 2012

본 논문에서는 IEEE와 ZigBee Alliance에서 제정한 국제 표준안과 호환성을 가지고, 저전력 저비용을 강점으로 하는 지그비를 이용하여 상대적으로 열악한 전송 환경을 갖지만 적용이 용이한 무선망과, 기존 무선 기반 센서 네트워크 단점을 극복하기 위해 이미 구축되어 있는 동축케이블을 이용한 유선망을 연동함에 있어서 RSSI 모니터링을 통한 출력파워 조절 알고리즘을 이용하여 저전력 소모를 특징으로 하는 지그비 장치의 새로운 저전력 소모 방안을 제시하였다. 보다 최적화된 저전력 소모를 가능하도록 실험을 통해 RSSI 모니터링을 통한 출력 파워 조절 알고리즘의 유효성을 검증하였다.

• Current Photovoltaic Research
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• 제9권3호
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• pp.106-109
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• 2021

In order to perform photovoltaic (PV) operation and management (O&M) efficiently, individual PV module monitoring is becoming more important. In this research, we developed wireless IoT sensor which can monitor individual photovoltaic modules. This IoT sensor can detect the output voltage, current and module temperature of individual modules and provide monitored data by wireless communication. Measured voltage error was 1.23%, and it shows 16.6 dBM, 0.42sec and 7.1 mA for voltage, transmittance output, response time and mean power consumption, respectively. IoT sensors were demonstrated in the test field with real climate environment condition and each of 5 sensors showed precise results of voltage, current and temperature. Also, sensors were compared with commercial power-optimizers and showed result difference within 5%.

• 조명전기설비학회논문지
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• 제21권5호
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• pp.120-125
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• 2007

1997년 지구온난화 문제를 해결하기 위해 교토 기후협약이 체결되었고, 우리나라도 2013년부터 기후협약 규제가 거의 확실시 되고 있다. 우리나라 온실가스 배출량은 연간 약6억 톤이며, 그 중 CO2가 5억 톤이다. 특히 화석연료의 연소로 대부분의 전력을 생산하는 발전 산업은 우리나라 CO2 배출량의 20[%] 이상을 차지하고 있다. 따라서 발전소의 전력생산에 따른 화석연료의 소모량과 이에 따른 CO2 배출량을 monitoring하는 것이 매우 중요해졌다. 본 논문은 발전소의 성능시험 결과 얻어지는 입출력 특성계수와 IPCC(Intergovernmental Panel on Climate Change)의 온실가스 추계 방법론을 이용하여 순시 발전출력에 따른 CO2 대기배출량을 계산하는 방법을 제시하였다. 4모선 모형계통의 시뮬레이션을 통하여 전력계통의 조류계산과 발전소별 CO2 배출량을 연립 연산하는 예를 도시하였다.

• 한국정보통신학회논문지
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• 제17권4호
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• pp.959-964
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• 2013

본 논문에서는 스완-간즈 카테터(Swan-Ganz catheter)를 사용하여 심장질환 중환자의 상태를 정확하게 파악하기 위해 기존의 모듈들보다 더 정밀한 데이터 측정이 가능한 모듈을 구현한다. 기존 방식은 차가운 수액을 심장에 주입하여 혈액의 온도변화와 회복시간을 측정하는 CO(Cardiac Output)를 측정하던 Bolus타입의 CO모듈이 존재하였지만, 지속적 환자 징후 관찰이 어렵다는 한계 때문에 현재는 대부분의 병원에서 사용하지 않는 상태이다. 이러한 한계를 극복하기 위해 지속적 심박출량 측정 플랫폼을 통해 효율적인 환자 징후관찰 및 상태파악이 가능 하도록 한다. 또한, 현재 기존 병원에서 심폐기능 이상 중환자의 징후관찰 및 상태파악을 위해 고가의 해외기기 도입으로 낭비되는 비용 문제를 해결하고 정밀한 데이터 수집으로 정확한 환자의 진단이 이루어 질 수 있도록 기존 모듈들의 문제점을 보완하였다.

• Smart Structures and Systems
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• 제6권3호
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• pp.183-196
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• 2010

Monitoring and economical design of alternative energy generators such as wind turbines is becoming increasingly critical; however acquisition of the dynamic output data can be a time-consuming and costly process. In recent years, low-cost wireless sensors have emerged as an enabling technology for structural monitoring applications. In this study, wireless sensor networks are installed in three operational turbines in order to demonstrate their efficacy in this unique operational environment. The objectives of the first installation are to verify that vibrational (acceleration) data can be collected and transmitted within a turbine tower and that it is comparable to data collected using a traditional tethered system. In the second instrumentation, the wireless network includes strain gauges at the base of the structure. Also, data is collected regarding the performance of the wireless communication channels within the tower. In both turbines, collected wireless sensor data is used for off-line, output-only modal analysis of the ambiently (wind) excited turbine towers. The final installation is on a turbine with embedded braking capabilities within the nacelle to generate an "impulse-like" load at the top of the tower. This ability to apply such a load improves the modal analysis results obtained in cases where ambient excitation fails to be sufficiently broad-band or white. The improved loading allows for computation of true mode shapes, a necessary precursor to many conditional monitoring techniques.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.168-168
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• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• Journal of Dental Anesthesia and Pain Medicine
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• 제19권6호
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• pp.353-360
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• 2019

Background: Controlled hypotension (CH) provides a better surgical environment and reduces operative time. However, there are some risks related to organ hypoperfusion. The EV1000/FloTrac system can provide continuous cardiac output monitoring without the insertion of pulmonary arterial catheter. The present study investigated the efficacy of this device in double jaw surgery under CH. Methods: We retrospectively reviewed the medical records of patients who underwent double jaw surgery between 2010 and 2015. Patients were administered conventional general anesthesia with desflurane; CH was performed with remifentanil infusion and monitored with an invasive radial arterial pressure monitor or the EV1000/FloTrac system. We allocated the patients into two groups, namely an A-line group and an EV1000 group, according to the monitoring methods used, and the study variables were compared. Results: Eighty-five patients were reviewed. The A-line group reported a higher number of failed CH (P = 0.005). A significant correlation was found between preoperative hemoglobin and intraoperative packed red blood cell transfusion (r = 0.525; P < 0.001). In the EV1000 group, the mean arterial pressure (MAP) was significantly lower 2 h after CH (P = 0.014), and the cardiac index significantly decreased 1 h after CH (P = 0.001) and 2 h after CH (P = 0.007). Moreover, venous oxygen saturation (ScVO₂) decreased significantly at both 1 h (P = 0.002) and 2 h after CH (P = 0.029); however, these values were within normal limits. Conclusion: The EV1000 group reported a lower failure rate of CH than the A-line group. However, EV1000/FloTrac monitoring did not present with any specific advantage over the conventional arterial line monitoring when CH was performed with the same protocol and same mean blood pressure. Preoperative anemia treatment will be helpful to decrease intraoperative transfusion. Furthermore, ScVO₂ monitoring did not present with sufficient benefits over the risk and cost.

• 반도체디스플레이기술학회지
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• 제18권1호
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• pp.5-9
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• 2019

Optical emission spectroscopy is used to identify chemical species and monitor the changes of process results during the plasma process. However, plasma process monitoring or fault detection by using emission signal variation monitoring is vulnerable to background signal fluctuations. IR heaters are used in semiconductor manufacturing chambers where high temperature uniformity and fast response are required. During the process, the IR lamp output fluctuates to maintain a stable process temperature. This IR signal fluctuation reacts as a background signal fluctuation to the spectrometer. In this research, we evaluate the effect of infrared background signal fluctuation on plasma process monitoring and improve the plasma process monitoring accuracy by using simple infrared background signal subtraction method. The effect of infrared background signal fluctuation on plasma process monitoring was evaluated on $SiO_2$ PECVD process. Comparing the $SiO_2$ film thickness and the measured emission line intensity from the by-product molecules, the effect of infrared background signal on plasma process monitoring and the necessity of background signal subtraction method were confirmed.

• 설비공학논문집
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• 제21권8호
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• pp.462-467
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• 2009

Researches and developments on BEMS are performed world-widely through sustainable management in various conditions. However, there are many obstacles to adapt the system in existing buildings because it needs highly expensive equipments, which are designed for newly built buildings, to install. Therefore, there are numerous limits exist when applying the BEMS in established buildings. The purpose of this study estimates the optimization of RF output power in WSN(Wireless Sensor Networks), which is the essential technology to develop PEMS. The results of this study is as follows ; 1) Applying WSN technique in buildings was possible. 2) As RF output power increases, the number of relay node reduced, therefore, the WSN showed more stability. 3) When estimating optimal RF output power in school, it should be considered between the number of relay node and RF output power. 4) Considering battery consumption and possibility of reception, the best suited RF output power is -20dbm in apartment house.