• Smart Structures and Systems
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• 제6권5_6호
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• pp.525-543
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• 2010

Testing and validation processes are critical tasks in developing a new hardware platform based on a new technology. This paper describes a series of experiments to evaluate the performance of a newly developed MEMS-based wireless sensor node as part of a wireless sensor network (WSN). The sensor node consists of a sensor board with four accelerometers, a thermometer and filtering and digitization units, and a MICAz mote for control, local computation and communication. The experiments include calibration and linearity tests for all sensor channels on the sensor boards, dynamic range tests to evaluate their performance when subjected to varying excitation, noise characteristic tests to quantify the noise floor of the sensor board, and temperature tests to study the behavior of the sensors under changing temperature profiles. The paper also describes a large-scale deployment of the WSN on a long-span suspension bridge, which lasted over three months and continuously collected ambient vibration and temperature data on the bridge. Statistical modal properties of a bridge tower are presented and compared with similar estimates from a previous deployment of sensors on the bridge and finite element models.

• 한국정밀공학회지
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• 제13권10호
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• pp.62-70
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• 1996

We experimented on cutting characteristics-cutting force, behavior of cutting temperature, surface roughness. chip thickness under low temperature, which generated by liquid nitrogen(77K). The work-pieces were freezed to-195 .deg. C and liquid nitrogen was also sprinkled on cutting area in order to decrease an experimental error of machining in low temperature. The workpiece was became to -195 .deg. C in5 minutes. In cooled condition surface roughness of workpiece was better than normal condition. In addition, we investigated the possibility that surface roughness of workpiece and cutting force can be predicted analyzing cutting conditions by the trained neural network.

• ETRI Journal
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• 제31권5호
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• pp.601-603
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• 2009

This letter presents a high performance 2.4 GHz two-stage power amplifier (PA) operating in the temperature range from $-30^{\circ}C$ to $+85^{\circ}C$ for IEEE 802.11g, wireless local area network application. It is implemented in InGaP/GaAs hetero-junction bipolar transistor technology and has a bias circuit employing a temperature compensation technique for error vector magnitude (EVM) performance. The technique uses a resistor made with a base layer of HBT. The design improves EVM performance in cold temperatures by increasing current. The implemented PA has a dynamic EVM of less than 4%, a gain of over 26 dB, and a current less than 130 mA below the output power of 19 dBm across the temperature range from $-30^{\circ}C$ to $+85^{\circ}C$.

• 산업기술연구
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• 제25권B호
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• pp.183-188
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• 2005

Instead of the incandescent lamps the LED lamps have been used on the traffic signal lamp with the advantages of small loss, no lens and long life. In this paper, we have compared three kinds of the LED controller structures and showed the LED array decision methods. We studied the temperature characteristics on LED and the temperature compensation network. The experimental results showed that the electrical characteristics of three kinds of the LED controller structures were different each other. We concluded that the temperature compensation is the important technique, the best compensation network has the ${\pm}10%$ variation for the luminous intensity.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.90-93
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• 2009

최근 차량용 네트워크 시스템으로 사용되고 있는 CAN(Controller Area Network)은 많은 ECU들이 필요한 미래형 스마트차량에 적합한 네트워크 프로토콜로서 안정성과 신뢰성을 보장해주며, 많은 ECU들의 장착으로 Wiring Harness의 공간과 중량이 늘어남으로 인해 발생되는 에너지 소비와 비용의 증가를 대폭 줄일 수 있는 것으로 나타났다. 본 논문에서는 CAN프로토콜을 이용하여 미래형 스마트 자동차에 요구되는 편의주행, 쾌적주행을 위해 Air conditioner 와 Heater를 제어하여 차량 내부 온도를 운전자의 요구에 맞도록 자동으로 제어할 수 있는 시스템을 구현하고자 한다.

• KIEAE Journal
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• 제16권6호
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• pp.69-75
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• 2016

Climate change has resulted in the urban heat island (UHI) effect throughout the globe, contributing to heat-related illness and fatalities. In order to reduce such damage, it is necessary to improve the climate observation network for precise observation of the urban thermal environment and quick UHI forecasting system. Purpose: This study analyzed the effective range of the climate observation network and the distribution of the existing Automatic Weather Stations (AWS) in Seoul to propose optimal locations for additional installment of AWS. Method: First, we performed quality analysis to pinpoint missing values and outliers within the high-density temperature data measured. With the result from the analysis, a spatial autocorrelation structure in the temperature data was tested to draw the effective range and correlation distance for each major time period. Result: As a result, it turned out that the optimal effective range for the climate observation network in Seoul in July was a radius of 2.8 kilometers. Based on this result, population density, and temperature data, we selected the locations for additional installment of AWS. This study is expected to be used to generate urban temperature maps, select and move measurement locations since it is able to suggest valid, specific spatial ranges when the data measured in point is converted into surface data.

• 한국유기농업학회지
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• 제25권3호
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• pp.555-567
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• 2017

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper's yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.110-115
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• 2022

Accurate current-voltage modeling of solar cell systems plays an important role in power prediction. Solar cells have nonlinear characteristics that are sensitive to environmental conditions such as temperature and irradiance. In this paper, the output characteristics of photovoltaic module are accurately predicted by combining the artificial neural network and physical model. In order to estimate the performance of PV module under varying environments, the artificial neural network model is trained with randomly generated temperature and irradiance data. With the use of proposed model, the current-voltage and power-voltage characteristics under real environments can be predicted with high accuracy.

• 전기전자학회논문지
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• 제23권1호
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• pp.159-163
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• 2019

최근 생활수준의 향상으로 외식문화가 발전하고 있고 온라인과 오프라인으로도 냉동식품의 택배수요가 날로 증가하고 있다. 현재 이러한 식품유통의 대부분은 냉동차량에 의해서 이루어지고 있다. 냉동차량에서 가장 중요한 요소 중에 하나는 정확히 온도를 측정하는 것이다. 기존의 상용 온도 기록 시스템은 일반적으로 온도 센서 모듈을 기록계에 직접 연결하는 형태이다. 이러한 기존의 방법은 전선을 사용하여 기록계에 온도 데이터를 전송하기 때문에 케이블의 길이에 따라 저항 오차를 보상해야한다. 본 논문은 케이블 내의 저항으로 인해 발생하는 오차를 극복하기 위해서 디지털 처리와 CAN (Controller Area Network) 통신을 사용하는 것을 제안한다. 또한 온도 측정의 정확도를 높이기 위해서 백금 센서인 PT-1000을 사용한다.

• 설비공학논문집
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• 제24권8호
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.