• Wind and Structures
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• 제11권1호
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• pp.35-50
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• 2008

Wind and temperature have been shown to be the critical sources causing changes in the modal properties of large-scale bridges. While the individual effects of wind and temperature on modal variability have been widely studied, the investigation about the effects of multiple environmental factors on structural modal properties was scarcely reported. This paper addresses the modeling of the simultaneous effects of wind and temperature on the modal frequencies of an instrumented cable-stayed bridge. Making use of the long-term monitoring data from anemometers, temperature sensors and accelerometers, a neural network model is formulated to correlate the modal frequency of each vibration mode with wind speed and temperature simultaneously. Research efforts have been made on enhancing the prediction capability of the neural network model through optimal selection of the number of hidden nodes and an analysis of relative strength of effect (RSE) for input reconstruction. The generalization performance of the formulated model is verified with a set of new testing data that have not been used in formulating the model. It is shown that using the significant components of wind speeds and temperatures rather than the whole measurement components as input to neural network can enhance the prediction capability. For the fundamental mode of the bridge investigated, wind and temperature together apply an overall negative action on the modal frequency, and the change in wind condition contributes less to the modal variability than the change in temperature.

• 한국구조물진단유지관리공학회 논문집
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• 제16권5호
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• pp.129-136
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• 2012

본 연구는 타설 콘크리트의 온도를 무선센서 방식으로 현장에서 직접 간편하게 계측할 수 있는 장치를 개발하고, 무선 전송네트워크시스템을 통하여 현장사무실 및 본사 등에서 실시간 효율적 온도이력관리를 할 수 있는 시스템을 구축하는데 목적이 있다. 실험결과, 우선 무선센서네트워크시스템의 기본이 되는 온도센서는 콘크리트 타설시 안정적으로 측정될 수 있도록 무선방식의 막대타입의 스텐레스 프로브형으로 제작하였으며, 거푸집에서의 탈부착이 간편하고 장기간의 내장전력공급이 가능한 거푸집일체형의 무선센서네트워크 장치를 개발하였다. 또한 무선센서네트워크시스템의 구성은 센서노드와 라우터, 게이트웨이 및 CDMA 통신방식으로 구성하였으며, 콘크리트의 동일한 양생조건 및 상이한 양생조건에서 온도를 측정한 결과, 기존의 유선방식과 동일한 온도분포를 보였다. 향후, 개발된 무선센서네트워크 장치를 현장에서 사용할 경우, 현장 사무실에서의 정량적인 콘크리트 온도관리가 효율적으로 이루어 질 것으로 판단되며, 감리 감독업무의 생산성 향상과 더불어 전반적인 콘크리트 구조체의 품질에 크게 기여할 것으로 판단된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1404-1407
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• 1996

A quality monitoring system in butt welding process is proposed to estimate weld pool sizes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to prove the integrity of the weld quality. The monitoring variables used are the surface temperatures measured at three points on the top surface of the weldment. The temperature profile is assumed that it has a gaussian distribution in vertical direction of torch movement and verify this assumption through temperature analysis. A neural network estimator is designed to estimate weld pool size from temperature informations. The experimental results show that the proposed neural network estimator which used gaussian distribution as temperature information can estimate the weld pool sizes accurately than used three point temperatures as temperature information. Considering the change of gap size in butt welding, the experiment were performed on various gap size.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.948-951
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• 2008

Ubiquitous sensor network (USN) based on ZigBee communication protocol has been used in various application fields, such as home-network, intelligent building and machine, logistics, environmental monitoring, military field, security field and etc. The ZigBee is targeted at radio-frequency application that require a low data rate, long battery life and secure network. Especially, the USN system can be applied efficiently to building-indoor where the complex geometry is adopted. In this study, all 90 points of railway cabin indoor were monitored for temperature and humidity using USN technology. All sensors were pre/post-calibrated and the temperature/humidity change were analyzed in a railway cabin in real-time. The results would be useful to develop the cabin heating, ventilating and air conditing (HVAC) system to meet all passengers' thermal comfort regardless of their seat position.

• 제어로봇시스템학회논문지
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• 제13권6호
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• pp.568-573
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• 2007

Environmental information (temperature, humidity, vibration, $CO_2$, gas leakage, etc.) of building is an essential item to manage and monitor a building. For intelligent building, it is necessary to get temperature and illumination information to save energy and crack information to prevent structural problems. Moreover, temperature and gas leakage information to alarm a tire precaution, or humidity information to maintain comfortable environment. However, there have not been many researches on systems for gathering environmental information and building maintenance due to high costs. In this paper, wireless sensor network technology is applied to collecting building environmental information. Wireless sensor network is one of the latest issues and has low-power consumption, low-cost, self-configuration features.

• Asian Journal of Atmospheric Environment
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• 제9권1호
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• pp.12-21
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• 2015

Air pollution is a challenging issue in some of the large cities in developing countries. In this regard, data interpretation is one of the most important parts of air quality management. Several methods exist to analyze air quality; among these, we applied the Multilayer Perceptron (MLP) and Radial Basis Function (RBF) methods to predict the hourly air concentration of benzene in 14 districts in the municipality of Tehran. Input data were hourly temperature, wind speed and relative humidity. Both methods determined reliable results. However, the RBF neural network performance was much closer to observed benzene data than the MLP neural network. The correlation determination resulted in 0.868 for MLP and 0.907 for RBF, while the Index of Agreement (IA) was 0.889 for MLP and 0.937 for RBF. The sensitivity analysis related to the MLP neural network indicated that the temperature had the greatest effect on prediction of benzene in comparison with the wind speed and humidity in the study area. The temperature was the most significant factor in benzene production because benzene is a volatile liquid.

• KIEAE Journal
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• 제17권5호
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• Journal of Power Electronics
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• 제12권1호
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• pp.1-9
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• 2012

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.

• 한국항행학회논문지
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• 제27권4호
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• pp.469-472
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• 2023

If temperature management is required in factory or environmental monitoring, temperature can be measured by connecting various sensors wired or wirelessly. Surface acoustic wave sensors measure temperature using changes in acoustic waves on the sensor surface according to temperature, and are useful for wireless networks. In this paper, in order to build a wireless temperature measurement system in the 900 MHz frequency band, the temperature characteristics of the passive SAW sensor were measured, and the analysis and removal of multipath reflection wave effect inside the high temperature chamber were conducted. The resonant frequency of the SAW sensor was measured, and radio transmission/reception and multipath reflected wave removal techniques were proposed in the shielded chamber.

• 한국가스학회지
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• 제5권2호
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• pp.43-51
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• 2001

본 연구에서는 상용프로그램(STONER) 상에 천연가스 전국 배관망 모델이 수립되었다. 이를 위해 STONER 상에 전국 배관망도가 작성되었으며, 배관망도 상에 기존 자료를 토대로 전국 각 배관의 배관 관경, 연장 길이가 입력되었다. 그리고 배관내를 흐르는 가스를 정의하기 위해 천연가스의 비중값이 입력되었다. 마지막으로 배관의 성질과 가스 온도를 결정하기 위해 실측치와의 비교 검증을 통하여 오류를 최소화하였다. 이에 먼저 주 배관성질 값이 가정되었고, 이에 대해 실측 수요 자료를 가지고 배관망 분석이 수행되었다. 그리고 분석 결과 계산된 공급압력과 실측된 공급 압력을 분산 비교하여 최종적으로 배관의 성질값을 결정하였다. 이렇게 배관의 성질 결정에 있어서 기존의 배관망 모델 수립 방법과는 다르게 실측자료와의 검증 방법을 사용하여 좀더 실제에 접근한 모델을 수립할 수 있었다.