• Wind and Structures
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• v.11 no.1
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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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.129-136
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• 2012

Temperature of fresh concrete can be effectively used to predict the strength of concrete being cured and make an informed decision for stripping the molds. A hygrothermograph and thermo-couple sensors that require an extensive wiring have been applied to measure a temperature of concrete at the early stage of the curing process on site. However, these methods have limits to provide the temperature data in real time due to harsh working environment including frequent cutting of wires. Therefore, this study is aiming at developing a device based on wireless sensor network to measure the temperature of concrete being cured in formwork. The result showed that the wireless sensor with probe type thermistor which is developed had the same temperature data compared to the existed wire type thermistor, and we confirmed the temperature history of concrete in real time for 28 days throughout the gateway by wireless network that collects the temperature data measured from specimens in laboratory. Also, the network device for transmission can be easily separated from the probe sensor part and reused consistently. If the wireless sensor network device developed uses in the field, the temperature management of concrete will be systematically conducted from at the early stage of the curing, and especially be effective for cold weather concrete construction. In addition, it will contribute to the establishment of advanced quality control system for concrete and productivity of supervisors on site will be increased in the future.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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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.

• Proceedings of the KSR Conference
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• 2008.06a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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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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• v.9 no.1
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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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• v.17 no.5
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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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• v.12 no.1
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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.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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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.

• Journal of the Korean Institute of Gas
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• v.5 no.2 s.14
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• pp.43-51
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• 2001

ln this study, a natural gas pipeline network model was established using STONER. First a map of natural gas pipeline network was drawn on STONER and then the length and diameter of the pipe were inputted. And as the specific gravity of gas flowing in the pipeline which is the value of natural gas was inputted. Finally in order to decide the pipeline variables and gas temperature, through the verification with observed real data, the possible error was minimized. For the verification, the pipeline variables and gas temperature were assumed and the pipeline network analysis was accomplished with real demand data. The square deviation of analysed pressure from observed pressure was calculated and the minimum case was selected for the optimum pipeline variables and gas temperature. Thus a proper natural gas pipeline network model for real network was established.