Design and Implementation of an Infrared Radiant Source for Humidity Testing

Zhang, Hong; Wang, Chuansheng; Li, Xiaorui; Sun, Boyan; Dong, Jiwen

doi:10.3390/s18093088

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…An experimental concept has recently been proposed by Zhang et al [ 4 ], where total transmissivity of a radiator–imager system is proposed to be calibrated against varying moisture levels in order to measure relative humidity of air with high response rate.…”

Section: Introductionmentioning

confidence: 99%

Influence of Atmosphere on Calibration of Radiation Thermometers

Mlačnik

Pušnik

2021

Sensors

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Current process of calibrating radiation thermometers, including thermal imagers, relies on measurement comparison with the temperature of a black body at a set distance. Over time, errors have been detected in calibrations of some radiation thermometers, which were correlated with moisture levels. In this study, effects of atmospheric air on thermal transmission were evaluated by the means of simulations using best available resources of the corresponding datasets. Sources of spectral transmissivity of air were listed, and transmissivity data was obtained from the HITRAN molecular absorption database. Transmissivity data of molecular species was compiled for usual atmospheric composition, including naturally occurring isotopologs. Final influence of spectral transmissivity was evaluated for spectral sensitivities of radiation thermometers in use, and total transmissivity and expected errors were presented for variable humidity and measured temperature. Results reveal that spectral range of measurements greatly influences susceptibility of instruments to atmospheric interference. In particular, great influence on measurements is evident for the high-temperature radiation pyrometer in the spectral range of 2–2.7 µm, which is in use in our laboratory as a traceable reference for high-temperature calibrations. Regarding the calibration process, a requirement arose for matching the humidity parameters during the temperature reference transfer to the lower tiers in the chain of traceability. Narrowing of the permitted range of humidity during the calibration, monitoring, and listing of atmospheric parameters in calibration certificates is necessary, for at least this thermometer and possibly for other thermometers as well.

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Section: Introductionmentioning

confidence: 99%

Influence of Atmosphere on Calibration of Radiation Thermometers

Mlačnik

Pušnik

2021

Sensors

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“…Subsequently, the temperature data from the infrared thermometer were transmitted to the host computer. The host computer compared the actual front surface temperature provided by the thermometer with the rear surface temperature measured by the 6 ⃝ PT100 in the 7 ⃝ PT1000 mounting hole on the 3 ⃝ vapor chamber. An interpolated temperature value was obtained, which was transmitted to the temperature controller after weighing.…”

Section: Experimental Measurementmentioning

confidence: 99%

“…Infrared remote sensing technology can accurately provide a wealth of information about the Earth’s surface or environmental conditions. Consequently, it is extensively utilized in military reconnaissance, meteorological observation, environmental monitoring, resource surveys, and disaster prevention, among other applications [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. With the continuous development of infrared remote sensing technology, the measurement of infrared radiation characteristics has become an essential tool for acquiring features and identifying complex or weak targets [ 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Temperature-Automated Calibration Methods for a Large-Area Blackbody Radiation Source

Yang,

Cao,

Huang

et al. 2024

Sensors

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High-precision temperature control of large-area blackbodies has a pivotal role in temperature calibration and thermal imaging correction. Meanwhile, it is necessary to correct the temperature difference between the radiating (surface of use) and back surfaces (where the temperature sensor is installed) of the blackbody during the testing phase. Moreover, large-area blackbodies are usually composed of multiple temperature control channels, and manual correction in this scenario is error-prone and inefficient. At present, there is no method that can achieve temperature-automated calibration for a large-area blackbody radiation source. Therefore, this article is dedicated to achieving temperature-automated calibration for a large-area blackbody radiation source. First, utilizing two calibrated infrared thermometers, the optimal temperature measurement location was determined using a focusing algorithm. Then, a three-axis movement system was used to obtain the true temperature at the same measurement location on a large-area blackbody surface from different channels. This temperature was subtracted from the blackbody’s back surface. The temperature difference was calculated employing a weighted algorithm to derive the parameters for calibration. Finally, regarding experimental verification, the consistency error of the temperature measurement point was reduced by 85.4%, the temperature uniformity of the surface source was improved by 40.4%, and the average temperature measurement deviation decreased by 43.8%. In addition, this system demonstrated the characteristics of strong environmental adaptability that was able to perform temperature calibration under the working conditions of a blackbody surface temperature from 100 K to 573 K, which decreased the calibration time by 9.82 times.

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“…However, these algorithms still need paired data for training. It is very difficult to many tasks to collect images, such as image dehazing and image denoising [31]- [36]. CycleGAN [37], Dual-GAN [38], DiscoGAN [39] and Coupled GAN [40] were proposed to solve this problem.…”

Section: B Image-to-image Style Transfermentioning

confidence: 99%

MSGAN: Generative Adversarial Networks for Image Seasonal Style Transfer

Zhang

Wang

2020

IEEE Access

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Although Generative Adversarial Networks (GANs) have shown remarkable successes in various computer vision tasks, they still face challenges in image season style transfer task. In this paper, we propose a multi-season Generative Adversarial Networks (MSGANs) aimed to transfer input images into other season styles. To improve the quality of the simulated images generated by the proposed MSGAN, we propose a novel loss function to guide the optimization direction of the network. Besides, we adopt the saliency information to guide the seasonal style transformation task, so as to ensure that different image contents can have different optimization weights in MSGAN. The experimental results show that the proposed MSGAN can generate high-quality simulated images from real images, and is superior to other latest methods. Not only that, the synthetic image generated by the proposed method also be used to perform depth estimation task so that prove that the synthetic images can be well applied to other computer vision tasks.

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Design and Implementation of an Infrared Radiant Source for Humidity Testing

Cited by 5 publications

References 23 publications

Influence of Atmosphere on Calibration of Radiation Thermometers

Influence of Atmosphere on Calibration of Radiation Thermometers

Temperature-Automated Calibration Methods for a Large-Area Blackbody Radiation Source

MSGAN: Generative Adversarial Networks for Image Seasonal Style Transfer

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