A Laboratory Intercomparison of Real-Time Gaseous Ammonia Measurement Methods

2007 ◽  
Vol 41 (24) ◽  
pp. 8412-8419 ◽  
Author(s):  
James J. Schwab ◽  
Yongquan Li ◽  
Min-Suk Bae ◽  
Kenneth L. Demerjian ◽  
Jian Hou ◽  
...  
2020 ◽  
Vol 17 (3) ◽  
pp. 867-890
Author(s):  
Jun-Hee Choi ◽  
Hyun-Sug Cho

The gravimetric method, which is mainly used among particulate matter (PM) measurement methods, includes the disadvantages that it cannot measure PM in real time and it requires expensive equipment. To overcome these disadvantages, we have developed a light scattering type PM sensor that can be manufactured at low cost and can measure PM in real time. We have built a big data system that can systematically store and analyze the data collected through the developed sensor, as well as an environment where PM states can be monitored mobile in real time using such data. In addition, additional studies were conducted to analyze and correct the collected big data to overcome the problem of low accuracy, which is a disadvantage of the light scattering type PM sensor. We used a linear correction method and proceeded to adopt the most suitable value based on error and accuracy.


2012 ◽  
Vol 195-196 ◽  
pp. 195-199
Author(s):  
Jian Wen Wang ◽  
Zheng Feng Wang ◽  
Peng Li

In the paper, it proceeds in-depth research and analysis to the existing capacitance current measurement methods, and the infuse signal method is chosen to achieve capacitance current accurate measurement on this basis. The method has the advantage of measurement error small, real-time good and safe reliable.


2021 ◽  
Vol 15 (4) ◽  
pp. 93-102
Author(s):  
Kyunghoon Kim ◽  
Gyutae Park ◽  
Seokwon Kang ◽  
Rahul Singh ◽  
Jeongin Song ◽  
...  

2017 ◽  
Vol 72 ◽  
pp. 583-589 ◽  
Author(s):  
Sanduru Thamarai Krishnan ◽  
Kuk Hui Son ◽  
Namhyoung Kim ◽  
Buddolla Viswanath ◽  
Sanghyo Kim ◽  
...  

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 81 ◽  
Author(s):  
Shengyu Hao ◽  
Peiyi Wang ◽  
Yanzhu Hu

At present, the identification of haze levels mostly relies on traditional measurement methods, the real-time operation and convenience of these methods are poor. This paper aims to realize the identification of haze levels based on the method of haze images processing. Therefore, this paper divides the haze images into five levels, and obtains the high-quality haze images in each level by the brightness correction of the optimization solution and the color correction of the feature matching. At the same time, in order to reduce the noise of the haze images, this article improved the Butterworth filter. Finally, based on the processed haze images, this paper uses the Faster R-CNN network to identify the haze levels. The results of multiple sets of comparison experiments demonstrate the accuracy of the study.


Electronics ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 171 ◽  
Author(s):  
Song-Woo Choi ◽  
Siyeong Lee ◽  
Min-Woo Seo ◽  
Suk-Ju Kang

Because the interest in virtual reality (VR) has increased recently, studies on head-mounted displays (HMDs) have been actively conducted. However, HMD causes motion sickness and dizziness to the user, who is most affected by motion-to-photon latency. Therefore, equipment for measuring and quantifying this occurrence is very necessary. This paper proposes a novel system to measure and visualize the time sequential motion-to-photon latency in real time for HMDs. Conventional motion-to-photon latency measurement methods can measure the latency only at the beginning of the physical motion. On the other hand, the proposed method can measure the latency in real time at every input time. Specifically, it generates the rotation data with intensity levels of pixels on the measurement area, and it can obtain the motion-to-photon latency data in all temporal ranges. Concurrently, encoders measure the actual motion from a motion generator designed to control the actual posture of the HMD device. The proposed system conducts a comparison between two motions from encoders and the output image on a display. Finally, it calculates the motion-to-photon latency for all time points. The experiment shows that the latency increases from a minimum of 46.55 ms to a maximum of 154.63 ms according to the workload levels.


Sign in / Sign up

Export Citation Format

Share Document