Fuzzy modeling and prediction of combustion layers′ temperature for power plant

This paper develops a fuzzy modeling strategy to study the temperature of different combustion layers in a power plant. First, a new infrared temperature measurement system is developed to measure three layers (bottom, middle and upper) temperature on both sides of the boiler. Then, a fuzzy clustering modeling algorithm is designed based on entropy to determine the structure of the fuzzy model and the corresponding fuzzy memberships of local models. The effect of modeling mismatches are overcome by the use of online identification of parameters. Simulation results show that the effectiveness of the proposed method can be achieved for a 660 MW power plant.

Research on Algorithm to Improve Accuracy of Temperature Measurement of Moving Strong Reflector

The roll is necessary and important equipment in aluminum processing, and its surface temperature will change its thermal expansion. The gap shape between rolls will change accordingly, affecting the quality of aluminum products. Therefore, it is important to monitor and control the roll surface temperature. The roller is a highly reflective body that rotates continuously during work. This article proposes an infrared temperature sensor to measure its surface temperature. Through field investigation and a literature review, we know that a roller’s motion will affect the accuracy of its surface temperature measurement. An infrared temperature measurement compensation algorithm based on rotation speed is constructed to eliminate this influence. Experimental results show that this method can make up for the temperature measurement error caused by the change of rotation speed and hence improve measurement accuracy. The algorithm is simple and adaptable and provides a new method to improve the accuracy of temperature measurement given speed change.

Theoretical Modeling and Analysis of Directional Spectrum Emissivity and Its Pattern for Random Rough Surfaces with a Matrix Method

The emissivity is an important surface property parameter in many fields, including infrared temperature measurement. In this research, a symmetry theoretical model of directional spectral emissivity prediction is proposed based on Gaussian random rough surface theory. A numerical solution based on a matrix method is determined based on its symmetrical characteristics. Influences of the index of refraction n and the root mean square (RMS) roughness σrms on the directional spectrum emissivity ε are analyzed and discussed. The results indicate that surfaces with higher n and lower σrms tend to have a peak in high viewing angles. On the contrary, surfaces with lower n and higher σrms tend to have a peak in low viewing angles. Experimental verifications based on infrared (IR) temperature measurement of Inconel 718 sandblasted surfaces were carried out. This model would contribute to understand random rough surfaces and their emitting properties in fields including machining, process controlling, remote sensing, etc.

Prediction of cutting temperature in the milling of wood-plastic composite using artificial neural network

In the milling of wood-plastic composites, the cutting temperature has a great influence on tool life and cutting quality. The effects of cutting parameters on the cutting temperatures in the cutting zone were analyzed using infrared temperature measurement technology. The results indicated that the cutting temperature increased with the increase of spindle speed and cutting depth but decreased with the increase of feed rates. In addition, based on experimental data, a BP neural network model was proposed for predicting the cutting temperatures. The value of R2 was 0.97354 for the testing data, which indicates that the developed model achieved high prediction accuracy, respectively. The results of the study can play a guiding role in the prediction and control of cutting temperature, which is of great importance in the improvement of tool life, machining quality, and machining efficiency.

Report on Three Round COVID-19 Risk Blind Tests by Screening Eye-region Manifestations

The Coronavirus disease 2019 (COVID-19) has affected several million people since 2019. Despite various vaccines of COVID-19 protect million people in many countries, the worldwide situations of more the asymptomatic and mutated strain discovered are urging the more sensitive COVID-19 testing in this turnaround time. Unfortunately, it is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts. On the other hand, there are more and more ocular manifestations that have been reported in the COVID-19 patients as growing clinical evidence[1]. This inspired this project. We have conducted the joint clinical research since January 2021 at the ShiJiaZhuang City, Heibei province, China, which approved by the ethics committee of The fifth hospital of ShiJiaZhuang of Hebei Medical University. We undertake several blind tests of COVID-19 patients by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Meantime as an important part of the ongoing globally COVID-19 eye test program by AIMOMICS since February 2020, we propose a new fast screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras. This could reliably make a rapid risk screening of COVID-19 with the sustainable stable high performance in different countries and races. For this clinical trial in ShiJiaZhuang, we compare and analyze 1194 eye-region images of 115 patients, including 66 COVID-19 positive patients, 44 rehabilitation patients (nucleic acid changed from positive to negative), 5 liver patients, as well as 117 healthy people. Remarkably, we consistently achieved very high testing results (> 0.94) in terms of both sensitivity and specificity in our blind test of COVID-19 patients. This confirms the viability of the COVID-19 fast screening by the eye-region manifestations. Particularly and impressively, the results have the similar conclusion as the other clinical trials of the globally COVID-19 eye test program[1]. Hopefully, this series of ongoing globally COVID-19 eye test study, and potential rapid solution of fully self-performed COVID risk screening method, can be inspiring and helpful to more researchers in the world soon. Our model for COVID-19 rapid prescreening have the merits of the lower cost, fully self-performed, non-invasive, importantly real-time, and thus enables the continuous health surveillance. We further implement it as the open accessible APIs, and provide public service to the world. Our pilot experiments show that our model is ready to be usable to all kinds of surveillance scenarios, such as infrared temperature measurement device at airports and stations, or directly pushing to the target people groups smartphones as a packaged application.