http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Research on soft sensing modeling method of gas turbine's difficult-to-measure parameters
Qiwei Cao,Shiyi Chen,Dongdong Zhang,Wenguo Xiang 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.8
During the operation of a gas turbine, there are many key parameters that are difficult to directly measure or to ensure measurement accuracy, which can only be measured by offline analysis methods. However, the data obtained by offline analysis has a large time lag, and it is difficult to realize real-time monitoring, control and optimization of gas turbines. In recent years, with the widespread application of data-driven methods, data-driven soft sensing technology has become a breakthrough method for online prediction of difficult-to-measure variables. Due to the time-varying nature of the gas turbine operation process, the predictive performance of the offline modeling method will inevitably degrade over time. Therefore, an adaptive soft-sensing multi-level modeling method based on the combination of the just in time learning and the ensemble learning is proposed in this paper. Taking compressor inlet air flow and turbine inlet temperature as examples, the research is carried out and verified by actual operating data. The results verify the effectiveness of the method.
Xinyu Yu,Liangtao Xia,Qingqing Jiang,Yupeng Wei,Xiang Wei,Shiyi Cao 대한뇌졸중학회 2020 Journal of stroke Vol.22 No.1
Background and Purpose Patients with aortic disease might have an increased risk of intracranial aneurysm (IA). We conducted this research to assess the prevalence of IA in patients with aortopathy, considering the impact of gender, age, and cardiovascular risk factors. Methods We searched PubMed and Scopus from inception to August 2019 for epidemiological studies reporting the prevalence of IA in patients with aortopathy. Random-effect meta-analyses were performed to calculate the overall prevalence, and the effect of risk factors on the prevalence was also evaluated. Anatomical location of IAs in patients suffered from distinct aortic disease was extracted and further analyzed. Results Thirteen cross-sectional studies involving 4,041 participants were included in this systematic review. We reported an estimated prevalence of 12% (95% confidence interval [CI], 9% to 14%) of IA in patients with aortopathy. The pooled prevalence of IA in patients with bicuspid aortic valve, coarctation of the aorta, aortic aneurysm, and aortic dissection was 8% (95% CI, 6% to 10%), 10% (95% CI, 7% to 14%), 12% (95% CI, 9% to 15%), and 23% (95% CI, 12% to 34%), respectively. Gender (female) and smoking are risk factors related to an increased risk of IA. The anatomical distribution of IAs was heterogeneously between participants with different aortic disease. Conclusions According to current epidemiological evidence, the prevalence of IA in patients with aortic disease is quadrupled compared to that in the general population, which suggests that an early IA screening should be considered among patients with aortic disease for timely diagnosis and treatment of IA.