Correlation Analysis of Influencing Factors of Switching Impulse Discharge Voltage in Rod-Plane Air Gap

2021 ◽  
pp. 667-675
Author(s):  
Xiuyuan Yao ◽  
Bingxue Yang ◽  
Zhanhui Lu ◽  
Ge Xing ◽  
Ding Yujian
Keyword(s):  
Correlation Analysis ◽  
Influencing Factors ◽  
Discharge Voltage ◽  
Air Gap ◽  
Impulse Discharge

Test Research on Phase-to-Ground Air-Gap Discharge Characteristics of UHV Substation

2014 ◽  
Vol 492 ◽  
pp. 162-168
Author(s):  
Qi Ming Ye ◽  
Liang Xie ◽  
Xiao Qing Luo ◽  
Feng Huo
Keyword(s):  
Minimum Distance ◽  
Discharge Voltage ◽  
Air Gap ◽  
Test Results ◽  
Discharge Characteristics ◽  
Voltage Rise ◽  
Test Base ◽  
Impulse Discharge ◽  
Lightning Impulse

In order to optimize the design of UHV substation and reduce its construction investment, it is necessary to take further research of UHV substation air-gap discharge characteristics. In this paper, by using sub-conductor and tower to simulate UHV substation air-gap, lightning and switching impulse discharge characteristics tests of UHV substation are taken in the UHV AC test base of SGCC. The results show that, when the distance of conductor between tower is in range of 4m to 7m, the 50% lightning impulse and switching impulse discharge voltage rise along with the rise of air gap distance. As the air-gap increases, the switching impulse discharge voltage presents the trend of saturation. According to the analysis of test results, we can draw a conclusion that the gap factor of switching impulse discharge can be 1.22 when the minimum distance between conductors and tower is 5~8m.

Cognition differences and improvement countermeasures of non-technical medical services based on grey correlation analysis

2019 ◽  
Vol 9 (2) ◽  
pp. 155-163
Author(s):  
Hangyu Liu ◽  
Saina Liu ◽  
Lizhong Duan ◽  
Chongxu Zhang ◽  
Lili Yin ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Influencing Factors ◽  
Design Methodology ◽  
Doctoral Degree ◽  
Medical Services ◽  
Literature Analysis ◽  
Expert Interviews ◽  
Content Type ◽  
Grey Correlation ◽  
Grey Correlation Analysis

Purpose The purpose of this paper is to explore the rationality differences of cognition of non-technical medical services in different groups, and to provide countermeasures for improving non-technical medical services. Design/methodology/approach Literature analysis, expert interviews, questionnaire survey and frequency analysis were taken to reveal the influencing factors of non-technical medical services. Grey correlation methods were taken to compare the rationality differences of cognition of non-technical medical services by analysis influencing factors’ scores marked by different groups. Findings A total of 12 influencing factors of non-technical medical services were obtained, including “doctor’s working career”, “doctor’s strict implementation of medical treatment norms and medication guidelines”, “doctor’s service awareness”, etc. And rationality differences of cognition of non-technical medical services were confirmed as follows: the doctors’ cognition was more reasonable compared with patients; the women’s cognition was more reasonable compared with men; the lower aged groups’ cognition was more reasonable compared with higher aged groups; and people with doctoral degree had a less reasonable cognition compared with others. Originality/value The authors systematically discussed the cognition differences of non-technical medical services among different people, and provided some countermeasures reasonably.

scholarly journals Investigation on the Stress of Chinese Pediatricians Under the Outbreak of COVID-19

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Lan Yang ◽  
Bingbing Zhang ◽  
Xiaoxing Kong ◽  
Weifang Zhou ◽  
Jianmei Tian ◽  
...  
Keyword(s):  
Linear Regression ◽  
Correlation Analysis ◽  
Sleep Quality ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Perceived Stress ◽  
Physical Condition ◽  
Work Experience ◽  
Mental Stress ◽  
Professional Title

Background: The Coronavirus disease 2019 (COVID-19) pandemic has posed a severe threat to international health and the economy. Clinicians, the main staff involved in fighting against the pandemic, are under great pressure. However, relevant mental stress studies are lacking at present. Objectives: This study aimed to explore the mental stress level and its influencing factors among Chinese pediatricians under the outbreak of COVID-19, aiming to provide a certain theoretical basis for relevant psychological intervention among medical staff. Methods: In this cross-sectional study, 352 in-service pediatricians were selected from nine hospitals in Jiangsu province, China, in February 2020. The online survey was performed to collect general information. Meanwhile, the Perceived Stress Scale (PSS-10), Self-rating Anxiety Scale (SAS), and Pittsburgh Sleep Quality Index (PSQI) scale were employed for assessment. Afterward, the stress level and influencing factors among pediatricians were analyzed through descriptive analysis, one-way analysis of variance (ANOVA), correlation analysis, and multiple linear regression analysis. Results: The mean score of PSS-10 showed moderate stress on the whole. Pediatricians from outpatient and emergency departments had significantly higher perceived stress than those from other departments. The perceived stress was significantly associated with age, educational level, professional title, work experience, and physical condition. Stepwise multiple linear regression showed that age, professional title, and physical condition had a linear relationship with the individual’s perceived stress. Besides, Pearson correlation analysis indicated that perceived stress was associated with anxiety level and sleep quality. Conclusions: Under the outbreak of COVID-19, pediatricians suffer from relatively high levels of mental stress. The influencing factors include education, age, professional title, work experience, and physical condition. Typically, the anxiety level and sleep quality are correlated with the mental stress among pediatricians.

scholarly journals Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Meiping Wang ◽  
Qi Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Correlation Analysis ◽  
Support Vector Regression ◽  
Influencing Factors ◽  
Heat Supply ◽  
Particle Swarm ◽  
Heating System ◽  
Support Vector ◽  
Swarm Optimization ◽  
Input Variables

We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

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