Research on real-time admittance matrix identification based on WAMS and multiple linear regression

2014 ◽  
Author(s):  
Wang Jing ◽  
Zhou Huizhi ◽  
Liu Dichen ◽  
Guo Ke ◽  
Han Xiangyu
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Real Time ◽  
Admittance Matrix

Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing

2001 ◽  
Vol 45 (2) ◽  
pp. 289-298 ◽  
Author(s):  
Christopher Smyser ◽  
Thomas J. Grabowski ◽  
Randall J. Frank ◽  
John W. Haller ◽  
Lizann Bolinger
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Real Time ◽  
Time Aware

A structural health monitoring system for data analysis of segment joint opening in an underwater shield tunnel

2019 ◽  
Vol 19 (4) ◽  
pp. 1032-1050 ◽  
Author(s):  
Xuyan Tan ◽  
Weizhong Chen ◽  
Guojun Wu ◽  
Luyu Wang ◽  
Jianping Yang
Keyword(s):  
Structural Health Monitoring ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Water Pressure ◽  
Shield Tunnel ◽  
Joint Opening ◽  
Health Monitoring System

The opening of segment joints is considered critical for the stability analysis of a shield tunnel lining. In order to prevent tunnel disasters, an integrated framework was developed in this study based on the laboratory experiment, real-time monitoring, and statistic theory. As a case study, this presented method was employed in a typical underwater shield tunnel to prevent leakage disasters. The water pressure experiment was used to analyze the importance of joint openings for the tunnel waterproofing. Then, an automatic structural health monitoring system was installed in the study site to have real-time monitoring of segment joint opening and the variation of external load applied to the structure. Based on the monitoring data, a multiple linear regression model was developed to explain the response of joint opening to water pressure and temperature at the important positions of arch crown, spandrel, and hance of the tunnel. The multiple linear regression results were verified to be in agreement with those of the numerical simulation; they denote that (1) joint opening decreased with the rise in temperature, but increased with the rise in water pressure at arch crown and hance, and (2) the segment joint opening decreased with the rise in water pressure at spandrel. As a potential application, the developed model was applied to predict future behaviors of structure, which is vital to prevent disasters and provides a reference to underwater constructions.

scholarly journals Evaluation of MJO Forecast Skill from Several Statistical and Dynamical Forecast Models

2009 ◽  
Vol 22 (9) ◽  
pp. 2372-2388 ◽  
Author(s):  
Kyong-Hwan Seo ◽  
Wanqiu Wang ◽  
Jon Gottschalck ◽  
Qin Zhang ◽  
Jae-Kyung E. Schemm ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Real Time ◽  
Linear Regression Model ◽  
Southern Oscillation ◽  
Multiple Linear Regression Model ◽  
Forecast Skill ◽  
Maritime Continent ◽  
Forecast System

Abstract This work examines the performance of Madden–Julian oscillation (MJO) forecasts from NCEP’s coupled and uncoupled general circulation models (GCMs) and statistical models. The forecast skill from these methods is evaluated in near–real time. Using a projection of El Niño–Southern Oscillation (ENSO)-removed variables onto the principal patterns of MJO convection and upper- and lower-level circulations, MJO-related signals in the dynamical model forecasts are extracted. The operational NCEP atmosphere–ocean fully coupled Climate Forecast System (CFS) model has useful skill (>0.5 correlation) out to ∼15 days when the initial MJO convection is located over the Indian Ocean. The skill of the CFS hindcast dataset for the period from 1995 to 2004 is nearly comparable to that from a lagged multiple linear regression model, which uses information from the previous five pentads of the leading two principal components (PCs). In contrast, the real-time analysis for the MJO forecast skill for the period from January 2005 to February 2006 using the lagged multiple linear regression model is reduced to ∼10–12 days. However, the operational CFS forecast for this period is skillful out to ∼17 days for the winter season, implying that the coupled dynamical forecast has some usefulness in predicting the MJO compared to the statistical model. It is shown that the coupled CFS model consistently, but only slightly, outperforms the uncoupled atmospheric model (by one to two days), indicating that only limited improvement is gained from the inclusion of the coupled air–sea interaction in the MJO forecast in this model. This slight improvement may be the result of the existence of a propagation barrier around the Maritime Continent and the far western Pacific in the NCEP Global Forecast System (GFS) and CFS models, as shown in several previous studies. This work also suggests that the higher horizontal resolution and finer initial data might contribute to improving the forecast skill, presumably as a result of an enhanced representation of the Maritime Continent region.

Pengaruh Motivasi Kerja Terhadap Kinerja Pegawai Kantor Sekretariat Daerah Kabupaten Gunungkidul

2017 ◽  
Vol 23 (2) ◽  
pp. 121-137
Author(s):  
Ary Sutrischastini ◽  
Agus Riyanto
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Work Motivation ◽  
Probability Sampling ◽  
Random Probability

This paper will discuss the effect of work motivation (incentives, motives and expectations) on the performance of the staff of the Regional Secretariat Gunungkidul. The purpose of this paper is: 1) Determine the effect of incentives on the performance of the staff of the Regional Secretariat Gunungkidul, 2) Determine the effect of motive on the performance of the staff of the Regional Secretariat Gunungkidul, 3) To know the effect of expectations on the performance of the staff of the Regional Secretariat Gunungkidul, 4)To know the effect of incentives, motives and expectations on the performance of the staff of the Regional Secretariat Gunungkidul.Research sites in the Regional Secretariat Gunungkidul and the population is 162entire employee in the Regional Secretariat Gunungkidul. Samples amounted to 116 respondents taken with simple random probability sampling method. Data were analyzed using multiple linear regression. Results obtained: (1) incentives positive and significant effect on the performance of, (2) motif positive and significant effect on the performance of, (3) expectations positive and significant impact on the performance of , and (4) incentives, motives and expectations of positive and significant impact on the performance of the staff of the Regional Secretariat Gunungkidul.

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

Sign in / Sign up

Export Citation Format

Share Document