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IJEAS0406053 96%

2394-3661, Volume-4, Issue-6, June 2017 Robust Least Squares Dummy Variable Estimation Of Dynamic Panel Models In The Presence Of Outliers Okeke Joseph Uchenna, Okeke Evelyn Nkiruka, Obi Jude Chukwura  Abstract— This research is focused on the consistent, robust least squares dummy variable (LSDVR) estimator which is predicated on the correction of the bias of the inconsistency of the least squares dummy variable estimator of the parameters of the dynamic panel data model, as an extension of earlier results.

https://www.pdf-archive.com/2017/09/10/ijeas0406053/

10/09/2017 www.pdf-archive.com

4I18-IJAET0118717 v6 iss6 2354-2362 94%

22311963 ESTIMATION OF STRESS-STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION USING RANKED SET SAMPLING M.

https://www.pdf-archive.com/2014/07/04/4i18-ijaet0118717-v6-iss6-2354-2362/

04/07/2014 www.pdf-archive.com

sparse-inverse-covariance 93%

sparse inverse covariance Sparse inverse covariance with the graphical Lasso January 19, 2017 Sitbon Pascal Abstract This paper reviews the estimation of sparse graphical model by Lasso estimations and its implementation (Friedman et al., 2007).

https://www.pdf-archive.com/2017/01/19/sparse-inverse-covariance/

19/01/2017 www.pdf-archive.com

4 (1) 93%

Since the future events cannot be known with certainty at the time of estimation, there is often substantial uncertainty surrounding the estimation process.

https://www.pdf-archive.com/2017/08/05/4-1/

05/08/2017 www.pdf-archive.com

Merged PDF Solutions Manual 93%

Merged PDF Solutions Manual Chapter 1:

https://www.pdf-archive.com/2015/03/26/merged-pdf-solutions-manual/

26/03/2015 www.pdf-archive.com

IJETR011834 90%

2321-0869, Volume-1, Issue-8, October 2013 Function Point Analysis S.Sowmya, N.Vignesh  Section III deals with Full Function points being used for estimation of real time software.

https://www.pdf-archive.com/2017/12/27/ijetr011834/

27/12/2017 www.pdf-archive.com

Translate Shark-Human Translation 90%

Translate Shark Human Translation Translate Shark-Human Translation Published by:

https://www.pdf-archive.com/2016/09/08/translate-shark-human-translation/

08/09/2016 www.pdf-archive.com

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm 89%

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm   Article  Joint Estimation of the Electric Vehicle Power Battery  State of Charge Based on the Least Squares Method  and the Kalman Filter Algorithm  Xiangwei Guo 1,2,3, Longyun Kang 1,2,*, Yuan Yao 1,2, Zhizhen Huang 1,2 and Wenbiao Li 1,2    New Energy Research Center of Electric Power College, South China University of Technology,  Guangzhou 510640, China; gxw8611@163.com (X.G.); HeinzYao@outlook.com (Y.Y.);  hzz465288@yahoo.com (Z.H.); epangelo@mail.scut.edu.cn (W.L.)  2  Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology,  Guangzhou 510640, China  3  College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China  *  Correspondence: lykang@scut.edu.cn; Tel.: +86‐137‐2809‐8863  1 Academic Editor: Sheng S. Zhang  Received: 6 October 2015; Accepted: 22 January 2016; Published: 8 February 2016  Abstract:  An  estimation  of  the  power  battery  state  of  charge  (SOC)  is  related  to  the  energy  management, the battery cycle life and the use cost of electric vehicles. When a lithium‐ion power  battery is used in an electric vehicle, the SOC displays a very strong time‐dependent nonlinearity  under the influence of random factors, such as the working conditions and the environment. Hence,  research  on  estimating  the  SOC  of  a  power  battery  for  an  electric  vehicle  is  of  great  theoretical  significance and application value. In this paper, according to the dynamic response of the power  battery terminal voltage during a discharging process, the second‐order RC circuit is first used as  the  equivalent  model  of  the  power  battery.  Subsequently,  on  the  basis  of  this  model,  the  least  squares  method  (LS)  with  a  forgetting  factor  and  the  adaptive  unscented  Kalman  filter  (AUKF)  algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show  that the joint estimation algorithm proposed in this paper has higher precision and convergence of  the initial value error than a single AUKF algorithm.  Keywords: least square method with a forgetting factor; AUKF; joint estimation    1. Introduction  In an electric vehicle, the power battery State of Charge (SOC), an important parameter of the  battery state, is used to directly reflect the remaining capacity of the battery and provide a basis for  the formulation of an optimal energy management strategy for the vehicle control system. An inaccurate  SOC will result in a reduced performance of the vehicle and lead to potential damage to the battery  system; therefore, it is critical to develop algorithms that can accurately estimate the battery SOC in 

https://www.pdf-archive.com/2018/08/16/untitled-pdf-document-32/

16/08/2018 www.pdf-archive.com

5528 89%

Although the estimated surface area was significantly different from the measured surface area by IP method, this mathematical estimation represented a good approximation of actual surface area.

https://www.pdf-archive.com/2016/06/16/5528/

16/06/2016 www.pdf-archive.com

outofhour co.uk 88%

outofhour co.uk Contractor Login Instant Estimate.

https://www.pdf-archive.com/2018/03/20/outofhour-co-uk/

20/03/2018 www.pdf-archive.com