scholarly journals Low-temperature thermal hydrolysis of sludge prior to anaerobic digestion: principal component analysis (PCA) of experimental data

Data in Brief ◽  
2021 ◽  
pp. 107323
Author(s):  
Mohamed N.A. Meshref ◽  
Seyed Mohammad Mirsoleimani Azizi ◽  
Wafa Dastyar ◽  
Rasha Maal-Bared ◽  
Bipro Ranjan Dhar
Author(s):  
Waqar Qureshi ◽  
Francesca Cura ◽  
Andrea Mura

Fretting wear is a quasi-static process in which repeated relative surface movement of components results in wear and fatigue. Fretting wear is quite significant in the case of spline couplings which are frequently used in the aircraft industry to transfer torque and power. Fretting wear depends on materials, pressure distribution, torque, rotational speeds, lubrication, surface finish, misalignment between spline shafts, etc. The presence of so many factors makes it difficult to conduct experiments for better models of fretting wear and it is the case whenever a mathematical model is sought from experimental data which is prone to noisy measurements, outliers and redundant variables. This work develops a principal component analysis based method, using a criterion which is insensitive to outliers, to realize a better design and interpret experiments on fretting wear. The proposed method can be extended to other cases too.


2006 ◽  
Vol 14 (2) ◽  
pp. 160-185 ◽  
Author(s):  
Jean-François Laslier

This article provides a model for analyzing approval voting elections. Within a standard probabilistic spatial voting setting, we show that principal component analysis makes it possible to derive candidates' relative locations from the approval votes. We apply this technique to original experimental data from the French 2002 presidential election.


Author(s):  
Mustofa Ahda

Meatball is favorite food for Indonesian society. Nowadays, many issues about the food ingredient on meatball were mixed with Lard. Therefore, this study was focused on applications of HPLC UV for the lard detection in meatball. The results showed that the beef and pork meatball can be distinguished using HPLC which was combined Principal Component Analysis (PCA). However, the application of HPLC has a problem for lard detection in meatball because can’t to control the hydrolysis of triglyceride (TGA). Hydrolysis of triglycerides can also be detected using HPLC UV and giving the interference for halal authentication.


2015 ◽  
Vol 235 ◽  
pp. 1-8
Author(s):  
Jacek Pietraszek ◽  
Ewa Skrzypczak-Pietraszek

Experimental studies very often lead to datasets with a large number of noted attributes (observed properties) and relatively small number of records (observed objects). The classic analysis cannot explain recorded attributes in the form of regression relationships due to lack of sufficient number of data points. One of method making available a filtering of unimportant attributes is an approach known as ‘dimensionality reduction’. Well-known example of such approach is principal component analysis (PCA) which transforms the data from the high-dimensional space to a space of fewer dimensions and gives heuristics to select least but necessary number of dimensions. Authors used such technique successfully in their previous investigations but a question arose: whether PCA is robust and stable? This paper tries to answer this question by re-sampling experimental data and observing empirical confidence intervals of parameters used to make decision in PCA heuristics.


Sign in / Sign up

Export Citation Format

Share Document