Principal Component Analysis (PCA) for Data Fusion and Navigation of Mobile Robots

2005 ◽  
pp. 610-611 ◽  
Author(s):  
Zeng-Guang Hou
Keyword(s):  
Principal Component Analysis ◽  
Data Fusion ◽  
Mobile Robots ◽  
Principal Component ◽  
Component Analysis

scholarly journals Ancient human bones studied and compared by near infrared spectroscopy, thermogravimetry and chemometrics

2018 ◽  
Vol 27 (1) ◽  
pp. 6-14 ◽  
Author(s):  
Alessandra Biancolillo ◽  
Mauro Tomassetti ◽  
Remo Bucci ◽  
Simona Izzo ◽  
Francesca Candilio ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Data Fusion ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Human Bones ◽  
Fusion Approach ◽  
Ancient Human Bones

Near infrared spectroscopy and thermogravimetry have been coupled with chemometric exploratory methods in order to investigate ancient (pre-Roman/Roman) human bones from two different necropolises in Central-South Italy (Cavo degli Zucchi and Elea Velia). These findings have been investigated by principal component analysis and they have also been compared with ancient human bones from two Sudanese necropolises (Saggai and Geili). Samples coming from African and European necropolises, mainly differ in two aspects: the burial procedures and their historical period. The ritual applied in the European region involved cremation, while the one applied in the African necropolises did not. Bones from Italian sites (Cavo degli Zucchi and Elea Velia) are Pre-Roman/Roman while the others (from middle Nile) come from the Prehistoric, Meroitic, and Christian Sudanese age. Near infrared spectroscopy and thermogravimetric measures have been analysed either individually or by a mid-level data-fusion approach. Principal component analysis of the near infrared spectroscopy data allowed differentiation between burnt and unburnt samples, while from the scores plots extracted from the principal component analysis model based on the entire derived thermograms, it was possible to recognize the different clusters related to the various dating of samples. The data-fusion analysis led to considerations similar to those obtained from the model based on thermogravimetry data. Finally, instead of inspecting the entire thermogravimetry curves, principal component analysis was carried out on carbonates, total collagen and water losses only. In this case, the data-fusion approach has led to extremely interesting results; in fact, this model clearly shows that samples group in separate clusters in agreement with their age and the different burial rituals.

Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion

2021 ◽  
Vol 339 ◽  
pp. 128125
Author(s):  
Michael Pérez-Rodríguez ◽  
Pamela Maia Dirchwolf ◽  
Zenaida Rodríguez-Negrín ◽  
Roberto Gerardo Pellerano
Keyword(s):  
Principal Component Analysis ◽  
Data Fusion ◽  
Fraud Detection ◽  
Principal Component ◽  
Rice Flour ◽  
Component Analysis

SAR Data Fusion Using Nonlinear Principal Component Analysis

2020 ◽  
Vol 17 (9) ◽  
pp. 1543-1547
Author(s):  
Luca Fasano ◽  
Daniele Latini ◽  
Alina Machidon ◽  
Chiara Clementini ◽  
Giovanni Schiavon ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Data Fusion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonlinear Principal Component Analysis ◽  
Sar Data

scholarly journals An Improved Principal Component Analysis in the Fault Detection of Multi-sensor System of Mobile Robot

2018 ◽  
Vol 14 (09) ◽  
pp. 82
Author(s):  
Zhaihe Zhou ◽  
Qianyun Zhang ◽  
Qingtao Zhao ◽  
Ruyi Chen ◽  
Qingxi Zeng
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Mobile Robots ◽  
Measurement Data ◽  
Principal Component ◽  
Component Analysis ◽  
Allocation Model ◽  
Dynamic Conditions ◽  
Pca Method ◽  
Detection Capabilities

<p class="0abstract"><span lang="EN-US">To cope with the fault detection in dynamic conditions of inertial components in the mobile robots, an improved principal component analysis (PCA) method was proposed. This work took a five gyroscopes redundancy allocation model to realize the measurement of the attitude. It is hard to distinguish the fault message from dynamic message in dynamic system that results in false alarm and missing inspection, so we firstly used the parity vector to preprocess the measurement data from the sensors. A fault was detected when the preprocessed data was dealt with PCA method. The effectiveness of the improved PCA method introduced in this paper was verified by comparing fault detection capabilities of conventional PCA method under the dynamic conditions of the step fault. The results of the simulation and experimental verification of the method was expected to contribute to the fault detection and improve the accuracy and reliability of the multi-sensors system in dynamic conditions.</span></p>

scholarly journals Data Fusion of UV-Vis and FTIR Spectra Combined with Principal Component Analysis for Distinguishing of Andrographis paniculata Extracts Based on Cultivation Ages and Solvent Extraction

2021 ◽  
Vol 21 (3) ◽  
pp. 753
Author(s):  
Antonio Kautsar ◽  
Wulan Tri Wahyuni ◽  
Utami Dyah Syafitri ◽  
Syifa Muflihah ◽  
Nursifa Mawadah ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Solvent Extraction ◽  
Data Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Ftir Spectra ◽  
Andrographis Paniculata ◽  
Score Plot ◽  
Using Data

Andrographis paniculata is one of the medicinal plants used for the treatment of antidiabetic. Cultivation ages and solvent extraction affected metabolites' composition and concentration that directly cause the plant's efficacies. This research aimed to distinguish A. paniculata based on cultivation ages and solvent extraction using data fusion of UV-Vis and FTIR spectra combined with principal component analysis (PCA). A. paniculata with 2, 3, and 4 months post-planting were extracted by water, 50% ethanol, 70% ethanol, and ethanol. In each extract, we measured UV-Vis and FTIR spectra. Then, we used the data fusion from both spectra. We used UV-Vis and FTIR absorbance from 200–400 nm and 1800–400 cm–1, respectively. Each extract gives a similar pattern of UV-Vis and FTIR spectra, only differ in their intensities. PCA score plot in two and three-dimensional showed A. paniculata extracts could be distinguished based on cultivation ages and solvent extraction with a total variance of 86 and 92%, respectively. Furthermore, this study confirms the data fusion of UV-Vis and FTIR spectra could distinguished A. paniculata extracts combined with chemometrics based on cultivation ages and solvent extraction.

scholarly journals Comparison of Geographical Traceability of Wild and Cultivated Macrohyporia Cocos With Different Data Fusion Approaches

2021 ◽  
Author(s):  
Qinqin Wang ◽  
Yuan-Zhong Wang ◽  
Yunmei Wang
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Random Forest ◽  
Data Fusion ◽  
Principal Component ◽  
Component Analysis ◽  
Level Data ◽  
Significant Difference ◽  
High Level ◽  
Geographical Traceability

Abstract Background Poria originated from the dried sclerotium of Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated M. cocos, the study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos. Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Methods Supervised pattern recognition techniques like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid- and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method—Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis were considered in data fusion. Results (1) the difference of wild and cultivated samples did exist in terms of the content analysis of vital chemical component and fingerprint analysis. (2) the cultivated samples from different origins could be easily identified by Fourier transform infrared spectroscopy or liquid chromatography, while the wild required data fusion. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) Mid-level-Boruta preceded Mid-level-PCA, low-level and high-level data fusion and individual techniques. The Mid-level-Boruta PLS-DA model took full advantage of information synergy and showed the best performance. Conclusions This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification.

Sign in / Sign up

Export Citation Format

Share Document