Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks by Hussein A. M. Hussein, Sharafiz B. Abdul Rahim, Faizal B. Mustapha, Prajindra S. Krishnan, Nawal Aswan B. Abdul Jalil

Subjects: Get full text

Effect of Roasting on Endogenous Functional Components and Antioxidant Activity of Walnuts with Shells by Mengyang WANG, Hexin ZHANG, Mei LI, Huaide XU, Ting ZHANG, Wenge ZHAO, Hongjie LEI

“…After roasting, the acid value and peroxide value of walnuts were within the specified range by the Codex Alimentarius Commission (CAC). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that moderate roasting (140 ℃, 60 min) improved the endogenous functional composition and antioxidant capacity of walnuts. …”
Get full text

Spatial and Temporal Distribution Characteristics of Water Quality in Beiluo River and Pollution Sources Based on Principal Component Analysis by Zhang Xiang, Li Su

“…[Methods] The main pollution indicators of the Beiluo River were screened and evaluated using principal component analysis and a comprehensive water quality identification index method according to the law of intra-annual distribution of water volume in the Beiluo River. …”
Get full text

Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by Ayuba John, Ismail Fauzi Bin Isnin, Syed Hamid Hussain Madni, Farkhana Binti Muchtar

“…Several IDS models have various security problems, such as low detection accuracy and high false alarms, which can be caused by the network traffic dataset's excessive dimensionality and class imbalance in the creation of IDS models. Principal Component Analysis (PCA) has proven to be a helpful feature selection technique for dimensionality reduction. …”
Get full text

Cutting-Edge Approaches in the Co-Amorphization Process by Azza A. K. Mahmoud, Géza Regdon, Katalin Kristó

Subjects: Get full text

Assessing Roles of Aggregate Structure on Hydraulic Properties of Saline/Sodic Soils in Coastal Reclaimed Areas by Yuanhang Fei, Dongli She, Shengqiang Tang, Hongde Wang, Xiaoqin Sun, Xiao Han, Dongdong Liu

Subjects: Get full text

Self-assessment of quality of life in patients after suffering from aneurysmal subarachnoid hemorrhage, principal component analysis by Sergio A. Calero-Martinez, Nazeer Aboud, Georg Simion, Ricardo Borda, Daniel Sarmiento, Marcel Kamp, Christian Senft, Nazife Dinc

Get full text

The Multi-Component Structure of Core Strength by Sarah Schulte, Jessica Bopp, Volker Zschorlich, Dirk Büsch

Get full text

SVM-Based Optical Detection of Retinal Ganglion Cell Apoptosis by Mukhit Kulmaganbetov, Ryan Bevan, Andrew Want, Nantheera Anantrasirichai, Alin Achim, Julie Albon, James Morgan

Subjects: Get full text

Screening and comprehensive evaluation of low nitrogen tolerance of Zhejiang photosensitive japonica rice cultivars by ZHAI Rongrong, YU Peng, YE Shenghai, WANG Junmei, WU Mingguo, LIN Jianrong, ZHU Guofu, ZHANG Xiaoming

Subjects: Get full text

Testing a New Star Formation History Model from Principal Component Analysis to Facilitate Spectral Synthesis Modeling by Yanzhe Zhang, H. J. Mo, Katherine E. Whitaker, Shuang Zhou

“…In addition to some commonly used SFH models (Γ, τ , and nonparametric), we also examine a model developed from principal component analysis (PCA), trained by a set of SFHs from IllustrisTNG. …”
Get full text

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites by Yunfeng Dong, Xiaona Wei, Lu Tian, Fengrui Liu, Guangde Xu

“…To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. …”
Get full text

Reduction of Spike-like Noise in Clinical Practice for Thoracic Electrical Impedance Tomography Using Robust Principal Component Analysis by Meng Dai, Xiaopeng Li, Zhanqi Zhao, Lin Yang

“…To address this issue, we propose a robust Principal Component Analysis (RPCA)-based approach that models EIT data as the sum of a low-rank matrix and a sparse matrix. …”
Get full text

Assessment of visual function under various lighting conditions in a cohort of active older drivers: dimensionality and principal metrics by Deyue Yu, Landon Perry, Thomas Kerwin, Jingzhen Yang, Zhong-Lin Lu, Zhong-Lin Lu, Zhong-Lin Lu

“…Additionally, VA and CSF were assessed in the presence of glare under mesopic condition. Correlations and principal component analysis (PCA) were conducted to identify principal visual function metrics.ResultsVA and CSF exhibited variation across lighting conditions (ps < 0.005), with significant correlations observed between multiple pairs of visual functions. …”
Get full text

Functional Data Analysis in Forecasting Serbian Fertility by Vladimir Nikitović

“… A new approach, combining functional data analysis and principal components decomposition in order to forecasting demographic rates, introduced recently by Hyndman and his associates, is tested on official data series of Serbian age-specific fertility rates available for period 1950-2009. …”
Get full text

Functional data analysis in forecasting Serbian fertility by Nikitović Vladimir

“…A new approach, combining functional data analysis and principal components decomposition in order to forecasting demographic rates, introduced recently by Hyndman and his associates, is tested on official data series of Serbian age-specific fertility rates available for period 1950-2009. …”
Get full text

Comprehensive Assessment of Technological Challenges In Photovoltaic Waste Recovery In India Using Principal Component Analysis and Analytic Hierarchy Process Models by Dinesh Yadav, Sanjeev Kumar, Prabhu Paramasivam, Praveen Kumar Kanti, Rupesh Gupta, Mohamed Yusuf

“…This study presents a smart multi‐criteria decision‐making (MCDM) approach that integrates Principal Component Analysis (PCA) and the Analytic Hierarchy Process (AHP) to assess technological challenges in PV waste management. …”
Get full text

Application of Functional Data Analysis in Complex Human Movement Analysis by Baifa Zhang, Zhi-Cheng Lin

“…This study clarifies the relevant concepts and basic processes of functional data analysis, outlines the computational framework of functional principal component analysis, and focuses on exploring its applications in sports science, clinical rehabilitation, and motor development. …”
Get full text

Klasifikasi Aktivitas Manusia Menggunakan Algoritme Computed Input Weight Extreme Learning Machine dengan Reduksi Dimensi Principal Component Analysis by M. Sofyan Irwanto, Fitra A. Bachtiar, Novanto Yudistira

“…Salah satu metode yang dapat digunakan untuk mengurangi dimensi fitur dari sebuah data adalah Principal Component Analysis (PCA), dan salah satu metode klasifikasi yang dapat digunakan adalah Computed Input Weight Extreme Learning Machine (CIW-ELM). …”
Get full text

Research on FTTR WLAN indoor wireless location algorithm based on frequency response by Zhifeng LONG, Jing ZHANG

“…Highly accurate and reliable indoor wireless positioning services have been widely used.In order to obtain good positioning accuracy, the design of positioning algorithms needs to be matched with wireless positioning facilities.fiber to the room (FTTR) is an indoor access network solution based on IEEE 802.11 ax, a new generation of wireless local area network (WLAN) standard.Compared with the existing Wi-Fi networks, FTTR has a much larger available band width.However, FTTR WLAN also lacks of a public valid data set to support localization functions, which makes the localization research based on FTTR scenarios face huge obstacles.In order to solve the above problems, firstly, a frequency response-based FTTR scene dataset generation method was proposed, which uses the existing Wi-Fi localization dataset to generate the frequency response matrix within the available band width of FTTR.Then, the parallel path principal component analysis (PCA) method was used to generate the classification matrix.And the generated dataset was trained using a fully connected neural network to improve the accuracy.The experimental results on the real measurement dataset show that the proposed localization algorithm can achieve a localization accuracy of less than 1 m, which is not only more accurate than the traditional location estimation algorithm, but also basically meets the fine-grained localization requirements for practical applications.…”
Get full text