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Salvatore, Stefania, Jørgen G. Bramness, and Jo Røislien. "Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data." BMC Medical Research Methodology 16, no. 1 (2016): 81. https://doi.org/10.1186/s12874-016-0179-2.
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<strong>Background: </strong>Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally.<strong>Methods: </strong>We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA) were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data.<strong>Results: </strong>The first three principal components (PCs), functional principal components (FPCs) and wavelet principal components (WPCs) explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data.<strong>Conclusion: </strong>FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.
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Pesaresi, Simone, Adriano Mancini, Giacomo Quattrini, and Simona Casavecchia. "Mapping Mediterranean Forest Plant Associations and Habitats with Functional Principal Component Analysis Using Landsat 8 NDVI Time Series." Remote Sensing 12, no. 7 (2020): 1132. http://dx.doi.org/10.3390/rs12071132.
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The classification of plant associations and their mapping play a key role in defining habitat biodiversity management, monitoring, and conservation strategies. In this work we present a methodological framework to map Mediterranean forest plant associations and habitats that relies on the application of the Functional Principal Component Analysis (FPCA) to the remotely sensed Normalized Difference Vegetation Index (NDVI) time series. FPCA, considering the chronological order of the data, reduced the NDVI time series data complexity and provided (as FPCA scores) the main seasonal NDVI phenological variations of the forests. We performed a supervised classification of the FPCA scores combined with topographic and lithological features of the study area to map the forest plant associations. The supervised mapping achieved an overall accuracy of 87.5%. The FPCA scores contributed to the global accuracy of the map much more than the topographic and lithological features. The results showed that (i) the main seasonal phenological variations (FPCA scores) are effective spatial predictors to obtain accurate plant associations and habitat maps; (ii) the FPCA is a suitable solution to simultaneously express the relationships between remotely sensed and ecological field data, since it allows us to integrate these two different perspectives about plant associations in a single graph. The proposed approach based on the FPCA is useful for forest habitat monitoring, as it can contribute to produce periodically detailed vegetation-based habitat maps that reflect the “current” status of vegetation and habitats, also supporting the study of plant associations.
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K, N. Kusuma, and Lakshmi Ram Prasath H. "Application of Feature Based Principal Component Analysis (FPCA) technique on Landsat8 OLI multispectral data to map Kimberlite pipes." Indian Journal of Science and Technology 14, no. 4 (2021): 361–72. https://doi.org/10.17485/IJST/v14i4.1741.
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Abstract <strong>Objectives:</strong> To map the kimberlite pipes emplaced in parts of Anantpur District, India using Landsat-8 OLI multispectral data. Kimberlite are considered as the primary host of natural diamond. Kimberlite pipes have very limited exposure and are altered, therefore the indirect surface indicators associated with kimberlite such as ferric iron bearing minerals (hematite, goethite), hydroxyl (clay) and carbonate (calcrete) minerals, were mapped to trace kimberlite pipe. <strong>Methods:</strong> Feature based Principal Component Analysis (FPCA) was applied over the OLI bands 2, 4, 5 and 6, and 2, 5, 6 and 7 to generate ferric iron (F image) and hydroxyl/carbonate image (H/C images). The color composite was generated by assigning RGB colours to F, H/C and F+H/C images. <strong>Findings:</strong> When matched with the pre-explored kimberlite pipe locations, it was observed that the kimberlitic pipes display different colours in the above colour composite. Hence, the Isodata clustering was carried out to segregate the classes, which resulted in 12 unique classes. Of these, the kimberlite pipes fall in 4 classes. However, due to the moderate resolution of OLI, false positive areas were also noted. Further the target area was found to be reduced by incorporating the structural control (lineament) over the emplacement of Kimberlite pipes.<strong> Novelty:</strong> The present work highlights the usefulness of the moderate resolution multispectral image in mapping the Kimberlite pipes in semiarid region, in absence of a hyperspectral sensor. <strong>Keywords:</strong> Kimberlite; Landsat8 OLI; feature based Principal Component Analysis (FPCA); Lineaments; Dharwar Craton
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Montanino, Andrea, Gianluca Alaimo, and Ettore Lanzarone. "A gradient-based optimization method with functional principal component analysis for efficient structural topology optimization." Structural and Multidisciplinary Optimization 64, no. 1 (2021): 177–88. http://dx.doi.org/10.1007/s00158-021-02872-9.
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AbstractStructural topology optimization (STO) is usually treated as a constrained minimization problem, which is iteratively addressed by solving the equilibrium equations for the problem under consideration. To reduce the computational effort, several reduced basis approaches that solve the equilibrium equations in a reduced space have been proposed. In this work, we apply functional principal component analysis (FPCA) to generate the reduced basis, and we couple FPCA with a gradient-based optimization method for the first time in the literature. The proposed algorithm has been tested on a large STO problem with 4.8 million degrees of freedom. Results show that the proposed algorithm achieves significant computational time savings with negligible loss of accuracy. Indeed, the density maps obtained with the proposed algorithm capture the larger features of maps obtained without reduced basis, but in significantly lower computational times, and are associated with similar values of the minimized compliance.
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Hael, Mohanned A., Hai Qiang Ma, Hamas A. AL-kuhali, and Zeinab Rizk. "Quantile-based Clustering for Functional Data via Modelling Functional Principal Components Scores." Journal of Physics: Conference Series 2449, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2449/1/012016.
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Abstract Clustering tasks of functional data arise naturally in many applications, and efficient classification approaches are needed to find groups. The current paper combines the quantile-based model with the principal component analysis of functional data (FPCA). In our proposed procedures, the projection of functional data is first approximated based on (rotated) FPCA. The quantile-based model is then implemented on the space of rotated scores to identify the potential features of underlying clusters. The proposed method overcomes the limitation of using direct basis function expansion such as Fourier, B-spline, or linear fitting, besides representing a nonparametric clustering alternative based on a quantile approach. The proposed method’s performance has been evaluated in a comprehensive simulation study and afterward compared with existing functional and non-functional clustering methods. The simulation study results showed that the proposed method performs well in terms of correct classification rate and computing time average. Finally, a real-world application concerning temporal wind speed data has been analyzed to demonstrate the proposed method’s advantages and usefulness.
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P., Gopinath. "Raspberry PI-Based Finger Vein Recognition System Using PCA NET." International Research Journal of Computer Science 10, no. 06 (2023): 414–18. http://dx.doi.org/10.26562/irjcs.2023.v1006.24.
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Despite simultaneously ignoring the intensity distribution that is formed by the finger tissue and, in some instances, processing it as background noise, the majority of finger vein feature extraction algorithms achieve satisfactory performance due to their ability to represent texture. This project makes use of two- directional two-dimensional Fisher Principal Component Analysis, also known as (2D) 2 FPCA, for feature extraction. This type of "noise" is presented as a novel soft biometric trait for improving finger vein recognition performance. In order to demonstrate that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition, a comprehensive analysis of the finger vein imaging principle and the characteristics of the image are first presented.
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Kusuma, K. N. "Application of Feature Based Principal Component Analysis (FPCA) technique on Landsat8 OLI multispectral data to map Kimberlite pipes." Indian Journal of Science and Technology 14, no. 4 (2021): 361–72. http://dx.doi.org/10.17485/ijst/v14i4.1741.
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Zhou, Xin, and Xianqing Lei. "Fault Diagnosis Method of the Construction Machinery Hydraulic System Based on Artificial Intelligence Dynamic Monitoring." Mobile Information Systems 2021 (July 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/1093960.
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This paper aims to study the fault diagnosis method of the mechanical hydraulic system based on artificial intelligence dynamic monitoring. According to the characteristics of functional principal component analysis (FPCA) and neural network in the fault diagnosis method in the feature extraction process, the fault diagnosis method combining functional principal component analysis and BP neural network is studied and it is applied to the fault of the coordinator hydraulic system diagnosis. This article mainly completed the following tasks: analyzing the structure and working principle of the mechanical hydraulic system, studying the failure mechanism and failure mode of the mechanical hydraulic system, summarizing the common failures of the hydraulic system and the individual failures of the mechanical hydraulic system, and establishing the mechanical hydraulic system. Description of failure mode and effects analysis (FMEA): then, a joint simulation model of the mechanical hydraulic system was established in ADAMS and AMESim, and the fault detection signal of the hydraulic system was determined and compared with the experimental data. At the same time, the simulation data of the cosimulation model were compared with the simulation data of the hydraulic model in MATLAB to further verify the correctness of the model. The functional principal component analysis is used to perform functional processing on sample data, feature parameters are extracted, and the BP neural network is used to train the mapping relationship between feature parameters and fault parameters. The consistency is verified, and the fault diagnosis method is finally completed. The experimental results show that the diagnostic accuracy rates are 0.9848 and 0.9927, respectively, the reliability is significantly improved, close to 100%, and the uncertainty is basically 0, which significantly improves the accuracy of fault diagnosis.
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Hou, Yunhui, Na Shen, and Yubin Lin. "A Classification Method for Multichannel MI-EEG Signal with FPCA and DNN." Journal of Physics: Conference Series 2891, no. 11 (2024): 112014. https://doi.org/10.1088/1742-6596/2891/11/112014.
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Abstract A new accurate identification method has been proposed to address the lack of interpretability in current deep learning-based feature extraction methods for motor imagery electroencephalogram (MI-EEG) signals. This method combines functional principal component analysis (FPCA) and deep neural networks (DNN) for four classifications of MI-EEG signals. The process involves preprocessing the acquired MI-EEG signals and obtaining power spectral density (PSD) versus frequency curves in the alpha band for multiple channels and samples through FIR filtering. All PSD-frequency curves are then functionally smoothed according to the theory of functional data analysis (FDA). Feature parameters are derived using FPCA, and the parameters of all samples are normalized. Training samples are selected randomly for clustering training with DNNs. Category prediction is carried out on the test data classification samples. This method is applied to 4×120 four-categorized MI-EEG samples, each from six channels obtained from Enobio test, a wireless EEG system from Spain Neuroelectrics, involving left hand, right hand, left foot, and right foot motor imagery at a sampling rate of 500Hz. 80% of the samples were used for training, and the remaining 20% were used for testing. The prediction accuracy ranged from 84.3% to 91.66%. While this multivariate feature parameter extraction method has clear mathematical and physical significance, it does demand a high sampling rate of 500Hz.
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B D, Mr Darshan, Vyshnavi Shekhar B S, Meghana M. Totiger, Priyanka N, and Spurthi A. "Classification of Emotion using Eeg Signals: an FPGA Based Implementation." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 2 (2023): 102–9. http://dx.doi.org/10.35940/ijrte.b7808.0712223.
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An electroencephalograph is a device that records all electrical energy in the human brain using wearable metal electrodes placed on the skull. Electrical impulses connect brain cells and are always mobile, even at rest. This activity appears as a squiggly line in EEG recordings. Activity gaze data is pre-processed to a frequency range of 0 to 75 Hz. This creates a new matrix with a sample rate of 200 Hz and a range of 0-75 Hz. A finite-impulse-response low-pass filter was used because the bandpass would distort his EEG data after processing. Each pre-processed EEG signal has an output, which completes feature extraction. Principal Component Analysis or PCA is passed in the feature reduction phase. PCA is an analytical process that uses singular value decomposition to transform a collection of corresponding features into mutually uncorrelated features or principal components. Principal component analysis: (a) mean normalization of features (b) covariance matrix (c) eigenvectors (d) reduced features or principal components. The above steps are passed to the SVM classifier for sentiment output. His VHDL code and testbench for 2*2 matrices were written, waveforms and RTL schemes were created in Xilinx 14.5. For the FPGA implementation, a Simulink model was designed, and the eigenvalues were pre-determined using a system generator.
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Mr., Darshan B. D., Shekhar B. S. Vyshnavi, M. Totiger Meghana, N. Priyanka, and A. Spurthi. "Classification of Emotion using Eeg Signals: an FPGA Based Implementation." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 2 (2023): 102–9. https://doi.org/10.35940/ijrte.B7808.0712223.
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<strong>Abstract: </strong>An electroencephalograph is a device that records all electrical energy in the human brain using wearable metal electrodes placed on the skull. Electrical impulses connect brain cells and are always mobile, even at rest. This activity appears as a squiggly line in EEG recordings. Activity gaze data is pre-processed to a frequency range of 0 to 75 Hz. This creates a new matrix with a sample rate of 200 Hz and a range of 0-75 Hz. A finite-impulse-response low-pass filter was used because the bandpass would distort his EEG data after processing. Each pre-processed EEG signal has an output, which completes feature extraction. Principal Component Analysis or PCA is passed in the feature reduction phase. PCA is an analytical process that uses singular value decomposition to transform a collection of corresponding features into mutually uncorrelated features or principal components. Principal component analysis: (a) mean normalization of features (b) covariance matrix (c) eigenvectors (d) reduced features or principal components. The above steps are passed to the SVM classifier for sentiment output. His VHDL code and testbench for 2*2 matrices were written, waveforms and RTL schemes were created in Xilinx 14.5. For the FPGA implementation, a Simulink model was designed, and the eigenvalues were pre-determined using a system generator.
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Pesaresi, Simone, Adriano Mancini, Giacomo Quattrini, and Simona Casavecchia. "Functional Analysis for Habitat Mapping in a Special Area of Conservation Using Sentinel-2 Time-Series Data." Remote Sensing 14, no. 5 (2022): 1179. http://dx.doi.org/10.3390/rs14051179.
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The mapping and monitoring of natural and semi-natural habitats are crucial activities and are regulated by European policies and regulations, such as the 92/43/EEC. In the Mediterranean area, which is characterized by high vegetational and environmental diversity, the mapping and monitoring of habitats are particularly difficult and often exclusively based on in situ observations. In this scenario, it is necessary to automate the generation of updated maps to support the decisions of policy makers. At present, the availability of high spatiotemporal resolution data provides new possibilities for improving the mapping and monitoring of habitats. In this work, we present a methodology that, starting from remotely sensed time-series data, generates habitat maps using supervised classification supported by Functional Data Analysis. We constructed the methodology using Sentinel-2 data in the Mediterranean Special Area of Conservation “Gola di Frasassi” (Code: IT5320003). In particular, the training set uses 308 field plots with 11 target classes (five forests, two shrubs, one grassland, one mosaic, one extensive crop, and one urban land). Starting from vegetation index time-series data, Functional Principal Component Analysis was applied to derive FPCA scores and components. In particular, in the classification stage, the FPCA scores are considered as features. The obtained results out-performed a previous map derived from photo-interpretation by domain experts. We obtained an overall accuracy of 85.58% using vegetation index time-series, topography, and lithology data. The main advantages of the proposed approach are the capability to efficiently compress high dimensional data (dense remote-sensing time series) providing results in a compact way (e.g., FPCA scores and mean seasonal time profiles) that: (i) facilitate the link between remote sensing with habitat mapping and monitoring and their ecological interpretation and (ii) could be complementary to species-based approaches in plant community ecology and phytosociology.
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Vittorietti, Martina, Javier Hidalgo, Jesús Galán López, Jilt Sietsma, and Geurt Jongbloed. "A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel." Materials 15, no. 3 (2022): 892. http://dx.doi.org/10.3390/ma15030892.
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This study proposes a new approach to determine phenomenological or physical relations between microstructure features and the mechanical behavior of metals bridging advanced statistics and materials science in a study of the effect of hard precipitates on the hardening of metal alloys. Synthetic microstructures were created using multi-level Voronoi diagrams in order to control microstructure variability and then were used as samples for virtual tensile tests in a full-field crystal plasticity solver. A data-driven model based on Functional Principal Component Analysis (FPCA) was confronted with the classical Voce law for the description of uniaxial tensile curves of synthetic AISI 420 steel microstructures consisting of a ferritic matrix and increasing volume fractions of M23C6 carbides. The parameters of the two models were interpreted in terms of carbide volume fractions and texture using linear mixed-effects models.
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Din, Shahab Ud, Khan Muhammad, Muhammad Fawad Akbar Khan, Shahid Bashir, Muhammad Sajid, and Asif Khan. "A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan." Applied Sciences 11, no. 23 (2021): 11486. http://dx.doi.org/10.3390/app112311486.
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Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.
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Abubakar, A. J., M. Hashim, and A. B. Pour. "HYDROTHEMAL ALTERATION MAPPING USING FEATURE-ORIENTED PRINCIPAL COMPONENT SELECTION (FPCS) METHOD TO ASTER DATA:WIKKI AND MAWULGO THERMAL SPRINGS, YANKARI PARK, NIGERIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W5 (October 5, 2017): 1–5. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w5-1-2017.
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Geothermal systems are essentially associated with hydrothermal alteration mineral assemblages such as iron oxide/hydroxide, clay, sulfate, carbonate and silicate groups. Blind and fossilized geothermal systems are not characterized by obvious surface manifestations like hot springs, geysers and fumaroles, therefore, they could not be easily identifiable using conventional techniques. In this investigation, the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were evaluated in discriminating hydrothermal alteration minerals associated with geothermal systems as a proxy in identifying subtle Geothermal systems at Yankari Park in northeastern Nigeria. The area is characterized by a number of thermal springs such as Wikki and Mawulgo. Feature-oriented Principal Component selection (FPCS) was applied to ASTER data based on spectral characteristics of hydrothermal alteration minerals for a systematic and selective extraction of the information of interest. Application of FPCS analysis to bands 5, 6 and 8 and bands 1, 2, 3 and 4 datasets of ASTER was used for mapping clay and iron oxide/hydroxide minerals in the zones of Wikki and Mawulgo thermal springs in Yankari Park area. Field survey using GPS and laboratory analysis, including X-ray Diffractometer (XRD) and Analytical Spectral Devices (ASD) were carried out to verify the image processing results. The results indicate that ASTER dataset reliably and complementarily be used for reconnaissance stage of targeting subtle alteration mineral assemblages associated with geothermal systems.
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Ding, Xiaojun, Tao Li, Jingyu Chen, Ling Ma, and Fengyuan Zou. "Research on the Clothing Classification of the She Ethnic Group in Different Regions Based on FPA-CNN." Applied Sciences 13, no. 17 (2023): 9676. http://dx.doi.org/10.3390/app13179676.
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In order to achieve the effective computer recognition of the She ethnic clothing from different regions through the extraction of color features, this paper proposes a She ethnic clothing classification method based on the Flower Pollination Algorithm-optimized color feature fusion and Convolutional Neural Network (FPA-CNN). The method consists of three main steps: color feature fusion, FPA optimization, and CNN classification. In the first step, a color histogram and color moment features, which can represent regional differences in She ethnic clothing, are extracted. Subsequently, FPA is used to perform optimal weight fusion, obtaining an optimized ratio. Kernel principal component analysis is then applied to reduce the dimensionality of the fused features, and a CNN is constructed to classify the She ethnic clothing from different regions based on the reduced fused features. The results show that the FPA-CNN method can effectively classify the She ethnic clothing from different regions, achieving an average classification accuracy of 98.38%. Compared to SVM, BP, RNN, and RBF models, the proposed method improves the accuracy by 11.49%, 7.7%, 6.49%, and 3.92%, respectively. This research provides a reference and guidance for the effective recognition of clothing through the extraction of color features.
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Beiranvand Pour, Amin, Tae-Yoon Park, Yongcheol Park, et al. "Application of Multi-Sensor Satellite Data for Exploration of Zn–Pb Sulfide Mineralization in the Franklinian Basin, North Greenland." Remote Sensing 10, no. 8 (2018): 1186. http://dx.doi.org/10.3390/rs10081186.
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Geological mapping and mineral exploration programs in the High Arctic have been naturally hindered by its remoteness and hostile climate conditions. The Franklinian Basin in North Greenland has a unique potential for exploration of world-class zinc deposits. In this research, multi-sensor remote sensing satellite data (e.g., Landsat-8, Phased Array L-band Synthetic Aperture Radar (PALSAR) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) were used for exploring zinc in the trough sequences and shelf-platform carbonate of the Franklinian Basin. A series of robust image processing algorithms was implemented for detecting spatial distribution of pixels/sub-pixels related to key alteration mineral assemblages and structural features that may represent potential undiscovered Zn–Pb deposits. Fusion of Directed Principal Component Analysis (DPCA) and Independent Component Analysis (ICA) was applied to some selected Landsat-8 mineral indices for mapping gossan, clay-rich zones and dolomitization. Major lineaments, intersections, curvilinear structures and sedimentary formations were traced by the application of Feature-oriented Principal Components Selection (FPCS) to cross-polarized backscatter PALSAR ratio images. Mixture Tuned Matched Filtering (MTMF) algorithm was applied to ASTER VNIR/SWIR bands for sub-pixel detection and classification of hematite, goethite, jarosite, alunite, gypsum, chalcedony, kaolinite, muscovite, chlorite, epidote, calcite and dolomite in the prospective targets. Using the remote sensing data and approaches, several high potential zones characterized by distinct alteration mineral assemblages and structural fabrics were identified that could represent undiscovered Zn–Pb sulfide deposits in the study area. This research establishes a straightforward/cost-effective multi-sensor satellite-based remote sensing approach for reconnaissance stages of mineral exploration in hardly accessible parts of the High Arctic environments.
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Guerrero-Morejón, Katherine, José María Hinojo-Montero, Jorge Jiménez-Sánchez, Cristian Rocha-Jácome, Ramón González-Carvajal, and Fernando Muñoz-Chavero. "Efficient Real-Time Isotope Identification on SoC FPGA." Sensors 25, no. 12 (2025): 3758. https://doi.org/10.3390/s25123758.
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Efficient real-time isotope identification is a critical challenge in nuclear spectroscopy, with important applications such as radiation monitoring, nuclear waste management, and medical imaging. This work presents a novel approach for isotope classification using a System-on-Chip FPGA, integrating hardware-accelerated principal component analysis (PCA) for feature extraction and a software-based random forest classifier. The system leverages the FPGA’s parallel processing capabilities to implement PCA, reducing the dimensionality of digitized nuclear signals and optimizing computational efficiency. A key feature of the design is its ability to perform real-time classification without storing ADC samples, directly processing nuclear pulse data as it is acquired. The extracted features are classified by a random forest model running on the embedded microprocessor. PCA quantization is applied to minimize power consumption and resource usage without compromising accuracy. The experimental validation was conducted using datasets from high-resolution pulse-shape digitization, including closely matched isotope pairs (12C/13C, 36Ar/40Ar, and 80Kr/84Kr). The results demonstrate that the proposed SoC FPGA system significantly outperforms conventional software-only implementations, reducing latency while maintaining classification accuracy above 98%. This study provides a scalable, precise, and energy-efficient solution for real-time isotope identification.
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VÎJDEA, Anca-Marina, and Stefan SOMMER. "A STANDARDIZED REMOTE SENSING METHODOLOGY FOR MAPPING MINERALOGICAL MINING WASTE ANOMALIES ACROSS EUROPEAN COUNTRIES USING PRINCIPAL COMPONENT ANALYSIS AND GIS DATA INTEGRATION." Geo-Eco-Marina 29 (2023) (December 31, 2023): 059–81. https://doi.org/10.5281/zenodo.10254736.
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This paper presents a methodology based on medium resolution satellite images (Landsat Thematic Mapper and Enhanced Thematic Mapper), adapted and further developed from a variant of a Principal Components Analysis used for geological exploration, which took into consideration the spectral bands in the visible and infrared wavelengths where the minerals of interest exhibited diagnostic features. The method was developed to be applied for an inventory of mining wastes at pan-European scale and was successfully tested in known mining regions with closed exploitations in Upper Silesia (Poland), Romania and Slovakia. The results were validated against European (CORINE Land Cover) and national data sets. A Mining Anomaly Index was computed at catchment scale, to determine the most vulnerable river basins from which pollutants from the extractive industry could be further transported to the seas. In view of the present increased demand for metals and critical raw materials, the principles of the described methodology could be applied to the new satellite sensors, with increased ground and spectral resolution.
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Yu, Fengmin, Liming Liu, Nanxiang Yu, Lianghao Ji, and Dong Qiu. "A Method of L1-Norm Principal Component Analysis for Functional Data." Symmetry 12, no. 1 (2020): 182. http://dx.doi.org/10.3390/sym12010182.
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Recently, with the popularization of intelligent terminals, research on intelligent big data has been paid more attention. Among these data, a kind of intelligent big data with functional characteristics, which is called functional data, has attracted attention. Functional data principal component analysis (FPCA), as an unsupervised machine learning method, plays a vital role in the analysis of functional data. FPCA is the primary step for functional data exploration, and the reliability of FPCA plays an important role in subsequent analysis. However, classical L2-norm functional data principal component analysis (L2-norm FPCA) is sensitive to outliers. Inspired by the multivariate data L1-norm principal component analysis methods, we propose an L1-norm functional data principal component analysis method (L1-norm FPCA). Because the proposed method utilizes L1-norm, the L1-norm FPCs are less sensitive to the outliers than L2-norm FPCs which are the characteristic functions of symmetric covariance operator. A corresponding algorithm for solving the L1-norm maximized optimization model is extended to functional data based on the idea of the multivariate data L1-norm principal component analysis method. Numerical experiments show that L1-norm FPCA proposed in this paper has a better robustness than L2-norm FPCA, and the reconstruction ability of the L1-norm principal component analysis to the original uncontaminated functional data is as good as that of the L2-norm principal component analysis.
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Górecki, Tomasz, and Mirosław Krzyśko. "A kernel version of functional principal component analysis." Statistics in Transition new series 13, no. 3 (2013): 559–68. http://dx.doi.org/10.59170/stattrans-2012-040.
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In this paper a new construction of functional principal components (FPCA) is proposed, based on principal components for vector data. A kernel version of FPCA is also presented. The quality of the two described methods was tested on 20 different data sets.
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ALEOTTI, JACOPO, and STEFANO CASELLI. "FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR RECOGNITION OF ARM GESTURES AND HUMANOID IMITATION." International Journal of Humanoid Robotics 10, no. 04 (2013): 1350033. http://dx.doi.org/10.1142/s0219843613500333.
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This paper investigates the use of functional principal component analysis (FPCA) for automatic recognition of dynamic human arm gestures and robot imitation. FPCA is a statistical technique of functional data analysis that generalizes standard multivariate principal component analysis. Functional data analysis signals (e.g., gestures) are functions that are considered as observations of a random variable on a functional space. In particular, FPCA reduces the dimensionality of the input data by projecting them onto a finite-dimensional space spanned by a few prominent eigenfunctions. The main contribution of this work is the proposal of a novel technique for unsupervised clustering of training data and dynamic gesture recognition based on FPCA. FPCA has not been considered in previous studies on humanoid learning. The proposed approach has been evaluated in two experimental settings for motion capture. In the first setup single arm gestures are recognized from inertial sensors attached to the arm of the user. In the second setup the method is extended to two-arm gestures acquired from a range sensor. Recognized gestures are reproduced by a small humanoid robot. The FPCA method has also been compared to a high performance algorithm for gesture classification based on dynamic time warping (DTW). The FPCA algorithm achieves comparable results in both recognition rate and robustness to missing data, while it outperforms DTW in terms of efficiency in execution time.
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Feng, Pan, and Junhui Qian. "Analyzing and forecasting the Chinese term structure of interest rates using functional principal component analysis." China Finance Review International 8, no. 3 (2018): 275–96. http://dx.doi.org/10.1108/cfri-06-2017-0065.
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Purpose The purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA). Design/methodology/approach The authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors. Findings Monthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting. Practical implications The authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue. Originality/value To the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.
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Canuto, Raquel, Suzi Camey, Denise P. Gigante, Ana M. B. Menezes, and Maria Teresa Anselmo Olinto. "Focused Principal Component Analysis: a graphical method for exploring dietary patterns." Cadernos de Saúde Pública 26, no. 11 (2010): 2149–56. http://dx.doi.org/10.1590/s0102-311x2010001100016.
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The aim of the present study was to introduce Focused Principal Component Analysis (FPCA) as a novel exploratory method for providing insight into dietary patterns that emerge based on a given characteristic of the sample. To demonstrate the use of FPCA, we used a database of 1,968 adults. Food intake was obtained using a food frequency questionnaire covering 26 food items. The focus variables used for analysis were age, income, and schooling. All analyses were carried out using R software. The graphs generated show evidence of socioeconomic inequities in dietary patterns. Intake of whole-wheat foods, fruit, and vegetables was positively correlated with income and schooling, whereas for refined cereals, animal fats (lard), and white bread this correlation was negative. Age was inversely associated with intake of fast-food and processed foods and directly associated with a pattern that included fruit, green salads, and other vegetables. In an easy and direct fashion, FPCA allowed us to visualize dietary patterns based on a given focus variable.
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Sej-Kolasa, Małgorzata, and Mirosława Sztemberg-Lewandowska. "Funkcjonalna analiza głównych składowych w badaniu zmian liczby studentów w czasie w krajach europejskich." Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie, no. 916 (December 16, 2015): 71–81. http://dx.doi.org/10.15678/krem.740.
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Principal component analysis (PCA) transforms an original set of variables into a new orthogonal set called principal components. Functional principal component analysis (FPCA) has the same advantages as classical principal component analysis while also enabling the analysis of dynamic data. The main difference between them is that PCA is based on multidimensional data and FPCA is based on functional data. The functional data are curves, surfaces or anything else varying over a continuum. They are not a single observation. The main aim of the paper is to show the usefulness of applying functional principal component analysis in order to analyse longitudinal data. The paper presents an example of how this method has been used based on the analysis of changes in the number of students (over time) in chosen European countries. Visualisation of the results makes it possible to compare countries and detect outliers.
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Tang, Bing, Yao Hu, and Huan Chen. "A Functional Data Approach to Outlier Detection and Imputation for Traffic Density Data on Urban Arterial Roads." Promet 34, no. 5 (2022): 755–65. http://dx.doi.org/10.7307/ptt.v34i5.4069.
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In traffic monitoring data analysis, the magnitude of traffic density plays an important role in determining the level of traffic congestion. This study proposes a data imputation method for spatio-functional principal component analysis (s-FPCA) and unifies anomaly curve detection, outlier confirmation and imputation of traffic density at target intersections. Firstly, the detection of anomalous curves is performed based on the binary principal component scores obtained from the functional data analysis, followed by the determination of the presence of outliers through threshold method. Secondly, an improved method for missing traffic data estimation based on upstream and downstream is proposed. Finally, a numerical study of the actual traffic density data is carried out, and the accuracy of s-FPCA for imputation is improved by 8.28%, 8.91% and 7.48%, respectively, when comparing to functional principal component analysis (FPCA) with daily traffic density data missing rates of 5%, 10% and 20%, proving the superiority of the method. This method can also be applied to the detection of outliers in traffic flow, imputation and other longitudinal data analysis with periodic fluctuations.
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Acal, Christian, Ana M. Aguilera, and Manuel Escabias. "New Modeling Approaches Based on Varimax Rotation of Functional Principal Components." Mathematics 8, no. 11 (2020): 2085. http://dx.doi.org/10.3390/math8112085.
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Functional Principal Component Analysis (FPCA) is an important dimension reduction technique to interpret the main modes of functional data variation in terms of a small set of uncorrelated variables. The principal components can not always be simply interpreted and rotation is one of the main solutions to improve the interpretation. In this paper, two new functional Varimax rotation approaches are introduced. They are based on the equivalence between FPCA of basis expansion of the sample curves and Principal Component Analysis (PCA) of a transformation of the matrix of basis coefficients. The first approach consists of a rotation of the eigenvectors that preserves the orthogonality between the eigenfunctions but the rotated principal component scores are not uncorrelated. The second approach is based on rotation of the loadings of the standardized principal component scores that provides uncorrelated rotated scores but non-orthogonal eigenfunctions. A simulation study and an application with data from the curves of infections by COVID-19 pandemic in Spain are developed to study the performance of these methods by comparing the results with other existing approaches.
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Yu, Zhixuan, and Xiaolong Chai. "Utilizing BIC for the intelligent selection of functional data with principal components." Journal of Physics: Conference Series 2898, no. 1 (2024): 012011. http://dx.doi.org/10.1088/1742-6596/2898/1/012011.
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Abstract Functional Principal Component Analysis (FPCA) is a technique for dimension reduction of functional data. Considering the impact of different data ownership, the paper innovatively proposes a weighted B-spline basis function and provides a continuous curve fitting function. Then, smoothing processing is carried out through the basis function expansion method and the maximum penalized likelihood function method. Subsequently, the process of selecting the number of principal components based on the BIC criterion is elaborated. Parameter estimation is realized through the newly proposed BIC-FPCA optimization algorithm. In the empirical analysis section, the paper uses student performance data randomly generated by Python to demonstrate the application of FPCA in data reconstruction. The results show that although the reconstructed data is smoother, there is still room for improvement in the selection of principal components. Finally, the paper summarizes the research findings, suggesting that the BIC criterion be combined with the cumulative variance contribution rate to improve the method of principal component selection, and the algorithm be optimized to reduce computational load. With these improvements, the application effect of FPCA in functional data analysis can be further enhanced.
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Dzulkalnine, Mohamad Faiz, Roselina Sallehuddin, Yusliza Yussof, Nor Haizan Mohd Radzi, Noorfa Haszlinna Binti Mustaffa, and Lizawati Mi Yusuf. "Determination of Vital Cancer Sites in Malaysian Colorectal Cancer Dataset by Using A Fuzzy Feature Selection Method." Journal of Physics: Conference Series 2129, no. 1 (2021): 012022. http://dx.doi.org/10.1088/1742-6596/2129/1/012022.
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Abstract In Malaysia, Colorectal Cancer (CRC) is one of the most common cancers that occur in both men and women. Early detection is very crucial and it can significantly increase the rate of survival for the patients and if left untreated can lead to death. With the lack of high-quality CRC data, expert systems and machine learning analysis are burdened with the presence of irrelevant features, outliers, and noise. This can reduce the classification accuracy for data analysis. Accordingly, it is essential to find a reliable feature selection method that can identify and remove any irrelevant feature while being resistant to noise and outliers. In this paper, Fuzzy Principal Component Analysis (FPCA) was tested for the classification of Malaysian’s CRC dataset. With the utilization of fuzzy membership in FPCA, the experimental results showed that the proposed method produces higher accuracy compared to PCA and SVM by almost 2% and 5% respectively. Empirical results showed that FPCA is a reliable feature selection method that can find the most informative features in the CRC dataset that could assist medical practitioners in making an informed decision.
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Koichubekov, Berik, Bauyrzhan Omarkulov, Nazgul Omarbekova, Khamida Abdikadirova, Azamat Kharin, and Alisher Amirbek. "Forecasting the Regional Demand for Medical Workers in Kazakhstan: The Functional Principal Component Analysis Approach." International Journal of Environmental Research and Public Health 22, no. 7 (2025): 1052. https://doi.org/10.3390/ijerph22071052.
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The distribution of the health workforce affects the availability of health service delivery to the public. In practice, the demographic and geographic maldistribution of the health workforce is a long-standing national crisis. In this study, we present an approach based on Functional Principal Component Analysis (FPCA) of data to identify patterns in the availability of health workers across different regions of Kazakhstan in order to forecast their needs up to 2033. FPCA was applied to the data to reduce dimensionality and capture common patterns across regions. To evaluate the forecasting performance of the model, we employed rolling origin cross-validation with an expanding window. The resulting scores were forecasted one year ahead using Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) methods. LSTM showed higher accuracy compared to ARIMA. The use of the FPCA method allowed us to identify national and regional trends in the dynamics of the number of doctors. We identified regions with different growth rates, highlighting where the most and least intensive growth is taking place. Based on the FPSA, we have predicted the need for doctors in each region in the period up to 2033. Our results show that the FPCA can serve as a significant tool for analyzing the situation relating to human resources in healthcare and be used for an approximate assessment of future needs for medical personnel.
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Cao, Xinyue, and Fan Liu. "Exploring Temporal Patterns of Urban Management Cases Based on Functional Principal Component Data Analysis." Journal of Physics: Conference Series 2202, no. 1 (2022): 012037. http://dx.doi.org/10.1088/1742-6596/2202/1/012037.
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Abstract This paper indentifies temporal patterns of urban management cases (UMCs) to facilitate relevant administrations finding UMCs from passive to active in a timely and effective way by employing functional principal component analysis (FPCA). Finally, this work obtains temporal patterns of UMCs which are different variability modes of case time series globally, providing practical decision making supports to build up precaution against cases and efficiency quality of related administrations. Furthermore, this work will contribute to the development of Smart City with social harmony greatly. Besides, the study also enriches research in related fields and has a specific academic value.
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Kim, Jong-Min. "Copula Dynamic Conditional Correlation and Functional Principal Component Analysis of COVID-19 Mortality in the United States." Axioms 11, no. 11 (2022): 619. http://dx.doi.org/10.3390/axioms11110619.
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This paper shows a visual analysis and the dependence relationships of COVID-19 mortality data in 50 states plus Washington, D.C., from January 2020 to 1 September 2022. Since the mortality data are severely skewed and highly dispersed, a traditional linear model is not suitable for the data. As such, we use a Gaussian copula marginal regression (GCMR) model and vine copula-based quantile regression to analyze the COVID-19 mortality data. For a visual analysis of the COVID-19 mortality data, a functional principal component analysis (FPCA), graphical model, and copula dynamic conditional correlation (copula-DCC) are applied. The visual from the graphical model shows five COVID-19 mortality equivalence groups in the US, and the results of the FPCA visualize the COVID-19 daily mortality time trends for 50 states plus Washington, D.C. The GCMR model investigates the COVID-19 daily mortality relationship between four major states and the rest of the states in the US. The copula-DCC models investigate the time-trend dependence relationship between the COVID-19 daily mortality data of four major states.
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Diederichs, Claas, and Sergej Fatikow. "FPGA-Based Object Detection and Motion Tracking in Micro- and Nanorobotics." International Journal of Intelligent Mechatronics and Robotics 3, no. 1 (2013): 27–37. http://dx.doi.org/10.4018/ijimr.2013010103.
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Object-detection and classification is a key task in micro- and nanohandling. The microscopic imaging is often the only available sensing technique to detect information about the positions and orientations of objects. FPGA-based image processing is superior to state of the art PC-based image processing in terms of achievable update rate, latency and jitter. A connected component labeling algorithm is presented and analyzed for its high speed object detection and classification feasibility. The features of connected components are discussed and analyzed for their feasibility with a single-pass connected component labeling approach, focused on principal component analysis-based features. It is shown that an FPGA implementation of the algorithm can be used for high-speed tool tracking as well as object classification inside optical microscopes. Furthermore, it is shown that an FPGA implementation of the algorithm can be used to detect and classify carbon-nanotubes (CNTs) during image acquisition in a scanning electron microscope, allowing fast object detection before the whole image is captured.
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Cao, Peng, and Jun Sun. "Robust estimation for partial functional linear regression models based on FPCA and weighted composite quantile regression." Open Mathematics 19, no. 1 (2021): 1493–509. http://dx.doi.org/10.1515/math-2021-0095.
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Abstract In this paper, we consider a novel estimation for partial functional linear regression models. The functional principal component analysis method is employed to estimate the slope function and the functional predictive variable, respectively. An efficient estimation based on principal component basis function approximation is used for minimizing the proposed weighted composite quantile regression (WCQR) objective function. Since the proposed WCQR involves a vector of weights, we develop a computational strategy for data-driven selection of the optimal weights. Under some mild conditions, the theoretical properties of the proposed WCQR method are obtained. The simulation study and a real data analysis are provided to illustrate the numerical performance of the resulting estimators.
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Shoriat Ullah, MD, and Kangwon Seo. "Prediction of Lithium-Ion Battery Capacity by Functional Principal Component Analysis of Monitoring Data." Applied Sciences 12, no. 9 (2022): 4296. http://dx.doi.org/10.3390/app12094296.
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The lithium-ion (Li-ion) battery is a promising energy storage technology for electronics, automobiles, and smart grids. Extensive research was conducted in the past to improve the prediction of the remaining capacity of the Li-ion battery. A robust prediction model would improve the battery performance and reliability for forthcoming usage. In the development of a data-driven capacity prediction model of Li-ion batteries, most past studies employed capacity degradation data; however, very few tried using other performance monitoring variables, such as temperature, voltage, and current data, to estimate and predict the battery capacity. In this study, we aimed to develop a data-driven model for predicting the capacity of Li-ion batteries adopting functional principal component analysis (fPCA) applied to functional monitoring data of temperature, voltage, and current observations. The proposed method is demonstrated using the battery monitoring data available in the NASA Ames Prognostics Center of Excellence repository. The main contribution of the study the development of an empirical data-driven model to diagnose the state-of-health (SOH) of Li-ion batteries based on the health monitoring data utilizing fPCA and LASSO regression. The study obtained encouraging battery capacity prediction performance by explaining overall variation through eigenfunctions of available monitored discharge parameters of Li-ion batteries. The result of capacity prediction obtained a root mean square error (RMSE) of 0.009. The proposed data-driven approach performs well for predicting the capacity by employing functional performance measures over the life span of a Li-ion battery.
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Salazar, Pascal, Patrick Cheung, Balaji Ganeshan, and Anastasia Oikonomou. "Predefined and data-driven CT radiomics predict recurrence-free and overall survival in patients with pulmonary metastases treated with stereotactic body radiotherapy." PLOS ONE 19, no. 12 (2024): e0311910. https://doi.org/10.1371/journal.pone.0311910.
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Background This retrospective study explores two radiomics methods combined with other clinical variables for predicting recurrence free survival (RFS) and overall survival (OS) in patients with pulmonary metastases treated with stereotactic body radiotherapy (SBRT). Methods 111 patients with 163 metastases treated with SBRT were included with a median follow-up time of 927 days. First-order radiomic features were extracted using two methods: 2D CT texture analysis (CTTA) using TexRAD software, and a data-driven technique: functional principal components analysis (FPCA) using segmented tumoral and peri-tumoural 3D regions. Results Using both Kaplan-Meier analysis with its log-rank tests and multivariate Cox regression analysis, the best radiomic features of both methods were selected: CTTA-based “entropy” and the FPCA-based first mode of variation of tumoural CT density histogram: “F1.” Predictive models combining radiomic variables and age showed a C-index of 0.62 95% with a CI of (0.57–0.67). “Clinical indication for SBRT” and “lung primary cancer origin” were strongly associated with RFS and improved the RFS C-index: 0.67 (0.62–0.72) when combined with the best radiomic features. The best multivariate Cox model for predicting OS combined CTTA-based features—skewness and kurtosis—with size and “lung primary cancer origin” with a C-index of 0.67 (0.61–0.74). Conclusion In conclusion, concise predictive models including CT density-radiomics of metastases, age, clinical indication, and lung primary cancer origin can help identify those patients with probable earlier recurrence or death prior to SBRT treatment so that more aggressive treatment can be applied.
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Kim, Jong-Min, Ning Wang, and Yumin Liu. "Multi-Stage Change Point Detection with Copula Conditional Distribution with PCA and Functional PCA." Mathematics 8, no. 10 (2020): 1777. http://dx.doi.org/10.3390/math8101777.
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A global uncertainty environment, such as the COVID-19 pandemic, has affected the manufacturing industry severely in terms of supply and demand balancing. So, it is common that one stage statistical process control (SPC) chart affects the next-stage SPC chart. It is our research objective to consider a conditional case for the multi-stage multivariate change point detection (CPD) model for highly correlated multivariate data via copula conditional distributions with principal component analysis (PCA) and functional PCA (FPCA). First of all, we review the current available multivariate CPD models, which are the energy test-based control chart (ETCC) and the nonparametric multivariate change point model (NPMVCP). We extend the current available CPD models to the conditional multi-stage multivariate CPD model via copula conditional distributions with PCA for linear normal multivariate data and FPCA for nonlinear non-normal multivariate data.
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Naiman, Joseph, and Peter Xuekun Song. "Multivariate Functional Kernel Machine Regression and Sparse Functional Feature Selection." Entropy 24, no. 2 (2022): 203. http://dx.doi.org/10.3390/e24020203.
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Motivated by mobile devices that record data at a high frequency, we propose a new methodological framework for analyzing a semi-parametric regression model that allow us to study a nonlinear relationship between a scalar response and multiple functional predictors in the presence of scalar covariates. Utilizing functional principal component analysis (FPCA) and the least-squares kernel machine method (LSKM), we are able to substantially extend the framework of semi-parametric regression models of scalar responses on scalar predictors by allowing multiple functional predictors to enter the nonlinear model. Regularization is established for feature selection in the setting of reproducing kernel Hilbert spaces. Our method performs simultaneously model fitting and variable selection on functional features. For the implementation, we propose an effective algorithm to solve related optimization problems in that iterations take place between both linear mixed-effects models and a variable selection method (e.g., sparse group lasso). We show algorithmic convergence results and theoretical guarantees for the proposed methodology. We illustrate its performance through simulation experiments and an analysis of accelerometer data.
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Chen, Yudong, Zhihui Lai, Jiajun Wen, and Can Gao. "Nuclear norm based two-dimensional sparse principal component analysis." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (2018): 1840002. http://dx.doi.org/10.1142/s0219691318400027.
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Two-Dimensional Principal Component Analysis (2D-PCA) is one of the most simple and effective feature extraction methods in the field of pattern recognition. However, the traditional 2D-PCA lacks robustness and the function of sparse feature extraction. In this paper, we propose a new feature extraction approach based on the traditional 2D-PCA, which is called Nuclear Norm Based Two-Dimensional Sparse Principal Component Analysis (N-2D-SPCA). To improve the robustness of 2D-PCA, we utilize nuclear norm to measure the reconstruction error of loss function. At the same time, we obtain sparse feature extraction by adding [Formula: see text]-norm and [Formula: see text]-norm regularization terms to the model. By designing an alternatively iterative algorithm, we can solve the optimization problem and learn a projection matrix for use with feature extraction. Besides, we present a bilateral projections model (BN-2D-SPCA) to further compress the dimensions of the feature matrix. We verify the effectiveness of our method on four benchmark face databases including AR, ORL, FERET and Yale databases. Experimental results show that the proposed method is more robust than some state-of-the-art methods and the traditional 2D-PCA.
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Rahman, Azizur, and Depeng Jiang. "Forecasting Canadian Age-Specific Mortality Rates: Application of Functional Time Series Analysis." Mathematics 11, no. 18 (2023): 3808. http://dx.doi.org/10.3390/math11183808.
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In the insurance and pension industries, as well as in designing social security systems, forecasted mortality rates are of major interest. The current research provides statistical methods based on functional time series analysis to improve mortality rate prediction for the Canadian population. The proposed functional time series-based model was applied to the three-mortality series: total, male and female age-specific Canadian mortality rate over the year 1991 to 2019. Descriptive measures were used to estimate the overall temporal patterns and the functional principal component regression model (fPCA) was used to predict the next ten years mortality rate for each series. Functional autoregressive model (fAR (1)) was used to measure the impact of one year age differences on mortality series. For total series, the mortality rates for children have dropped over the whole data period, while the difference between young adults and those over 40 has only been falling since about 2003 and has leveled off in the last decade of the data. A moderate to strong impact of age differences on Canadian age-specific mortality series was observed over the years. Wider application of fPCA to provide more accurate estimates in public health, demography, and age-related policy studies should be considered.
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Zhang, Bin, Kai Zheng, Qingqing Huang, Song Feng, Shangqi Zhou, and Yi Zhang. "Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis." Sensors 20, no. 3 (2020): 920. http://dx.doi.org/10.3390/s20030920.
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Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.
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Latinwo, B., A. Falohun, and E. Omidiora. "Principal Component Analysis - Based Ethnicity Prediction Using Iris Feature." British Journal of Applied Science & Technology 16, no. 4 (2016): 1–5. http://dx.doi.org/10.9734/bjast/2016/26131.
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LI, YUN, BAO-LIANG LU, and TENG-FEI ZHANG. "COMBINING FEATURE SELECTION WITH EXTRACTION: UNSUPERVISED FEATURE SELECTION BASED ON PRINCIPAL COMPONENT ANALYSIS." International Journal on Artificial Intelligence Tools 18, no. 06 (2009): 883–904. http://dx.doi.org/10.1142/s0218213009000445.
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Principal components analysis (PCA) is a popular linear feature extractor, and widely used in signal processing, face recognition, etc. However, axes of the lower-dimensional space, i.e., principal components, are a set of new variables carrying no clear physical meanings. Thus we propose unsupervised feature selection algorithms based on eigenvectors analysis to identify critical original features for principal component. The presented algorithms are based on k-nearest neighbor rule to find the predominant row components and eight new measures are proposed to compute the correlation between row components in transformation matrix. Experiments are conducted on benchmark data sets and facial image data sets for gender classification to show their superiorities.
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Soe, Moe Myint, Moe Myint Moe, and Aye Cho Aye. "National Flags Recognition Based on Principal Component Analysis." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1610–14. https://doi.org/10.5281/zenodo.3591157.
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Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdf
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Zhang, Yingwei, Chuanfang Zhang, and Wei Zhang. "Statistical Analysis of Nonlinear Processes Based on Penalty Factor." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/945948.
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A new process monitoring approach is proposed for handling the nonlinear monitoring problem in the electrofused magnesia furnace (EFMF). Compared to conventional method, the contributions are as follows: (1) a new kernel principal component analysis is proposed based on loss function in the feature space; (2) the model of kernel principal component analysis based on forgetting factor is updated; (3) a new iterative kernel principal component analysis algorithm is proposed based on penalty factor.
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Wang, Qiang, Yong Bao Liu, Xing He, Shu Yong Liu, and Jian Hua Liu. "Fault Diagnosis of Bearing Based on KPCA and KNN Method." Advanced Materials Research 986-987 (July 2014): 1491–96. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1491.
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Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear system modeling. The combination of Kernel Principal Component Analysis (KPCA) and K-Nearest Neighbor (KNN) is applied to fault diagnosis of bearing. In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of vibration signals to high dimensional feature space, and structure and statistics in the feature space to extract the feature vector from the fault signal with the principal component analytic method. Assessment method using the feature vector of the Kernel Principal Component Analysis, and then enter the sensitive features to K-Nearest Neighbor classification. The experimental results indicated that this method has good accuracy.
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Liu, Wen Bin, Yu Xin He, Hua Qing Wang, and Jian Feng Yang. "Bearing Condition Recognition Based on Kernel Principal Component Analysis and Genetic Programming." Applied Mechanics and Materials 397-400 (September 2013): 1282–85. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1282.
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In order to extract the fault feature validity in early fault diagnosis, method based on kernel principal component analysis and genetic programming (GP) is presented. The time domain features of the vibration signal are extracted and the initial symptom parameters (SP) are constructed. Then the combination to the initial SPs is carried on to optimize and build composite characteristics by GP. Through kernel principal component analysis (KPCA), the nonlinear principal component of the original characteristics is produced. Finally, the nonlinear principal components are selected as the feature subspace to classify the conditions of rolling bearing. Meanwhile, the within-class and among-class distance is introduced to compare and analyze the bearing condition recognition effect by using KPCA and GP plus KPCA separately. Experimental results show that the features extracted by kernel principal component analysis and genetic programming perform better ability in identifying the working states of the rolling bearing.
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He, Feng Ying, Shang Ping Zhong, and Kai Zhi Chen. "JPEG Steganalysis Based on Feature Fusion by Principal Component Analysis." Applied Mechanics and Materials 263-266 (December 2012): 2933–38. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2933.
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Aiming to the problems in the existing JPEG steganalysis schemes, such as high redundancy in features and failure to make good use of the complementarities among them, this study proposed a JPEG steganalysis approach based on feature fusion by the principal component analysis (PCA) and analysis of the complementarities among features. The study fused complementary features and isolated redundant components by PCA, and finally used RBaggSVM classifier for classification. Experimental results show that this scheme effectively improves the detection rate of steganalysis in JPEG images and achieves faster speed of image classification.
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HUANG, Wei, Xian-Feng ZHAO, Deng-Guo FENG, and Ren-Nong SHENG. "JPEG Steganalysis Based on Feature Fusion by Principal Component Analysis." Journal of Software 23, no. 7 (2012): 1869–79. http://dx.doi.org/10.3724/sp.j.1001.2012.04107.
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Nasution, Muhammad Zulfahmi. "Face Recognition based Feature Extraction using Principal Component Analysis (PCA)." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 182–91. http://dx.doi.org/10.31289/jite.v3i2.3132.
Full textAbstract:
The human face is an entity that has semantic features. Face detection is the first step before face recognition. Face recognition technique is an identification process based on facial features. One feature extraction approach for facial recognition techniques is the Principal Component Analysis (PCA) method. The PCA method is used to simplify facial features and characteristics in order to obtain proportions that are able to represent the characteristics of the original face. The purpose of this research is to construct facial patterns stored in a digital image database. The process of pattern construction and face recognition starts from objects in the form of face images, side detection, pattern construction until it can determine the similarity of face patterns to proceed as face recognition. In this research, a program has been designed to test some samples of face data stored in a digital image database so that it can provide a similarity in the face patterns being observed and its introduction using PCA