Principal Component Analysis Algorithm Research Articles

Unmanned Helicopter Airborne Fourier Transform Infrared Spectrometer Remote Sensing System for Hazardous Vapors Detection

The rapid development of unmanned aerial vehicles (UAVs) provides a new application mode for gas remote sensing. Compared with fixed observation and vehicle-mounted platforms, a Fourier transform infrared spectrometer (FTIR) integrated in the UAV can monitor chemical gases across a large area, can collect data from multiple angles in three-dimensional space, and can operate in contaminated or hazardous environments. The unmanned helicopter has a larger payload and longer endurance than the rotary-wing drone, which relaxes the weight, size and power consumption limitations of the spectrometer. A FTIR remote sensing system integrated in an unmanned helicopter was developed. In order to solve the data acquisition and analysis problem caused by vibration and attitude instability of the unmanned helicopter, a dual-channel parallel oscillating mirror was designed to improve the stability of the interferometer module, and a robust principal component analysis algorithm based on kernel function was used to separate background spectrum and gas features. The flight experiment of sulfur hexafluoride gas detection was carried out. The results show that the system operates stably and can collect and identify the target spectrum in real time under the motion and hovering modes of an unmanned helicopter, which has broad application prospects.

Towards Detection and Attribution of Cyber Attacks in IoT Enabled Cyber-Physical Systems

Internet of Things (IoT) enabled cyber physical systems such as Industrial equipment’s and operational IT to send and receive data over internet. This equipment’s will have sensors to sense equipment condition and report to centralized server using internet connection. Sometime some malicious users may attack or hack such sensors and then alter their data and this false data will be report to centralized server and false action will be taken. Due to false data many countries equipment and production system got failed and many algorithms was developed to detect attack, but all these algorithms suffer from data imbalance (one class my contains huge records (for example NORMAL records and other class like attack may contains few records which lead to imbalance problem and detection algorithms may failed to predict accurately). To deal with data imbalance, existing algorithms were using OVER and UNDER sampling which will generate new records for FEWER class only. Securing Internet of Things (IoT)-enabled cyberphysical systems (CPS) can be challenging, as security solutions developed for general information / operational technology (IT / OT) systems may not be as effective in a CPS setting. Thus, this paper presents a two-level ensemble attack detection and attribution framework designed for CPS, and more specifically in an industrial control system (ICS). At the first level, a decision tree combined with a novel ensemble deep representation learning model is developed for detecting attacks imbalanced ICS environments. At the second level, an ensemble deep neural network is designed for attack attribution.The connection between ICS or IIoT-based systems with public networks, however, increases their attack surfaces and risks of being targeted by cyber criminals.To overcome from this issue, we are introducing novel technique without using any under or oversampling algorithms. The proposed technique consists of 2 parts. Auto encoder: It will get trained on imbalanced dataset and then extract features from it and these extracted features will get trained with DECISION TREE algorithm to predict label for known or unknown attacks. Decision tree get trained on reduced number of features obtained from PCA (principal component analysis) algorithm. Deep Neural Network (DNN): In this level, DNN algorithm get trained on known and unknown attacks. Key Word: Cyber Physical Systems (CPS), Industrial Control System (ICS).

Characterization and depth detection of internal delamination defects in CFRP based on line laser scanning infrared thermography

In this article, the shape characterization and depth detection of delamination defects in carbon fiber reinforced polymer (CFRP) sandwich are achieved by detecting delamination defects through line laser infrared thermography. In terms of defect shape characterization, the temperature matrix difference imaging method is proposed to characterize the shape of internal defects, and the defect shape distortion is corrected by the principal component analysis algorithm to optimize the effect of defect shape characterization. In terms of defect depth detection, this study constructs a dataset of cooling curves for defects of different depths and builds an encoder–decoder convolutional neural network structure based on the attention mechanism, which is centered on a streamlined encoder–decoder structure as well as a highly integrated attention gate, which is capable of accelerating the extraction of features and reducing the number of iterations of the algorithm. The results demonstrate that we achieved an average classification accuracy of 88.9% for defects with a depth gradient difference of 0.2 mm, attaining the highest classification accuracy of 95% for a single defect. Furthermore, our approach outperformed several classical classifiers by achieving superior accuracy with fewer iterations. The experimental results demonstrate that the line laser scanning inspection method is effective in the task of detecting internal defects in CFRP materials.

Granite Exposure Mapping Through Sentinel‐2 Visible and Short Wave Infrared Bands

AbstractNonmetallic minerals like granite and limestone have calcite and biotitic ingredients as their major part which exhibit wonderful absorption features in the visible and short wave range of the electromagnetic spectrum. This research puts emphasis on delineating granite and limestone deposits of the Mardan district through the latest multispectral Landsat‐9 and Sentinel‐2 sensors of which the latter provided 94% mapping accuracy in delineating granites (biotitic bearing minerals) and limestone (calcite‐bearing minerals). The Image processing techniques of minimum noise fraction, which is double cascaded principal components analysis and pixel purity index algorithms proved helpful to bring significant improvements in classification results and in the reduction of noise and data size. The outcomes of the research study show that supervised machine learning algorithms are impactful to map such minerals provided that the data must be well organized and limited in size. The results achieved were verified through validation steps like, (a) Independent reference data of high‐resolution Google Earth maps and (b) Ground survey reports. Arc GIS 10.2 and Envi 5.3 software suite were used to create (a) ground truth points at random for accuracy assessment (b) portraying study area maps (c) Image Processing and Preprocessing tools and (d) implementation of machine learning algorithms. Access to the data and software suite is being provided for open research work.

Construction and optimization of representative actual driving cycles based on the improved autoencoder

In this study, much work has been performed to accurately and efficiently develop representative actual driving cycles. Electric vehicle road tests were conducted and the associated data were gathered based on the manual driving method, and the Changsha Driving Cycle Construction (CS-DCC) method was proposed to achieve systematical construction of a representative driving cycle from the original data. The results show that the refined data exhibit greater stability and a smoother pattern in contrast to the original data after noise reduction by five-scale wavelet analysis. The Gaussian Kernel Principal Component Analysis (KPCA) algorithm is chosen to reduce the dimensionality of the characteristic matrix, and the number of principal components is selected as 5 with a cumulative contribution rate of 85.99%. The average error of the characteristic parameters between the optimized drive cycle and the total data is further reduced from 13.6 to 6.1%, with a reduction ratio of 55.1%. Meanwhile, the constructed driving cycle has prominent local characteristics compared with four standard driving cycles, demonstrating the necessity of constructing an actual driving cycle that reflects localized driving patterns. The findings present a powerful application of artificial intelligence in advancing engineering technologies.

Assessment of unmixing approaches for the quantitation of SERS nanoparticles in highly multiplexed spectral images

AbstractSurface‐enhanced Raman scattering nanoparticles (SERS NPs) offer powerful optical contrast features for imaging assays. Their gold core enhances the inelastic scattering cross section, allowing highly sensitive and rapid detection, and their characteristic sets of narrow spectral bands give them unsurpassed multiplexing capabilities. Multiplexed hyperspectral images are commonly unmixed using a compensation matrix of reference spectra to produce quantitative image channels illustrating the distribution of each material. It is these unmixed channels that are fit for interpretation from assays utilizing SERS NP contrast agents. Some factors that may impact SERS NP quantitative and dynamic range capabilities may include endogenous background heterogeneity, the ability of unmixing algorithms to account for signal variances, and linear system conditioning imposed by contrast agent signals. We report on hyperspectral Raman imaging of mixtures of SERS NPs from an expanded library of contrast agents. We study increasing plexity and varying degrees of system conditioning as inputs to a diverse set of classical, non‐negatively constrained, and regularized regression algorithms to investigate which signal features and unmixing methods deliver the most promising quantitation performance with the least error. Raman imaging of SERS NP mixtures is performed on controlled substrates and representative biological specimens, and experimental results are compared against ground truth data. We evaluate spectral fitting fidelity, quantitation, and specificity correlations with system conditioning. Spectral unmixing with a regularized hybrid of least squares regression with principal component analysis (HLP) algorithm approximated spectra with 3.5× better fitting fidelity and 3× better quantitation robustness with tissue background compared with simpler unmixing routines.

Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm

To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection.

Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM.

In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

Retracted: Prediction and Analysis of the Physical Test Scores Based on BP Neural Network and Principal Component Analysis Algorithm

Retracted: Prediction and Analysis of the Physical Test Scores Based on BP Neural Network and Principal Component Analysis Algorithm

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study.

Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800-700 cm-1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

An improved Back Propagation Neural Network framework and its application in the automatic calibration of Storm Water Management Model for an urban river watershed

The use of the Storm Water Management Model (SWMM) to simulate flows in urban river watersheds necessitates the proper calibration of the various parameters involved in the process. Back Propagation Neural Network (BPNN) is often used to establish relationship between two sets of multivariate variables, such as parameters and simulation results of SWMM. The aim of this study is to establish an improved BPNN to calibrate SWMM. It was found that when using gauged flow data obtained from the urban river management system as calibration data, only using BPNN was not sufficient. An improved BPNN framework was proposed with integrating Principal Component Analysis (PCA) and Genetic Algorithm (GA) process, abbreviated as PCA-GA-BPNN. It was proved to be effective for calibration. The BPNN combined with GA process made 90 % of the predicted parameters within reasonable range, which was only 8 % using BPNN alone. The PCA process reduced the training time up to 64 %. Using a hydrograph of 196 h, compared with the nondominated sorting genetic algorithm (NSGA), PCA-GA-BPNN training time can be reduced from 18,142 s down to 4.5 s. Nash efficiency coefficients (NSE) of hydrographs fitting was 0.75. Including more rainfall events data in calibration achieved better fitting than including more gauging station data.

The Importance and Practice of College Students’ Ideological and Political Education in Higher Education-Based on the Perspective of Principal Components Analysis

Abstract With the rapid development of artificial intelligence technology, the traditional mode of civic education has not adapted to the development of the times and is in urgent need of innovation. This paper proposes an evaluation study of civic education in colleges and universities based on the hierarchical analysis method (AHP)-principal component analysis (PCA). Starting from the current situation of civic education, based on the principle of evaluation index system construction, the evaluation index system of civic education is initially formulated. Then, the expert argumentation is used to modify and improve the initially formulated evaluation index system and then to determine the final version of the evaluation index system of civic education. On the basis of the evaluation index system, a combination of AHP and PCA algorithms is used to construct the evaluation model of civic and political education, and the statistical analysis software SPSS20.0 is used to analyze the constructed model. The results show that the Sig value between the evaluation indicators of the first level is less than 0.05, which means that the data are judged to be suitable for principal component analysis. The percentage of variance of the first three components after the rotation is 28.69%, 28.10%, and 25.48%, which indicates that the three principal components explaining the evaluation indicators of the first level are relatively average. This study promotes the development of civic education, thereby improving the level of civic education in colleges and universities.

A Linkage Cultivation Model of Students’ Information Literacy and Critical Thinking Skills in Education Informatization Environment

Abstract This paper first screens the indicators by examining the current state of information literacy and critical thinking. The sparse principal component analysis (SPCA) algorithm and factor analysis model are combined to deeply mine the influencing factors of information literacy and critical thinking and provide an optimization path for the linkage cultivation model of information literacy and critical thinking. Finally, we validate and analyze the enhanced critical thinking abilities. The results show that the first four eigenroot coefficients of the correlation coefficient matrices of the proposed four main factor variables are 4.31, 3.4, 3.07, and 2.74, respectively. The eigenroots’ explanatory degrees are 33.2%, 25.7%, 21.9%, and 15.1%, respectively. The four extracted principal factors can explain 95.85% of the information in the original index. The four critical indicators of questioning, discriminating, comparing, and controlling are significantly positively correlated between the two. The synthesis of the usefulness of the four critical indicators revealed that 97.5% agreed that they could improve students’ information literacy and critical thinking skills. This study positively supports and promotes the improvement of students’ information literacy and critical thinking skills in the educational information technology environment.

Digitized Perception of Piano Accompaniment in Songs with the Involvement of Deep Learning Techniques

Abstract In this paper, we use the Hanning window function to add windows to the piano accompaniment human brain perception signal, and the noise and redundant features in the piano accompaniment human brain perception signal are downgraded by principal component analysis and Relief algorithm for dimensionality reduction processing and feature selection. Based on the basic structure of RCNN, the ReConv-EEG Net EEG identity recognition method is established, and the feature vectors in the recurrent network are classified and computed through the feed-forward layer, the normalization layer and the output layer to obtain the feature values of the EEG signals. Under different speed accompaniments, the subjects perceived the high-speed piano accompaniment with the lowest α -wave power spectral value of 47.85 on average, while the original speed accompaniment had the most significant α -wave power spectral value of 49.12 on average.Meanwhile, there were differences in the perception of piano accompaniment among subjects, and the mean EEG δ -wave synchronization indexes of the non-musicians and the musicians differed by an average of 0.2297 when they were listening to the same accompaniment.The method in this paper contributes to developing a new method for deep learning-assisted music perception research in the field. Also, it lays the data foundation for applying the technique to the field.

A hybrid PCA-SOA-BP approach for predicting converter endpoint temperature in steelmaking

Accurately controlling the temperature of the converter end is a crucial element of the steelmaking process. To enhance the accuracy of predicting the converter end temperature, we propose a hybrid model that utilizes principal component analysis (PCA) and snake optimization algorithm (SOA) in conjunction with a backpropagation algorithm (BP) neural network. The 16 parameters for smelting in converter steelmaking were reduced using principal component analysis to remove shared features. The nine principal components derived from this analysis were then used as input variables for an optimized BP neural network. An optimization algorithm was then employed to refine the initialized weights and thresholds of the BP neural network. The impact of neuron node quantity in the hidden layer on the BP neural network was examined. Results show that the ideal BP neural network is achieved with 19 neuron nodes in the hidden layer. Compared with ordinary BP neural network, PCA-BP neural network and SOA-BP neural network, the model proposed in this study can predict the end temperature of converter most accurately. In the temperature error range of ±10 °C and ±15 °C, the prediction accuracy of the model is 93% and 96%, respectively. Meanwhile, the model has been effectively applied in the industrial production of a steel plant in China. The results show that the prediction results of the model are in good agreement with the actual production data in the field. This accurate prediction can optimize the field operation process and realize the stable control of product quality.

A Hybrid Data Preprocessing-Based Hierarchical Attention BiLSTM Network for Remaining Useful Life Prediction of Spacecraft Lithium-Ion Batteries.

As a crucial energy storage for the spacecraft power system, lithium-ion batteries degradation mechanisms are complex and involved with external environmental perturbations. Hence, effective remaining useful life (RUL) prediction and model reliability assessment confronts considerable obstacles. This article develops a new RUL prediction method for spacecraft lithium-ion batteries, where a hybrid data preprocessing-based deep learning model is proposed. First, to improve the correlation between battery capacity and features, the empirically selected high-dimensional features are linearized by using the Box-Cox transformation and then denoised via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, the principal component analysis (PCA) algorithm is employed to perform feature dimensionality reduction, and the output of PCA is further processed by the sliding window technique. Third, a multiscale hierarchical attention bi-directional long short-term memory (MHA-BiLSTM) model is constructed to estimate the capacity in future cycles. Specifically, the MHA-BiLSTM model can predict the RUL of lithium-ion batteries by considering the correlation and significance of each cycle's information during the degradation process on different scales. Finally, the proposed method is validated based on multiple types of experiments under two lithium-ion battery datasets, demonstrating its superior performance in terms of feature extraction and multidimensional time series prediction.

Real-time EEG-based emotion recognition for neurohumanities: perspectives from principal component analysis and tree-based algorithms.

Within the field of Humanities, there is a recognized need for educational innovation, as there are currently no reported tools available that enable individuals to interact with their environment to create an enhanced learning experience in the humanities (e.g., immersive spaces). This project proposes a solution to address this gap by integrating technology and promoting the development of teaching methodologies in the humanities, specifically by incorporating emotional monitoring during the learning process of humanistic context inside an immersive space. In order to achieve this goal, a real-time emotion recognition EEG-based system was developed to interpret and classify specific emotions. These emotions aligned with the early proposal by Descartes (Passions), including admiration, love, hate, desire, joy, and sadness. This system aims to integrate emotional data into the Neurohumanities Lab interactive platform, creating a comprehensive and immersive learning environment. This work developed a ML, real-time emotion recognition model that provided Valence, Arousal, and Dominance (VAD) estimations every 5 seconds. Using PCA, PSD, RF, and Extra-Trees, the best 8 channels and their respective best band powers were extracted; furthermore, multiple models were evaluated using shift-based data division and cross-validations. After assessing their performance, Extra-Trees achieved a general accuracy of 94%, higher than the reported in the literature (88% accuracy). The proposed model provided real-time predictions of VAD variables and was adapted to classify Descartes' six main passions. However, with the VAD values obtained, more than 15 emotions can be classified (reported in the VAD emotion mapping) and extend the range of this application.

Activation of fluoride anion as nucleophile in water with data-guided surfactant selection.

A principal component surfactant_map was developed for 91 commonly accessible surfactants for use in surfactant-enabled organic reactions in water, an important approach for sustainable chemical processes. This map was built using 22 experimental and theoretical descriptors relevant to the physicochemical nature of these surfactant-enabled reactions, and advanced principal component analysis algorithms. It is comprised of all classes of surfactants, i.e. cationic, anionic, zwitterionic and neutral surfactants, including designer surfactants. The value of this surfactant_map was demonstrated in activating simple inorganic fluoride salts as effective nucleophiles in water, with the right surfactant. This led to the rapid development (screening 13-15 surfactants) of two fluorination reactions for β-bromosulfides and sulfonyl chlorides in water. The latter was demonstrated in generating a sulfonyl fluoride with sufficient purity for direct use in labelling of chymotrypsin, under physiological conditions.

An Exploration of the Application of Principal Component Analysis in Big Data Processing

Abstract With the arrival of the significant data era, efficiently processing large-scale multidimensional data has become challenging. As a powerful data dimensionality reduction tool, Principal Component Analysis (PCA) plays a vital role in big data processing, especially in information extraction and data simplification, showing unique advantages. The research aims to simplify the data processing process and improve the data processing efficiency by PCA method. The research method adopts the basic theory of PCA, the improvement of the weighted principal component analysis algorithm, and standardized and homogenized data processing techniques to process large-scale multidimensional data sets. The results show that the data dimensionality is significantly reduced after using PCA, for example, in the Analysis of the earnings quality of listed companies in the e-commerce industry, the cumulative variance contribution rate of the first four principal components extracted by PCA reaches 81.623%, which effectively removes the primary information of the original data. PCA not only reduces the complexity of the data, but also retains a large amount of crucial information, which is a significant application value for the processing of big data, especially in the fields of data compression and pattern recognition.

A Resource-Efficient Feature Extraction Framework for Image Processing in IoT Devices

Extracting features from image data on Internet of Things (IoT) devices to reduce the amount of data that needs to be uploaded to cloud/edge servers has received increasing attention. However, most of the existing related approaches suffer from two major limitations, (i) low performance and high network traffic, and (ii) a lot of storage resource consumption. To this end, we propose a resource-efficient feature extraction framework for image processing in IoT devices. The proposed framework consists of the edge-assisted extractor generation method and the NestE method. The extractor generated by the edge-assisted extractor generation method can extract the features required by the application, which can not only avoid the IoT device uploading useless feature data but also improve application performance. The proposed NestE generates a nonredundant subextractor by splitting the extractor into multiple subextractors, removing redundant subextractors, and nesting small-capacity subextractors in large-capacity subextractors in a parameter-sharing manner. Compared with deploying multiple independent subextractors on IoT devices, deploying the nonredundant multifunctional extractor can save considerable storage resources and switching overhead. Extensive experimental results show that the proposed framework reduces the storage footprint by approximately 90.7% and switching overhead by approximately 92.4% compared with deploying independent subextractors when using the classical principal component analysis algorithm.