Principal Component Neural Network Research Articles

Near-Infrared spectroscopy combined with machine learning methods for distinguishment of the storage years of rice

Rice is one of the most important food crops that provide essential nutrients, micronutrients and daily energy for humans. The freshness of rice determines the quality and nutrition supply property, but the freshness decreases along with the storage time. A simple, nondestructive and rapid detection technology is needed to estimate the time of storage rice as for a fast evaluation of the rice quality. To accomplish this objective, near-infrared spectroscopy (NIRS) is employed in combination with three machine learning methods, including least square support vector machine (LSSVM), random forest (RF) and principal component-neural network (PC-NN). With specific design on grid search of the relevant parameters, the LSSVM model optimally performed classification with the highest accuracy of 95.7% in the distinguishment of three labeled storage years, the RF model and PC-NN models have close accuracies in model training and optimization processes. In comparison to the PLS method, which is the typical chemometric method in NIRS data analysis, the three presented machine learning methods all perform excellent over the PLS model for model training and for model testing. Especially the RF and PC-NN model were optimized by hyperparameter training, to obtain 90% of testing accuracy and reduced the error differences to ∼5.0% between model training and testing. This study indicated the potential of NIRS in combination with machine learning methods as practical chemometric tools for discrimination of the rice storage freshness by distinguishing their storage years. The design of adaptive tuning on hyperparameters provide a valuable approach to improve the model prediction abilities.

A 3D attention U-Net network and its application in geological model parameterization

To solve the problems of convolutional neural network–principal component analysis (CNN-PCA) in fine description and generalization of complex reservoir geological features, a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model, and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study. The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects. The results show that compared with CNN-PCA method, the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction, better reflect the fluid flow features in the original geologic model, and improve history matching results.

An Index for Rail Weld Health Assessment in Urban Metro Using In-Service Train

Rail welds are considered as the weak part of a railway track. Their defects and health can directly affect wheel-rail interaction, track safety, and reliability. Current practices for rail welds health assessment are based on 2D vertical and lateral wear measurement which needs time and track blocking. The development of inertia-based condition monitoring methods such as measuring axle box acceleration (ABA) comes with a crucial question on criteria or index for each rail track component health monitoring. In this study, an index for evaluation of rail weld health is proposed through integrated numerical and field experiment data within a metro line using the ABA technique. The relationship between the speed, wheel structural vibration, and acceleration amplitude is investigated using fast Fourier transformation (FFT) and a nonlinear neural network principal component analysis (PCA) model. An index is introduced to assess weld severity level based on the statistical method. This index is simple and applicable for maintenance practice.

Application of Cmic/Corg in the Soil Fertility Evaluation of Typical Forests in the Yulin Sandy Area

The microbial quotient (Cmic/Corg) has been used extensively to evaluate agriculture soil fertility, but the microbial sensitivity should be considered during the forestry process. Therefore, the objective of this study was to examine a soil fertility evaluation method applied to four vegetation types in the Mu Us Sandland in northwestern China, using the relationship between the Cmic/Corg ratio and soil moisture, and soil temperature under the premise of microbial diversity. The final predictive value was C. microphylla (0.2198) > P. sylvestris (0.2175) > P. tabulaeformis (0.0872) > S.psammophila (0.0767). We verified the evaluation results using two traditional methods, the back-propagation (BP) artificial neural network model and principal component analysis, which are widely used to evaluate soil quality based on the soil nutrient concentration. The results were the same as the Cmic/Corg predictions. We conclude that when the soil microbes are used in soil quality evaluations, the changing pattern should be fully considered.

Comparison of Models for Spatial Distribution and Prediction of Cadmium in Subtropical Forest Soils, Guangdong, China

Cadmium (Cd) is a toxic metal and found in various soils, including forest soils. The great spatial heterogeneity in soil Cd makes it difficult to determine its distribution. Both traditional soil surveys and spatial modeling have been used to study the natural distribution of Cd. However, traditional methods are highly labor-intensive and expensive, while modeling is often encumbered by the need to select the proper predictors. In this study, based on intensive soil sampling (385 soil pits plus 64 verification soil pits) in subtropical forests in Yunfu, Guangdong, China, we examined the impacting factors and the possibility of combining existing soil information with digital elevation model (DEM)-derived variables to predict the Cd concentration at different soil depths along the landscape. A well-developed artificial neural network model (ANN), multi-variate analysis, and principal component analysis were used and compared using the same dataset. The results show that soil Cd concentration varied with soil depth and was affected by the top 0–20 cm soil properties, such as soil sand or clay content, and some DEM-related variables (e.g., slope and vertical slope position, varying with depth). The vertical variability in Cd content was found to be correlated with metal contents (e.g., Cu, Zn, Pb, Ni) and Cd contents in the layer immediately above. The selection of candidate predictors differed among different prediction models. The ANN models showed acceptable accuracy (around 30% of predictions have a relative error of less than 10%) and could be used to assess the large-scale Cd impact on environmental quality in the context of intensifying industrialization and climate change, particularly for ecosystem management in this region or other regions with similar conditions.

A Novel Method for Multivariant Pneumonia Classification Based on Hybrid CNN-PCA Based Feature Extraction Using Extreme Learning Machine With CXR Images

In this era of COVID19, proper diagnosis and treatment of pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite challenging and time-consuming to diagnose accurately due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgement. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network-principal component analysis (CNN-PCA) based feature extraction, and CNN-PCA-ELM with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.

On the combination of kernel principal component analysis and neural networks for process indirect control

ABSTRACT A new adaptive kernel principal component analysis (KPCA) for non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-processing techniques. Indeed, the input vector of neural network controller is pre-processed by the KPCA method. Then, the obtained reduced neural network controller is applied in the indirect adaptive control. The influence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi-input multi-output system. It is concluded that, using the KPCA method, a significant reduction in the control error and the identification error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best.

Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus

The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the large number of efficacious predictors (factors) and their nonlinear interrelationships, developing predictive models for dynamic modulus can be a challenging task. In this research, results obtained from a series of laboratory tests including mixture dynamic modulus, aggregate gradation, dynamic shear rheometer (on asphalt binder), and mixture volumetric are used to create a database. The created database is used to develop a model for estimating the dynamic modulus. First, the highly correlated predictor variables are detected, then Principal Component Analysis (PCA) is used to first reduce the problem dimensionality, then to produce a set of orthogonal pseudo-inputs from which two separate predictive models were developed using linear regression analysis and Artificial Neural Networks (ANN). These models are compared to existing predictive models using both statistical analysis and Receiver Operating Characteristic (ROC) Analysis. Empirically-based predictive models can behave differently outside of the convex hull of their input variables space, and it is very risky to use them outside of their input space, so this is not common practice of design engineers. To prevent extrapolation, an input hyper-space is added as a constraint to the model. To demonstrate an application of the proposed framework, it was used to solve design-based optimization problems, in two of which optimal and inverse design are presented and solved using a mean-variance mapping optimization algorithm. The design parameters satisfy the current design specifications of asphalt pavement and can be used as a first step in solving real-life design problems.

Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

Productivity prediction method based on analysis model of principal component neural network

In order to improve the accuracy of prediction model for labor productivity in regional economic growth in China, a prediction method for labor productivity in regional economic growth in China based on analysis model of neural network of principal components is proposed. At first, principal component analysis method is used to simplify multiple index parameters describing characteristics of labor productivity samples of regional economic growth which may have certain correlation into several aggregative indexes; then, BP neural network algorithm is introduced to construct prediction model for labor productivity in regional economic growth in China; at last, the performance advantage of this algorithm is verified through simulation experiment.

English

Near infrared spectroscopy (NIRS) technology is widely used on agricultural products for quality detection, classification and variety identification due to its rapid speed and high-efficiency. NIRS experiments were conducted to identify varieties of DUN millet, JIN 21 millet and 5 other types of millet. The NIRS characteristic curves and data of millet samples were collected. The spectroscopic data on different types of millet were analyzed by discriminant analysis, principal component analysis and neural network technology. The calibration set correct classification was 98.9%. A BP neural network prediction model for millet was also built. It was found that the forecast results of original wave spectrum prediction model were best, with its correlation coefficient of validation (Rv) at 0.9999, the standard error of prediction (SEP) was 0.0191 and the root mean square error of prediction (RMSEP) was 0.0189. Moreover, the Rv of first derivative spectra was 0.9976, the SEP and RMSEP were 0.1043 and 0.1437, respectively, and the Rv, SEP and RMSEP of second derivative spectra were 0.9835, 0.28735 and 0.2720 respectively. This study laid the foundation for identification of millet varieties by NIRS. Key words: Millet, near infrared spectroscopy (NIRS), principal component analysis, neural network prediction, variety identification.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

Neural network modeling and principal component analysis of antibacterial activity of chitosan/AgCl-TiO2 colloid treated cotton fabric

The present work was undertaken to predict the antibacterial activity of chitosan/AgCl-TiO2 colloid treated cotton fabric with artificial neural network (ANN) using chitosan/AgCl-TiO2 concentration and curing time as predictors. Cotton fabric samples were prepared by treating with different blends of chitosan/AgCl-TiO2 colloid and varying curing time. The antibacterial activity against Staphylococcus aureus (gram positive) and Escherichia coli (gram negative) was measured in terms of % bacterial reduction (% BR). Feedforward neural network models were trained with combination of Levenberg- Marqaurdt algorithm and Bayesian regularization support incorporated in backpropagation. The 10 % cross-validation technique was also carried out to rule out any chance of over-fitting of the trained networks. Furthermore trend analysis was also performed with developed models to understand the effect of input parameters. The promising results realized with excellent coefficient of determination and acceptable mean absolute error during network training and their testing on the novel data patterns. The developed models will benefit the design and development of clean and hygienic textiles. Principal component analysis (PCA) was also performed to visualize and analyze the correlation between the variables and the trend of individual observation on two-dimensional space.

Utilising improved cellular automata to simulate and analyse image evolution and optimisation

This paper introduces the cellular automata, current research of the field and so on, comprehensively uses principal component analysis, decision tree and neural network methods to improve cellular automata model and makes simulation and analysis of the spatial evolution of experiment image by using the improved models, then from optimisation strategy and other aspects it gives some improvement discussion and optimisation methods.

The Indicator Variable Principal Component Neural Network Model for the Prediction of the Opening of Dam Crack

In order to effectively predict the opening of crack, the variation law of the crack is studied, and the indicator variable principal component neural network model is established by combining indicator variable model and principal component analysis with neural network theory. This new model can solve the problems of nonlinearity between the effect variable and the affecting factors and the colinearity between the affecting factors effectively and improved the prediction accuracy. By a case study, it showed that the prediction results are close to the actual values.

A hierarchical framework for estimating neuroretinal rim area using 3D spectral domain optical coherence tomography (SD-OCT) optic nerve head (ONH) images of healthy and glaucoma eyes.

Glaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer (RNFL). Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new approach for locating the Bruch's membrane opening BMO and then estimating the optic disc size and rim area of 3D Spectralis SD-OCT images. To deal with the overlapping of the Bruch's membrane BM layer and the border tissue of Elschnig due to the poor image resolution, we propose the use of image deconvolution approach to separate these layers. To estimate the optic disc size and rim area, we propose the use of a new regression method based on the artificial neural network principal component analysis (ANN-PCA), which allows us to model irregularity in the BMO estimation due to scan shifts and/or poor image quality. The diagnostic accuracy of rim area, and rim to disc area ratio is compared to the diagnostic accuracy of global RNFL thickness measurements provided by two commercially available SD-OCT devices using receiver operating characteristic curve analyses.

Prediction Model of the Cement Strength Based on the Principal Component Wavelet Neural Network

In this paper, we study the integration of principal component analysis and wavelet neural net work to establish the principle component wavelet neural network model, and apply the model to predict the strength of cement, and using the average prediction error of the strength of cement by the model to compare with the results through the BP neural network model and the principal component neural network model, the average prediction error in this paper is the smallest. Therefore, the method in this paper has guiding significance to control the quality of the cement production.

Neural network for control chart pattern recognition based on kernel principle component analysis

Considering the problem that the abnormal features have great similarity so that simple structure and high precision modeling cannot be achieved,a control chart pattern recognition method based on Kernel Principal Component Analysis(KPCA) and neural network was proposed.Firstly,the kernel method was used to translate the nonlinear feature into a higher dimensional linear feature space.Secondly this feature was projected to lower dimensional feature space.Finally the BP neural network classifier was introduced to identify the control chart pattern.This method was verified through stochastic simulation.The result demonstrates that the model can cluster each control chart pattern effectively and improve recognition accuracy.

Fault Diagnosis System for NPC Inverter based on Multi-Layer Principal Component Neural Network

This paper presents a fault diagnosis method for a neutral point clamped (NPC) inverter using a multi-layer artificial neural network (MANN). The considered possible faults of NPC inverter include the open-circuit fault occurring in one single device or more devices. The upper, middle and down bridge voltages are adopted the test signals because of the difficulties in isolating some fault modes. A novel multi-layer neural network is proposed to diagnose all possible open-circuit faults. Furthermore, the principal component analysis (PCA) is utilized to reduce the input size of neural network. The comparison between neural network with and without PCA is performed. The simulation and experimental results prove the feasibility of the diagnostic method and show that the proposed method has the advantages of good classification performance and high reliability.

Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.