Component Analysis Neural Network Research Articles

Hyperspectral image classification using principle component analysis and deep convolutional neural network

Hyperspectral image classification using principle component analysis and deep convolutional neural network

Automatic Scoring of Spoken English Based Principal Component Analysis and Convolutional Neural Network

Using computers to help people practice spoken language is a common method, but there are currently some problems. Firstly, because fluency feature is calculated depending on expert knowledge, the key information contained in the original data set may be lost. Secondly, optimize each model’s parameters separately to make the model’s performance in sub-optimal state. In order to solve these problems, a spoken English fluency scoring method based on convolutional neural network is proposed, in order to make the feature extraction consider the short-, medium-, and long-term characteristics of speech signal; three convolution layers are superimposed in this paper, which jointly learns feature extraction and scoring models from the original time-domain signal input. In the feature extraction process, we applied principal component analysis to make useful data extraction of audio features. The experimental results show that the scoring results of the proposed method are more accurate.

Unsupervised Clustering in Epidemiological Factor Analysis

Background: The analysis of epidemiological data at an early phase of an epidemiological situation, when the confident correlation of contributing factors to the outcome has not yet been established, may present a challenge for conventional methods of data analysis. Objective: This study aimed to develop approaches for the early analysis of epidemiological data that can be effective in the areas with less labeled data. Methods: An analysis of a combined dataset of epidemiological statistics of national and subnational jurisdictions, aligned at approximately two months after the first local exposure to COVID-19 with unsupervised machine learning methods, including principal component analysis and deep neural network dimensionality reduction, to identify the principal factors of influence was performed. Results: The approach and methods utilized in the study allow to clearly separate milder background cases from those with the most rapid and aggressive onset of the epidemics. Conclusion: The findings can be used in the evaluation of possible epidemiological scenarios and as an effective modeling approach to identify possible negative epidemiological scenarios and design corrective and preventative measures to avoid the development of epidemiological situations with potentially severe impacts.

Business performance assessment of small and medium-sized enterprises: Evidence from the Czech Republic

Business performance assessment is one of the basic tasks of management. Business performance can be assessed using a number of methods. The basic ones include financial analysis, Balanced Scorecard or Economic Value Added (EVA). The paper is focused on SME business performance assessment based on Economic Value Added, calculated using the INFA build-up model. According to this method, companies were divided into four categories. The first category included companies with a positive EVA value. The second category included companies with negative EVA, but with the economic result above the risk-free rate. The third category included companies with a positive economic result above the risk-free rate. The fourth category included companies with a negative economic result. The model did not include companies with negative equity. The input represented 15 predictors based on their financial statements. The data were normalized and all extreme values, likely caused by a data rewriting error, were removed. Company performance is visualized by comparing Principal Component Analysis and Kohonen neural networks. Compared to similar research, the methods are compared using the data that analyzes the performance of companies. Both methods made it possible to visualize the given task. With regard to the purpose of facilitating the interpretation of the results, for the given case, the use of PC seems to be more appropriate. AcknowledgmentThis study has been supported by the Technology Agency of the Czech Republic under project No TL01000349.

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals.

Insert conditions significantly influence the product quality and manufacturing efficiency of lathe machining. This study used the power spectral density distribution of the vibration signals of a lathe machining accelerometer to design an insert condition classification system applicable to different machining conditions. For four common lathe machining insert conditions (i.e., built-up edge, flank wear, normal, and fracture), herein, the insert condition classification system was established with two stages—insert condition modeling and machining model fusion. In the insert condition modeling stage, the magnitude features of the segmented frequencies were captured according to the power spectral density distributions of the accelerometer vibration signals. Principal component analysis and backpropagation neural networks were used to develop insert condition models for different machining conditions. In the machining model fusion stage, a backpropagation neural network was employed to establish the weight function between the machining conditions and insert condition models. Subsequently, the insert conditions were classified based on the calculated weight values of all the insert condition models. Cutting tests were performed on a computer numerical control (CNC) lathe and utilized to validate the feasibility of the designed insert condition classification system. The results of the cutting tests showed that the designed system could perform insert condition classification under different machining conditions, with a classification rate exceeding 80%. Using a triaxial accelerometer, the designed insert condition classification system could perform identification and classification online for four common insert conditions under different machining conditions, ensuring that CNC lathes could further improve manufacturing quality and efficiency in practice.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

A new method to production prediction for the shale gas reservoir

ABSTRACT It is a common phenomenon that shale gas production differs significantly among wells, and it is difficult to find a relationship between engineering variables and gas rate by utilizing a simple plotting of production data against well and treatment variables. Data such as completion method, flow back, and gas production have been collected for 180 shale gas wells. Firstly, the collected data were standardized, and then related or dependent variables were removed by gray correlation analysis. Finally, principal component analysis and BP Neural-network methods are applied separately to analyze the collected data. It is the first-of-its-kind study that was conducted to define the relationship between gas production and engineering parameters for Fuling shale gas reservoir in China. By use of principal component analysis, it is concluded that the number of stages, the lateral length, the distance between clusters, and the average volume of slick-water are the main factors that impact shale gas production. The results showed that stage numbers account for 42.9% of the total variance, while the horizontal length accounts for about 21.4%; therefore, these two factors account for 64.3% among all of the factors. However, the next important factors are the average distance between clusters and the average volume of slick-water, both account for 24.1% of the total variance. The importance of each variable was also defined by the BP Neural-network method. The number of stages and the horizontal length are still the two major factors, and both account for 52%. The volume of slick-water and the proppant size of 40/70 mesh for both ceramic and sand are considered the second important factor and both account for 37%. This implies that shale gas well configuration and treatment parameters are the dominant factors for gas production in shale reservoirs. Therefore, in order to enhance gas production, it is crucial to increase the number of stages and horizontal length firstly and to raise the volume of slick-water by using 40/70 mesh for both ceramic and sand. However, another driver to accelerate production is to decrease the distance of clusters. These measures will be helpful to improve production in the future shale gas development of Fuling reservoir. This study is believed to be the first-of-its-kind to define the relationship between gas production and engineering parameters for Fuling shale gas reservoir in China.

Modulation format/bit rate recognition based on principal component analysis (PCA) and artificial neural networks (ANNs): erratum

The aim of this erratum is to add an omitted reference.

Nitrate nitrogen concentration detection method based on principal component analysis and BP neural network

Nitrate nitrogen concentration detection method based on principal component analysis and BP neural network

Topological neural network of combined AE and EN signals for assessment of SCC damage

ABSTRACTStress corrosion cracking (SCC) is a common corrosion form that involves undetected premature failures during service life of structures. Since SCC is a combination of electrochemical and mechanical phenomena, coupled acoustic emission and electrochemical noise techniques were proposed to investigate the evolution of SCC damage tomartensitic stainless steel samples. Tests were carried out using a precipitation hardening martensitic stainless steel in an aqueous MgCl2 environment at 100°C, with an applied mechanical stress equal to the 90% of 0.2% yield strength. The synergistic use of the two non-destructive analysis technique was performed using a synchronisation process. The combination of two advanced multivariate analysis techniques (Principal Component Analysis and Self Organising Map neural network) highlighted damage-sensitive features. Characteristic clusters of variables related to specific damage mechanisms were identified discriminating electrochemical activation processes (i.e. pitting), SCC initiation and propagation until final failure phenomena during SCC.

No-Reference Image Quality Assessment Using Independent Component Analysis and Convolutional Neural Network

As digital images have become a significantly primary medium in a broad area, there is a growing interest in the development of automatic objective image quality assessment (IQA) methods. In this paper, a novel no-reference IQA (NRIQA) algorithm is proposed based on independent component analysis and convolutional neural network. The proposed NRIQA algorithm consists of the following three steps: selection of some representative patches, extraction of the features of the selected image patches, and prediction of the image quality by exploiting the features. Initially, an image is divided into non-overlapping patches and then some patches are selected with the suitable property for assessing the overall image quality. In this paper, we refer to the selected patches as image quality patches. The largest infinity norm of the gradient of each image quality patch is employed as a basis when the image quality patches being selected. Second, we employ independent component analysis (ICA) to extract the features of image quality patches. At the last moment, a convolutional neural network (CNN) is applied to the independent component coefficients of image quality patches to predict the corresponding differential mean opinion score (DMOS). We compared the performance of the proposed NQIRM with other IQMs in terms of PCC, SROCC, and RMSE on the database LIVE2, CSIQ and TID2008/2013. The PCC, SROCC and RMSE values achieve respectively to 0.996, 0.999 and 6.011 on the database TID2013. The performance comparison results show the proposed NRIQM is superior to commonly used IQMs.

Development and Deployment of Organizational Culture Maturity Model (TMKC) for the Business Excellence Success

The paper describes the development of the maturity-based organisational culture model (TMKC - team bonding-motivation-knowledge management-commitment for improvement). Each level of the model has practices for the improvements. The model deployment in 11 different organisations indicates the earnings before interest, depreciation, taxes and amortisation (EBITDA) are significantly higher. Delphi method-based survey, dipstick survey and correlation analysis were applied to discover the extent of the problem. The cluster analysis, principal components analysis and artificial neural network were used to prioritise organisation-based cultural factors from 13 to 4. The impact of TMKC model was validated through pilots and statistical tests. The higher maturity levels of 4 and 5 have significantly increased business excellence (BE) initiative goal achievement rate from 34% to 86%. The successful TMKC model deployment positively impacts % EBITDA change. The model has been piloted in 11 cases and its finding can change with deployments in varied instances.

ICA와 DNN을 이용한 방송 드라마 콘텐츠에서 음악구간 검출 성능*

ICA와 DNN을 이용한 방송 드라마 콘텐츠에서 음악구간 검출 성능*

ICA와 DNN을 이용한 방송 드라마 콘텐츠에서 음악구간 검출 성능*

ICA와 DNN을 이용한 방송 드라마 콘텐츠에서 음악구간 검출 성능*

Prediction of wind drift and evaporation losses of a sprinkler irrigation system using principal component analysis and artificial neural network technique

Principal component analysis was merged with the artificial neural network (ANN) technique to predict wind drift and evaporation losses (WDEL) from a sprinkler irrigation system. For this purpose, field experiments were conducted to determine WDEL under different conditions. Data from field experiments and previous studies were used as sample data to train the ANN model. Three models were developed to predict WDEL. In the first model (ANN1), 9 neurons (riser height, main nozzle diameter, auxiliary nozzle diameter, discharge rate of the main nozzle, discharge rate of the auxiliary nozzle, operating pressure, wind speed, air temperature and relative humidity) were used as the input layer. In the second model (ANN2), 7 neurons (riser height, operating pressure, wind speed, air temperature and relative humidity, diameter ratio and discharge ratio) were used as the input layer. The third model (ANN3) used a multivariate technique (PC1, PC2, and PC3). Results revealed that the ANN3 model had the highest coefficient of determination (R2 = 0.8349). The R2 values for the ANN1 and ANN2 models were 0.7792 and 0.4807, respectively. It can be concluded that the ANN3 model has the highest predictive capacity.

Noise reduction in feature level and its application in rolling element bearing fault diagnosis

Vibration analysis is an effective way to accurately diagnose bearing faults, because it carries abundant information regarding mechanical health conditions. However, noise interference makes the features, extracted from vibration signals at different time periods, show randomness fluctuation that will reduce the bearing diagnostic accuracy. To solve this problem, this article proposes a noise reduction method in feature level and tries to use it in bearing fault diagnosis with principal component analysis and radial basis function neural network. First, original feature space, including time, frequency, and energy features, is constructed from these obtained vibration signals. Second, compendious feature sets of the considered bearing faults are created by principal component analysis and random statistical average algorithm. In this step, random statistical average is designed to weaken the influence of noise to features and principal component analysis is used to reduce the dimension of features for co...

Research on the Prediction Model of Transformer Bidding

Aiming at the problem of transformer manufacturing enterprises bidding is lacking scientific theoretical guidance and low bid probability, in order to predict the next bid price, based on principal component analysis (PCA) and artificial neural network (ANN) pre-tender estimate forecast model is proposed. The model uses PCA to preprocess the original high dimensional data, select principal components (PC) as the radial basis function (RBF) neural network's input. PCA eliminates the correlation of the input variables, at the same time of simplifying the structure of ANN, improving the accuracy of the prediction model. The simulation results show the applicability of the pre-tender estimate forecast model.

Reinforced Concrete Bridge Structural Health Classification using Hybrid Principal Component Analysis and Artificial Neural Network Approach with Wireless Sensor Network

Reinforced Concrete Bridge Structural Health Classification using Hybrid Principal Component Analysis and Artificial Neural Network Approach with Wireless Sensor Network

Classification of Chinese Herbal Medicine by Laser-Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network

ABSTRACTChinese herbal medicine has attracted increasing attention because of the unique and significant efficacy in various diseases. In this paper, three types of Chinese herbal medicine, the roots of Angelica pubescens, Codonopsis pilosula, and Ligusticum wallichii with different places of origin or parts, are analyzed and identified using laser-induced breakdown spectroscopy (LIBS) combined with principal component analysis (PCA) and artificial neural network (ANN). The study of the roots of A. pubescens was performed. The score matrix is obtained by principal component analysis, and the backpropagation artificial neural network (BP-ANN) model is established to identify the origin of the medicine based on LIBS spectroscopy of the roots of A. pubescens with three places of origin. The results show that the average classification accuracy is 99.89%, which exhibits better prediction of classification than linear discriminant analysis or support vector machine learning methods. To verify the effectiveness of PCA combined with the BP-ANN model, this method is used to identify the origin of C. pilosula. Meanwhile, the root and stem of L. wallichii are analyzed by the same method to distinguish the medicinal materials accurately. The recognition rate of C. pilosula is 95.83%, and that of L. wallichii is 99.85%. The results present that LIBS combined with PCA and BP-ANN is a useful tool for identification of Chinese herbal medicine and is expected to achieve automatic real-time, fast, and powerful measurements.

Colorimetric sensor array-based artificial olfactory system for sensing Chinese green tea’s quality: A method of fabrication

ABSTRACTTea quality is often evaluated by experienced tea tasters; however, their assessments are subjective, being influenced by their individual physiological and psychological condition. Herein, we fabricated a colorimetric sensor array-based artificial olfactory system for sensing the quality of Chinese green tea. First, the colorimetric sensors array was man-made using printing 12 chemically responsive dyes (9 porphyrins, metalloporphyrins and 3 pH indicators) on silica-gel flat plate. The plate was exposed to volatile organic compounds, and the colour changes in each sample were obtained by distinguishing between the images of sensor array before and after contact with tea sample. The values of colour composition changes were extracted from the dyes’ colour sections. Multivariate calibrations were applied through principal component analysis and back propagation artificial neural network (BP-ANN) for modelling. The optimum BP-ANN model was obtained with nine principal components, and the discrimination rate was equal to 85% and 86% in the calibration and prediction sets, respectively. We thus conclude that the low cost colorimetric sensor array-based artificial olfactory technique has great potential for application in intelligent evaluation of the quality of green tea.