Correlation Analysis Algorithm Research Articles

A Fusion Method Based on EEMD, Pearson Correlation Analysis, Improved LSTM, and Gaussian Function-Trust Region Algorithm for State of Health Prediction of Lithium-Ion Batteries

Abstract Aiming at the phenomenon of battery capacity regeneration, which leads to inaccurate prediction of lithium-ion battery state of health (SOH), a new fusion method based on ensemble empirical mode decomposition (EEMD), Pearson correlation analysis (PCA), and improved long short-term memory (LSTM) network and Gaussian function-trust region (GS-TR) algorithm is introduced to predict battery SOH. First, the EEMD method is adopted to process the battery SOH data to eliminate the impact of capacity recovery. Second, the decomposed data signals are classified by the PCA method, and the signals classified as high frequency and low frequency are respectively predicted by the improved LSTM algorithm and the GS-TR algorithm. Finally, the prediction results of the improved LSTM and GS-TR algorithms are integrated. The proposed fusion method avoids the complexity of the hybrid neural network model and improves the prediction efficiency. Based on the average results of the three data sets from NASA, the RMSE result of the proposed algorithm is reduced by 9.56% compared with the improved LSTM with the EEMD algorithm and 37.57% compared with the improved LSTM without the EEMD algorithm. The results show that the proposed method has higher adaptability and prediction accuracy.

Multi-modal physiological signals based fear of heights analysis in virtual reality scenes

Fear of heights (FoH) analysis and its association to physiological signals can better help understand people’s emotion and quantify human’s behavior, which have been found important in many applications, such as disease analysis, affective computing, etc. Existing studies are mainly on how to alleviate FoH while little literature on FoH analysis has been reported in the past. In this paper, we present the studies of correlation on FoH to multi-modal physiological signals in the virtual reality (VR). To stimulate the FoH to participants, 4 types of VR scenarios that consist of the virtual scene of the VR game “Richie’s plank experience” and the realistic stimulus of hitting by basketball are adopted in the experiment. The synchronized eye movement (EMO), pupil, and electrocardiogram (ECG) of 17 healthy subjects with an even mix of men and women are recorded for FoH analysis. The observations on the FoH analysis carried out in the paper are two-fold: (1) The recognition ability of multi-modal physiological signals for HoF has been evaluated using ReliefF feature selection algorithm. The results show that pupil feature based model generally achieves the best classification performance for FoH. Multiple features consisted of pupil diameter, power spectral density (PSD) of EMO, pupil, ECG, and mean value of EMO can effectively quantify the variability. (2) The FoH analysis model built on the correlation analysis algorithms combined with multi-modal feature-level fusion and strategy-level fusion methods can effectively overcome the drawbacks of conventional statistical models.

Research on Fault Diagnosis of Launch Vehicle’s Power Transformation and Transmission System Based on Big Data

The on-board power supply system provides power for the launch vehicle. The power transmission and transformation system plays an irreplaceable role to ensure that the on-board power supply system receives the normal working voltage of the launch vehicle. There are many types of faults in power transmission and transformation systems. The traditional faulty diagnosis method of power transmission and transformation equipment has the disadvantages of being susceptible to experts’ subjectivity and model’s ossification. In this paper, a new method of equipment fault diagnosis based on big data is proposed. On the basis of big data, this paper introduces the failure mode clustering algorithm, the state parameter correlation analysis algorithm, the fault diagnosis method based on the correlation matrix, and other key fault diagnosis technologies. The fault record data of the 400 kV voltage grade oil-immersed transformer bushing in the past ten years by a Chinese combat unit is used as a case for demonstration. The results show that the accuracy rate of SC-LSTM-K-means clustering model exceeds 95%. And the fault classification mode can be accurately obtained. A priori correlation algorithm with TA coefficient can be used to evaluate the strong and weak relationship between the state parameters; the fault diagnosis matrix based on Pearson’s correlation coefficient can accurately determine the fault mode consistent with the actual operation and maintenance test results. Therefore, the fault diagnosis method of power transmission and transformation system based on big data can both effectively obtain the inherent laws of historical data and realize more accurate fault diagnosis with data adaptability.

Research on Recognition of Topological Relations Between Residential Lines in Low Voltage Station Area Based on Correlation Analysis Algorithm and Probabilistic Decision Method

In order to solve the problems of low accuracy and incomprehensive recognition of the topological relationship between households in the station area and the incomplete recognition results in traditional methods, a method for identifying topological relationships between household changes in low-voltage stations based on correlation analysis algorithm and probabilistic decision method is proposed. The BIRCH method is used to cluster the topological relationship characteristics of the household line changes in the low-voltage station area, and the topological relationship characteristics are obtained through clustering parameter initialization, clustering implementation and clustering evaluation, and the user phases in the topological relationship are identified according to the feature clustering results. The correlation analysis method is used to analyze the similarity of the voltage sequence of the points to be identified and the comprehensive similarity of all the faults of the target distribution transformer and the auxiliary distribution transformer, and set a similarity threshold to determine whether the points to be identified belong to the same station area. Finally, based on the probabilistic decision-making method, the identification of the topological relationship of the low-voltage station area household line change is completed. The experimental results show that this method can not only identify the topological relationship of single distribution transformer outage, but also identify the topological relationship of multiple distribution transformer outage. The accuracy of the identification result is high, and the identification loss function is low, which indicates that the identification result of this method is reliable and comprehensive.

Plant-wide troubleshooting and diagnosis using dynamic embedded latent feature analysis

Plant-wide process data are usually high dimensional with dynamics residing in a reduced dimensional latent space. In this paper, we propose a novel procedure for diagnosing and troubleshooting plant-wide process anomalies using dynamic embedded latent feature analysis (DELFA). To remove the impact of external disturbances or exogenous variables, a dynamic inner canonical correlation analysis algorithm with exogenous variables is proposed. Composite loadings and composite weights are derived and applied for diagnosing a feature that is contained in several latent variables. The dynamic embedded latent features are usually related to poor control performance or malfunctioning control instrumentation. The proposed DELFA procedure with dynamic latent scores and composite loadings is applied to two industrial datasets of a chemical plant before and after a troubled control valve was fixed. The case study demonstrates convincingly that latent dynamic features are powerful for troubleshooting of process anomalies and diagnosing their causes in a plant-wide setting.

A method for degradation features extraction of diesel engine valve clearance based on modified complete ensemble empirical mode decomposition with adaptive noise and discriminant correlation analysis feature fusion

The health assessment of the valve clearance is a key link to realize the failure prediction and health management of the valve mechanism. To accurately evaluate the state of valve clearance, this article proposes a diesel engine valve clearance degradation feature extraction method based on modified complete ensemble empirical mode decomposition with adaptive noise and discriminant correlation analysis feature fusion algorithm. First, we use modified complete ensemble empirical mode decomposition with adaptive noise to adaptively filter the cylinder head vibration signal. Then, power spectrum entropy and improved hierarchical dispersion entropy are proposed as degenerate feature entropy. To improve the sensitivity of the degraded feature entropy to the degraded state, the discriminant correlation analysis algorithm is used to fuse the two types of feature entropy to obtain fused degraded feature entropy. Finally, the degenerate fusion features are input into the least squares support vector machine to realize the health status assessment of the valve mechanism. Through the verification of test data, the results show that the proposed method can effectively evaluate the health state of the valve clearance of diesel engines.

An Efficient and Scalable Heart Disease Diagnosis System with Attribute Impact Based Weights and Genetic Correlation Analysis

Fuzzy neural network (FNN) is playing a vital role in processing of complex data mining applications like medical diagnosis, speech recognition, text processing, image processing etc. Fuzzy neural networks simulate the human brain functionality with fuzzy logic decision making capabilities, to achieve more accuracy in feature selection process of complex data mining applications. Today cardiovascular diseases become a serious global health issue and approximately more than 31% of all global deaths are happening due to cardiovascular diseases reported by WHO. In order to prevent and control the cardiovascular diseases, an efficient and accurate heart disease diagnosis system (HDDS) has to be designed with the state of the art feature based data classifiers. In recent, some research articles introduced HDDS using popular data mining techniques like FNN, but they are suffering from accuracy in allocation of attribute weights and attribute correlation analysis, pattern recognition, forecasting. To address the problems in designing the HDDS, in this paper, Fuzzy Neural Networks has been used with empowered input layer and hidden layers to achieve the high accuracy and performance, while processing the huge set of medical data records. We designed an Attribute Impact calculation procedure to assign the accurate weight values to the attributes and we proposed a Genetic Correlation Analysis algorithm to do correlation analysis which helps in improving the performance.

A hybrid method for muscle artifact removal from EEG signals

BackgroundElectroencephalogram (EEG) signals may be contaminated with muscle artifacts that are usually difficult to be removed. New methodIn this article, a new hybrid method for suppressing muscle artifacts is proposed. Our method leverages variational mode decomposition (VMD) and canonical correlation analysis (CCA) algorithms. Each channel of EEG is decomposed into intrinsic mode functions (IMFs) with VMD to achieve an extended data set that contains more channels than the original data set. The potential artifact components are decomposed by CCA for further isolation. ResultsThe proposed method is evaluated with semi-simulation and real contaminated EEG signals. The results show that the performance of removing artifacts for VMD-CCA exceeds the comparison methods. Comparison with existing methodsRegardless of the number of EEG channels and the signal-to-noise ratio of the signal, the VMD-CCA approach is superior to the existing methods. As the number of EEG channels decreases, the average de-artifact effects of VMD-CCA and the comparison approaches are basically the same, but the randomness increases. ConclusionsThe VMD-CCA method can effectively isolate muscle artifacts in EEG in case of multiple channels or few channels.

Identification and validation of the clinical roles of the VHL-related LncRNAs in clear cell renal cell carcinoma.

Accumulating evidence suggests that lncRNAs (long non-coding RNAs) function as oncogenes or tumor suppressor genes in ccRCC (clear cell renal cell carcinoma). Although VHL (Von Hippel-Lindau) gene inactivation is by far the most common carcinogenic driving event in ccRCC, the roles of VHL-related lncRNAs in ccRCC remain unknown. In this study, using RNA-seq and clinical data in TCGA-KIRC (the Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma), we identified VHL-related lncRNAs through WGCNA (Weighted Gene Co-expression Network Analysis), correlation analysis and catRAPID algorithm, and explored their clinical characteristics in ccRCC. Results showed that 10 lncRNAs (AC112220.2, AL391121.1, USP46-AS1, AL450326.1, MID1IP1-AS1, SUCLG2-AS1, RAP2C-AS1, FGD5-AS1, AC018647.2 and AC015922.2) were identified as VHL-related lncRNAs, and they were down-regulated in ccRCC tissues. Survival analysis results indicated that high expression groups of AC112220.2, AL391121.1, USP46-AS1, AL450326.1, SUCLG2-AS1, RAP2C-AS1, FGD5-AS1, AC018647.2 and AC015922.2 had significantly longer OS (Overall Survival) than their respective low expression groups. Meanwhile high AC112220.2, USP46-AS1, AL450326.1, SUCLG2-AS1, FGD5-AS1, AC018647.2 and AC015922.2 expression groups had remarkably longer DFS (Disease Free Survival) than their respective low expression groups. Besides, FGD5-AS1 and AL391121.1 expression were decreased in VHL mutant tissues compared with VHL non-mutant tissues. Moreover, high expression group of FGD5-AS1 had significantly longer OS and DFS than their respective low expression groups in VHL mutant ccRCC. In addition, we found that DNA hypermethylation may also play an important role in decreased FGD5-AS1 expression. Furthermore, we validated the expression of FGD5-AS1 in VHL mutant and non-mutant ccRCC tissues and cell lines. In conclusion, our results demonstrated that lncRNA FGD5-AS1 was significantly associated with VHL and can serve as a novel biomarker of ccRCC.

Implementation of BFP-Typed Apertometer: Reference Location and Accuracy

This work is to investigate the instrumentation implementing issues of back focal plane (BFP)-typed apertometer. Firstly, we introduce a general instrument architecture of the BFP-typed apertometer, including the hardware (optics and mechanics) of the system, operation procedures of control and measurement system, and also the digital image processing algorithm/software. Secondly, we are the first to discuss the gradual rising effect of total-internal-reflection (TIR) phenomenon, the criteria on how to “exactly” locate the reference ring on BFP images, and the corresponding accuracies. As a significantly important complement, we further discuss three important issues: 1) the influence of Goos–Hanchen shift on numerical aperture (NA) measurement; 2) comparison of one-dimension and two-dimension identification algorithms; and 3) comparison of center locating using Fourier correlation analysis (FCA) and Hough transformation (HT) algorithms.

Multi-view face pose recognition model construction based on a typical correlation analysis algorithm

Multi-view face pose recognition model construction based on a typical correlation analysis algorithm

Multi-view face pose recognition model construction based on a typical correlation analysis algorithm

In order to overcome the problems of large recognition error and low recognition accuracy in the existing face pose recognition models, the paper proposes and constructs a new multi-view face pose recognition model based on typical correlation analysis algorithm. First, the AdaBoost algorithm is used to realise multi-pose face detection and positioning. Secondly, the face image is pre-processed, including image greying, image denoising, and face image geometric normalisation, and then the typical correlation analysis algorithm is used to extract face features. Finally, multi-view facial gesture recognition is realised through convolutional neural network. Experimental results show that, compared with the traditional recognition model, the recognition accuracy of the constructed model is greatly improved, and the average accuracy (mAP) is 96.334%, which proves that the recognition performance of the constructed model is better.

Multi-Modal Emotion Recognition by Fusing Correlation Features of Speech-Visual

To effectively fuse speech and visual features, this letter proposes a multi-modal emotion recognition method by fusing correlation features of speech-visual. Firstly, speech and visual features are extracted by two-dimensional convolutional neural network (2D-CNN) and three-dimensional convolutional neural network (3D-CNN), respectively. Secondly, the speech and visual features is processed by feature correlation analysis algorithm in multi-modal fusion. In addition, the class information of speech and visual features are also applied to the feature correlation analysis algorithm, which can effectively fuse speech and visual features and improve the performance of multi-modal emotion recognition. Finally, support vector machines (SVM) completes the classification of multi-modal speech and visual emotion recognition. Experimental results on the RML, eNTERFACE05, BAUM-1 s datasets show that the recognition rate of our method is higher than other state-of-the-art methods.

Brain-Computer-Spinal Interface Restores Upper Limb Function After Spinal Cord Injury

Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and require frequent calibration and high computational complexity. Furthermore, most applications of closed-loop stimulation act on peripheral nerves or muscles, resulting in rapid muscle fatigue. Here we show that a local field potential-based BCI can control spinal stimulation and improve forelimb function in rats with cervical SCI. We decoded forelimb movement via multi-channel local field potentials in the sensorimotor cortex using a canonical correlation analysis algorithm. We then used this decoded signal to trigger epidural spinal stimulation and restore forelimb movement. Finally, we implemented this closed-loop algorithm in a miniaturized onboard computing platform. This Brain-Computer-Spinal Interface (BCSI) utilized recording and stimulation approaches already used in separate human applications. Our goal was to demonstrate a potential neuroprosthetic intervention to improve function after upper extremity paralysis.

Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.

Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study.

BackgroundThe novel COVID-19 disease has spread worldwide, resulting in a new pandemic. The Chinese government implemented strong intervention measures in the early stage of the epidemic, including strict travel bans and social distancing policies. Prioritizing the analysis of different contributing factors to outbreak outcomes is important for the precise prevention and control of infectious diseases. We proposed a novel framework for resolving this issue and applied it to data from China.ObjectiveThis study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China.MethodsDaily COVID-19 case data and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and influenza-like illness factors, from 343 cities in China were collected. A correlation analysis and interpretable machine learning algorithm were used to evaluate the quantitative contribution of factors to new cases and COVID-19 growth rates during the epidemic period (ie, January 17 to February 29, 2020).ResultsMany factors correlated with the spread of COVID-19 in China. Travel-related population movement was the main contributing factor for new cases and COVID-19 growth rates in China, and its contributions were as high as 77% and 41%, respectively. There was a clear lag effect for travel-related factors (previous vs current week: new cases, 45% vs 32%; COVID-19 growth rates, 21% vs 20%). Travel from non-Wuhan regions was the single factor with the most significant impact on COVID-19 growth rates (contribution: new cases, 12%; COVID-19 growth rate, 26%), and its contribution could not be ignored. City flow, a measure of outbreak control strength, contributed 16% and 7% to new cases and COVID-19 growth rates, respectively. Socioeconomic factors also played important roles in COVID-19 growth rates in China (contribution, 28%). Other factors, including medical, environmental, and influenza-like illness factors, also contributed to new cases and COVID-19 growth rates in China. Based on our analysis of individual cities, compared to Beijing, population flow from Wuhan and internal flow within Wenzhou were driving factors for increasing the number of new cases in Wenzhou. For Chongqing, the main contributing factor for new cases was population flow from Hubei, beyond Wuhan. The high COVID-19 growth rates in Wenzhou were driven by population-related factors.ConclusionsMany factors contributed to the COVID-19 outbreak outcomes in China. The differential effects of various factors, including specific city-level factors, emphasize the importance of precise, targeted strategies for controlling the COVID-19 outbreak and future infectious disease outbreaks.

Development of Technologies for Reducing the Error of Traditional Algorithms of Correlation Analysis of Noisy Signals

Development of Technologies for Reducing the Error of Traditional Algorithms of Correlation Analysis of Noisy Signals

Mild Cognitive Impairment Detection Using Association Rules Mining

A single Mild cognitive impairment (MCI) is a transitional state between normal cognition and dementia. The typical diagnostic procedure relies on neuropsychological testing, which is insufficiently accurate and does not provide information on patients’ clinical profiles. The objective of this paper is to improve the recognition of elderly primary care patients with MCI by using an approach typically applied in the market basket analysis – association rules mining. In our case, the association rules represent various combinations of the clinical features or patterns associated with MCI. The analytical process was performed in line with the CRISP-DM, the methodology for data mining projects widely used in various research or industry domains. In the data preparation phase, we applied several approaches to improve the data quality like the k-Nearest Neighbour, correlation analysis, Chi Merge and K-Means algorithms. The analytical solution´s success was confirmed not only by the novelty and correctness of new knowledge, but also by the form of visualization that is easily understandable for domain experts. This iterative approach provides a set of rules (patterns) that meet minimum support and reliability. The extracted rules may help medical professionals recognize clinical patterns; however, the final decision depends on the expert. A medical expert has a crucial role in this process by enabling the link between the information contained in the rules and the evidence-based knowledge. It markedly contributes to the interpretability of the results.

Association Analysis of Peripheral and CSF Biomarkers in Late Mild Cognitive Impairment.

Research shows that late mild cognitive impairment (LMCI) has a high risk of turning into Alzheimer’s disease (AD). Due to the invasion of detection methods and physical damage to the patients, it is not a convenient way to diagnose and detect early AD and LMCI by cerebrospinal fluid (CSF) data. So there is an urgent need to find the correlation between peripheral biological data and CSF data in the brain, and to find new diagnostic methods through changes in the peripheral biological data. Studies have shown that during the pathogenesis of LMCI and AD, peripheral immune cells specifically infiltrate into the brain through the blood–brain barrier, causing an imbalance in the brain’s immune response and dysregulating the clearance of Aβ in CSF. Therefore, in this paper, canonical correlation analysis (CCA) algorithm is presented to derive the correlation between peripheral and CSF biomarkers based on LMCI peripheral gene expression data and plasma marker information. Firstly, to explore the influence of the infiltration of peripheral blood immune cells on the brain, the abundance of 28 immune cells were calculated by using the gene set enrichment analysis algorithm of LMCI samples. Then, to identify the correlation between biomarkers inside and outside of the brain, we performed CCA to calculate the relationship between CSF and peripheral biomarkers. Results of CCA showed significant correlations between the variable sets of 8 peripheral biomarkers and the variable sets of CSF biomarkers (at 0.794). Finally, according to Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis, it was found that the obtained peripheral biomarkers are involved in many immune-related pathways and functions which can be activated in peripheral blood of LMCI patients. Most related genes enriched in immune-related pathways and functions were up-regulated. Through receiver operating characteristic curve (ROC) analysis, it was also found that FP40/FP42 and type 1 T helper can accurately predict the pathological changes of LMCI (at 0.747).

Methods and Algorithms for Correlation Analysis in R

Methods and Algorithms for Correlation Analysis in R