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This paper presents full-scale observations of wind-induced vibrations on a 140m high cylindrical reinforced concrete tower under non-stationary and strong wind conditions. The vibration responses of the tower in two planar orthogonal axes and rotation, in conjunction with wind speed and direction data, have been utilized to extract the response characteristics. The primary focus is on discussing the correlation of wind-induced responses in three components: Along-wind, across-wind, and torsion. Throughout the varying levels of oscillation, the tower’s responses clearly exhibit correlations among these components. The correlation coefficients between along-wind and across-wind responses, as well as along-wind and torsional responses, remain negligible across the entire dataset. In contrast, during intense oscillations, there is a near-perfect correlation between the across-wind and torsional responses. Furthermore, the probability density functions (PDFs) of the response components under strong wind conditions significantly deviate from the Gaussian distribution and closely resemble the Cauchy distribution. The joint PDFs also indicate that the along-wind response is statistically independent of the other response components, while the across-wind response correlates well with the torsion, as indicated by the non-Gaussian distribution. This paper also includes distinct observations regarding dominant oscillating frequencies, torsional amplitudes, and vertical vibration amplitudes. The importance of this research lies in its examination of the relationship between different response components, a crucial aspect in understanding how structures behave under wind loading. By emphasizing the need for full-scale measurements, this study aims to provide valuable validation for previous theoretical and experimental research findings. [ABSTRACT FROM AUTHOR]

To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively. [ABSTRACT FROM AUTHOR]

Soil thermal conductivity (λ) is an important parameter in thermal calculation and temperature-field analysis in geotechnical engineering. To accurately predict it, this paper uses individual and ensemble machine learning methods to establish predictive models. The λ measurements were obtained (n = 337) and saturation, dry density, quartz content, sand content and clay content were selected as input parameters for the predictive models. The performance of the prediction model is evaluated by Inspection parameters. The predictive model was k-fold cross-validated and compared with traditional empirical models. The results show that individual and ensemble machine learning models accurately predict λ. The random forest model had the best predictive accuracy, with a correlation coefficient R2 = 0.979, root mean square error (RMSE) = 0.097 Wm−1 K−1 and mean absolute error (MAE) = 0.07 Wm−1 K−1. The MLR model had the worst predictive accuracy. The accuracy of ensemble machine learning models was obviously better than those of individual machine learning models. The accuracy of the proposed predictive model was significantly higher than that of the traditional empirical model. According to the results of performance tests and k-fold cross-validation, the RF and decision tree + bagging models are recommended for predicting λ. [ABSTRACT FROM AUTHOR]

Breast cancer is perceived as the most common cause of mortality among women globally. Early detection of this disease is critical to reduce significantly the possibility of death. Machine learning techniques have been proved to be efficient and very successful for an accurate breast cancer diagnosis. In this paper, an efficient hybrid Feature Selection (FS) method named a Correlation technique-Modified GreyWolf Optimizer (CMGWO) was proposed for accurate breast cancer classification based on dimensionality reduction. The suggested technique is based on two stages: the feature selection step and the classification step. Feature selection is the process of picking the most significant characteristics from a dataset. This stage is crucial in machine learning. Firstly, we focus on the filter method by using a Correlation technique for dimensionality reduction. This technique is intended to eliminate and reduce the number of features by selecting one feature from the other correlated features. Secondly, we use the Modified Grey Wolf Optimization algorithm (MGWO) to locate and determine the most significant features from uncorrelated features. After that, we use multiple classifiers to classify breast cancer disease based on the selected features. The Wisconsin Diagnostic Breast Cancer (WDBC) database was used to prove the performance of our proposed work. The experimental results show that the combination of the correlation method and MGWO for feature selection increases the accuracy rate of classification with a minimum number of features. The performances of different machine learning algorithms were evaluated, including Random Forest classifier (RF), Support Vector Machine (SVM) Classifier, and Naïve Bayes (NB) Classifier for the classification step. The suggested technique proves to be the best approach and reliable one among all studied approaches since it increases classification accuracy to 99.12% obtained by CMGWO using Random Forest classifier and demonstrates its significance in detecting breast cancer. [ABSTRACT FROM AUTHOR]

This paper estimates a correlation model and joint distribution model of spectral accelerations of vertical ground motions based on the NGA-West2 ground motion database. The correlation model calculated via BC16 vertical GMPE in this study is compared with those published models via other vertical NGA-West2 GMPEs. It is verified that the calculated residuals of vertical ground motions at multiple periods follow the multivariate normal distributions via both a qualitative validation method and a set of quantitative validation methods. The correlation model and the joint distribution model of vertical spectral accelerations are the theoretical basis for research on the joint occurrence of spectral accelerations of vertical ground motions, and an example of applied research on joint occurrence of spectral accelerations of vertical ground motions is studied: the conditional mean spectrum of vertical ground motions. Finally, the effects of different vertical correlation models and ground motion prediction equations on vertical conditional spectra are investigated for wide application of the correlation model and the joint distribution model of spectral accelerations of vertical ground motions. [ABSTRACT FROM AUTHOR]

Nowadays, web applications play central roles in information systems using the Internet. Then, client-side web programming using HTML, CSS, and JavaScript should be mastered first by novice students. Previously, we have presented the element fill-in-blank problem (EFP) for its self-study. An EFP instance requests to fill in the blank elements in the given source code by referring to the screenshots of the corresponding web page. The correctness of any answer is marked through string matching. However, these blanks were manually selected by considering the importance of elements and the uniqueness of their correct answers. In this paper, we propose a blank element selection algorithm to automatically generate a new EFP instance from a given source code for client-side web programming. We define the seven rules on blank element selections from the code, and implement the procedure in Python using the open source BeautifulSoup and regular expressions' ' For evaluations, we applied the algorithm to the 47 source codes used for manual generations and obtained the better EFP instances with more blanks. Besides, we verified the effectiveness by generating 10 new instances with the algorithm and assigning them to 40 students. In addition, we extended its application to three source codes for games and verified the effectiveness by assigning them to 20 students, to further validate the applicability of the algorithm in EFP instance generations. We also evaluated the relationships between the number of blanks, the number of lines in source codes, the submission times and answer rates of students to further assess the adaptability of the algorithm. These results allow us to measure the algorithm's versatility in generating a wide range of EFP instances and contributes to comprehensive understanding of instance difficulties and learning outcomes. [ABSTRACT FROM AUTHOR]

• A DC-Gaussian process regression model is used to the prediction of Dst index. • DC-GPR model has the best stability among the three models among DC-GPR, DC-SVM, and DC-NN models • DC-GPR model exhibits the better forecasting performance than DC-SVM and DC-NN models in Dst forecasting. In this paper we apply a class of Bayesian machine learning model, Gaussian Process Regression, to the prediction of Dst index by using 80 intense geomagnetic storms data (Dst ⩽ - 100 nT) from 1995 to 2014. The purpose of this paper is to compare the performance of Gaussian process regression model with Support Vector Machine model combined together with Distance Correlation (DC-SVM) and Neural Network model combined together with Distance Correlation (DC-NN) (Lu et al., 2016). For comparison, we estimate the correlation coefficients (CC), the RMS errors, the absolute value of difference in minimum Dst (Δ Dst min ) and the absolute value of difference in minimum time (Δ t Dst ) between observed Dst and predicted one.In order to compare the prediction effects and the generalizability of the three models to magnetic storm events, we combined 70 intense magnetic storm events and 10 super large magnetic storm events into one group. It is shown that DC-GPR model exhibits the better forecasting performance than by DC-SVM model and DC-NN model in magnetic storm. The CC, the RMS errors, the Δ Dst min , and the Δ t Dst of GPR are 0.65, 35.45 nT , 16.12 nT and 1.16 h , respectively. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze the applicability conditions for the significant wave height (SWH) measurement approach based on measuring the cross-correlation function of two signals with similar frequencies reflected by the sea surface in the bistatic problem statement (the transmitting antenna and the receiving antenna are separated in space). When implementing this approach, the modulus of the normalized cross-correlation function for several pairs of signals with different frequency bases will be measured in the experiment. The advantage of this approach over the traditional method for radar altimetry, based on the analysis of the shape of the reflected pulse, is the high accuracy in measuring the SWH for weak waves. In the bistatic formulation of the problem, an important advantage of the approach under study is the possibility of obtaining analytical formulas for solving the direct problem. This paper presents the derivation of a formula for the modulus of the normalized cross-correlation function of reflected signals, which expresses an explicit relationship with the parameters of sea waves and the measurement geometry in the bistatic formulation of the problem. This paper considers the influence on the modulus of the normalized cross-correlation function of the antenna patterns of the transmitting and receiving antennas, the distances to the sea surface, the wave slope variances, the SWH and the frequency base of the transmitted signals. The optimal variants of the measurement scheme are discussed. The results and conclusions obtained can be easily expanded to underwater acoustic sounding. [ABSTRACT FROM AUTHOR]

Turquoise is one of the key ingredients in some magical Tibetan medicines, and its quality and content directly affect the medicine's effectiveness. In this paper, laser-induced breakdown spectroscopy (LIBS) technology was first applied to detect the raw materials of Tibetan medicine. The traditional data analysis methods could not meet the practical requirements of modern Tibetan medicine factories due to matrix effects. The concept of correlation coefficient (ρ) in pattern recognition technique was introduced as an evaluation index, and the model was established based on the intensities of the four characteristic Al and Cu spectral lines of the samples for different contents of turquoise, which was applied to estimate the contents of turquoise in the samples to be tested. We detected the LIBS on 126 samples of raw ore from 42 areas in China and evaluated the turquoise content using self-developed software with an error of <10%. This paper's technical testing process and methods can also be applied to test other mineral compositions and provide technical support for modernizing and standardizing Tibetan medicines. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

In the field of geodetic data processing, the existing literature on the treatment of the multiplicative random error model assumes that the random multiplicative error elements are independent of one another. However, there is no research exploring the correlation between these elements of the multiplicative random error. In this paper, we have developed three parameter estimation methods for the correlated observation multiplicative random error model based on existing literature research. These methods are derived using formulas for variance and correlation coefficients. The three methods are the correlated observation least squares method, the correlated observation weighted least squares method, and the correlated observation bias-corrected weighted least squares method. Additionally, the corresponding formulas for the unit weight mean square error and standard deviation are provided. The numerical simulation results demonstrate that, for the multiplicative random error model of correlated observations, the correlated observation bias-corrected weighted least squares method yields the optimal parameter estimation with higher accuracy, making it the most effective approach for solving this model. [ABSTRACT FROM AUTHOR]

In this paper, we come up with a unique technique to generate the ideal pattern diversity with the help of 24 Vivaldi antennas. With the help of a mathematical concept called golden angle, the 24 antenna elements are arranged in such a way that the radiation patterns will never overlap and the mutual coupling will be very low. It is also verified from the simulations and the measured results that the envelope correlation coefficient is less than 0.01 in the 3–4.2 GHz which is a sub‐6 GHz band for 5G. This same concept can be extended to a greater number of antennas also. [ABSTRACT FROM AUTHOR]

This paper studies the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in a new multiplicative fashion by an observed confounding variable, namely, a parametric distortion measurement errors model. The least squares estimation and weighted least squares estimation are used to estimate parameters in the multiplicative distortion functions. An interesting finding is that the calibrated variables obtained by using the weighted least squares estimators perform asymptotic equivalent to the calibrated variables obtained from the nonparametric kernel smoothing estimators in the literature. We then used the calibrated variables to estimate the correlation coefficient, and show that the proposed estimators of correlation coefficient are all asymptotically efficient. Moreover, we suggest an asymptotic normal approximation and an empirical likelihood-based statistic to construct the confidence intervals. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators. These methods are applied to analyze two real datasets for an illustration. [ABSTRACT FROM AUTHOR]

The paper presents the study of bread wheat's chlorophyll content and grain yield traits under changing environmental conditions -- optimal water supply and simulated water-deficit conditions. Selecting 15 wheat cultivars from different regions (Russia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkey, and CIMMYT) based on economically valuable characteristics became the specimens for the presented study that evaluated the SPAD indicators in correlation with the grain yield in bread wheat and determined the vital role of genotypes, environments, and genotype by environment interaction effects. The relationship between chlorophyll content and yield parameters under different growing conditions was also well-defined. The results revealed that ecosystems had more influence on the chlorophyll content than the wheat genotypes. Several wheat cultivars with soil moisture tolerance have gained identification, along with the correlation coefficient between chlorophyll content and grain yield under varied environmental conditions of water supply. The study validated the role of genotypes and environments in the manifestation of responses to stress conditions. [ABSTRACT FROM AUTHOR]

In the realm of data analysis and machine learning, achieving an optimal balance of feature importance, known as feature weighting, plays a pivotal role, especially when considering the nuanced interplay between the symmetry of data distribution and the need to assign differential weights to individual features. Also, avoiding the dominance of large-scale traits is essential in data preparation. This step makes choosing an effective normalization approach one of the most challenging aspects of machine learning. In addition to normalization, feature weighting is another strategy to deal with the importance of the different features. One of the strategies to measure the dependency of features is the correlation coefficient. The correlation between features shows the relationship strength between the features. The integration of the normalization method with feature weighting in data transformation for classification has not been extensively studied. The goal is to improve the accuracy of classification methods by striking a balance between the normalization step and assigning greater importance to features with a strong relation to the class feature. To achieve this, we combine Min–Max normalization and weight the features by increasing their values based on their correlation coefficients with the class feature. This paper presents a proposed Correlation Coefficient with Min–Max Weighted (CCMMW) approach. The data being normalized depends on their correlation with the class feature. Logistic regression, support vector machine, k-nearest neighbor, neural network, and naive Bayesian classifiers were used to evaluate the proposed method. Twenty UCI Machine Learning Repository and Kaggle datasets with numerical values were also used in this study. The empirical results showed that the proposed CCMMW significantly improves the classification performance through support vector machine, logistic regression, and neural network classifiers in most datasets. [ABSTRACT FROM AUTHOR]

Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as the research object. In this paper, the uni-feature is one of the X and Y coordinates of the signature trajectory point, pen pressure, pen tilt, and pen azimuth feature. Aiming to solve the unequal length of feature vectors and the low accuracy of signature verification when using uni-features, we innovatively introduced the idea of correlation analysis and proposed a dynamic signature verification method based on the correlation coefficient of uni-features. Firstly, an alignment method of two feature vector lengths was proposed. Secondly, the correlation coefficient calculation formula was determined by analyzing the distribution type of the feature data, and then the correlation coefficient of the same uni-feature between the genuine signatures or between the genuine and forged signatures was calculated. Finally, the signature was verified by introducing a Gaussian density function model and combining it with the signature verification discrimination threshold. Experimental results showed that the proposed method could improve the performance of dynamic signature verification based on uni-features. In addition, the pen pressure feature had the best signature verification performance, with the highest signature verification accuracy of 93.46% on the SVC 2004 dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a feature extraction method combining adaptive variational mode decomposition (AVMD) and singular value decomposition (SVD) for electric shock fault‐type identification. The AVMD algorithm is utilized to adaptively decompose the electric shock signal into intrinsic mode components, each containing distinct frequency information. Subsequently, the correlation coefficient is employed to extract the intrinsic mode component with amplitudes greater than or equal to 0.1 (γk${\gamma }_k$ ≥ 0.1). Feature extraction is then performed using SVD on the γk${\gamma }_k$ ≥ 0.1 intrinsic mode component, based on its maximum singular value and singular entropy. This approach effectively overcomes the limitation of the traditional VMD that necessitates manual K value setting. Moreover, it achieves dimensionality reduction and feature extraction of the intrinsic mode components through SVD, resulting in enhanced computational efficiency and fault identification accuracy. Extensive simulations demonstrate the remarkable recognition rates of electric shock fault types in animals and plants using the proposed AVMD‐SVD method, achieving a recognition rate as high as 99.25%. Comparative performance analysis further verifies the superiority of AVMD‐SVD over similar empirical mode decomposition‐SVD feature extraction techniques. [ABSTRACT FROM AUTHOR]

The paper presents the dynamics of the hydrothermal coefficients characterizing agroclimatic resources on the territory of Siberia within the coordinates of 50°–65° N, 60°–120° E during modern climate change. The intensity, frequency, and duration of adverse weather phenomena for agriculture (the atmospheric droughts and moisture excess conditions) are analyzed. The Ped' drought index () and the standardized precipitation evapotranspiration index (SPEI) calculated from the data of 134 weather stations and the ERA5 reanalysis are used as an indicator. In Eastern Siberia and in the west of the analyzed territory, there is an increase in aridity (a trend is 0.2–0.5 per decade). Northern regions of Kazakhstan, the Sayan Mountains, and the West Siberian Plain were characterized by a relative stability of hydrothermal conditions in 1950–2020 (statistically significant changes are not revealed). A direct linear dependence of hydrothermal conditions on large-scale circulation processes have been not detected for most cases. The greatest number of statistically significant correlation coefficients has been obtained for the drought index and the SCAND (Scandinavia teleconnection pattern) index. [ABSTRACT FROM AUTHOR]

Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information using 360° observation of the target. Due to the anisotropy of target scattering characteristics in the actual scene, the sub-aperture imaging method is usually used for CSAR imaging. However, the uniformly divided overlapping sub-aperture CSAR imaging algorithm only considers phase compensation, ignoring the effect of target scattering characteristics on echo amplitude. In CSAR imaging scenarios carried by small rotor unmanned aerial vehicles (SRUAVs), the size of the observed scene cannot be ignored compared to the distance between the target and the antenna and the effect of the anisotropy of the target scattered energy on the echo amplitude should be considered. In this paper, a sub-aperture CSAR imaging method based on adaptive overlapping sub-aperture is proposed. First, the boundary points of the sub-aperture are determined by analyzing the correlation coefficient and the variation coefficient of the energy function. Next, the overlapping sub-aperture division schemes are automatically generated by screening and combining the boundary points. The sub-aperture images are then generated by a Back Projection (BP) algorithm. Finally, sub-aperture image registration and incoherent superposition are used to generate the final CSAR image. Verified by the CSAR field echo data, the proposed method can realize imaging of the original echo data without the Inertial Navigation System (INS) and Global Positioning System (GPS) observation data. Compared with the CSAR full-aperture BP imaging algorithm, the entropy of the image generated by the proposed method increased by 66.77%. Compared with the sub-aperture CSAR imaging algorithm, the entropy of the image generated by the proposed method was improved by 11.12%, retaining more details of the target, improving the target contour features, and enhancing the focusing effect. [ABSTRACT FROM AUTHOR]

The paper deals with finding power system nodes where reactive powers have the greatest influence on system voltages. The problem to be solved is important in reactive power planning. Its proper solution indicates in which nodes new sources of reactive power should be installed in order to achieve the assumed goals in the aforementioned planning. So far, the problem formulated earlier has not been satisfactorily resolved. The paper presents an original method, which, based on the entire history of the system operation states, allows a solution to the problem mentioned above to be found. The proposed method assumes the use of measurement data of nodal-voltage magnitudes and nodal reactive power. Correlational relationships between the above-mentioned quantities are investigated. The paper shows that the considered correlational relationships are not linear. In this situation, Kendall's rank correlation coefficient is used to evaluate the strength of these correlational relationships. Analysis of the strongest relationships allows us to identify those nodal reactive powers that have the greatest influence on the voltages in the power system. The results of the analysis are the basis for determining the location of additional reactive power sources in the power system, which is very essential in reactive power planning. The proposed method is relatively easy to implement and does not require complicated calculations. The paper additionally shows that failure to use the entire spectrum of representative system states when solving the problem under consideration can adversely affect the result. [ABSTRACT FROM AUTHOR]

A model for the life expectancy of West African nations is proposed in this paper using the regression analysis approach. The model shows how income is the major determining factor in the calculation of the life expectancy of individual citizens of West African countries. The model predictions were validated by using the sourced data, as the absolute difference between the model predictions and the sourced data was small with a high correlation between them. A variable sensitivity analysis was conducted using the model which indicated that the income variable of the citizens was the most sensitive variable. It is recommended that the income of every nation must be given priority when targeting areas of life expectancy intervention and improvement projects. This can be followed by the quality of health, unemployment rate and the economic status of the nation, respectively. The model optimisation confirmed that life expectancy of a nation could attain both low and high values depending on the policies/efforts of the government of that nation. [ABSTRACT FROM AUTHOR]

Human reliability analysis (HRA) is a systematic technique for determining the impact that humans have on high-risk systems. Dependence assessment is a crucial aspect of HRA, as it quantifies the impact of an operator's inability to accomplish one task on the failure probabilities of subsequent tasks. Many traditional methods for dependence assessment usually need analysts to make a definitive judgment all by themselves. It strongly relies on the knowledge and experience of domain specialists. To address this issue, a model based on the correlation coefficient in the Dempster–Shafer evidence theory and the analytic hierarchy process method is proposed in this paper. We use Dempster–Shafer evidence theory (D–S evidence theory) to model uncertainty and subjectivity in the assessment process. And the correlation coefficient is used to modify the basic probability assignment function to make the fusion formula get more accurate results. At the same time, the analytic hierarchy process is used to model the hierarchical relationship of multifactor decision-making problems. Benefiting from these functions and a united framework structure, the method has an extraordinary ability to reduce subjectivity and ambiguity in language evaluation. In the case study, the proposed method was used to analyze the probability of dependence between two human operations on actual nuclear power plant data. The proposed method is particularly suitable for dependency assessments that are intrinsically linguistic in nature. [ABSTRACT FROM AUTHOR]

Aiming at the problem of mutual interference between millimeter-wave frequency-modulation continuous-wave (FMCW) radars, an interference mitigation method based on outlier detection and variational mode decomposition (VMD) is proposed in this paper. Firstly, by differential processing of the raw millimeter-wave FMCW radar data, combined with threshold detection, the interfered sample area is located. Adaptive amplitude limiting is applied to the interfered samples to achieve initial suppression of the interference. Then, based on the VMD algorithm, the processed data are adaptively decomposed to obtain multiple intrinsic mode functions (IMFs). The Pearson correlation coefficient between each IMF and the signal before decomposition is calculated, and the IMF with the maximum Pearson correlation coefficient is extracted as the signal component to achieve the separation of the target signal from the interference and noise. The proposed method was validated based on simulation and experimental data. The results show that the proposed method achieves the best performance in terms of signal-to-interference-plus-noise ratio (SINR), mean square error (MSE), and kurtosis in frequency (KF) compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition (CEEMD). Further comparison was made with two typical methods, and the Range–Doppler (RD) map and SINR results showed that the proposed method exhibited certain performance advantages. [ABSTRACT FROM AUTHOR]

Online reviews play a vital role in providing multidimensional information for tourists. It also has an effect on the ranking and overall score of hotels. As a powerful tool, the Fermatean fuzzy set efficiently models dealing with uncertain information. Considering that there is no study using the correlation coefficient in Fermatean fuzzy context to assess the effect of online reviews on ratings and overall score of hotels. Therefore, a correlation coefficient measure is put forward to determine the relationship between two Fermaten fuzzy numbers and then they are utilized to assess the impact of online reviews on ranking and overall rating of hotels. The paper first introduces the TOPSIS–based ranking model using a new distance under Fermatean set. Then, we construct a new correlation coefficient between two Fermatean fuzzy numbers to measure the effect of online reviews with ranking, overall score and score of hotels under given criteria. A case study on TripAdvisor.com is performed to illustrate the proposed operator and model. [ABSTRACT FROM AUTHOR]

The work developed a modification of the algorithm for detecting cloning of frames in video sequences. It is impossible to imagine the life of a modern person without the constant presence of digital technologies in it. They have become an integral part of our daily life. Everyone has a smartphone, almost everyone owns a personal computer or laptop. Photo and video recording technologies, as well as the distribution and publication of images and videos, such as social networks, have become an integral part of digitalization. Digitization has also spread to very important areas for human life, such as forensics or mass media. In order to have confidence in video materials, which is extremely important in criminal investigations, court cases and information networks, it is necessary to develop the field of exposing modifications and falsifications in video and photo materials, develop new methods that allow you to verify the integrity of the video or detect falsification, and modify and improve already developed ones. The paper analyzes the methods for finding falsifications in video sequences and modern formats and algorithms for saving and compressing video sequences. As a result of the analysis, the current state of development in the field of detection of fake videos was revealed and the most popular video formats were determined. Experiments were conducted with videos of different formats and resolutions, data were collected and analyzed, after which the necessary threshold values of the Pearson correlation coefficient were established for each format studied. Threshold values for searching for cloned frames were proposed in the work. The threshold value is chosen depending on the format and resolution of the video. Parameters such as video format and resolution were taken to modify the algorithm for detecting cloning of frames in a video sequence by analyzing the threshold values of the Pearson correlation coefficient. A software implementation of the modification of the algorithm for detecting cloning of frames in video sequences was created. Errors of the first and second kind are cited in the work. [ABSTRACT FROM AUTHOR]

With the increasing complexity of decision making (DM) problems, powerful mathematical tools are needed to represent and process fuzzy and uncertain DM information, and Pythagorean fuzzy set (PFS) is such a mathematical tool. PFS has been successfully applied in the field of fuzzy multiple criteria decision making (MCDM). Correlation coefficient is an information measure of PFS, and plays an important role in the application of PFS. At present, there is a problem that the existing correlation coefficients cannot moderately measure the correlation degree between PFSs, so this paper proposes the new correlation coefficients of PFS. The TODIM method has been proved to be effective in dealing with MCDM problems that consider the psychological behavior of decision makers. This paper extends the TODIM method with the new correlation coefficients of PFS, and the extended TODIM method is called Pythagorean fuzzy CC-TODIM method. By numerical examples, it is verified that the new correlation coefficients of PFS are more reasonable and valid. By case analysis, it is verified that the Pythagorean fuzzy CC-TODIM method can effectively solve the MCDM problems, and the Pythagorean fuzzy CC-TODIM method based on the new correlation coefficients is more accurate and reliable. [ABSTRACT FROM AUTHOR]

This paper studies how to estimate and test the symmetry of a continuous variable under the additive distortion measurement errors setting. The unobservable variable is distorted in a additive fashion by an observed confounding variable. In this paper, a direct plug-in estimation procedure and two-step direct plug-in estimation procedure for correlation coefficient are proposed to measure the symmetry or asymmetry of a continuous variable, and empirical likelihood based confidence intervals are constructed to test the symmetry of the unobserved variable. The asymptotic properties of the proposed estimators and test statistics are investigated. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators and test statistics. These methods are applied to analyze a real dataset for an illustration. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for software-defect prediction based on self-organizing data mining; this method can establish a causal relationship between software metrics and defects. Defect-prediction models were established for intra-project and cross-project scenarios. For intra-project forecasting, this article establishes a self-organizing data mining model, adding a method of smooth data preprocessing to solve the problem of data imbalance. For cross-project forecasting, this article establishes a self-organizing data mining model, solves the difference between the two by finding a source-project instance with a larger correlation coefficient with the target project, and establishes a defect-prediction model for the selected source-project instance. This paper aims to achieve classification and ranking prediction. The proposed method is tested on public-defect datasets. In the classification-prediction experiment, the precision, F-measure, and AUC evaluation indicators of this method are used. In the ranking-prediction experiment, AAE and ARE evaluation by this method are optimized. The algorithm is found to be an efficient and feasible method for software-defect prediction. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel tracking system based on composite trend representation and historical portfolio data (CTRHP) for portfolio optimization (PO). In the part of obtaining prediction of price, we introduce the important data of historical portfolio, which is rarely utilized before, to improve the accuracy of measuring investment performance. In addition, we propose a set of correlation coefficient-based similarity measurement functions (CSMFs) to automatically assign different weights to different trend representations, which enables each trend representation to have an impact on future price predictions and set the strength according to their investment performance. In the part of portfolio optimization, a new optimization objective based on generalized increasing factors is proposed to optimize the investment portfolio, and a fast solution algorithm is presented. Extensive experiments on six standard datasets from real financial markets across different assets and different time horizons show that our CTRHP achieves significantly better performance compared with previous state-of-the-art PO systems in investing returns and risk control. Moreover, it has the advantages of being able to tolerate certain transaction fees and running fast, which shows that it is suitable for real financial environments. [ABSTRACT FROM AUTHOR]

With the implementation and steady progress of the Belt and Road (B&R) initiative, China's investment in countries along the B&R has maintained a high growth trend. Generally speaking, investment problems are often accompanied by high risk and uncertainty, and how to make the suitable investment decision is a difficult issue. This paper investigates an investment decision approach under the probabilistic hesitant fuzzy environment. Firstly, a new probabilistic hesitant fuzzy distance and correlation coefficient are defined to overcome the defects of the existing probabilistic hesitant fuzzy information measures. Secondly, an attribute weight integrated model is constructed by combining the maximum deviation method, the CRITIC method and the maximum entropy principle, which is able to take into account the correlation between attributes and make full use of the decision information. In addition, a disappointment theory-based probabilistic hesitant fuzzy multi-attribute decision making (PHFMADM) method is proposed to solve the investment decision problem, which can integrate the psychological behavior of decision makers into the decision making process and make the decision results more authentic and reliable. Finally, the rationality and validity of the method are verified by comparing with the existing methods. [ABSTRACT FROM AUTHOR]

The all-sky meteor radar is an important means to detect 70–110 km wind fields. Previous studies have shown that the wind field retrieved by all-sky meteor radars is closely related to the meteor count detected by the radar. However, the precision of the wind field is still unclear. In this paper, the influence of the meteor counts detected by two all-sky meteor radars operating simultaneously at Kunming station on wind fields is analyzed based on the observations of the two radars from 1 November 2013 to 31 December 2014. First, the meteor counts detected by the two meteor radars are approximately 100–3000 per hour, and the meteor count detected by the 37.5 MHz meteor radar is more than that according to the 53.1 MHz meteor radar. The meteor counts detected by the two radars vary with the local time and altitude. The meteor counts detected from 20 UTC to 02 UTC are the largest in the altitude range of 84–92 km, while the meteor counts detected from 09 UTC to 15 UTC are the lowest at other altitudes. Second, the more meteors detected by the two radars, the smaller the wind field differences retrieved by the two radars, and the closer the wind fields are to the real average wind field. Third, because the performance of the two radars is basically identical, except that the meteor counts detected by the two radars are different due to their different operating frequencies, the meteor count is the main system error of the wind fields retrieved, and the errors can be determined by the correlation coefficients of the wind fields retrieved by the two radars. Finally, in the altitude range of 76–100 km, the mean wind field differences of the two radars are less than 5 m/s. [ABSTRACT FROM AUTHOR]

This paper proposes a method based on the circular track synthetic aperture radar (CSAR) mode to directly obtain the digital elevation model (DEM) information of the target area through single-circle observation data. This paper improves and innovates the existing methods by using sub-aperture correlation. The circular motion trajectory of the entire radar observation platform is divided into multiple overlapping sub-aperture arcs. Each sub-aperture is projected in the height direction to observe the position deviation of the sub-aperture images more intuitively at different heights. Then, a new calculation method is used to calculate the correlation coefficient between sub-apertures, which enhances the accuracy of extracting DEM results. Finally, the measured data of the Ku band recorded by us are used to verify the effectiveness and accuracy of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

In the paper industry, to determine the installation positions of concentration sensor and flow sensor of pulp pipe transportation system, the length of pipe inlet section of pulp fluid must be determined. In order to solve the problem that it is difficult to determine the length of pipe inlet section of pulp fluid, the paper presents a method to determine the length of pipe inlet section of pulp fluid, and the relationship between the length of inlet section and pulp fluid parameters is studied by this method. Firstly, CFD (Computational Fluid Dynamics) method is applied to obtain flow velocity and solid phase concentration distribution data of pulp fluid at different axial positions in the pipe. Then, Pearson correlation coefficient method is applied to analyze these data. Finally, the length of pipe inlet section of pulp fluid is determined according to the obtained correlation coefficient, and the relationship between the length of pipe inlet section of pulp fluid and initial average flow velocity and solid phase concentration is obtained. The conclusion shows that the method can well obtain the length of pipe inlet section of pulp fluid, which provide some theoretical basis for the design of pulp pipe transportation system. [ABSTRACT FROM AUTHOR]

Cotton fabric selection is a challenging task in the garment product design and development process, and the selection of optimal alternative under the presence of multiple decision criteria becomes complex, and hence it is considered as a multi-criteria decision-making (MCDM) problem. In addition, the selection process involves fuzziness and uncertainty. In this study, Pythagorean fuzzy sets (PFSs) are introduced to handle uncertain information. Elimination and choice translating reality (ELECTRE) is a well-known outranking method for solving MCDM problems. Therefore, we extend the ELECTRE method under the PFS environment, and a correlation-based closeness coefficient is proposed to compare Pythagorean fuzzy numbers (PFNs). This paper applies the proposed PF-ELECTRE approach in solving a practical case involving the ranking cotton fabrics. To exhibit the superiority and robustness of the suggested method, sensitivity analysis is performed to examine the impacts of weights variation, as well as a comparative analysis is carried out between the PF-ELECTRE with several existing MCDM methods. The research contributes to the advancement and development of outranking MCDM methods through a novel PF-ELECTRE approach that utilizes the weighted correlation coefficient. Moreover, the developed method can obtain reliable results and can be used to other textile domains. [ABSTRACT FROM AUTHOR]

This paper studies the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in a multiplicative fashion by an observed confounding variable. We propose a new identifiability condition by using the absolute logarithmic calibration to obtain calibrated variables and the direct-plug-in estimator for the correlation coefficient. We show that the direct-plug-in estimator is asymptotically efficient. Moreover, we suggest an asymptotic normal approximation and an empirical likelihood-based statistic to construct the 2 confidence intervals. Next, we propose several test statistics for testing whether the true correlation coefficient is zero or not. The asymptotic properties of the proposed test statistics are examined. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators and test statistics. These methods are applied to analyze a real dataset for an illustration. [ABSTRACT FROM AUTHOR]

Frequency modulated thermal wave imaging (FMTWI) is an efficient and affective thermal nondestructive testing and evaluation (NDT&E) technique for qualitative and quantitative analysis for defects in test materials. FMTWI utilizes low peak power heat sources modulated within a frequency sweep to excite the material under test. The paper demonstrates a novel analytical approach for heat diffusion in isotropic material using FMTWI technique to distinguish defects located at different depths inside the test sample. The frequency modulated thermal excitation has been illuminated over carbon fibre reinforced polymer (CFRP) material to compute thermal response over the object under test. The mapped temperature response is analysed further for defect detectability in terms of correlation coefficient and time delay. Further, the presented analytical approach for defects located at different depths are comparted with the simulation model of CFRP test material. Lastly, different frequency and time domain data analysis schemes have been applied to detect defects in terms of thermal contrast. Results demonstrates the detection capability of FMTWI technique using correlation based matched filter method to provide better test resolution and sensitivity. [ABSTRACT FROM AUTHOR]

Medical imaging technology is one of the most critical applications necessitating data protection, particularly if we need to keep track of any important patient information. This medical imaging system employs encryption and decryption. Using several cryptographic techniques, the security key was established to protect the data. Every network that sends and receives data needs to be secure in some way. In this paper, ALO along with the encryption algorithm honey is used to enhance the security of medical imaging technologies, the proposed study uses a variety of ways to protect important health information. In comparison to the existing one, the proposed honey algorithm attains better results. Further, the antlion optimizer uses random keys throughout the encryption and decryption. In the next step, the keys are remodeled using antlion optimization. After that, the updated key is optimized by analyzing every element and generating paths that trigger the traps and latching functions. The mean square error (MSE) is reduced to 1% and the peak signal-to-noise ratio (PSNR) is increased to 98% by using a hybrid strategy. [ABSTRACT FROM AUTHOR]

The focus of this paper is on the empirical relationship between item difficulty and item discrimination. Two studies—an empirical investigation and a simulation study—were conducted to examine the association between item difficulty and item discrimination under classical test theory and item response theory (IRT), and the effects of the association on various quantities of interest. Results from the empirical investigation show that item difficulty and item discrimination are negatively correlated under classical test theory, mostly negatively correlated under the two‐parameter logistic model, and mostly positively correlated under the three‐parameter logistic model; the magnitude of the correlation varied over the different data sets. Results from the simulation study reveal that a failure to incorporate the correlation between item difficulty and item discrimination in IRT simulations may provide the investigator with inaccurate values of important quantities of interest, and may lead to incorrect operational decisions. Implications to practice and future directions are discussed. [ABSTRACT FROM AUTHOR]

Differential privacy is widely used as a privacy protection mechanism. Although there are many methods for publishing histograms on static data sets, there are fewer methods for publishing histograms based on sliding windows in a data stream environment,and they face the problem of high publishing errors of histograms. For this problem, this paper proposed a data stream differential privacy histogram publishing algorithm suitable for sliding window model HPA-SW. The algorithm first divided a sliding window into k sub-blocks based on the idea of data partitioning, and used this parameter to control and adjusted the statistical error of the data histogram. Then, the algorithm compared the data distribution of two adjacent histograms to optimize the privacy budget allocation of the current window, so as to quickly calculate the local optimal histogram. In order to verify the effectiveness of the proposed algorithm, this paper firstly proved through rigorous theoretical derivation that the designed algorithm met the requirements of differential privacy, and its approximation error did not exceed W/2k. Secondly, through experimental comparison on real data sets, it shows that the publishing error of the proposed algorithm is low, which 50% lower than SSHP algorithm. [ABSTRACT FROM AUTHOR]

A single-valued neutrosophic set (SVNS) is a subcategory of neutrosophic set that is used to represent uncertainty and fuzziness in three tiers, namely truthfulness, indeterminacy, and falsity. The measure of entropy of a SVNS plays an important role to determine the ambiguity in a variety of situations. The knowledge measure is a dual form of entropy and is helpful in certain counterintuitive situations. In this paper, we introduce a knowledge measure for the SVNS and contrast the same with existing measures. The comparative study reveals that the proposed knowledge measure is more effective in modeling the structured linguistic variables. We provide the relations of the proposed knowledge measure with single valued neutrosophic similarity and distance measures. We also investigate the application of the proposed measure in multi-attribute group decision making (MAGDM). The proposed MAGDM model is helpful when the decision makers in the group have varied background and the hiring organization is unable to assign the level of importance or weight to a decision-maker. [ABSTRACT FROM AUTHOR]

This paper defines incidence degree with fixed-ratio l -order-difference for any 1 ≤ l ≤ n-1. The substance of their respective jiilly correlation and near fully correlation is discussed from differential and original sequence perspectives, respectively, Namely, fully correlated algebraic meanings, nearly fully correlated topological meanings, and their geometric meaning. The axioms of correlation classes ofincidence degreewithjixed-ratio l -order-difference for any 1 ≤ l ≤ n-1 are refined. [ABSTRACT FROM AUTHOR]

To accurately predict and quantify the potential structural damage (PSD) of ground motion records (GMRs), an intensity measure (IM) that correlates well with the structural response is needed. This paper proposes an exploratory factor analysis (EFA)-based approach for developing a compound intensity measure (CIM) for characterizing the PSD of GMRs. EFA is used to preferentially extract principal factors that are related to the acceleration, velocity and displacement of GMRs through compression of the selected IMs of three groups of GMRs. Then, the CIM is developed with a linear combination of the extracted principal factors on a log-scale. The explanations of variance corresponding to each principal factor are used to quantify the contributions of a single IM to the developed CIM. Applications to single-degree-of-freedom (SDOF) systems modeled with different hysteretic models and multiple-degree-of-freedom systems subjected to three groups of GMRs demonstrate the feasibility of the proposed approach. The results of the correlation between the developed CIM and structural demand confirm the robust performance of the proposed approach in estimating the predictability of the developed CIM for measuring the PSD of GMRs. The efficiency of the developed CIM for SDOF systems is found to be approximately of the same magnitude irrespective of hysteretic models, and the developed CIM is sufficient and proficient in structural demand evaluation with good confidence. [ABSTRACT FROM AUTHOR]

A distributed multiple-radar system has natural advantages in anti-deception jamming. However, most of the anti-jamming methods are proposed in full spatial registration. In practice, the registration error is difficult to eliminate, which will seriously degrade the performance of cooperative anti-jamming. Therefore, it is of great significance to consider the problem of cooperative anti-deception jamming under registration error. In this paper, the cooperative anti-deception jamming method is proposed in a distributed multiple-radar system under registration errors. On the premise of the known registration error, target received signal vectors are estimated from an uncertainty region in each channel by maximum likelihood (ML) algorithm. With the estimated received signal vectors, a target discrimination algorithm is introduced based on the difference in target spatial scattering characteristics, which calculate the correlation coefficient between different target received signal vectors and discriminate a false target with a designed threshold. Furthermore, since the registration error depends on the radar site errors, theoretical derivation for the registration error is given as a function of the transmitter and receiver site errors. Finally, simulation results verify the feasibility of the proposed discrimination method, and its performance due to the influence of the jamming-to-noise ratio (JNR), the registration error, the target size, and the discrimination threshold are considered. [ABSTRACT FROM AUTHOR]

When the "Guide to the expression of uncertainty in measurement" (GUM) is used to evaluate the uncertainty of wind speed sensors, there is a lack of input quantities correlation research, which leads to the deviation of the uncertainty evaluation result. This paper takes the calibration result of an automatic weather station's three-cup wind speed sensor as an example to evaluate the uncertainty of wind speed sensor measurement based on the correlation of input quantities. First, the calibration point of 10.00 m/s wind speed is taken as an example, and the original wind speed record is obtained by calibrating the three-cup wind speed sensor. Then, according to the GUM evaluation method, the uncertainty of the wind speed measurement result is analyzed and evaluated, And analyze and explore the correlation between the input quantities (considering the influence of the stability and uniformity of the wind tunnel on the measurement result, and the existence of a certain interdependence between them, an experimental plan was developed to test the stability and uniformity of the wind tunnel, and the correlation coefficient between them was calculated to be 0.1561 at the wind speed point of 10.00 m/s). Finally, the input quantities correlation (the correlation coefficient of wind tunnel stability and uniformity) is integrated into the uncertainty evaluation, and the uncertainty evaluation result is obtained at 10.00 m/s wind speed point (where U = 0.22 m/s, U 95 = 0.22 m/s). Through comparison, the uncertainty results evaluated without considering the correlation of input quantities have generated negative deviation, with deviation values being −0.00022 m/s. Due to the relatively small deviation, in practical applications, it can be determined whether to correct it based on the accuracy requirements of the measurement result and actual needs. • Evaluate the Measurement Uncertainty of three cup wind speed sensor. • Design an experimental plan to test the stability and uniformity of the wind tunnel. • Calculate the correlation coefficient between wind tunnel stability and uniformity. • Integrating the correlation coefficient between wind tunnel stability and uniformity into uncertainty evaluation. • Calculate the deviation of uncertainty without considering the correlation of input quantities. [ABSTRACT FROM AUTHOR]

The blood pressure (BP) is generally measured using a cuff based sphygmomanometer. However, it is inconvenient to be used. Recently, an alternative solution only using the photoplethesmograms (PPGs) was proposed. In this case, the continuous BP estimation could be performed. First, the features were extracted from the PPGs. Then, a regression network was employed to estimate the BP values. Nevertheless, the accuracy of this approach was not so high. In order to improve the estimation accuracy, this paper proposes to cascade a two layer piecewise neural network to the output of the existing regression network to correct the estimation error. In particular, the overall system is a three layer network. The first layer of the network is the existing regression network. It generates the initial estimated BP values. The second layer of the network consists of the window functions. It segments the range of the BP values into various regions for the further processing. The final layer of the network performs the estimation correction. The performance of our proposed network is evaluated via two practical datasets and three common regression networks including the three layer artificial neural network (ANN) based regression network, the random forest (RF) based regression network and the support vector regression (SVR) based network. For the first dataset, our proposed method with the RF model and the piecewise neural network achieves the systolic BP (SBP) estimation error and the diastolic BP (DBP) estimation error at $3.01{\pm }2.22$ mmHg with the correlation coefficient at 0.926 and $4.43{\pm }3.37$ mmHg with the correlation coefficient at 0.935, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $5.34{\pm }4.08$ mmHg with the correlation coefficient at 0.740 and $5.89{\pm }4.98$ mmHg with the correlation coefficient at 0.840, respectively. For the second dataset, our proposed method with the RF model and the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $7.91{\pm }8.06$ mmHg with the correlation coefficient at 0.876 and $3.47{\pm }5.59$ mmHg with the correlation coefficient at 0.859, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $9.77{\pm }9.01$ mmHg with the correlation coefficient at 0.805 and $7.08{\pm }5.55$ mmHg with the correlation coefficient at 0.612, respectively. It can be seen that our proposed network yields the estimated BP values highly correlated to the reference BP values. Also, our proposed method yields the higher accuracies compared to the existing networks. This demonstrates the effectiveness of our proposed network. [ABSTRACT FROM AUTHOR]

One of the most convenient biometrics approaches for identifying a person is dorsal hand veins recognition. In recent years, the dorsal hand veins have acquired increasing attention because of its characteristics such as universal, unique, permanent, contactless, and difficulty of forging, also, the veins remain unchanged when a human being grows. The captured dorsal hand veins image suffers from the many differences in lighting conditions, brightness, existing hair, and amount of noise. To solve these problems, this paper aims to extract and recognize dorsal hand veins based on the largest correlation coefficient. The proposed system consists of three stages: 1) preprocessing the image, 2) feature extraction, and 3) matching. In order to evaluate the proposed system performance, two databases have been employed. The test results illustrate the correct recognition rate (CRR), and accuracy of the first database are 99.38% and 99.46%, respectively, whereas the CRR, and accuracy of the second database are 99.11% and 99.07% respectively. As a result, we conclude that our proposed method for recognizing dorsal hand veins is feasible and effective. [ABSTRACT FROM AUTHOR]

This paper consider a new exponential parametric distortion measurement errors model for the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted with exponential parametric distortion measurement errors models, which are exponential functions of an observed confounding variable. The nonlinear least squares estimation and weighted nonlinear least squares estimation are used to estimate parameters in the multiplicative distortion functions. We use the fully parametrical calibration and mixed calibration to estimate the correlation coefficient, and show that the proposed estimators of correlation coefficient are all asymptotically efficient. Moreover, we suggest several asymptotic normal approximation statistics to construct the confidence intervals. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators. These methods are applied to analyse a real dataset for an illustration. [ABSTRACT FROM AUTHOR]

Intrusions (or threats) have been considerably increased due to the rapid growth in Internet and network technologies. Nowadays, the world is moving more toward a digital world in this era of networks; it makes more vulnerable to attacks. Intrusion detection models have proved to be a robust method in achieving high security in the network. The detection capacity of the intrusion detection model depends on the training set. High-dimensional dataset increases complexities, higher resource utilization, and affects system accuracy. Many researchers have suggested intrusion detection methods with reduced dimensions training set. However, they have not applied the multi-level-based correlation among attributes. This paper analyzed the network data and proposed a multi-level correlation-based feature selection method. It selects significant features and reduces the size of the training set. We have applied a classifier that learns from the training set and detects attacks; the proposed method enhanced the detection capacity. This work provides a detailed analysis of the UNSW-NB'15 dataset with binary classes (normal and attack) and multi-classes (normal and attack categories); it also shows the effectiveness of the UNSW-NB'15 dataset, which maintains a high category. The proposed method is executed on a high-dimensional dataset UNSW-NB'15. Finally, the experimental results are compared with existing techniques that show the better performance of the proposed method. [ABSTRACT FROM AUTHOR]

Winding fault is one of the most common types of transformer faults. The frequency response method is a common diagnosis method for winding fault detection. In order to improve the feature extraction ability of the frequency response curve before and after the winding fault, this paper proposes a winding fault feature extraction method based on the moving window algorithm to improve the Euclidean distance and correlation coefficient and uses a support vector machine to diagnose winding fault. "Moving window meter algorithm" refers to the fixed moving window width and window moving interval, scanning the entire frequency response curve from the initial point to the end point of the frequency response curve, using the correlation coefficient (CC) and Euclidean distance (ED) to calculate the mathematical index of each window. The mathematical index of each window is used as the characteristic quantity of fault type classification. Finally, the grid search algorithm is used to optimize the support vector machine to classify and identify the type of winding fault. At the same time, the standard support vector machine s(SVM) and back propagation neural network algorithm (BPNN) are compared with the support vector machine optimized by the grid search method to diagnose the fault type. The research shows that the improved correlation coefficient and Euclidean distance using the moving window algorithm are more sensitive to winding faults than the traditional calculation methods. The combination of the two calculation methods makes up for the shortcomings of their respective methods. The fault features obtained meet the requirements of the support vector machine for fault diagnosis, and the grid search method-optimized support vector machine classification algorithm has a good classification and recognition effect on the identification of fault types. The effectiveness and superiority of this method are further illustrated. [ABSTRACT FROM AUTHOR]

Low accuracy is the main challenge that plagues the application of engine modeling technology at present. In this paper, correlation analysis technology is used to analyze the main influencing factors of engine torque and NOx (nitrogen oxides) raw emission performance from a statistical point of view, and on this basis, the regression algorithm is used to construct the engine torque and NOx emission prediction model. The prediction RMSE between engine torque prediction value and true value reaches 4.6186, and the torque prediction R2 reaches 1.00. Prediction RMSE between NOx emission prediction value and true value reaches 67.599, and NOx emission prediction R2 reaches 0.99. When using the new WHTC data for model prediction verification, the RMSE between the engine torque predicted value and true value reaches 4.9208, and the prediction accuracy reaches 99.60%, the RMSE between NOx emission prediction value and true value reaches 72.38, and the prediction accuracy reaches 99.2%, indicating that the model is relatively accurate. The evaluation result of the ambient temperature impact on torque shows that ambient temperature is positively correlated with engine torque. [ABSTRACT FROM AUTHOR]

The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers. [ABSTRACT FROM AUTHOR]