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The negative pressure wave (NPW) signal caused by leakage of water supply pipeline contains a lot of noise due to the influence of water flow state and circuit noise, which will adversely affect the accuracy of leakage detection and leak location. To solve this problem, a novel pipeline pressure signal denoising method based on variational mode decomposition (VMD) is proposed in the paper. Firstly, the key parameters which affect the performance of VMD method: IMFs K and penalty factor $\alpha $ are optimized by genetic algorithm (GA) without prior knowledge. At the same time, different parameters are combined to form different reconstructed signals based on the correlation coefficient (CC). Secondly, the optimal combination of parameters is adaptively determined by using a fitness evaluation function based on the information entropy (IE). Thirdly, based on the optimal parameter combination, reconstruct the signal to achieve noise reduction. Finally, the denoised signal will be used for leak detection and leak location calculation. In order to verify the effectiveness of the proposed method, simulation analysis and laboratory experiments are carried out. Simulation analysis results show that the noise suppression effect of our method is better than that of wavelet, empirical mode decomposition (EMD) and VMD. The highest SNR obtained by our method is 16.187. The laboratory experiment results showed that the NPW signal denoised by proposed method had less error and more stable results in location, with an average relative error of 2.97%.

The demand for electric vehicles is increasing due to the recent focus on reducing greenhouse gas emissions. Hence, the number of electric-vehicle charging stations must be increased to meet the growing demand for electric vehicles. To this end, supportive policies are being established to increase the deployment of electric vehicle charging stations worldwide. On the other hand, it is not possible to subsidize all facilities simultaneously. This study developed a model to prioritize the deployment of electric vehicle charging stations. The Republic of Korea was analyzed as a case study region to develop this model, and the correlations between the amount of charging energy of electric vehicle charging stations and socioeconomic factors (traffic volume, population, number of electric vehicles, and land value) were analyzed. The correlations were analyzed differently depending on the purpose (e.g., residential or commercial) of the facilities where electric vehicle charging stations were installed. Correlation analysis was conducted to determine the factors that affect the amount of charging energy, and a model was developed to prioritize the deployment of electric vehicle charging stations through a genetic algorithm. A model with a correlation of more than 0.2 was developed, except for residential facilities with slow chargers and public institutions with slow chargers. The results of this paper can help identify the key factors to be analyzed by facility use when installing electric vehicle charging stations and determine the priorities for subsidizing the deployment of electric vehicle charging stations.

With the development of high-frequency carriers and high-capacity communications, ultrahigh-speed spectrum analyzer based on all-optical methods has been demonstrated in many fields. Limited by current data processing algorithms, ultrahigh-speed spectrum analyzers based on time-lens, such as frequency-domain light intensity spectrum analyzer (f-LISA) and parametric spectro-temporal analyzer (PASTA), are not yet to automatically process acquired data, which hinders their practicality. In this paper, a frame segmentation algorithm based on time-domain waveform analysis is proposed to achieve the automatic data processing of these analyzers, which can directly generate the spectro-temporal image of the signal under test. In a single-soliton test based on the f-LISA platform, the algorithm achieves an accuracy of 93.28% when the soliton changes and 90.04% when the soliton is stable. And in this test, the single-frame processing time of the algorithm is 419us. We believe the proposed algorithm will facilitate the practical application of spectrum analyzers like f-LISA and PASTA and provide a reference for other real-time measurement schemes.

The significance of clustering algorithms lies in their ability to distinguish problems and devise customized solutions. In the broader context of clustering, fuzzy clustering is one of the crucial aspects. In response to the real-world clustering problems, this research suggests a new fuzzy cluster scheme of data under the linear diophantine fuzzy set(LDFS) framework. More precisely, LDF clustering is initiated with the aid of the correlation coefficient( $\mathcal {CC}$ ) and weighted correlation coefficient( $\mathcal {WCC}$ ) for LDFS. Due to their ability to quantify the degree of similarity between two elements, $\mathcal {CC}$ are valuable in clustering problems. The LDF- clustering algorithm comprises a well-integrated algorithm for managing uncertainty and $\mathcal {CC}$ among LDFS. Also, our approach to LDF clustering is compared to existing fuzzy clustering studies to assess its effectiveness. Since LDFS broadens the score space, the experimental evaluation of our proposed scheme enables Decision makers(DM) to freely select their score values. The theme of this study is to impart the commencement of LDF-clustering analysis and attempt to apply $\mathcal {CC}$ to the clustering problem. An interpretative example provides the analysis of the logistic efficiency of food products by employing an LDF-clustering algorithm.

Rich datasets related to the Earth have been obtained because of the rapid development of Earth observation technologies. A broad range of prior research has investigated how to obtain the correlation relationships of relevant features from a large number of data containing spatio-temporal information, which is also the technical basis for big data analysis. Based on the dynamic time warping (DTW) algorithm, this research proposes a correlation analysis method of time series data and applies it to the correlation analysis between the time series features of the surface temperature and melting area of the Antarctic ice sheet. The results show that our method based on the DTW algorithm can effectively distinguish the change details of the nonlinear time series. The correlation coefficients between these two highly correlated factors computed by our method are higher than Pearson’s correlation coefficients by more than 0.2 almost in all study areas where data are available. In summary, the method proposed in this study provides a new feasible way for the correlation study of time series data. It outperforms than traditional correlation coefficients such as Pearson’s correlation coefficient in some fields, specifically when complex nonlinear time series data with certain periodicity are used. [ABSTRACT FROM AUTHOR]

The National Weather Service has initiated an effort to upgrade the scan strategy of the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) to improve its hydrometeorological applications. The lowest scan elevation angle has been changed from 0.5° to 0° (or even lower in the future) for some WSR-88D stations. Using the KMUX WSR-88D radar deployed in mountainous terrain in Northern California as an example, this letter quantifies the impacts of lowering the minimum scan elevation angle of WSR-88D radar on surface rainfall mapping, with an emphasis on shallow orographic precipitation. In order to estimate surface rainfall using radar observations, polarimetric radar rainfall relations are established using local disdrometer data, which are then implemented with the KMUX radar observations at 0° and 0.5° scan elevation angles to derive rainfall estimates. Comparative evaluation of the radar-based rainfall estimates using rainfall measurements from surface rain gauges has demonstrated the superior performance of the lower scan elevation angle. When the distance from the radar is long (i.e., when the radar beam is likely within or above the melting layer), the improvement gained by the 0° scan relative to the 0.5° scan is 16.1% and 19.5% in terms of the normalized standard error (NSE) and the Pearson correlation coefficient (CORR), respectively. [ABSTRACT FROM AUTHOR]

With the increasing scale of energy storage batteries, the number of retired energy storage batteries is also rapidly increasing, and the energy storage life, as an important indicator for evaluating the safety of retired energy storage, has received widespread attention. The existing methods for estimating the life of retired energy storage have the problem of considering attenuation characteristics (AC) that do not fully reflect the actual operating characteristics of batteries, which can affect the estimation accuracy. Therefore, this article proposes a precise estimation method for the life of retired energy storage batteries to improve the accuracy of estimating the life of retired energy storage. Firstly, an AC extraction method was proposed to fully explore the potential relationship between various feature data and the operational status of retired energy storage. Then, a method was proposed to screen and optimize ACs that are highly correlated with the life of retired energy storage batteries, selecting ACs that are highly correlated with the life of retired energy storage batteries. Finally, an accurate method for estimating the life of retired energy storage batteries was proposed, which effectively improved the accuracy of estimating the life. The proposed method was validated on the NASA dataset with an accuracy of no less than 99.784% for estimating life.

In brain-computer interface (BCI) work, how correctly identifying various features and their corresponding actions from complex Electroencephalography (EEG) signals is a challenging technology. However, most current methods do not consider EEG feature information in spatial, temporal and spectral domains, and the structure of these models cannot effectively extract discriminative features, resulting in limited classification performance. To address this issue, we propose a novel text motor-imagery EEG discrimination method, namely wavelet-based temporal-spectral-attention correlation coefficient (WTS-CC), to simultaneously consider the features and their weighting in spatial, EEG-channel, temporal and spectral domains in this study. The initial Temporal Feature Extraction (iTFE) module extracts the initial important temporal features of MI EEG signals. The Deep EEG-Channel-attention (DEC) module is then proposed to automatically adjust the weight of each EEG channel according to its importance, thereby effectively enhancing more important EEG channels and suppressing less important EEG channels. Next, the Wavelet-based Temporal-Spectral-attention (WTS) module is proposed to obtain more significant discriminative features between different MI tasks by weighting features on two-dimensional time-frequency maps. Finally, a simple discrimination module is used for MI EEG discrimination. The experimental results indicate that the proposed text WTS-CC method can achieve promising discrimination performance that outperforms the state-of-the-art methods in terms of classification accuracy, Kappa coefficient, F1 score, and AUC on three public datasets.

The key to accurately locate pipeline leakage is to effectively reduce the noise in the leakage signal. However, the leakage signal has the characteristics of nonlinear and dynamic change, and the denoising effect of the traditional method is limited. In order to reduce the positioning error caused by noise, a novel denoising method based on variational mode decomposition (VMD) is proposed. First, the correlation coefficient is used to screen effective intrinsic mode function components. Then, an optimization method of VMD decomposition layers $k$ by using minimum information entropy is utilized. Thus, the optimal number of layers and noise reduction signal can be obtained. Finally, the leakage point can be obtained by the principle of negative pressure wave (NPW). Simulation results show that the SNR can effectively improve by using the method in this paper. In laboratory experiments, this method can be used to effectively denoise the pressure signal while preserving the original signal characteristics as much as possible. Furthermore, From the perspective of positioning accuracy, compared other methods, the proposed method can achieve better positioning effect, and the average relative positioning error is 2.03%.

Hyperspectral images possess the characteristics of high dimensionality, which causes “dimensional disaster” and low classification accuracy. In order to solve the problems, based on traditional k-means algorithm, considering the importance of different bands for classification, and also combining both intra-class and inter-class information, a Kmeans-CM (K-means with correlation coefficient and maximize inter-class distance) algorithm with spectral angle mapper for hyperspectral image classification is proposed. First, we define weights of bands by introducing coefficient of variation and spectral angle mapping, which measures the importance of bands for classification, so as to make up for the deficiency of the traditional K-means algorithm to treat each band equally. Second, we introduce correlation coefficient to reset the intra-class distances in order to intensify correlation between pixels in the same category. Then, in order to reduce effect of local optimum of clustering effect, we introduce inter-class information for clustering by maximizing the distance between class centers and global center. Finally, the K-means clustering objective function is redefined according to the band weights and intra-class, inter-class information, and also solving optimally. The overall classification accuracy of the method reached 84.47%, 90.08% and 80.45% on classical hyperspectral data sets Pavia University, Salinas and Botswana respectively. And comparing with the traditional K-means algorithm, the CV-K-means algorithm and the CK-means algorithm, the experimental results show that the proposed algorithm can effectively improve classification accuracy of hyperspectral images, thus proving that the algorithm has good classification performance.

In this paper a broadband graded index all-dielectric (GID) lens that enables field decorrelation and decoupling of closely spaced wideband multiple-input-multiple-output (MIMO) antenna elements, is presented. Reduction in correlation coefficient $\rho $ for field decorrelation and port decoupling is realized by tilting the individual antenna element radiation patterns away from each other and by suppressing the surface wave propagation inside the antenna substrate through judicious control of the permittivity profile of the GID lens using the concept of transformation electromagnetics (TE). The permittivity profile of the GID lens is designed using air-hole technology and is fabricated by additive manufacturing. To validate the proposed concept, six different examples of lens loaded wideband patch-based MIMO antenna elements arranged in two different orientations are investigated. Simulated and measured results verify the effectiveness of the proposed technique, which constitutes the reduction in $\rho $ for both isotropic and non-isotropic propagation environments along with port-isolation enhancement without reducing the fractional bandwidth (FBW) in all of the six tested cases. Further results indicate a wide FBW having a minimum of 8.5% and a maximum of 33%, while radiation patterns with no extra backward radiation are obtained.

Transcranial direct current stimulation (tDCS) has become a new method of rehabilitation treatment for stroke and is gradually gaining acceptance. However, the neurophysiological mechanisms of tDCS in the treatment of stroke still need to be further explored. In our research, we aimed to investigate the effects of tDCS on neural oscillation power and brain functional network (BFN) connectivity in stroke patients based on electroencephalogram (EEG). Fifteen patients with ischemic stroke attended two experimental sessions in a randomized crossover trial and received real and sham tDCS. Resting-state EEG signals were acquired before and after stimulation. First, the power of EEG delta, theta, alpha, beta, and gamma bands were analyzed before and after stimulation. Then, BFNs in different bands were constructed, and network characteristic parameters such as degree, clustering coefficient, characteristic path length, global efficiency, and small-world attribute were analyzed. The results showed that the delta power decreased significantly (${P}< {0.05}$) and the alpha power increased significantly (${P}< {0.05}$) after real tDCS, whereas the theta, beta, and gamma power did not change significantly (${P}>{0.05}$). The degree and global efficiency of the delta band BFN decreased significantly after real tDCS (${P}< {0.05}$), the characteristic path length increased significantly (${P}< {0.05}$), and the clustering coefficient and small-world attribute decreased but not statistically significant (${P}>{0.05}$). The degree, clustering coefficient, global efficiency, and small-world attribute of the alpha band BFN increased significantly after real tDCS (${P}< {0.05}$), and the characteristic path length decreased significantly (${P}< {0.05}$). The BFN characteristic parameters in theta, beta, and gamma bands did not change significantly after real tDCS (${P}>{0.05}$). There was no significant difference in sham tDCS group (${P}>{0.05}$). Our study found that the power of delta oscillations decreased and the global connectivity of delta band BFN weakened after tDCS in stroke patients, whereas the power of alpha oscillations increased and the global and local connectivity of alpha band BFN was enhanced. The findings implied that one of the neurophysiological mechanisms of tDCS treatment in stroke patients may be the modulation of the power of delta and alpha oscillations, as well as the improvement of the connectivity of delta and alpha oscillation networks. [ABSTRACT FROM AUTHOR]

When the single-phase grounding fault is removed due to interruptions in circuit breakers, a secondary arc emerges and continues to burn, which weakens the continuity and stability of electrical power supplies. Therefore, analyses of the influencing factors of the arcing time are necessary to find suppression technologies for secondary arcs. Scatter plots of the arcing time as a function of the relevant parameters for each experiment are plotted to determine the distribution rule for the scattered points. In addition, the correlation coefficients between the arcing time and the relevant parameters are calculated through statistical correlation algorithm processing. Finally, multivariate fitting is performed between the arcing time and the relevant parameters, and a function is obtained from the fitting for testing. The results show that the arc length and secondary current are significantly positively correlated with the arcing time, while the maximum recovery voltage is significantly negatively correlated. However, the arc area and diameter have little correlation with the arcing time. The proposed method not only provides theoretical support and suppression measures for secondary arcs but can also be applied as an effective means to improve the continuity and security of electrical power supplies. [ABSTRACT FROM AUTHOR]

In this letter, two compact, colocated, low-coupled, quad-polarized (QP) antennas are proposed based on two dual-mode radiation bodies to achieve compactness and high isolation. The first antenna obtains four polarizations by orthogonally placing two dual-mode loops; the second antenna includes a dual-mode loop and a miniaturized dual-polarized patch based on fractal structures inside the loop. To investigate the performance of QP multiple-input–multiple-output (MIMO) systems, channel parameters including correlation coefficients, degree of power balance, and channel capacities are analyzed based on the measured data. Results show that, high channel capacities can be achieved both in a realistic office room and a rich scattered reverberation chamber, which are very close to that of the 4 × 4 independently identically distributed (i.i.d.) Rayleigh channel. It is revealed that both correlation coefficient and degree of received power balance have strong influence on the performance of a MIMO system, which are related to the radiation properties of the antenna and the polarimetric characteristics of the propagation environment. [ABSTRACT FROM AUTHOR]

In this research work, a new harmonic responsibility measure is proposed to extract the amount of harmonic responsibility of each plant supplied from the point of common coupling (PCC). The proposed method uses a function of the correlation coefficients between the voltage and current signals measured synchronously at the PCC. After the verification of the method on synthetic data generated in simulation environment, field data measurements of voltage and current are used to test the practicability of the proposed method. Harmonic contributions of the iron and steel (I&S) plants obtained using the proposed method are compared with the results of one existing method in the literature and it has been shown that harmonic responsibilities of the plants can be obtained for each harmonic order. The harmonic currents absorbed by the shunt harmonic filters of the I&S plants from the rest of the system are also identified effectively with the proposed method to determine the actual source of the harmonics. The proposed method can serve the needs of the active power filters and other compensation systems to improve the effectiveness of those systems in reducing individual distorting effects of the I&S plants supplied from a PCC of the electricity transmission system. [ABSTRACT FROM AUTHOR]

In this article, we introduce a design methodology of reinforced fuzzy models based both on univariate analysis and multivariable analysis to cope with high-dimensional problems. This approach is aimed at reducing the design process and curbing computing overhead inherently associated with the increasing volume of data in terms of both their number and the dimensionality of the feature space. The critical features of the proposed fuzzy models are highlighted as follows: First, the essential input variables of the model are selected by running the univariable and multivariable analyses. In univariate analysis, input variables with a strong linear relationship with the output variable are selected through correlation analysis completed for each input space and output space. On the contrary, in multivariable analysis, input variables are chosen by comparing the determination coefficients obtained from the subsets of input variables. Second, according to the analysis of the input variable, we construct two different kinds of fuzzy models. The first fuzzy model comprises the design of the univariable-based fuzzy model (UFM) and its aggregation. The UFMs are made by the individual input variables selected from the univariable analysis using a correlation coefficient. The subspaces formed by correlation analysis are applied for determining the centers of the membership function (MF). The results produced by individual fuzzy models are aggregated through some t-conorms. The second fuzzy model is with the fuzzy clustering for improving the form of fuzzy space in the premise part of a fuzzy rule. To curb the dramatic increase in size of fuzzy rule in high-dimensional problems, the clustering space is employed as the fuzzy space, and the partition matrix produced by fuzzy provided the required degrees of the MF. Experimental studies include a suite of synthetic and publicly available data. The superiority of the proposed design methodology was demonstrated by using 34 publicly available datasets and also compared with the conventional models associated with the fuzzy rule-based models as well as the state-of-the-art models reported in the literature. [ABSTRACT FROM AUTHOR]

Approximate arithmetic circuit (AAC) has emerged as a promising high-performance and energy-efficient circuit paradigm, which can be used in many applications with inherent error tolerance. To guarantee the usability of AACs and the availability of resilient applications, it is necessary to analyze the reliability of AACs. Most current literature focus on the error characteristics of AACs and few methods can be applied to estimate the reliability of AACs. These methods mostly have exponential time complexities and evaluate the average reliability assuming the input combinations are equally likely. In reality, the primary input (PI) signals can be given with any probability from 0 to 1. In this article, we assume that the PIs have random signal probabilities and propose approaches to reliability boundary estimation for AACs. First, we propose a new efficient and accurate method to evaluate the reliability of AACs. The method mainly calculates the AAC reliability for an input vector set, and furthermore, during the calculation, the correlation problem is considered to increase accuracy. Then, based upon the proposed AAC reliability evaluation method, we present the approaches to finding the reliability boundary. Randomly given signal probabilities of every PI, two heuristic search algorithms are utilized to find the lowest reliability. A comparison of the results on three series of AACs and the circuits in the EvoApprox8b library confirms that the proposed reliability evaluation method is more accurate and efficient than the previous method. Further experiments verify the plausibility of the calculated reliability boundary of AACs. [ABSTRACT FROM AUTHOR]

Understanding complicated city traffic patterns has been recognized as a critical goal by twenty-first-century urban planners and traffic management systems, resulting in a significant rise in the quantity and variety of traffic data gathered. For example, in a growing number of large cities, taxi firms have begun collecting metadata for each vehicle trip, such as origin, destination, and travel duration. Taxi data offer information on traffic patterns, allowing the study of urban flow—what will traffic look like between two sites on a particular day and time in the future? This paper proposes a method based on sparse GPS probe data, that focuses on allocating travel time data to the different links traveled between GPS observations. This model incorporates the progressive spatial correlations between the links in a network. The main goal of this work is to show how we can consider progressive spatial correlations and improve our results more realistically with a simple adjustment in the previously known parametric methods. For estimating arterial travel time, the methodology is applied to a case study for the partial network of New York City-based on the data collected from the taxicabs in New York City, providing the locations of origins, destinations, and travel times. The model estimates quarter-hourly averages of urban link travel times using OD trip data. This study proposes a more accurate approach for estimating link travel times, that fully utilizes the partial information received from taxi data in cities.

Noise radars, as well as certain types of quantum radar, can be understood in terms of a correlation coefficient which characterizes their detection performance. Although most results in the noise radar literature are stated in terms of the signal-to-noise ratio (SNR), we show that it is possible to carry out performance prediction in terms of the correlation coefficient. To this end, we derive the range dependence of the correlation coefficient under the assumption that all external noise is additive white Gaussian noise. We then combine our result with a previously-derived expression for the receiver operating characteristic (ROC) curve of a coherent noise radar, showing that we can obtain ROC curves for varying ranges. A comparison with corresponding results for a conventional radar employing coherent integration shows that our results are sensible. The aim of our work is to show that the correlation coefficient is a viable adjunct to SNR in understanding noise radar performance.

This paper develops a novel pseudo-measurement construct termed a Correlational Measurement (CM) to improve observability and state estimation accuracy. CMs utilize the correlation between similar classes of loads and DERs. The modifications required to accommodate CMs in traditional node voltage or branch current based state estimators are derived. Additionally, an observability analysis framework for distribution systems with DERs is presented to seamlessly integrate CMs. The framework assembles an observability test and an observable island identification procedure from the literature along with a novel observability metric. Finally, an on-line CM parameter estimation procedure is presented. Simulations on the IEEE 123 bus test system and real field data from Pecan Street demonstrate the utility of the proposed CMs and the observability analysis framework in improving observability and state estimation accuracy.

In this study, an algorithm combining feature tracking and maximum cross-correlation (FT-MCC) for the extraction of sea ice motion (SIM) vectors were applied to Gaofen-3 (GF-3) imagery, filling the gap of SIM extraction using GF-3 imagery. The locally consistent flow field filtering method is proposed to replace the filtering method based on the correlation coefficient threshold in FT-MCC to improve filtering effectiveness of SIM results extracted by FT-MCC. A comparison of the probability density distributions (PDDs) of the correlation coefficients of SIM vectors extracted by FT-MCC from images with different resolutions revealed high reliability for SIM vectors extracted for images with an 80 m spatial resolution. A comparison of the PDDs of the correlation coefficients of SIM vectors obtained from images with different polarization modes showed more reliable SIM vectors were extracted from vertical transmit horizontal receive (VH) polarization images than from corresponding vertical transmit vertical receive (VV) polarization images. The SIM vectors extracted from GF-3 images by two methods (FT(A-KAZE)-MCC and FT(ORB)-MCC) derived from the FT-MCC algorithm were highly consistent in terms of accuracy and reliability. SIM vectors extracted manually and from Sentinel-1 images were used as reference data to verify the SIM results extracted from GF-3 images, for which the uncertainties in the magnitude and direction of the extracted SIM vectors were found to be 0.119 cm/s–0.287 cm/s (103 m/d–248 m/d) and 4.119°–5.930°, respectively.

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]

This article proposes an unsupervised online spike sorter, dedicated to brain-implantable neural recording microsystems. The main (online) spike sorting phase in the proposed approach is based on the wave shape resemblance between spike classes, realized by template matching. This phase follows an offline training phase, implemented off the implant. In the training phase, the number and centroids of the clusters are automatically determined and subsequently sent to the implant to configure the on-implant online spike sorter. Comprehensively verified using two separate datasets with a wide spectrum of spike wave shapes, the proposed approach presents average classification accuracies of $\sim 85$ % (unsupervised) and $\sim 92$ % (supervised). A 64-channel spike sorter was designed using a computational core with folded architecture. To make the very large-scale integration (VLSI) implementation of this spike sorter appropriate for brain implants in terms of both power and area consumption, the computations realizing the proposed approach were significantly reduced. Designed in a standard 180-nm CMOS technology, the circuit consumes $1.74~\mu \text{W}$ /channel and per-channel area of 0.047 mm2. The circuit is capable of clustering neural spikes in real-time with a latency of as short as 1.36 ms. A prototype of the circuit was implemented and successfully tested. [ABSTRACT FROM AUTHOR]

Standard noise radars, as well as noise-type radars such as quantum two-mode squeezing (QTMS) radar, are characterized by a covariance matrix with a very specific structure. This matrix has four independent parameters: the amplitude of the received signal, the amplitude of the internal signal used for matched filtering, the correlation between the two signals, and the relative phase between them. In this article, we derive estimators for these four parameters using two techniques. The first is based on minimizing the Frobenius norm between the structured covariance matrix and the sample covariance matrix; the second is maximum likelihood (ML) parameter estimation. The two techniques yield the same estimators. We then give probability density functions (pdf’s) for all four estimators. Because some of these pdf’s are quite complicated, we also provide approximate pdf’s. Finally, we apply our results to the problem of target detection and derive expressions for the receiver operating characteristic (ROC) curves of two different noise radar detectors. In summary, our work gives a broad overview of the basic statistical behavior of noise-type radars. [ABSTRACT FROM AUTHOR]

It is difficult to obtain linear output displacements for stepping piezoelectric platform due to their discontinuous outputs. Inspired by the theory that the maximum static friction force is greater than the sliding friction force, in this letter, we propose a bipedal alternating inertial actuation mode (BAIAM) to improve the displacement linearity of the stepping platform. The output displacements are measured, the linear fitting is carried out by the least square method, the correlation coefficient and linear fitting function are used to evaluate the displacement linearity, and the correlation coefficients of the forward and backward motions are calculated as 0.9998 and 0.9999 in BAIAM, respectively, which verifies the effectiveness of the proposed method. The closed-loop experiments in BAIAM are also carried out, and the steady-state errors for the target position of ±40 μm within ±0.2 μm in the point-to-point positioning control experiments are achieved. The experimental results confirm that the developed platform can be applied to precision fields requiring a large travel range, planar two-degree-of-freedom motion, and linear outputs. [ABSTRACT FROM AUTHOR]

With the extensive application of active magnetic bearings (AMBs), the robust multiobjective optimization of the structure seems to be a priority. However, it is a challenge due to the high dimension and huge computational cost of finite-element analysis. In this article, a robust multi-objective optimization method is proposed to pursue good performance for a three-pole AMB. To increase the efficiency of the optimization process, the Kendall correlation coefficient is applied to assist in determining the sensitivity. The parameters are divided into three layers, and a three-level multiobjective optimization structure is established. Meanwhile, Kriging model is employed to improve the optimization efficiency. The selection of the final solution in Pareto curves is always an issue. The proposed optimization structure can only ensure the performance of the AMB, rather than robustness. Thus, a robust solution selection method is proposed based on the climbing algorithm. The robustness can be easily shown in the Pareto curve obtained through the optimization structure. The final solution is selected with good robustness in terms of suspension force and force ripple. The experimental results based on a prototype are provided to verify the effectiveness of the proposed optimization method. [ABSTRACT FROM AUTHOR]

Distributed tactile pressure recognition is an important task in intelligent industry. In this article, convolutional neural network (CNN) has attracted attention for their ability to automatically extract features and complete recognition. However, there are still the following weaknesses. The pressure information is low-resolution based on sensor density, resulting in limited configurable network depth. The current CNNs are deep network for high-resolution image, so they are not applicable for pressure task. What CNN has learned from the pressure is not clear. Therefore, a bilinear feature fusion CNN (BF-CNN) is proposed. The bilinear features are used to improve the feature extraction capability by depicting second-order characteristics, and fusion strategy is applied to promote the efficiency of feature utilization through the complementarity of hierarchical features. To understand BF-CNN intuitively, we develop the fused gradient class activation map (F-Grad-CAM) algorithm to visualize the regions of interest of the samples in detail, using multi-scale features to provide high-resolution heat maps. We also construct a pressure image dataset of 26 (A-Z) letter-shapes with complex contours collected by a 16 × 16 pressure array to validate the proposed models. The results show our work achieved highest accuracy of 98.23% and provided a more detailed location on activated areas. [ABSTRACT FROM AUTHOR]

The effect of delay spread (DS), the specular multipath components (SMCs), and the impact of dense multipath components (DMCs) on multiple-input/multiple-output (MIMO) system performance in a tunnel environment are studied by considering a Richter’s maximum-likelihood (RiMAX)-based estimator. The path loss (PL), angular spread, interuser spatial correlation, and capacity of a massive MIMO framework are analyzed at the 3.5-GHz frequency band through a measurement campaign inside a subway tunnel to implement the latest 5G standards. The radio channel consists of a uniform rectangular array of 32 transmitting elements and a uniform cylindrical array of 64 receiving elements with horizontal and vertical polarizations. The power ratio of the DMCs is found to be distance dependent, as the DMCs and SMCs behave differently in tunnels. Moreover, the study shows that the channel capacity, angular spread, and DS are reduced as the distance between the transmitting and receiving antenna arrays increases, whereas the PL exponent increases with the distance. This research highlights the importance of considering spatial characteristics in 5G massive MIMO system models and provides guidelines to enhance the networks’ accuracy in underground mobile communications. [ABSTRACT FROM AUTHOR]

We analyze the impact of arbitrary dependence between the forward and backward links in backscatter communication systems. Specifically, we quantify the effect of positive and negative dependence between these fading links on channel capacity by using Copula theory. The benefits of this approach are highlighted over the classical framework of linear dependence based on Pearson’s correlation coefficient, which is also analyzed. Results show that for a fixed transmit power budget, capacity grows with positive dependence as well as with fading severity in the low signal-to-noise ratio (SNR) regime. Conversely, fading dependence becomes immaterial in the high SNR regime. [ABSTRACT FROM AUTHOR]

The traditional superresolution methods based on image patches often ignore the consistency between the overlapped patches, causing block effects in produced images. The convolutional sparse coding based superresolution method uses the translation invariance of the convolution filter to directly encode the entire image, maintaining consistency and good performance. In this article, we propose a novel approach to single-image superresolution reconstruction based on hybrid nonlocal similarity constrained convolution sparse coding. We first decompose the input image into a smooth part and a texture part. The Bayesian nonparametric model can use more prior information of the original image, so we replace the previous bicubic interpolation with this method to better reconstruct the residual high-frequency information in the smooth part. When reconstructing the texture part, this article proposes a nonlocal similarity constrained convolutional sparse coding method, which transforms the reconstruction of the texture part to minimize the convolution sparse coding noise of the feature maps and classifies the image patches in the search space by using the correlation coefficients as the structural information, avoiding unnecessary weight calculation. Several methods were tested on satellite images extensively. Both visual inspection and quantitative analysis results demonstrate that our method outperforms other state-of-the-art methods and increases noise immunity effectively.

Prediction of incidences of thunderstorms using different techniques is quite well established. To add to this knowledge, in this paper, a heuristic equation is proposed which relates the correlation coefficient of atmospheric parameters with the number of hourly incidences of thunderstorms. There are four ways to compute the indices values from the proposed heuristic equation. These indices values are used in the classification of the hourly incidences of thunderstorms. The proposed equation and indices work well, as tested on pre-monsoon hourly atmospheric data from 2018 and validated with hourly data from April 2019 and 2020. From the four indices, the first index value is computed with normalized average values of parameters of only hourly incidence of thunderstorms data of 2016-2017 in the month-wise method. The other three indices use optimization techniques namely, the Teaching Learning Based Optimization (TLBO) technique, Differential Evaluation (DE), and Simulated Annealing (SA). TLBO shows better classification of hourly incidences of thunderstorms for 2018 atmospheric data. TLBO is also precisely validating the hourly incidences of thunderstorms for April 2019 and April 2020 hourly atmospheric data. It performed better with 2020 data by 88%. The variations of atmospheric parameters before, after or, during the incidences of thunderstorms are also depicted.

Odor sensing enables us to sense odorant stimuli. This sensation causes a deep subconscious response in humans in various ways and represents an indispensable sensation in daily life. However, unwanted compounds called fixatives, which can cause interference and contradiction in odorant analysis especially using mass spectrometry and human subject, are included in odorant samples. Moreover, we do not know the pure odorant mass spectrum. Therefore, it is essential to eliminate the interference of fixatives from the odor sample mass spectrum data and to extract the pure odor mass spectrum. In the present study, we performed independent component analysis (ICA) on the mass spectra of odor samples to remove the influence of fixatives. The advantage of the ICA in separating independent components without a priori knowledge of the original data is useful. The abundance of essential-oil mass spectra that we gathered were utilized as odorant samples. The results were compared with sensory test data from a human subject for a better study of fixative influence. It was revealed that ICA could extract the pure odor sample mass spectrum data without the influence of fixatives, even if the fixatives were added to the odor samples. This study’s outcomes allow us to analyze more odor samples for odorant analysis, not hindered by the influence of unwanted compounds.

Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.

The Mixed distribution model of time headway plays an important role in Intelligent Transportation System. A more accurate improved model based on a new traffic state classification method and correlation coefficient method was established in this study. Upon analyzing traffic data from three unban roads in Nanjing, China from December 8, 2017 to April 24, 2018 in clear days free of fogs and haze, it was found that 1) the improved model dwarfs the other models, for it boasts higher goodness-of-fit and it represents the only one to successfully pass the Chi-square test. 2) the improved model shows better performance in application because the relative errors between the field traffic rate and calculated traffic rate obtained from the improved model are the smallest. 3) the goodness-of-fit of the improved model is affected by traffic state, and the fitting precision of the improved model under four states is the best. Thus, it is hard to tell performance of which model established under two states or three-state model is better. 4) correlation coefficients between time headway and the absolute value of relative speed make the improved Mixed model more fitted and precise. The improved model proposed in this study is a more accurate time headway distribution model.

When a fault occurs on the tie line of ac/dc hybrid system, the change rule of electrical quantity on the inverter side is different from that of infinite ac power source. Sensitivity and reliability of the traditional relay protection methods will be affected. A longitudinal protection method for ac/dc hybrid system based on voltage wave similarity comparison is proposed. The protection compares the similarity of the measuring voltage wave and the calculated reference voltage wave, and determines the fault location roughly. By comparing the judgment results of voltage similarity between both ends of line, a reliable judgment can be made to distinguish between internal and external faults. In order to improve the reliability of voltage wave similarity comparison, dynamic time warping method is used to overcome the influence caused by phase error, and Pearson correlation coefficient method is used to judge the similarity of voltage wave. The simulation results of real time digital simulation system (RTDS) show that the proposed method can identify the fault which occurs on the tie line of ac/dc hybrid system accurately which has high reliability and good applicability. [ABSTRACT FROM AUTHOR]

Based on the spatial structure correlation in different geophysical parameters, we propose a new 3-D joint inversion method for frequency-domain airborne electromagnetic (AEM) and airborne magnetic (AirMag) data by incorporating a local Pearson correlation constraint (LPCC). For each iteration, the entire model is separated into multiple subdomains and the Pearson correlation coefficients of resistivity and magnetization in the subdomain are employed as the additional regularization term to do the joint constraint. This new regularization term is continuously updated in the inversion process to ensure that the resistivity and magnetization models in two separated inversions converge to a similar spatial structure. As a statistics technology, the LPCC-based joint inversion scheme not only has the advantages of the conventional joint inversions, but also can implement the structural constraints in different scales by selecting different sizes of the subdomain. This provides the flexibility for solving multiscale problems. Synthetic examples show that the joint inversion can improve the overall inversion resolution by combining the high vertical resolution of the EM method and large exploration depth and high horizontal resolution of the magnetic method. In the application to field survey datasets, the joint inversion delivers better results than those of separate inversions, which further verifies the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Correlations and mutual couplings in antenna arrays lead to performance deterioration of multiple-input-multiple-output (MIMO) systems. The relation between mutual coupling and correlation is complex, especially in non-isotropic multipath environments. Reducing mutual coupling does not guarantee an eventual reduction of the overall correlation and vice versa. In general, it is not a trivial task to reduce mutual coupling and correlation simultaneously in arbitrary multipath environments. In this correspondence, a planar metamaterial structure (PMS) and an array-antenna decoupling surface (ADS) are applied to the antenna array for simultaneous reductions of correlations and mutual couplings. The PMS comprises regularly arranged metamaterial elements holding certain transmission features. The ADS placed between the PMS and array is a substrate with printed rectangular metal strips. A 1 × 4 patch array is taken as an example to demonstrate the effectiveness of the proposed scheme. According to the simulated and measured results, the combination of PMS and ADS can work together without interfering or affecting the matching condition of the antennas. As a result, the mutual coupling and overall correlation between the antenna elements are reduced in a general multipath environment. [ABSTRACT FROM AUTHOR]

Age of information (AoI) has been widely accepted as a measure quantifying freshness of status information in real-time status update systems. In many of such systems, multiple sources share a limited network resource and therefore the AoIs defined for the individual sources should be correlated with each other. However, there are not found any results in the literature studying the correlation of two or more AoIs in a status update system with multiple sources. In this work, we consider a multi-source system sharing a common service facility and provide a framework to investigate joint performance of the multiple AoIs. We then apply our framework to a simple pushout server with multiple sources and derive a closed-form expression for the joint Laplace transform of the AoIs in the case with independent M/G inputs. We further show some properties of the correlation coefficient of AoIs in the two-source system. [ABSTRACT FROM AUTHOR]

Due to the single and limited prior information in electromagnetic tomography (EMT), the existing reconstruction methods are often unable to reconstruct the image with high accuracy for different kinds of flow patterns. In this article, an image reconstruction algorithm based on the sequential Monte Carlo principle for EMT is proposed. It can increase the diversity of samples to extract more image features for EMT images reconstruction and maintain a high image reconstruction effect for different flow patterns. First, the EMT image reconstruction process is modeled as a dynamic time-varying system to search for the optimal estimation in state space. At the same time, controllable adjustment of probability density distribution can improve the reconstruction effect under different flow patterns. Second, samples are obtained from the initial sample space, and their weights are updated according to the samples’ information. The numbers of effective samples and the diversity of samples are increased by resampling. Finally, the system state estimation is calculated according to the samples and their updated weights, and the system state is updated iteratively until the optimal solution is obtained. Simulation results have shown that the proposed method is superior to other existing methods in image error and correlation coefficient, and has better imaging quality. [ABSTRACT FROM AUTHOR]

Dynamic response of the seismic sensor is crucial, particularly for ultra-weak vibration detection. The related researches almost focus on traditional electrical ones. However, for fiber-optic seismic sensor (FOSS), the dynamic response, as well as the on-site results, is rarely reported. This study thoroughly evaluates the theoretical mechanism for improving the dynamic response of FOSS and its applications in the oilfield. The Ricker wavelet is utilized to simulate the applied seismic signal, and its dynamic response output is investigated. The influences of peak frequency and damping ratio on resonance wave suppression and correlation coefficient improvement are discussed. Besides, the perforation shot detection based on ten-level 3-component FOSS arrays is demonstrated in the oilfield. The basic FOSSs and damping-optimized FOSSs are both manufactured for dynamic response comparison. The signal features, including attenuation time, correlation coefficient, and principal frequency component distribution, all confirm the dynamic response improvement of the proposed damping-optimized FOSS array. [ABSTRACT FROM AUTHOR]

Acoustic Bessel beams are commonly used as ideal sources to study the characteristics for acoustic-vortex (AV) beams, exhibiting prosperous perspectives in contactless object manipulations and acoustic communications. However, accurate Bessel beams are difficult to construct using 2-D arrays in practical applications. By integrating active phase control and passive phase modulation to a ring array of sectorial planar transducers, quasi-Bessel AV (QB-AV) beams of arbitrary order are built by the line focus of AV fields in the current study. Based on Snell’s refraction law, a circular sawtooth lens of phase modulation is designed to converge incident waves toward the beam axis at the same deflection angle. QB-AV beams constructed by the main lobes of the sectorial sources are demonstrated by theoretical derivations, numerical simulations, and quality evaluations, while those created by the sidelobes are neglected to avoid the pressure fluctuations in the near field. Experimental measurements for AV beams of different orders coincide basically with the simulations, demonstrating that line-focused QB-AV beams can be generated along the beam axis across the pressure peak. With the increase of the topological charge, the peak pressure of the beam decreases accordingly with a reduced effective axial range. The favorable results prove that, as a special kind of diffraction sources, the adjustable QB-AV beams may enable more important biomedical applications where Bessel beams are necessary, especially for the line-focused manipulation of biological and drug particles. [ABSTRACT FROM AUTHOR]

The accurate state of charge (SoC) estimator has great significance for ensuring the safety and reliability of Li-ion battery systems. However, the accurate SoC estimation for a series–parallel connected battery pack is a remaining challenge due to the strong inconsistency characteristic caused by cell open-circuit (COC) faults. Therefore, a novel fault-tolerant multimodule SoC estimation method for the series–parallel connected battery pack is proposed. An average module model is used to describe the performance of the battery pack. A bias correction model considering SoC, series resistance, and polarization characteristic inconsistency is developed to describe in-pack modules. A noise adaptive H ∞ filter-unscented H ∞ filter is employed to estimate SoC for the average module. The SoCs of in-pack modules are obtained by an optimized recursive least square method. A correlation coefficient-based method is developed to detect COC fault and isolate the fault module. Finally, several experiments on a 4s-3p battery pack are taken to evaluate and verify the real-time performance, stability, and accuracy of the proposed method. The result shows that both online and offline SoC estimation error is less than 1% for healthy modules, and it is less than 2.5% for the fault module in which one-third capacity is lost randomly. [ABSTRACT FROM AUTHOR]

An interval-valued Pythagorean hesitant fuzzy set (IVPHFS) not only can be regarded as the union of some interval-valued Pythagorean fuzzy sets but also represent the Pythagorean hesitant fuzzy elements in the form of interval values. So IVPHFSs are extensions of Pythagorean hesitant fuzzy sets (PHFSs) and interval-valued Pythagorean fuzzy sets (IVPFSs), which are powerful tools to represent more complicated, uncertain and vague information. This paper focuses on the four kinds of correlation coefficients for PHFSs, and extends them to the correlation coefficients and the weighted correlation coefficients for IVPHFSs. In the processing, we develop the least common multiple expansion (LCME) method to solve the problem that the cardinalities of Pythagorean hesitant fuzzy elements (PHFEs) (or interval-valued Pythagorean hesitant fuzzy elements (IVPHFEs)) are different. In addition, we propose score functions and accuracy functions of Pythagorean fuzzy elements (PFEs) (or interval-valued Pythagorean fuzzy elements (IVPFEs)) to rank all the PFEs (or IVPFEs) in a PHFE (or an IVPHFE). Especially, score functions and accuracy functions of IVPFEs are both presented as interval numbers. Then use the comparison method of interval numbers to compare two revised IVPHFEs in order to keep the original fuzzy information as far as possible. What's more, we define the local correlations and local informational energies which can depict the similarity between two IVPHFEs more meticulously and completely. At last the numerical examples to show the feasibility and applicability of the proposed methods in multiple criteria decision making (MCDM) and clustering analysis.

Currently, X-band marine radar images are commonly utilized to retrieve sea wave parameters. However, the marine radar image usually contains rainfall interference noise, which has an effect on the inversion accuracy of the wave parameters. To control the quality of the radar image when retrieving sea wave parameters, research on rainfall detection is investigated in this paper. Based on the correlation characteristics of the sea clutter and the difference in the correlation coefficients between sea wave images with and without rain, a novel method of rainfall detection is proposed. By analyzing the spatial and temporal correlation characteristics of sea clutter, the detection threshold of the proposed method is determined. To verify the effectiveness of the proposed method, X-band marine radar data collected at Pingtan, which is located on the coast of the East China Sea, are utilized in this paper. The experimental results demonstrate that the proposed method can effectively detect rainfall from X-band marine radar images.

The echo signal of light detection and ranging (LiDAR) system is easily disturbed by various noises under the influence of intense background light. The signal to noise ratio (SNR) decreases with increasing distance, which seriously affects the retrieval accuracy of the LiDAR system. This paper combines local mean decomposition and an improved thresholding method (LMD-ITM) to process the Lidar signal with a lot of noise, so as to avoid the loss of useful information. In this research, the correlation coefficient and energy entropy are used to distinguish between the relevant and irrelevant components decomposed by the local mean decomposition. Subsequently, the improved thresholding method is applied to process the irrelevant components. It can extract the effective signals which are difficult to be separated from the high-frequency signals. Finally, all components are reconstructed to get the denoising signal. The experimental results demonstrate that this method can effectively avoid the short-range migration, suppress the long-range noise, and adapt to different weather conditions. Compared with the previous denoising method, the proposed method increases the SNR by about 15%.

The technique of binary code similarity detection (BCSD) has been applied in many fields, such as malware detection, plagiarism detection and vulnerability search, etc. Existing solutions for the BCSD problem usually compare specific features between binaries based on the control flow graphs of functions from binaries or compute the embedding vector of binary functions and solve the problem based on deep learning algorithms. In this paper, from another research perspective, we propose a new and lightweight method to solve cross-version BCSD problem based on multiple features. It transforms binary functions into vectors and signals and computes the similarity coefficient value and correlation coefficient value for solving cross-version BCSD problem. Without relying on the CFG of functions, deep learning algorithms and other related attributes, our method works directly on the raw bytes of each binary and it can be used as an alternative method to coping with various complex situations that exist in the real-world environment. We implement the method and evaluate it on a custom dataset with about 423,282 samples. The result shows that the method could perform well in cross-version BCSD field, and the recall of our method could reach 96.63%, which is almost the same as the state-of-the-art static solution.

In this paper, we present an optimal channel selection method to improve common spatial pattern (CSP) related features for motor imagery (MI) classification. In contrast to existing channel selection methods, in which channels significantly contributing to the classification in terms of the signal power are selected, distinctive channels in terms of correlation coefficient values are selected in the proposed method. The distinctiveness of a channel is quantified by the number of channels with which it yields large difference in correlation coefficient values for binary motor imagery (MI) tasks, rather than by the largeness of the difference itself. For each distinctive channel, a group of channels is formed by gathering strongly correlated channels and the Fisher score is computed using the feature output, based on the filter-bank CSP (FBCSP) exclusively applied to the channel group. Finally, the channel group with the highest Fisher score is chosen as the selected channels. The proposed method selects the fewest channels on average and outperforms existing channel selection approaches. The simulation results confirm performance improvement for two publicly available BCI datasets, BCI competition III dataset IVa and BCI competition IV dataset I, in comparison with existing methods.

This paper presents a novel feature extraction method for electroencephalogram (EEG)-based cognitive task classification based on the correlation coefficients of EEG channel pairs by introducing preprocessing of the EEG signals. The preprocessing attempts to optimally demix each pair of EEG channels using a two-dimensional rotation matrix in order to mitigate the interference between channel pairs and, consequently, to enhance the resulting correlation coefficient features for cognitive task classification. For the optimization, the following criteria are proposed with an optimal rotation angle approximated for each criterion: i) maximum inter-class correlation coefficient distance (ICCD); ii) minimum within-class correlation coefficient distance (WCCD); and iii) maximum Fisher ratio (FR), which is the ratio of ICCD to WCCD. Performance evaluation based on the cognitive task dataset, dataset IV and Ib in BCI competition II, and Keirn and Aunon's dataset, shows that ICCD optimization with the `above the mean' and 1.5 interquartile range (IQR) feature selection method yields the best classification performance in comparison with other existing cognitive task classification methods.

Raw statistical features can imitate the amplitude, average, energy and time, and frequency series distribution of a raw vibration signal. However, these raw statistical features are either not very sensitive to weak incipient faults or are unsuitable for more severe faults, thus affecting the fault detection and classification accuracy. To tackle this problem, this paper proposes a discriminant feature extraction method for Centrifugal Pump (CP) fault diagnosis. In order to obtain the discriminant feature pool, the proposed method is divided into three phases. In the first phase, a healthy baseline signal is selected. In the second phase, the healthy baseline signal is cross-correlated with the CP vibration signals of different classes, and a set of new features are extracted from the resulting correlation sequence. In the third phase, raw hybrid features in time, frequency, and the time-frequency domain are extracted from both the healthy baseline signals and the CP vibration signals of different classes. The correlation coefficient is calculated between the raw hybrid feature pools, which results in a new set of discriminant features. Discriminant features help the machine learning classifiers to effectively detect and classify the data into its respective classes. Furthermore, the proposed method combines all these features into a single feature vector that forms a vulnerable feature pool. The vulnerable feature pool describes the CP's vulnerability to a fault and is provided as an input to a multiclass support vector machine (MSVM) for CP fault detection and classification. The experimental results illustrate that the accuracy obtained from the proposed method shows promising improvements over the state-of-the-art conventional methods.

In Dempster-Shafer (D-S) evidence theory, how to deal with conflict is an important and open topic. Two key strategies for resolving conflicts of evidence are considered, namely information averaging and information focus. How to balance this relationship is still a question worth considering. Recently, Ma et al. studied evidence conflicts from the perspective of complete conflict set and proposed a flexible rule for conflict evidence combination. The proposed combination rule seems to take into account the above two strategies. However, through analysis, we find that Ma et al.’s method tends to use the average method to solve conflicting propositions, while Dempster’s combined rule has a weak focusing function. In this paper, based on the concept of non-conflict element set, a new conflict handling method is proposed. First, the similarity between the evidences is characterized by the correlation coefficient; based on this, a new weighting scheme of evidence is developed. In addition, the propositional support is reasonably allocated through discounts.Through numerical examples, the applicability and superiority of the method are compared and analyzed. The results show that the proposed method takes two strategies of averaging and focusing into consideration, and the information variance is small.