Statistical Characteristics Research Articles

The Parameterized Oceanic Front-Guided PIX2PIX Model: A Limited Data-Driven Approach to Oceanic Front Sound Speed Reconstruction

In response to the demand for high-precision acoustic support under the condition of limited data, this study utilized high-resolution reanalysis data and in situ observation data to extract the Kuroshio Extension Front (KEF) section through front-line identification methods. By combining the parameterized oceanic front model and the statistical features of big data, the parameterized oceanic front was reconstructed. A proxy dataset was generated using the Latin hypercube sampling method, and the sound speed reconstruction model based on the PIX2PIX model was trained and validated using single sound speed profiles at different positions of the oceanic front, combined with the parameterized oceanic front model. The experimental results show that the proposed sound speed reconstruction model can significantly improve the reconstruction accuracy by introducing the parameterized front model as an additional input, especially in the shallow-water area. The mean absolute error (MAE) of the full-depth sound speed reconstruction for this model is 0.63~0.95 m·s−1, and the structural similarity index (SSIM) is 0.76~0.78. The MAE of the sound speed section within a 1000 m depth is reduced by 6.50~37.62%, reaching 1.95~3.31 m·s−1. In addition, the acoustic support capabilities and generalization of the model were verified through ray tracing models and in situ data. This study contributes to advancing high-precision acoustic support in data-limited oceanic environments, laying a solid groundwork for future innovations in marine acoustics.

Statistical Characteristics of Wavelike Banded Convection Associated With Ducted Gravity Waves Over Southern China

AbstractWavelike banded convection occurs frequently and persistently in southern China, significantly impacting local weather. This study presents the first multi‐year investigation of wavelike banded convection associated with ducted gravity waves in southern China from 2013 to 2021 using radar mosaic maps. These convective bands are highly dependent on the wave ducting environment, which typically exhibits a three‐layer stratification structure. Out of 112 identified cases, two types were further classified based on the presence of a wave ducting vertical structure in the soundings: WaveDuct and nonWaveDuct type. Unlike nonWaveDuct type, the WaveDuct type predominantly happens on winter or spring nights. The phase speeds of WaveDuct convective bands also show a highly positive correlation with the depth of the ducting layer, consistent with wave ducting theory. This study provides a climatological understanding of the frequencies, movements, and environment conditions of wavelike banded convection related to ducted gravity waves over southern China.

Enhancing induction machine fault detection through machine learning: Time and frequency analysis of vibration signals

The integration of machine learning algorithms into fault diagnosis is regarded as an advanced and effective method for detecting electrical system faults. Vibration signals are identified as valuable and easily accessible data for fault identification in electrical machines. In this study, experiments were conducted to assemble a dataset consisting of 525 × 3 instances, each containing 200 three-axis vibration signal measurements. These measurements were obtained from a laboratory test bench equipped with a customized winding three-phase induction machine. Instead of directly analyzing the vibration signals, a feature extraction approach was employed, calculating a comprehensive set of statistical, harmonic, and impulsive features from the time domain (e.g., ShapeFactor, SINAD, ClearanceFactor, ImpulseFactor, CrestFactor, Kurtosis, Skewness, THD, Std, RMS, Mean, PeakValue, SNR), along with spectral features from the frequency domain (e.g., Peak Frequencies, Amplitudes, BandPower). To improve model efficiency, Analysis of Variance (ANOVA) was applied to select only the most significant features, with F-statistics used to rank feature importance. Features with higher F-statistics were prioritized for their ability to explain more variance in motor fault classification. This selective approach reduced model complexity, helping to minimize overfitting and focus on features that had a substantial impact on predictive accuracy. By concentrating on these high-impact features, a balance between simplicity and performance was achieved. These selected features were then used to train five machine learning classifiers: Ensemble (Bagged Trees), Quadratic Support Vector Machine, Weighted K-Nearest Neighbors, Efficient Logistic Regression, and Neural Networks. The performance of these classifiers was evaluated using various metrics, with validation accuracy rates ranging from 78.3% to 98.8%. A comparative study with similar works in the literature was conducted, demonstrating that high performances were obtained with the proposed approach while maintaining computational efficiency.

Association between Radiological and First-Order Statistical Features of the Mammogram, and the Tumor Phenotype in Breast Cancer Patients

Background: Breast cancer is among the most prevalent cancers which can effectively be screened by mammography. The spatial distribution of grey levels of the mammogram known as first-order statistical features (FOSFs) contain higher dimensional data which describe the breast composition. We aim to test these basic measures to differentiate density categories in breast cancer patients, and use them as covariates to investigate the relationship between radiologic and pathologic features of the tumor. Methods: Data from Eighty-five breast cancer subjects, their BI-RADS breast density category (a to d), percentage density (PD), and FOSFs of the mammogram including median, mean, interquartile range, kurtosis, maximum, minimum, standard deviation, skewness, and energy, were extracted. The tumor grade and the percentage of Ki67, ER, PR, and Her2 status were recorded. A linear discriminant analysis, and a support vector machine (SVM) were used to discriminate each density category from others. Then, the relation between variables were investigated using ANCOVA and regression analysis. Results: Density categories a and d were classified by SVM with high accuracy. The key feature of a and d were interquartile range and maximum intensities respectively. Reported tumor margins were related to Her2 overexpression and PR positivity. Spiculated tumor margin predicted percentage of PR expression, with a cumulative odds ratio of 7.85 (CI 2.5- 24.78), when adjusted for age, area of breast, density, and FOSFs, (p=0.0004). Conclusion: The findings of this study suggest that FOSFs can be incorporated in computer-aided systems to adjust for differences in breast composition and to refine risk profiles.

Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.

To establish and validate a new clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer. A total of 593 breast cancer patients who underwent preoperative breast MRI from Jan 2017 to Dec 2020 were enrolled, which were randomly divided into the training (n=474) and test sets (n=119) at the ratio of 8:2. Intratumoral region (ITR) of interest were manually delineated, and peritumoral regions of 3mm and 5mm (PTR-3 mm and PTR-5 mm) were automatically obtained by dilating the ITR. Intratumoral and peritumoral radiomics features were extracted from the fat-suppressed T2-weighted images, including first-order statistical features, shape features, texture features, and filtered features. The Mann-Whitney U Test, Z score normalization, K-best method, and least absolute shrinkage and selection operator (LASSO) algorithm were applied to select key features to construct radscores based on ITR, PTR-3 mm, PTR-5 mm, ITR+PTR-3 mm and ITR+ PTR-5 mm. Risk factors were selected by univariate and multivariate logistic regressions and were used to construct a clinical model and a clinical-radiomics model that presented as a nomogram. The performance of models was assessed by sensitivity, specificity, accuracy, the area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA). ITR+PTR-3 mm radsore and histological grade were selected as risk factors. A clinical-radiomics model was constructed by adding ITR+PTR-3mm radscore to the clinical factor, which was presented as a nomogram. The clinical-radiomics nomogram showed the highest AUC (0.873), sensitivity (72.3%), specificity (78.9%) and accuracy (77.0%) in the training set and the highest AUC (0.851), sensitivity (71.4%), specificity (79.8%) and accuracy (77.3%) in the test set. DCA showed that the clinical-radiomics nomogram had the greatest net clinical benefit compared to the other models. The clinical-radiomics nomogram showed promising clinical application value in differentiating luminal and non-luminal breast cancer.

Impact of rogue waves on semi-submersible platforms: An experimental investigation of wave run-up and air-gap responses

The wave run-up and air-gap responses of semi-submersible platforms under extreme sea conditions are crucial design considerations for ensuring the structural integrity and safety of marine engineering operations. This study investigated the impact of rogue waves on the wave run-up and air-gap responses of semi-submersible platforms using a transient focusing wave group to accurately simulate irregular seas. Comparative model tests were conducted on a semi-submersible platform under both rogue and non-rogue-wave conditions. The platform motions and air-gap responses were analysed using spectral analysis, extreme-value statistics, and wavelet methods. Rogue waves were found to significantly affect the air-gap response spectrum, increasing the nonlinearity of both the wave run-up and air-gap responses. Different locations on the platform exhibited varying statistical and spectral response characteristics. Rogue waves modified the instantaneous properties of wave elevation, thereby increasing the energy levels in the system. This study confirmed that rogue waves significantly influence the air-gap responses and wave run-up of semi-submersible platforms, suggesting that they should be considered when designing and planning the operations of these platforms. This study offers a comprehensive understanding of the complex interactions between rogue waves and large maritime structures and provides insights for improving safety and performance under extreme sea conditions.

Uncertainty quantification of 3D acoustic shape sensitivities with generalized [formula omitted]-order perturbation boundary element methods

This paper presents a novel perburbation-based method for uncertainty quantification of acoustic fields and their shape sensitivities. In this work, the frequencies of impinging acoustic waves are regarded as random variables. Taylor’s series expansions of acoustic boundary integral equations are derived to obtain nth-order derivatives of acoustic state functions with respect to frequencies. Acoustic shape sensitivity is obtained by directly differentiating acoustic boundary integral equation with respect to shape design variables, and then the nth-order derivatives of shape sensitivity with respect to random frequencies are formulated with Taylor’s series expansions. Based on the nth-order perturbation theory, the statistical characteristics of acoustic state functions and their shape sensitivities can be evaluated. Numerical examples are presented to demonstrate the validity and effectiveness of the proposed algorithm.

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.

Q‐Learning Based Adaptive Kalman Filtering With Adaptive Window Length

ABSTRACTIn this article, we propose an adaptive Kalman filtering with adaptive window length based on Q‐learning for dynamic systems with unknown model information. The iteration step length of the Q‐function is quantitatively adjusted through the influence function. The adaptive Kalman filtering algorithm is used to set an appropriate weight matrix for the Q‐function to estimate unknown model parameters. One numerical example and a practice‐oriented case are given to illustrate the effectiveness of the proposed method. It is shown that this filtering can provide state estimates of best accuracy among all the compared methods when the model mismatch and noise statistical characteristics change.

Observed response of microwave land surface emissivity to antecedent rainfall in Hainan Island

ABSTRACT Microwave emissivity is an important parameter for over-land retrieval of atmospheric variables such as water vapour, rainfall, and snowfall. However, it remains unclear for the dynamic response of multi-frequency microwave emissivity to prior rainfall over heterogeneous land surfaces. This study combined multi-frequency Microwave Land Surface Emissivity (MLSE) under all-weather conditions with hourly in-situ rainfall to investigate the response of MLSE to rainfall over different vegetation types in Hainan Island in China. Specifically, we explored the change of MLSE at 6.925, 10.65, 18.7, 23.8 and 36.5 Ghz to rainfall intensity in four rainfall cases, followed with statistical characteristics of MLSE to rainfall and dry duration (DD) across different vegetation types during 2003–2010. We found that the magnitude of decline in MLSE (0.004–0.06) is dependent on the rainfall intensity and duration, and the reduction caused by short-lasting heavy rain (daily rainfall >100 mm) was 2–6 times higher than that by light long-lasting rain (daily rainfall <80 mm). Among different microwave frequencies, the MLSE at 23.8 Ghz was most reduced by rainfall, while less reduction was found at low microwave frequencies such as at 6.925 and 10.65 Ghz. More importantly, the change of MLSE after rainfall was significantly different among vegetation types. Over woody savannas and forested lands, MLSE slightly decreased (about 0.004) in the first two hours of DD, whereas a rapid increase was observed in the first hour of DD over areas dominated with woody savanna, grassland, and cropland. These findings improve our understanding of the multi-frequency MLSE in response to rainfall over complex land surfaces, and benefit the microwave retrieval of rainfall over land.

Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light

AbstractAdvancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single photon measurements requires efficient detectors and longer measurement times to obtain high‐quality photon statistics. Here, a deep learning‐based variational autoencoder (VAE) method is introduced for classifying single photon added coherent state (SPACS), single photon added thermal state (SPATS), and mixed states between coherent and SPACS as well as between thermal and SPATS of light. The semi‐supervised learning‐based VAE efficiently maps the photon statistics features of light to a lower dimension, enabling quasi‐instantaneous classification with low average photon counts. The proposed VAE method is robust and maintains classification accuracy in the presence of losses inherent in an experiment, such as finite collection efficiency, non‐unity quantum efficiency, finite number of detectors, etc. Additionally, leveraging the transfer learning capabilities of VAE enables successful classification of data of any quality using a single trained model. It is envisioned that such a deep learning methodology will enable better classification of quantum light and light sources even in poor detection.

On the Definition of Velocity in Discrete-Time, Stochastic Langevin Simulations

We systematically develop beneficial and practical velocity measures for accurate and efficient statistical simulations of the Langevin equation with direct applications to computational statistical mechanics and molecular dynamics sampling. Recognizing that the existing velocity measures for the most statistically accurate discrete-time Verlet-type algorithms are inconsistent with the simulated configurational coordinate, we seek to create and analyze new velocity companions that both improve existing methods as well as offer practical options for implementation in existing computer codes. The work is based on the set of GJ methods that, of all methods, for any time step within the stability criteria correctly reproduces the most basic statistical features of a Langevin system; namely correct Boltzmann distribution for harmonic potentials and correct transport in the form of drift and diffusion for linear potentials. Several new and improved velocities exhibiting correct drift are identified, and we expand on an earlier conclusion that, generally, only half-step velocities can exhibit correct, time-step independent Maxwell–Boltzmann distributions. Specific practical and efficient algorithms are given in familiar forms, and these are used to numerically validate the analytically derived expectations. One especially simple algorithm is highlighted, and the ability of one of the new on-site velocities to produce statistically correct averages for a particular damping value is specified.

Statistical characterization of electricity use profile: Leveraging data analytics for stochastic simulation in a smart campus

On the path to energy transition, advanced metering infrastructures have been installed in distribution systems to support sustainability goals, generating a substantial volume of electricity consumption data that are essential for planning and management studies. Additionally, given the stochastic nature of electricity consumption, understanding and quantifying statistical properties such as data distribution, normality, stationarity, and autocorrelation are crucial for the development of more sustainable systems and the enhancement of building performance. In this context, this paper presents a statistical methodology for assessing key aspects of electricity consumption in buildings on a smart campus, which is an initiative originated on university campuses that integrates sustainable energy systems, efficient electrical infrastructure, and data-driven technologies to establish a sustainable learning environment. Using 28 months of electricity consumption data from a Brazilian smart campus, Electricity Use Profile models are developed and several hypothesis tests and probability distribution fittings are conducted to extract statistical features from the models of 128 buildings. The results indicate that each building exhibits unique statistical properties that cannot be generalized, emphasizing the need for data analysis for each building before using the data in decision-making processes.

An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the breeder's experience, leading to feed wastage and environmental pollution. To achieve precision in feeding, it is crucial to adjust the feed according to the fish's feeding state. Existing computer vision-based methods for assessing feeding intensity are limited by their dependence on a single spatial feature and manual threshold setting for determining feeding status constraints. These models lack practicality due to their specificity to certain scenarios and objectives. To address these limitations, we propose an integrated approach that combines computer vision technology with a Convolutional Neural Net-work (CNN) to assess the feeding intensity of farmed fish. Our method incorporates temporal, spatial, and data statistical features to provide a comprehensive evaluation of feeding intensity. Using computer vision techniques, we preprocessed feeding images of pearl gentian grouper, extracting temporal features through optical flow, spatial features via binarization, and statistical features using the gray-level co-occurrence matrix. These features are input into their respective specific feature discrimination networks, and the classification results of the three networks are fused to construct a three-stream network for feeding intensity discrimination. The results of our proposed three-stream network achieved an impressive accuracy of 99.3% in distinguishing feeding intensity. The model accurately categorizes feeding states into none, weak, and strong, providing a scientific basis for intelligent fish feeding in aquaculture. This advancement holds promise for promoting sustainable industry development by minimizing feed wastage and optimizing environmental impact.

Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode

This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing online adaptive FD. The goal of this study is to present an artificial intelligence (AI) technique for detecting seven faults: inverter fault, feedback sensor fault, grid anomaly, nonhomogeneous partial shading, open circuit in PV array, MPPT controller fault, and boost converter controller fault. In this work, it was found that the application of Extreme Learning Machine (ELM) plays an important role in fault detection and localization. Nine (9) statistical features and eight (8) wavelet packet parameters are extracted from the data based on multiple default values. These features were used as an input vector to train and test the ELM and determine whether the system is operating under normal conditions or is faulty. The BDE feature selection algorithm is adopted to optimize the seven-fault classification procedure to reduce the number of features. The results showed that the Extreme Learning Machine (ELM), based on statistical parameters followed by BDE, can detect faults with high accuracy (98.3%) compared to a case without optimization.

Overlapping microbubble localization based on multiscale statistical features for ultrasound super-resolution imaging

Ultrasound localization microscopy enables visualization of the microvasculature through the localization and tracking of isolated microbubbles (MBs). Currently, improving the precision for localizing overlapping MBs at high concentration to decrease the acquisition time remains a challenge. This study introduces multiscale statistical feature localization (MSFL) to address this challenge. First, the overlapping MBs are separated by local energy and blobness filters constructed from the Hessian matrix of texture features and its eigenvalues. Then, multiscale and orientation kernels are modeled to adapt to the appearance of various MBs; their amplitude and gradient convolution response results jointly determine the credibility of the MBs. Finally, precise MB localization is obtained by the weighted average of the local maxima of the response maps at each scale to achieve super-resolution (SR) imaging. The proposed method is evaluated using simulation and in-vivo datasets. Simulation results demonstrate that MSFL can adapt to asymmetric Gaussian distributions and improve overlapping MB localization, with a 9.28 % improvement in localization precision and a 5 % greater Jaccard index compared to the Gaussian fitting method. Additionally, the strong robustness of MSFL under noise interference maintains its ability to locate more MBs, and reconstruct the same saturation vessels with 36 % acquisition time reduction compared to Radial symmetry method. For SR images, MSFL can draw smoother interlaced vessels when more overlapping MBs are present, and quickly reconstructs more saturated in vivo microvasculature with a resolution is 10.5 μm(λ/10). These results validate the advantages of MSFL in achieving precise SR imaging with a short acquisition time.

Weighted joint LRTs for cooperative spectrum sensing using K-means clustering

In this paper, a blind centralized cooperative spectrum sensing (CSS) with soft decision fusion is considered. The fusion center (FC) constructs the energy vector from the collaborating secondary nodes. The energy feature is assumed to be an efficient measure, as it is widely used and directly correlates with the strength of the received signal. The K-means clustering algorithm is employed to extract descriptive statistical features about the distributions of the absence and presence of the primary user (PU) signal, such as the mean and non-centrality parameter. In the framework of this statistical analysis, the signal-to-noise ratio (SNR) at each node is easily estimated and examined. Equal, selective, and weighted combining techniques are applied to develop three joint likelihood ratio test (JLRT)-based algorithms. These algorithms are implemented in the context of simple hypothesis testing, where the distribution of the data is fully specified. Furthermore, they are justified by the Neyman-Pearson theorem, which constructs the most powerful test for a given significance level. The proposed selective and weighted JLRT approaches are based on the estimated SNRs at each sensor, reflecting their reliability. Several comparison scenarios between the proposed algorithms, K-means, fuzzy c-means (FCM), and OR-rule are simulated over Rayleigh fading channel with low average SNR and few samples. The simulation results reveal that the proposed tests outperform other CSS techniques. Additionally, asymptotic theoretical expressions for probability of detection and the probability of false alarm are derived, which show high agreement with the simulated results.

The entropy-transformed Gompertz distribution: Distributional insights and cross-disciplinary utilizations

A novel two-parameter continuous model titled the entropy-transformed Gompertz (ETGPZ) distribution has been developed via the entropy transform. A new framework has been investigated and found to meet the criteria of the probability function. By significantly improving the functional shape and having the ability to model the most likely form of the hazard rate function, this new modification has increased the adaptability of the typical distribution. Some of its core characteristics, such as its statistical and computational features, are clearly presented. A thorough simulation analysis has been done to examine the final behavior of maximum likelihood estimators while estimating model parameters. We assess the performance and practical applicability of the ETGPZ distribution using eight real datasets from engineering and biomedical fields. The results demonstrate that the ETGPZ outperforms the baseline Gompertz (GPZ) distribution, highlighting its superiority and broader potential for various applications.

Enhancing machine learning multi-class fault detection in electric motors through entropy-based analysis

Abstract In the field of electric motor maintenance, this study introduces a transformative approach by integrating entropy-based algorithms with machine learning for enhanced multi-class fault detection. Employing Shannon, Renyi, and Tsallis entropy algorithms on standard fault detection measurements, the research significantly advances predictive maintenance strategies through a robust, early-indication, system-agnostic analysis. Detailed examination is conducted, comparing results derived from datasets that include statistical features (excluding entropy) against the proposed entropy-based datasets, when applied to a multi-layer perceptron classifier (MLPC). Optimization of the MLPC and all compared algorithms’ hyperparameters is done using the state-of-the-art Optuna tool to dynamically explore each search space, ensuring that each methodology performs adequately in a timely fashion while allowing for adaptation. The results showcase significant enhancement in classification accuracy of diverse electric motor operational states, facilitating the differentiation between healthy and various levels of fault conditions under assorted load scenarios. Computational analyses reveal favorable results related to execution time and memory overhead, thereby supporting the practicality in operations constrained by memory resources. Validation of the approach is achieved through laboratory experiments on a purpose-built test bench. Versatility of entropy-based measures through their proposed utilization in diverse fault indications is achieved by a demonstration in the field of mechanical fault detection with a focus on bearing faults through well-respected datasets.

Automation of vegetable production analysis in the Russian Federation using a modern information system

The presented study is aimed at solving the problems of information support for making effective management decisions in the field of vegetable growing in Russia based on the use of technologies and tools that automate complex algorithms of statistical analysis, which expands the range of users of up-to-date, reliable and complete information on the factors that shape the volumes and efficiency of production in the vegetable growing industries. The information system proposed by the authors, developed on the basis of the PyCharm2023.1 programming environment using the Python programming language, allows the user to obtain statistical characteristics of the phenomenon under study, apply methods of correlation and regression analysis, ANOVA to identify cause-and-effect relationships, modeling processes and phenomena in order to predict their development. The web application was developed taking into account officially published information on the conditions and results of vegetable production, and its operation has been tested. At the same time, the information system has the potential for development and can be adapted to any data set.