Search

Statistical analysis of the time-to-event scenarios commonly involves the use of the exponential and Rayleigh distributions. However, the exponential distribution is constrained by its constant failure rate, while the Rayleigh distribution exhibits a growing (or increasing) failure rate. The linear failure rate distribution provides a broader perspective by encompassing both of these distributions, including the possibility of a non-increasing hazard function. In this paper, a new distribution is introduced, which could be considered as one of the major modifications to the linear failure rate distribution, known as the weighted linear failure rate distribution. The model proposed illustrates both monotonic and a known non-monotonic failure rate. This paper extensively explores the characteristics of the proposed distribution. It presents the maximum likelihood estimates for the unknown parameters and includes a simulation study. The analysis focuses on three real data sets obtained from the fields of musical education and engineering. By employing three decisive tools, it is observed that the current distribution provides a superior fit in comparison to several other widely recognized distributions.

Abstract This paper investigates a novel category of probability distributions and a specific member within this category. We have formulated a new family of trigonometric distributions by utilizing the odds ratio derived from the distribution function of a base distribution. This newly devised distribution family termed the “Sine pie-power odd-G family” of distributions, is constructed through a transformation involving the sine function. The paper presents an overview of the fundamental characteristics inherent to this proposed distribution family. Using the Weibull distribution as a base reference, we have introduced a member belonging to the proposed distribution family. This member demonstrates various hazard functions such as j, reverse-j, increasing, decreasing, or bathtub shapes. The paper examines essential statistical attributes of this newly introduced distribution. The estimation of the distribution’s parameters is carried out via the maximum likelihood estimation method. The accuracy of the parameter estimation procedure is validated through Monte Carlo simulations. The outcomes of these simulations reveal a reduction in biases and mean square errors as sample sizes increase, even for small samples. Two sets of real-engineering data are considered to demonstrate the proposed distribution’s applicability. The performance of the suggested distribution is evaluated using some model selection criteria and goodness-of-fit test statistics. Empirical evidence from these evaluations substantiates that the proposed model outperforms six existing models.

Battery Management System (BMS) including measurements errors that causes decrease in ‎the quality of ‎calculated State of the Charge (SOC). It will limit the accurate estimation of ‎the SOC that is a critical challenge in ‎some of the engineering fields such as medical science, ‎robotics, navigation and industrial applications. These ‎facts implies on the significance of ‎SOC estimation from battery measurements that is the matter of the literature ‎through the ‎recent years. Due to the dependency of the EKF to the system model, the change in the ‎battery ‎parameters and noise information cause losing performance in the SOC estimation ‎over the time. In this paper, we ‎assume that the battery parameters including internal ‎resistance and capacitor and also the noise information are ‎varying over the time. To solve ‎that, two separate on-line identification algorithms for parameters and noise ‎information are ‎introduced. In more details, a Recursive Least Square (RLS) algorithm is used to identify ‎the ‎resistance and capacitor values. Moreover, the process and measurement noise covariance are ‎estimated based ‎on iterative noise information identification algorithm. Then all of the ‎updated values are used in the EKF ‎algorithm. This paper aims to address the issue of uncertainty in SOC estimation by proposing two algorithms. ‎The first algorithm focuses on identifying deterministic uncertainty, which refers to uncertainty in model ‎parameters. To address the challenge of uncertain model parameters, RLS is introduced.

Sensors calibration plays a crucial role in controlling systems and achieving fault-tolerant control by ensuring accuracy, performance, safety, energy efficiency, and compliance with standards. It is an essential to maintain the reliability and effectiveness of modern control systems across various applications. In this paper, we represent a new algorithm that processes a set of raw data collected by a sensor to find the mapping function that relates the raw data to the real value of the measured signal by the sensor. Working on sensors with an unknown mapping function, unknown parameters, or with external disturbances, that affects their behaviour, represents a problem; moreover, it takes a lot of time and effort to calibrate the sensor before each use. Several techniques were used to overcome these aspects mostly by recording the output of the sensor for different input values that change manually, to calibrate the sensor. However, the represented technique in this paper can easily provide us with the input/output model of a specific sensor by doing only one experiment; it also improves the accuracy of the measurements as it is a self-calibrating technique that reduces the nonlinearity and noise problems to deliver a better estimation of the measured signal, which is validated in this paper experimentally using a low-cost current sensor by comparing the obtained results from this algorithm with the results using the extracted input/output model illustrated in the datasheet. Furthermore, if the sensor is pretty poor, and if the application requires more precision, the provided estimation by the mapping function can be mixed with other sensor/s readings using sensor fusion algorithms to find a more precise value of the input. The represented algorithm can also perform self-calibration while evaluating the functionality of the application and the variations of the temperature and other external disturbances that affect the sensor.

The purpose of this paper is to review the scientific literature on direct load control (DLC) technology in the context of a smart grid (SG). This study synthesized and compared various articles from the literature with the aim of conducting a comprehensive analysis. This study aims to provide a detailed understanding of the landscape, recent advancements, and the relationships among various aspects within this field. The evaluation is conducted through a systematic literature review, examining recent advancements in the field. A survey of over 200 papers published between 1983 and 2022 is classified based on appliance information, aim/objective, constraint, management method and tool, implementation, and publication status. In the DLC application, the study identifies the type and population of appliances used in and the aim/objective and constraint considered. It also determines whether forecast, prediction, and estimation structures, optimization and control algorithms, and specific structures are employed. The practical aspect of the study examines various perspectives, including the modeling and presentation of the research. This involves identifying the validation methods, time period for implementation, solver, program, software, toolbox, and platform structures used during validation. Additionally, the characteristics of the database used for implementation and the location of the implementation are detailed. The publication year and the countries of the authors are also provided. Comparative relationships between these elements are presented based on the aforementioned information.

Abstract Several experimental evidences and field data documented that zooplankton may alter its behavioral response in the presence of toxic phytoplankton, reducing its consumption to the point of starvation. This paper is devoted to the mathematical study of such interactions of toxic phytoplankton with grazer zooplankton. The non-toxic phytoplankton is assumed to adopt a density-dependent refuge strategy to avoid over-predation by zooplankton. Both groups of phytoplankton are assumed to suffer direct harm from anthropogenic toxicants, while zooplankton is affected indirectly by ingesting contaminated phytoplankton. We calibrate the proposed model with the field data from Talsari and Digha Mohana, India, and estimate some crucial model parameters consistent with the behavior of the observed data. Our results demonstrate that zooplankton grazing on toxic phytoplankton plays a key role in the emergence or mitigation of plankton blooms. We also highlight the system’s potential to exhibit multiple stable configurations under the same ecological conditions. The plankton system experiences significant regime shifts, which are explored through various bifurcation scenarios, such as transcritical and saddle-node bifurcations. These shifts are influenced by changes in refuge capacity, species growth rates, and environmental carrying capacity. Furthermore, we incorporate environmental variations due to seasonal periodic or almost periodic changes, allowing the refuge parameter to be time-dependent. We observe that the forced system exhibits double periodic solutions. Moreover, stronger seasonal variations in the refuge pattern lead to irregular chaotic blooms. In conclusion, the results offer valuable insights into the sustainability of biodiversity, potentially shedding light on the origin of diverse plankton bloom phenomena.

The assessment of the load-bearing capacity of piles holds significant importance in the design of pile foundations. This paper presents hybridized support vector regression (SVR) models by utilizing the Artificial Rabbit Optimization (ARO) and the Black Widow Optimization Algorithm (BWOA) to predict the bearing capacity of concrete piles. A repository comprising 472 reports on static load tests conducted on driven piles was employed for the study. The dataset was allocated into three parts: the training set (70%), the validation set ( 15% ), and the testing set ( 15% ). Multiple criteria for assessing quality were utilized to evaluate the effectiveness of the models. The first rank belonged to the SVR model integrated with the ARO algorithm, where it could gain the higher value of R^2 in all of training (R^2 = 0.9876), validating (R^2 = 0.9778), and testing sections (R^2 = 0.9874), and the lowest value of RMSE in all the training ( RMSE = 39.393 ), validating ( RMSE = 53.727 ) and testing sections ( RMSE = 38.082). The findings indicate that the suggested model is highly appropriate for predicting the capacity of concrete piles.

This paper presents an automated tool for comparative estimates of earthmoving productivity, focusing on excavation operations. The developed system includes three modules (computational, data storage, operational) and a series of forms that estimate construction productivity for three types of equipment (excavator, loader, truck) based on fourteen estimation methodologies. To the authors’ best knowledge, it is the first research attempt at developing an automated tool based on the comparative evaluation of such a diverse set of estimation methodologies. All system components have been developed in a Microsoft (MS) environment, thus taking advantage of their user-friendly nature. A numerical example corroborated the tool’s validity. Future research may optimize the module integrated into the developed application to assist construction estimators in reaching informed decisions when deploying critical project resources.

In this research paper, we present an innovative investigation into a novel two parameter semicircular distribution, termed the “stereographic semicircular Erlang distribution,” which is constructed using the inverse stereographic projection (ISP) technique. This distribution serves as advancement over the existing stereographic semicircular exponential distribution. We delve into essential mathematical properties of this distribution and execute a simulation study to estimate its parameter values. Furthermore, we perform an empirical analysis utilizing a dataset comprising posterior corneal curvature measurements extracted from the eyes of 23 patients. This empirical assessment is designed to evaluate the adaptability and potential applicability of the proposed distribution within the realm of ophthalmology in medical science.

Building and other construction assets constitute a significant part of a country’s physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries’ specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980–2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country’s construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development.

Abstract In the count data set, the frequency of some points may occur more than expected under the standard data analysis models. Indeed, in many situations, the frequencies of zero and of some other points tend to be higher than those of the Poisson. Adapting existing models for analyzing inflated observations has been studied in the literature. A method for modeling the inflated data is the inflated distribution. In this paper, we extend this inflated distribution. Indeed, if inflations occur in three or more of the support point, then the previous models are not suitable. We propose a model based on zero, one, $$\ldots ,$$ … , and k inflated points with probabilities $$w_{0},w_1,\ldots ,$$ w 0 , w 1 , … , and $$w_{k},$$ w k , respectively. By choosing the appropriate values for the weights $$w_{0},\ldots ,w_{k},$$ w 0 , … , w k , various inflated distributions, such as the zero-inflated, zero–one-inflated, and zero–k-inflated distributions, are derived as special cases of the proposed model in this paper. Various illustrative examples and real data sets are analyzed using the obtained results.

This article explores the mathematical and statistical performances and connections of the two well-known ordinary least-squares estimators (OLSEs) and best linear unbiased estimators (BLUEs) of unknown parameter matrices in the context of a multivariate general linear model (MGLM) for regression, both of which are defined under two different optimality criteria. Tian and Zhang [38] once collected a series of existing and novel identifying conditions for OLSEs to be BLUEs under general linear models: On connections among OLSEs and BLUEs of whole and partial parameters under a general linear model, Stat. Probabil. Lett., 112 (2016), 105–112. In this paper, we show how to extend this kind of results to multivariate general linear models. We shall give a direct algebraic procedure to derive explicit formulas for calculating the OLSEs and BLUEs of parameter spaces in a given MGLM, discuss the relationships between OLSEs and BLUEs of parameter matrices in the MGLM, establish many algebraic equalities related to the equivalence of OLSEs and BLUEs, and give various intrinsic statistical interpretations about the equivalence of OLSEs and BLUEs of parameter matrices in a given MGLM using some matrix analysis tools concerning ranks, ranges, and generalized inverses of matrices.

In recent years, the modeling of time-to-events has emerged as a highly promising and dynamic research area. This field has witnessed a surge of research studies dedicated to developing novel statistical methodologies aimed at effectively handling time-to-event phenomena. These studies are motivated by the increasing recognition of the importance of time-related factors in various fields such as medicine, epidemiology, finance, and engineering. Researchers have been actively engaged in proposing innovative approaches to address the complexities associated with time-to-event data. The overarching goal is to enhance our understanding of event occurrence and duration, enabling more accurate predictions and informed decision-making. This research encompasses a wide range of topics, including survival analysis, reliability modeling, and event prediction. The motivation behind these research efforts stems from the need to overcome traditional limitations in time-to-event analysis and to explore new avenues for modeling and interpretation. By introducing advanced statistical techniques, researchers seek to capture the intricate dynamics of event processes, considering factors such as censoring, competing risks, and time-varying covariates. The proliferation of research studies in this domain reflects a collective effort to push the boundaries of statistical modeling and analysis, paving the way for more comprehensive and robust methodologies. As researchers continue to delve deeper into the intricacies of time-to-event data, the impact of these advancements extends to diverse applications, ultimately fostering innovation and progress across interdisciplinary fields. This paper adopts and implements a new statistical approach to propose a family of flexible distributions, namely, a new generalized-O family of distributions. For the newly obtained family, certain mathematical properties such as identifiability, quantile function, rth non-central moment, Lorenz curve, incomplete moments, and the expression of the Bonferroni curve are obtained. Furthermore, an extension of the Weibull model is introduced using the newly developed approach, namely, a new generalized Weibull model. The parameters of the new generalized version of the Weibull model are estimated by adopting a well-known estimation approach. Finally, a data set consists of sixty (60) observations representing the times of the survival of some patients infected by the COVID-19 epidemic is analyzed to illustrate the new generalized Weibull model.

This study investigates the correlation between the California Bearing Ratio (CBR) and the Dynamic Cone Penetrometer (DCP) for subgrade soil analysis. The paper aims to provide practical equations for predicting CBR values from DCP test results, therefore enhancing the efficiency of soil assessments in engineering practice. By analyzing test data and proposing correlations for different soil groups, the study introduces recalibrated correlations that demonstrate high accuracy in predicting CBR values. The newly proposed equations offer reliable predictions with R2 values of 0.89, 0.92, and 0.94 for clean sand, silty sand or sandy silt, and cohesive soil, respectively. These correlations serve as valuable tools for engineers, enabling rapid and accurate CBR estimations for improved decision-making in various engineering projects.

Rangelands or grasslands are the main nutrition sources of grazing ruminants. They are mainly consisted of legumes, grasses and other species. Although feeding and grazing behaviors of grazing animals in grasslands are complicated, estimating herbage intake (HI) of grazing animals is significant for both producers and range management. For this purpose, there are some physical, chemical, empirical and technological methods are used for estimating HI of grazing animals. Direct, clipping or herbage disappearance method based on herbage differences before and after grazing, marker techniques, some empirical equations based on organic matter digestibility or requirements of animals, faecal near-infrared reflectance spectroscopy (FNIRS) and remote sensing-global positioning system technologies are the methods used for estimation of individual or herd HI. All methods have different features, deficiencies and conveniences. In this paper it was aimed to discuss about advantages, disadvantages, patterns of usage and potentials of these methods. In conclusion, technological methods such as remote sensing and FNIRS have the potential for displacing other methods in near future.

This paper presents a novel two-parameter distribution derived from the Rayleigh distribution, thoroughly investigating its essential mathematical properties. We employ estimation techniques to determine the proposed distribution's estimated parameters. Through extensive simulation studies, we analyze and evaluate the asymptotic behavior of the model estimators. Furthermore, we calculate various actuarial measures to highlight the practical utility of the proposed distribution in actuarial science. To further substantiate the applicability of our distribution, we perform a comprehensive regression analysis. The practical relevance of the proposed distribution is demonstrated by modeling a lifetime dataset from the insurance field, where it exhibits a superior fit compared to existing distributions. The findings suggest that the new distribution significantly improves modeling capabilities, making it a valuable tool for theoretical research and practical applications in fields requiring accurate lifetime data modeling.

This paper introduces the power new XLindley (PNXL) distribution, a novel two-parameter distribution derived using the power transformation method applied to the XLindley distribution. We thoroughly explore the structural properties of the PNXL distribution, including the rth moment about the origin, moment generating function, survival rate function, distribution function, hazard rate function, skewness, kurtosis, and coefficient of variation. Additionally, we derive the quantile function, fuzzy reliability, reliability measures, stochastic ordering, and actuarial measures for this new distribution. To estimate the parameters of the PNXL distribution, we propose several estimators and evaluate their performance through extensive simulation studies. To demonstrate the applicability and superiority of the PNXL distribution over existing distributions, we fit it to two real datasets and compare its performance with potential competing models. The results highlight the PNXL distribution's effectiveness and potential as a robust tool for modeling and analyzing real-world data.

Complementary Linear Filter (CLF) is a common techinque employed for estimating the ground projection of body Centre of Mass starting from ground reaction forces. This method fuses centre of pressure position and double integration of horizontal forces, selecting best cut-off frequencies for low-pass and high-pass filters. Classical Kalman filter is a substantially equivalent approach, as both methods rely on an overall quantification of error/noise and don’t analyze its origin and time-dependence. In order to overcome such limitations, a Time-Varying Kalman Filter (TVKF) is proposed in this paper: the effect of unknown variables is directly taken into account by employing a statistical description which is obtained from experimental data. To this end, in this paper we have employed a dataset of 8 walking healthy subjects: beside supplying gait cycles at different speeds, it deals with subjects in age of development and provides a wide range of body sizes, allowing therefore to assess the observers’ behaviour under different conditions. The comparison carried out between CLF and TVKF appears to highlight several advantages of the latter method in terms of better average performance and smaller variability. Results presented in this paper suggest that a strategy which incorporates a statistical description of unknown variables and a time-varying structure can yield a more reliable observer. The demonstrated methodology sets a tool that can undergo a broader investigation to be carried out including more subjects and different walking styles.

Abstract Many issues in real life are riddled with confusion, vagueness, and ambiguity. The Topp-Leone distribution is a significant one-parameter probability distribution in the context of classical probability theory. There is a gap in the literature when it comes to dealing with circumstances involving interval-valued data with a classical Topp-Leone distribution. So, in this connection, neutrosophic Topp-Leone distribution is presented in this paper as an extension of the traditional Topp-Leone distribution. The new neutrosophic distribution takes into account the indeterminacy and crisp form of interval-valued distributions. The suggested distribution's mathematical features were derived, including moments and related measures, quantile function, survival, hazard, inverted hazard functions, and mills ratio. Maximum likelihood is used to estimate model parameters. Finally, a complex dataset is utilized to show the usefulness of the proposed distributions.

A recent paper introduced the interpolating family (IF) of distributions, and they also derived various mathematical properties of the family. Some of the most important properties discussed were the integer order moments of the IF distributions. The moments were expressed as an integral (which were not evaluated) or as finite sums of the beta function. In this paper, more general expressions for moments of any integer order or any real order are derived. Apart from being more general, our expressions converge for a wider range of parameter values. The expressions for entropies are also derived, the maximum likelihood estimation is considered and the finite sample performance of maximum likelihood estimates is investigated.

Vietnam's current bridge design code is based on AASHTO LRFD Specifications. Many field survey projects are being conducted to localize some environmental action data. This paper presents a temperature monitoring project on three large concrete box girder bridges in different climate regions of Vietnam, which have recently seen increased construction in the country. Additionally, some results of a data regression analysis to build predictive formulas on temperature distribution in such bridges are also introduced. In this data analysis, the solar radiation is calculated based on the air temperature. The good agreement of prediction and monitoring data shows that this method is appropriate, especially for Vietnam’s condition, where the air temperature data are available for every region but not the solar radiation data. Furthermore, an estimation of extreme values for temperature gradients in the top and web slabs, as well as in the entire section, using the Generalized Pareto Distribution (GPD) has been conducted. This estimation indicates that webs in box girders can be subjected to very large horizontal temperature gradients.

Streamflow, a pivotal variable in water resources management, holds profound significance in shaping the decision-making processes of hydrologic projects. This paper tries to delve into the exploration of the stage-discharge relationship using three machine learning methods (MLMs) namely multi-layer neural networks (MLNN), radial basis neural networks (RBNN), and neuro-fuzzy systems (ANFIS) to predict and simulate mean daily stage-discharge data derived from two monitoring stations, Bulakbasi and Karaozü, Kizilirmak River, Turkey. Root mean square error (RMSE), Mean absolute percentage error (MAPE), coefficient of determination (R2), and the Developed Discrepancy Ratio (DDR) metrics were utilized to MLMs' performance assessment. The performance evaluation indices (RMSE, MAEP, R2, DDR) for the preeminent MLNN model applied to Bulakhbashi and Karasu stations were determined as (0.29, 1.57, 0.9998, 17.62) and (1.71, 6.56, 0.9980, 6.65), respectively. The MLNN model contributed to a notable enhancement in the RMSE performance index for the aforementioned stations, exhibiting improvements of 87% and 56%, respectively. These results affirm the MLNN's proficiency in accurately capturing the stage-discharge at both monitoring stations.

The log-normal distribution is widely used to model positive valued data in many areas of applied research. However, sometimes the log-normal distribution does not completely satisfy the fitting expectations in every real life situations. In this paper, we introduce, investigate, and discuss a more flexible shifted hybrid log-normal distribution for which the log-normal distribution is a special case. Also, various properties, special cases and estimation procedure of the new distribution are discussed. Moreover, the performances of maximum likelihood estimators of the parameters are examined using a brief simulation study. The flexibility and performance of the newdistribution is also illustrated through two applications by fitting two real datasets of different situations.

The Hessian matrix conveys important information about the curvature, spectrum and partial derivatives of a function, and is required in a variety of tasks. However, computing the exact Hessian is prohibitively expensive for high-dimensional input spaces, and is just impossible in zeroth-order optimization, where the objective function is a black-box of which only input-output pairs are known. In this work we address this relevant problem by providing a rigorous analysis of an Hessian estimator available in the literature, allowing it to be used as a provably accurate replacement of the true Hessian matrix. The Hessian estimator is randomized and incremental, and its computation requires only point function evaluations. We provide non-asymptotic convergence bounds on the estimation error and derive the minimum number of function queries needed to achieve a desired accuracy with arbitrarily high probability. In the second part of the paper we show a practical application of our results, introducing a novel optimization algorithm suitable for non-convex and black-box federated learning. The algorithm only requires clients to evaluate their local functions at certain input points, and builds a sufficiently accurate estimate of the global Hessian matrix in a distributed way. The algorithm exploits inexact cubic regularization to escape saddle points and guarantees convergence with optimal iteration complexity and high probability. Numerical results show that the proposed algorithm outperforms the existing zeroth-order federated algorithms in both convex and non-convex problems. Furthermore, we achieve similar performance to state-of-the-art algorithms for federated convex optimization that use exact gradients and Hessian matrices.

ABSTRACTAlthough the surface energy budget is essential to determine Earth’s climate, site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations. This has led to the development of a growing number of surface radiation products, mainly including remotely sensed data, model reanalysis data, and simulations using General Circulation Models (GCMs). This collection of papers introduces new techniques, including the use of machine learning methods for radiation estimation, and evaluates and compares various radiation products, as well as their spatio-temporal variations. These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations. However, remotely sensed radiation products perform relatively better than others. Despite this, there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates. Even though the major radiative parameters including downward shortwave radiation, net longwave radiation, and albedo, from most products show insignificant long-term variation trends on a global scale, only specific regions, such as the Yunnan-Kweichow Plateau (YKP) and regions with permafrost (i.e. Qinghai-Tibet Plateau and Arctic) and glaciers (i.e. Altai Mountains) exhibit remarkable trends.

Inertial sensors have become increasingly popular in human activity classification due to their ease of use and affordability. This paper proposes a novel algorithm for human activity recognition that is a combination of a high-gain observer and deep learning computer vision classification algorithms. The nonlinear high-gain observer designed using Lyapunov analysis accurately estimates the attitude of the chest of a human subject using measurements from a single Inertial Measurement Unit (IMU). The signals processed by the observer are then converted into spectrograms to obtain “images” of the frequency response of the signals. The images for activities from a dataset of 7 human subjects are annotated and used for training/ fine-tuning of several well-known deep learning algorithms for image processing. The results from the best combination of our algorithms shows an exceptional accuracy of 98% for activity recognition. Using deep learning computer vision algorithms, this paper shows how to perform transfer learning from networks pre-trained on millions of images, thus showing how we can train a powerful deep learning network for activity recognition even with just small datasets. The algorithm that uses the high gain observer is shown to perform significantly better than an algorithm based on raw accelerometer and gyro signals.

Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation in a subway station. Our sensing solution uses the change in attenuation of the communication links between sensor nodes to estimate the number of people standing on the platform. In order to achieve this, we use the same attenuation information coming from the WSN to detect the presence of a rail vehicle in the station and compensate for the channel fading caused by the introduced rail vehicle. We make use of two separately trained regression models depending on the presence or absence of a rail vehicle to estimate the people count. The detection of rail vehicles occurred with a near-perfect accuracy. When evaluating the resulting estimation model on our test set, we achieved a mean average error of 3.567 people, which is a significant improvement over 6.192 people when using a single regression model. This demonstrates that device-free sensing technologies can be successfully implemented in dynamic environments by implementing detection techniques and using different regression models depending on the environment’s state.

High-speed dual-motor electric drive tracked vehicles (DDTVs) have emerged as a research hotspot in the field of tracked vehicles in recent years due to their advantages in fuel economy and the scalability of electrical equipment. The emergency braking of a DDTV at high speed can lead to slipping or even yawing (which is caused by a large deviation of forces at each track directly), posing significant challenges to the vehicle’s stability and safety. Therefore, the accurate real-time acquisition of critical driving parameters, such as the longitudinal force and vehicle speed, is crucial for the stability control of a DDTV. This paper developed a novel estimating algorithm of critical driving parameters for DDTVs equipped with conventional sensors such as rotary transformers at PMSMs and onboard accelerometers on the basis of their dynamics models. The algorithm includes a sensor signal preprocessing module, a longitudinal force estimation method based on a nonlinear observer, and a speed estimation method based on an adaptive Kalman filter. Through hardware-in-loop experiments based on a Speedgoat real-time target machine, the proposed algorithm is proven to estimate the longitudinal force of the track and vehicle speed accurately, whether the vehicle has stability control functions or not, providing a foundation for the further development of vehicle stability control algorithms.

This paper is the first one from the coming soon articles with a description of the results and findings of the project “Tuning of educational structures”. The article highlights the main goals and results of the project “Tuning Russia” conducted in Russia in the period of 2010–2013. Special attention is paid to the basic approaches of the drafting the Reference points of the educational programs in different subject areas. Some findings from the survey of the opinion of academy, alumni, students and employers on the importance and achieved level of the professional competencies in Economics are given. The paper describes several kinds of activities to be done by the Tuning centers were established in 13 Russian universities participated in the project “Tuning Russia”.

Most conventional data association based multi-target tracking (MTT) algorithms typically suffer from intractable computational complexities and could not perform in an environment where large number of closely spaced multiple targets move across each other in clutters. Unlike to the existing MTT systems, the linear multi-target (LM) algorithm modifies the measurement detection followed by neighbored tracks as a clutter, hence, it updates the track without the influence of other tracks. Thus, LM technique is a computationally efficient algorithm that allows the multi-target system to play like a single target tracking algorithm. Smoothing maximizes the state estimation accuracy and reduces estimation error based on future scan measurement. However, only few research paper focused on the LM algorithm without utilizing the benefits of the smoothing. This paper presents Rauch-Tung-Striebel Smoothing in the linear multi-target based on integrated probabilistic data association (RTS-LMIPDA). The RTS-LMIPDA algorithm fuses forward and backward LM track predictions to obtain the smoothing prediction which is required to calculate the smoothing multi-target state estimates in the forward track. Numerical analysis is presented to illustrate the estimation accuracy and false track discrimination (FTD) performances of RTS-LMIPDA in comparison to the existing MTT algorithms using the simulations.

The objective of this paper is to present the properties and potentials of Gumbel Power function (GuPF) distribution to rainfall and wind speed datasets using the T-X methodology. The density and hazard rate function of the GuPF distribution are unimodal and increasing respectively. Statistical properties of the new distribution such as quantile, moments, and probability weighted moments (PWMs), order statistics and entropy are derived. The Maximum likelihood estimation method is used to estimate the parameters of the proposed model. The superiority of GuPF distribution over other distributions with the same baseline is illustrated using two environmental datasets.

Most modern large-scale multi-agent systems operate by taking actions based on local data and cooperate by exchanging information over communication networks. Due to the abundance of sensors, each agent is capable of generating more data than what could be supported by communication channels in near real-time. Thus, not every piece of information can be transmitted perfectly over the network. Such communication constraints lead to a large number of challenging research problems, some of which have been solved, and many more that remain open. The focus of this paper is to present a comprehensive treatment of this new class of fundamental problems in information dissemination over networks, which is based on the notion of extremum information. The unifying theme herein is that the strategic communication, i.e., when the agents decide on what to transmit based on their observed data (or state), leads to the optimality of extremum (or outlier) information. In other words, when a random information source deviates from the average by a certain amount, that realization should be prioritized for transmission. This creates a natural ranking of the data points based on their magnitude such that if an agent has access to more than one piece of information, the ones that display the largest deviation from the average are transmitted and the rest is discarded. We show that the problem of finding the top-K largest measurements over a network can be cast and efficiently implemented as a distributed inference problem. Curiously, the same principle holds for the framework of distributed optimization, leading to a class of state-dependent protocols known as max-dissent. While still a heuristic, max-dissent can considerably accelerate the convergence to an optimal solution in distributed convex optimization. We provide open problems and present several directions for future work including questions related to cyber-security and robustness of such networks as well as new architectures for distributed learning and optimization.

Owing that ensuring true representative samples from a population is dependent on sampling via sample estimators, in this paper, we established the sample maximum likelihood (ML) estimators of the location (μ), scale (σ2) and shape (β) parameters of Log-Exponential Power Distribution (LEPD), under simple random sampling (SRS), ranked set sampling (RSS) and another introduced sampling called general RSS (GRSS). The study then obtained the Fisher information (FI) of the obtained estimators under different cases: when one parameter, two parameters and when all the parameters are unknown, for each of the sampling procedure and compute its asymptotic relative efficiency (RE). The FI estimate, when GRSS samples are obtained from ith or (ith,jth) ordered samples, were established along with its RE. Application from simulated and real life data with imperfect units ranking was used to demonstrate the relevance of the study, in terms of sampling efficiency within the shortest length of interval estimate. Application of GRSS to the Food and Agricultural Organization (FAO) projected data on cattle meat boom by 2050 was considered, the implication of the sudden increase in cattle population in Nigeria and the recommendation to prevent subsequent crises due to competition for land among farmers, herders and other populace was analytically established. Finally, the procedure was used to establish the cause of increased suicide rate in Nigeria has recently published by World Health Organization (WHO).

The Laplace distribution, also known as the double exponential distribution, is a continuous probability distribution that is often used for modelling the data having heavy tails. In this paper, we proposed the Neutrosophic Laplace distribution which is the extension of the classical Laplace Distribution. We derived various statistical properties of the Neutrosophic Laplace Distribution such as mean, variance, skewness, rth moment, quartiles, and moment-generating function. The expressions for the estimation of the parameters are also derived using the maximum likelihood function of the distribution. A simulation study has been done to evaluate the performance of estimates. An application of the Neutrosophic Laplace Distribution is discussed to study the daily return of the NIFTY50 from Indian Stock Market. The analysis shows that the neutrosophic Laplace Model is acceptable, effective, and adequate for dealing with uncertainty in an unpredictable context.

The Laplace distribution, also known as the double exponential distribution, is a continuous probability distribution that is often used for modelling the data having heavy tails. In this paper, we proposed the Neutrosophic Laplace distribution which is the extension of the classical Laplace Distribution. We derived various statistical properties of the Neutrosophic Laplace Distribution such as mean, variance, skewness, rth moment, quartiles, and moment-generating function. The expressions for the estimation of the parameters are also derived using the maximum likelihood function of the distribution. A simulation study has been done to evaluate the performance of estimates. An application of the Neutrosophic Laplace Distribution is discussed to study the daily return of the NIFTY50 from Indian Stock Market. The analysis shows that the neutrosophic Laplace Model is acceptable, effective, and adequate for dealing with uncertainty in an unpredictable context.

In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery pack’s SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the models’ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower.

In this paper, a new distribution of the Muth-generated family is introduced by considering the Pareto model as baseline with the goal of having increased flexibility and improved goodness of fit in terms of studying tail characteristics. Maximum likelihood estimated parameters of the distribution were found to be consistent and asymptotically unbiased. From a practical point of view, it is shown that the proposed distribution is more flexible than some common statistical distributions. In particular, the proposed model proved to fit well into unimodal data structures. Some mathematical properties were derived, and characterization investigated by truncated first moment where a product of reverse hazard rate and another function of the truncated point is considered. Other characterizations by order statistics and upper record values based on the characterization by the first truncated moment were also established.

The COVID-19 series is obviously one of the most volatile time series with lots of spikes and oscillations. The conventional integer-valued auto-regressive time series models (INAR) may be limited to account for such features in COVID-19 series such as severe over-dispersion, excess of zeros, periodicity, harmonic shapes and oscillations. This paper proposes alternative formulations of the classical INAR process by considering the class of high-ordered INAR models with harmonic innovation distributions. Interestingly, the paper further explores the bivariate extension of these high-ordered INARs. South Africa and Mauritius’ COVID-19 series are re-scrutinized under the optic of these new INAR processes. Some simulation experiments are also executed to validate the new models and their estimation procedures.

State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset, resulting in SOC and SOH estimations over the entire battery life. The results show that the dataset can be used to extract parameters, design models based on it, and validate it with dynamically degraded battery cells. The work provides an approach and dataset for better performance evaluations, applicability, and reliability investigations.

When data exhibit a high frequency of small to medium values and a low frequency of large values, fitting a classical distribution might fail. This is why spliced models defined from different distributions on distinct intervals are proposed in the literature. In contrast to the intensive study of two-spliced distributions, the case with more than two components is scarcely approached. In this paper, we focus on three-spliced distributions and on their ability to improve the modeling of extreme data. For this purpose, we consider a popular insurance data set related to Danish fire losses, to which we fit several three-spliced distributions; moreover, the results are compared to the best-fitted two-spliced distributions from previous studies.

This paper proposes a variance upper bound based interval Kalman filter that enhances the interval Kalman filter based on the same principle proposed by Tran et al. (2017) for uncertain discrete time linear models. The systems under consideration are subject to bounded parameter uncertainties not only in the state and observation matrices, but also in the covariance matrices of the Gaussian noises. By using the spectral decomposition of a symmetric matrix and by optimizing the gain matrix of the proposed filter, we lower the minimal upper bound on the state estimation error covariance for all admissible uncertainties. This paper contributes with an improved algorithm that provides a less conservative error covariance upper bound than the approach proposed by Tran et al. (2017). The state estimates are determined using interval analysis in order to enclose the set of all possible solutions of the classical Kalman filter consistent with the uncertainties.

AbstractIn this paper, we propose a tractable Kumaraswamy Bell exponential (KwBE) distribution as a submodel of the Kumaraswamy Bell-G family of distributions. Several well-established properties are obtained for the KwBE distribution, such as the linear functional representation, [Formula: see text]th moment, incomplete moment, moment generating function using Wright generalized hyper-geometric function, conditional moment and Réyni entropy. Based on the KwBE model, a group acceptance sampling plan (GASP) for the truncated life test is presented using median life as a quality index. Moreover, the essential design parameters are derived by defining the consumer risk and the test termination duration. The comparative study of GASP with ordinary sampling plan (OSP) is performed. A simulation study is performed to highlight the behavior of the estimates. On the inferential side, the associated parameters are estimated using a well-established maximum likelihood estimation method. The detailed model’s comparison analysis, graphical as well as numerical evidence to real-data applications, supports the theoretical work.

This paper proposes an improved fast charging strategy for electric vehicles (EVs) by considering available battery capacity. According to previous research and battery experiment reports, the energy capacity of batteries is not fixed, and it can decrease temporarily depending on the magnitude of charging or discharging power. This paper addresses the decreased capacity in proportion to the magnitude of the charging power which leads to a reduction in the driving range. For effective and practical use for EV users, the change in battery capacity is expressed through an equation, and a new state of charged indicator is proposed. To improve the conventional charging method that uniformly supplies power regardless of the battery capacity, this paper proposes an optimal charging strategy injecting constant power. The proposed charging strategy provides an optimal charging power reference to minimize costs considering charged energy, charging time, and usable energy loss based on billing system of EV charging. To verify the effectiveness of the proposed charging method, the optimal charging power reference for each battery is calculated based on capacity and characteristics, and the total cost is compared. The results show that the performance of the proposed charging strategy is effective in minimizing both the reduced battery capacity and economic burden on EV users.

Predictive control has emerged as a promising control method in a variety of technological fields. Model predictive control, as one of the subdivisions of this control method, has found a growing number of applications in power electronics and motor drives. In practical implementations, model predictive control faces performance degradation of the controlled plant due to its dependency on a model. There are considerable numbers of review papers that are devoted to the different points of view of predictive control. However, the existing literature lacks a review study that addresses the solutions for parameter dependency of the model predictive control method. Recently, model-free predictive control has been used in drives and power electronics as a solution for dealing with the model-dependency of the model predictive control method. There are many papers that have used such methods. In this paper, a classification is proposed for the different implementation types of model-free predictive control or similar methods that address model parameter uncertainties. Additionally, a comparison between the methods is also presented.

In this work, we presented a new model called the alpha power inverse power Burr-Hatke distribution (APIPBHD). It provides several greater advantages in fitting a variety of different types of data. Estimates of the model parameters are provided and based on traditional research methods. We established the superiority of the proposed distribution by utilizing the importance and adaptability of the APIPBHD compared to other well-known distributions. The real data set includes 63 observations, all of which were manufactured to approximate the strengths of glass fibers to highlight the relevance and flexibility of the provided technique. We proved our superiority using one set of real data. Finally, major findings and conclusions are recorded at the end of the paper. Also, we added future work on the upcoming research depending on the proposed model.

The Weibull distribution has always been important in numerous areas because of its vast variety of applications. In this paper, basic properties of the neutrosophic Weibull distribution are derived. The effect of indeterminacy is studied on parameter estimation. The application of the neutrosophic Weibull distribution will be discussed with the help of two real-life datasets. From the analysis, it can be seen that the neutrosophic Weibull model is adequate, reasonable, and effective to apply in an uncertain environment.

The aim of this paper is to show the problems of implementing the wireless adaptive networks with the free space optical (FSO) technology. Implementing adaptive networks with the wireless optical communication technology has several benefits and also some hindering problems. The thermal optical noise modeled with Gaussian distribution and link turbulence is two of the major problems of this implementation. In this paper, the theoretical analysis of the FSO link effects that are modeled with K-distribution and Negative exponential distributions are considered on the estimation performance of the adaptive incremental networks. These distributions arise when the FSO link is contaminated with strong optical turbulence. Experiments are designed to cover these conditions and the analysis is based on the steady state mean square deviation (MSD) and excess mean square error (EMSE) values for the incremental LMS (ILMS) algorithm and these are the metrics that show how well the adaptive network performs. Simulation results are presented for different parameters of K -distribution and negative exponential distribution and the results show perfect match with the theoretical outcomes. Based on these results, we show that implementing the incremental adaptive networks in the strong turbulence conditions is not feasible and we must think of some countermeasures for these cases.

Hydrogen energy has become an important way to solve energy crises owing to its non-pollution, high level of efficiency, and wide application. Proton exchange membrane fuel cells (PEMFCs) have received wide attention as an energy conversion device for hydrogen energy. The hydrogen concentration in the PEMFC anode directly determines the output voltage of the stack. The performance of the PEMFC gradually decreases due to the accumulation of nitrogen. However, the continuous circulation of anode gas and the nitrogen accumulation at the anode due to transmembrane diffusion lead to difficulties in estimating the anode gas concentration. The relationship between anode nitrogen concentration and voltage variation characteristics was studied by increasing the anode hydrogen concentration through the method of increasing nitrogen concentration and conducting experiments on a 16-cell stack. In this paper, an estimation method for nitrogen concentration in the anode is proposed to evaluate the nitrogen concentration in the anode on the basis of voltage variation characteristics, and the method was recalibrated and validated using experimental data. Due to the inhomogeneity of the gas distribution within the PEMFC stack, the mean cell voltage can provide a more accurate estimation of the anode nitrogen concentration compared to a single cell voltage. It is shown that the proposed approach can offer a new method to estimate anode nitrogen concentration. Compared with the conventional method, the new method is simpler as it does not require additional equipment or complex algorithms. In this paper, the anode nitrogen concentration was estimated by applying this method with a maximum error of only 0.35%.

As the second-largest contributor to historical global warming, methane emissions must be controlled to slow down temperature increase and achieve climate benefits. Due to a lack of knowledge about the specific sources and processes, a quantitative approach will lead to inaccurate estimation. In this paper, a typical local distribution company with more than 100 years of operation history was chosen. Detailed procedures of pipeline constructions and accidents were investigated, and critical steps leading to methane emissions were clarified. Then emission quantification methodologies for all processes were proposed, including a new pipeline connection, new regulator connection, emergency repair and third-party damages. As a basis for emission estimation, the distribution of parameters, such as diameter, length and pressure, was counted. Then the emission rates of all projects were calculated, and emission factors were established. The average emission rates of the new pipeline connection, new regulator connection, emergency repair, third-party damage (medium pressure) and third-party damage (low pressure) were 234 kg, 147 kg, 217 kg, 17,282 kg and 62 kg, respectively. In addition, the total methane emission in China from these sources was estimated to be about 5 × 104 t, which is large enough to attract attention. The work in this paper aims to establish a reasonable framework to evaluate venting methane emissions from the distribution process of natural gas.

Objective: Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered as to whether it is possible to estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation from fNIRS can provide complementary information that can be used alongside the cerebral activity analysis, e.g., sport studies. Thus, the objective of this paper is to propose a method for RR estimation from fNIRS. Our primary presumption is that changes in the baseline wander of oxygenated hemoglobin concentration (O2Hb) signal are related to RR. Methods: fNIRS and respiratory signals were concurrently collected from subjects during controlled breathing tasks at a constant rate from 0.1 Hz to 0.4 Hz. Firstly, the signal quality index algorithm is employed to select the best O2Hb signal, and then a band-pass filter with cut-off frequencies from 0.05 to 2 Hz is used to remove very low- and high-frequency artifacts. Secondly, troughs of the filtered O2Hb signal are localized for synthesizing the baseline wander (S1) using cubic spline interpolation. Finally, the fast Fourier transform of the S1 signal is computed, and its dominant frequency is considered as RR. In this paper, two different datasets were employed, where the first one was used for the parameter adjustment of the proposed method, and the second one was solely used for testing. Results: The low mean absolute error between the reference and estimated RRs for the first and second datasets (2.6 and 1.3 breaths per minute, respectively) indicates the feasibility of the proposed method for RR estimation from fNIRS. Significance: This paper provides a novel view on the respiration interference as a source of complementary information in fNIRS.