486 results
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2. ESTIMATES OF CONSTRUCTION INFRASTRUCTURE STOCK FOR CAPE VERDE: 1980-2019.
- Author
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LOPES, Jorge and TAVARES, Admir
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CAPITAL stock , *INFRASTRUCTURE (Economics) , *CONSTRUCTION industry , *INVESTMENTS - Abstract
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 FROM AUTHOR]
- Published
- 2024
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3. Goodness-of-fit tests for the one-sided Lévy distribution based on quantile conditional moments.
- Author
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Pączek, Kewin, Jelito, Damian, Pitera, Marcin, and Wyłomańska, Agnieszka
- Abstract
In this paper we introduce a novel statistical framework based on the first two quantile conditional moments that facilitates effective goodness-of-fit testing for one-sided Lévy distributions. The scale-ratio framework introduced in this paper extends our previous results in which we have shown how to extract unique distribution features using conditional variance ratio for the generic class of
α -stable distributions. We show that the conditional moment-based goodness-of-fit statistics are a good alternative to other methods introduced in the literature tailored to the one-sided Lévy distributions. The usefulness of our approach is verified using an empirical test power study. For completeness, we also derive the asymptotic distributions of the test statistics and show how to apply our framework to real data. [ABSTRACT FROM AUTHOR]- Published
- 2024
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4. Cox processes driven by transformed Gaussian processes on linear networks—A review and new contributions.
- Author
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Møller, Jesper and Rasmussen, Jakob G.
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GAUSSIAN processes , *POINT processes , *STATISTICAL correlation , *GEODESIC distance , *GAUSSIAN function - Abstract
There is a lack of point process models on linear networks. For an arbitrary linear network, we consider new models for a Cox process with an isotropic pair correlation function obtained in various ways by transforming an isotropic Gaussian process which is used for driving the random intensity function of the Cox process. In particular, we introduce three model classes given by log Gaussian, interrupted, and permanental Cox processes on linear networks, and consider for the first time statistical procedures and applications for parametric families of such models. Moreover, we construct new simulation algorithms for Gaussian processes on linear networks and discuss whether the geodesic metric or the resistance metric should be used for the kind of Cox processes studied in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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5. The Maxwell-Boltzmann-Exponential distribution with regression model.
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Altun, Emrah and Altun, Gökçen
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DISTRIBUTION (Probability theory) , *REGRESSION analysis , *PARAMETER estimation , *PROBABILITY theory , *MIXTURES - Abstract
This paper proposes a new probability model called as Maxwell-Boltzmann-Exponential (MBE) distribution. The MBE distribution arises as a mixture distribution of the Maxwell-Boltzmann and exponential distributions. The statistical properties of the distributions are studied and obtained in closed-form expressions. Three methodologies are assessed and compared for the estimation of parameters in the MBE distribution. The MBE regression model is defined, with the proposed regression model being an alternative to the gamma regression model for response variables that are extremely right-skewed and bimodal. Two real data sets are used to demonstrate the applicability of the proposed models against the existing models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Estimation of the neighborhood of metric regularity for quadratic functions.
- Author
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Xu, Wending
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MATHEMATICAL optimization , *CONVEX functions , *NEIGHBORHOODS - Abstract
Metric regularity is widely concerned since its important applications in optimization and control theory. For promoting the application of metric regularity, it is valuable to study the estimation of the neighborhood which makes the regularity hold. However, it seems that no result has been established about this issue. This paper investigates the estimation of the neighborhood of metric regularity for quadratic functions. The main result gives the expression of the neighborhood of metric regularity for a kind of convex quadratic functions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. In-flight estimation of quadrotor mass and inertia using all-accelerometer.
- Author
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Al-Rawashdeh, Yazan M., Elshafei, Moustafa, and Ouakad, Hassen M.
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MOTION analysis , *CLOSED loop systems , *RIGID bodies , *GRAVITY , *TORQUE - Abstract
In this paper, an on-line closed-loop identification of mainly mass and inertia of an under-actuated aerial vehicle, namely a quadrotor acting as an aerial manipulator, is presented. Being treated as a rigid body, only one set of eighteen- or six tri-axial linear accelerometers is used to facilitate such estimation. Force and torque disturbances acting upon the vehicle during identification are also estimated which will refine the overall estimation quality of the unknown parameters. Namely, recursive linear and nonlinear least squares methods are used to obtain initial and refined estimations, respectively. Random disturbances are introduced to the closed-loop system to ensure enough excitation of the overall system. Issues related to identifiability, stability and performance are discussed. Since the proposed method depends on general kinematical and dynamical analyses of motion, it is claimed that the presented method will be also applicable to a wider range of, mainly, aerial vehicles. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Modeling Data with Extreme Values Using Three-Spliced Distributions.
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Bâcă, Adrian and Vernic, Raluca
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EXTREME value theory , *DATA modeling , *INSURANCE - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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9. Nonparametric identification of Wiener system with a subclass of wide‐sense cyclostationary excitations.
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Mzyk, Grzegorz and Maik, Gabriel
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SYSTEM identification , *TELECOMMUNICATION channels , *TELECOMMUNICATION systems , *IMPULSE response , *TIME series analysis - Abstract
Summary: The paper identifies a Wiener system, which is excited by a cyclostationary time series. To estimate the first subsystem's linear dynamic impulse response: this proposed algorithm first kernel‐windows the Wiener system's input measurements, then cross‐correlates with the output time series. To identify the second subsystem's static nonlinearity: this proposed algorithm first estimates the unobservable inter‐block internal signal (consistently in the statistical sense), and then kernel‐windows these estimates with the Wiener system output. This estimator provides the unusual capability to identify non‐invertible nonlinearities. This strategy removes any restrictive requirement for a Gaussian random excitation or a sinusoidal deterministic excitation. This paper further proves the estimator's asymptotic consistency and determines the kernel bandwidth for algorithmic convergence. The proposed algorithm's efficacy is verified in the context of two common applications: a servo mechanical system and a telecommunication channel. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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10. On the Interpolating Family of Distributions.
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Nadarajah, Saralees and Okorie, Idika E.
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MAXIMUM likelihood statistics , *BETA functions , *MAXIMUM entropy method - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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11. Post-occurrence cybersecurity of Power-to-Gas hydrogen-based energy hubs using real-time optimal adaptive feasibility area estimation.
- Author
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Khani, Hadi, Farag, Hany E.Z., and El-Saadany, Ehab F.
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RENEWABLE energy sources , *GREEN fuels , *NETWORK hubs , *CLEAN energy , *ENERGY consumption , *INTERNET security - Abstract
There is a great desire to integrate multiple energy systems by forming low-carbon energy hubs. Among various energy platforms, the integration of power and gas networks via Power-to-Gas (PtG) technologies is introduced as a promising solution to enhance the flexibility of power grids and facilitate seamless adoption for high penetration levels of renewable energy resources. In addition, the production of green hydrogen from waste/inexpensive clean electricity has recently been identified as a potential means for large-scale energy storage, deployment of hydrogen mobility, and decarbonization of several processes that utilize hydrogen. As integrated hydrogen-based energy hubs (H 2 Hubs) attract more attention for widespread deployment, the security of such new platforms against various cyberattacks is also becoming a critical topic for investigation. This paper proposes a new data estimation technique, developed and integrated with H 2 Hubs scheduling models, for real-time optimal adaptive collection and organization of the system operating parameters for cybersecurity assessment. A new formulation is presented for the adaptive formation of a newly proposed concept, referred to as the system feasibility area , via real-time acquisition of the system operating information. The created feasibility areas are utilized for real-time post-occurrence detection of the bad scheduling data that is invisible to the existing cybersecurity layers. Numerical studies are conducted to demonstrate the efficacy and feasibility of the proposed model. The results demonstrate that the historical data can be adaptively collected in real time and successfully utilized to create feasibility areas to categorize the system operating points. • An optimal scheduling model is proposed for hydrogen-based energy hubs (H 2 Hubs). • A new data estimation method is developed and integrated with the scheduling model. • A new formulation is given for the adaptive creation of the feasibility area. • Simulation case studies are presented and discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. Uniformly Shifted Exponential Distribution.
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Alzaid, Abdulhamid. A. and Qarmalah, Najla
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DISTRIBUTION (Probability theory) , *ENGINEERING reliability theory , *PROGRAMMING languages , *STOCHASTIC orders , *WEIBULL distribution - Abstract
The use of life distributions has increased over the past decade, receiving particular attention in recent years, both from a practical and theoretical point of view. Life distributions can be used in a number of applied fields, such as medicine, biology, public health, epidemiology, engineering, economics, and demography. This paper presents and investigates a new life distribution. The proposed model shows favorable characteristics in terms of reliability theory, which makes it competitive against other commonly used life distributions, such as the exponential, gamma, and Weibull distributions. The methods of maximum likelihood and moments are used to estimate the parameters of the proposed model. Additionally, real-life data drawn from different fields are used to illustrate the usefulness of the new distribution. Further, the R programming language is used to perform all computations and produce all graphs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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13. Two-staged approach for estimation of sequences in partially observable P-time Petri nets on a sliding horizon with schedulability analysis.
- Author
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Declerck, P. and Bonhomme, P.
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PETRI nets , *DISMISSAL of employees - Abstract
In this paper, we consider the on-line estimation of current subsequences for Partially Observable P-time Petri Nets and their starting markings on a sliding horizon composed of steps defined by two successive occurrences of observable transition firings. We propose a general strategy composed of two phases: Phase 1 exploits a simplification of the P-time Petri net under the form of a Timed Petri net; considering a candidate count vector and the relevant starting marking proposed at Phase 1, Phase 2 makes a schedulability analysis by building a system of relations which can be represented by an acyclic conflict-free computation graph. The complete approach avoids the generation of sets which is generally time and space consuming, and provides an optimal solution for each subproblem by using efficient standard tools. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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14. Measuring 1-mm-accurate local survey ties over kilometer baselines at McDonald Geodetic Observatory.
- Author
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Rivera, Jullian, Bettadpur, Srinivas, Griffin, John, Kang, Zhigui, and Ries, John
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GLOBAL Positioning System , *LASER ranging , *OBSERVATORIES - Abstract
The goal for the next generation of terrestrial reference frames (TRF) is to achieve a 1-mm- and 0.1-mm/yr-accurate frame realization through the combination of reference station solutions by multi-technique geodetic observatories. A potentially significant source of error in TRF realizations is the inter-system ties between the instruments at multi-technique stations, usually independently determined through ground-based local surveying. The quality of local tie surveys is varied and inconsistent, largely due to differences in measurement techniques, surveying instruments, site conditions/geometries, and processing methods. The Global Geodetic Observing System (GGOS) has tried to address these problems by issuing guidelines for the construction and layout of future multi-technique observatories, promoting uniformity and quality while minimizing existing problems with local surveying that are exacerbated over longer baseline distances. However, not every observatory is going to be able to completely satisfy these guidelines, and in this work, a successful endeavor to satisfy the accuracy goals while exceeding the GGOS baseline guideline is detailed for the McDonald Geodetic Observatory (MGO) in the Davis Mountains of Texas, USA. MGO consists of a VLBI Geodetic Observing System (VGOS), infrastructure in place for a Space Geodesy Satellite Laser Ranging (SGSLR) telescope, and several Global Navigation Satellite Systems (GNSS) stations spanning a 900 m baseline and a 120 m elevation change. The results of the local ties between the GNSS stations across the near-kilometer baseline, as measured from their antenna reference points, show sub-mm precision and 1 mm accuracy validated through repeatability across several surveys conducted in 2021as well as 1 mm consistency with the monthly averaged daily solutions of the GNSS-based positioning. In this paper, we report these results as well as the framework of the surveys with sufficient detail and rigor in order to give confidence to the quality claims and to present the novel design and techniques employed in the procedure, processing, and error-budget analysis, which were determined through iterative research methods across repeated survey campaigns. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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15. First-order spatial random coefficient non-negative integer-valued autoregressive (SRCINAR(1,1)) model.
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Tabandeh, Atefeh and Ghodsi, Alireza
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AUTOREGRESSIVE models , *STANDARD deviations , *AUTOCORRELATION (Statistics) , *PARAMETER estimation , *WITCHWEEDS , *LOGITS - Abstract
In this paper we introduce first-order spatial random coefficient non-negative integer-valued autoregressive (SRCINAR(1,1)) model to fit the spatial count data on a two-dimensional regular lattice. We use a thinning operator in order to define the processes. The mean, variance and autocorrelation function of the model are derived. Three methods (Yule-Walker, Conditional least squares and Conditional maximum likelihood) for the estimation of the parameters of the model are discussed and are compared with respect to the root mean squared error (RMSE), in a simulation study. Finally, SRCINAR(1,1) model is applied to a real data set of striga counts at the ICRISAT Sahelian Center near Niamey (Niger). [ABSTRACT FROM AUTHOR]
- Published
- 2024
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16. The Raise Regression: Justification, Properties and Application.
- Author
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Salmerón‐Gómez, Román, García‐García, Catalina B., and García‐Pérez, José
- Abstract
Summary Multicollinearity results in inflation in the variance of the ordinary least squares estimators due to the correlation between two or more independent variables (including the constant term). A widely applied solution is to estimate with penalised estimators such as the ridge estimator, which trade off some bias in the estimators to gain a reduction in the variance of these estimators. Although the variance diminishes with these procedures, all seem to indicate that the inference and goodness of fit are controversial. Alternatively, the raise regression allows mitigation of the problems associated with multicollinearity without the loss of inference or the coefficient of determination. This paper completely formalises the raise estimator. For the first time, the norm of the estimator, the behaviour of the individual and joint significance, the behaviour of the mean squared error and the coefficient of variation are analysed. We also present the generalisation of the estimation and the relation between the raise and the residualisation estimators. To have a better understanding of raise regression, previous contributions are also summarised: its mean squared error, the variance inflation factor, the condition number, adequate selection of the variable to be raised, the successive raising, and the relation between the raise and the ridge estimator. The usefulness of the raise regression as an alternative to mitigate multicollinearity is illustrated with two empirical applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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17. A Modified Anisotropic Diffusion Scheme for Signal-Dependent Noise Filtering.
- Author
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Ben Abdallah, Mariem, Malek, Jihene, Bajahzar, Abdullah, and Belmabrouk, Hafedh
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IMAGE denoising , *PATTERN recognition systems , *IMAGE reconstruction , *COMPUTER vision , *IMAGE analysis , *IMAGE processing , *NOISE - Abstract
Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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18. Estimating neighborhood-level population characteristics from parcel data.
- Author
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Ruther, Matthew H.
- Abstract
This paper investigates how ancillary geographic data – in particular, information on land or assessor parcels – might be used to improve estimates for small area populations and population characteristics. It seeks to determine whether parcel land use codes can be used to reliably replicate population and housing distributions within small (subcounty) areas and whether other parcel attributes – in addition to land use – exhibit any explanatory power in replicating population and housing characteristics within these same places. The basis for this paper was Professor Barbara Buttenfield’s service on the Census Scientific Advisory Committee, in which her working group explored the utility of administrative source data as an alternative or complement to federal survey data. This analysis highlights some of the benefits and complications of the incorporation of parcel data into geodemographic estimation, and the findings demonstrate that such a use is problematic but encouraging. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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19. COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.
- Author
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Bougourzi, Fares, Distante, Cosimo, Dornaika, Fadi, Taleb-Ahmed, Abdelmalik, Hadid, Abdenour, Chaudhary, Suman, Yang, Wanting, Qiang, Yan, Anwar, Talha, Breaban, Mihaela Elena, Hsu, Chih-Chung, Tai, Shen-Chieh, Chen, Shao-Ning, Tricarico, Davide, Chaudhry, Hafiza Ayesha Hoor, Fiandrotti, Attilio, Grangetto, Marco, Spatafora, Maria Ausilia Napoli, Ortis, Alessandro, and Battiato, Sebastiano
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COVID-19 , *COMPUTED tomography , *COVID-19 pandemic , *CONVOLUTIONAL neural networks , *DEEP learning - Abstract
COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
20. Moving-horizon estimation approach for nonlinear systems with measurement contaminated by outliers.
- Author
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Awawdeh, Moath, Faisal, Tarig, Bashir, Anees, Nour Alshbatat, Abdel Ilah, and Momani, Rana T. H.
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NONLINEAR systems , *NONLINEAR estimation , *LINEAR time invariant systems , *LINEAR systems , *KALMAN filtering - Abstract
An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
21. Research on Excitation Estimation for Ocean Wave Energy Generators Based on Extended Kalman Filtering.
- Author
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Zhang, Yuchen, Zhang, Zhenquan, Wang, Jun, Qin, Jian, Huang, Shuting, Xue, Gang, and Liu, Yanjun
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KALMAN filtering , *OCEAN waves , *WAVE energy , *WAVE forces , *MOORING of ships , *PERMANENT magnet generators , *NONLINEAR systems , *OCEAN - Abstract
Wave energy generation methods have significant energy costs. The implementation of sophisticated control techniques in wave energy generators can lower the cost of power generation by optimizing the energy recovered from wave energy converters (WECs). To determine control inputs, most control systems rely on knowledge of the wave excitation force, including information on past, present, and future excitation forces. For the excitation of WEC devices, wave excitation force can only be inferred and predicted because it is an unmeasurable quantity. One of the more widely used observers in wave excitation estimates at the moment is the Kalman filter, but its use is primarily restricted to linear Kalman filtering. The mooring system is an integral component of floating wave energy producers. The mooring force of the device is actually nonlinear; however, the majority of current studies on excitation estimates for wave energy producers based on Kalman filter methods employ an ideal motion model based on the linearization of the mooring force. This paper, in an attempt to make things more realistic, creates a WEC system with highly nonlinear mooring forces, suggests a way to build a wave excitation force estimator for a nonlinear WEC system using the extended Kalman filtering method, and assesses the impact of various factors, such as measurement noise, random phase, and the number of equal-energy methods dividing the frequency, on the accuracy of the wave excitation force estimate. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
22. The Zero-Inflated Poisson - Probit regression model: a new model for count data.
- Author
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Pho, Kim-Hung and Truong, Buu-Chau
- Abstract
AbstractThis paper wishes to propose a new model for count data and it is shortly called a Zero-Inflated Poisson - Probit (ZIP-P) model. The way of setting up for this new model is also based on the traditional model, the Zero-Inflated Poisson (ZIP) model. Note that this new model as well as the ZIP model has a wide range of practical applications and it can be easily applied and analyzed to the real data sets. More specifically, both of these models can be applied very well to count data sets where the frequency of zeros occupies a very high percentage. To achieve the goal of this study, we first recall the construction and general formula of the ZIP model. Then we present the most detailed way for building a new model includes the general formula and related equations necessary for estimating the parameters of the ZIP-P model. In addition, asymptotic results, numerical simulations and analysis of a real data set are also performed in this work. Moreover, we also clarify that this actual data set fits the ZIP-P model better than the other models considered. Finally, several lines of discussions and inferences are made in the study. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
23. Higher-Order INAR Model Based on a Flexible Innovation and Application to COVID-19 and Gold Particles Data.
- Author
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Almuhayfith, Fatimah E., Krishna, Anuresha, Maya, Radhakumari, Irshad, Muhammad Rasheed, Bakouch, Hassan S., and Almulhim, Munirah
- Subjects
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MAXIMUM likelihood statistics , *COVID-19 , *TIME series analysis , *GOLD - Abstract
INAR models have the great advantage of being able to capture the conditional distribution of a count time series based on their past observations, thus allowing it to be tailored to meet the unique characteristics of count data. This paper reviews the two-parameter Poisson extended exponential (PEE) distribution and its corresponding INAR(1) process. Then the INAR of order p (INAR(p)) model that incorporates PEE innovations is proposed, its statistical properties are presented, and its parameters are estimated using conditional least squares and conditional maximum likelihood estimation methods. Two practical data sets are analyzed and compared with competing INAR models in an effort to gauge the performance of the proposed model. It is found that the proposed model performs better than the competitors. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
24. Asymmetric delayed relay feedback identification based on the n-shifting approach.
- Author
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Sánchez Moreno, José, Dormido Bencomo, Sebastián, Escrig, Oscar Miguel, and Romero Pérez, Julio Ariel
- Subjects
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FREQUENCIES of oscillating systems , *PID controllers , *MANUFACTURING processes , *PSYCHOLOGICAL feedback , *POINT set theory , *SYSTEM identification - Abstract
The paper presents an improvement of the n-shifting technique to identify the frequency response of an industrial process using a fully asymmetric and delaying relay. The n-shifting approach allows the calculation of n + 1 points of G(s) by an asymmetric relay experiment. This set of n points is composed of G(0), G(jωosc), ... , G(jnωosc), being ωosc the oscillation frequency, and where G(jωosc) is in most cases located in the third quadrant of the Nyquist map. By delaying the relay output and repeating a similar experiment it can be generated n additional points of G(s) where the first point is G(jω'osc) with 0 < ω'osc < ωosc. In this way, it is possible to depict the full output spectrum of G(s) from zero to very high frequencies by a short relay experiment. An example of identification and tuning of a PID controller with data from the n-shifting are presented to show the validity of the approach. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. A partially periodic oscillation model combined with heterogeneous autoregression and its application to COVID-19.
- Author
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Hwang, Eunju
- Subjects
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COVID-19 , *OSCILLATIONS , *MONTE Carlo method , *TIME series analysis , *REGRESSION analysis , *PARAMETER estimation , *AUTOREGRESSION (Statistics) - Abstract
This paper proposes a partially periodic oscillation model, which is motivated by time series modelling for COVID-19 daily confirmed cases, in particular, to represent more accurately the dynamic features of the 7-day periodicity. In order to express the phenomenon of the partial 7-day cycle in the COVID-19 data, some partial periodic part is added to a heterogeneous autoregression model. Estimation algorithm based on the least squares errors and regression analysis is provided and parameter estimation consistency is given along with its proof. A Monte-Carlo simulation study is carried out to investigate the finite-sample performance. The proposed model is applied to the COVID-19 daily confirmed cases of the most affected eight countries which posses the partially periodic oscillation. Model criteria such as RMSE, MAE, HMAE, AIC and BIC are compared with other existing models. Efficiency of the model, relative to the benchmark, is evaluated to reveal its better accuracy performance. Out-of-sample forecasting analysis is conducted as well. The novelty is that this work is a challenging trial to identify the partially periodic oscillation of COVID-19 data, without smoothing, as well as the proposed model outperforms the existing time series models in the empirical analysis of the worldwide COVID-19. • A partially periodic oscillation model combined with heterogeneous autoregression is proposed. • The model is applied to the COVID-19 daily confirmed cases in the top eight countries with partially periodic oscillation. • A simple and robust estimation method is developed. • Model criteria measures such as RMSE, MAE, HMAE, AIC and BIC are evaluated and compared with existing models. • Efficiency of the proposed model, relative to benchmark models, is reported as 126%-163% improvement in some countries. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Efficient Crowd Counting via Dual Knowledge Distillation.
- Author
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Wang, Rui, Hao, Yixue, Hu, Long, Li, Xianzhi, Chen, Min, Miao, Yiming, and Humar, Iztok
- Subjects
- *
DISTILLATION , *CROWDS , *RESEARCH personnel , *COUNTING , *KNOWLEDGE transfer - Abstract
Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. RELAXED EXCITATION CONDITIONS FOR ROBUST IDENTIFICATION AND ADAPTIVE CONTROL USING ESTIMATION WITH MEMORY.
- Author
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GALLEGOS, JAVIER and AGUILA-CAMACHO, NORELYS
- Subjects
- *
ADAPTIVE control systems , *NONLINEAR systems , *LINEAR systems , *PLANT identification , *MEMORY , *UNCERTAIN systems - Abstract
In this paper, adaptive controllers are designed to track a given trajectory for linear and nonlinear systems. No condition on the tracked trajectory, other than continuity and boundedness, is needed to simultaneously ensure exponential convergence to the tracking reference, exponential convergence to the identification of the plant, and robustness to nonparametric uncertainties. To achieve this, the formulation of the excitation condition associated with the identification part of the adaptive scheme is proposed without employing closed-loop signals, allowing the use of a transient enrichment of the reference. The effect of this transient modification is attenuated by using relaxed requirements for the identification, obtained through a generalization of several estimation algorithms found in recent literature that use memory mechanisms. Consequently, no spectral content of the tracked trajectory--a classic requirement in adaptive theory--is needed to guarantee the mentioned features when the proposed scheme is used. A numerical example is given to illustrate the design aspects involved and the distinctive features of the proposed strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. A new bivariate autoregressive model driven by logistic regression.
- Author
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Wang, Zheqi, Wang, Dehui, and Cheng, Jianhua
- Subjects
- *
AUTOREGRESSIVE models , *LOGISTIC regression analysis , *STATISTICAL models , *CHRONIC myeloid leukemia , *TIME series analysis , *BIVARIATE analysis , *AUTOREGRESSION (Statistics) - Abstract
In this paper, we propose a new bivariate random coefficient autoregressive (BOD-RCAR(1)) process driven by both explanatory variables and past observations. Firstly, some statistical properties of this model are derived. Secondly, three methods are used for estimating the unknown parameters: conditional least squares (CLS), conditional maximum likelihood (CML) and maximum empirical likelihood (MEL). The asymptotic properties of the estimators are given. Besides, two kinds of test based on empirical likelihood (EL) are established. A simulation experiment is presented to demonstrate the performance of the proposed method. Finally, an application to a real data example is investigated to assess the performance of the model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Zero-inflated probit Bernoulli model: a new model for binary data.
- Author
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Pho, Kim-Hung
- Abstract
Abstract This study proposes a new model in the family zero-inflated (ZI) binary models, it is called as a ZI Probit Bernoulli (ZIPBer) model. This model can be performed to model or simulate binary data that has an excessive amount of zero counts. At the framework of this research, we first introduce the classical formula, estimating equation (EE) and related functions for the ZIPBer model. Next, the results of asymptotic inferences for the ZIPBer model is studied based on the widespread regularity conditions. For the numerical illustrations, many simulation studies and a practical data set are executed in this paper. Specifically, we perform the fishing data set (FDS) to check the working effectiveness and robustness of the maximum likelihood estimation (MLE) approach in estimating parameters for the ZIPBer model. We also illustrate that this actual data set is more suitable for the proposed model: ZIPBer than other binary models such as logit, probit and ZIBer. The achieved results in this study are very unanimous with reality. It also illustrates that this is a dependable verification for us to know the solution as well as how to proceed to how to catch or take the most fish. This is the contribution of this work in terms of application. Finally, some crucial discussions and conclusions are given in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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30. EXPLORING THE ADAPTABILITY OF THE UNIT INVERSE WEIBULL DISTRIBUTION FOR MODELING DATA ON THE UNIT INTERVAL.
- Author
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T., Shameera and P. P., Bindu
- Subjects
- *
WEIBULL distribution , *SKEWNESS (Probability theory) - Abstract
This paper derives a new lifetime distribution called the unit inverse Weibull distribution(UIWD) from inverse weibull distribution. Various statistical properties such as the survival function, hazard rate function, revised hazard rate function, cumulative hazard rate function, moments, and quartiles have been discussed. Additionally, we have explored other properties like skewness, kurtosis, order statistics, and the quantile function. Various methods of estimation, including maximum likelihood, moments, percentiles, and the Cramer-Von Mises, have been discussed. Simulation studies were conducted to assess the accuracy and precision of the parameters. Comparative analyses were performed to highlight the effectiveness and utility of the proposed model in comparison to other existing models, using two real-life applications. Finally, real life data analysis reveals that derived distribution can provide a better fit than several well-known distributions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
31. Study on the assessment of cognitive and functional status for a segment of the population during the ageing process.
- Author
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Vizitiu, Elena, Costea, Andrei-Ioan, Silişteanu, Sînziana-Călina, and Constantinescu, Mihai
- Subjects
- *
FUNCTIONAL status , *POPULATION aging , *FUNCTIONAL assessment , *PHYSICAL mobility , *BODY mass index - Abstract
Background: The aim of the paper is to assess the cognitive and functional status of a segment of the population for a senescent age group. These assessments can help develop intervention strategies to improve the cognitive and functional status of these individuals as well as promote a healthy and active lifestyle. The assessment of cognitive and functional status in a segment of the population during senescence can identify certain problems and deficiencies in cognitive and physical functioning and can help develop physical activities to optimize the cognitive and functional state of senescent people. Objectives: To identify the cognitive and functional profile of the studied age group; Pointing out risk factors for cognitive and functional deterioration; Suggesting physical activities to improve cognitive and functional status. Methods: The recorded data helps us develop personalized aquatic programs to improve health and physical performance, tailored to the individual needs of participants, to be used over 6 months in 2024. Results: The findings suggested by the collected data, recognises a significant link between subjects' body mass index and their physical test scores. Conclusions: Analysis of the data allows us to identify risk factors, including chronic conditions, adopted lifestyle, genetic influences or other environmental conditioning, that could contribute to cognitive and functional decline in the older population. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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32. Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models.
- Author
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Fadhil, Diyar and Oliveira, Rodolfo
- Subjects
- *
DEEP learning , *GAUSSIAN mixture models , *TIME delay estimation , *END-to-end delay , *SUPERVISED learning , *MACHINE learning , *EXPECTATION-maximization algorithms - Abstract
Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an end-to-end (E2E) delay estimation method for 5G networks through deep learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are subsequently used as labeled data in a supervised deep learning stage. A multi-layer neural network model is trained using the labeled data and assuming different numbers of E2E delay observations for each training sample. The accuracy and computation time of the proposed deep learning estimator based on the Gaussian Mixture Model (DLEGMM) are evaluated for different 5G network scenarios. The simulation results show that the DLEGMM outperforms the GMM method based on the EM algorithm, in terms of the accuracy of the E2E delay estimates, although requiring a higher computation time. The estimation method is characterized for different 5G scenarios, and when compared to GMM, DLEGMM reduces the mean squared error (MSE) obtained with GMM between 1.7 to 2.6 times. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Empirical study of periodic autoregressive models with additive noise – estimation and testing.
- Author
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Żuławiński, Wojciech and Wyłomańska, Agnieszka
- Abstract
AbstractPeriodic autoregressive (PAR) time series with finite variance is considered as one of the most common models of second-order cyclostationary processes. However, in the real applications, the signals with periodic characteristics may be disturbed by additional noise related to measurement device disturbances or to other external sources. Thus, the known estimation techniques dedicated for PAR models may be inefficient for such cases. When the variance of the additive noise is relatively small, it can be ignored and the classical estimation techniques can be applied. However, for extreme cases, the additive noise can have a significant influence on the estimation results. In this paper, we propose four estimation techniques for the noise-corrupted PAR models with finite-variance distributions. The methodology is based on Yule-Walker equations utilizing the autocovariance function. It can be used for any type of the finite-variance additive noise. The presented simulation study clearly indicates the efficiency of the proposed techniques, also for extreme case, when the additive noise is a sum of the Gaussian additive noise and additive outliers. The proposed estimation techniques are also applied for testing if the data correspond to noise-corrupted PAR model. This issue is strongly related to the identification of informative component in the data in case when the model is disturbed by additive non-informative noise. The power of the test is studied for simulated data. Finally, the testing procedure is applied for two real time series describing particulate matter concentration in the air. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Estimation of <italic>Cpm </italic> for autocorrelated data in the presence of random measurement errors.
- Author
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Bera, Kuntal and Anis, M. Z.
- Abstract
Abstract Process capability analysis is a widely used process improvement metric. There are several capability indices used in industries to assess the performance of production process. C p m is one of the most important capability index which is often used in many industries for assessing the performance of the production process by taking into consideration the target value. Although capability indices are estimated under the assumption that the data are independent but in many industries, process outputs are often autocorrelated. At the same time, no measuring device gives accurate results. In this paper, we discuss the statistical properties of the estimator of C p m for autocorrelated data and in the presence of random measurement errors. Here, we show that under the combined effect of autocorrelation and measurement errors, the estimator of C p m behaves differently depending upon the variability of the measurement errors. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. Interval Observers for Continuous-Time Systems with Parametric Uncertainties.
- Author
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Zhirabok, A. N., Zuev, A. V., Filaretov, V. F., Shumsky, A. E., and Ir, Kim Chkhun
- Subjects
- *
ADMISSIBLE sets , *LINEAR systems , *DYNAMICAL systems - Abstract
In this paper, interval observers are designed for linear dynamic systems described by continuous-time models with exogenous disturbances, measurement noises, and parametric uncertainties. Jordan canonical form-based relations are presented for an interval observer that estimates the set of admissible values of a given linear function of the system state vector. The theoretical results are illustrated by a practical example. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. Mixture‐modelling‐based Bayesian MH‐RM algorithm for the multidimensional 4PLM.
- Author
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Guo, Shaoyang, Chen, Yanlei, Zheng, Chanjin, and Li, Guiyu
- Subjects
- *
GIBBS sampling , *ALGORITHMS , *MIXTURES - Abstract
Several recent works have tackled the estimation issue for the unidimensional four‐parameter logistic model (4PLM). Despite these efforts, the issue remains a challenge for the multidimensional 4PLM (M4PLM). Fu et al. (2021) proposed a Gibbs sampler for the M4PLM, but it is time‐consuming. In this paper, a mixture‐modelling‐based Bayesian MH‐RM (MM‐MH‐RM) algorithm is proposed for the M4PLM to obtain the maximum a posteriori (MAP) estimates. In a comparison of the MM‐MH‐RM algorithm to the original MH‐RM algorithm, two simulation studies and an empirical example demonstrated that the MM‐MH‐RM algorithm possessed the benefits of the mixture‐modelling approach and could produce more robust estimates with guaranteed convergence rates and fast computation. The MATLAB codes for the MM‐MH‐RM algorithm are available in the online appendix. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Anode Nitrogen Concentration Estimation Based on Voltage Variation Characteristics for Proton Exchange Membrane Fuel Cell Stacks.
- Author
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Guo, Ruifeng, Chen, Dongfang, Li, Yuehua, Wu, Wenlong, Hu, Song, and Xu, Xiaoming
- Subjects
- *
PROTON exchange membrane fuel cells , *VOLTAGE-gated ion channels , *ELECTRODIALYSIS , *ANODES , *GAS distribution , *ENERGY conversion - Abstract
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%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Evaluation of Methane Emissions in Daily Operations and Accidents: A Case Study of a Local Distribution Company in China.
- Author
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Xie, Yitong, Qin, Chaokui, Wu, Cong, Zhang, Lubing, and Cao, Ping
- Subjects
- *
WATER pipelines , *GAS distribution , *METHANE , *GLOBAL warming - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Coherent Multi-Dwell Processing of Un-Synchronized Dwells for High Velocity Estimation and Super-Resolution in Radar.
- Author
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Levy, Benzion, Maman, Lior, Shvartzman, Shlomi, and Pinhasi, Yosef
- Subjects
- *
RADAR , *VELOCITY , *PHASED array antennas , *SIGNAL processing , *SIGNAL-to-noise ratio , *AMBIGUITY , *MIMO radar , *TIME management - Abstract
This paper describes a coherent multi-dwell processing (CMDP) method for high velocity estimation and super-resolution in search and track, while search (TWS) radar modes use an un-conventional signal processing algorithm that exploits multi-dwell transmissions. The existence of the multi-dwell waveform is necessary for visibility needs by un-folding the target's velocity and range ambiguity and is proposed to be utilized for high velocity estimation and super-resolution. In this paper, the proposed scheme is shown to result in improved velocity estimation and doppler resolution performance for un-ambiguous targets in comparison to classical radar processing. The processing concept uses the same transmitted waveform (WF) and time duration without the need to increase the time on target (TOT) through sophisticated coherent concatenation of the received dwells with velocity compensation between the dwells. The phase compensation in receive mode is implemented for each target according to its characteristics, which means that target velocities are estimated in each dwell separately. The notable result of the CMDP is the linear doppler resolution improvement obtained with the given search resources and without knowing the target characteristics in advance or the dwell delay time. Other possible benefits of this process are the ability to achieve larger detection ranges and high-angle measurement precisions in search mode due to the higher signal-to-noise ratio (SNR) of the extended dwell and the ability to track more targets due to efficient time and resource management. An outstanding opportunity to exploit the CMDP is by combining missions in phased array (PA) radars, meeting the multi-objective needs of both high spatial scan rates for illuminating the target and high doppler estimation and resolution performance. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. A Bayesian look at classical estimation: The shape of the pareto distribution and its functions.
- Author
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Toribio, Sherwin G. and Elfessi, Abdulaziz
- Subjects
- *
PARETO distribution , *MAXIMUM likelihood statistics , *BAYES' estimation , *HAZARD function (Statistics) , *PHENOMENOLOGICAL biology - Abstract
The Pareto Distribution has been found to be useful in describing socio-economic, physical, and biological phenomena. This paper explores different methods of estimating the shape parameter of the Pareto distribution and two of its functions (reliability and hazard functions), assuming the scale parameter is known. In particular, this paper shows that the Bayesian approach, using both the quadratic and entropy loss functions, can provide a general method for estimating the shape parameter and these two functions. These Bayesian estimators generalize the maximum likelihood estimator (MLE) and the uniformly most powerful unbiased estimator (UMVUE). Simulation results show that the general Bayesian method can perform better than the MLE and UMVUE, especially when the sample size is small. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
41. An assessment of the accuracy of surgical time estimation by orthopaedic theatre staff.
- Author
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Butler, Stephen, Loseli, Tau, Graham, David, Watson, Anna, Kao, Mark, Saxena, Akshat, Sivakumar, Brahman, and Van der Rijt, Adrian
- Subjects
- *
ANALYSIS of variance , *ORTHOPEDIC surgery , *TREATMENT duration , *TREATMENT effectiveness , *MEDICAL care use , *DESCRIPTIVE statistics , *CHI-squared test , *DATA analysis software , *LONGITUDINAL method , *EVALUATION - Abstract
Objective: Optimal utilisation of theatre time increases efficiency and reduces the cost of health care. The accuracy of surgical time estimation between different members of the theatre team has not been well documented, and may aid in more efficient utilisation of available theatre time. This study aims to identify the cohort of theatre staff with greatest accuracy in estimating orthopaedic surgical time. Methods: This study was conducted in a prospective fashion using consecutive orthopaedic trauma and elective operative lists over a period of 3 months. Prior to each operating list, a senior member of each of the anaesthetic, orthopaedic and scrub/scout nursing teams predicted the surgical duration for orthopaedic procedures after being provided with information regarding the individual cases. The absolute difference between estimated and actual surgical times was calculated. Results: When expressed as a percentage difference from true surgical time, the orthopaedic team provided the most accurate estimates, with a mean difference of 33.0%. This was followed by nursing staff (40.5%) and anaesthetics (50.9%). Similarly, a higher proportion of estimates by the orthopaedic team were within the limits of 20% underestimation and 10% overestimation (deemed clinically significant). Conclusions: Surgical times for orthopaedic trauma and elective cases are most accurately estimated by the operating team. These estimates should be implemented when planning theatre utilisation, and may benefit computer algorithms for theatre scheduling. What is known about the topic? The ability of surgeons, nurses and anaesthetists to accurately predict surgical times is often debated, with heated discussions if additional cases can fit onto a scheduled list. What does the paper add? Our paper demonstrated that despite all groups being inaccurate with timing predictions, orthopaedic surgeons were the most accurate. What are the implications for practitioners? With ever-growing pressure on health systems, it is paramount that available theatre resources are utilised with maximal efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. Stability and Risk Bounds of Iterative Hard Thresholding.
- Author
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Yuan, Xiao-Tong and Li, Ping
- Subjects
- *
STATISTICAL learning , *ORTHOGONAL matching pursuit , *EIGENFUNCTIONS , *COMPRESSED sensing , *REGRESSION analysis , *PARAMETER estimation - Abstract
In this paper, we analyze the generalization performance of the Iterative Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The parameter estimation and sparsity recovery consistency of IHT has long been known in compressed sensing. From the perspective of statistical learning, another fundamental question is how well the IHT estimation would predict on unseen data. This paper makes progress towards answering this open question by introducing a novel sparse generalization theory for IHT under the notion of algorithmic stability. Our theory reveals that: 1) under natural conditions on the empirical risk function over $n$ samples of dimension $p$ , IHT with sparsity level $k$ enjoys an $\tilde {\mathcal {O}}(n^{-1/2}\sqrt {k\log (n)\log (p)})$ rate of convergence in sparse excess risk; 2) a tighter $\tilde {\mathcal {O}}(n^{-1/2}\sqrt {\log (n)})$ bound can be established by imposing an additional iteration stability condition on a hypothetical IHT procedure invoked to the population risk; and 3) a fast rate of order $\tilde {\mathcal {O}}\left ({n^{-1}k(\log ^{3}(n)+\log (p))}\right)$ can be derived for strongly convex risk function under proper strong-signal conditions. The results have been substantialized to sparse linear regression and sparse logistic regression models to demonstrate the applicability of our theory. Preliminary numerical evidence is provided to support our theoretical predictions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Dynamic harmonics–interharmonics identification and compensation through optimal control of a power conditioning application.
- Author
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Ramos-Paz, Serafin, Ornelas-Tellez, Fernando, and Rico-Melgoza, J. Jesus
- Subjects
- *
ELECTRIC power distribution grids , *HARDWARE-in-the-loop simulation , *ELECTRIC circuits , *REAL-time control , *ELECTRICAL load , *IDENTIFICATION , *GYROTRONS - Abstract
This paper proposes an online identification and compensation scheme for a distorted waveform's harmonic and interharmonic content in electrical circuits. The proposed novel identification scheme allows the simultaneous estimation of the harmonic components' frequency, amplitude, and phase. One of the main characteristics of the proposed online identification scheme is how the harmonics and interharmonics are estimated using a fast-convergent state estimator based on the state-space representation of the harmonic content. The proposed estimator has a decentralized approach that relies on efficient implementation, a relevant result for real-time feedback control applications, such as power conditioners control. The decentralized structure allows, if needed, selective harmonic suppression. The paper exposes a case study of identifying and suppressing the harmonic and interharmonic content produced by a three-phase load connected to the electrical power grid to demonstrate the proposed estimator's efficiency. Hardware-in-the-loop and simulation results validate the identifier effectiveness to determine in real time the harmonic content and how it can be used to reduce the total harmonic distortion through a nonlinear optimal tracking control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
44. Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVs.
- Author
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Doostmohammadian, Mohammadreza, Taghieh, Amin, and Zarrabi, Houman
- Subjects
- *
TRACKING radar , *COMPUTER systems , *EARTH stations , *INDUSTRIALIZATION , *WIRELESS communications , *MICROCONTROLLERS , *INTERIOR-point methods - Abstract
This paper considers distributed estimation methods to enable the formation of Unmanned-Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with communication devices to receive a beacon signal from the target and share information with neighboring UAVs. The shared information includes the time-of-arrival (TOA) of the beacon signal from the target and estimates on the target’s location. Every UAV processes the received information from the neighbors using a single-time-scale distributed estimation protocol. This differs from multi-time-scale protocols that require (i) many consensus iterations on a-priori estimates, (ii) fast communication among agents (in general, much faster than the sampling rate of the target dynamics), and thus, more-costly communication equipment and processing units. Further, our approach outperforms most single-time-scale methods in terms of observability assumption as these methods assume that the target is observable via the measurement data received from neighboring UAVs (referred to as local-observability). This requires more communications among the sensors. In contrast, our approach is only based on global-observability assumption, and thus, requires less networking (only strong-connectivity) and communication traffic along with less computational load by data-processing once at the same time-scale of sampling target dynamics. We consider modified time-difference-of-arrival (TDOA) measurements with a constant output matrix for the linearized model. UAVs make a pre-specified formation, and by estimating the target’s location via these measurements, move along with the target. Note to Practitioners—Inspired by recent development in industrial UAVs along with emerging progress in low-cost processing units, fog computing systems, and wireless communications, this paper considers mobile tracking of a moving target via a group of wireless-connected autonomous drones. In the classical tracking methods, which are prone to single-point-of-failure, all the sensors need to send their information to a ground central station over a long-range and costly data-transmission channel. In contrast, by collaborative tracking the processing and decision-making are distributed among a swarm of drones equipped with onboard miniaturized electronic parts such as sensors, microcontrollers, microprocessors, and communication units. This article provides an efficient algorithm to enable such drones to autonomously track the moving target in real-time. Note that the cost and tracking ability of the UAV swarm are directly determined by the computational efficiency and communication burden of the estimation algorithm. In this regard, most available estimation algorithms are over budget and even infeasible due to the need for fast data-transmission channels, fast CPUs, and high network traffic. Our proposed estimation technique outperforms similar algorithms in terms of required communication bandwidth, data-transmission rate, and computational resources, which considerably reduces the hardware cost and improves tracking efficiency in real-time large-scale applications. We show the feasibility and efficiency of our distributed tracking method by simulation. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
45. ON SOME STATISTICAL PROPERTIES AND APPLICATIONS OF THREE-PARAMETER SUJATHA DISTRIBUTION.
- Author
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Prodhani, Hosenur Rahman and Shanker, Rama
- Subjects
- *
PARAMETERS (Statistics) , *MAXIMUM likelihood statistics - Abstract
In this paper some important statistical properties of three-parameter Sujatha distribution including descriptive measures based on moments, reliability properties, mean deviations, stochastic ordering and Bonferroni and Lorenz curves have been discussed. The estimation of parameters using maximum likelihood estimation has been discussed. Finally, the goodness of fit of the distribution has been presented for two real lifetime datasets and compared with several one and two-parameter well-known lifetime distributions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
46. Statistical modelling of COVID-19 pandemic development applying branching processes.
- Author
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Atanasov, D., Stoimenova, Vessela, and Yanev, Nikolay M.
- Subjects
- *
BRANCHING processes , *COVID-19 pandemic , *STATISTICAL models , *COVID-19 , *PARAMETER estimation - Abstract
In this paper, a statistical model for COVID-19 infection dynamics is described, using only the observed daily statistics of infected individuals. For this purpose, two special classes of branching processes without or with an immigration component are considered. These models are intended to estimate the main parameter of the infection and to give a prediction of the mean value of the non-observed population of the infected individuals. This is a serious advantage in comparison with other more complicated models where the officially reported data are not sufficient for estimation of the model parameters. The model is applied for different regions in the world and the corresponding parameters of the infection dynamics are estimated. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
47. A new INAR model based on Poisson-BE2 innovations.
- Author
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Zhang, Jiayue, Zhu, Fukang, and Khan, Naushad Mamode
- Subjects
- *
MAXIMUM likelihood statistics , *LEAST squares , *PARAMETER estimation , *DIFFUSION of innovations - Abstract
In this paper, two-parameter Poisson binomial-exponential 2 (PBE2) distribution is firstly reviewed, then a new integer-valued autoregressive (INAR) model with PBE2 innovations is proposed. The definition and statistical properties of the proposed model are given, including the mean, variance, covariance, strict stationarity and ergodicity. Two-step conditional least squares and conditional maximum likelihood estimation methods are considered to estimate the parameters. To assess the proposed model, a crime data set is analyzed and a comparison with other competing INAR models is given, which shows that the proposed model yields a better performance. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. A reversed-hazard-based nonlinear model for one-way classification.
- Author
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Baratnia, Mohammad and Doostparast, Mahdi
- Subjects
- *
LIKELIHOOD ratio tests , *CLASSIFICATION , *HAZARD function (Statistics) - Abstract
This paper proposes a new statistical method for one-way classification which is based on the (reversed) hazard function of the response variable. The model parameters are estimated according the maximum likelihood approach. Several testing procedures, e.g., generalized likelihood ratio test, are investigated to assess homogeneity of populations. A non-parametric method for data analysis is also proposed. Two data sets are studied using the obtained results. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
49. Neutrosophic Laplace Distribution with Application in Financial Data Analysis.
- Author
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Thakur, Rahul, Malik, S. C., and Raj, Masum
- Subjects
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LAPLACE distribution , *CORPORATE finance , *DISTRIBUTION (Probability theory) , *RATE of return on stocks , *FINANCIAL markets , *CONTINUOUS distributions - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
50. Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation.
- Author
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Storcz, Tamás, Várady, Géza, Kistelegdi, István, and Ercsey, Zsolt
- Subjects
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ENERGY consumption , *REGRESSION analysis , *HUMAN comfort , *POINT cloud , *ENERGY futures - Abstract
Optimal building design in terms of comfort and energy performance means designing and constructing a building that requires the minimum energy demand under the given conditions while also providing a good level of human comfort. This paper focuses on replacing the complex energy and comfort simulation procedure with fast regression model-based processes that encounter the building shape as input. Numerous building shape descriptors were applied as inputs to several regression models. After evaluating the results, it can be stated that, with careful selection of building geometry describing design input variables, complex energy and comfort simulations can be approximated. Six different models with five different building shape descriptors were tested. The worst results were around R2 = 0.75, and the generic results were around R2 = 0.92. The most accurate prediction models, with the highest level of accuracy (R2 > 0.97), were linear regressions using 3rd power and dense neural networks using 1st power of inputs; furthermore, averages of mean absolute percentage errors are 1% in the case of dense neural networks. For the best performance, the building configuration was described by a discrete functional point cloud. The proposed method can effectively aid future building energy and comfort optimization processes. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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