Relations between the continuous and the discrete Lotka power function

One confusing question over a long period of time is how transfer the discrete function transfers into continuous function. Recently the issue has been resolved but some details of the transformation process will be introduced in the paper. The correlation coefficients of 100,000 values are established from the two groups of data with the range between -1 and 1, creating a histogram from these correlation coefficient values known as “the probability mass function.” The coefficient values are brought into the discrete distribution function, so that transfers into the discrete cumulative function, next converted into a continuous cumulative function, next which is differentiated to get the density function, so that it is easy to being research analysis. A model will be established during the process of the conversion what the medium is “the least squares algorithm.” Finally, when the integral of the area within the range of the density function equals to 1, this implies that the transformation complete succeeds from the discrete function to the continuous function.

Number of software Reliability growth models has been proposed in the literature. A mathematical technique which describes the software testing phenomenon known as the software reliability growth model. Software reliability growth models are used to predict the number of faults and reliability of the software. In the view of this software reliability growth models are basically differentiated as the continuous and discrete models. There is a plenty of development in the continuous models but little towards the discrete models. In this paper we have presented a discrete reliability growth model with different discrete testing effort functions and the same time software release policy is discussed. A new imperfect debugging discrete software reliability growth model with testing effort is proposed. All calculations are done on real data. The results shows the proposed testing effort models are perfectly fit to the data.

The identification of continuous plants from sampled data is a technique commonly used. This method is normally divided into two parts: the determination of the discrete transfer function from the sampled data and the calculation of the continuous transfer function from the discrete one. An original method to perform the second part of this problem is described. The method, which establishes the minimum condition under which the continuous model can be obtained from the discrete one, has shown to have important advantages in the treatment of multiple poles. The method allows one to assume any temporal behavior of the input signal between sampling instants.

This article focuses on the integral theory of discrete regular quaternionic functions and the connection between discrete and classical quaternionic analysis. We develop a new approach to the discrete integral theory based on the concepts ‘discrete boundary measure’ and ‘discrete normal vector’. By combining the discrete integral theory and our results on discrete approximations to domains in \({\mathbb {R}}^4\), we reveal a convergence relation between the discrete and continuous regular functions: a quaternionic function is regular if and only if it is the scaling limit of some discrete regular functions.

Continuous action systems (CAS) is a formalism intended for modeling hybrid systems (systems that combine discrete control with continuous behavior), and proving properties about the model within refinement calculus. We use a symbolic manipulation program to build a tool for simulating CAS models by, calculating symbolically the time evolution of the discrete and continuous CAS model functions, as explicit and exact expressions of a continuous time variable. We may then study the time behavior and general properties of the model by plotting these functions with respect to time. For certain models our tool eliminates the need for introducing tolerances into the model structure. The tool is useful for checking that the model behaves correctly, and we can sometimes study the behavior of CAS models with in principle infinite precision.

Comparison between error correcting output codes and fuzzy support vector machines

We study threshold regression models that allow the relationship between the outcome and a covariate of interest to change across a threshold value in the covariate. In particular, we focus on continuous threshold models, which experience no jump at the threshold. Continuous threshold regression functions can provide a useful summary of the association between outcome and the covariate of interest, because they offer a balance between flexibility and simplicity. Motivated by collaborative works in studying immune response biomarkers of transmission of infectious diseases, we study estimation of continuous threshold models in this article with particular attention to inference under model misspecification. We derive the limiting distribution of the maximum likelihood estimator, and propose both Wald and test-inversion confidence intervals. We evaluate finite sample performance of our methods, compare them with bootstrap confidence intervals, and provide guidelines for practitioners to choose the most appropriate method in real data analysis. We illustrate the application of our methods with examples from the HIV-1 immune correlates studies.

A genetic algorithm with discrete cost functions is used to design and optimise a 42 V-14 V/8 A buck converter. The use of discrete and continuous cost functions are confronted. Discrete cost functions produces better designs. Electrical time domain simulation is used for verification. Experimental measurement, electrical and thermal, validates the design.

Typhoid fever is a contagious disease that is generally caused by bacteria known as Salmonella typhi. This disease spreads through manure contamination of food or water and infects unprotected people. In this work, our focus is to numerically examine the dynamical behavior of a typhoid fever nonlinear mathematical model. To achieve our objective, we utilize a conditionally stable Runge–Kutta scheme of order 4 (RK-4) and an unconditionally stable non-standard finite difference (NSFD) scheme to better understand the dynamical behavior of the continuous model. The primary advantage of using the NSFD scheme to solve differential equations is its capacity to discretize the continuous model while upholding crucial dynamical properties like the solutions convergence to equilibria and its positivity for all finite step sizes. Additionally, the NSFD scheme does not only address the deficiencies of the RK-4 scheme, but also provides results that are consistent with the continuous system's solutions. Our numerical results demonstrate that RK-4 scheme is dynamically reliable only for lower step size and, consequently cannot exactly retain the important features of the original continuous model. The NSFD scheme, on the other hand, is a strong and efficient method that presents an accurate portrayal of the original model. The purpose of developing the NSFD scheme for differential equations is to make sure that it is dynamically consistent, which means to discretize the continuous model while keeping significant dynamical properties including the convergence of equilibria and positivity of solutions for all step sizes. The numerical simulation also indicates that all the dynamical characteristics of the continuous model are conserved by discrete NSFD scheme. The theoretical and numerical results in the current work can be engaged as a useful tool for tracking the occurrence of typhoid fever disease.

Optimal configuration of a discrete fluid power force system utilised in the PTO for WECs

Computation of bounds for transient measures of large rewarded Markov models using regenerative randomization

While frequency-based methods for the characterization of fractals are popular and effective in many applications, they have limitations when applied to irregularly shaped images, such as nuclear images. The irregularity renders texture characterization by frequency domain methods, based upon Fourier transform, problematic. To address this situation, this paper presents an algorithm based upon the signal analysis in the spatial domain. An autocovariance function can be estimated regardless of the shape and size of regions where the image is defined. As in the continuous fractional Brownian motion (FBM) that results from inputting white noise into a specific fractional integral system, a discrete FBM can be related to white noise by a specific fractional summation system (FSS) that is linear, causal and shift-invariant. Although the method of direct sampling is not valid for converting a continuous fractional integral to a discrete fractional summation, discrete fractional summations similar to the sampled system functions can be obtained through an iterative process. While the continuous system function of a fractional integral is linear in the frequency domain when plotted in log-log scales, unfortunately, it is not true for the comparable discrete system function. The discrete system function is actually approximately linear in the log-log scales over a very limited range. The slope of the straight line that approximates the function curve in the mean-square-error (MSE) sense in a specific time range provides a description of the autocovariance function that reveals the statistical relations among the local textures. Applications to characterization of ovary nuclear images in groups of normal, atypical and cancer cases are studied and presented.

This article provides a brief analysis of the error in calculating the discrete crosscorrelation function of the transit-time ultrasonic flowmeter signals. Special attention is paid to the study of the influence of the obtained discrete correlation function inaccuracy on the ultrasonic flowmeter’s propagation times determining error. It is known that for real time-limited acoustic signals, the discrete correlation function values are calculated with a significant error. The consequence of this is the appearance of the measurement error of the time delay between signals. The reason for this phenomenon is incorrect truncation of finite length digital sequences of the received acoustic signals. The report presents and describes an improved cross-correlation method for determining the time difference. The new algorithm takes into account the existing discretizing parameters of the received UPS – sampling frequency, sequence size and the truncated signal’s shape. Theoretical analytical expressions for the signals discrete cross-correlation function estimating are obtained as an approximation of a continuous function (the method of trapezoids and Simpson is used as an integral replacement). The numerical simulation by MatLab explains the error formation essence in the signal times difference calculating.

There are multiscale coupling relationships during the operation of dc/dc converters, where the macroscale is the overall working principle of the circuit and the microscale is the device mechanism model of the circuit components. Modeling only at the macroscale cannot reflect the effect of the device's internal effects on the circuit, while modeling the device at the microscale cannot show the operating characteristics of the circuit. To obtain the analytical solutions of the state variables for dc/dc converters at circuit- and device-level scales, a method for establishing a multiscale model of a dc/dc converter is proposed. Firstly, the device mechanism at the microscale is appropriately combined with the circuit model at the macroscale. Secondly, the traditional piecewise switching function that represents the switching state is replaced by a continuous switching fitting function. Thereby, a continuous multiscale unified model of the dc/dc converter is reached. Afterward, by adopting the improved equivalent small parameter (ESP) method proposed in this article, the steady-state analytical solutions of the model in the open- and closed-loop states can be obtained, respectively. Finally, the steady-state analytical solutions are compared with the results of simulation and experiment.

Numeracy is the ability to understand and work with numbers. It is a necessary skill for composing and understanding documents in clinical, scientific, and other technical domains. In this paper, we explore different strategies for modelling numerals with language models, such as memorisation and digit-by-digit composition, and propose a novel neural architecture that uses a continuous probability density function to model numerals from an open vocabulary. Our evaluation on clinical and scientific datasets shows that using hierarchical models to distinguish numerals from words improves a perplexity metric on the subset of numerals by 2 and 4 orders of magnitude, respectively, over non-hierarchical models. A combination of strategies can further improve perplexity. Our continuous probability density function model reduces mean absolute percentage errors by 18% and 54% in comparison to the second best strategy for each dataset, respectively.