On Modeling Bivariate Left Censored Data using Reversed Hazard Rates

When the observations are not quantified and are known to be less than a threshold value, the concept of left censoring needs to be included in the analysis of such datasets. In many real multi component lifetime systems left censored data is very common. The usual assumption that components which are part of a system, work independently seems not appropriate in a number of applications. For instance it is more realistic to acknowledge that the working status of a component affects the remaining components. When you have left-censored data, it is more meaningful to use the reversed hazard rate, proposed as a dual to the hazard rate. In this paper, we propose a model for left-censored bivariate data incorporating the dependence enjoyed among the components, based on a dynamic bivariate vector reversed hazard rate proposed in Gurler (1996). The properties of the proposed model is studied. The maximum likelihood method of estimation is shown to work well for moderately large samples. The Bayesian approach to the estimation of parameters is also presented. The complexity of the likelihood function is handled through the Metropolis - Hastings algorithm. This is executed with the MH adaptive package in r. Different interval estimation techniques of the parameters are also considered. Applications of this model is demonstrated by illustrating the usefulness of the model in analyzing real data

On a Model for Bivariate Left Censored Data

The lifetimes of subjects which are left-censored lie below a threshold value or a limit of detection. A popular tool used to handle left-censored data is the reversed hazard rate. In this work, we study the properties and develop characterizations of a class of distributions based on proportional reversed hazard rates used for analyzing left censored data. These characterizations are applied to simulate samples as well as analyze real data using distributions belonging to this class.Comment: 16 pages, 4 figure

Further Results on the Bivariate Semi-parametric Singular Family of Distributions

General classes of bivariate distributions are well studied in literature. Most of these classes are proposed via a copula formulation or extensions of some characterisation properties in the univariate case. In Kundu(2022) we see one such semi-parametric family useful to model bivariate data with ties. This model is a general semi-parametric model with a baseline. In this paper we present a characterisation property of this class of distributions in terms of a functional equation. The general solution to this equation is explored. Necessary and sufficient conditions under which the solution becomes a bivariate distribution is investigated

A New Modified MPPT Controller for Indirect Vector Controlled Induction Motor Drive with Variable Irradiance and Variable Temperature

Due to the increase in power demand and the earth natural resources are depleting day by day, renewable energy sources have become an important alternate and solar energy is mainly used. In order to track the radiations from the sun in an efficient manner the maximum power point tracking (MPPT) controller is used. But the existed MPPT controllers were developed based upon the ideal characteristics of constant irradiation and temperature. To overcome the above problem a practical data is considered for designing of MPPT controller which is based upon variable irradiance under various temperatures. The output obtained from the MPPT is given to the boost converter with an inverter to find the performance of an indirect vector controlled induction motor drive under different operating conditions. For inverter control, a SVM algorithm in which the calculation of switching times proportional to the instantaneous values of the reference phase voltage. It eliminates the calculation of sector and angle information. The torque ripple and the performance of induction motor drive with ideal and practical data MPPT controllers are compared under different operating conditions. An experimental validation is carried out and the comparison is made with the simulation results. Keywords: maximum power point tracking, variable irradiance, indirect vector controlled, total harmonic distortions, space vector modulation, induction motor drive and torque ripple

Total harmonic distortion analysis of inverter fed induction motor drive using neuro fuzzy type-1 and neuro fuzzy type-2 controllers

Introduction. When the working point of the indirect vector control is constant, the conventional speed and current controllers operate effectively. The operating point, however, is always shifting. In a closed-system situation, the inverter measured reference voltages show higher harmonics. As a result, the provided pulse is uneven and contains more harmonics, which enables the inverter to create an output voltage that is higher. Aim. A space vector modulation (SVM) technique is presented in this paper for type-2 neuro fuzzy systems. The inverter’s performance is compared to that of a neuro fuzzy type-1 system, a neuro fuzzy type-2 system, and classical SVM using MATLAB simulation and experimental validation. Methodology. It trains the input-output data pattern using a hybrid-learning algorithm that combines back-propagation and least squares techniques. Input and output data for the proposed technique include information on the rotation angle and change of rotation angle as input and output of produced duty ratios. A neuro fuzzy-controlled induction motor drive’s dynamic and steady-state performance is compared to that of the conventional SVM when using neuro fuzzy type-2 SVM the induction motor, performance metrics for current, torque, and speed are compared to those of neuro fuzzy type-1 and conventional SVM. Practical value. The performance of an induction motor created by simulation results are examined using the experimental validation of a dSPACE DS-1104. For various switching frequencies, the total harmonic distortion of line-line voltage using neuro fuzzy type-2, neuro fuzzy type-1, and conventional based SVMs are provided. The 3 hp induction motor in the lab is taken into consideration in the experimental validations.Вступ. Коли робоча точка непрямого векторного управління стала, традиційні регулятори швидкості та струму працюють ефективно. Проте робоча точка постійно змінюється. У ситуації закритої системи виміряна інвертором опорна напруга показує вищі гармоніки. В результаті імпульс, що подається, нерівномірний і містить більше гармонік, що дозволяє інвертору створювати більш високу вихідну напругу. Мета. У цій статті представлена методика просторової векторної модуляції (SVM) для нейронечітких систем типу 2. Продуктивність інвертора порівнюється з продуктивністю нейронечіткої системи типу 1, нейронечіткої системи типу 2 та класичної SVM з використанням моделювання MATLAB та експериментальної перевірки. Методологія. Навчається шаблон даних введення-виводу, використовуючи алгоритм гібридного навчання, який поєднує у собі методи зворотного поширення помилки та методу найменших квадратів. Вхідні та вихідні дані для запропонованої методики включають інформацію про кут повороту і зміну кута повороту як отримані вхідні і вихідні коефіцієнти заповнення. Динамічні характеристики приводу асинхронного двигуна з нейронечітким управлінням порівнюються з характеристиками звичайного SVM. При використанні нейронечіткого SVM типу 2 асинхронний двигун, показники продуктивності по струму обертаючого моменту і швидкості порівнюються з показниками приводу асинхронного двигуна з нейронечітким управлінням типу 1 та традиційного SVM. Практична цінність. Продуктивність асинхронного двигуна, створеного за результатами моделювання, досліджується з використанням експериментальної перевірки dSPACE DS-1104. Для різних частот перемикання розраховуються загальні гармонічні спотворення лінійної напруги з використанням нейронечіткого управління  типу 2, нейронечіткого управління типу 1 і традиційного SVM. Асинхронний двигун потужністю 3 л.с. у лабораторії враховується під час експериментальних перевірок

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs

Let G be a finite graph with p vertices and q edges. A vertex magic total labeling is a bijection f from V(G)∪E(G) to the consecutive integers 1, 2, ..., p+q with the property that for every u∈V(G) , f( u)+ ∑f(uv)=K for some constant k. Such a labeling is E-super if f :E(G)→{1, 2,..., q}. A graph G is called E-super vertex magic if it admits an E-super vertex magic labeling. In this paper, we solve two open problems given by Marimuthu, Suganya, Kalaivani and Balakrishnan (Marimuthu et al., 2015)

An Empirical Study On Text Summarization Techniques By Integrating NLP With Machine And Deep Learning Techniques

From the past few decades, data storage in multiple sources is getting more attention. Due to either time constraints in the current scenario or busy life in the co-operate world irrespective of the age factor, people did attract towards reading the in short news to get acquainted with the national and international news especially in their regional languages. So, the proposed paper has conducted an empirical study on the regional language "Telugu," summarization that generates a brief note of huge texts stored in multiple databases. In the early days, text summarization does perform with traditional NLP approaches, with the advancement of Artificial Intelligence; it has spread its wings to the world of NLP also, to summarize the text smartly. Smart Text Summarization technique can reduce the time and work a lot for any human to understand the exact purpose of that document. However, the real complexity arises while developing such an abstract summary by choosing the required words or phrases that fit the whole document. Some kinds of texts also were used to find its sensitivity which is frequently used in social media texts, reviews, and e-commerce sites to know the exact view of the customer or the person

Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism

Segmenting skin lesions from dermoscopic images is essential for diagnosing skin cancer. But the automatic segmentation of these lesions is complicated due to the poor contrast between the background and the lesion, image artifacts, and unclear lesion boundaries. In this work, we present a deep learning model for the segmentation of skin lesions from dermoscopic images. To deal with the challenges of skin lesion characteristics, we designed a multi-scale feature extraction module for extracting the discriminative features. Further in this work, two attention mechanisms are developed to refine the post-upsampled features and the features extracted by the encoder. This model is evaluated using the ISIC2018 and ISBI2017 datasets. The proposed model outperformed all the existing works and the top-ranked models in two competitions

Evaluation of puddle splash in automotive applications using smoothed particle hydrodynamics

The impact of splashing water as a car moves through water-soil puddle or flooded areas is significant to the automotive industry. Due to the multifold advantages of simulating this problem, several numerical approaches exists to understand the fluid dynamics and the fluid-structure interaction in such scenarios. The current research focuses on obtaining fluid dynamics of the splashing phenomena as a first step towards simulating such problems through a computational Fluid Dynamic (CFD) approach by adopting the Smoothed Particle Hydrodynamics (SPH). As a mesh-free Lagrangian-based method, the SPH framework tracks particle behavior in the computational domain at each instant of dynamic simulations. In contrast to traditional grid-based methods, SPH is well suited for simulating fluid dynamic problems involving free-surfaces, multi-phase flows, and involving objects with high degree of deformations. The current study presents results and discusses the observations from simulating a car Body-In-White (BIW) geometry with four tires that moves through a water puddle as a normal car would. The SENSE solver developed at ESS Engineering Software Steyr adopts the SPH framework and presents several formulations. The solvers are implemented on Graphics Processing Units (GPU) to enable its usage for industrial applications. The framework is developed to be easily parallelizable allowing multiple GPU simulations, that are useful for industrial problems involving huge number of particles. In addition to the ease of scaling, it also permits computations at higher particle resolutions when needed to handle specific physical constraints of a problem. The simulations discussed in the current study were performed using the Divergence-Free (DF-SPH) formulation in SPH, and implemented on multiple-GPU devices, with a particle discretization that allows to see the properties to the order of 5 mm