Stochastic Comparisons of Lifetimes of Series and Parallel Systems with Dependent Heterogeneous MOTL-G Components under Random Shocks

To measure the magnitude among random variables, we can apply a partial order connection defined on a distribution class, which contains the symmetry. In this paper, based on majorization order and symmetry or asymmetry functions, we carry out stochastic comparisons of lifetimes of two series (parallel) systems with dependent or independent heterogeneous Marshall–Olkin Topp Leone G (MOTL-G) components under random shocks. Further, the effect of heterogeneity of the shape parameters of MOTL-G components and surviving probabilities from random shocks on the reliability of series and parallel systems in the sense of the usual stochastic and hazard rate orderings is investigated. First, we establish the usual stochastic and hazard rate orderings for the lifetimes of series and parallel systems when components are statistically dependent. Second, we also adopt the usual stochastic ordering to compare the lifetimes of the parallel systems under the assumption that components are statistically independent. The theoretical findings show that the weaker heterogeneity of shape parameters in terms of the weak majorization order results in the larger reliability of series and parallel systems and indicate that the more heterogeneity among the transformations of surviving probabilities from random shocks according to the weak majorization order leads to larger lifetimes of the parallel system. Finally, several numerical examples are provided to illustrate the main results, and the reliability of series system is analyzed by the real-data and proposed methods.

Real-Time Monitoring of the Vacuum Degree Based on the Partial Discharge and an Insulation Supplement Design for a Distribution Class Vacuum Interrupter

The internal pressure of a vacuum interrupter (VI) is increased by arc heat, ceramic cracking, gas leakage, and manufacturing defects. Accordingly, the dielectric strength of VI rapidly decreases. To improve the reliability of power transmission, efficient maintenance through the real-time monitoring of the vacuum degree is essential. However, real-time monitoring of the vacuum degree is difficult, and related research is scarce. Additionally, due to the insulation problems of this technology, there are few commercially available products. Therefore, this paper proposes a method for real-time monitoring of the vacuum degree and an insulation supplement design for a distribution class VI. First, dielectric experiments were conducted to identify the section in which the dielectric strength of the VI rapidly decreased according to the vacuum degree. Second, for real-time monitoring of the VI, several factors were proposed through the partial discharge in the VI, while the capacitance characteristics of the VI were calculated to improve the signal of the internal partial discharge. Finally, to supplement the dielectric problems of the solid insulation high voltage apparatus that occur when real-time monitoring technology is applied, the insulation supplement design was performed through the finite element method (FEM).

Further Results of the TTT Transform Ordering of Order n

To compare the variability of two random variables, we can use a partial order relation defined on a distribution class, which contains the anti-symmetry. Recently, Nair et al. studied the properties of total time on test (TTT) transforms of order n and examined their applications in reliability analysis. Based on the TTT transform functions of order n, they proposed a new stochastic order, the TTT transform ordering of order n (TTT-n), and discussed the implications of order TTT-n. The aim of the present study is to consider the closure and reversed closure of the TTT-n ordering. We examine some characterizations of the TTT-n ordering, and obtain the closure and reversed closure properties of this new stochastic order under several reliability operations. Preservation results of this order in several stochastic models are investigated. The closure and reversed closure properties of the TTT-n ordering for coherent systems with dependent and identically distributed components are also obtained.

Sinus Augmentation—Expect the Unexpected: Diagnostic Anatomical Study

“Big-nose variant” is an anatomical phenomenon defined as the pneumatization of inferior third of the nasal cavity within the alveolar ridge while simultaneously displacing the maxillary sinus laterally. The purpose of the present study was to assess the prevalence of the big-nose variant phenomenon and suggest a morphology classification system. Diagnostic anatomical evaluation was performed in a tertiary medical center on 321 randomly selected maxillary cone beam computerized tomography scans of patients who presented at an oral and maxillofacial department. Two anatomical categories were defined for anatomical identification: classes for horizontal mesiodistal distribution, and divisions for vertical distribution. Class 2, defined as location of the nasal/sinus border between the distal edge of the canine up to the distal edge of second premolar, was found to be the most prevalent (64.6%). Class 3, defined as location of the nasal/sinus border distal to mesial edge of the first molar, was found in 17.9% of cases. Regarding the divisions category, in 96% and 58.2% of teeth examined, nasal cavity alone was found to be superior to the canine and first premolar, respectively, defined as Division A. In 46.9% and 85.6% of teeth examined, maxillary sinus alone was located above the second premolar and first molar, respectively, defined as Division C. Identifying Class 3 on the paraxial reconstruction is the first step in identifying big-nose variant, with further assurance gained from each determining division. The use of the classes and divisions may enable better maxillary treatment planning, alert surgeons for the unexpected, and avoid complications.

AlertNet: Deep convolutional-recurrent neural network model for driving alertness detection

Drowsy driving is one of the major problems which has led to many road accidents. Electroencephalography (EEG) is one of the most reliable sources to detect sleep on-set while driving as there is the direct involvement of biological signals. The present work focuses on detecting driver’s alertness using the deep neural network architecture, which is built using ResNets and encoder-decoder based sequence to sequence models with attention decoder. The ResNets with the skip connections allow training the network deeper with a reduced loss function and training error. The model is built to reduce the complex computations required for feature extraction. The ResNets also help in retaining the features from the previous layer and do not require different filters for frequency and time-invariant features. The output of ResNets, the features are input to encoder-decoder based sequence to sequence models, built using Bi-directional long-short memories. Sequence to Sequence model learns the complex features of the signal and analyze the output of past and future states simultaneously for classification of drowsy/sleepstage-1 and alert stages. Also, to overcome the unequal distribution (class-imbalance) data problem present in the datasets, the proposed loss functions help in achieving the identical error for both majority and minority classes during the raining of the network for each sleep stage. The model provides an overall-accuracy of 87.92% and 87.05%, a macro-F1-core of 78.06%, and 79.66% and Cohen's-kappa score of 0.78 and 0.79 for the Sleep-EDF 2013 and 2018 data sets respectively.

Modelling Long-Term Monthly Rainfall Variability in Selected Provinces of South Africa: Trend and Extreme Value Analysis Approaches

Extreme rainfall events have made significant damages to properties, public infrastructure and agriculture in some provinces of South Africa notably in KwaZulu-Natal and Gauteng among others. The general global increase in the frequency and intensity of extreme precipitation events in recent years is raising a concern that human activities might be heavily disturbed. This study attempts to model long-term monthly rainfall variability in the selected provinces of South Africa using various statistical techniques. The study investigates the normality and stationarity of the underlying distribution of the whole body of rainfall data for each selected province, the long-term trends of the rainfall data and the extreme value distributions which model the tails of the rainfall distribution data. These approaches were meant to help achieve the broader purpose of this study of investigating the long-term rainfall trends, stationarity of the rainfall distributions and extreme value distributions of monthly rainfall records in the selected provinces of South Africa in this era of climate change. The five provinces considered in this study are Eastern Cape, Gauteng, KwaZulu-Natal, Limpopo and Mpumalanga. The findings revealed that the long-term rainfall distribution for all the selected provinces does not come from a normal distribution. Furthermore, the monthly rainfall data distribution for the majority of the provinces is not stationary. The paper discusses the modelling of monthly rainfall extremes using the non-stationary generalised extreme value distribution (GEVD) which falls under the block maxima extreme value theory (EVT) approach. The maximum likelihood estimation method was used to obtain the estimates of the parameters. The stationary GEVD was found as the best distribution model for Eastern Cape, Gauteng, and KwaZulu-Natal provinces. Furthermore, model fitting supported non-stationary GEVD model for maximum monthly rainfall with nonlinear quadratic trend in the location parameter and a linear trend in the scale parameter for Limpopo, while in Mpumalanga the non-stationary GEVD model with a nonlinear quadratic trend in the scale parameter and no variation in the location parameter fitted well to the monthly rainfall data. The negative values of the shape parameters for Eastern Cape and Mpumalanga suggest that the data follow the Weibull distribution class, while the positive values of the shape parameters for Gauteng, KwaZulu-Natal and Limpopo suggest that the data follow the Fréchet distribution class. The findings from this paper could give information that can assist decision makers establish strategies for proper planning of agriculture, infrastructure, drainage system and other water resource applications in the South African provinces.

A Study of Multilayer Perceptron Networks Applied to Classification of Ceramic Insulators Using Ultrasound

Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator’s experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks’ application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory.

Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

<p><span>The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.</span></p>

DISTRIBUIÇÃO ESPACIAL E ESTRUTURA POPULACIONAL DE Parkia platycephala Benth

O objetivo conhecer o padrão espacial e a estrutura de duas populações de Parkia platycephala, em duas áreas no sul do Piauí. Realizou-se um censo de indivíduos que apresentaram pelo menos um fuste com diâmetro altura do peito (DAP) ≥ 5 cm. Para tanto, as duas áreas amostradas foram divididas em faixas contendo 20 metros de largura. Dentro destas faixas, foram coletadas as coordenadas cartesianas o diâmetro dos indivíduos. Para o cálculo da distribuição espacial, foi aplicado a função K de Ripley univariada. Para a distribuição diamétrica foi utilizada intervalos de classes com amplitudes crescentes. Na área I foram amostrados 101 indivíduos e área basal de 7,744 m2/ha, para a área II, 66 indivíduos e área basal de 4,654 m2/ha. Os indivíduos da P. platycephala apresentou padrão de ocorrência agregado em ambas as áreas. Os indivíduos menores ocorrem próximos dos indivíduos maiores, formando população agregada. Em relação ao padrão espacial dos indivíduos por classe diamétrica predominou-se o aleatório, diferindo em intensidades, à medida que aumenta os diâmetros. As classes de diâmetro e altura revelam possíveis dificuldades de recrutamento, isso porque a maioria dos indivíduos estão acima de 10cm de diâmetro e 6m de altura respectivamente.Palavras-chave: função K de Ripley; análise espacial; conservação. SPATIAL DISTRIBUTION AND POPULATION STRUCTURE OF Parkia platycephala Benth ABSTRACT: The objective was to know the spatial pattern and structure of two populations of Parkia platycephala, in two areas in the south of Piauí. A census was taken of individuals who had at least one bole with breast height diameter (DBH) ≥ 5 cm. To do so, the two areas sampled were divided into tracks containing 20 meters wide. Within these ranges, the Cartesian coordinates were the diameter of the individuals. To calculate the spatial distribution, the univariate Ripley K function was applied. For the diametrical distribution, class intervals with increasing amplitudes were used. In area I, 101 individuals and basal area of 7,744 m2 / ha were sampled, for area II, 66 individuals and basal area of 4,654 m2 / ha. The individuals of P. platycephala showed an aggregate occurrence pattern in both areas. Smaller individuals occur close to larger individuals, forming an aggregate population. In relation to the spatial pattern of the individuals by diametric class the random was predominant, differing in intensities, as the diameters increase. The diameter and height classes reveal possible recruitment difficulties, which is why most individuals are above 10cm in diameter and 6m in height, respectively.Keywords: Ripley's K function; spatial analysis; conservation.