International Journal of Reliability, Quality and Safety Engineering

In this paper, we consider a phased mission system consisting of $k$ phases (subsystems) wherein the control flows sequentially. The mission is successful if tasks associated with every phase are successful. Density and reliability functions of the lifetime of such a system are worked out when the lifetimes of components of the subsystems follow exponential distribution. It is assumed that components in different subsystems are in series configuration and function independently. The subsystems are assumed to operate independently. A simulation study is conducted to find bias of the estimates of the parameters and the reliability function. The Root Mean Square Error (RMSE) of the estimates is also calculated.

Under the condition of small sample, the performance parameters’ interval quantization and instantaneous reliability calculation of solid rocket motor grain structure are researched. Two important mechanical properties of material: Relaxation modulus and Poisson’s ratio are obtained by experiments of grain material. Since the data of parameters obtained are a small sample, the method of gray theory is used to mine the experimental data. It realizes the uncertainty quantitative analysis of grain material performance parameters and obtains the quantitative interval of performance parameters. Considering that the evidence theory can directly attribute probabilistic mass to the set or interval, the instantaneous reliability of grain structure is analyzed based on the evidence theory. By establishing the relationship between failure surface and the frame of discernment, and using the belief function and plausibility function to obtain the upper and lower bound probability distribution of structural reliability and failure probability, the instantaneous reliability probability interval of structure is obtained.

This paper considers a competing risk model defined on the basis of minimum of two dependent failures where the two failures are assumed to jointly follow Marshall–Olkin bivariate Weibull distribution. This paper explores some important features of corresponding likelihood functions and performs a full Bayesian analysis of the model for data resulting from normal as well as accelerated life tests. The accelerated model is described by regressing the scale parameters of the model through inverse power-law relationship. Posterior-based inferences are drawn using the Gibbs sampler algorithm after specifying proper but vague priors for the model parameters. The numerical illustration is provided using real datasets. The performance of the model is assured by Bayesian tools of model compatibility and then the entertained model is compared with the competing risk model based on Marshall–Olkin bivariate exponential assumption.

Transliteration is the process of mapping the character of one language to the character of some other language based on its phonetics. India is very much diverse in languages where people speak different languages. Though they speak different languages, it might be difficult for them to read the script of those many languages. In a situation like this, transliteration process plays a major role. It helps in various Natural Language Processing applications such as Information retrieval, Machine translation, Speech recognition. These are NLP applications which make the computer understand the natural language as to how human being interprets. It helps in translating technical terms and proper names from one language to another language. Moreover, transliteration works have been carried out in languages such as Japanese, Chinese and English. But when considering Indian languages, especially Tamil language, very few recognizable works have been carried out. In this paper, transliteration process is carried out on Unicode Tamil characters. The phonetics-based forward list processing is implemented for transliterating from English language to Tamil language which yields promising results.

In recent years, various researchers have designed a software bug prediction model for classifying the nonfaulty and faulty modules in software that are correlated with software constraints. Software bug or defect prediction helps programmers or developers discover the possibilities of bugs and minimize maintenance costs. However, most approaches do not solve the class-imbalance issue regarding the software bug prediction model. To solve these issues, the latest software bug prediction model using enhanced deep-structured architecture is developed. Here, the software modules are obtained from online sources, which undergo pre-processing to remove unnecessary data. These pre-processed texts are considered for deep feature extraction, performed using a Convolutional Neural Network (CNN) with an adaptive target-based pooling method to get effective deep features. Here, the parameter tuning in CNN is performed using Hybrid Rat-Barnacle Mating Swarm Optimization (HR-BMSO) to enhance the prediction performance. These deep features are inserted into Adaptive Features-based Parameter-Tuned Attention Long Short Term Memory (AF-PTALSTM) for predicting the software bugs, in which the optimization of certain parameters takes place with the same HR-BMSO to get accurate predicted results. The accuracy and $F$1-score of the designed AF-PTALSTM method attain 97% and 94% through analysis. Thus, the experimental analysis of the designed software bug prediction model depicts higher efficiency while estimating with traditional approaches.

This paper introduces the concept of cumulative extropy in a past lifetime using the quantile function and establishes its monotone and ordering properties. It also demonstrates an application of this new measure for various distributions commonly used in lifetime data analysis, and provides a nonparametric estimator for the quantile-based cumulative extropy in a past lifetime.

It is important to carefully monitor the development status of an open source projects because of the large number of people involved in the projects. One of the methods for evaluating project stability is earned value management (EVM), applying EVM to projects requires time-series effort data. However, there are few researches on the calculation of time-series effort data for open source projects, because open source projects do not manage effort as a whole. In this research, we have examined the validity of the calculation method and results of the time-series effort data in the open source projects. In addition, we have verified similarity of proprietary software development projects to open source projects in terms of effort. In particular, the similarity between open source projects and proprietary software development projects is verified by applying the effort data of open source projects to the software reliability growth model (SRGM).