Search results

The purpose of this study is to develop Markovian model to obtain the transient probabilities to determine mean-time-to-failure and reliability function and further steady state availability of the repairable system. As the system parameters are uncontrollable factors; thus the life times, repair times and recovery/reboot time are assumed to be as uncertain or fuzzified distributions.

The fuzzy approach is introduced to investigate the reliability measures of load sharing repairable system which consists of two operating units and one standby unit. On the failure of an operating component, it is instantly spotted, located and sent for recovery procedures with coverage probability. In case of imperfect recovery, reboot takes place.

On the basis of extension principle and mathematical programming approach, the authors establish membership functions for system characteristics with the help of α-cuts. To demonstrate the practical validity of the proposed fuzzified model, numerical illustrations are performed.

The model proposed for reliability analysis may cheer up the continuance of the work towards more applications in repairable systems. Therefore, the reader is provided with useful intuition into the nature of fuzzy computations and practical amendments while measuring ambiguous data.

The current‐voltage (Id—Vd) characteristics and microwave performance of Si1−xGex MESFETs are discussed. The 2D Poisson's equation along with the drift and diffusion equation are solved using a finite difference technique to calculate device parameters such as gm and fT. The low field carrier mobility is computed by using a single partice Monte Carlo program. In the simulation all relevant scattering mechanisms are accounted for.

The purpose of this paper is to develop an efficient numerical algorithm for the self‐consistent solution of Schrodinger and Poisson equations in one‐dimensional systems. The goal is to compute the charge‐control and capacitance‐voltage characteristics of quantum wire transistors.

The paper presents a numerical formulation employing a non‐uniform finite difference discretization scheme, in which the wavefunctions and electronic energy levels are obtained by solving the Schrödinger equation through the split‐operator method while a relaxation method in the FTCS scheme (“Forward Time Centered Space”) is used to solve the two‐dimensional Poisson equation.

The numerical model is validated by taking previously published results as a benchmark and then applying them to yield the charge‐control characteristics and the capacitance‐voltage relationship for a split‐gate quantum wire device.

The paper helps to fulfill the need for C‐V models of quantum wire device. To do so, the authors implemented a straightforward calculation method for the two‐dimensional electronic carrier density n(x,y). The formulation reduces the computational procedure to a much simpler problem, similar to the one‐dimensional quantization case, significantly diminishing running time.

The purpose of this paper is to propose that the grey tolerance rough set (GTRS) and construct the GTRS-based classifiers.

The authors use grey relational analysis to implement a relationship-based similarity measure for tolerance rough sets.

The proposed classification method has been tested on several real-world data sets. Its classification performance is comparable to that of other rough-set-based methods.

The authors design a variant of a similarity measure which can be used to estimate the relationship between any two patterns, such that the closer the relationship, the greater the similarity will be.

Notes that the real test of maintenance stratagem success (or failure in financial terms) can only be resolved when a comparison of machine maintenance costs can be made to some benchmark standard. Presents a comparative study between two models developed to predict the average hourly maintenance cost of tracked hydraulic excavators operating in the UK opencast mining industry. The models use the conventional statistical technique multiple regression, and artificial neural networks. Performance analysis using mean percentage error, mean absolute percentage error and percentage cost accuracy intervals was conducted. Results reveal that both models performed well, having low mean absolute percentage error values (less than 5 percent) indicating that predictor variables were reliable inputs for modelling average hourly maintenance cost. Overall, the neural network model performed slightly better as it was able to predict up to 95 percent of cost observations to within ≤q £5. Moreover, summary statistical analysis of residual values highlighted that predicted values using the neural network model are less subject to variance than the multiple regression model.

Energy demand is an important economic index, and demand forecasting has a significant role when devising energy development plans for cities or countries. GM(1,1) model has become popular because it needs only a few data points to construct a time-series model without statistical assumptions. Several methods have been developed to improve prediction accuracy of the original GM(1,1) model by only estimating the sign of each residual. This study aims to address that this is too tight a restriction for the modification range.

Based on the predicted residual, this study uses the functional-link net (FLN) with genetic-algorithm-based learning to estimate the modification range for its corresponding predicted value obtained from the original GM(1,1) model.

The forecasting ability of the proposed grey prediction model is verified using real energy demand cases from China. Experimental results show that the proposed prediction model performs well compared to other grey residual modification models with sign estimation.

The proposed FLNGM(1,1) model can improve prediction accuracy of the original GM(1,1) model using residual modification. The distinctive feature of the proposed model is to use an FLN to estimate sign and modification range simultaneously for the predicted value based on its corresponding predicted residual obtained from the residual GM(1,1) model.

Continuous glucose monitoring (CGM) is a notable invention introduced in the biomedical industry. It provides valuable information about intermittent capillary blood glucose that is normally unattainable by regular clinical blood sample tests. CGM includes several progressive facilities such as instantaneous and real-time display of blood glucose level, “24/7” coverage, continuous motion of alerts for actual or impending hypo- and hyperglycemia and the ability to characterize glycemic variability. CGM allows users and physicians to visualize and diagnose more accurate and precise rate of change of glucose by capacitating small, comfortable, user-friendly sensor devices. Sometimes, this vital information is shared to the personal message box over Internet. In short, CGM is capable to inform, educate, motivate and alert (IEMA) people with diabetes. Despite the huge expectation with CGM, the available solutions have not attracted much attention among people. The huge potential of CGM in future diabetic study relies on the successful implication of the CGM. This paper aims at disseminating of state-of-the-art knowledge about existing work around the CGM.

This paper presents a comprehensive systematic review on the recent developments in CGM development techniques that have been reported in credible sources, namely PubMed, IEEE Xplore, Science Direct, Springer Link, Scopus and Google Scholar. Detailed analysis and systematic comparison are provided to highlight the achievement and future direction of CGM deployment.

Several key challenges are also portrayed for suitable opportunistic orientation. CGM solutions from four leading manufacturers such as Tandem, Dexcom, Abbott and Medtronic are compared based on the following factors including accuracy (% MARD); sensor lifetime, calibration requirement, smart device, compatibility and remote monitoring. Qualitative and quantitative analyses are performed.

This work can be a valuable source of reference and guidance for future research in this field.

The purpose of this paper is to study the performance metrics of redundant repairable machining system which is applicable in various systems like computer and communication system, manufacturing and production system, etc.

In the present investigation, the authors develop Markov model for the system consisting of identical active operating machines which are prone to breakdown. The operating machines are under the care of one permanent repair facility that provides time-sharing basis repair services. The maintenance is facilitated with the provision of standby machines of mixed type and permanent as well as additional repair facility. From the economic point of view, F-policy and N-policy to control the service and arrival of failed machines effectively are included.

For the performance analysis of the system in long run, the authors compute steady-state probabilities using product-type solution method recursively. Sensitivity analysis is performed numerically for various parameters by developing code in MATLAB.

The performance prediction done may be helpful for the system designers and decision makers for the improvement of the existing machining systems in various industries.

Markovian model for the performance prediction of fault tolerant multi-identical operating and standby machines redundant system is developed in generic frameworks by incorporating many noble features which were not all taken together by other researchers working on the same lines. The key concepts incorporated for the modeling of the concerned system is: F-policy, N-policy, time-sharing, and sensitivity analysis of availability and cost function.

Service-oriented architecture is an emerging software architecture, in which web service (WS) plays a crucial role. In this architecture, the task of WS composition and verification is required when handling complex requirement of services from users. When the number of WS becomes very huge in practice, the complexity of the composition and verification is also correspondingly high. In this paper, the authors aim to propose a logic-based clustering approach to solve this problem by separating the original repository of WS into clusters. Moreover, they also propose a so-called quality-controlled clustering approach to ensure the quality of generated clusters in a reasonable execution time.

The approach represents WSs as logical formulas on which the authors conduct the clustering task. They also combine two most popular clustering approaches of hierarchical agglomerative clustering (HAC) and k-means to ensure the quality of generated clusters.

This logic-based clustering approach really helps to increase the performance of the WS composition and verification significantly. Furthermore, the logic-based approach helps us to maintain the soundness and completeness of the composition solution. Eventually, the quality-controlled strategy can ensure the quality of generated clusters in low complexity time.

The work discussed in this paper is just implemented as a research tool known as WSCOVER. More work is needed to make it a practical and usable system for real life applications.

In this paper, the authors propose a logic-based paradigm to represent and cluster WSs. Moreover, they also propose an approach of quality-controlled clustering which combines and takes advantages of two most popular clustering approaches of HAC and k-means.