Critical systems are becoming increasingly software intensive, necessitating reliable software to ensure proper operation. Non-homogeneous Poisson process software reliability growth models are commonly used to characterize fault detection as a function of testing time, which enables quantitative assessment of software reliability. Many early models assumed that the testing-effort was constant throughout software testing. To remove this assumption, researchers have proposed models incorporating testing-effort, yet this significantly increases model complexity to the degree that most previous studies utilized a two-step procedure involving least squares estimation (LSE) and algorithms, including Newton's method to estimate the parameters of a testing-effort model. This approach may limit the quality of the model fit achieved. Moreover, the research trend over the past 30 years has been to propose progressively more complex models, sacrificing practical considerations such as predictive accuracy. This paper proposes a two-step procedure that utilizes the expectation conditional maximization (ECM) algorithm, referred to as the ECM/ECM approach, to obtain the parameter estimates of a software reliability growth model incorporating testing-effort. The results of the proposed approach are compared to past methods as well as a simpler model that does not consider testing-effort to assess whether the additional complexity introduced by testing-effort functions compromises predictive accuracy. Our results indicate that the ECM/ECM approach achieves a better goodness of fit with respect to four measures, including three predictive measures. In some cases, the simpler model omitting testing-effort outperforms methods considering testing-effort. These results suggest that the proposed ECM/ECM approach can achieve better parameter estimates than the previously proposed LSE/MLE approach and that algorithms to improve fit and predictive accuracy may better serve users of software reliability models.

Interval estimation in assessment in software systems must be useful because it is hard to obtain a sufficient amount of software reliability data for conducting point estimation and the software reliability data is essentially incomplete. We discuss flexible software reliability measurement considering the uncertainty of model parameters in a reliability model. Concretely, applying a discrete-time domain model, we analyze the Bayesian inference of the parameters in the model by using the Markov chain Monte Carlo method. Furthermore, numerical illustrations of the approach for our method are also shown in this paper.

Software maintenance, as a long-term, cost-intensive activity and an unmanaged structure of a software system, further increases maintenance efforts and thereby development costs. A well-structured software system must achieve an optimal balance between cohesion and coupling among different software elements. Therefore, in this paper, a coupling measurement technique is proposed. This technique helps measure the conceptual similarity between different elements (classes) of a software system. It utilizes two kinds of relations, structural and semantic, in order to obtain more accurate coupling measures. In particular, our proposed approach mainly extracts lexical information from four portions of the underlying source code of a software element. The four main parts are comments, Javadoc, signatures, and member variable zone. Similarly, the structural coupling is measured by counting calls to member functions of other classes generated from a given class. The proposed approach is first tested on a student record management system software and finally applied to three standard open source Java software systems. While applying on standard open source software systems, three coupling measurement schemes are designed (including the proposed one): structural, semantic, and structural+semantic (proposed). Finally, the results are presented. The results obtained are very promising and reflect the actual coupling present between classes.

Software managers can obtain useful information from many fault data sets recorded on bug tracking systems (BTS). However, it is difficult to find helpful measures for software reliability, maintainability, and performability, because the data collected on the BTS are mixed with qualitative and quantitative ones. This paper discusses the methods of reliability, maintainability, and performability assessment by deep learning for big data in terms of software faults. Specifically, we implement the reliability, maintainability, and performability analysis tool discussed in our method by using the latest programing technology. Moreover, we show several performance examples of the implemented application software by using the fault big data observed in the practical projects.

Today's IT infrastructure demands for an automated yet stringently controlled solution to manage patches for vulnerable software applications. The use of patch management tools is the best practice that tests all the available patches before installation to ensure that the released patch will not break the existing software. However, the availability of several patch management software poses a challenge for the system administrator to decide which software facilitates the operational competence and effectiveness of the computer system in terms of revenue and system security. Therefore, selecting the appropriate patch management software that automatically patches all the Microsoft and non-Microsoft products simultaneously is an important and complex concern, leading to the multi-criteria decision approach. Here, we implement a hybrid approach that combines the intuitionistic fuzzy set and entropy weight-based multi-criteria decision making model with TOPSIS to select the best defense against vulnerabilities (or patch management software) in the group decision making environment. As most real world decision problems involve a group of decision makers that may have multiple opinions for individual criteria, the intuitionistic fuzzy weighted averaging operator is explicitly considered here and generates optimal weights for the attributes. A numerical example is provided to illustrate the application of the intuitionistic fuzzy TOPSIS method that helps identify the best patch management tool based on selected criteria.

In this paper, we present a new distribution function that characterizes using a Vtub-shaped failure rate function. We discuss the characteristics of the distribution and determine the confidence intervals of the Vtub-shaped failure rate. We also illustrate the proposed distribution function with an application in software reliability modeling by using the new Vtub-shaped failure rate as the time-dependent fault detection rate per fault in a recent generalized software reliability model with the uncertainty of operating environments.

Assessment of system availability has been studied from the design stage to the operational stage in various system configurations using either analytic or simulation techniques. However, the former cannot handle complicated state changes, and the latter is computationally expensive. This study proposes a Bayesian approach to evaluate system availability. In this approach: 1) Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) are treated as distributions instead of being "averaged" to better describe real scenarios and overcome the limitations of data sample size; 2) Markov Chain Monte Carlo (MCMC) simulations are applied to take advantage of the analytical and simulation methods; and 3) a threshold is set up for Time to Failure (TTR) data and Time to Repair (TTR) data, and new datasets with right-censored data are created to reveal the connections between technical and "Soft" KPIs. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined by a Bayesian Weibull model and a Bayesian lognormal model, respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems). By comparing the results with and without considering the threshold for censoring data, we show the threshold can be used as a monitoring line for continuous improvement in the investigated mining company.

This study proposes a framework to monitor the patient safety culture by means of a safety attitudes questionnaire by using the bootstrap method to determine the performance of each dimension of safety attitudes questionnaire in each year based on a small sample size. The focus is not the bootstrap method itself but the philosophy of using the bootstrap method to construct the control chart-like limits to observe the performance of each dimension based on the small sample size. Hospital management can better understand how each dimension performs on a yearly basis from the data available. When more new data are available, the boundaries generated by the bootstrap method can be adjusted. Besides, the trends and changes for each dimension can be traced in a control chart that enables hospital management to observe how the patient safety culture changes from time to time. This study demonstrates the philosophy of using the bootstrap method when the sample size is seven for each dimension. The trends and changes for each dimension can be observed for hospital management by construction its control chart.

In the degradation process of the rolling bearing, the traditional feature trends appear steady in the early stage and then show a sudden change trend in the later stage. For this reason, it is difficult to predict the bearing degradation trend accurately. In order to solve this problem, this paper puts forward a new degradation index extraction method based on dual-tree complex wavelet transform (DTCWT) and isometric feature mapping (ISOMAP). Compared with the traditional characteristic parameters, the new degradation index can better reflect the degradation tendency of the bearing. Considering the data of different time points has different contributions to degradation trend prediction, the improved BP neural network is applied to predict the degradation trend of bearing. The method is verified by using the bearing degradation data.

This paper gives a system dynamic reliability maintenance that jointly considers the system residual life and relative importance. Firstly, the component failure time of the system is generated by a Weibull model with unknown shape and scale parameters, and then the two unknown parameters are updated based on the Bayesian rules. The system residual life distribution is estimated by using the theory of survival signature. A novel component relative importance is extended to identify the most critical component groups that need to be maintained. Finally, a system with two cross-linked modules is used to illustrate the usage of our research. Simulation results show that the proposed strategies are effective and convenient.

When the traditional Bayesian method is used to verify software reliability, prior information cannot be distinguished from experimental information. In this paper, a hybrid Bayesian method is used to verify software reliability. At the same time, the correlation coefficient conception is introduced. This paper determines the correlation coefficient in the hybrid beta distribution respectively through the subjective quantitative method, the goodness of fit method, the Kullback information method, the Spearman correlation coefficient method, and the Kendall correlation coefficient method. The feasibility of five kinds of methods for calculating the correlation coefficients is proven by case analysis, and it is also demonstrated that the distinction between prior information and experimental information has an impact on the acceptable quality level of software reliability demonstration testing.

In recent years, the Unmanned Aerial System (UAS) has experienced rapid development and has been frequently deployed on the battlefield. The UAS for the export of military trade in China, represented by the CH-4 Medium Altitude Long Endurance (MALE) UAS, has been equipped in the air forces of many countries in the Middle East. When the UAS is deployed and operated, it produces a large amount of data and information of Integrated Logistical Support (ILS). Recording, processing, and applying this information and data are important contents of ILS. At present, the information and data sources of ILS are various and difficult to manage. Also, it is difficult to effectively integrate and use, so it cannot effectively analyze and evaluate equipment performance and support efficiency nor provide insight into the market demand and profit model. According to the requirements of information and data management for ILS of UAS under the military trade model, this paper puts forward an information data management platform for ILS of UAS. Firstly, the design ideas and principles of the platform is determined. Then, a general framework of the platform is established. Finally, the design contents and requirements are provided, with a detailed description of the hardware layer, software layer, data layer, function layer, and interface layer. The content of this paper can help the research and development of the information data management platform for ILS of UAS.

For the facility location problem of operations equipment support, a locating decision-making model of operations equipment support is proposed. The model decision-making target model is the minimum support-delayed time of the equipment system, and influence factors are considered including the operational task facility effect on repair capabilities of facility location, equipment technical faults, and battle damage. A two-phase heuristics algorithm using location and decision-service-relation, neighborhood search, and genetic algorithm are used. The decision-making target is acquired through Monte Carlo simulation. An example is used to verify the models and the effectuality of the algorithm, and it is demonstrated that the mission conditions have a significant influence on the operations equipment support facility location.

Aiming at solving the problem that the current on-condition maintenance (OCM) time is not accurate, this paper proposes an OCM policy based on historical failure data, real-time condition monitoring data, and the Weibull proportional intensity model (WPIM). Firstly, taking the mean distance between failures (MDBF) during the actual operation of Electric Multiple Unit (EMU) as the design variable and the failure rate of the structure and the wheel set as the adjoint variable, a WPIM model is established. Then, based on the solved model, a physical programming method is introduced to ensure the cost and reliability under the expected range. Finally, taking the bogie as the research object, the effectiveness and application value of the proposed maintenance policy in the bogie maintenance decision-making are verified through analysis, providing a theoretical basis for the OCM maintenance classification and maintenance system optimization of bogies.

In this paper, driving behavior reliability was applied to the subjective human-machine evaluation of the G1 method. A reliability model for G1 evaluation was established. By comparing the single G1 method to evaluate the cab design of a vehicle model and the reliability model of G1 method to evaluate the cab design results of the model, it is concluded that the scores of the two evaluation results are the same, and both are excellent. Therefore, for the G1 method in driver's machine evaluation, the evaluation results are reasonable and reliable.

Concerning the problem of lubrication failure of high speed rotor bearings, a unified model of mixed lubrication is built for angular contact ball bearings with comprehensive consideration of the effects of contact geometry, real rough surface topography, elastic deformation, rheological properties of lubricants, high speed spinning properties of balls, and other factors. On this basis, the effect laws of rotate speed, load, vacuum, high and low temperature, and other working and environmental conditions on the contact and lubrication properties of the microscopic transmission interface of a bearing are analyzed, laying a theoretical basis for the application of high speed rotor bearings in a multi-stress space environment.

The current design of the movable jaw structure of the traditional jaw crusher is unreasonable with heavy weight. The parameterized entity modeling of the movable jaw is carried out by using Pro/Engineer Wildfire5.0 software. Through finite element analysis by using the ANSYS software, the response surface method (RSM) is utilized to optimize and analyze the dimension parameters of the movable jaw, and the optimum design parameters are obtained by taking the movable jaw weight as the optimization objective. Under the condition of satisfying the strength, the weight of the movable jaw is reduced about 15.975%. The reliability of movable jaw structure before and after optimization is analyzed by six sigma standard. The results shown that the structural reliability of the movable jaw before and after optimization is maintained, which provides a feasible scheme for the development and optimization improvement of crushers.

In order to solve the problems that the traditional circular target is not convenient for automatic camera calibration, the accuracy of circle centering is not high, and the circle centering array is greatly influenced by the rotation angle of the circular target, a circular target suitable for automatic camera calibration was designed. For this target, a least squares center-fitting method based on random sampling and a centroid lattice sequential sorting method based on vector angle were proposed. The experimental results showed that the average centering error was 0.0061 pixels using this center extraction algorithm. Compared with the traditional least squares fitting method, the accuracy was higher; for the calibration target 0-360 degree rotation, it could more quickly and accurately complete the center-point lattice sorting. The average sorting time was 0.0096s, and it was easier to implement on-line camera automatic calibration.

This paper is devoted to redundancy allocation in series (parallel) systems subject to random shocks. The non-homogeneous Poisson process is used to describe the shock process, and redundant series (parallel) system reliability is given. The majorization order allows for stochastic comparison between random lifetimes of systems under two redundancy allocation policies. Then, the redundancy allocation policy for maximizing (minimizing) the series (parallel) system reliability is presented. Finally, the effect of the number of subsystems on system reliability is analyzed.

The DICOM system contains patients' personal privacy and may present a risk of information leakage in telemedicine diagnostics. In order to solve the security problem of the DICOM system, we propose a medical image encryption method based on the cross-diffusion of the logistic map and the Chebyshev map. We first parse the DICOM image and read the data elements, and then we use the logistic map and the Chebyshev map to generate a key. Then, we cross-use these two sequences for further image encryption diffusion. Finally, we conduct in-depth experiments and comparisons with existing algorithms to validate the characteristics of high security and low correlation of adjacent pixels of the proposed encryption algorithm.

To get over the limited energy of nodes and unbalanced energy consumption in wireless sensor networks (WSN), this paper puts forward a WSN clustering routing algorithm based on weight function timing. The algorithm was applied to build the weight function between node aggregation degree and residual energy. Then, the weight function was based on producing the timing time for all nodes. Both the iteration number and the energy consumption were reduced in cluster head selection. At the same time, the node energy consumption rate and the distance from the node to the sink node were taken into consideration. Next, the reasonable cluster head was chosen according to each node's weight function value and the timing time. In the periodic clustering process, the proposed algorithm removes the aggregation degree exchange between the nodes, thus reducing the network traffic and lowering the network energy consumption. Simulation results show that the algorithm achieves excellent cluster convergence and stable cluster size.

In this paper, a novel analog circuit fault prognostic approach is presented. The Pearson product-moment correlation coefficient (PPMCC) is used to calculate the circuit's health degree on the basis of the extracted output voltages. The relevance vector machine (RVM) algorithm with kernel function optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm is utilized to estimate the remaining useful performance (RUP). A leapfrog filter is used in a fault prognostic experiment to verify the prognostic approach, and the experimental results reveal that the presented approach can forecast the analog circuit's RUP precisely.

Due to the multipath reflection in adaptive beamforming, it is likely that target signals are leaked and speeches are distorted, reducing the auditory effect. The transfer function generalized sidelobe canceller (GSC) uses the transfer function ratio to construct a block matrix that minimizes leaking target signals. However, the inhibitory effect to reverberations outside the beam direction is insignificant. In light of this, we developed a de-reverberation algorithm according to the transfer function GSC and minimum-phase decomposition, which was proven to be strongly adjustable to the environment. Speeches that were double processed by the spatial domain and the complex ceptrum domain could approach the noise-free state. In addition, we simulated the de-reverberated speech waveforms and their effects with subjective and objective evaluation indices, verifying the positive effect of the optimization algorithm in de-reverberation and increasing resolution.

Aiming at the problem of discontinuous edge details in the wavelet transform modulus maxima edge detection algorithm, the shortcomings of the algorithm are addressed by improving the smoothness of the wavelet function and selecting appropriate wavelet filters. The order of vanishing moments of a wavelet function determines the ability of the wavelet to approximate smooth function. Therefore, this paper focuses on improving the order of vanishing moments of the B-spline dyadic wavelet, giving a new lifting scheme and lifting parameters, and realizing a B-spline dyadic wavelet filter with high-order vanishing moments, symmetry, and compact support. At the same time, an edge detection algorithm based on lifting the B-spline dyadic wavelet is proposed. The experimental results show that the algorithm can effectively suppress noise and display the continuous details of the image edges.

In connection with the life aging problem of valve-regulated lead-acid batteries (VRLA), to ensure that batteries have a good performance and long life, we have considered VRLA as the backup power that works in a complex environment for a long time. The noise signal is analyzed, a VRLA health estimation model of the double adaptive Kalman algorithm is built, and a method of estimating battery health based on the improved unscented Kalman filter is put forward by using the battery Thevenin equivalent circuit model. The test results show that the average relative error of the VRLA health state estimated by the improved UKF algorithm is 3.1% and it can estimate the VRLA health state effectively.

Word sense disambiguation (WSD) is one of the most important research issues in the field of natural language processing. In this paper, a new method of word sense disambiguation is proposed, in which words and parts of speech (POS) are extracted as discriminative features. At the same time, a maximum entropy classifier is adopted to determine ambiguous words' semantic categories. Training data of SemEval-2007: Task#5 is used to optimize the maximum entropy model. A test corpus is applied to test the performance of the WSD classifier. Experimental results show that the performance of word sense disambiguation is improved after the proposed approach is used.