<![CDATA[ IEEE Transactions on Reliability - new TOC ]]>
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TOC Alert for Publication# 24 2019April 18<![CDATA[Table of Contents]]>681C11161<![CDATA[IEEE Transactions on Reliability publication information]]>681C2C2213<![CDATA[Toward Better Summarizing Bug Reports With Crowdsourcing Elicited Attributes]]>6812225980<![CDATA[Security Testbed for Internet-of-Things Devices]]>68123446826<![CDATA[Automated Testing of Android Apps: A Systematic Literature Review]]>68145665422<![CDATA[Mobile GUI Testing Fragility: A Study on Open-Source Android Applications]]>68167903081<![CDATA[Increasing Validity of Simulation Models Through Metamorphic Testing]]>681911083788<![CDATA[Modeling the Ageing Effect of Cycling Using a Supercapacitor-Module Under High Temperature With Electrochemical Impedance Spectroscopy Test]]>6811091214989<![CDATA[<inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-Weibull Applied to Brazilian Hydropower Equipment]]>2, Akaike information criterion, and mean squared error were calculated to verify the suitability of the model for the sample data and to compare the distributions adjustments. The maximum likelihood method was applied to determine the parameters of the q-Weibull distribution and the results were compared with those of the probability plotting method. An analysis using 3-D contour plots for the likelihood function was performed for four types of equipment. The results were analyzed by reliability and failure rate plots. The failure rate plots were grouped according to their shapes. The modeling of the failure phenomenon done by q-Weibull is closer to reality and allows better planning of maintenance and replacement policies. Hydropower data bank q-Weibull parameters have not been introduced in the literature.]]>6811221324536<![CDATA[A Reliability Model for Dependent and Distributed MDS Disk Array Units]]>681133148968<![CDATA[Identification of Cascading Failure Initiated by Hidden Multiple-Branch Contingency]]>6811491602794<![CDATA[Start-Up Demonstration Tests With the Intent of Equipment Classification for Balanced Systems]]>(1)CS^{(1)(2)}TF^{(1)(2)}TF^{(2)}_AMGC start-up demonstration tests. By using the finite Markov chain imbedding approach, several probabilistic indexes are presented for the new start-up demonstration tests based on the assumption that the tests are independent and identically distributed cases. Two types of optimization models are formulated and solved for the new start-up demonstration tests. Numerical examples are then presented to demonstrate the new start-up demonstration tests. This basic idea of equipment classification can be easily extended to other traditional start-up demonstration tests.]]>6811611741125<![CDATA[Time-Variant Reliability-Based Design Optimization Using an Equivalent Most Probable Point]]>6811751862139<![CDATA[CMV: Clustered Majority Voting Reliability-Aware Task Scheduling for Multicore Real-Time Systems]]>6811872006268<![CDATA[Dependability Analysis of Data Storage Systems in Presence of Soft Errors]]>6812012153934<![CDATA[An Approach for the Prediction of Number of Software Faults Based on the Dynamic Selection of Learning Techniques]]>6812162362238<![CDATA[Highly Reliable Redundant TSV Architecture for Clustered Faults]]>6812372475122<![CDATA[A Study on Effects of Copper Wrap Specifications on Printed Circuit Board Reliability]]>6812482667652<![CDATA[Reliability Importance Measures for Network Based on Failure Counting Process]]>6812672791306<![CDATA[Design and Evaluation of Algorithms for Energy Efficient and Complete Determination of Critical Nodes for Wireless Sensor Network Reliability]]>2) and bit complexity to O(clog_{2} n) where Δ is the maximum node degree, c is the critical node count, and n is the node count. The testbed experiments and simulation results show that this algorithm detects up to 93% of critical nodes and achieves up to 91% of state determination with low energy consumption. The second proposed algorithm, which is based on the first one, finds the states of all nodes by running a limited distributed depth-first search algorithm in unrecognized parts of the network without traversing the whole network. Comprehensive testbed experiments and simulation results reveal that, in the presence of a CDS, this algorithm finds all critical nodes with lower energy consumption than all existing algorithms.]]>6812802901588<![CDATA[A Novel Multistate Minimal Cut Vectors Problem and Its Algorithm]]>6812913011539<![CDATA[An Adaptive Prognostic Approach Incorporating Inspection Influence for Deteriorating Systems]]>6813023161494<![CDATA[A Sequential Bayesian Approach for Remaining Useful Life Prediction of Dependent Competing Failure Processes]]>6813173294751<![CDATA[A Modular Fault Diagnosis and Prognosis Method for Hydro-Control Valve System Based on Redundancy in Multisensor Data Information]]>6813303413568<![CDATA[3-D Printed Object Authentication Based on Printing Noise and Digital Signature]]>6813423533481<![CDATA[Novel Double-Node-Upset-Tolerant Memory Cell Designs Through Radiation-Hardening-by-Design and Layout]]>6813543631968<![CDATA[Defense Resource Allocation Against Sequential Unintentional and Intentional Impacts]]>6813643742151<![CDATA[A Parametric Predictive Maintenance Decision-Making Framework Considering Improved System Health Prognosis Precision]]>6813753962409<![CDATA[IEEE Transactions on Reliability institutional listings]]>681C3C31916<![CDATA[IEEE Transactions on Reliability institutional listings]]>681C4C4665