Hybrid intelligent method for fuzzy reliability analysis of corroded X100 steel pipelines

Epistemic uncertainties are critical for reliable design of corroded pipes made of high-strength grade steel. In this work, corrosion defects geometries and operating pressure are provided as the epistemic uncertainties in reliability analysis. A framework of an iterative approach-based bi-loop is presented for fuzzy reliability analysis (FRA) of corroded pipelines to evaluate the fuzzy reliability index-based various fuzzy-random variables (FRVs). In the inner loop, the conjugate first-order reliability method using adaptive finite-step size is applied for carried out the reliability analysis. The outer loop is structured based on the fuzzy analysis corresponding to a modified particle swarm optimization as an intelligent tool. The adaptive conjugate fine step size is dynamically computed to adjust the conjugate sensitivity vector in the reliability loop. The sufficient descent condition is satisfied based on three-term conjugate first-order reliability method. The performance function of corroded pipelines is defined based on average shear stress yield-based plastic flow theory, remaining strength factor, and operating pressure. Two applicable examples as corroded pipelines made from X100 high-strength steel are given to illustrate the effects of epistemic uncertainties under corrosion defects. Investigation the results has shown that modeling of epistemic uncertainty in the reliability analysis of high-grade steel pipelines could result more reasonable reliability indexes. In addition, results indicate that FRVs have significant influence on fuzzy reliability index calculations, especially corrosion defect depth and operating pressure (as FRVs). The sensitivity measure of FRA demonstrated that fuzzy reliability index of corroded X100 steel pipelines is more sensitive to the FRVs means.

     

A hybrid sufficient performance measure approach to improve robustness and efficiency of reliability-based design optimization

The stable convergence and efficiency of reliability-based design optimization (RBDO) using performance measure approach (PMA) are the major issue to develop the reliability methods based on modified chaos control (MCC), hybrid chaos control (HCC) and finite-step length adjustment (FSL). However, these methods may be inefficient for RBDO problems with convex and concave probabilistic constraints. In this paper, an adaptive modified chaos control (AMC) is proposed to provide the robust and efficient results in RBDO. The proposed AMC is adjusted using dynamical chaos control factor, which is extracted using sufficient descent condition for PMA. Using sufficient criterion, the proposed AMC is adaptively combined with advanced mean value (AMV) to improve the performance of PMA, named as hybrid adaptive modified chaos control (HAMC). Considering the robustness and efficiency, the proposed HAMC is compared with several existing reliability methods by three nonlinear structural/mathematical performance functions and two RBDO problems. The results indicate that the proposed HAMC with sufficient descent condition provides superior convergences in terms of both robustness and efficiency, compared to existing PMA methods using AMV, MCC, HCC and FSL.

     

DDBWS: a dynamic deadline and budget-aware workflow scheduling algorithm in workflow-as-a-service environments

The Journal of Supercomputing - Workflow scheduling has been excessively studied in different environments like clusters, grids, and clouds. Cloud is a scalable, cost-effective environment that...     

Simulation-based optimization of ecological leasing: a step toward extended producer responsibility (EPR)

In recent years, due to severe environmental legislations and extended producer responsibility, leasing has been recommended as a strategy for increasing resources productivity and minimizing waste generation during consumption and product end-of-life (EOL) phase. Hence, it is essential to develop a model that can help leasing company for achieving sustainable production management. In this paper, a model is developed which optimizes the environmental and economical impact of product during leasing and its EOL phase from customers and leasing company point of view. To solve the problem, simulation-based optimization approach is investigated and the integrated mathematical and simulation model is developed for incorporating uncertainty during leasing period. Finally a case study with data from industry is used to validate the approach and potential applications of the developed model are illustrated.     

Scalable Linear Array Architecture with Data-Driven Control for Ultrahigh-Speed Vector Quantization

Current and future requirements for adaptive real-time image compression challenge even the capabilities of highly parallel realizations in terms of hardware performance. Previously proposed linear array structures for full-search vector quantization do not offer scalability and adaptivity in this context, because they require separate data/control pins for dynamically updating the codevectors and complicated interlock mechanisms to ensure that the regular data flow is not corrupted as a result of updates. We explore the design space for full-search vector quantizers and propose a novel linear processor array architecture in which global wiring is limited to clock and power supply distribution, thus allowing high-speed processing in spite of only limited communication with the host via the boundary processors. The resulting fully pipelined design is not only area-efficient for VLSI implementation but is also readily scalable and offers extremely high performance.     

Structural properties of Cayley digraphs with applications to mesh and pruned torus interconnection networks

Despite numerous interconnection schemes proposed for distributed multicomputing, systematic studies of classes of interprocessor networks, that offer speed-cost tradeoffs over a wide range, have been few and far in between. A notable exception is the study of Cayley graphs that model a wide array of symmetric networks of theoretical and practical interest. Properties established for all, or for certain subclasses of, Cayley graphs are extremely useful in view of their wide applicability. In this paper, we obtain a number of new relationships between Cayley (di)graphs and their subgraphs and coset graphs with respect to subgroups, focusing in particular on homomorphism between them and on relating their internode distances and diameters. We discuss applications of these results to well-known and useful interconnection networks such as hexagonal and honeycomb meshes as well as certain classes of pruned tori.     

A Group Construction Method with Applications to Deriving Pruned Interconnection Networks

A number of low degree and, thus, low complexity, Cayley-graph interconnection structures, such as honeycomb and diamond networks, are known to be derivable by systematic pruning of 2D or 3D tori. In this paper, we extend these known pruning schemes via a general algebraic construction based on commutative groups. We show that, under certain conditions, Cayley graphs based on the constructed groups are pruned networks when Cayley graphs of the original commutative groups are kD tori. Thus, our results offer a general mathematical framework for synthesizing and exploring pruned interconnection networks that offer lower node degrees and, thus, smaller VLSI layout and simpler physical packaging. Our constructions also lead to new insights, as well as new concrete results, for previously known interconnection schemes such as honeycomb and diamond networks     

The effect of ginger on diabetic nephropathy,plasma antioxidant capacity and lipid peroxidation in rats

Oxidative stress is a major factor in the pathogenesis of diabetic complications. We studied the effects of ginger powder on nephropathy induced by diabetes, and measured changes in plasma antioxidant capacity and lipid peroxidation.