An analysis of the exponential stability of linear stochastic neutral delay systems

This paper is concerned with the analysis of the mean square exponential stability and the almost sure exponential stability of linear stochastic neutral delay systems. A general stability result on the mean square and almost sure exponential stability of such systems is established. Based on this stability result, the delay partitioning technique is adopted to obtain a delay‐dependent stability condition in terms of linear matrix inequalities (LMIs). In obtaining these LMIs, some basic rules of the Ito calculus are also utilized to introduce slack matrices so as to further reduce conservatism. Some numerical examples borrowed from the literature are used to show that, as the number of the partitioning intervals increases, the allowable delay determined by the proposed LMI condition approaches h_max, the maximal allowable delay for the stability of the considered system, indicating the effectiveness of the proposed stability analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Novel neural network modeling method and applications

Neural networks play an important role for designing the parametric model of electromagnetic structures. The current neural network methods are unfit for a circuit model with many input variables because it is costly to extract a large number of the training data and test data to complete the highly nonlinear mapping approximation. This article proposes a new neural network modeling method—the multidimensional neural network model, which can be used to solve the issue of multivariable radiofrequency and microwave passive device modeling. The entire multidimensional neural network modeling problem is simplified into a set of neural network submodels through decomposition method. Then the submodels are combined into an equivalent model, and the final entire model is produced through the neural‐network mapping model developed with the submodels and equivalent model. A microstrip hairpin filter model is developed using the proposed method. The simulation results show the correctness and the effectivity of the proposed method. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:769–779, 2015.     

Output performance optimization for RTD fluxgate sensor based on dynamic permeability基于动态磁导率参数的时间差型磁通门传感器的输出响应优化

The output performance of residence times difference (RTD) fluxgate may vary under different driving conditions (driving currents and frequencies) and core materials. To optimize the RTD fluxgate and simplify its design process, an analytical model is employed to select the parameters and identify the effective factors that dominate the performance. The dynamic permeability parameters (P_i), which reflect the changes in the magnetization curve, are mathematically analyzed in detail. The linear variation functions of P_i in different driving conditions are fitted by using the dynamic arctangent hysteresis model. Consequently, the selection of driving conditions and core materials, which are assessed by comparing the experiment and simulation results, has an important role in achieving the optimal output performance of the RTD fluxgate.     

Petri net based test case generation for evolved specification

Model-based testing can use a model to test a concrete program’s implementation. When the model is changed due to the evolution of the specification, it is important to maintain the test suites up to date, such that it can be used for regression testing. A complete regeneration of the whole test suite from the new model, although inefficient, is still frequently used in practice. To address this problem effectively, we propose a test case reusability analysis technique to identify reusable test cases of the original test suite based on graph analysis, such that we can generate new test cases to cover only the change-related parts of the new model. The Market Information System (MIS) is employed to demonstrate the feasibility and effectiveness of the proposed method. Our experimental results show that the use of our method saves about 31.5% test case generation cost.     

A yield-enhanced global optimization methodology for analog circuit based on extreme value theory

The challenge in variation-aware circuit optimization with consideration of yield is the trade-off between optimized performance, yield and optimization runtime. This paper presents a practical variationaware circuit global optimization framework named GOYE, which shows the advantages on performance, yield and runtime. It uses an approach called constraint violation elimination (CVE) in global search phase to prune initial starting points and uses the gradient-based method in local search to locate optimum. The worst-case analysis (WCA), which is necessary for variation-aware circuit optimization, is nested in the local optimization process. The efficiency is significantly improved by a novel method based on extreme value theory (EVT). Our EVT-based method is also the first one that allows users to control the target yield such that under-design or over-design can be avoided. A design example in TSMC 65 nm technology is illustrated in the paper where all performance achieves three-sigma yield with consideration of environmental and inter-die/intra-die process variations.     

Similarity assessment for scientific workflow clustering and recommendation基于相似性评估策略的科学工作流聚类与推荐

This article proposes to identify and recommend scientific workflows for reuse and repurposing. Specifically, a scientific workflow is represented as a layer hierarchy that specifies the hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows. A graph-skeleton based clustering technique is adopted for grouping layer hierarchies into clusters. Barycenters in each cluster are identified, which serve as core workflows in this cluster, for facilitating the cluster identification and workflow ranking and recommendation with respect to the requirement of scientists.     

Biologically inspired model of path integration based on head direction cells and grid cells

Some neurons in the brain of freely moving rodents show special firing pattern. The firing of head direction cells (HDCs) and grid cells (GCs) is related to the moving direction and distance, respectively. Thus, it is considered that these cells play an important role in the rodents’ path integration. To provide a bionic approach for the vehicle to achieve path integration, we present a biologically inspired model of path integration based on the firing characteristics of HDCs and GCs. The detailed implementation process of this model is discussed. Besides, the proposed model is realized by simulation, and the path integration performance is analyzed under different conditions. Simulations validate that the proposed model is effective and stable.     

A virtual service placement approach based on improved quantum genetic algorithm

Despite the critical role that middleboxes play in introducing new network functionality, management and innovation of them are still severe challenges for network operators, since traditional middleboxes based on hardware lack service flexibility and scalability. Recently, though new networking technologies, such as network function virtualization (NFV) and software-defined networking (SDN), are considered as very promising drivers to design cost-efficient middlebox service architectures, how to guarantee transmission efficiency has drawn little attention under the condition of adding virtual service process for traffic. Therefore, we focus on the service deployment problem to reduce the transport delay in the network with a combination of NFV and SDN. First, a framework is designed for service placement decision, and an integer linear programming model is proposed to resolve the service placement and minimize the network transport delay. Then a heuristic solution is designed based on the improved quantum genetic algorithm. Experimental results show that our proposed method can calculate automatically the optimal placement schemes. Our scheme can achieve lower overall transport delay for a network compared with other schemes and reduce 30% of the average traffic transport delay compared with the random placement scheme.