A service-oriented business performance evaluation model and the performance-aware service selection method
In service-oriented architecture, services and business processes are closely related and therefore the research on service-oriented business process (SOBP) attracts the attention of academia as well as industry. Because of the loosely coupled, autonomic and dynamic nature of services, the operation and the performance evaluation of business process meet some challenges, such as the definition of the key performance indicators (KPIs) and the alignment of business performance and IT performance. In this paper, we address these challenges. First, the definition and the characteristics of an SOBP are presented with a motivating scenario. Then a service-oriented business performance evaluation model is described, which integrates the performance of strategy layer, business process layer, business activity layer, service composition layer and IT infrastructure layer. The KPIs corresponding to each layer are also defined with their formal representations. The qualitative and quantitative performance analysis methods, based on the proposed model, are presented, respectively. Finally, the improved analytic hierarchy process is explicated to calculate the correlation between different KPIs and select the most suitable service. An online-shopping example is taken to prove the soundness and the feasibility of the method.

title: Constrained Nonlinear State Estimation - A Differential Evolution Based Moving Horizon Approach
abstract: A solution is proposed to estimate the states in the nonlinear discrete time system. Moving Horizon Estimation (MHE) is used to obtain the approximated states by minimizing a criterion that is the Euclidean form of the difference between the estimated outputs and the measured ones over a finite time horizon. The differential evolution (DE) algorithm is incorporated into the implementation of MHE in order to solve the optimization problem which is presented as a nonlinear programming problem due to the constraints. The effectiveness of the approach is illustrated in simulated systems that have appeared in the moving horizon estimation literature.

title: A New Performance Evaluation Model and AHP-Based Analysis Method in Service-Oriented Workflow
abstract: In service-oriented architecture, services and workflows are closely related so that the research on service-oriented workflow attracts the attention of academia. Because of the loosely-coupled, autonomic and dynamic nature of service, the operation and performance evaluation of workflow meet some challenges, such as how to judge the quality of service (QoS) and what is the relation between QoS and workflow performance. In this paper we are going to address these challenges. First the definition of service is proposed, and the characteristics and operation mechanism of service-oriented workflow are presented. Then a service-oriented workflow performance evaluation model is described which combines the performance of the business system and IT system. The key performance indicators (KPI) are also depicted with their formal representation. Finally the improved Analytic Hierarchy Process is brought forward to analyze the correlation between different KPIs and select services.

title: DE and NLP Based QPLS Algorithm
abstract: As a novel evolutionary computing technique, Differential Evolution (DE) has been considered to be an effective optimization method for complex optimization problems, and achieved many successful applications in engineering. In this paper, a new algorithm of Quadratic Partial Least Squares (QPLS) based on Nonlinear Programming (NLP) is presented. And DE is used to solve the NLP so as to calculate the optimal input weights and the parameters of inner relationship. The simulation results based on the soft measurement of diesel oil solidifying point on a real crude distillation unit demonstrate that the superiority of the proposed algorithm to linear PLS and QPLS which is based on Sequential Quadratic Programming (SQP) in terms of fitting accuracy and computational costs.

title: Manufacturing Grid: Needs, Concept, and Architecture
abstract: As a new approach, grid technology is rapidly used in scientific computing, large-scale data management, and collaborative work. But in the field of manufacturing, the application of grid is just at the beginning. The paper proposes the concept of manufacturing. The needs, definition and architecture of manufacturing gird are discussed, which explains why needs manufacturing grid, what is manufacturing grid and how to construct a manufacturing grid system.

title: An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
abstract: This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed

title: Dynamic Complexities in a Lotka-volterra Predator-prey Model Concerning impulsive Control Strategy
abstract: Based on the classical Lotka–Volterra predator–prey system, an impulsive differential equation to model the process of periodically releasing natural enemies and spraying pesticides at different fixed times for pest control is proposed and investigated. It is proved that there exists a globally asymptotically stable pest-eradication periodic solution when the impulsive period is less than some critical value. Otherwise, the system can be permanent. We observe that our impulsive control strategy is more effective than the classical one if we take chemical control efficiently. Numerical results show that the system we considered has more complex dynamics including period-doubling bifurcation, symmetry-breaking bifurcation, period-halving bifurcation, quasi-periodic oscillation, chaos and nonunique dynamics, meaning that several attractors coexist. Finally, a pest–predator stage-structured model for the pest concerning this kind of impulsive control strategy is proposed, and we also show that there exists a ...

title: Subsequence Similarity Search under Time Shifting
abstract: Time series data naturally arise in many application domains, and the similarity search for time series under dynamic time shifting is prevailing. But most recent research focused on the full length similarity match of two time series. In this paper a basic subsequence similarity search algorithm based on dynamic programming is proposed. For a given query time series, the algorithm can find out the most similar subsequence in a long time series. Furthermore two improved algorithms are also given in this paper. They can reduce the computation amount of the distance matrix for subsequence similarity search. Experiments on real and synthetic data sets show that the improved algorithms can significantly reduce the computation amount and running time compared to the basic algorithm

title: Nesting Algorithm for Multi-Classification Problems
abstract: Support vector machines (SVMs) are originally designed for binary classifications. As for multi-classifications, they are usually converted into binary ones. In the conventional multi-classifiable algorithms, One-against-One algorithm is a very power method. However, there exists a middle unclassifiable region. In order to overcome this drawback, a novel method called Nesting Algorithm is presented in this paper. Our ideas are as follows: firstly, construct the optimal hyperplanes based on One-against-One approach. Secondly, if there exist data points in the middle unclassifiable region, select them to construct the optimal hyperplanes with the same hyperparameters. Thirdly, repeat the second step until there are no data points in the unclassifiable region or the region is disappeared. In this paper, we also prove the validity of the proposed algorithm for unclassifiable region and give the computational complexity analysis of the method. In order to examine the training accuracy and the generalization performance of the proposed algorithm, One-against-One algorithm, fuzzy least square support vector machine (FLS-SVM) and the proposed algorithm are applied to five UCI datasets. The results show that the training accuracy of the proposed algorithm is higher than the others, and its generalization performance is also comparable with them.

title: Reactive-ion etching of Ge2Sb2Te5 in CF4/Ar plasma for non-volatile phase-change memories
abstract: Etching of Ge"2Sb"2Te"5 (GST) is a critical step in the fabrication of chalcogenide random access memories. In this paper, the etch characteristics of GST films were studied with a CF"4/Ar gas mixture using a reactive-ion etching system. We observed a monotonic decrease in etch rate with decreasing CF"4 concentration indicating its importance in defining the material removal rate. Argon, on the other hand, plays an important role in defining the smoothness of the etched surface and sidewall edge acuity. We have studied the importance of gas mixture and RF power on the quality of the etched film. The smoothest surfaces and most vertical sidewalls were achieved using a CF"4/Ar gas mixture ratio of 10/40, a background pressure of 80mTorr, and power of 200W.

title: Entropy-based metrics in swarm clustering
abstract: Ant-based clustering methods have received significant attention as robust methods for clustering. Most ant-based algorithms use local density as a metric for determining the ants' propensities to pick up or deposit a data item; however, a number of authors in classical clustering methods have pointed out the advantages of entropy-based metrics for clustering. We introduced an entropy metric into an ant-based clustering algorithm and compared it with other closely related algorithms using local density. The results strongly support the value of entropy metrics, obtaining faster and more accurate results. Entropy governs the pickup and drop behaviors, while movement is guided by the density gradient. Entropy measures also require fewer training parameters than density-based clustering. The remaining parameters are subjected to robustness studies, and a detailed analysis is performed. 
 
In the second phase of the study, we further investigated Ramos and Abraham's ( Proc 2003   2003. pp 1370–1375) contention that ant-based methods are particularly suited to incremental clustering. Contrary to expectations, we did not find substantial differences between the efficiencies of incremental and nonincremental approaches to data clustering.