Log-based mining techniques applied to Web service composition reengineering

Web service compositions are becoming more and more complex, involving numerous interacting ad-hoc services. These services are often implemented as business processes themselves. By analysing such complex web service compositions one is able to better understand, control and eventually re-design them. Our contribution to this problem is a mining algorithm, based on a statistical technique to discover composite web service patterns from execution logs. Our approach is characterised by a “local” pattern’s discovery that covers partial results through a dynamic programming algorithm. Those locally discovered patterns are then composed iteratively until the composite Web service is discovered. The analysis of the disparities between the discovered model and the initial ad-hoc composite model (delta-analysis) enables initial design gaps to be detected and thus to re-engineer the initial Web service composition.     

A multilevel computing architecture for embedded multimedia applications

We have designed a new architecture that simplifies integration of heterogeneous IP for multimedia and streaming applications. The multilevel computing architecture (MLCA) is a template architecture featuring multiple processing units. This template architecture for SOC systems uses superscalar techniques to exploit task-level parallelism among different processing units. It supports a natural programming model that relieves programmers from explicitly synchronizing tasks and communicating data. code transformations that improve application performance are easy to incorporate in compilers for this architecture.     

Improving performance of node clustering in wireless sensor networks using meta-heuristic algorithms and a novel validity index

The use of wireless sensor networks has significantly increased in the last decade. These networks consist of a large number of small sensors, which are efficient tools for data collection from different environments. The data collected by these sensors will be usually transmitted to a base station that will provide the end user’s data. In order to improve scalability of such networks, sensor nodes can be grouped into non-overlapped clusters. These clusters create a hierarchical design in wireless sensor networks that leads to better energy utilization and thus increase the network’s lifetime. Using validity indexes and meta-heuristic algorithms are common ways to improve performance of clustering. In this paper, we provide a new validity index called ASCS by enhancing the Chou and Su (CS) validity index, and improve the performance of the meta-heuristic algorithms for clustering using this new validity index as their objective functions. Differential evolution and harmony search are two algorithms that will be used for this purpose. The experimental results show the better performance of ASCS index comparing to Davies and Bouldin (DB) and CS validity indexes in determining appropriate number of clusters and determining proper clusters’ members.

     

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.     

Layered Working-Set Trees

The working-set bound [Sleator and Tarjan in J. ACM 32(3), 652–686, 1985] roughly states that searching for an element is fast if the element was accessed recently. Binary search trees, such as splay trees, can achieve this property in the amortized sense, while data structures that are not binary search trees are known to have this property in the worst case. We close this gap and present a binary search tree called a layered working-set tree that guarantees the working-set property in the worst case. The unified bound [B?doiu et al. in Theor. Comput. Sci. 382(2), 86–96, 2007] roughly states that searching for an element is fast if it is near (in terms of rank distance) to a recently accessed element. We show how layered working-set trees can be used to achieve the unified bound to within a small additive term in the amortized sense while maintaining in the worst case an access time that is both logarithmic and within a small multiplicative factor of the working-set bound.     

Cancellation of bulging effect on mould level in continuous casting: Experimental validation

During the continuous casting of steel, several disturbances occur and affect all the parts of the caster, including the mould where the molten steel has to be stable for good quality of the final product. Especially at high casting velocities, the bulging generates important level fluctuations which cannot be efficiently rejected by the classical controllers. To address these problems, this paper proposes an architecture combining disturbances estimation and compensation techniques. It operates as an additional module and does not need any change of the main controller currently implemented. The entire structure has been tested successfully through several simulations and by means of intensive water model experiments.     

Verbesserung des Schwingungsrißkorrosionsverhaltens von Stahlproben durch Beschichtung

Improvement of the corrosion fatigue behavior of steels by paint-coating Paint-coatings have been used extensively to prevent corrosion of steel. In order to investigate the influence of an anticorrosive paint on the corrosion fatigue strength of steel, specimens of SAE 4135 in a quenched and tempered state, and in salt bath nitrocarburized state, on the one hand, and specimens of the steel SAE 5115 in a salt bath carburized state, on the other hand, were subjected to comparative rotating bending fatigue tests in inert media and in aqueous 5 pct NaCl. The results demonstrate that the 5 pct NaCl environment is reducing fatigue life drastically, but the painting treatment, however, was found to improve significantly the corrosion fatigue life. Regarding the beneficial effect of the painting treatment on the corrosion fatigue behavior, a superiority of the nitrocarburized variant in contrast to the other variants was ascertained.     

Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

Multimedia Tools and Applications - Skin lesion recognition is one of the most important tasks in dermoscopic image analysis. Current Convolutional Neural Network (CNN) algorithms based recognition...