Modal Analysis of Induction Generator Based Wind Farm Connected to Series-compensated Transmission Line and Line Commutated Converter High-Voltage DC Transmission Line

This article presents a comprehensive analysis of the potential of sub-synchronous resonance in induction generator based wind farms connected to a line commutated converter based high-voltage DC transmission line. Since series-compensated lines are also known to cause sub-synchronous resonance in induction generator based (Type 1) wind farms, this study also considers a series-compensated line in parallel with the high-voltage DC line for the investigation of sub-synchronous resonance potential. The CIGRE benchmark high-voltage DC system and the IEEE first benchmark system are considered as the study system components. A linearized state-space model of the study system is developed for eigenvalue analysis followed by participation factor analysis. A sensitivity study of the sub-synchronous resonance modes with respect to variation in different model parameters, such as rectifier firing angle, DC line power flow, and series compensation level, is also reported. It is found that the high-voltage DC rectifier station current regulator does not interact with any of the sub-synchronous modes of the wind farm. However, in the event that the high-voltage DC line is blocked during a contingency, the wind farm will operate radially with the series-compensated line and may experience sub-synchronous resonance oscillation due to the induction generator effect.     

Computing and Social Welfare

Social Welfare Computing is an emerging discipline that seeks to direct technology to cause minimum societal disruption, and in particular seeks to minimize the harm caused directly by technology itself. This is markedly different from the better-understood uses of technology to create value or to address existing social needs. Innovative technologies that are widely adopted created significant value for their users; otherwise, they would not be widely adopted. Often the companies that create them obtain new sources of wealth and power, which inevitably lead to new forms of abuse of power and new forms of societal disruption. Societal disruption in turn requires social adaptation, including new regulations to influence the behavior of firms and to define and to protect the rights of individuals in the changed society. The governance of online business models is complex because regulators must meet the conflicting objectives of different segments of society, and because regulators must avoid imposing restrictions that stifle innovation. Social Welfare Computing seeks to guide social adaptation, combining insights from disciplines as varied as anthropology, business strategy, economics, strategic planning, and law.

     

Personalized email management at network edges

A new technique for managing and disseminating Web-based email prefetches messages and generates dynamic pages, displaying them at the network edge. Compared to other popular Web-based email servers, the prefetching and caching emails (PACE) prototype shows an improved performance with respect to user-perceived latency. Additionally, PACE'S centralized neural-network-based personalized spam filter will filter spam and viruses at the server's origin, thus saving bandwidth. Another major concern for users is the email accounts being clogged with spam. Spam filters can be classified as server-side or client-side. Server-side filters are integrated with email servers and filter out spam at the server end.     

Spreading of social contagions without key players

Contagion models have been used to study the spread of social behavior among agents of a networked population. Examples include information diffusion, social influence, and participation in collective action (e.g., protests). Key players, which are typically agents characterized by structural properties of the underlying network (e.g., high degree, high core number or high centrality) are considered important for spreading social contagions. In this paper, we ask whether contagions can propagate through a population that is devoid of key players. We justify the use of Erd?s-Rényi random graphs as a representation of unstructured populations that lack key players, and investigate whether complex contagions—those requiring reinforcement—can spread on them. We demonstrate that two game-theoretic contagion models that utilize common knowledge for collective action can readily spread such contagions, thus differing significantly from classic complex contagion models. We compare contagion dynamics results on unstructured networks to those on more typically-studied, structured social networks to understand the role of network structure. We test the classic complex contagion and the two game-theoretic models with a total of 18 networks that range over five orders of magnitude in size and have different structural properties. The two common knowledge models are also contrasted to understand the effects of different modeling assumptions on dynamics. We show that under a wide range of conditions, these two models produce markedly different results. Finally, we demonstrate that the disparity between classic complex contagion and common knowledge models persists as network size increases.     

Combining the Power of Taverna and caGrid: Scientific Workflows that Enable Web-Scale Collaboration

Service-oriented architecture represents a promising approach to integrating data and software across different institutional and disciplinary sources, thus facilitating Web-scale collaboration while avoiding the need to convert different data and software to common formats. The US National Cancer Institute's Biomedical Information Grid program seeks to create both a service-oriented infrastructure (caGrid) and a suite of data and analytic services. Workflow tools in caGrid facilitate both the use and creation of services by accelerating service discovery, composition, and orchestration tasks. The authors present caGrid's workflow requirements and explain how they met these requirements by adopting and extending the Taverna system.     

Quaternized Poly(4-Vinylpyridine) Coated Activated Carbon: Diffusion Controlled Sorption of Chromium(VI)

Quaternized poly(4-vinylpyridine) coated activated carbon has been proven to be successful in removal and recovery of Cr(VI) from aqueous solution. However, the key component of the mass transfer operations and the diffusion coefficient has not been determined. In this study, adsorption of Cr(VI) was investigated at different initial concentrations. A finite bath diffusion control model with changing bulk concentrations was derived analytically, and a constant related to radius of the absorbent particle and the fractional attainment of adsorption was first introduced into this model. Results indicated that the adsorbent provided fast adsorption kinetics and the modified diffusion-control model fitted the experimental data well. The observed sorption kinetics was consistent with the finite bath diffusion, with an average value of 4.10×10?6?cm2/s for the product of the distribution coefficient and the effective diffusivity in the adsorption of Cr(VI) on the quaternized poly(4-vinylpyridine) coated activated carbon.     

Model-driven approach to enterprise interoperability at the technical service level

Enterprise Interoperability is the ability of enterprises to interoperate in order to achieve their business goals. Although the purpose of enterprise interoperability is determined at the business level, the use of technical (IT) services to support business services implies that interoperability solutions at both the business and technical level should be aligned. This paper introduces and demonstrates the suitability of an approach based on model transformations to automate enterprise interoperability. We start by considering that a set of enterprises are willing to interoperate in the context of their individual goals. The interactions necessary for their cooperation are then properly captured in terms of a so-called choreography. Our approach allows a choreography to be mapped and transformed to an orchestration, which defines the operation of the actual technical services of the interoperating enterprises. The paper discusses the technical challenges of implementing the transformation, and illustrates our approach with two application scenarios.     

Association rule mining using binary particle swarm optimization

In this paper, we developed a binary particle swarm optimization (BPSO) based association rule miner. Our BPSO based association rule miner generates the association rules from the transactional database by formulating a combinatorial global optimization problem, without specifying the minimum support and minimum confidence unlike the a priori algorithm. Our algorithm generates the best M rules from the given database, where M is a given number. The quality of the rule is measured by a fitness function defined as the product of support and confidence. The effectiveness of our algorithm is tested on a real life bank dataset from commercial bank in India and three transactional datasets viz. books database, food items dataset and dataset of the general store taken from literature. Based on the results, we infer that our algorithm can be used as an alternative to the a priori algorithm and the FP-growth algorithm.     

Geospatial analysis of terrain through optimized feature extraction and regression model with preserved convex region

In this paper, cat optimization algorithm for feature extraction in satellite image has been proposed. In cat optimization, cost function computes the pixel in the satellite image to preserve the boundary shape and avoid non-convex part of the contour of the image. However, the existing feature extraction optimization algorithm measures the distinct data framework and thematic information to insight land cover such as waterbody, urban and vegetation. The land cover is obtained from different optimized feature extraction algorithms never provide proper boundary shape and land feature. Furthermore, the proposed cat optimized algorithm distinguishes the inner, outer and extended boundary along with the land cover. The cat-optimised algorithm for low and high-resolution satellite image shows the better result of 85%, with the preserved convex region when compared with the existing feature extraction algorithm such as fuzzy and Particle Swarm Optimization (PSO).