Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2850

Search results for: distributed algorithms

2850 Distributed Minimum Spanning Forest

Authors: Ahmad Shahzad, Frans Coenen

Abstract:

Minimum spanning forest (MSF) is widely studied in graph related problems due to its importance in various applications from collaborative filtering as used in recommender engines to coarsening of graphs for Multilevel graph partitioning. However, with advent to data becoming larger enough to be handled in a single machine it has increasingly becoming important to distribute the data over a cluster of machine. That requires re-engineering and implementation of classical algorithms to be used in distributed environment and gain efficiency in terms of lower run time costs. In this paper a distributed approach of MSF is been presented which is based on classical algorithm of Kruskal to calculate Minimum Spanning Trees (MST). The presented approach is cross compared with current state of the art distributed algorithms and it has shown to outperform existing algorithms.

Keywords: distributed algorithms, distributed Kruskal’s Algorithm, minimum spanning forest, minimum spanning tree

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2849 Implementation of Distributed Randomized Algorithms for Resilient Peer-to-Peer Networks

Authors: Richard Tanaka, Ying Zhu

Abstract:

This paper studies a few randomized algorithms in application-layer peer-to-peer networks. The significant gain in scalability and resilience that peer-to-peer networks provide has made them widely used and adopted in many real-world distributed systems and applications. The unique properties of peer-to-peer networks make them particularly suitable for randomized algorithms such as random walks and gossip algorithms. Instead of simulations of peer-to-peer networks, we leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and these distributed randomized algorithms running on top of them. We can thus analyze their behaviour and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations.

Keywords: distributed randomized algorithms, peer-to-peer networks, virtual container technology, resilient networks

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2848 A Survey on Concurrency Control Methods in Distributed Database

Authors: Seyed Mohsen Jameii

Abstract:

In the last years, remarkable improvements have been made in the ability of distributed database systems performance. A distributed database is composed of some sites which are connected to each other through network connections. In this system, if good harmonization is not made between different transactions, it may result in database incoherence. Nowadays, because of the complexity of many sites and their connection methods, it is difficult to extend different models in distributed database serially. The principle goal of concurrency control in distributed database is to ensure not interfering in accessibility of common database by different sites. Different concurrency control algorithms have been suggested to use in distributed database systems. In this paper, some available methods have been introduced and compared for concurrency control in distributed database.

Keywords: distributed database, two phase locking protocol, transaction, concurrency

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2847 A High-Level Co-Evolutionary Hybrid Algorithm for the Multi-Objective Job Shop Scheduling Problem

Authors: Aydin Teymourifar, Gurkan Ozturk

Abstract:

In this paper, a hybrid distributed algorithm has been suggested for the multi-objective job shop scheduling problem. Many new approaches are used at design steps of the distributed algorithm. Co-evolutionary structure of the algorithm and competition between different communicated hybrid algorithms, which are executed simultaneously, causes to efficient search. Using several machines for distributing the algorithms, at the iteration and solution levels, increases computational speed. The proposed algorithm is able to find the Pareto solutions of the big problems in shorter time than other algorithm in the literature. Apache Spark and Hadoop platforms have been used for the distribution of the algorithm. The suggested algorithm and implementations have been compared with results of the successful algorithms in the literature. Results prove the efficiency and high speed of the algorithm.

Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, multi-objective optimization

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2846 A Unique Multi-Class Support Vector Machine Algorithm Using MapReduce

Authors: Aditi Viswanathan, Shree Ranjani, Aruna Govada

Abstract:

With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Keywords: distributed algorithm, MapReduce, multi-class, support vector machine

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2845 A Hybrid Distributed Algorithm for Solving Job Shop Scheduling Problem

Authors: Aydin Teymourifar, Gurkan Ozturk

Abstract:

In this paper, a distributed hybrid algorithm is proposed for solving the job shop scheduling problem. The suggested method executes different artificial neural networks, heuristics and meta-heuristics simultaneously on more than one machine. The neural networks are used to control the constraints of the problem while the meta-heuristics search the global space and the heuristics are used to prevent the premature convergence. To attain an efficient distributed intelligent method for solving big and distributed job shop scheduling problems, Apache Spark and Hadoop frameworks are used. In the algorithm implementation and design steps, new approaches are applied. Comparison between the proposed algorithm and other efficient algorithms from the literature shows its efficiency, which is able to solve large size problems in short time.

Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, neural network

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2844 A Hybrid Distributed Algorithm for Multi-Objective Dynamic Flexible Job Shop Scheduling Problem

Authors: Aydin Teymourifar, Gurkan Ozturk

Abstract:

In this paper, a hybrid distributed algorithm has been suggested for multi-objective dynamic flexible job shop scheduling problem. The proposed algorithm is high level, in which several algorithms search the space on different machines simultaneously also it is a hybrid algorithm that takes advantages of the artificial intelligence, evolutionary and optimization methods. Distribution is done at different levels and new approaches are used for design of the algorithm. Apache spark and Hadoop frameworks have been used for the distribution of the algorithm. The Pareto optimality approach is used for solving the multi-objective benchmarks. The suggested algorithm that is able to solve large-size problems in short times has been compared with the successful algorithms of the literature. The results prove high speed and efficiency of the algorithm.

Keywords: distributed algorithms, apache-spark, Hadoop, flexible dynamic job shop scheduling, multi-objective optimization

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2843 Diffusion Adaptation Strategies for Distributed Estimation Based on the Family of Affine Projection Algorithms

Authors: Mohammad Shams Esfand Abadi, Mohammad Ranjbar, Reza Ebrahimpour

Abstract:

This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results.

Keywords: selective partial update, affine projection, dynamic selection, diffusion, adaptive distributed networks

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2842 The Parallelization of Algorithm Based on Partition Principle for Association Rules Discovery

Authors: Khadidja Belbachir, Hafida Belbachir

Abstract:

subsequently the expansion of the physical supports storage and the needs ceaseless to accumulate several data, the sequential algorithms of associations’ rules research proved to be ineffective. Thus the introduction of the new parallel versions is imperative. We propose in this paper, a parallel version of a sequential algorithm “Partition”. This last is fundamentally different from the other sequential algorithms, because it scans the data base only twice to generate the significant association rules. By consequence, the parallel approach does not require much communication between the sites. The proposed approach was implemented for an experimental study. The obtained results, shows a great reduction in execution time compared to the sequential version and Count Distributed algorithm.

Keywords: association rules, distributed data mining, partition, parallel algorithms

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2841 A General Variable Neighborhood Search Algorithm to Minimize Makespan of the Distributed Permutation Flowshop Scheduling Problem

Authors: G. M. Komaki, S. Mobin, E. Teymourian, S. Sheikh

Abstract:

This paper addresses minimizing the makespan of the distributed permutation flow shop scheduling problem. In this problem, there are several parallel identical factories or flowshops each with series of similar machines. Each job should be allocated to one of the factories and all of the operations of the jobs should be performed in the allocated factory. This problem has recently gained attention and due to NP-Hard nature of the problem, metaheuristic algorithms have been proposed to tackle it. Majority of the proposed algorithms require large computational time which is the main drawback. In this study, a general variable neighborhood search algorithm (GVNS) is proposed where several time-saving schemes have been incorporated into it. Also, the GVNS uses the sophisticated method to change the shaking procedure or perturbation depending on the progress of the incumbent solution to prevent stagnation of the search. The performance of the proposed algorithm is compared to the state-of-the-art algorithms based on standard benchmark instances.

Keywords: distributed permutation flow shop, scheduling, makespan, general variable neighborhood search algorithm

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2840 Selection of Relevant Servers in Distributed Information Retrieval System

Authors: Benhamouda Sara, Guezouli Larbi

Abstract:

Nowadays, the dissemination of information touches the distributed world, where selecting the relevant servers to a user request is an important problem in distributed information retrieval. During the last decade, several research studies on this issue have been launched to find optimal solutions and many approaches of collection selection have been proposed. In this paper, we propose a new collection selection approach that takes into consideration the number of documents in a collection that contains terms of the query and the weights of those terms in these documents. We tested our method and our studies show that this technique can compete with other state-of-the-art algorithms that we choose to test the performance of our approach.

Keywords: distributed information retrieval, relevance, server selection, collection selection

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2839 The Primitive Code-Level Design Patterns for Distributed Programming

Authors: Bing Li

Abstract:

The primitive code-level design patterns (PDP) are the rudimentary programming elements to develop any distributed systems in the generic distributed programming environment, GreatFree. The PDP works with the primitive distributed application programming interfaces (PDA), the distributed modeling, and the distributed concurrency for scaling-up. They not only hide developers from underlying technical details but also support sufficient adaptability to a variety of distributed computing environments. Programming with them, the simplest distributed system, the lightweight messaging two-node client/server (TNCS) system, is constructed rapidly with straightforward and repeatable behaviors, copy-paste-replace (CPR). As any distributed systems are made up of the simplest ones, those PDAs, as well as the PDP, are generic for distributed programming.

Keywords: primitive APIs, primitive code-level design patterns, generic distributed programming, distributed systems, highly patterned development environment, messaging

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2838 A Highly Efficient Broadcast Algorithm for Computer Networks

Authors: Ganesh Nandakumaran, Mehmet Karaata

Abstract:

A wave is a distributed execution, often made up of a broadcast phase followed by a feedback phase, requiring the participation of all the system processes before a particular event called decision is taken. Wave algorithms with one initiator such as the 1-wave algorithm have been shown to be very efficient for broadcasting messages in tree networks. Extensions of this algorithm broadcasting a sequence of waves using a single initiator have been implemented in algorithms such as the m-wave algorithm. However as the network size increases, having a single initiator adversely affects the message delivery times to nodes further away from the initiator. As a remedy, broadcast waves can be allowed to be initiated by multiple initiator nodes distributed across the network to reduce the completion time of broadcasts. These waves initiated by one or more initiator processes form a collection of waves covering the entire network. Solutions to global-snapshots, distributed broadcast and various synchronization problems can be solved efficiently using waves with multiple concurrent initiators. In this paper, we propose the first stabilizing multi-wave sequence algorithm implementing waves started by multiple initiator processes such that every process in the network receives at least one sequence of broadcasts. Due to being stabilizing, the proposed algorithm can withstand transient faults and do not require initialization. We view a fault as a transient fault if it perturbs the configuration of the system but not its program.

Keywords: distributed computing, multi-node broadcast, propagation of information with feedback and cleaning (PFC), stabilization, wave algorithms

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2837 Double Clustering as an Unsupervised Approach for Order Picking of Distributed Warehouses

Authors: Hsin-Yi Huang, Ming-Sheng Liu, Jiun-Yan Shiau

Abstract:

Planning the order picking lists of warehouses to achieve when the costs associated with logistics on the operational performance is a significant challenge. In e-commerce era, this task is especially important productive processes are high. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, the definition of which features should be processed by such algorithms is not a simple task, being crucial to the proposed technique’s success. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A Zone2 picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.

Keywords: order picking, warehouse, clustering, unsupervised learning

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2836 Parallel Querying of Distributed Ontologies with Shared Vocabulary

Authors: Sharjeel Aslam, Vassil Vassilev, Karim Ouazzane

Abstract:

Ontologies and various semantic repositories became a convenient approach for implementing model-driven architectures of distributed systems on the Web. SPARQL is the standard query language for querying such. However, although SPARQL is well-established standard for querying semantic repositories in RDF and OWL format and there are commonly used APIs which supports it, like Jena for Java, its parallel option is not incorporated in them. This article presents a complete framework consisting of an object algebra for parallel RDF and an index-based implementation of the parallel query engine capable of dealing with the distributed RDF ontologies which share common vocabulary. It has been implemented in Java, and for validation of the algorithms has been applied to the problem of organizing virtual exhibitions on the Web.

Keywords: distributed ontologies, parallel querying, semantic indexing, shared vocabulary, SPARQL

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2835 An Algorithm for Herding Cows by a Swarm of Quadcopters

Authors: Jeryes Danial, Yosi Ben Asher

Abstract:

Algorithms for controlling a swarm of robots is an active research field, out of which cattle herding is one of the most complex problems to solve. In this paper, we derive an independent herding algorithm that is specifically designed for a swarm of quadcopters. The algorithm works by devising flight trajectories that cause the cows to run-away in the desired direction and hence herd cows that are distributed in a given field towards a common gathering point. Unlike previously proposed swarm herding algorithms, this algorithm does not use a flocking model but rather stars each cow separately. The effectiveness of this algorithm is verified experimentally using a simulator. We use a special set of experiments attempting to demonstrate that the herding times of this algorithm correspond to field diameter small constant regardless of the number of cows in the field. This is an optimal result indicating that the algorithm groups the cows into intermediate groups and herd them as one forming ever closing bigger groups.

Keywords: swarm, independent, distributed, algorithm

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2834 Cloud Computing in Data Mining: A Technical Survey

Authors: Ghaemi Reza, Abdollahi Hamid, Dashti Elham

Abstract:

Cloud computing poses a diversity of challenges in data mining operation arising out of the dynamic structure of data distribution as against the use of typical database scenarios in conventional architecture. Due to immense number of users seeking data on daily basis, there is a serious security concerns to cloud providers as well as data providers who put their data on the cloud computing environment. Big data analytics use compute intensive data mining algorithms (Hidden markov, MapReduce parallel programming, Mahot Project, Hadoop distributed file system, K-Means and KMediod, Apriori) that require efficient high performance processors to produce timely results. Data mining algorithms to solve or optimize the model parameters. The challenges that operation has to encounter is the successful transactions to be established with the existing virtual machine environment and the databases to be kept under the control. Several factors have led to the distributed data mining from normal or centralized mining. The approach is as a SaaS which uses multi-agent systems for implementing the different tasks of system. There are still some problems of data mining based on cloud computing, including design and selection of data mining algorithms.

Keywords: cloud computing, data mining, computing models, cloud services

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2833 Considering the Reliability of Measurements Issue in Distributed Adaptive Estimation Algorithms

Authors: Wael M. Bazzi, Amir Rastegarnia, Azam Khalili

Abstract:

In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition.

Keywords: adaptive filter, distributed estimation, sensor network, IDLMS algorithm

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2832 An Investigation Enhancing E-Voting Application Performance

Authors: Aditya Verma

Abstract:

E-voting using blockchain provides us with a distributed system where data is present on each node present in the network and is reliable and secure too due to its immutability property. This work compares various blockchain consensus algorithms used for e-voting applications in the past, based on performance and node scalability, and chooses the optimal one and improves on one such previous implementation by proposing solutions for the loopholes of the optimally working blockchain consensus algorithm, in our chosen application, e-voting.

Keywords: blockchain, parallel bft, consensus algorithms, performance

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2831 Hierarchical Clustering Algorithms in Data Mining

Authors: Z. Abdullah, A. R. Hamdan

Abstract:

Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering.

Keywords: clustering, unsupervised learning, algorithms, hierarchical

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2830 HPPDFIM-HD: Transaction Distortion and Connected Perturbation Approach for Hierarchical Privacy Preserving Distributed Frequent Itemset Mining over Horizontally-Partitioned Dataset

Authors: Fuad Ali Mohammed Al-Yarimi

Abstract:

Many algorithms have been proposed to provide privacy preserving in data mining. These protocols are based on two main approaches named as: the perturbation approach and the Cryptographic approach. The first one is based on perturbation of the valuable information while the second one uses cryptographic techniques. The perturbation approach is much more efficient with reduced accuracy while the cryptographic approach can provide solutions with perfect accuracy. However, the cryptographic approach is a much slower method and requires considerable computation and communication overhead. In this paper, a new scalable protocol is proposed which combines the advantages of the perturbation and distortion along with cryptographic approach to perform privacy preserving in distributed frequent itemset mining on horizontally distributed data. Both the privacy and performance characteristics of the proposed protocol are studied empirically.

Keywords: anonymity data, data mining, distributed frequent itemset mining, gaussian perturbation, perturbation approach, privacy preserving data mining

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2829 A Preliminary Conceptual Scale to Discretize the Distributed Manufacturing Continuum

Authors: Ijaz Ul Haq, Fiorenzo Franceschini

Abstract:

The distributed manufacturing methodology brings a new concept of decentralized manufacturing operations close to the proximity of end users. A preliminary scale, to measure distributed capacity and evaluate positioning of firms, is developed in this research. In the first part of the paper, a literature review has been performed which highlights the explorative nature of the studies conducted to present definitions and classifications due to novelty of this topic. From literature, five dimensions of distributed manufacturing development stages have been identified: localization, manufacturing technologies, customization and personalization, digitalization and democratization of design. Based on these determinants a conceptual scale is proposed to measure the status of distributed manufacturing of a generic firm. A multiple case study is then conducted in two steps to test the conceptual scale and to identify the corresponding level of distributed potential in each case study firm.

Keywords: distributed manufacturing, distributed capacity, localized production, ordinal scale

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2828 Fault Diagnosis of Manufacturing Systems Using AntTreeStoch with Parameter Optimization by ACO

Authors: Ouahab Kadri, Leila Hayet Mouss

Abstract:

In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

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2827 Optimizing Communications Overhead in Heterogeneous Distributed Data Streams

Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

Abstract:

In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

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2826 Performance Analysis of Ad-Hoc Network Routing Protocols

Authors: I. Baddari, A. Riahla, M. Mezghich

Abstract:

Today in the literature, we discover a lot of routing algorithms which some have been the subject of normalization. Two great classes Routing algorithms are defined, the first is the class reactive algorithms and the second that of algorithms proactive. The aim of this work is to make a comparative study between some routing algorithms. Two comparisons are considered. The first will focus on the protocols of the same class and second class on algorithms of different classes (one reactive and the other proactive). Since they are not based on analytical models, the exact evaluation of some aspects of these protocols is challenging. Simulations have to be done in order to study their performances. Our simulation is performed in NS2 (Network Simulator 2). It identified a classification of the different routing algorithms studied in a metrics such as loss of message, the time transmission, mobility, etc.

Keywords: ad-hoc network routing protocol, simulation, NS2, delay, packet loss, wideband, mobility

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2825 Method of Successive Approximations for Modeling of Distributed Systems

Authors: A. Torokhti

Abstract:

A new method of mathematical modeling of the distributed nonlinear system is developed. The system is represented by a combination of the set of spatially distributed sensors and the fusion center. Its mathematical model is obtained from the iterative procedure that converges to the model which is optimal in the sense of minimizing an associated cost function.

Keywords: mathematical modeling, non-linear system, spatially distributed sensors, fusion center

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2824 Semi-Supervised Hierarchical Clustering Given a Reference Tree of Labeled Documents

Authors: Ying Zhao, Xingyan Bin

Abstract:

Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data.

Keywords: semi-supervised clustering, hierarchical agglomerative clustering, reference trees, distance constraints

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2823 Optimal Sizing and Placement of Distributed Generators for Profit Maximization Using Firefly Algorithm

Authors: Engy Adel Mohamed, Yasser Gamal-Eldin Hegazy

Abstract:

This paper presents a firefly based algorithm for optimal sizing and allocation of distributed generators for profit maximization. Distributed generators in the proposed algorithm are of photovoltaic and combined heat and power technologies. Combined heat and power distributed generators are modeled as voltage controlled nodes while photovoltaic distributed generators are modeled as constant power nodes. The proposed algorithm is implemented in MATLAB environment and tested the unbalanced IEEE 37-node feeder. The results show the effectiveness of the proposed algorithm in optimal selection of distributed generators size and site in order to maximize the total system profit.

Keywords: distributed generators, firefly algorithm, IEEE 37-node feeder, profit maximization

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2822 Sensitivity Analysis for 14 Bus Systems in a Distribution Network with Distributed Generators

Authors: Lakshya Bhat, Anubhav Shrivastava, Shiva Rudraswamy

Abstract:

There has been a formidable interest in the area of Distributed Generation in recent times. A wide number of loads are addressed by Distributed Generators and have better efficiency too. The major disadvantage in Distributed Generation is voltage control- is highlighted in this paper. The paper addresses voltage control at buses in IEEE 14 Bus system by regulating reactive power. An analysis is carried out by selecting the most optimum location in placing the Distributed Generators through load flow analysis and seeing where the voltage profile rises. MATLAB programming is used for simulation of voltage profile in the respective buses after introduction of DG’s. A tolerance limit of +/-5% of the base value has to be maintained. To maintain the tolerance limit, 3 methods are used. Sensitivity analysis of 3 methods for voltage control is carried out to determine the priority among the methods.

Keywords: distributed generators, distributed system, reactive power, voltage control, sensitivity analysis

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2821 Distributed Leadership: An Alternative at Higher Education Institutions in Turkey

Authors: Sakine Sincer

Abstract:

In today’s world, which takes further steps towards globalization each and every day, societies and cultures are re-shaped while the demands of the changing world are described once more. In this atmosphere, where the speed of change sometimes reaches a terrifying point, it is possible to state that effective leaders are needed more than ever in order to meet the above-stated needs and demands. The question of what effective leadership is keeping its importance on the agenda. Most of the answers to this question has mostly focused on the approach of distributed leadership recently. This study aims at analyzing the applicability of distributed leadership, which is accepted to be an example of effective leadership that can meet the needs of global world, which is changing more and more rapidly nowadays, at higher education institutions in Turkey. Within the framework of this study, first of all, the historical development of distributed leadership is addressed, and then a theoretical framework is drawn for this approach by means of underlying what distributed leadership is and is not. After that, different points of view about the approach are laid out within the borders of opinions expressed by Gronn and Spillane, who are accepted to be the most famous advocators of distributed leadership. Then, exemplar practices of distributed leadership are included in the study before drawing attention to the strengths and weaknesses of this approach. Lastly, the applicability of distributed leadership at higher education institutions in Turkey is analyzed. This study is carried out with the method of literature review by resorting to first- and second-hand sources on distributed leadership.

Keywords: globalization, school leadership, distributed leadership, higher education, management

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