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Abstract: Graphical Semantic Wiki for use in the Pre-coding Phase of Discrete Event Simulation Abstract This research concerns the development and application of graphical wiki software to facilitate knowledge management in the pre-coding phase of discrete simulation projects. 1. Description The objective of this research is to develop a tool to facilitate the collaborative capturing of knowledge for discrete event simulation projects, and while capturing it, put it into a form that will be reused and continue to be developed. Discrete Event Simulation projects often generate a significant amount of knowledge - at considerable effort - and involve gathering knowledge from many human users in a system. However, this knowledge is often only stored in the simulation itself. A problem with this is that Discrete Event simulations are typically used to solve a specific problem, and then put aside. This means that the only knowledge that is gained in such a project may be the solution to the specific problem being examined. It would be preferable to store the generated knowledge in a manner oriented towards reuse. The field of knowledge management is concerned with facilitating collaboration between humans, and the sharing and dissemination of knowledge. A lot of literature has been published indicating the need for knowledge management in Discrete Event Simulation. However, far fewer papers have explicitly examined the field of knowledge management and how it can be applied in Discrete Event Simulation projects. One of the most popular and proven knowledge management tools is the wiki. This facilitates true collaboration and knowledge reuse in a simple and accessible manner. However traditional wikis are limited when it comes to describing systems: They don’t provide support for editing graphical content. The primary objective of this research is to show how a graphical wiki may be developed and used to facilitate collaborative generation of system descriptions, suitable for use in discrete event simulation projects, and that this description could continue to exist as a useful artifact of knowledge after the DES project has been completed. This wiki would support collaborative editing of diagrams. The semantic wiki is a wiki which contains a logical definition of its content, and captures the relationship between different parts. This allows the content to be Peter William Dongan PhD Supervisor: Dr Cathal Heavey University of Limerick pwdongan@hotmail.com 157 “understood” by machines. It offers such benefits as integration with external software, enhanced querying of the knowledge-base, and automatic verification of consistency in the descriptions. The level of detail that could be captured in a graphical wiki populated with diagrams would be extensive. In a text-based wiki, the main naturally occurring relationship is links between pages; however in a graphical wiki, relationships could be captured for every connection in every diagram. This would increase the potential reuse of the knowledge base. It may also facilitate a level of logical integration between heterogeneous types of diagrams, making knowledge gathering more flexible. Finally, it could feasibly be integrated with software to automatically generate simulations from the descriptions in the wiki. Therefore a secondary objective of this research is to examine how a semantic layer in a graphical wiki could be used to enhance it as a knowledge management tool. 2. Status Prototype Graphical Semantic Wiki software has been developed using ASP.NET and Silverlight. Diagrams and diagram elements and connectors may be edited collaboratively by users. Typical wiki features are supported, such as versioning, and accessibility via typical Web browsers. The software allows ontologies to be defined as the content is generated. Graphical content is associated with semantic objects. When a connector is drawn between two graphical objects, a relationship [associated with the connector type] is created between the associated logical objects. These ontologies are defined in a simple frame-based manner. Semantic information may be generated from different diagram types [to varying degrees], and heterogeneous diagrams based on the same logical objects may be validated against each other. Ongoing work includes the development of case studies to further explore the concepts being applied, and to prove them, as well as further examination of how ontologies may be defined.
Particle Swarm Optimisation with Gradually Increasing Directed Neighbourhoods Abstract—Particle swarm optimisation (PSO) is an intelligent random search algorithm, and the key to success is to effectively balance between exploration and exploitation of the solution space. This paper presents a new dynamic topology called “gradually increasing directed neighbourhoods (GIDN)” that provides an effective way to balance between exploration and exploitation. In our model, each particle begins with a small number of connections and there are many small isolated swarms that improve the exploration ability. At each iteration, we gradually add a number of new connections between particles which improves the ability of exploitation gradually. Furthermore, these connections among particles are created randomly and have directions. We formalise this topology using random graph representations. Experiments are conducted on 31 benchmark test functions to validate our proposed topology. The results show that the PSO with GIDN performs much better than a number of the state of the art algorithms on almost all of the 31 functions. Index Terms—PSO, Dynamic Topologies. I. Introduction Particle Swarm Optimisation (PSO) was proposed by Eberhart and Kennedy in 1995 [1], [2]. It is inspired by the socially self-organised populations such as bird flocking and fish schooling. The vital property of the PSO is the interactions or connections between particles. These connections are generally known as the “neighbourhood topology” of the algorithm. The neighbourhood topologies of the swarm determine the speed of information flow in the entire population, and furthermore, the speed of information flow could be used to control exploration and exploitation of the search space [3]. This paper presents a dynamic neighborhood topology through gradually increasing the number of connections for each particle in the population. We have formalised this topology using random graph representations. In order to validate our proposed method, we have tested the PSO on 31 benchmark test functions. The results show that the changes in the PSO result on better performance than a number of the state of the art algorithms on almost all of the functions. II. PSO with Gradually Increasing Directed Neighbourhoods In our model, we gradually add connections between particles and these connections between particles are randomly chosen and also have directions. So we choose a random directed graph G(N, b, γ, t) to formally define our GIDN. We define the in-neighbourhood set H + t (pi) for any particle (pi) Hongliang Liu Enda Howley and Jim Duggan are with the Department of Information Technology, National University of Ireland, Galway (email: h.liu1@nuigalway.ie and jim.duggan@nuigalway.ie) Hongliang Liu, Enda Howley and Jim Duggan TABLE I Comparisons between our PSO-GIDN and other PSO Algorithms (T-test Results Summary) SPSO GPSO VPSO PSO-NO PSO-RDN PSO-DMS Statistically Better 21 26 15 20 17 27 Statistically Same 9 4 14 10 12 2 Statistically Worse 1 1 2 1 2 2 in the G(N, b, γ, t) first. The size of H + t (pi) at iteration t is determined by the following equation. |H + t t (pi)| = ⌊( ) maxt γ ∗ N + b⌋ (1) where ⌊x⌋ is the largest integer not greater than x (the floor function), maxt is the maximum iteration number, N denotes the size of the population, b and γ are two parameters. The parameter b is the number of neighbours that each particle begins with. We suggest that b is set to a small number such as 2 or 3 in order to create lots of small swarms in the population. The parameter γ controls the neighbourhood size increasing speed and subsequently the information flow speed. Thus, γ can be used to control exploration and exploitation. f (γ) = t ( maxt )γ is a decreasing function because of 0 < t ≤ 1. Our maxt results show that γ = 2 provides a better balance between exploration and exploitation. 158 III. Some Experimental Results Table I shows a summary of comparisons between our PSO- GIDN and several well-known algorithms including SPSO, GPSO, VPSO, PSO-NO, PSO-RDN, PSO-DMS. There are only a very few cases that the PSO-GIDN performs statistically significant worse than the other PSO algorithms. In contrast, the PSO-GIDN performs statistically significant better on the majority of the functions. IV. Summary To conclude, our paper provides an optimistic answer to the challenge of balancing the opposing forces of exploration and exploitation, a key concern in the PSOs. References [1] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Networks, vol. 4. IEEE Press, Nov./Dec. 1995, pp. 1942–1948. [2] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Oct. 1995, pp. 39–43. [3] P. Suganthan, “Particle swarm optimiser with neighbourhood operator,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999), 1999. 1
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NUI Galway - UL Alliance First Annu
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FULL TABLE OF CONTENTS 1 GAMES, VIS
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4 MECHANICAL AND BIOMEDICAL ENGINEE
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5.21 Detecting Topics and Events in
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8.7 Modelling Extreme Flood Events
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GAMES, VISUALISATION & EDUCATION 1.
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Generation and Analysis of Graph St
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Evolution and Analysis of Strategie
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Abstract The delivery of multimedia
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Applications of Reinforcement Learn
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Assessing the effects of interactiv
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Real-time depth map generation usin
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An analysis of the capability of pr
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Building Information Modelling duri
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Dwelling Energy Measurement Procedu
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Numerical Modelling of Tidal Turbin
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Energy Storage using Microencapsula
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Data Centre Energy Efficiency Mark
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An embodied energy and carbon asses
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SmartOp - Smart Buildings Operation
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Ocean Wave Energy Exploitation in D
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Future Smart Grid Synchronization C
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Web-Based Building Energy Usage Vis
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Image Recognition and Classificatio
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Android Based Multi-Feature Elderly
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Determining Subjects’ Activities
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New Analysis Techniques for ICU Dat
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National E-Prescribing Systems in I
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Using Mashups to Satisfy Personalis
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3D Computational Modeling of Blood
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Experimental and Computational Inve
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Experimental Analysis of the Therma
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Simulating Actin Cytoskeleton Remod
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Computational Analysis of Transcath
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An In vitro Shear Stress System for
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Development of a Micropipette Aspir
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A Computational Test-Bed to Examine
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Computational Modeling of Ceramic-b
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Multi-Scale Computational Modelling
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Development of a mixed-mode cohesiv
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Active Computational Modelling of C
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Modelling the Management of Medical
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SOCIAL MEDIA, SEARCH & RECOMMENDATI
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Improving Twitter Search by Removin
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Abstract The goal of this research
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Generalized Blockmodeling Samantha
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Life-Cycles and Mutual Effects of S
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dcat: Searching Public Sector Infor
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The Effect of User Features on Chur
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User Similarity and Interaction in
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Improving Categorisation in Social
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Natural Language Queries on Enterpr
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Studying Forum Dynamics from a User
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Provenance in the Web of Data: a bu
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Towards Social Descriptions of Serv
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ENVIRONMENTAL ENGINEERING 6.1 Asses
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Novel Agri-engineering solutions fo
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Evaluation of amendments to control
Page 118 and 119: Determination of optimal applicatio
Page 120 and 121: Treatment of Piggery Wastewaters us
Page 122 and 123: NEXT GENERATION INTERNET 7.1 Extens
Page 124 and 125: Enabling Federation of Government M
Page 126 and 127: Curated Entities for Enterprise Uma
Page 128 and 129: Mobile Web + Social Web + Semantic
Page 130 and 131: Engaging Citizens in the Policy-Mak
Page 132 and 133: Preference-based Discovery of Dynam
Page 134 and 135: RDF On the Go: An RDF Storage and Q
Page 136 and 137: Policy Modeling meets Linked Open D
Page 138 and 139: A Contextualized Perspective for Li
Page 140 and 141: Improving discovery in Life Science
Page 142 and 143: The Semantic Public Service Portal
Page 144 and 145: Personalized Content Delivery on Mo
Page 146 and 147: A Framework to Describe Localisatio
Page 148 and 149: The influence of secondary settleme
Page 150 and 151: Analysis of Shear Transfer in Void-
Page 152 and 153: Cost-Effective Sustainable Construc
Page 154 and 155: Modelling Extreme Flood Events due
Page 156 and 157: Axial Load Capacity of a Driven Cas
Page 158 and 159: Chemical amendment of dairy cattle
Page 160 and 161: Seismic Design of Concentrically Br
Page 162 and 163: MODELLING, ALGORITHMS & CONTROL 9.1
Page 164 and 165: Eigen-based Approach for Leverage P
Page 166 and 167: Evolutionary Modelling of Industria
Page 170 and 171: Low Coverage Genome Assembly Using
Page 172 and 173: Evolving a Robust Open-Ended Langua
Page 174 and 175: Context Stamp - A Topic-based Conte
Page 176 and 177: DSP-Based Control of Multi-Rail DC-
Page 178 and 179: Topographical Cues - Controlling Ce
Page 180 and 181: Creep Relaxation and Crack Growth P
Page 182 and 183: Finite Element Modelling of Failure
Page 184 and 185: Influence of Fluorine and Nitrogen
Page 186 and 187: Phase Decompositions of Bioceramic
Page 188 and 189: High Resolution Microscopical Analy
Page 190 and 191: An Experimental and Numerical Analy
Page 192 and 193: Thermomechanical characterisation o
Page 194 and 195: A multiaxial damage mechanics metho
Page 196: The effect of citrate ester plastic
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NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

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