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Search Results: 1 - 10 of 35598 matches for " intelligence system "
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Swarm Intelligence in Power System Planning  [PDF]
Ke Meng, Z.Y. Dong, Yichen Qiao
International Journal of Clean Coal and Energy (IJCCE) , 2013, DOI: 10.4236/ijcce.2013.22B001
Abstract: Power system planning is one of the essential tasks in the power system operation management, which requires in-depth knowledge of the system under consideration. It can be regarded as a nonlinear, discontinuous, constrained multi objective optimization problem. Although the traditional optimization tools can be used, the modern planning problem requires more advanced optimization tools. In this paper, a survey of state-of-the-art mathematical optimization methods that facilitates power system planning is provided, and the needs of introducing swarm intelligence approaches into power system planning are discussed.
Implementation of Artificial Intelligence Techniques for Steady State Security Assessment in Pool Market
I. S. Saeh,A. Khairuddin
International Journal of Engineering , 2009,
Abstract: Various techniques have been implemented to include steady state securityassessment in the analysis of trading in deregulated power system, howevermost of these techniques lack requirements of fast computational time withacceptable accuracy. The problem is compounded further by the requirements toconsider bus voltages and thermal line limits. This work addresses the problemby presenting the analysis and management of power transaction between powerproducers and customers in the deregulated system using the application ofArtificial Intelligence (AI) techniques such as Neural Network (ANN), DecisionTree (DT) techniques and Adaptive Network based Fuzzy Inference System(ANFIS). Data obtained from Newton Raphson load flow analysis method areused for the training and testing purposes of the proposed techniques and alsoas comparison in term of accuracy against the proposed techniques. The inputvariables to the AI systems are loadings of the lines and the voltage magnitudesof the load buses. The algorithms are initially tested on the 5 bus system andfurther verified on the IEEE 30 57 and 118 bus test system configured as pooltrading models. By comparing the results, it can be concluded that ANNtechnique is more accurate and better in term of computational time takencompared to the other two techniques. However, ANFIS and DT’s can be moreeasily implemented for practical applications. The newly developed techniquescan further improve security aspects related to the planning and operation ofpool-type deregulated system.
Design & Development of a Software System for Swarm Intelligence based Research Studies
Utku K?se
Brain. Broad Research in Artificial Intelligence and Neuroscience , 2012,
Abstract: This paper introduce a software system including widely-used Swarm Intelligence algorithms or approaches to be used for the related scientific research studies associated with the subject area. The programmatic infrastructure of the system allows working on a fast, easy-to-use, interactive platform to perform Swarm Intelligence based studies in a more effective, efficient and accurate way. In this sense, the system employs all of the necessary controls for the algorithms and it ensures an interactive platform on which computer users can perform studies on a wide spectrum of solution approaches associated with simple and also more advanced problems.
A Rule-Based Expert System for Industrial Training  [PDF]
Wensheng Liu, Zandong Sun
Technology and Investment (TI) , 2012, DOI: 10.4236/ti.2012.32016
Abstract: In this paper, a rule-based expert system, named CHECKOP has been presented for industrial training to avoid potential hazards which result from incorrect operating. It also enables a range of operating scenarios for a chemical plant to be depicted by Delta 3D software. The system facilitates the input of operating sequences into the rule-based CLIPS data base. By analysing these operating sequences enables unsafe plant operation to be corrected and the consideration of preventative measures. Operating sequences can be captured and then operation instructions generated automatically.
An Expert System for Advising to Buy a FootballPlayer Using Visual Prolog  [PDF]
Mahdi Gholami mehr, Hossein Shirazi
Intelligent Information Management (IIM) , 2012, DOI: 10.4236/iim.2012.44020
Abstract: This work presents the design of an Expert System that aims to advice the club teams to buy a football player in the post that they needed. Suggesting different player in many posts by an expert person is based on football experience, knowledge about the player and the club that he works. For mechanization the ability of this person, we use Expert System because it can model the ability of a person in solving a problem. Visual Prolog language is used as a tool for designing our Expert System.
An Overview of Functional Components of Artificial Intelligence Financial Decision Support System  [PDF]
Qi Wang
Open Journal of Social Sciences (JSS) , 2018, DOI: 10.4236/jss.2018.68009
Abstract: The development of artificial intelligence technology makes the intelligent decision-making of enterprise finance possible. Combined with the traditional enterprise financial decision support system, this paper summarizes and introduces the structure and function of the enterprise Financial Decision Support System and its subsystems under the development of artificial intelligence in detail.
Analysis of Drug Patent in American Universities Based on Xlpat Platform  [PDF]
Bin Li
Open Journal of Social Sciences (JSS) , 2018, DOI: 10.4236/jss.2018.612023
Abstract: This paper uses Xlpat patent intelligence system to analyze the drug patents of American universities, and to visualize the search results and analyze the core patents. It is a summary of the research and development of drug patents in American universities, aiming to provide reference for researchers in this field.
Collective Intelligence Systems: Classification and Modeling
Ioanna Lykourentzou,Dimitrios J. Vergados,Epaminondas Kapetanios,Vassili Loumos
Journal of Emerging Technologies in Web Intelligence , 2011, DOI: 10.4304/jetwi.3.3.217-226
Abstract: Collective intelligence (CI) is an emerging research field that seeks to merge human and machine intelligence, with an aim to achieve results unattainable by either one of these entities alone. CI systems may significantly vary in nature, from collaborative systems, like open source software development communities, to competitive ones, like problem-solving companies that benefit from the competition among participating user teams to identify solutions to various R&D problems. The advantages that CI systems earn user communities, together with the fact that they share a number of basic common features, provide the potential for designing a general methodology for their efficient modeling, development and evaluation. In this paper we describe a modeling process which identifies the common features, as well as the main challenges that the construction of generic collective intelligence systems poses. First a basic categorization of CI systems is performed, followed by a description of the proposed modeling approach. This approach includes concepts such as the set of possible user actions, the CI system state and the individual and community objectives, as well as a number of necessary functions, which estimate various parameters of the CI system, such as the expected user actions, the future system state and the level of objective fulfillment. Finally, based on the proposed modeling approach, certain current CI systems are described, a number of problems that they face are identified and specific solutions are suggested. The proposed modeling approach is expected to promote more efficient CI system design, so that the benefit gained by the participating community and individuals, will be maximized.
National Crime Intelligence System
S.O. Adeola,B.K. Alese,S.O. Falaki
Information Technology Journal , 2007,
Abstract: In this research we developed an online National Crime Intelligence System, which helps to store and retrieve all information about persons with criminal related record in Nigeria. As a buildup to this work, we visited all relevant organizations that are responsible for monitoring and controlling crime in Nigeria with a view to understanding their modus operandi and to examining the current shortcomings in crime monitoring and control. Legal experts opinions were sought in respect of administration of criminal cases in Nigeria. With the help of the intelligence information gathered, we were able to build a robust database using the oracle database tool as backend. Also, Visual Basic program was used to build the interface, which provides access medium to the database. The developed package is a crime intelligent system consisting of a distributed database, which makes it very easy to access the criminal status of every citizen and resident in a Nigerian law court and in every part of the country. It facilitates the computer storage, retrieval and processing criminal cases in Nigeria law courts. With the help of the developed software, the problem of identifying an ex-convict, the time and where a crime was committed and all other related problems would have been solved.
Business intelligence
Cebotarean Elena
Journal of Knowledge Management, Economics and Information Technology , 2011,
Abstract: Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes. BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics. Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS). Though the term business intelligence is sometimes used as a synonym for competitive intelligence, because they both support decision making, BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. Business intelligence understood broadly can include the subset of competitive intelligence.
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