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Sample records for model intelligence mathias

  1. Modelling intelligent behavior

    Science.gov (United States)

    Green, H. S.; Triffet, T.

    1993-01-01

    An introductory discussion of the related concepts of intelligence and consciousness suggests criteria to be met in the modeling of intelligence and the development of intelligent materials. Methods for the modeling of actual structure and activity of the animal cortex have been found, based on present knowledge of the ionic and cellular constitution of the nervous system. These have led to the development of a realistic neural network model, which has been used to study the formation of memory and the process of learning. An account is given of experiments with simple materials which exhibit almost all properties of biological synapses and suggest the possibility of a new type of computer architecture to implement an advanced type of artificial intelligence.

  2. Mathias forcing and combinatorial covering properties of filters

    Czech Academy of Sciences Publication Activity Database

    Chodounský, David; Repovš, D.; Zdomskyy, L.

    2015-01-01

    Roč. 80, č. 4 (2015), s. 1398-1410 ISSN 0022-4812 R&D Projects: GA AV ČR IAA100190902 Institutional support: RVO:67985840 Keywords : Menger space * Hurewicz space * Mathias forcing Subject RIV: BA - General Mathematics Impact factor: 0.510, year: 2015 http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=10080752

  3. Modelling traffic flows with intelligent cars and intelligent roads

    NARCIS (Netherlands)

    van Arem, Bart; Tampere, Chris M.J.; Malone, Kerry

    2003-01-01

    This paper addresses the modeling of traffic flows with intelligent cars and intelligent roads. It will describe the modeling approach MIXIC and review the results for different ADA systems: Adaptive Cruise Control, a special lane for Intelligent Vehicles, cooperative following and external speed

  4. The reality of fiction: the ECO by Mathias Goeritz

    Directory of Open Access Journals (Sweden)

    Fernando Quesada

    2016-05-01

    Full Text Available Abstract This article covers the full biography of a building, the Experimental Museum El Eco, designed by Germanborn and Mexican émigré artist and architect Mathias Goeritz. It provides an approach intersecting the biography of the author, the history of the building, and prominent  individuals of the two cultural traditions, German and Mexican, who  participated in the creation of a very special and unique building: El Eco. On the one hand, the ethics of Expressionism, the interest in  non-European art, the cult of primitivism and the aesthetic system of the  pair of concepts abstraction-empathy, all stemming from German culture. On the other, the pantheistic religiosity of landscape, zoomorphism and anthropomorphism, the interest in masks, and the aesthetics of  monumental scale, stemming from pre-Cortesian Mexican culture. Taking  the stance of intertwining Mathias Goeritz parcours with those of individuals and issues from his German past and his Mexican future – highlighting the figures of Wilhelm Worringer, Paul Westheim, Luis Barragán, Edmundo O'Gorman, and Ida Rodríguez Prampolini – this article proposes a return  trip from fiction to reality, following in the footsteps of the author and  comparing them with the pathway of the very building El Eco.

  5. Modeling Interactive Intelligences

    Science.gov (United States)

    2002-08-01

    New York: Basic Books, 1999. P. 207-10. [5] Piaget , Jean . Play, Dreams, and Imitation in Childhood. New York: Norton, 1962. [6] Dillard, Annie. Living...concepts of reentry and binding. Next, I rely on Jean Piaget’s model of adaptation in order to examine the function of imitation and play in an...rather than metrics should be used. 2. ADAPTATION, SELECTION, IMITATION, AND PLAY Piaget presented adaptive behavior as a combination of accommodation and

  6. Transparency of Computational Intelligence Models

    Science.gov (United States)

    Owotoki, Peter; Mayer-Lindenberg, Friedrich

    This paper introduces the behaviour of transparency of computational intelligence (CI) models. Transparency reveals to end users the underlying reasoning process of the agent embodying CI models. This is of great benefit in applications (e.g. data mining, entertainment and personal robotics) with humans as end users because it increases their trust in the decisions of the agent and their acceptance of its results. Our integrated approach, wherein rules are just one of other transparency factors (TF), differs from previous related efforts which have focused mostly on generation of comprehensible rules as explanations. Other TF include degree of confidence measure and visualization of principal features. The transparency quotient is introduced as a measure of the transparency of models based on these factors. The transparency enabled generalized exemplar model has been developed to demonstrate the TF and transparency concepts introduced in this paper.

  7. Intelligent structural optimization: Concept, Model and Methods

    International Nuclear Information System (INIS)

    Lu, Dagang; Wang, Guangyuan; Peng, Zhang

    2002-01-01

    Structural optimization has many characteristics of Soft Design, and so, it is necessary to apply the experience of human experts to solving the uncertain and multidisciplinary optimization problems in large-scale and complex engineering systems. With the development of artificial intelligence (AI) and computational intelligence (CI), the theory of structural optimization is now developing into the direction of intelligent optimization. In this paper, a concept of Intelligent Structural Optimization (ISO) is proposed. And then, a design process model of ISO is put forward in which each design sub-process model are discussed. Finally, the design methods of ISO are presented

  8. A measurement model of multiple intelligence profiles of management graduates

    Science.gov (United States)

    Krishnan, Heamalatha; Awang, Siti Rahmah

    2017-05-01

    In this study, developing a fit measurement model and identifying the best fitting items to represent Howard Gardner's nine intelligences namely, musical intelligence, bodily-kinaesthetic intelligence, mathematical/logical intelligence, visual/spatial intelligence, verbal/linguistic intelligence, interpersonal intelligence, intrapersonal intelligence, naturalist intelligence and spiritual intelligence are the main interest in order to enhance the opportunities of the management graduates for employability. In order to develop a fit measurement model, Structural Equation Modeling (SEM) was applied. A psychometric test which is the Ability Test in Employment (ATIEm) was used as the instrument to measure the existence of nine types of intelligence of 137 University Teknikal Malaysia Melaka (UTeM) management graduates for job placement purposes. The initial measurement model contains nine unobserved variables and each unobserved variable is measured by ten observed variables. Finally, the modified measurement model deemed to improve the Normed chi-square (NC) = 1.331; Incremental Fit Index (IFI) = 0.940 and Root Mean Square of Approximation (RMSEA) = 0.049 was developed. The findings showed that the UTeM management graduates possessed all nine intelligences either high or low. Musical intelligence, mathematical/logical intelligence, naturalist intelligence and spiritual intelligence contributed highest loadings on certain items. However, most of the intelligences such as bodily kinaesthetic intelligence, visual/spatial intelligence, verbal/linguistic intelligence interpersonal intelligence and intrapersonal intelligence possessed by UTeM management graduates are just at the borderline.

  9. Prediction of speech intelligibility based on an auditory preprocessing model

    DEFF Research Database (Denmark)

    Christiansen, Claus Forup Corlin; Pedersen, Michael Syskind; Dau, Torsten

    2010-01-01

    Classical speech intelligibility models, such as the speech transmission index (STI) and the speech intelligibility index (SII) are based on calculations on the physical acoustic signals. The present study predicts speech intelligibility by combining a psychoacoustically validated model of auditory...

  10. Computational Intelligence, Cyber Security and Computational Models

    CERN Document Server

    Anitha, R; Lekshmi, R; Kumar, M; Bonato, Anthony; Graña, Manuel

    2014-01-01

    This book contains cutting-edge research material presented by researchers, engineers, developers, and practitioners from academia and industry at the International Conference on Computational Intelligence, Cyber Security and Computational Models (ICC3) organized by PSG College of Technology, Coimbatore, India during December 19–21, 2013. The materials in the book include theory and applications for design, analysis, and modeling of computational intelligence and security. The book will be useful material for students, researchers, professionals, and academicians. It will help in understanding current research trends and findings and future scope of research in computational intelligence, cyber security, and computational models.

  11. Corpo e spazio. Una teoria compositiva nell’opera di Oswald Mathias Ungers / Matter and Space. Compositional Theory in the Work of Oswald Mathias Ungers

    Directory of Open Access Journals (Sweden)

    Gilda Giangipoli

    2016-04-01

    Full Text Available Tra i numerosi approfondimenti teorici e sperimentazioni condotti da Oswald Mathias Ungers in più di cinquant’anni della sua opera, emerge una delle prime teorie compositive applicate al tema dell’abitazione: la teoria di “corpo e spazio” che introduce una visione gerarchica degli spazi domestici anche nell’intento di ridurre il più possibile le superfici di distribuzione, per lasciare più spazio agli ambiti di vita collettiva della casa. / Between the multifarious theoretical studies and experimentations done by Oswald Mathias Ungers, in more than fifty years of his work, comes out one of the first compositional theory applied to the house-subject: the theory of “matter and space”, which introduces a hierarchic view of domestic spaces, also to reduce as much as possible distributive surfaces and to give more space to collective rooms of the house.

  12. Model SH intelligent instrument for thickness measuring

    International Nuclear Information System (INIS)

    Liu Juntao; Jia Weizhuang; Zhao Yunlong

    1995-01-01

    The authors introduce Model SH Intelligent Instrument for thickness measuring by using principle of beta back-scattering and its application range, features, principle of operation, system design, calibration and specifications

  13. An Intelligence Collection Management Model.

    Science.gov (United States)

    1984-06-01

    classification of inteligence collection requirements in terms of. the a-.- metnodo"c, .ev--e in Chaster Five. 116 APPgENDIX A A METHOD OF RANKING...of Artificial Intelligence Tools and Technigues to!TN’X n~l is n rs aa~emfft-.3-ufnyva: ’A TZ Ashby W. Ecss. An Introduction to Cybernetics. New York

  14. Modelling speech intelligibility in adverse conditions

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2013-01-01

    Jørgensen and Dau (J Acoust Soc Am 130:1475-1487, 2011) proposed the speech-based envelope power spectrum model (sEPSM) in an attempt to overcome the limitations of the classical speech transmission index (STI) and speech intelligibility index (SII) in conditions with nonlinearly processed speech....... Instead of considering the reduction of the temporal modulation energy as the intelligibility metric, as assumed in the STI, the sEPSM applies the signal-to-noise ratio in the envelope domain (SNRenv). This metric was shown to be the key for predicting the intelligibility of reverberant speech as well...... subjected to phase jitter, a condition in which the spectral structure of the intelligibility of speech signal is strongly affected, while the broadband temporal envelope is kept largely intact. In contrast, the effects of this distortion can be predicted -successfully by the spectro-temporal modulation...

  15. A Transitive Model For Artificial Intelligence Applications

    Science.gov (United States)

    Dwyer, John

    1986-03-01

    A wide range of mathematical techniques have been applied to artificial intelligence problems and some techniques have proved more suitable than others for certain types of problem. We formally define a mathematical model which incorporates some of these successful techniques and we discuss its intrinsic properties. Universal applicability of the model is demonstrated through specific applications to problems drawn from rule-based systems, digital hardware design and constraint satisfaction networks. We also give indications of potential applications to other artificial intelligence problems, including knowledge engineering.

  16. Mathias-Prikry and Laver type forcing; Summable ideals, coideals, and +-selective filters

    Czech Academy of Sciences Publication Activity Database

    Chodounský, David; Guzmán Gonzáles, O.; Hrušák, M.

    2016-01-01

    Roč. 55, č. 3 (2016), s. 493-504 ISSN 0933-5846 R&D Projects: GA ČR(CZ) GF15-34700L Institutional support: RVO:67985840 Keywords : Mathias–Prikry forcing * Laver type forcing * Mathias like real Subject RIV: BA - General Mathematics Impact factor: 0.394, year: 2016 http://link.springer.com/article/10.1007/s00153-016-0476-9

  17. Intelligence and the brain: a model-based approach

    NARCIS (Netherlands)

    Kievit, R.A.; van Rooijen, H.; Wicherts, J.M.; Waldorp, L.J.; Kan, K.-J.; Scholte, H.S.; Borsboom, D.

    2012-01-01

    Various biological correlates of general intelligence (g) have been reported. Despite this, however, the relationship between neurological measurements and g is not fully clear. We use structural equation modeling to model the relationship between behavioral Wechsler Adult Intelligence Scale (WAIS)

  18. A model for Business Intelligence Systems’ Development

    Directory of Open Access Journals (Sweden)

    Manole VELICANU

    2009-01-01

    Full Text Available Often, Business Intelligence Systems (BIS require historical data or data collected from var-ious sources. The solution is found in data warehouses, which are the main technology used to extract, transform, load and store data in the organizational Business Intelligence projects. The development cycle of a data warehouse involves lots of resources, time, high costs and above all, it is built only for some specific tasks. In this paper, we’ll present some of the aspects of the BI systems’ development such as: architecture, lifecycle, modeling techniques and finally, some evaluation criteria for the system’s performance.

  19. Modeling speech intelligibility in adverse conditions

    DEFF Research Database (Denmark)

    Dau, Torsten

    2012-01-01

    by the normal as well as impaired auditory system. Jørgensen and Dau [(2011). J. Acoust. Soc. Am. 130, 1475-1487] proposed the speech-based envelope power spectrum model (sEPSM) in an attempt to overcome the limitations of the classical speech transmission index (STI) and speech intelligibility index (SII......) in conditions with nonlinearly processed speech. Instead of considering the reduction of the temporal modulation energy as the intelligibility metric, as assumed in the STI, the sEPSM applies the signal-to-noise ratio in the envelope domain (SNRenv). This metric was shown to be the key for predicting...... the intelligibility of reverberant speech as well as noisy speech processed by spectral subtraction. However, the sEPSM cannot account for speech subjected to phase jitter, a condition in which the spectral structure of speech is destroyed, while the broadband temporal envelope is kept largely intact. In contrast...

  20. A review on intelligent sensory modelling

    Science.gov (United States)

    Tham, H. J.; Tang, S. Y.; Teo, K. T. K.; Loh, S. P.

    2016-06-01

    Sensory evaluation plays an important role in the quality control of food productions. Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain. Classically, factorial multivariate methods such as Principle Component Analysis (PCA), Partial Least Square (PLS) method, Multiple Regression (MLR) method and Response Surface Method (RSM) are the common tools used to analyse sensory data. These methods can model some of the sensory data but may not be robust enough to analyse nonlinear data. In these situations, intelligent modelling techniques such as Fuzzy Logic and Artificial neural network (ANNs) emerged to solve the vagueness and uncertainty of sensory data. This paper outlines literature of intelligent sensory modelling on sensory data analysis.

  1. Intelligent Mechatronic Systems Modeling, Control and Diagnosis

    CERN Document Server

    Merzouki, Rochdi; Pathak, Pushparaj Mani; Ould Bouamama, Belkacem

    2013-01-01

    Acting as a support resource for practitioners and professionals looking to advance their understanding of complex mechatronic systems, Intelligent Mechatronic Systems explains their design and recent developments from first principles to practical applications. Detailed descriptions of the mathematical models of complex mechatronic systems, developed from fundamental physical relationships, are built on to develop innovative solutions with particular emphasis on physical model-based control strategies. Following a concurrent engineering approach, supported by industrial case studies, and drawing on the practical experience of the authors, Intelligent Mechatronic Systems covers range of topic and includes:  • An explanation of a common graphical tool for integrated design and its uses from modeling and simulation to the control synthesis • Introductions to key concepts such as different means of achieving fault tolerance, robust overwhelming control and force and impedance control • Dedicated chapters ...

  2. The Development of an Intelligent Leadership Model for State Universities

    OpenAIRE

    Aleme Keikha; Reza Hoveida; Nour Mohammad Yaghoubi

    2017-01-01

    Higher education and intelligent leadership are considered important parts of every country’s education system, which could potentially play a key role in accomplishing the goals of society. In theories of leadership, new patterns attempt to view leadership through the prism of creative and intelligent phenomena. This paper aims to design and develop an intelligent leadership model for public universities. A qualitativequantitative research method was used to design a basic model of intellige...

  3. Top-Down, Intelligent Reservoir Model

    Science.gov (United States)

    Mohaghegh, Shahab

    2010-05-01

    Conventional reservoir simulation and modeling is a bottom-up approach. It starts with building a geological model of the reservoir that is populated with the best available petrophysical and geophysical information at the time of development. Engineering fluid flow principles are added and solved numerically so as to arrive at a dynamic reservoir model. The dynamic reservoir model is calibrated using the production history of multiple wells and the history matched model is used to strategize field development in order to improve recovery. Top-Down, Intelligent Reservoir Modeling approaches the reservoir simulation and modeling from an opposite angle by attempting to build a realization of the reservoir starting with the measured well production behavior (history). The production history is augmented by core, log, well test and seismic data in order to increase the accuracy of the Top-Down modeling technique. Although not intended as a substitute for the conventional reservoir simulation of large, complex fields, this novel approach to reservoir modeling can be used as an alternative (at a fraction of the cost) to conventional reservoir simulation and modeling in cases where performing conventional modeling is cost (and man-power) prohibitive. In cases where a conventional model of a reservoir already exists, Top-Down modeling should be considered as a compliment to, rather than a competition for the conventional technique, to provide an independent look at the data coming from the reservoir/wells for optimum development strategy and recovery enhancement. Top-Down, Intelligent Reservoir Modeling starts with well-known reservoir engineering techniques such as Decline Curve Analysis, Type Curve Matching, History Matching using single well numerical reservoir simulation, Volumetric Reserve Estimation and calculation of Recovery Factors for all the wells (individually) in the field. Using statistical techniques multiple Production Indicators (3, 6, and 9 months cum

  4. Modeling Speech Intelligibility in Hearing Impaired Listeners

    DEFF Research Database (Denmark)

    Scheidiger, Christoph; Jørgensen, Søren; Dau, Torsten

    2014-01-01

    Models of speech intelligibility (SI) have a long history, starting with the articulation index (AI, [17]), followed by the SI index (SI I, [18]) and the speech transmission index (STI, [7]), to only name a few. However, these models fail to accurately predict SI with nonlinearly processed noisy...... speech, e.g. phase jitter or spectral subtraction. Recent studies predict SI for normal-hearing (NH) listeners based on a signal-to-noise ratio measure in the envelope domain (SNRenv), in the framework of the speech-based envelope power spectrum model (sEPSM, [20, 21]). These models have shown good...... is not yet available. As a firrst step towards such a model, this study investigates to what extent eects of hearing impairment on SI can be modeled in the sEPSM framework. Preliminary results show that, by only modeling the loss of audibility, the model cannot account for the higher speech reception...

  5. Modeling Speech Intelligibility in Hearing Impaired Listeners

    DEFF Research Database (Denmark)

    Scheidiger, Christoph; Jørgensen, Søren; Dau, Torsten

    2014-01-01

    speech, e.g. phase jitter or spectral subtraction. Recent studies predict SI for normal-hearing (NH) listeners based on a signal-to-noise ratio measure in the envelope domain (SNRenv), in the framework of the speech-based envelope power spectrum model (sEPSM, [20, 21]). These models have shown good...... agreement with measured data under a broad range of conditions, including stationary and modulated interferers, reverberation, and spectral subtraction. Despite the advances in modeling intelligibility in NH listeners, a broadly applicable model that can predict SI in hearing-impaired (HI) listeners...... is not yet available. As a firrst step towards such a model, this study investigates to what extent eects of hearing impairment on SI can be modeled in the sEPSM framework. Preliminary results show that, by only modeling the loss of audibility, the model cannot account for the higher speech reception...

  6. Model Pembelajaran Berbasis Penstimulasian Multiple Intelligences Siswa

    OpenAIRE

    Edy Legowo

    2017-01-01

    Tulisan ini membahas mengenai penerapan teori multiple intelligences dalam pembelajaran di sekolah. Pembahasan diawali dengan menguraikan perkembangan konsep inteligensi dan multiple intelligences. Diikuti dengan menjelaskan dampak teori multiple intelligences dalam bidang pendidikan dan pembelajaran di sekolah. Bagian selanjutnya menguraikan tentang implementasi teori multiple intelligences dalam praktik pembelajaran di kelas yaitu bagaimana pemberian pengalaman belajar siswa yang difasilita...

  7. Artificial Intelligence Software Engineering (AISE) model

    Science.gov (United States)

    Kiss, Peter A.

    1990-01-01

    The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

  8. Swarm Intelligence for Urban Dynamics Modelling

    Science.gov (United States)

    Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gérard H. E.

    2009-04-01

    In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

  9. Swarm Intelligence for Urban Dynamics Modelling

    International Nuclear Information System (INIS)

    Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gerard H. E.

    2009-01-01

    In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

  10. Emotional Intelligence Model for Managers in Mumbai

    OpenAIRE

    Vaibhav P. Birwatkar

    2014-01-01

    Much of the literature pertinent to management indicates that managers with high emotional intelligence are morale boosters in their workplaces. Previous studies offer limited evidence regarding the impact of manager's emotional intelligence on workplace psychology, productivity and job satisfaction. This research examines the awareness level of the concept of emotional intelligence, the emotional intelligence level of managers across the organizations, whether managers use emotional intellig...

  11. Model Pembelajaran Berbasis Penstimulasian Multiple Intelligences Siswa

    Directory of Open Access Journals (Sweden)

    Edy Legowo

    2017-03-01

    Full Text Available Tulisan ini membahas mengenai penerapan teori multiple intelligences dalam pembelajaran di sekolah. Pembahasan diawali dengan menguraikan perkembangan konsep inteligensi dan multiple intelligences. Diikuti dengan menjelaskan dampak teori multiple intelligences dalam bidang pendidikan dan pembelajaran di sekolah. Bagian selanjutnya menguraikan tentang implementasi teori multiple intelligences dalam praktik pembelajaran di kelas yaitu bagaimana pemberian pengalaman belajar siswa yang difasilitasi guru dapat menstimulasi multiple intelligences siswa. Evaluasi hasil belajar siswa dari pandangan penerapan teori multiple intelligences seharusnya dilakukan menggunakan authentic assessment dan portofolio yang lebih memfasilitasi para siswa mengungkapkan atau mengaktualisasikan hasil belajarnya melalui berbagai cara sesuai dengan kekuatan jenis inteligensinya.

  12. Toward Intelligent Assessment: An Integration of Constructed Response Testing, Artificial Intelligence, and Model-Based Measurement.

    Science.gov (United States)

    Bennett, Randy Elliot

    A new assessment conception is described that integrates constructed-response testing, artificial intelligence, and model-based measurement. The conception incorporates complex constructed-response items for their potential to increase the validity, instructional utility, and credibility of standardized tests. Artificial intelligence methods are…

  13. Business intelligence modeling in launch operations

    Science.gov (United States)

    Bardina, Jorge E.; Thirumalainambi, Rajkumar; Davis, Rodney D.

    2005-05-01

    The future of business intelligence in space exploration will focus on the intelligent system-of-systems real-time enterprise. In present business intelligence, a number of technologies that are most relevant to space exploration are experiencing the greatest change. Emerging patterns of set of processes rather than organizational units leading to end-to-end automation is becoming a major objective of enterprise information technology. The cost element is a leading factor of future exploration systems. This technology project is to advance an integrated Planning and Management Simulation Model for evaluation of risks, costs, and reliability of launch systems from Earth to Orbit for Space Exploration. The approach builds on research done in the NASA ARC/KSC developed Virtual Test Bed (VTB) to integrate architectural, operations process, and mission simulations for the purpose of evaluating enterprise level strategies to reduce cost, improve systems operability, and reduce mission risks. The objectives are to understand the interdependency of architecture and process on recurring launch cost of operations, provide management a tool for assessing systems safety and dependability versus cost, and leverage lessons learned and empirical models from Shuttle and International Space Station to validate models applied to Exploration. The systems-of-systems concept is built to balance the conflicting objectives of safety, reliability, and process strategy in order to achieve long term sustainability. A planning and analysis test bed is needed for evaluation of enterprise level options and strategies for transit and launch systems as well as surface and orbital systems. This environment can also support agency simulation based acquisition process objectives. The technology development approach is based on the collaborative effort set forth in the VTB's integrating operations, process models, systems and environment models, and cost models as a comprehensive disciplined

  14. Business Intelligence Modeling in Launch Operations

    Science.gov (United States)

    Bardina, Jorge E.; Thirumalainambi, Rajkumar; Davis, Rodney D.

    2005-01-01

    This technology project is to advance an integrated Planning and Management Simulation Model for evaluation of risks, costs, and reliability of launch systems from Earth to Orbit for Space Exploration. The approach builds on research done in the NASA ARC/KSC developed Virtual Test Bed (VTB) to integrate architectural, operations process, and mission simulations for the purpose of evaluating enterprise level strategies to reduce cost, improve systems operability, and reduce mission risks. The objectives are to understand the interdependency of architecture and process on recurring launch cost of operations, provide management a tool for assessing systems safety and dependability versus cost, and leverage lessons learned and empirical models from Shuttle and International Space Station to validate models applied to Exploration. The systems-of-systems concept is built to balance the conflicting objectives of safety, reliability, and process strategy in order to achieve long term sustainability. A planning and analysis test bed is needed for evaluation of enterprise level options and strategies for transit and launch systems as well as surface and orbital systems. This environment can also support agency simulation .based acquisition process objectives. The technology development approach is based on the collaborative effort set forth in the VTB's integrating operations. process models, systems and environment models, and cost models as a comprehensive disciplined enterprise analysis environment. Significant emphasis is being placed on adapting root cause from existing Shuttle operations to exploration. Technical challenges include cost model validation, integration of parametric models with discrete event process and systems simulations. and large-scale simulation integration. The enterprise architecture is required for coherent integration of systems models. It will also require a plan for evolution over the life of the program. The proposed technology will produce

  15. An intelligent model for liver disease diagnosis.

    Science.gov (United States)

    Lin, Rong-Ho

    2009-09-01

    Liver disease, the most common disease in Taiwan, is not easily discovered in its initial stage; early diagnosis of this leading cause of mortality is therefore highly important. The design of an effective diagnosis model is therefore an important issue in liver disease treatment. This study accordingly employs classification and regression tree (CART) and case-based reasoning (CBR) techniques to structure an intelligent diagnosis model aiming to provide a comprehensive analytic framework to raise the accuracy of liver disease diagnosis. Based on the advice and assistance of doctors and medical specialists of liver conditions, 510 outpatient visitors using ICD-9 (International Classification of Diseases, 9th Revision) codes at a medical center in Taiwan from 2005 to 2006 were selected as the cases in the data set for liver disease diagnosis. Data on 340 patients was utilized for the development of the model and on 170 patients utilized to perform comparative analysis of the models. This paper accordingly suggests an intelligent model for the diagnosis of liver diseases which integrates CART and CBR. The major steps in applying the model include: (1) adopting CART to diagnose whether a patient suffers from liver disease; (2) for patients diagnosed with liver disease in the first step, employing CBR to diagnose the types of liver diseases. In the first phase, CART is used to extract rules from health examination data to show whether the patient suffers from liver disease. The results indicate that the CART rate of accuracy is 92.94%. In the second phase, CBR is developed to diagnose the type of liver disease, and the new case triggers the CBR system to retrieve the most similar case from the case base in order to support the treatment of liver disease. The new case is supported by a similarity ratio, and the CBR diagnostic accuracy rate is 90.00%. Actual implementation shows that the intelligent diagnosis model is capable of integrating CART and CBR techniques to

  16. A Fuzzy Student Modeling with Two Intelligent Agents.

    Science.gov (United States)

    Huang, Mu-Jung

    1999-01-01

    A new fuzzy student modeling method with two intelligent agents, a diagnosis agent and a learning agent, are suggested by this article for several aspects of student modeling in Intelligent Tutoring Systems. Also integrated are fuzzy theories and Fuzzy-Hasse diagrams for student modeling. (Author/AEF)

  17. Artificial intelligence model for sustain ability measurement

    International Nuclear Information System (INIS)

    Navickiene, R.; Navickas, K.

    2012-01-01

    The article analyses the main dimensions of organizational sustain ability, their possible integrations into artificial neural network. In this article authors performing analyses of organizational internal and external environments, their possible correlations with 4 components of sustain ability, and the principal determination models for sustain ability of organizations. Based on the general principles of sustainable development organizations, a artificial intelligence model for the determination of organizational sustain ability has been developed. The use of self-organizing neural networks allows the identification of the organizational sustain ability and the endeavour to explore vital, social, antropogenical and economical efficiency. The determination of the forest enterprise sustain ability is expected to help better manage the sustain ability. (Authors)

  18. Computational Intelligence in a Human Brain Model

    Directory of Open Access Journals (Sweden)

    Viorel Gaftea

    2016-06-01

    Full Text Available This paper focuses on the current trends in brain research domain and the current stage of development of research for software and hardware solutions, communication capabilities between: human beings and machines, new technologies, nano-science and Internet of Things (IoT devices. The proposed model for Human Brain assumes main similitude between human intelligence and the chess game thinking process. Tactical & strategic reasoning and the need to follow the rules of the chess game, all are very similar with the activities of the human brain. The main objective for a living being and the chess game player are the same: securing a position, surviving and eliminating the adversaries. The brain resolves these goals, and more, the being movement, actions and speech are sustained by the vital five senses and equilibrium. The chess game strategy helps us understand the human brain better and easier replicate in the proposed ‘Software and Hardware’ SAH Model.

  19. Intelligent Model for Video Survillance Security System

    Directory of Open Access Journals (Sweden)

    J. Vidhya

    2013-12-01

    Full Text Available Video surveillance system senses and trails out all the threatening issues in the real time environment. It prevents from security threats with the help of visual devices which gather the information related to videos like CCTV’S and IP (Internet Protocol cameras. Video surveillance system has become a key for addressing problems in the public security. They are mostly deployed on the IP based network. So, all the possible security threats exist in the IP based application might also be the threats available for the reliable application which is available for video surveillance. In result, it may increase cybercrime, illegal video access, mishandling videos and so on. Hence, in this paper an intelligent model is used to propose security for video surveillance system which ensures safety and it provides secured access on video.

  20. La realidad de la ficción: el ECO de Mathias Goeritz

    Directory of Open Access Journals (Sweden)

    Fernando Quesada

    2016-05-01

    Full Text Available Resumen Este artículo recorre al completo la biografía de un edificio, el Museo  Experimental El Eco, del artista y arquitecto alemán emigrado a México Mathias Goeritz. Se entrecruzan la biografía del propio autor y la del edificio con las de personalidades de las dos tradiciones culturales, la  alemana y la mexicana, que intervinieron en la creación de una arquitectura tan particular y única como es la de El Eco. De Alemania provienen la ética del expresionismo, el interés por al arte no europeo, el culto al primitivismo y el sistema estético del par abstracción-empatía. De México la religiosidad panteísta del paisaje, el zoomorfismo y antropomorfismo, el interés por las máscaras y la estética monumental. A partir del cruce de Mathias Goeritz con personas y temas provenientes de su pasado alemán y de su futuro mexicano, en las que cabe resaltar las figuras de Wilhelm Worringer, Paul Westheim, Luis Barragán, Edmundo O’Gorman e Ida Rodríguez Prampolini, es posible en este artículo proponer un viaje de ida y vuelta de la ficción a la realidad, siguiendo los pasos de su autor y comparándolos con los que siguió el propio edificio.

  1. Emotional intelligence is a second-stratum factor of intelligence: evidence from hierarchical and bifactor models.

    Science.gov (United States)

    MacCann, Carolyn; Joseph, Dana L; Newman, Daniel A; Roberts, Richard D

    2014-04-01

    This article examines the status of emotional intelligence (EI) within the structure of human cognitive abilities. To evaluate whether EI is a 2nd-stratum factor of intelligence, data were fit to a series of structural models involving 3 indicators each for fluid intelligence, crystallized intelligence, quantitative reasoning, visual processing, and broad retrieval ability, as well as 2 indicators each for emotion perception, emotion understanding, and emotion management. Unidimensional, multidimensional, hierarchical, and bifactor solutions were estimated in a sample of 688 college and community college students. Results suggest adequate fit for 2 models: (a) an oblique 8-factor model (with 5 traditional cognitive ability factors and 3 EI factors) and (b) a hierarchical solution (with cognitive g at the highest level and EI representing a 2nd-stratum factor that loads onto g at λ = .80). The acceptable relative fit of the hierarchical model confirms the notion that EI is a group factor of cognitive ability, marking the expression of intelligence in the emotion domain. The discussion proposes a possible expansion of Cattell-Horn-Carroll theory to include EI as a 2nd-stratum factor of similar standing to factors such as fluid intelligence and visual processing.

  2. Multidimensional Learner Model In Intelligent Learning System

    Science.gov (United States)

    Deliyska, B.; Rozeva, A.

    2009-11-01

    The learner model in an intelligent learning system (ILS) has to ensure the personalization (individualization) and the adaptability of e-learning in an online learner-centered environment. ILS is a distributed e-learning system whose modules can be independent and located in different nodes (servers) on the Web. This kind of e-learning is achieved through the resources of the Semantic Web and is designed and developed around a course, group of courses or specialty. An essential part of ILS is learner model database which contains structured data about learner profile and temporal status in the learning process of one or more courses. In the paper a learner model position in ILS is considered and a relational database is designed from learner's domain ontology. Multidimensional modeling agent for the source database is designed and resultant learner data cube is presented. Agent's modules are proposed with corresponding algorithms and procedures. Multidimensional (OLAP) analysis guidelines on the resultant learner module for designing dynamic learning strategy have been highlighted.

  3. World modeling for cooperative intelligent vehicles

    NARCIS (Netherlands)

    Papp, Z.; Brown, C.; Bartels, C.

    2008-01-01

    Cooperative intelligent vehicle systems constitute a promising way to improving traffic throughput, safety and comfort. The state-of-the-art intelligent-vehicle applications usually can be described as a collection of interacting, highly autonomous, complex dynamical systems (the individual

  4. Model of key success factors for Business Intelligence implementation

    Directory of Open Access Journals (Sweden)

    Peter Mesaros

    2016-07-01

    Full Text Available New progressive technologies recorded growth in every area. Information-communication technologies facilitate the exchange of information and it facilitates management of everyday activities in enterprises. Specific modules (such as Business Intelligence facilitate decision-making. Several studies have demonstrated the positive impact of Business Intelligence to decision-making. The first step is to put in place the enterprise. The implementation process is influenced by many factors. This article discusses the issue of key success factors affecting to successful implementation of Business Intelligence. The article describes the key success factors for successful implementation and use of Business Intelligence based on multiple studies. The main objective of this study is to verify the effects and dependence of selected factors and proposes a model of key success factors for successful implementation of Business Intelligence. Key success factors and the proposed model are studied in Slovak enterprises.

  5. A watershed model of individual differences in fluid intelligence.

    Science.gov (United States)

    Kievit, Rogier A; Davis, Simon W; Griffiths, John; Correia, Marta M; Cam-Can; Henson, Richard N

    2016-10-01

    Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  6. Prediction of gas compressibility factor using intelligent models

    Directory of Open Access Journals (Sweden)

    Mohamad Mohamadi-Baghmolaei

    2015-10-01

    Full Text Available The gas compressibility factor, also known as Z-factor, plays the determinative role for obtaining thermodynamic properties of gas reservoir. Typically, empirical correlations have been applied to determine this important property. However, weak performance and some limitations of these correlations have persuaded the researchers to use intelligent models instead. In this work, prediction of Z-factor is aimed using different popular intelligent models in order to find the accurate one. The developed intelligent models are including Artificial Neural Network (ANN, Fuzzy Interface System (FIS and Adaptive Neuro-Fuzzy System (ANFIS. Also optimization of equation of state (EOS by Genetic Algorithm (GA is done as well. The validity of developed intelligent models was tested using 1038 series of published data points in literature. It was observed that the accuracy of intelligent predicting models for Z-factor is significantly better than conventional empirical models. Also, results showed the improvement of optimized EOS predictions when coupled with GA optimization. Moreover, of the three intelligent models, ANN model outperforms other models considering all data and 263 field data points of an Iranian offshore gas condensate with R2 of 0.9999, while the R2 for best empirical correlation was about 0.8334.

  7. Model of intelligent information searching system

    International Nuclear Information System (INIS)

    Yastrebkov, D.I.

    2004-01-01

    A brief description of the technique to search for electronic documents in large archives as well as drawbacks is presented. A solution close to intelligent information searching systems is proposed. (author)

  8. Computational intelligence applications in modeling and control

    CERN Document Server

    Vaidyanathan, Sundarapandian

    2015-01-01

    The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 16 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought ...

  9. EMOTIONAL INTELLIGENCE AND ORGANIZATIONAL COMPETITIVENESS: MANAGEMENT MODEL APPROACH

    Directory of Open Access Journals (Sweden)

    John N. N. Ugoani

    2016-09-01

    Full Text Available Modern organization theory considers emotional intelligence as the index of competencies that help organizations to develop a vision for competitiveness. It also allows organizational leaders to enthusiastically commit to the vision, and energize organizational members to achieve the vision. To maximize competiveness organizations use models to simplify and clarify thinking, to identify important aspects, to suggest explanations and to predict consequences, and explore other performance areas that would otherwise be hidden in an excess of words. The survey research design was used to explore the relationship between emotional intelligence and organizational competitiveness. The study found that emotional intelligence has strong positive relationship with organizational competitiveness

  10. Intelligence.

    Science.gov (United States)

    Sternberg, Robert J

    2012-09-01

    Intelligence is the ability to learn from past experience and, in general, to adapt to, shape, and select environments. Aspects of intelligence are measured by standardized tests of intelligence. Average raw (number-correct) scores on such tests vary across the life span and also across generations, as well as across ethnic and socioeconomic groups. Intelligence can be understood in part in terms of the biology of the brain-especially with regard to the functioning in the prefrontal cortex. Measured values correlate with brain size, at least within humans. The heritability coefficient (ratio of genetic to phenotypic variation) is between 0.4 and 0.8. But genes always express themselves through environment. Heritability varies as a function of a number of factors, including socioeconomic status and range of environments. Racial-group differences in measured intelligence have been reported, but race is a socially constructed rather than biological variable. As a result, these differences are difficult to interpret. Different cultures have different conceptions of the nature of intelligence, and also require different skills in order to express intelligence in the environment. WIREs Cogn Sci 2012 doi: 10.1002/wcs.1193 For further resources related to this article, please visit the WIREs website. Copyright © 2012 John Wiley & Sons, Ltd.

  11. Modeling of biological intelligence for SCM system optimization.

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  12. Modeling of Biological Intelligence for SCM System Optimization

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  13. Modeling of Biological Intelligence for SCM System Optimization

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

  14. A Model for an Intelligent Support Decision System in Aquaculture

    OpenAIRE

    Novac Ududec, Cornelia / C

    2009-01-01

    The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-...

  15. Intelligence

    Science.gov (United States)

    Sternberg, Robert J.

    2012-01-01

    Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain—especially with regard to the functioning in the prefrontal cortex—and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret. PMID:22577301

  16. Intelligence.

    Science.gov (United States)

    Sternberg, Robert J

    2012-03-01

    Intelligence is the ability to learn from experience and to adapt to, shape, and select environments. Intelligence as measured by (raw scores on) conventional standardized tests varies across the lifespan, and also across generations. Intelligence can be understood in part in terms of the biology of the brain-especially with regard to the functioning in the prefrontal cortex-and also correlates with brain size, at least within humans. Studies of the effects of genes and environment suggest that the heritability coefficient (ratio of genetic to phenotypic variation) is between .4 and .8, although heritability varies as a function of socioeconomic status and other factors. Racial differences in measured intelligence have been observed, but race is a socially constructed rather than biological variable, so such differences are difficult to interpret.

  17. Advances in Intelligent Modelling and Simulation Artificial Intelligence-Based Models and Techniques in Scalable Computing

    CERN Document Server

    Khan, Samee; Burczy´nski, Tadeusz

    2012-01-01

    One of the most challenging issues in today’s large-scale computational modeling and design is to effectively manage the complex distributed environments, such as computational clouds, grids, ad hoc, and P2P networks operating under  various  types of users with evolving relationships fraught with  uncertainties. In this context, the IT resources and services usually belong to different owners (institutions, enterprises, or individuals) and are managed by different administrators. Moreover, uncertainties are presented to the system at hand in various forms of information that are incomplete, imprecise, fragmentary, or overloading, which hinders in the full and precise resolve of the evaluation criteria, subsequencing and selection, and the assignment scores. Intelligent scalable systems enable the flexible routing and charging, advanced user interactions and the aggregation and sharing of geographically-distributed resources in modern large-scale systems.   This book presents new ideas, theories, models...

  18. Computational Intelligence Agent-Oriented Modelling

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman

    2006-01-01

    Roč. 5, č. 2 (2006), s. 430-433 ISSN 1109-2777 R&D Projects: GA MŠk 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : multi-agent systems * adaptive agents * computational intelligence Subject RIV: IN - Informatics, Computer Science

  19. Modeling intelligent adversaries for terrorism risk assessment: some necessary conditions for adversary models.

    Science.gov (United States)

    Guikema, Seth

    2012-07-01

    Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often-implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis. © 2011 Society for Risk Analysis.

  20. Sustainability Reporting Process Model using Business Intelligence

    OpenAIRE

    Alxneit, Thorsten Julius

    2015-01-01

    Sustainability including the reporting requirements is one of the most relevant topics for companies. In recent years, many software providers have launched new software tools targeting companies committed to implementing sustainability reporting. But it’s not only companies willing to use their Business Intelligence (BI) solution, there are also basic principles such as the single source of truth and tendencies to combine sustainability reporting with the financial reporting (...

  1. International Conference on Computational Intelligence, Cyber Security, and Computational Models

    CERN Document Server

    Ramasamy, Vijayalakshmi; Sheen, Shina; Veeramani, C; Bonato, Anthony; Batten, Lynn

    2016-01-01

    This book aims at promoting high-quality research by researchers and practitioners from academia and industry at the International Conference on Computational Intelligence, Cyber Security, and Computational Models ICC3 2015 organized by PSG College of Technology, Coimbatore, India during December 17 – 19, 2015. This book enriches with innovations in broad areas of research like computational modeling, computational intelligence and cyber security. These emerging inter disciplinary research areas have helped to solve multifaceted problems and gained lot of attention in recent years. This encompasses theory and applications, to provide design, analysis and modeling of the aforementioned key areas.

  2. Mathematical modeling and computational intelligence in engineering applications

    CERN Document Server

    Silva Neto, Antônio José da; Silva, Geraldo Nunes

    2016-01-01

    This book brings together a rich selection of studies in mathematical modeling and computational intelligence, with application in several fields of engineering, like automation, biomedical, chemical, civil, electrical, electronic, geophysical and mechanical engineering, on a multidisciplinary approach. Authors from five countries and 16 different research centers contribute with their expertise in both the fundamentals and real problems applications based upon their strong background on modeling and computational intelligence. The reader will find a wide variety of applications, mathematical and computational tools and original results, all presented with rigorous mathematical procedures. This work is intended for use in graduate courses of engineering, applied mathematics and applied computation where tools as mathematical and computational modeling, numerical methods and computational intelligence are applied to the solution of real problems.

  3. Forecasting rain events - Meteorological models or collective intelligence?

    Science.gov (United States)

    Arazy, Ofer; Halfon, Noam; Malkinson, Dan

    2015-04-01

    Collective intelligence is shared (or group) intelligence that emerges from the collective efforts of many individuals. Collective intelligence is the aggregate of individual contributions: from simple collective decision making to more sophisticated aggregations such as in crowdsourcing and peer-production systems. In particular, collective intelligence could be used in making predictions about future events, for example by using prediction markets to forecast election results, stock prices, or the outcomes of sport events. To date, there is little research regarding the use of collective intelligence for prediction of weather forecasting. The objective of this study is to investigate the extent to which collective intelligence could be utilized to accurately predict weather events, and in particular rainfall. Our analyses employ metrics of group intelligence, as well as compare the accuracy of groups' predictions against the predictions of the standard model used by the National Meteorological Services. We report on preliminary results from a study conducted over the 2013-2014 and 2014-2015 winters. We have built a web site that allows people to make predictions on precipitation levels on certain locations. During each competition participants were allowed to enter their precipitation forecasts (i.e. 'bets') at three locations and these locations changed between competitions. A precipitation competition was defined as a 48-96 hour period (depending on the expected weather conditions), bets were open 24-48 hours prior to the competition, and during betting period participants were allowed to change their bets with no limitation. In order to explore the effect of transparency, betting mechanisms varied across study's sites: full transparency (participants able to see each other's bets); partial transparency (participants see the group's average bet); and no transparency (no information of others' bets is made available). Several interesting findings emerged from

  4. Intelligent Cloud Learning Model for Online Overseas Chinese Education

    Directory of Open Access Journals (Sweden)

    Yidong Chen

    2015-02-01

    Full Text Available With the development of Chinese economy, oversea Chinese education has been paid more and more attention. However, the overseas Chinese education resource is relatively lack because of historical reasons, which hindered further development . How to better share the Chinese education resources and provide intelligent personalized information service for overseas student is a key problem to be solved. In recent years, the rise of cloud computing provides us an opportunity to realize intelligent learning mode. Cloud computing offers some advantages by allowing users to use infrastructure, platforms and software . In this paper we proposed an intelligent cloud learning model based on cloud computing. The learning model can utilize network resources sufficiently to implement resource sharing according to the personal needs of students, and provide a good practicability for online overseas Chinese education.

  5. Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

    Directory of Open Access Journals (Sweden)

    Aleksandar Sabljic

    2004-12-01

    Full Text Available This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

  6. Artificial intelligence support for scientific model-building

    Science.gov (United States)

    Keller, Richard M.

    1992-01-01

    Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

  7. Artificial intelligence and mathematical models for intelligent management of aircraft data

    OpenAIRE

    knight, Peter Robin

    2012-01-01

    Increasingly, large volumes of aircraft data are being recorded in an effort to adapt aircraft maintenance procedures from being time-based towards condition-based techniques. This study uses techniques of artificial intelligence and develops mathematical models to analyse this data to enable improvements to be made in aircraft management, affordability, availability, airworthiness and performance. In addition, it highlights the need to assess the integrity of data before further analysis and...

  8. Intelligent control of HVAC systems. Part I: Modeling and synthesis

    Directory of Open Access Journals (Sweden)

    Adrian TOADER

    2013-03-01

    Full Text Available This is the first part of a work on intelligent type control of Heating, Ventilating and Air-Conditioning (HVAC systems. The study is performed from the perspective of giving a unitary control method to ensure high energy efficiency and air quality improving. To illustrate the proposed HVAC control technique, in this first part it is considered as benchmark problem a single thermal space HVAC system. The construction of the mathematical model is performed only with a view to obtain a framework of HVAC intelligent control validation by numerical simulations. The latter will be reported in a second part of the study.

  9. Technologies for conceptual modelling and intelligent query formulation

    CSIR Research Space (South Africa)

    Alberts, R

    2008-11-01

    Full Text Available modelling and intelligent query formulation R ALBERTS1, K BRITZ1, A GERBER1, K HALLAND1,2, T MEYER1, L PRETORIUS1,2 (1) Knowledge Systems Group, Meraka Institute, CSIR, Pretoria, Gauteng, South Africa (2) School of Computing, University of South... this problem still more pressing: in this case an excess of information can be equivalent to an absence of information It is therefore necessary to use tools that organise data into intelligible and easily-accessible structures and return answers...

  10. The highly intelligent virtual agents for modeling financial markets

    Science.gov (United States)

    Yang, G.; Chen, Y.; Huang, J. P.

    2016-02-01

    Researchers have borrowed many theories from statistical physics, like ensemble, Ising model, etc., to study complex adaptive systems through agent-based modeling. However, one fundamental difference between entities (such as spins) in physics and micro-units in complex adaptive systems is that the latter are usually with high intelligence, such as investors in financial markets. Although highly intelligent virtual agents are essential for agent-based modeling to play a full role in the study of complex adaptive systems, how to create such agents is still an open question. Hence, we propose three principles for designing high artificial intelligence in financial markets and then build a specific class of agents called iAgents based on these three principles. Finally, we evaluate the intelligence of iAgents through virtual index trading in two different stock markets. For comparison, we also include three other types of agents in this contest, namely, random traders, agents from the wealth game (modified on the famous minority game), and agents from an upgraded wealth game. As a result, iAgents perform the best, which gives a well support for the three principles. This work offers a general framework for the further development of agent-based modeling for various kinds of complex adaptive systems.

  11. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    This study proposes an application of two techniques of artificial intelligence (AI) for rainfall–runoff modeling: the artificial neural networks (ANN) and .... conventional mathematical analysis does not, or cannot, provide analytical solutions, .... very simple where there exist one-to-one relation- ships between the symbols of the ...

  12. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    This study proposes an application of two techniques of artificial intelligence (AI) for rainfall–runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two diff- erent ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods ...

  13. A generic model for camera based intelligent road crowd control ...

    African Journals Online (AJOL)

    This research proposes a model for intelligent traffic flow control by implementing camera based surveillance and feedback system. A series of cameras are set minimum three signals ahead from the target junction. The complete software system is developed to help integrating the multiple camera on road as feedback to ...

  14. Harraga dans la littérature francophone : Boualem Sansal, Tahar Ben Jelloun, Mathias Enard et Marie Ndiaye

    Directory of Open Access Journals (Sweden)

    Désirée Schyns

    2016-03-01

    Full Text Available Résumé : Le présent article offre une analyse de l’immigration clandestine représentée en fiction. Je commenterai quatre productions romanesques francophones qui évoquent la recherche d’une vie meilleure en Europe par des personnages des pays du Sud, à savoir l’Algérie (Boualem Sansal, le Maroc (Tahar Ben Jelloun et Mathias Enard, et un pays d’Afrique subsaharienne qui ne sera nulle part mentionné (Marie Ndiaye. Nous allons voir que Sansal se sert de la fiction pour dénoncer surtout la situation socio-politique en Algérie qui fait que tant de jeunes veulent quitter le pays. Tahar Ben Jelloun et Mathias Enard ont recours à des références intertextuelles afin de replacer le sujet de l’immigration clandestine dans un contexte plus large, et notamment de critiquer les relations coloniales et d’établir un lien avec le passé. Quant à Marie Ndiaye, elle interpelle le lecteur en donnant un témoignage déchirant de l’errance de son personnage, mais en même temps insère des éléments fantastiques qui créent une distance esthétique et affective par rapport à l’expérience migratoire.

  15. Intelligent Transportation and Evacuation Planning A Modeling-Based Approach

    CERN Document Server

    Naser, Arab

    2012-01-01

    Intelligent Transportation and Evacuation Planning: A Modeling-Based Approach provides a new paradigm for evacuation planning strategies and techniques. Recently, evacuation planning and modeling have increasingly attracted interest among researchers as well as government officials. This interest stems from the recent catastrophic hurricanes and weather-related events that occurred in the southeastern United States (Hurricane Katrina and Rita). The evacuation methods that were in place before and during the hurricanes did not work well and resulted in thousands of deaths. This book offers insights into the methods and techniques that allow for implementing mathematical-based, simulation-based, and integrated optimization and simulation-based engineering approaches for evacuation planning. This book also: Comprehensively discusses the application of mathematical models for evacuation and intelligent transportation modeling Covers advanced methodologies in evacuation modeling and planning Discusses principles a...

  16. Employing the intelligence cycle process model within the Homeland Security Enterprise

    OpenAIRE

    Stokes, Roger L.

    2013-01-01

    CHDS State/Local The purpose of this thesis was to examine the employment and adherence of the intelligence cycle process model within the National Network of Fusion Centers and the greater Homeland Security Enterprise by exploring the customary intelligence cycle process model established by the United States Intelligence Community (USIC). This thesis revealed there are various intelligence cycle process models used by the USIC and taught to the National Network. Given the numerous differ...

  17. National Water Model: Providing the Nation with Actionable Water Intelligence

    Science.gov (United States)

    Aggett, G. R.; Bates, B.

    2017-12-01

    The National Water Model (NWM) provides national, street-level detail of water movement through time and space. Operating hourly, this flood of information offers enormous benefits in the form of water resource management, natural disaster preparedness, and the protection of life and property. The Geo-Intelligence Division at the NOAA National Water Center supplies forecasters and decision-makers with timely, actionable water intelligence through the processing of billions of NWM data points every hour. These datasets include current streamflow estimates, short and medium range streamflow forecasts, and many other ancillary datasets. The sheer amount of NWM data produced yields a dataset too large to allow for direct human comprehension. As such, it is necessary to undergo model data post-processing, filtering, and data ingestion by visualization web apps that make use of cartographic techniques to bring attention to the areas of highest urgency. This poster illustrates NWM output post-processing and cartographic visualization techniques being developed and employed by the Geo-Intelligence Division at the NOAA National Water Center to provide national actionable water intelligence.

  18. An intelligent diagnosis model based on rough set theory

    Science.gov (United States)

    Li, Ze; Huang, Hong-Xing; Zheng, Ye-Lu; Wang, Zhou-Yuan

    2013-03-01

    Along with the popularity of computer and rapid development of information technology, how to increase the accuracy of the agricultural diagnosis becomes a difficult problem of popularizing the agricultural expert system. Analyzing existing research, baseing on the knowledge acquisition technology of rough set theory, towards great sample data, we put forward a intelligent diagnosis model. Extract rough set decision table from the samples property, use decision table to categorize the inference relation, acquire property rules related to inference diagnosis, through the means of rough set knowledge reasoning algorithm to realize intelligent diagnosis. Finally, we validate this diagnosis model by experiments. Introduce the rough set theory to provide the agricultural expert system of great sample data a effective diagnosis model.

  19. A Cybernetic Model to Enhance Organizational Intelligence

    OpenAIRE

    Schwaninger, Markus

    2003-01-01

    The present paper focuses on the modeling of cognitive processes in organizations. This issue is approached from the perspective of Organizational Cybernetics, the science of control and communication applied to the management of organizations. First, the Team Syntegrity Model is described, which provides a structural architecture for processes of planning, knowledge generation and innovation in turbulent environments. The model is holographic and based on the mathematical structure of polyhe...

  20. Modeling and simulating human teamwork behaviors using intelligent agents

    Science.gov (United States)

    Fan, Xiaocong; Yen, John

    2004-12-01

    Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively gathering, fusing, and sharing information, and for building coherent teams with both humans and agents working effectively on intelligence-intensive problems. Teamwork modeling is also challenging because the research has spanned diverse disciplines from business management to cognitive science, human discourse, and distributed artificial intelligence. This article presents an extensive, but not exhaustive, list of work in the field, where the taxonomy is organized along two main dimensions: team social structure and social behaviors. Along the dimension of social structure, we consider agent-only teams and mixed human-agent teams. Along the dimension of social behaviors, we consider collaborative behaviors, communicative behaviors, helping behaviors, and the underpinning of effective teamwork-shared mental models. The contribution of this article is that it presents an organizational framework for analyzing a variety of teamwork simulation systems and for further studying simulated teamwork behaviors.

  1. THE FUZZY OVERLAY STUDENT MODEL IN AN INTELLIGENT TUTORING SYSTEM

    Directory of Open Access Journals (Sweden)

    D. I. Popov

    2015-01-01

    Full Text Available The article is devoted to the development of the student model for use in an intelligent tutoring system (ITS designed for the evaluation of students’ competencies in different Higher Education Facilities. There are classification and examples of the various student models, the most suitable for the evaluation of competencies is selected and finalized. The dynamic overlay fuzzy student model builded on the domain model based on the concept of didactic units is described in this work. The formulas, chart and diagrams are provided.

  2. Interoperation Modeling for Intelligent Domotic Environments

    Science.gov (United States)

    Bonino, Dario; Corno, Fulvio

    This paper introduces an ontology-based model for domotic device inter-operation. Starting from a previously published ontology (DogOnt) a refactoring and extension is described allowing to explicitly represent device capabilities, states and commands, and supporting abstract modeling of device inter-operation.

  3. Modeling intelligent agent beliefs in a card game scenario

    Science.gov (United States)

    Gołuński, Marcel; Tomanek, Roman; WÄ siewicz, Piotr

    In this paper we explore the problem of intelligent agent beliefs. We model agent beliefs using multimodal logics of belief, KD45(m) system implemented as a directed graph depicting Kripke semantics, precisely. We present a card game engine application which allows multiple agents to connect to a given game session and play the card game. As an example simplified version of popular Saboteur card game is used. Implementation was done in Java language using following libraries and applications: Apache Mina, LWJGL.

  4. Thermal Models for Intelligent Heating of Buildings

    DEFF Research Database (Denmark)

    Thavlov, Anders; Bindner, Henrik W.

    2012-01-01

    using a grey box approach, i.e. by formulating the model using physical knowledge about heat flow, while the parameters in the model are estimated using collected data and statistics. The physical parameters in the model, e.g. heat capacities and resistances to transfer heat, have been estimated...... share of renewable power generation, which is in general intermittent and non-controllable, the consumption side has to be much more flexible than today. To achieve such flexibility, methods for moving power consumption in time, within the hourly timescale, have to be developed. One approach currently...

  5. Electricity load modelling using computational intelligence

    NARCIS (Netherlands)

    Ter Borg, R.W.

    2005-01-01

    As a consequence of the liberalisation of the electricity markets in Europe, market players have to continuously adapt their future supply to match their customers' demands. This poses the challenge of obtaining a predictive model that accurately describes electricity loads, current in this thesis.

  6. The role of business intelligence in decision process modeling

    Directory of Open Access Journals (Sweden)

    Višnja Istrat

    2015-10-01

    Full Text Available Decision making is a very significant and complex function of management that requires methods and techniques that simplify the process of choosing the best alternative. In modern business, the challenge for managers is to find the alternatives for improving the decision-making process. Decisions directly affect profit generation and positioning of the company in the market. It is well-known that people dealt with the phenomenon of decision making in each phase of the development of society, which has triggered the need to learn more about this process. The main contribution of this paper is to show the significance of business intelligence tools and techniques as support to the decision making process of managers. Research results have shown that business intelligence plays an enormous role in modern decision process modeling.

  7. Modeling and Control of Intelligent Flexible Structures

    Science.gov (United States)

    1994-03-26

    Mexico , Feb. 1993. "Model Correction Using Constrained Eigenstructure Assignment,’llth International Modal Analysis Conference, February 1993, Orlando...repeated modes. Assume the firt p modes am repeared with identify the moda paripion tem #i,. a multiplicity of 2 and the remaining modes are distdnct...c"d & Aerosaace En .eer,.ng Saonu Hemck Pmfenor Stuie Uaivers... of.Vew Yore at •wiqalo Depwone of Enpuriwng Science an .Mec, namc qaio..Y ’ 14.60

  8. Modelling fuel cell performance using artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Ogaji, S.O.T.; Singh, R.; Pilidis, P.; Diacakis, M. [Power Propulsion and Aerospace Engineering Department, Centre for Diagnostics and Life Cycle Costs, Cranfield University (United Kingdom)

    2006-03-09

    Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed. (author)

  9. Modelling fuel cell performance using artificial intelligence

    Science.gov (United States)

    Ogaji, S. O. T.; Singh, R.; Pilidis, P.; Diacakis, M.

    Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed.

  10. Conceptual Model of Business Value of Business Intelligence Systems

    OpenAIRE

    Popovič, Aleš; Turk, Tomaž; Jaklič, Jurij

    2010-01-01

    With advances in the business intelligence area, there is an increasing interest for the introduction of business intelligence systems into organizations. Although the opinion about business intelligence and its creation of business value is generally accepted, economic justification of investments into business intelligence systems is not always clear. Measuring the business value of business intelligence in practice is often not carried out due to the lack of measurement methods and resourc...

  11. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

    Science.gov (United States)

    Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan

    2017-05-01

    Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

  12. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*

    Science.gov (United States)

    Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan

    2017-01-01

    Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111

  13. Addressing Diverse Learner Preferences and Intelligences with Emerging Technologies: Matching Models to Online Opportunities

    Science.gov (United States)

    Zhang, Ke; Bonk, Curtis J.

    2008-01-01

    This paper critically reviews various learning preferences and human intelligence theories and models with a particular focus on the implications for online learning. It highlights a few key models, Gardner's multiple intelligences, Fleming and Mills' VARK model, Honey and Mumford's Learning Styles, and Kolb's Experiential Learning Model, and…

  14. Dynamic intelligent cleaning model of dirty electric load data

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Xiaoxing [State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044 (China); Sun, Caixin [The Key Laboratory of High Voltage Engineering and Electrical New Technology, Ministry of Education, Electrical Engineering College of Chongqing University, Chongqing 400044 (China)

    2008-04-15

    There are a number of dirty data in the load database derived from the supervisory control and data acquisition (SCADA) system. Thus, the data must be carefully and reasonably adjusted before it is used for electric load forecasting or power system analysis. This paper proposes a dynamic and intelligent data cleaning model based on data mining theory. Firstly, on the basis of fuzzy soft clustering, the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means soft clustering. Then, the proposed dynamic algorithm can automatically find the new clustering center (the characteristic curve of the data) with the updated sample data; At last, it is composed with radial basis function neural network (RBFNN), and then, an intelligent adjusting model is proposed to identify the dirty data. The rapid and dynamic performance of the model makes it suitable for real time calculation, and the efficiency and accuracy of the model is proved by test results of electrical load data analysis in Chongqing. (author)

  15. Dynamic intelligent cleaning model of dirty electric load data

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Xiaoxing [State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044 (China)], E-mail: mikezxx@tom.com; Sun Caixin [Key Laboratory of High Voltage Engineering and Electrical New Technology, Ministry of Education, Electrical Engineering College of Chongqing University, Chongqing 400044 (China)

    2008-04-15

    There are a number of dirty data in the load database derived from the supervisory control and data acquisition (SCADA) system. Thus, the data must be carefully and reasonably adjusted before it is used for electric load forecasting or power system analysis. This paper proposes a dynamic and intelligent data cleaning model based on data mining theory. Firstly, on the basis of fuzzy soft clustering, the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means soft clustering. Then, the proposed dynamic algorithm can automatically find the new clustering center (the characteristic curve of the data) with the updated sample data; At last, it is composed with radial basis function neural network (RBFNN), and then, an intelligent adjusting model is proposed to identify the dirty data. The rapid and dynamic performance of the model makes it suitable for real time calculation, and the efficiency and accuracy of the model is proved by test results of electrical load data analysis in Chongqing.

  16. Dynamic intelligent cleaning model of dirty electric load data

    International Nuclear Information System (INIS)

    Zhang Xiaoxing; Sun Caixin

    2008-01-01

    There are a number of dirty data in the load database derived from the supervisory control and data acquisition (SCADA) system. Thus, the data must be carefully and reasonably adjusted before it is used for electric load forecasting or power system analysis. This paper proposes a dynamic and intelligent data cleaning model based on data mining theory. Firstly, on the basis of fuzzy soft clustering, the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means soft clustering. Then, the proposed dynamic algorithm can automatically find the new clustering center (the characteristic curve of the data) with the updated sample data; At last, it is composed with radial basis function neural network (RBFNN), and then, an intelligent adjusting model is proposed to identify the dirty data. The rapid and dynamic performance of the model makes it suitable for real time calculation, and the efficiency and accuracy of the model is proved by test results of electrical load data analysis in Chongqing

  17. A human performance modelling approach to intelligent decision support systems

    Science.gov (United States)

    Mccoy, Michael S.; Boys, Randy M.

    1987-01-01

    Manned space operations require that the many automated subsystems of a space platform be controllable by a limited number of personnel. To minimize the interaction required of these operators, artificial intelligence techniques may be applied to embed a human performance model within the automated, or semi-automated, systems, thereby allowing the derivation of operator intent. A similar application has previously been proposed in the domain of fighter piloting, where the demand for pilot intent derivation is primarily a function of limited time and high workload rather than limited operators. The derivation and propagation of pilot intent is presented as it might be applied to some programs.

  18. A psychoanalyst artificial intelligence model in a computer game

    OpenAIRE

    Muñoz Fernández, Enrique

    2012-01-01

    Projecte realitzat en el marc d'un programa de mobilitat amb la Vienna University of Technology. [ANGLÈS] Implementation of an artificial intelligence model based on the psychoanalytic theory of the ID-Ego-SuperEgo of Sigmund Freud into the computer game Unreal Tournament 2004. [CASTELLÀ] Implementación de un modelo de inteligencia artificial basado en la teoría psicoanalítica del ID-Ego-SuperEgo de Sigmund Freud en el videojuego Unreal Tournament 2004. [CATALÀ] Implementació d'un mo...

  19. Modeling the Structure and Effectiveness of Intelligence Organizations: Dynamic Information Flow Simulation

    National Research Council Canada - National Science Library

    Behrman, Robert; Carley, Kathleen

    2003-01-01

    This paper describes the Dynamic Information Flow Simulation (DIFS), an abstract model for analyzing the structure and function of intelligence support organizations and the activities of entities within...

  20. Application of Artificial Intelligence for Bridge Deterioration Model

    Science.gov (United States)

    Chen, Zhang; Wu, Yangyang; Sun, Lijun

    2015-01-01

    The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention. PMID:26601121

  1. Application of Artificial Intelligence for Bridge Deterioration Model.

    Science.gov (United States)

    Chen, Zhang; Wu, Yangyang; Li, Li; Sun, Lijun

    2015-01-01

    The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  2. A hierarchical distributed control model for coordinating intelligent systems

    Science.gov (United States)

    Adler, Richard M.

    1991-01-01

    A hierarchical distributed control (HDC) model for coordinating cooperative problem-solving among intelligent systems is described. The model was implemented using SOCIAL, an innovative object-oriented tool for integrating heterogeneous, distributed software systems. SOCIAL embeds applications in 'wrapper' objects called Agents, which supply predefined capabilities for distributed communication, control, data specification, and translation. The HDC model is realized in SOCIAL as a 'Manager'Agent that coordinates interactions among application Agents. The HDC Manager: indexes the capabilities of application Agents; routes request messages to suitable server Agents; and stores results in a commonly accessible 'Bulletin-Board'. This centralized control model is illustrated in a fault diagnosis application for launch operations support of the Space Shuttle fleet at NASA, Kennedy Space Center.

  3. Advances in Intelligent Modelling and Simulation Simulation Tools and Applications

    CERN Document Server

    Oplatková, Zuzana; Carvalho, Marco; Kisiel-Dorohinicki, Marek

    2012-01-01

    The human capacity to abstract complex systems and phenomena into simplified models has played a critical role in the rapid evolution of our modern industrial processes and scientific research. As a science and an art, Modelling and Simulation have been one of the core enablers of this remarkable human trace, and have become a topic of great importance for researchers and practitioners. This book was created to compile some of the most recent concepts, advances, challenges and ideas associated with Intelligent Modelling and Simulation frameworks, tools and applications. The first chapter discusses the important aspects of a human interaction and the correct interpretation of results during simulations. The second chapter gets to the heart of the analysis of entrepreneurship by means of agent-based modelling and simulations. The following three chapters bring together the central theme of simulation frameworks, first describing an agent-based simulation framework, then a simulator for electrical machines, and...

  4. Application of Artificial Intelligence for Bridge Deterioration Model

    Directory of Open Access Journals (Sweden)

    Zhang Chen

    2015-01-01

    Full Text Available The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  5. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Emotional intelligence: an integrative meta-analysis and cascading model.

    Science.gov (United States)

    Joseph, Dana L; Newman, Daniel A

    2010-01-01

    Research and valid practice in emotional intelligence (EI) have been impeded by lack of theoretical clarity regarding (a) the relative roles of emotion perception, emotion understanding, and emotion regulation facets in explaining job performance; (b) conceptual redundancy of EI with cognitive intelligence and Big Five personality; and (c) application of the EI label to 2 distinct sets of constructs (i.e., ability-based EI and mixed-based EI). In the current article, the authors propose and then test a theoretical model that integrates these factors. They specify a progressive (cascading) pattern among ability-based EI facets, in which emotion perception must causally precede emotion understanding, which in turn precedes conscious emotion regulation and job performance. The sequential elements in this progressive model are believed to selectively reflect Conscientiousness, cognitive ability, and Neuroticism, respectively. "Mixed-based" measures of EI are expected to explain variance in job performance beyond cognitive ability and personality. The cascading model of EI is empirically confirmed via meta-analytic data, although relationships between ability-based EI and job performance are shown to be inconsistent (i.e., EI positively predicts performance for high emotional labor jobs and negatively predicts performance for low emotional labor jobs). Gender and race differences in EI are also meta-analyzed. Implications for linking the EI fad in personnel selection to established psychological theory are discussed. Copyright 2009 APA, all rights reserved.

  7. Modeling the prediction of business intelligence system effectiveness.

    Science.gov (United States)

    Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

    2016-01-01

    Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

  8. Terrorism Risk Modeling for Intelligence Analysis and Infrastructure Protection

    National Research Council Canada - National Science Library

    Willis, Henry H; LaTourrette, Tom; Kelly, Terrence K; Hickey, Scot; Neill, Samuel

    2007-01-01

    ...? The Office of Intelligence and Analysis (OI&A) at DHS is responsible for using information and intelligence from multiple sources to identify and assess current and future threats to the United States...

  9. The experimentation of learning models viewed from interpersonal intelligence

    Science.gov (United States)

    Gerhana, M. T. C.; Mardiyana; Pramudya, I.

    2017-11-01

    This research aimed to know experimentation Project Based Learning (PjBL) and Problem Based Learning (PBL) with scientific approach viewed from interpersonal intelligence. The subjects of this research were grade X MIPA students in SMA N 1 Minggir. This research instruments used were test and questionnaire. The result of the research showed that: (1) Students subjected PjBL with scientific approach had a better learning achievement than PBL with scientific approach; (2) students with high interpersonal intelligence had a better learning achievement than low and medium interpersonal intelligence, students with low and medium interpersonal intelligence gave the same learning achievement; (3) In PjBL, students with high interpersonal intelligence had a better learning achievement than low and medium interpersonal intelligence, students with low and medium interpersonal intelligence gave the same learning achievement. In PBL, students with high and medium interpersonal intelligence had a better learning achievement than low interpersonal intelligence, students with high and medium interpersonal intelligence gave the same learning achievement; (4) In high interpersonal intelligence, students subjected PjBL had a better learning achievement than students subjected PBL. In medium and low interpersonal intelligence, students subjected PjBL gave same learning achievement than students subjected PBL.

  10. On prognostic models, artificial intelligence and censored observations.

    Science.gov (United States)

    Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

    2001-03-01

    The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

  11. Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective

    CERN Document Server

    Chen, Shyi-Ming

    2013-01-01

    Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological a...

  12. A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification

    Directory of Open Access Journals (Sweden)

    Mehdi Khashei

    2015-09-01

    Full Text Available Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the literature clearly indicates that, despite proposing numerous classification models, credit scoring is often a difficult task. On the other hand, there is no universal credit-scoring model in the literature that can be accurately and explanatorily used in all circumstances. Therefore, the research for improving the efficiency of credit-scoring models has never stopped. In this paper, a hybrid soft intelligent classification model is proposed for credit-scoring problems. In the proposed model, the unique advantages of the soft computing techniques are used in order to modify the performance of the traditional artificial neural networks in credit scoring. Empirical results of Australian credit card data classifications indicate that the proposed hybrid model outperforms its components, and also other classification models presented for credit scoring. Therefore, the proposed model can be considered as an appropriate alternative tool for binary decision making in business and finance, especially in high uncertainty conditions.

  13. The Construction of Intelligent English Teaching Model Based on Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Xiaoguang Li

    2017-12-01

    Full Text Available In order to build a modernized tool platform that can help students improve their English learning efficiency according to their mastery of knowledge and personality, this paper develops an online intelligent English learning system that uses Java and artificial intelligence language Prolog as the software system. This system is a creative reflection of the thoughts of expert system in artificial intelligence. Established on the Struts Spring Hibernate lightweight JavaEE framework, the system modules are coupled with each other in a much lower degree, which is convenient to future function extension. Combined with the idea of expert system in artificial intelligence, the system developed appropriate learning strategies to help students double the learning effect with half the effort; Finally, the system takes into account the forgetting curve of memory, on which basis the knowledge that has been learned will be tested periodically, intending to spare students’ efforts to do a sea of exercises and obtain better learning results.

  14. Modelling and Intelligent Control of an Elastic Link Robot Manipulator

    Directory of Open Access Journals (Sweden)

    Malik Loudini

    2013-01-01

    Full Text Available In this paper, precise control of the end-point position of a planar single-link elastic manipulator robot is discussed. The Timoshenko beam theory (TBT has been used to characterize the structural link elasticity including important damping mechanisms. A suitable nonlinear model is derived based on the Lagrangian assumed modes method. Elastic link manipulators are classified as systems possessing highly complex dynamics. In addition, the environment in which they operate may have a lot of disturbances. These give rise to special problems that may be solved using intelligent control techniques. The application of two advanced control strategies based on fuzzy set theory is investigated. The first closed-loop control scheme to be applied is the standard Proportional-Derivative (PD type fuzzy logic controller (FLC, also known as PD-type Mamdani's FLC (MPDFLC. Then, a genetic algorithm (GA is used to optimize the MPDFLC parameters with innovative tuning procedures. Both the MPDFLC and the GA optimized FLC (GAOFLC are implemented and tested to achieve a precise control of the manipulator end-point. The performances of the adopted closed-loop intelligent control strategies are examined via simulation experiments.

  15. Novel approach for dam break flow modeling using computational intelligence

    Science.gov (United States)

    Seyedashraf, Omid; Mehrabi, Mohammad; Akhtari, Ali Akbar

    2018-04-01

    A new methodology based on the computational intelligence (CI) system is proposed and tested for modeling the classic 1D dam-break flow problem. The reason to seek for a new solution lies in the shortcomings of the existing analytical and numerical models. This includes the difficulty of using the exact solutions and the unwanted fluctuations, which arise in the numerical results. In this research, the application of the radial-basis-function (RBF) and multi-layer-perceptron (MLP) systems is detailed for the solution of twenty-nine dam-break scenarios. The models are developed using seven variables, i.e. the length of the channel, the depths of the up-and downstream sections, time, and distance as the inputs. Moreover, the depths and velocities of each computational node in the flow domain are considered as the model outputs. The models are validated against the analytical, and Lax-Wendroff and MacCormack FDM schemes. The findings indicate that the employed CI models are able to replicate the overall shape of the shock- and rarefaction-waves. Furthermore, the MLP system outperforms RBF and the tested numerical schemes. A new monolithic equation is proposed based on the best fitting model, which can be used as an efficient alternative to the existing piecewise analytic equations.

  16. Analysis of Intelligent Transportation Systems Using Model-Driven Simulations

    Directory of Open Access Journals (Sweden)

    Alberto Fernández-Isabel

    2015-06-01

    Full Text Available Intelligent Transportation Systems (ITSs integrate information, sensor, control, and communication technologies to provide transport related services. Their users range from everyday commuters to policy makers and urban planners. Given the complexity of these systems and their environment, their study in real settings is frequently unfeasible. Simulations help to address this problem, but present their own issues: there can be unintended mistakes in the transition from models to code; their platforms frequently bias modeling; and it is difficult to compare works that use different models and tools. In order to overcome these problems, this paper proposes a framework for a model-driven development of these simulations. It is based on a specific modeling language that supports the integrated specification of the multiple facets of an ITS: people, their vehicles, and the external environment; and a network of sensors and actuators conveniently arranged and distributed that operates over them. The framework works with a model editor to generate specifications compliant with that language, and a code generator to produce code from them using platform specifications. There are also guidelines to help researchers in the application of this infrastructure. A case study on advanced management of traffic lights with cameras illustrates its use.

  17. Modeling Common-Sense Decisions in Artificial Intelligence

    Science.gov (United States)

    Zak, Michail

    2010-01-01

    A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation

  18. Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling.

    Science.gov (United States)

    Degiacomi, Matteo T; Dal Peraro, Matteo

    2013-07-02

    Proteins often assemble in multimeric complexes to perform a specific biologic function. However, trapping these high-order conformations is difficult experimentally. Therefore, predicting how proteins assemble using in silico techniques can be of great help. The size of the associated conformational space and the fact that proteins are intrinsically flexible structures make this optimization problem extremely challenging. Nonetheless, known experimental spatial restraints can guide the search process, contributing to model biologically relevant states. We present here a swarm intelligence optimization protocol able to predict the arrangement of protein symmetric assemblies by exploiting a limited amount of experimental restraints and steric interactions. Importantly, within this scheme the native flexibility of each protein subunit is taken into account as extracted from molecular dynamics (MD) simulations. We show that this is a key ingredient for the prediction of biologically functional assemblies when, upon oligomerization, subunits explore activated states undergoing significant conformational changes.

  19. Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Atris Suyantohadi

    2010-03-01

    Full Text Available The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN and Lindenmayer System (L-System methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N, Phosphor (P and Potassium (K. The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied.

  20. Space Environment Modelling with the Use of Artificial Intelligence Methods

    Science.gov (United States)

    Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

    1996-12-01

    Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore

  1. The Actualization of Literary Learning Model Based on Verbal-Linguistic Intelligence

    Science.gov (United States)

    Hali, Nur Ihsan

    2017-01-01

    This article is inspired by Howard Gardner's concept of linguistic intelligence and also from some authors' previous writings. All of them became the authors' reference in developing ideas on constructing a literary learning model based on linguistic intelligence. The writing of this article is not done by collecting data empirically, but by…

  2. A UML Profile Oriented to the Requirements Modeling in Intelligent Tutoring Systems Projects

    OpenAIRE

    Guedes , Gilleanes Thorwald Araujo; Vicari , Rosa Maria

    2010-01-01

    International audience; This paper describes a proposal for the creation of a UML profile oriented to the intelligent tutoring systems project. In this paper we shall describe the proposed profile as well as its application into the modeling of the AMEA intelligent tutoring system.

  3. Modelling intelligence-led policing to identify its potential

    NARCIS (Netherlands)

    Hengst-Bruggeling, M. den; Graaf, H.A.L.M. de; Scheepstal, P.G.M. van

    2014-01-01

    lntelligence-led policing is a concept of policing that has been applied throughout the world. Despite some encouraging reports, the effect of intelligence-led policing is largely unknown. This paper presents a method with which it is possible to identify intelligence-led policing's potential to

  4. Emotional Intelligence Competencies and the Army Leadership Requirements Model

    Science.gov (United States)

    2015-06-12

    cultural stereotype in the military that suggests the display of emotions is less than desirable, however the ability for military leaders to regulate...enlisting recruits better suited for service in the Air Force. Concerning age, gender , and emotional intelligence competency levels, Boyatzis and Sala...uses in the act of leading. Emotional Intelligence in Action Emotionally competent leaders underwrite a healthy organizational climate

  5. Penerapan Model Pembelajaran Atraktif Berbasis Multiple Intelligences Tentang Pemantulan Cahaya pada Cermin

    Directory of Open Access Journals (Sweden)

    Intan Kusumawati

    2016-03-01

    Full Text Available Penelitian ini bertujuan untuk mengetahui efektivitas penerapan model pembelajaran atraktif berbasis multiple intelligences dalam meremediasi miskonsepsi siswa tentang pemantulan cahaya pada cermin. Pada penelitian ini digunakan bentuk pre-eksperimental design dengan rancangan one group pretest-post test design. Alat pengumpulan data berupa tes pilihan ganda dengan reasoning. Hasil validitas sebesar 4,08 dan reliabilitas 0,537. Siswa dibagi menjadi lima kelompok kecerdasan, yaitu kelompok linguistic intelligence, mathematical-logical intelligence, visual-spatial intelligence, bodily-khinestetic intelligence, dan musical intelligence. Siswa membahas konsep fisika sesuai kelompok kecerdasannya dalam bentuk pembuatan pantun-puisi, teka-teki silang, menggambar kreatif, drama, dan mengarang lirik lagu. Efektivitas penerapan model pembelajaran multiple intelligences menggunakan persamaan effect size. Ditemukan bahwa skor effect size masing-masing kelompok berkategori tinggi sebesar 5,76; 3,76; 4,60; 1,70; dan 1,34. Penerapan model pembelajaran atraktif berbasis multiple intelligences efektif dalam meremediasi miskonsepsi siswa. Penelitian ini diharapkan dapat digunakan pada materi fisika dan sekolah lainnya.

  6. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  7. Electron beam lithographic modeling assisted by artificial intelligence technology

    Science.gov (United States)

    Nakayamada, Noriaki; Nishimura, Rieko; Miura, Satoru; Nomura, Haruyuki; Kamikubo, Takashi

    2017-07-01

    We propose a new concept of tuning a point-spread function (a "kernel" function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.

  8. Hidden Hearing Loss and Computational Models of the Auditory Pathway: Predicting Speech Intelligibility Decline

    Science.gov (United States)

    2016-11-28

    Title: Hidden Hearing Loss and Computational Models of the Auditory Pathway: Predicting Speech Intelligibility Decline Christopher J. Smalt...to utilize computational models of the auditory periphery and auditory cortex to study the effect of low spontaneous rate ANF loss on the cortical...representation of speech intelligibility in noise. The auditory-periphery model of Zilany et al. (JASA 2009,2014) is used to make predictions of

  9. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Saini, K. K.; Saini, Sanju

    2008-01-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  10. The Actualization of Literary Learning Model Based on Verbal-Linguistic Intelligence

    Directory of Open Access Journals (Sweden)

    Nur Ihsan Halil

    2017-10-01

    Full Text Available This article is inspired by Howard Gardner's concept of linguistic intelligence and also from some authors' previous writings. All of them became the authors' reference in developing ideas on constructing a literary learning model based on linguistic intelligence. The writing of this article is not done by collecting data empirically, but by developing and constructing an existing concept, namely the concept of linguistic intelligence, which is disseminated into a literature-based learning of verbal-linguistic intelligence. The purpose of this paper is to answer the question of how to apply the literary learning model based on the verbal-linguistic intelligence. Then, regarding Gardner's concept, the author formulated a literary learning model based on the verbal-linguistic intelligence through a story-telling learning model with five steps namely arguing, discussing, interpreting, speaking, and writing about literary works. In short, the writer draw a conclusion that learning-based models of verbal-linguistic intelligence can be designed with attention into five components namely (1 definition, (2 characteristics, (3 teaching strategy, (4 final learning outcomes, and (5 figures.

  11. Emotional intelligence model for directors of research centers in mexico

    Directory of Open Access Journals (Sweden)

    Mara Maricela Trujillo Flores

    2008-01-01

    H5 Social skills exhibited by directors, that are also part of interpersonal intelligence, allow a director to exert a greater influence on the working group, facilitating communication, conflict management, leadership, collaboration, cooperation and development of team skills.

  12. The effect of learning models and emotional intelligence toward students learning outcomes on reaction rate

    Science.gov (United States)

    Sutiani, Ani; Silitonga, Mei Y.

    2017-08-01

    This research focused on the effect of learning models and emotional intelligence in students' chemistry learning outcomes on reaction rate teaching topic. In order to achieve the objectives of the research, with 2x2 factorial research design was used. There were two factors tested, namely: the learning models (factor A), and emotional intelligence (factor B) factors. Then, two learning models were used; problem-based learning/PBL (A1), and project-based learning/PjBL (A2). While, the emotional intelligence was divided into higher and lower types. The number of population was six classes containing 243 grade X students of SMAN 10 Medan, Indonesia. There were 15 students of each class were chosen as the sample of the research by applying purposive sampling technique. The data were analyzed by applying two-ways analysis of variance (2X2) at the level of significant α = 0.05. Based on hypothesis testing, there was the interaction between learning models and emotional intelligence in students' chemistry learning outcomes. Then, the finding of the research showed that students' learning outcomes in reaction rate taught by using PBL with higher emotional intelligence is higher than those who were taught by using PjBL. There was no significant effect between students with lower emotional intelligence taught by using both PBL and PjBL in reaction rate topic. Based on the finding, the students with lower emotional intelligence were quite hard to get in touch with other students in group discussion.

  13. A Concept Map Knowledge Model of Intelligence Analysis

    Science.gov (United States)

    2011-05-01

    performance humaine . Le modèle couvre une vingtaine de sujets différents, il répertorie et définit des douzaines de concepts relevant de l’analyse des...analysis, such as Canadian Association of Professional Intelligence Analysts, and Canadian Association for Security and Intelligence Studies. The map...intentionally left blank. UNCLASSIFIED DOCUMENT CONTROL DATA ( Security classification of the title, body of abstract and indexing annotation must be

  14. The Kano Model Use to Evaluate the Perception of Intelligent and Active Packaging of Slovak Customers

    Directory of Open Access Journals (Sweden)

    Erika Loučanová

    2018-03-01

    Full Text Available Intelligent innovation represents any autonomic change with positive impact to the customer. They increase the comfort of the customer and concurrently they represent more effective, more economical, healthier and safer solution. This term is not so usual in Slovakia, however intelligent innovation are present on the market. For that in the article intelligent innovation assessment, we focused on intelligent and active packaging, the occurrence of which we have mostly noticed on the Slovak market. The paper deals with the evaluation of the perception of packaging innovations by using the Kano model. According to research results, intelligent and active packaging influence customers and therefore constitutes a tool of competitiveness in Slovakia. However, considering the specification of their requirements, the degree of impact is very variable and specific to customers of different gender and age.

  15. Does Reading Cause Later Intelligence? Accounting for Stability in Models of Change.

    Science.gov (United States)

    Bailey, Drew H; Littlefield, Andrew K

    2017-11-01

    This study reanalyzes data presented by Ritchie, Bates, and Plomin (2015) who used a cross-lagged monozygotic twin differences design to test whether reading ability caused changes in intelligence. The authors used data from a sample of 1,890 monozygotic twin pairs tested on reading ability and intelligence at five occasions between the ages of 7 and 16, regressing twin differences in intelligence on twin differences in prior intelligence and twin differences in prior reading ability. Results from a state-trait model suggest that reported effects of reading ability on later intelligence may be artifacts of previously uncontrolled factors, both environmental in origin and stable during this developmental period, influencing both constructs throughout development. Implications for cognitive developmental theory and methods are discussed. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

  16. Design, modelling, implementation, and intelligent fuzzy control of a hovercraft

    Science.gov (United States)

    El-khatib, M. M.; Hussein, W. M.

    2011-05-01

    A Hovercraft is an amphibious vehicle that hovers just above the ground or water by air cushion. The concept of air cushion vehicle can be traced back to 1719. However, the practical form of hovercraft nowadays is traced back to 1955. The objective of the paper is to design, simulate and implement an autonomous model of a small hovercraft equipped with a mine detector that can travel over any terrains. A real time layered fuzzy navigator for a hovercraft in a dynamic environment is proposed. The system consists of a Takagi-Sugenotype fuzzy motion planner and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including the right and left views from which he makes his next step towards the goal in the free space. It intelligently combines two behaviours to cope with obstacle avoidance as well as approaching a goal using a proportional navigation path accounting for hovercraft kinematics. MATLAB/Simulink software tool is used to design and verify the proposed algorithm.

  17. Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

    Science.gov (United States)

    Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

    2012-12-01

    This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

  18. Intelligent decision-making models for production and retail operations

    CERN Document Server

    Guo, Zhaoxia

    2016-01-01

    This book provides an overview of intelligent decision-making techniques and discusses their application in production and retail operations. Manufacturing and retail enterprises have stringent standards for using advanced and reliable techniques to improve decision-making processes, since these processes have significant effects on the performance of relevant operations and the entire supply chain. In recent years, researchers have been increasingly focusing attention on using intelligent techniques to solve various decision-making problems. The opening chapters provide an introduction to several commonly used intelligent techniques, such as genetic algorithm, harmony search, neural network and extreme learning machine. The book then explores the use of these techniques for handling various production and retail decision-making problems, such as production planning and scheduling, assembly line balancing, and sales forecasting.

  19. Intelligent Model Management in a Forest Ecosystem Management Decision Support System

    Science.gov (United States)

    Donald Nute; Walter D. Potter; Frederick Maier; Jin Wang; Mark Twery; H. Michael Rauscher; Peter Knopp; Scott Thomasma; Mayukh Dass; Hajime Uchiyama

    2002-01-01

    Decision making for forest ecosystem management can include the use of a wide variety of modeling tools. These tools include vegetation growth models, wildlife models, silvicultural models, GIS, and visualization tools. NED-2 is a robust, intelligent, goal-driven decision support system that integrates tools in each of these categories. NED-2 uses a blackboard...

  20. Weaving Emotional Intelligence into a Home Visiting Model

    Science.gov (United States)

    Enson, Beth; Imberger, Jaci

    2017-01-01

    This article details the impact of Emotional Intelligence (EI) training on the 10-year evolution of the Taos First Steps Home Visiting program. While EI has become standard fare in corporate training and practice, it is less well known in the world of early childhood services. This article highlights interviews with key personnel, both in-house…

  1. Modeling and Evaluation of LTE in Intelligent Transportation Systems

    NARCIS (Netherlands)

    Trichias, K.; van den Berg, Hans Leo; de Jongh, J.; Litjens, R.; Dimitrova, D.C.; Brogle, M.; Braun, T.; Heijenk, Gerhard J.; Meratnia, Nirvana

    The term Intelligent Transportation Systems (ITS) refers to adding information and communications technology to transport infrastructure and ve- hicles. The IEEE 802.11p standard is considered the main candidate for com- munication within the context of ITS and it performs well for active safety use

  2. A Conversation Model Enabling Intelligent Agents to Give Emotional Support

    NARCIS (Netherlands)

    Van der Zwaan, J.M.; Dignum, V.; Jonker, C.M.

    2012-01-01

    In everyday life, people frequently talk to others to help them deal with negative emotions. To some extent, everybody is capable of comforting other people, but so far conversational agents are unable to deal with this type of situation. To provide intelligent agents with the capability to give

  3. Underwater Signal Modeling for Subsurface Classification Using Computational Intelligence.

    Science.gov (United States)

    Setayeshi, Saeed

    In the thesis a method for underwater layered media (UWLM) modeling is proposed, and a simple nonlinear structure for implementation of this model based on the behaviour of its characteristics and the propagation of the acoustic signal in the media accounting for attenuation effects is designed. The model that responds to the acoustic input is employed to test the artificial intelligence classifiers ability. Neural network models, the basic principles of the back-propagation algorithm, and the Hopfield model of associative memories are reviewed, and they are employed to use min-max amplitude ranges of a reflected signal of UWLM based on attenuation effects, to define the classes of the synthetic data, detect its peak features and estimate parameters of the media. It has been found that there is a correlation between the number of layers in the media and the optimum number of nodes in the hidden layer of the neural networks. The integration of the result of the neural networks that classify and detect underwater layered media acoustic signals based on attenuation effects to prove the correspondence between the peak points and decay values has introduced a powerful tool for UWLM identification. The methods appear to have applications in replacing original system, for parameter estimation and output prediction in system identification by the proposed networks. The results of computerized simulation of the UWLM modeling in conjunction with the proposed neural networks training process are given. Fuzzy sets is an idea that allows representing and manipulating inexact concepts, fuzzy min-max pattern classification method, and the learning and recalling algorithms for fuzzy neural networks implementation is explained in this thesis. A fuzzy neural network that uses peak amplitude ranges to define classes is proposed and evaluated for UWLM pattern recognition. It is demonstrated to be able to classify the layered media data sets, and can distinguish between the peak points

  4. Intelligent Hydraulic Actuator and Exp-based Modelling of Losses in Pumps and .

    DEFF Research Database (Denmark)

    Zhang, Muzhi

    A intelligent fuzzy logic self-organising PD+I controller for a gearrotor hydraulic motor was developed and evaluated. Furthermore, a experimental-based modelling methods with a new software tool 'Dynamodata' for modelling of losses in hydraulic motors and pumps was developed.......A intelligent fuzzy logic self-organising PD+I controller for a gearrotor hydraulic motor was developed and evaluated. Furthermore, a experimental-based modelling methods with a new software tool 'Dynamodata' for modelling of losses in hydraulic motors and pumps was developed....

  5. Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

    Science.gov (United States)

    Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

    2014-12-01

    This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

  6. Gratitude mediates the effect of emotional intelligence on subjective well-being: A structural equation modeling analysis.

    Science.gov (United States)

    Geng, Yuan

    2016-11-01

    This study investigated the relationship among emotional intelligence, gratitude, and subjective well-being in a sample of university students. A total of 365 undergraduates completed the emotional intelligence scale, the gratitude questionnaire, and the subjective well-being measures. The results of the structural equation model showed that emotional intelligence is positively associated with gratitude and subjective well-being, that gratitude is positively associated with subjective well-being, and that gratitude partially mediates the positive relationship between emotional intelligence and subjective well-being. Bootstrap test results also revealed that emotional intelligence has a significant indirect effect on subjective well-being through gratitude.

  7. Optimal model of economic diplomacy of Republic of Croatia in the contexst of global intelligence revolution

    Directory of Open Access Journals (Sweden)

    Zdravko Bazdan

    2010-12-01

    Full Text Available The aim of this study is to point to the fact that economic diplomacy is a relatively new practice in international economics, specifically the expansion of the occurrence of Intelligence Revolution. The history in global relations shows that without economic diplomacy there is no optimal economic growth and social development. It is important to note that economic diplomacy should be important for our country and the political elite, as well as for the administration of Croatian economic subjects that want to compete in international market economy. Comparative analysis are particularly highlighted by French experience. Therefore, Croatia should copy the practice of those countries that are successful in economic diplomacy. And in the curricula - especially of our economic faculties - we should introduce the course of Economic Diplomacy. It is important to note, that in order to form our optimal model of economic diplomacy which would be headed by the President of Republic of Croatia formula should be based on: Intelligence Security Agency (SOA, Intelligence Service of the Ministry of Foreign Affairs and European Integration, Intelligence Service of the Croatian Chamber of Commerce and the Intelligence Service of the Ministry of Economy, Labor and Entrepreneurship. Described model would consist of intelligence subsystem with at least twelve components.

  8. Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model (PREPRINT)

    Science.gov (United States)

    2009-02-20

    Society for Risk Analysis, February 20, 2009    1. INTELLIGENT ADVERSARY RISK  ANALISIS  IS DIFFERENT THAN  HAZARD RISK ANALYSIS  Risk analysis has...future work and our conclusions. 1.1. Intelligent adversary risk analysis requires new approaches We believe that risk analysis of uncertain hazards...public panic and social disruption; and require special action for public health preparedness. B to Second highest priority agents include those

  9. Modeling speech intelligibility based on the signal-to-noise envelope power ratio

    DEFF Research Database (Denmark)

    Jørgensen, Søren

    background noise, reverberation and noise reduction processing on speech intelligibility, indicating that the model is more general than traditional modeling approaches. Moreover, the model accounts for phase distortions when it includes a mechanism that evaluates the variation of envelope power across...... (audio) frequency. However, because the SNRenv is based on the long-term average envelope power, the model cannot account for the greater intelligibility typically observed in fluctuating noise compared to stationary noise. To overcome this limitation, a multi-resolution version of the sEPSM is presented...... distorted by reverberation or spectral subtraction. The relationship between the SNRenv based decision-metric and psychoacoustic speech intelligibility is further evaluated by generating stimuli with different SNRenv but the same overall power SNR. The results from the corresponding psychoacoustic data...

  10. Swarm intelligence algorithm for interconnect model order reduction with sub-block structure preserving

    Science.gov (United States)

    Wang, Xinsheng; Wang, Chenxu; Yu, Mingyan

    2016-07-01

    In this paper, we propose a generalised sub-block structure preservation interconnect model order reduction (MOR) technique based on the swarm intelligence method, that is, particle swarm optimisation (PSO). The swarm intelligence-based structure preservation MOR can be used for a standard model as a criterion for different structure preservation interconnect MOR methods. In the proposed technique, the PSO method is used for predicting the unknown elements of structure-preserving reduced-order modelling of interconnect circuits. The prediction is based on minimising the difference of transform function between the original full-order and desired reduced-order systems maintaining the full-order structure in the reduced-order model. The proposed swarm-intelligence-based structure-preserving MOR method is compared with published work on structure preservation MOR SPRIM techniques. Simulation and synthesis results verify the accuracy and validity of the new structure-preserving MOR technique.

  11. Exploring an Emotional Intelligence Model With Psychiatric Mental Health Nurses.

    Science.gov (United States)

    Sims, Traci T

    A lack of emotional skills may affect a nurse's personal well-being and have negative effects on patient outcomes. To compare psychiatric-mental health nurses' (PMHN) scores on the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) to a normed population and compare the emotional intelligence (EI) scores of PMHNs using two tools, MSCEIT and Self-Rated Emotional Intelligence Scale (SREIS). Comparative descriptive and correlational study. PMHNs in the study had a higher mean EI compared with that of 5,000 participants in the normed MSCEIT sample. Significant weak correlations were seen between the perceiving and understanding emotion branches of the MSCEIT and SREIS. The current study added data about a sample of PMHN's EI levels in the United States, which may encourage dialog about EI among PMHNs. Future research is needed to examine the relationship between self-report EI tools (e.g., SREIS) and performance tools (e.g., MSCEIT) to determine if they are measuring the same construct.

  12. Extending Galactic Habitable Zone Modeling to Include the Emergence of Intelligent Life.

    Science.gov (United States)

    Morrison, Ian S; Gowanlock, Michael G

    2015-08-01

    Previous studies of the galactic habitable zone have been concerned with identifying those regions of the Galaxy that may favor the emergence of complex life. A planet is deemed habitable if it meets a set of assumed criteria for supporting the emergence of such complex life. In this work, we extend the assessment of habitability to consider the potential for life to further evolve to the point of intelligence--termed the propensity for the emergence of intelligent life, φI. We assume φI is strongly influenced by the time durations available for evolutionary processes to proceed undisturbed by the sterilizing effects of nearby supernovae. The times between supernova events provide windows of opportunity for the evolution of intelligence. We developed a model that allows us to analyze these window times to generate a metric for φI, and we examine here the spatial and temporal variation of this metric. Even under the assumption that long time durations are required between sterilizations to allow for the emergence of intelligence, our model suggests that the inner Galaxy provides the greatest number of opportunities for intelligence to arise. This is due to the substantially higher number density of habitable planets in this region, which outweighs the effects of a higher supernova rate in the region. Our model also shows that φI is increasing with time. Intelligent life emerged at approximately the present time at Earth's galactocentric radius, but a similar level of evolutionary opportunity was available in the inner Galaxy more than 2 Gyr ago. Our findings suggest that the inner Galaxy should logically be a prime target region for searches for extraterrestrial intelligence and that any civilizations that may have emerged there are potentially much older than our own.

  13. Means-End based Functional Modeling for Intelligent Control: Modeling and Experiments with an Industrial Heat Pump System

    DEFF Research Database (Denmark)

    Saleem, Arshad

    2007-01-01

    The purpose of this paper is to present a Multilevel Flow Model (MFM) of an industrial heat pump system and its use for diagnostic reasoning. MFM is functional modeling language supporting an explicit means-ends intelligent control strategy for large industrial process plants. The model is used...... in several diagnostic experiments analyzing different fault scenarios. The model and results of the experiments are explained and it is shown how MFM based intelligent modeling and automated reasoning can improve the fault diagnosis process significantly....

  14. From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond

    Science.gov (United States)

    Dimitrova, Vania; Brna, Paul

    2016-01-01

    STyLE-OLM (Dimitrova 2003 "International Journal of Artificial Intelligence in Education," 13, 35-78) presented a framework for interactive open learner modelling which entails the development of the means by which learners can "inspect," "discuss" and "alter" the learner model that has been jointly…

  15. A Multidisciplinary Model for Development of Intelligent Computer-Assisted Instruction.

    Science.gov (United States)

    Park, Ok-choon; Seidel, Robert J.

    1989-01-01

    Proposes a schematic multidisciplinary model to help developers of intelligent computer-assisted instruction (ICAI) identify the types of required expertise and integrate them into a system. Highlights include domain types and expertise; knowledge acquisition; task analysis; knowledge representation; student modeling; diagnosis of learning needs;…

  16. Using Game Theory Techniques and Concepts to Develop Proprietary Models for Use in Intelligent Games

    Science.gov (United States)

    Christopher, Timothy Van

    2011-01-01

    This work is about analyzing games as models of systems. The goal is to understand the techniques that have been used by game designers in the past, and to compare them to the study of mathematical game theory. Through the study of a system or concept a model often emerges that can effectively educate students about making intelligent decisions…

  17. Effective Stress Management: A Model of Emotional Intelligence, Self-Leadership, and Student Stress Coping

    Science.gov (United States)

    Houghton, Jeffery D.; Wu, Jinpei; Godwin, Jeffrey L.; Neck, Christopher P.; Manz, Charles C.

    2012-01-01

    This article develops and presents a model of the relationships among emotional intelligence, self-leadership, and stress coping among management students. In short, the authors' model suggests that effective emotion regulation and self-leadership, as mediated through positive affect and self-efficacy, has the potential to facilitate stress coping…

  18. An Intelligent Tutoring System for Learning Chinese with a Cognitive Model of the Learner

    Science.gov (United States)

    Kosek, Michal; Lison, Pierre

    2014-01-01

    We present an intelligent tutoring system that lets students of Chinese learn words and grammatical constructions. It relies on a Bayesian, linguistically motivated cognitive model that represents the learner's knowledge. This model is dynamically updated given observations about the learner's behaviour in the exercises, and employed at runtime to…

  19. A collision model for safety evaluation of autonomous intelligent cruise control.

    Science.gov (United States)

    Touran, A; Brackstone, M A; McDonald, M

    1999-09-01

    This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.

  20. A Model for Web-Intelligence Index to Evaluate the Web Intelligence Capacity of Government Web Sites of Sri Lanka

    OpenAIRE

    Abeysiriwardana, Prabath Chaminda; Kodituwakku, S. R.

    2015-01-01

    Web intelligence can be considered as a subset of Artificial Intelligence. It uses existing data in web to produce new data, knowledge and wisdom to support decision making and new predictions for web users. Artificial Intelligence is ever changing and evolving field of computer science and it is extensively used in wide array of web based business applications. Although it is used substantially in web based systems in developed countries, it is not examined whether it is being substantially ...

  1. MODEL OF SYNTHESIS OF A HARDWARE AND SOFTWARE SYSTEM DESIGNATED FOR AN INTELLIGENT OFFICE BUILDING

    Directory of Open Access Journals (Sweden)

    Ogirenko Andrey Grigor'evich

    2012-10-01

    Full Text Available The problem of synthesis of technology-intensive constituents of an intelligent office system, implemented in advanced office buildings, is the subject matter of this article. On the basis of the proposed classification and the analysis performed by the author, the general structure of the multilevel distributed system, that has radial treelike lines of communications, is developed. The structure of the intelligent office system designated for advanced real estate facilities represents an integration of two structures, including the functional constituent and the hardware constituent. The model of optimization of the hardware constituent is proposed by the author. The article also contains an overview of the model implementation within the framework of a set of intelligent buildings in the centre of Moscow.

  2. Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

    Science.gov (United States)

    Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

    Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

  3. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    Directory of Open Access Journals (Sweden)

    Hooman Aghaebrahimi Samani

    2012-03-01

    Full Text Available A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA, based on the psychology of love, Artificial Endocrine System (AES, based on the physiology of love, and Affective State Transition (AST, based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN. The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

  4. Traffic Route Modelling and Assignment with Intelligent Transport System

    Directory of Open Access Journals (Sweden)

    Kunicina Nadezhda

    2014-12-01

    Full Text Available The development of signal transmitting environment for multimodal traffic control will enhance the integration of emergency and specialized transport routing tools in usual traffic control paradigms - it is one of the opportunities offered by modern intelligent traffic control systems. The improvement of effective electric power use in public transport system is an advantage of Intelligent Transport System (ITS. The research is connected with the improvement of on-line traffic control and adaptation of special traffic lighting alternatives by ITS. The assignment of the nearest appropriate transport will be done by passenger request, but unlike information system, the transport planning is done on demand. The task can be solved with the help of modern technical methods and equipment, as well as by applying control paradigms of the distributed systems. The problem is solved with the help of calculations hyper-graph and scheduling theory. The goal of the research is to develop methods, which support scheduling of the emergency transport, using high performance computing.

  5. A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

    Directory of Open Access Journals (Sweden)

    Gang Ma

    2015-01-01

    Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

  6. Intelligence: Real or artificial?

    OpenAIRE

    Schlinger, Henry D.

    1992-01-01

    Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally r...

  7. Intelligent judgements over health risks in a spatial agent-based model.

    Science.gov (United States)

    Abdulkareem, Shaheen A; Augustijn, Ellen-Wien; Mustafa, Yaseen T; Filatova, Tatiana

    2018-03-20

    Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of

  8. Artificial intelligence and exponential technologies business models evolution and new investment opportunities

    CERN Document Server

    Corea, Francesco

    2017-01-01

    Artificial Intelligence is a huge breakthrough technology that is changing our world. It requires some degrees of technical skills to be developed and understood, so in this book we are going to first of all define AI and categorize it with a non-technical language. We will explain how we reached this phase and what historically happened to artificial intelligence in the last century. Recent advancements in machine learning, neuroscience, and artificial intelligence technology will be addressed, and new business models introduced for and by artificial intelligence research will be analyzed. Finally, we will describe the investment landscape, through the quite comprehensive study of almost 14,000 AI companies and we will discuss important features and characteristics of both AI investors as well as investments. This is the “Internet of Thinks” era. AI is revolutionizing the world we live in. It is augmenting the human experiences, and it targets to amplify human intelligence in a future not so distant from...

  9. Implementation of intelligent nuclear material diagnosis module based on the component object model

    International Nuclear Information System (INIS)

    Lee, Sang Yoon; Song, Dae Yong; Ko, Won Il; Ha, Jang Ho; Kim, Ho Dong

    2003-08-01

    In this paper, the implementation techniques of intelligent nuclear material surveillance system based on the COM (Component Object Model) and SOM (Self Organized Mapping) was described. The surveillance system that is to be developed is consist of CCD cameras, neutron monitors, and PC for data acquisition. To develop the system, the properties of the COM based software development technology was investigated, and the characteristics of related platform APIs was summarized. This report could be used for the developers who want to develop the intelligent surveillance system for various experimental environments based on the DVR and sensors using Borland C++ Builder

  10. Business intelligence tools for radiology: creating a prototype model using open-source tools.

    Science.gov (United States)

    Prevedello, Luciano M; Andriole, Katherine P; Hanson, Richard; Kelly, Pauline; Khorasani, Ramin

    2010-04-01

    Digital radiology departments could benefit from the ability to integrate and visualize data (e.g. information reflecting complex workflow states) from all of their imaging and information management systems in one composite presentation view. Leveraging data warehousing tools developed in the business world may be one way to achieve this capability. In total, the concept of managing the information available in this data repository is known as Business Intelligence or BI. This paper describes the concepts used in Business Intelligence, their importance to modern Radiology, and the steps used in the creation of a prototype model of a data warehouse for BI using open-source tools.

  11. The Relations between Implicit Intelligence Beliefs, Autonomous Academic Motivation, and School Persistence Intentions: A Mediation Model

    Science.gov (United States)

    Renaud-Dubé, Andréanne; Guay, Frédéric; Talbot, Denis; Taylor, Geneviève; Koestner, Richard

    2015-01-01

    This study attempts to test a model in which the relation between implicit theories of intelligence and students' school persistence intentions are mediated by intrinsic, identified, introjected, and external regulations. Six hundred and fifty students from a high school were surveyed. Contrary to expectations, results from ESEM analyses indicated…

  12. Implementing Adaptability in the Standard Reference Model for Intelligent Multimedia Presentation Systems

    NARCIS (Netherlands)

    L. Rutledge (Lloyd); L. Hardman (Lynda); J.R. van Ossenbruggen (Jacco); D.C.A. Bulterman (Dick)

    1998-01-01

    textabstractThis paper discusses the implementation of adaptability in environments that are based on the Standard Reference Model for Intelligent Multimedia Presentation Systems. This adaptability is explored in the context of style sheets, which are represented in such formats as DSSSL. The use of

  13. Decision Conceptual Model for Innovation Ways using the Competitive Intelligence System

    OpenAIRE

    Mateescu Mihaela; Muscalu Sabin; Bozga Raluca

    2017-01-01

    This paper proposes a theoretical model that can be used in making decisions regarding theinnovation ways. The innovation strategy is designed with the competitive intelligence system. Thismodel will be implemented software and will be applied in a service company. The results of thepresent study will be communicated in a future paper.

  14. Decision Conceptual Model for Innovation Ways using the Competitive Intelligence System

    Directory of Open Access Journals (Sweden)

    Mateescu Mihaela

    2017-01-01

    Full Text Available This paper proposes a theoretical model that can be used in making decisions regarding theinnovation ways. The innovation strategy is designed with the competitive intelligence system. Thismodel will be implemented software and will be applied in a service company. The results of thepresent study will be communicated in a future paper.

  15. Predicting speech intelligibility in adverse conditions: evaluation of the speech-based envelope power spectrum model

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2011-01-01

    conditions by comparing predictions to measured data from [Kjems et al. (2009). J. Acoust. Soc. Am. 126 (3), 1415-1426] where speech is mixed with four different interferers, including speech-shaped noise, bottle noise, car noise, and cafe noise. The model accounts well for the differences in intelligibility...

  16. Emotional Intelligence and Negative Feelings: A Gender Specific Moderated Mediation Model

    Science.gov (United States)

    Karakus, Mehmet

    2013-01-01

    This study aims to clarify the effect of emotional intelligence (EI) on negative feelings (stress, anxiety, burnout and depression) in a gender specific model. Four hundred and twenty-five primary school teachers (326 males, 99 females) completed the measures of EI, stress, anxiety, burnout and depression. The multi-group analysis was performed…

  17. Towards a value model for collaborative, business intelligence-supported risk assessment

    NARCIS (Netherlands)

    Liu, L.; Daniëls, H.A.M.; Johannesson, P.

    2012-01-01

    Collaborative business intelligence supports risk assessment and in return enhances management control on a business network. Nonetheless, it needs an incentive basis in the first place before it can be implemented, that is, the value model. Starting from the managerial challenges which arise from

  18. Design of an Intelligent Support Agent Model for People with a Cognitive Vulnerability

    NARCIS (Netherlands)

    Aziz, A.A.; Klein, M.C.A.; Zhang, B.; Wang, Y.; Kinser, W.

    2010-01-01

    This paper presents the design of an intelligent agent application aimed at supporting people with a cognitive vulnerability to prevent the onset of a depression. For this, a computational model of the cognitive processes around depression is used. The agent application uses the principles of

  19. Intelligent sensor-model automated control of PMR-15 autoclave processing

    Science.gov (United States)

    Hart, S.; Kranbuehl, D.; Loos, A.; Hinds, B.; Koury, J.

    1992-01-01

    An intelligent sensor model system has been built and used for automated control of the PMR-15 cure process in the autoclave. The system uses frequency-dependent FM sensing (FDEMS), the Loos processing model, and the Air Force QPAL intelligent software shell. The Loos model is used to predict and optimize the cure process including the time-temperature dependence of the extent of reaction, flow, and part consolidation. The FDEMS sensing system in turn monitors, in situ, the removal of solvent, changes in the viscosity, reaction advancement and cure completion in the mold continuously throughout the processing cycle. The sensor information is compared with the optimum processing conditions from the model. The QPAL composite cure control system allows comparison of the sensor monitoring with the model predictions to be broken down into a series of discrete steps and provides a language for making decisions on what to do next regarding time-temperature and pressure.

  20. An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

    Science.gov (United States)

    Ranganayaki, V; Deepa, S N

    2016-01-01

    Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

  1. Finite-element-model updating using computational intelligence techniques applications to structural dynamics

    CERN Document Server

    Marwala, Tshilidzi

    2010-01-01

    Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include: • multi-layer perceptron neural networks for real-time FEM updating; • particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models; • simulated annealing to put the methodologies into a sound statistical basis; and • response surface methods and expectation m...

  2. Mathias Théry, La Vie après la mort d'Henrietta Lacks, 2004, France, 23 min (ENSAD) / Mathias Théry et Etienne Chaillou, Cherche toujours, 2008, France, 52 min (Arte France / Les Films d'ici)

    OpenAIRE

    Blanchard, Antoine; Monfeuillard, Hélène; Sabuncu, Elifsu

    2009-01-01

    COMPTE RENDU DE FILMS; International audience; Comment entrer dans l’intimité d’un chercheur ? Comment rendre compte de la vision du monde qui est la sienne ? Comment accéder à une authentique recherche en train de se faire ? À ces questions, le réalisateur Mathias Théry donne quelques éléments de réponse dans ses deux films, La vie après la mort d’Henrietta Lacks (ci-après VAMHL) et Cherche toujours (CT). Le premier constitue son film d’études à l’École nationale supérieure des arts décorati...

  3. Intelligence in Artificial Intelligence

    OpenAIRE

    Datta, Shoumen Palit Austin

    2016-01-01

    The elusive quest for intelligence in artificial intelligence prompts us to consider that instituting human-level intelligence in systems may be (still) in the realm of utopia. In about a quarter century, we have witnessed the winter of AI (1990) being transformed and transported to the zenith of tabloid fodder about AI (2015). The discussion at hand is about the elements that constitute the canonical idea of intelligence. The delivery of intelligence as a pay-per-use-service, popping out of ...

  4. Comparison of two intelligent models to estimate the instantaneous ...

    Indian Academy of Sciences (India)

    Mostafa Zamani Mohiabadi

    2017-07-25

    Jul 25, 2017 ... 2014) has combined empirical models and a Bayesian neural network (BNN) model to estimate daily global solar radiation on a horizon- tal surface in Ghardaıa, Algeria. In their model, the maximum and minimum air temperatures of the year 2006 have been used to estimate the coefficients of the empirical ...

  5. Cognitive-Operative Model of Intelligent Learning Systems Behavior

    Science.gov (United States)

    Laureano-Cruces, Ana Lilia; Ramirez-Rodriguez, Javier; Mora-Torres, Martha; de Arriaga, Fernando; Escarela-Perez, Rafael

    2010-01-01

    In this paper behavior during the teaching-learning process is modeled by means of a fuzzy cognitive map. The elements used to model such behavior are part of a generic didactic model, which emphasizes the use of cognitive and operative strategies as part of the student-tutor interaction. Examples of possible initial scenarios for the…

  6. Model of facilitation of emotional intelligence to promote wholeness ...

    African Journals Online (AJOL)

    The facilitation of inherent affective and mental resourcefulness and resilience was the main concept of the model. Step two comprised the definition and classification of central and related concepts. Step three provides a description of the model. The model operates in three phases namely the dependent phase, partially ...

  7. Leader emotional intelligence, transformational leadership, trust and team commitment: Testing a model within a team context

    Directory of Open Access Journals (Sweden)

    Anton F. Schlechter

    2008-06-01

    Full Text Available This exploratory study tested a model within a team context consisting of transformational-leadership behaviour, team-leader emotional intelligence, trust (both in the team leader and in the team members and team commitment. It was conducted within six manufacturing plants, with 25 teams participating. Of the 320 surveys distributed to these teams, 178 were received (which equals a 56% response rate. The surveys consisted of the multi-factor leadership questionnaire (MLQ, the Swinburne University emotional intelligence test (SUEIT, the organisational-commitment scale (OCS (adapted for team commitment and the workplace trust survey (WTS. The validity of these scales was established using exploratory factor analysis (EFA and confrmatory factor analysis (CFA. The Cronbach alpha was used to assess the reliability of the scales. The model was tested using structural equation modelling (SEM; an acceptable level of model ft was found. Signifcant positive relationships were further found among all the constructs. Such an integrated model has not been tested in a team context before and the positive fndings therefore add to existing teamwork literature. The fnding that transformational leadership and leader emotional intelligence are positively related to team commitment and trust further emphasises the importance of effective leadership behaviour in team dynamics and performance.

  8. Leader emotional intelligence, transformational leadership, trust and team commitment: Testing a model within a team context

    Directory of Open Access Journals (Sweden)

    Anton F. Schlechter

    2008-09-01

    Full Text Available This exploratory study tested a model within a team context consisting of transformational-leadership behaviour, team-leader emotional intelligence, trust (both in the team leader and in the team members and team commitment. It was conducted within six manufacturing plants, with 25 teams participating. Of the 320 surveys distributed to these teams, 178 were received (which equals a 56% response rate. The surveys consisted of the multi-factor leadership questionnaire (MLQ, the Swinburne University emotional intelligence test (SUEIT, the organisational-commitment scale (OCS (adapted for team commitment and the workplace trust survey (WTS. The validity of these scales was established using exploratory factor analysis (EFA and confrmatory factor analysis (CFA. The Cronbach alpha was used to assess the reliability of the scales. The model was tested using structural equation modelling (SEM; an acceptable level of model ft was found. Signifcant positive relationships were further found among all the constructs. Such an integrated model has not been tested in a team context before and the positive fndings therefore add to existing teamwork literature. The fnding that transformational leadership and leader emotional intelligence are positively related to team commitment and trust further emphasises the importance of effective leadership behaviour in team dynamics and performance.

  9. Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

  10. Analysis of traversable pits model to make intelligent wheeled vehicles

    Directory of Open Access Journals (Sweden)

    F. Abbasi

    2017-11-01

    Full Text Available In this paper, the issue of passing wheeled vehicles from pits is discussed. The issue is modeled by defining the limits of passing wheeled vehicles. The proposed model has been studied based on changes in the effective parameters. Finally, in order to describe the problem, the proposed model has been solved for wheeled vehicles based on the effective parameters by using one of the numerical methods.

  11. A Heat Dynamic Model for Intelligent Heating of Buildings

    DEFF Research Database (Denmark)

    Thavlov, Anders; Bindner, Henrik W.

    2015-01-01

    This article presents a heat dynamic model for prediction of the indoor temperature in an office building. The model has been used in several flexible load applications, where the indoor temperature is allowed to vary around a given reference to provide power system services by shifting the heating...... of the building in time. This way the thermal mass of the building can be used to absorb energy from renewable energy source when available and postpone heating in periods with lack of renewable energy generation. The model is used in a model predictive controller to ensure the residential comfort over a given...

  12. Artificial intelligence techniques for modeling database user behavior

    Science.gov (United States)

    Tanner, Steve; Graves, Sara J.

    1990-01-01

    The design and development of the adaptive modeling system is described. This system models how a user accesses a relational database management system in order to improve its performance by discovering use access patterns. In the current system, these patterns are used to improve the user interface and may be used to speed data retrieval, support query optimization and support a more flexible data representation. The system models both syntactic and semantic information about the user's access and employs both procedural and rule-based logic to manipulate the model.

  13. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2014-01-01

    Full Text Available Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

  14. A methodology for the design of experiments in computational intelligence with multiple regression models.

    Science.gov (United States)

    Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

    2016-01-01

    The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

  15. The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model.

    Science.gov (United States)

    Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei

    2017-06-01

    We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers.

  16. A methodology for the design of experiments in computational intelligence with multiple regression models

    Directory of Open Access Journals (Sweden)

    Carlos Fernandez-Lozano

    2016-12-01

    Full Text Available The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

  17. An Intelligent System for Modelling, Design and Analysis of Chemical Processes

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    ICAS, Integrated Computer Aided System, is a software that consists of a number of intelligent tools, which are very suitable, among others, for computer aided modelling, sustainable design of chemical and biochemical processes, and design-analysis of product-process monitoring systems. Each...... of these tools are characterized by a framework that follows an established work-flow and data-flow, developed to guide the user through the many steps of the problem solution process. At each, the specific tool knows which data, model and/or algorithm to use. The tool also provides analysis of the calculated...... results so that the user can make intelligent decisions to proceed to the next step. The tools contain in-house databases, especially designed to work in an integrated manner with tool specific ontology for efficient knowledge management. Examples highlighting the use of the tools willl be given, where...

  18. Application of artificial intelligence models in water quality forecasting.

    Science.gov (United States)

    Yeon, I S; Kim, J H; Jun, K W

    2008-06-01

    The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error.

  19. Conceptual Modeling for the Design of Intelligent and Emergent Information Systems

    OpenAIRE

    Loucopoulos, Pericles; Fayoumi, Amjad

    2016-01-01

    A key requirement to today’s fast changing economic environment is the ability of organizations to adapt dynamically in an effective and efficient manner. Information and Communication Technologies play a crucially important role in addressing such adaptation requirements. The notion of ‘intelligent software’ has emerged as a means by which enterprises can respond to changes in a reactive manner but also to explore, in a pro-active manner, possibilities for new business models. The developmen...

  20. Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

    Science.gov (United States)

    Krishnan, G. S.

    1997-01-01

    A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

  1. SmartWeld/SmartProcess - intelligent model based system for the design and validation of welding processes

    Energy Technology Data Exchange (ETDEWEB)

    Mitchner, J.

    1996-04-01

    Diagrams are presented on an intelligent model based system for the design and validation of welding processes. Key capabilities identified include `right the first time` manufacturing, continuous improvement, and on-line quality assurance.

  2. Intelligence: Real or artificial?

    Science.gov (United States)

    Schlinger, Henry D.

    1992-01-01

    Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selectionist model instead. Such a strategic change should lead them to the science of behavior analysis. PMID:22477051

  3. Comparison of two intelligent models to estimate the instantaneous ...

    Indian Academy of Sciences (India)

    ... they are 85.46 (w/m2), 3.08 (w/m2) and 5.41, respectively. As the results indicate, both models are able to estimate the amount of radiation well, while the neural network has a higher accuracy. The output of the modes for six other cities of Iran, with similar climate conditions, also proves the ability of the proposed models.

  4. Modeling and Control of Multivariable Process Using Intelligent Techniques

    Directory of Open Access Journals (Sweden)

    Subathra Balasubramanian

    2010-10-01

    Full Text Available For nonlinear dynamic systems, the first principles based modeling and control is difficult to implement. In this study, a fuzzy controller and recurrent fuzzy controller are developed for MIMO process. Fuzzy logic controller is a model free controller designed based on the knowledge about the process. In fuzzy controller there are two types of rule-based fuzzy models are available: one the linguistic (Mamdani model and the other is Takagi–Sugeno model. Of these two, Takagi-Sugeno model (TS has attracted most attention. The fuzzy controller application is limited to static processes due to their feedforward structure. But, most of the real-time processes are dynamic and they require the history of input/output data. In order to store the past values a memory unit is needed, which is introduced by the recurrent structure. The proposed recurrent fuzzy structure is used to develop a controller for the two tank heating process. Both controllers are designed and implemented in a real time environment and their performance is compared.

  5. Model business intelligence system design of quality products by using data mining in R Bakery Company

    Science.gov (United States)

    Fitriana, R.; Saragih, J.; Luthfiana, N.

    2017-12-01

    R Bakery company is a company that produces bread every day. Products that produced in that company have many different types of bread. Products are made in the form of sweet bread and wheat bread which have different tastes for every types of bread. During the making process, there were defects in the products which the defective product turns into reject product. Types of defects that are produced include burnt, sodden bread and shapeless bread. To find out the information about the defects that have been produced then by applying a designed model business intelligence system to create database and data warehouse. By using model business Intelligence system, it will generate useful information such as how many defect that produced by each of the bakery products. To make it easier to obtain such information, it can be done by using data mining method which data that we get is deep explored. The method of data mining is using k-means clustering method. The results of this intelligence business model system are cluster 1 with little amount of defect, cluster 2 with medium amount of defect and cluster 3 with high amount of defect. From OLAP Cube method can be seen that the defect generated during the 7 months period of 96,744 pieces.

  6. A review on integration of artificial intelligence into water quality modelling.

    Science.gov (United States)

    Chau, Kwok-wing

    2006-07-01

    With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

  7. The implementation of multiple intelligences based teaching model to improve mathematical problem solving ability for student of junior high school

    Science.gov (United States)

    Fasni, Nurli; Fatimah, Siti; Yulanda, Syerli

    2017-05-01

    This research aims to achieve some purposes such as: to know whether mathematical problem solving ability of students who have learned mathematics using Multiple Intelligences based teaching model is higher than the student who have learned mathematics using cooperative learning; to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using Multiple Intelligences based teaching model., to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using cooperative learning; to know the attitude of the students to Multiple Intelligences based teaching model. The method employed here is quasi-experiment which is controlled by pre-test and post-test. The population of this research is all of VII grade in SMP Negeri 14 Bandung even-term 2013/2014, later on two classes of it were taken for the samples of this research. A class was taught using Multiple Intelligences based teaching model and the other one was taught using cooperative learning. The data of this research were gotten from the test in mathematical problem solving, scale questionnaire of the student attitudes, and observation. The results show the mathematical problem solving of the students who have learned mathematics using Multiple Intelligences based teaching model learning is higher than the student who have learned mathematics using cooperative learning, the mathematical problem solving ability of the student who have learned mathematics using cooperative learning and Multiple Intelligences based teaching model are in intermediate level, and the students showed the positive attitude in learning mathematics using Multiple Intelligences based teaching model. As for the recommendation for next author, Multiple Intelligences based teaching model can be tested on other subject and other ability.

  8. Forensic intelligence framework--Part I: Induction of a transversal model by comparing illicit drugs and false identity documents monitoring.

    Science.gov (United States)

    Morelato, Marie; Baechler, Simon; Ribaux, Olivier; Beavis, Alison; Tahtouh, Mark; Kirkbride, Paul; Roux, Claude; Margot, Pierre

    2014-03-01

    Forensic intelligence is a distinct dimension of forensic science. Forensic intelligence processes have mostly been developed to address either a specific type of trace or a specific problem. Even though these empirical developments have led to successes, they are trace-specific in nature and contribute to the generation of silos which hamper the establishment of a more general and transversal model. Forensic intelligence has shown some important perspectives but more general developments are required to address persistent challenges. This will ensure the progress of the discipline as well as its widespread implementation in the future. This paper demonstrates that the description of forensic intelligence processes, their architectures, and the methods for building them can, at a certain level, be abstracted from the type of traces considered. A comparative analysis is made between two forensic intelligence approaches developed independently in Australia and in Europe regarding the monitoring of apparently very different kind of problems: illicit drugs and false identity documents. An inductive effort is pursued to identify similarities and to outline a general model. Besides breaking barriers between apparently separate fields of study in forensic science and intelligence, this transversal model would assist in defining forensic intelligence, its role and place in policing, and in identifying its contributions and limitations. The model will facilitate the paradigm shift from the current case-by-case reactive attitude towards a proactive approach by serving as a guideline for the use of forensic case data in an intelligence-led perspective. A follow-up article will specifically address issues related to comparison processes, decision points and organisational issues regarding forensic intelligence (part II). Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  9. Intelligent Models Performance Improvement Based on Wavelet Algorithm and Logarithmic Transformations in Suspended Sediment Estimation

    Directory of Open Access Journals (Sweden)

    R. Hajiabadi

    2016-10-01

    Full Text Available Introduction One reason for the complexity of hydrological phenomena prediction, especially time series is existence of features such as trend, noise and high-frequency oscillations. These complex features, especially noise, can be detected or removed by preprocessing. Appropriate preprocessing causes estimation of these phenomena become easier. Preprocessing in the data driven models such as artificial neural network, gene expression programming, support vector machine, is more effective because the quality of data in these models is important. Present study, by considering diagnosing and data transformation as two different preprocessing, tries to improve the results of intelligent models. In this study two different intelligent models, Artificial Neural Network and Gene Expression Programming, are applied to estimation of daily suspended sediment load. Wavelet transforms and logarithmic transformation is used for diagnosing and data transformation, respectively. Finally, the impacts of preprocessing on the results of intelligent models are evaluated. Materials and Methods In this study, Gene Expression Programming and Artificial Neural Network are used as intelligent models for suspended sediment load estimation, then the impacts of diagnosing and logarithmic transformations approaches as data preprocessor are evaluated and compared to the result improvement. Two different logarithmic transforms are considered in this research, LN and LOG. Wavelet transformation is used to time series denoising. In order to denoising by wavelet transforms, first, time series can be decomposed at one level (Approximation part and detail part and second, high-frequency part (detail will be removed as noise. According to the ability of gene expression programming and artificial neural network to analysis nonlinear systems; daily values of suspended sediment load of the Skunk River in USA, during a 5-year period, are investigated and then estimated.4 years of

  10. Cognitive model of image interpretation for artificial intelligence applications

    International Nuclear Information System (INIS)

    Raju, S.

    1988-01-01

    A cognitive model of imaging diagnosis was devised to aid in the development of expert systems that assist in the interpretation of diagnostic images. In this cognitive model, a small set of observations that are strongly predictive of a particular diagnosis lead to a search for other observations that would support this diagnosis but are not necessarily specific for it. Then a set of alternative diagnoses is considered. This is followed by a search for observations that might allow differentiation of the primary diagnostic consideration from the alternatives. The production rules needed to implement this model can be classified into three major categories, each of which have certain general characteristics. Knowledge of these characteristics simplifies the development of these expert systems

  11. AN INTELLIGENT HYBRID NEURAL NETWORK MODEL IN RENEWABLE ENERGY SYSTEMS

    Directory of Open Access Journals (Sweden)

    K. Gnana Sheela

    2012-07-01

    Full Text Available This paper presents a hybrid neural network approach to predict wind speed automatically in renewable energy systems. Wind energy is one of the renewable energy systems with lowest cost of production of electricity with largest resources available. By the reason of the fluctuation and volatility in wind, the wind speed prediction provides the challenges in the stability of renewable energy system. The aim is to compute predicted wind speed based on hybrid model which integrates a Self Organizing Map (SOM and Back propagation (BP neural network. The simulation result shows that the proposed approach provides significant result of wind speed prediction with less error rates. Due to seasonality, single computing models have some disadvantages such as fluctuality, randomness and unstable. These disadvantages are rectified by using hybrid computing neural network models. Wind speed prediction is an important in the field of wind power plants.

  12. Programming Models and Tools for Intelligent Embedded Systems

    DEFF Research Database (Denmark)

    Sørensen, Peter Verner Bojsen

    Design automation and analysis tools targeting embedded platforms, developed using a component-based design approach, must be able to reason about the capabilities of the platforms. In the general case where nothing is assumed about the components comprising a platform or the platform topology......, analysis must be employed to determine its capabilities. This kind of analysis is the subject of this dissertation. The main contribution of this work is the Service Relation Model used to describe and analyze the flow of service in models of platforms and systems composed of re-usable components...

  13. Intelligence Essentials for Everyone

    Science.gov (United States)

    1999-06-01

    the IC adopt this Joint Intelli- gence Virtual Architecture model to take advantage of technological developments, reduce bureau- cratic barriers, and...intelligence. For example, major U.S. auto makers purchase their competitors’ models as soon as they appear in the showrooms . The new cars are taken...devised the Joint Intelligence Virtual Architecture (JIVA) concept to accelerate and streamline the entire intelligence process. Under JIVA, intelligence

  14. John Carroll’s Views on Intelligence: Bi-Factor vs. Higher-Order Models

    Directory of Open Access Journals (Sweden)

    A. Alexander Beaujean

    2015-10-01

    Full Text Available The development of factor models is inextricably tied to the history of intelligence research. One of the most commonly-cited scholars in the field is John Carroll, whose three-stratum theory of cognitive ability has been one of the most influential models of cognitive ability in the past 20 years. Nonetheless, there is disagreement about how Carroll conceptualized the factors in his model. Some argue that his model is best represented through a higher-order model, while others argue that a bi-factor model is a better representation. Carroll was explicit about what he perceived the best way to represent his model, but his writings are not always easy to understand. In this article, I clarify his position by first describing the details and implications of bi-factor and higher-order models then show that Carroll’s published views are better represented by a bi-factor model.

  15. Advances in Games Technology: Software, Models, and Intelligence

    Science.gov (United States)

    Prakash, Edmond; Brindle, Geoff; Jones, Kevin; Zhou, Suiping; Chaudhari, Narendra S.; Wong, Kok-Wai

    2009-01-01

    Games technology has undergone tremendous development. In this article, the authors report the rapid advancement that has been observed in the way games software is being developed, as well as in the development of games content using game engines. One area that has gained special attention is modeling the game environment such as terrain and…

  16. An artificial intelligence tool for complex age-depth models

    Science.gov (United States)

    Bradley, E.; Anderson, K. A.; de Vesine, L. R.; Lai, V.; Thomas, M.; Nelson, T. H.; Weiss, I.; White, J. W. C.

    2017-12-01

    CSciBox is an integrated software system for age modeling of paleoenvironmental records. It incorporates an array of data-processing and visualization facilities, ranging from 14C calibrations to sophisticated interpolation tools. Using CSciBox's GUI, a scientist can build custom analysis pipelines by composing these built-in components or adding new ones. Alternatively, she can employ CSciBox's automated reasoning engine, Hobbes, which uses AI techniques to perform an in-depth, autonomous exploration of the space of possible age-depth models and presents the results—both the models and the reasoning that was used in constructing and evaluating them—to the user for her inspection. Hobbes accomplishes this using a rulebase that captures the knowledge of expert geoscientists, which was collected over the course of more than 100 hours of interviews. It works by using these rules to generate arguments for and against different age-depth model choices for a given core. Given a marine-sediment record containing uncalibrated 14C dates, for instance, Hobbes tries CALIB-style calibrations using a choice of IntCal curves, with reservoir age correction values chosen from the 14CHRONO database using the lat/long information provided with the core, and finally composes the resulting age points into a full age model using different interpolation methods. It evaluates each model—e.g., looking for outliers or reversals—and uses that information to guide the next steps of its exploration, and presents the results to the user in human-readable form. The most powerful of CSciBox's built-in interpolation methods is BACON, a Bayesian sedimentation-rate algorithm—a powerful but complex tool that can be difficult to use. Hobbes adjusts BACON's many parameters autonomously to match the age model to the expectations of expert geoscientists, as captured in its rulebase. It then checks the model against the data and iteratively re-calculates until it is a good fit to the data.

  17. A Suitable Artificial Intelligence Model for Inventory Level Optimization

    Directory of Open Access Journals (Sweden)

    Tereza Sustrova

    2016-05-01

    Full Text Available Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings: Artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction. Conclusions: It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise. Conclusions resulting from the paper are beneficial for further research.

  18. Model Data Warehouse dan Business Intelligence untuk Meningkatkan Penjualan pada PT. S

    Directory of Open Access Journals (Sweden)

    Rudy Rudy

    2011-06-01

    Full Text Available Today a lot of companies use information system in every business activity. Every transaction is stored electronically in the database transaction. The transactional database does not help much to assist the executives in making strategic decisions to improve the company competitiveness. The objective of this research is to analyze the operational database system and the information needed by the management to design a data warehouse model which fits the executive information needs in PT. S. The research method uses the Nine-Step Methodology data warehouse design by Ralph Kimball. The result is a data warehouse featuring business intelligence applications to display information of historical data in tables, graphs, pivot tables, and dashboards and has several points of view for the management. This research concludes that a data warehouse which combines multiple database transactions with business intelligence application can help executives to understand the reports in order to accelerate decision-making processes. 

  19. A multi-resolution envelope-power based model for speech intelligibility

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Ewert, Stephan D.; Dau, Torsten

    2013-01-01

    The speech-based envelope power spectrum model (sEPSM) presented by Jørgensen and Dau [(2011). J. Acoust. Soc. Am. 130, 1475-1487] estimates the envelope power signal-to-noise ratio (SNRenv) after modulation-frequency selective processing. Changes in this metric were shown to account well...... to conditions with stationary interferers, due to the long-term integration of the envelope power, and cannot account for increased intelligibility typically obtained with fluctuating maskers. Here, a multi-resolution version of the sEPSM is presented where the SNRenv is estimated in temporal segments...... with a modulation-filter dependent duration. The multi-resolution sEPSM is demonstrated to account for intelligibility obtained in conditions with stationary and fluctuating interferers, and noisy speech distorted by reverberation or spectral subtraction. The results support the hypothesis that the SNRenv...

  20. Design and Optimization of Intelligent Service Robot Suspension System Using Dynamic Model

    International Nuclear Information System (INIS)

    Choi, Seong Hoon; Park, Tae Won; Lee, Soo Ho; Jung, Sung Pil; Jun, Kab Jin; Yoon, J. W.

    2010-01-01

    Recently, an intelligent service robot is being developed for use in guiding and providing information to visitors about the building at public institutions. The intelligent robot has a sensor at the bottom to recognize its location. Four wheels, which are arranged in the form of a lozenge, support the robot. This robot cannot be operated on uneven ground because its driving parts are attached to its main body that contains the important internal components. Continuous impact with the ground can change the precise positions of the components and weaken the connection between each structural part. In this paper, the design of the suspension system for such a robot is described. The dynamic model of the robot is created, and the driving characteristics of the robot with the designed suspension system are simulated. Additionally, the suspension system is optimized to reduce the impact for the robot components

  1. Complex system modelling and control through intelligent soft computations

    CERN Document Server

    Azar, Ahmad

    2015-01-01

    The book offers a snapshot of the theories and applications of soft computing in the area of complex systems modeling and control. It presents the most important findings discussed during the 5th International Conference on Modelling, Identification and Control, held in Cairo, from August 31-September 2, 2013. The book consists of twenty-nine selected contributions, which have been thoroughly reviewed and extended before their inclusion in the volume. The different chapters, written by active researchers in the field, report on both current theories and important applications of soft-computing. Besides providing the readers with soft-computing fundamentals, and soft-computing based inductive methodologies/algorithms, the book also discusses key industrial soft-computing applications, as well as multidisciplinary solutions developed for a variety of purposes, like windup control, waste management, security issues, biomedical applications and many others. It is a perfect reference guide for graduate students, r...

  2. Intelligent harmonic load model based on neural networks

    Science.gov (United States)

    Ji, Pyeong-Shik; Lee, Dae-Jong; Lee, Jong-Pil; Park, Jae-Won; Lim, Jae-Yoon

    2007-12-01

    In this study, we developed a RBFNs(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method implemented by using harmonic information as well as fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. The RBFNs have certain advantage such as simple structure and rapid computation ability compared with multilayer perceptron which is extensively applied for load modeling. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynominal 2nd equation method, MLP and RBF without considering harmonic components.

  3. Estimation of zero-intelligence models by L1 data

    Czech Academy of Sciences Publication Activity Database

    Šmíd, Martin

    2016-01-01

    Roč. 16, č. 9 (2016), s. 1423-1444 ISSN 1469-7688 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : Limit Order Market * Stochastic Models * Econometric Methods Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.960, year: 2016 http://library.utia.cas.cz/separaty/2016/E/smid-0458944.pdf

  4. Modeling and Simulation of a DG-Integrated Intelligent Microgrid

    Science.gov (United States)

    2010-02-01

    PMSG : Permanent Magnet Synchronous Generator PLL : Phase Lock Loop PV : Photovoltaic PWM : Pulse Width Modulation TOU : Time of Use VTES...during the synchronization of the microgrid to the main grid...........42   Fig. 33. DER output (kW) to serve critical and non-critical loads...model Solar Photovoltaic (PV) is a technology that converts sunlight directly into electrical energy. The output is direct current. The major

  5. Artificial Intelligence for Constructing Accurate, Low-Cost Models and

    Science.gov (United States)

    2005-01-01

    models are a virtual representation of a robotic car constructed from the Lego ™ MindStorms ® Robotics Invention System. The baseline vehicle is shown in...sampled using a Lego ® RCX computer coupled with a high accuracy voltage measuring interface from Lego Dacta®. Data were captured using LabVIEW® Software... Lego ® RCX computer coupled with a high accuracy voltage measuring interface from Lego Dacta®. Data were captured using LabVIEW® Software from

  6. A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

    Science.gov (United States)

    Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

    2017-01-01

    A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

  7. A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

    2014-01-01

    Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

  8. Intelligent systems

    CERN Document Server

    Irwin, J David

    2011-01-01

    Technology has now progressed to the point that intelligent systems are replacing humans in the decision making processes as well as aiding in the solution of very complex problems. In many cases intelligent systems are already outperforming human activities. Artificial neural networks are not only capable of learning how to classify patterns, such images or sequence of events, but they can also effectively model complex nonlinear systems. Their ability to classify sequences of events is probably more popular in industrial applications where there is an inherent need to model nonlinear system

  9. Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method

    CERN Document Server

    Quiza, Ramón; Davim, J Paulo

    2012-01-01

    Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

  10. Use of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shock.

    Science.gov (United States)

    Glass, Todd F; Knapp, Jason; Amburn, Philip; Clay, Bruce A; Kabrisky, Matt; Rogers, Steven K; Garcia, Victor F

    2004-02-01

    To determine whether a prototype artificial intelligence system can identify volume of hemorrhage in a porcine model of controlled hemorrhagic shock. Prospective in vivo animal model of hemorrhagic shock. Research foundation animal surgical suite; computer laboratories of collaborating industry partner. Nineteen, juvenile, 25- to 35-kg, male and female swine. Anesthetized animals were instrumented for arterial and systemic venous pressure monitoring and blood sampling, and a splenectomy was performed. Following a 1-hr stabilization period, animals were hemorrhaged in aliquots to 10, 20, 30, 35, 40, 45, and 50% of total blood volume with a 10-min recovery between each aliquot. Data were downloaded directly from a commercial monitoring system into a proprietary PC-based software package for analysis. Arterial and venous blood gas values, glucose, and cardiac output were collected at specified intervals. Electrocardiogram, electroencephalogram, mixed venous oxygen saturation, temperature (core and blood), mean arterial pressure, pulmonary artery pressure, central venous pressure, pulse oximetry, and end-tidal CO(2) were continuously monitored and downloaded. Seventeen of 19 animals (89%) died as a direct result of hemorrhage. Stored data streams were analyzed by the prototype artificial intelligence system. For this project, the artificial intelligence system identified and compared three electrocardiographic features (R-R interval, QRS amplitude, and R-S interval) from each of nine unknown samples of the QRS complex. We found that the artificial intelligence system, trained on only three electrocardiographic features, identified hemorrhage volume with an average accuracy of 91% (95% confidence interval, 84-96%). These experiments demonstrate that an artificial intelligence system, based solely on the analysis of QRS amplitude, R-R interval, and R-S interval of an electrocardiogram, is able to accurately identify hemorrhage volume in a porcine model of lethal

  11. Advanced intelligent systems

    CERN Document Server

    Ryoo, Young; Jang, Moon-soo; Bae, Young-Chul

    2014-01-01

    Intelligent systems have been initiated with the attempt to imitate the human brain. People wish to let machines perform intelligent works. Many techniques of intelligent systems are based on artificial intelligence. According to changing and novel requirements, the advanced intelligent systems cover a wide spectrum: big data processing, intelligent control, advanced robotics, artificial intelligence and machine learning. This book focuses on coordinating intelligent systems with highly integrated and foundationally functional components. The book consists of 19 contributions that features social network-based recommender systems, application of fuzzy enforcement, energy visualization, ultrasonic muscular thickness measurement, regional analysis and predictive modeling, analysis of 3D polygon data, blood pressure estimation system, fuzzy human model, fuzzy ultrasonic imaging method, ultrasonic mobile smart technology, pseudo-normal image synthesis, subspace classifier, mobile object tracking, standing-up moti...

  12. Forecasting the natural gas demand in China using a self-adapting intelligent grey model

    International Nuclear Information System (INIS)

    Zeng, Bo; Li, Chuan

    2016-01-01

    Reasonably forecasting demands of natural gas in China is of significance as it could aid Chinese government in formulating energy policies and adjusting industrial structures. To this end, a self-adapting intelligent grey prediction model is proposed in this paper. Compared with conventional grey models which have the inherent drawbacks of fixed structure and poor adaptability, the proposed new model can automatically optimize model parameters according to the real data characteristics of modeling sequence. In this study, the proposed new model, discrete grey model, even difference grey model and classical grey model were employed, respectively, to simulate China's natural gas demands during 2002–2010 and forecast demands during 2011–2014. The results show the new model has the best simulative and predictive precision. Finally, the new model is used to forecast China's natural gas demand during 2015–2020. The forecast shows the demand will grow rapidly over the next six years. Therefore, in order to maintain the balance between the supplies and the demands for the natural gas in the future, Chinese government needs to take some measures, such as importing huge amounts of natural gas from abroad, increasing the domestic yield, using more alternative energy, and reducing the industrial reliance on natural gas. - Highlights: • A self-adapting intelligent grey prediction model (SIGM) is proposed in this paper. • The SIGM has the advantage of working with exponential functions and linear functions. • The SIGM solves the drawbacks of fixed structure and poor adaptability of grey models. • The demand of natural gas in China is successfully forecasted using the SIGM model. • The study findings can help Chinese government reasonably formulate energy policies.

  13. Big cats as a model system for the study of the evolution of intelligence.

    Science.gov (United States)

    Borrego, Natalia

    2017-08-01

    Currently, carnivores, and felids in particular, are vastly underrepresented in cognitive literature, despite being an ideal model system for tests of social and ecological intelligence hypotheses. Within Felidae, big cats (Panthera) are uniquely suited to studies investigating the evolutionary links between social, ecological, and cognitive complexity. Intelligence likely did not evolve in a unitary way but instead evolved as the result of mutually reinforcing feedback loops within the physical and social environments. The domain-specific social intelligence hypothesis proposes that social complexity drives only the evolution of cognitive abilities adapted only to social domains. The domain-general hypothesis proposes that the unique demands of social life serve as a bootstrap for the evolution of superior general cognition. Big cats are one of the few systems in which we can directly address conflicting predictions of the domain-general and domain-specific hypothesis by comparing cognition among closely related species that face roughly equivalent ecological complexity but vary considerably in social complexity. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Hybrid Modeling for Testing Intelligent Software for Lunar-Mars Closed Life Support

    Science.gov (United States)

    Malin, Jane T.; Nicholson, Leonard S. (Technical Monitor)

    1999-01-01

    Intelligent software is being developed for closed life support systems with biological components, for human exploration of the Moon and Mars. The intelligent software functions include planning/scheduling, reactive discrete control and sequencing, management of continuous control, and fault detection, diagnosis, and management of failures and errors. Four types of modeling information have been essential to system modeling and simulation to develop and test the software and to provide operational model-based what-if analyses: discrete component operational and failure modes; continuous dynamic performance within component modes, modeled qualitatively or quantitatively; configuration of flows and power among components in the system; and operations activities and scenarios. CONFIG, a multi-purpose discrete event simulation tool that integrates all four types of models for use throughout the engineering and operations life cycle, has been used to model components and systems involved in the production and transfer of oxygen and carbon dioxide in a plant-growth chamber and between that chamber and a habitation chamber with physicochemical systems for gas processing.

  15. A review on the integration of artificial intelligence into coastal modeling.

    Science.gov (United States)

    Chau, Kwokwing

    2006-07-01

    With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

  16. Role of theory of mind and executive function in explaining social intelligence: a structural equation modeling approach.

    Science.gov (United States)

    Yeh, Zai-Ting

    2013-01-01

    Social intelligence is the ability to understand others and the social context effectively and thus to interact with people successfully. Research has suggested that the theory of mind (ToM) and executive function may play important roles in explaining social intelligence. The specific aim of the present study was to test with structural equation modeling (SEM) the hypothesis that performance on ToM tasks is more associated with social intelligence in the elderly than is performance on executive functions. One hundred and seventy-seven participants (age 56-96) completed ToM, executive function, and other basic cognition tasks, and were rated with social intelligence scales. The SEM results showed that ToM and executive function were strongly correlated (0.54); however, only the path coefficient from ToM to social intelligence, and not from executive function, was significant (0.37). ToM performance, but not executive function, was strongly correlated with social intelligence among elderly individuals. ToM and executive function might play different roles in social behavior during normal aging; however, based on the present results, it is possible that ToM might play an important role in social intelligence.

  17. Addressing diverse learner preferences and intelligences with emerging technologies: Matching models to online opportunities

    Directory of Open Access Journals (Sweden)

    Ke Zhang

    2009-03-01

    Full Text Available This paper critically reviews various learning preferences and human intelligence theories and models with a particular focus on the implications for online learning. It highlights a few key models, Gardner’s multiple intelligences, Fleming and Mills’ VARK model, Honey and Mumford’s Learning Styles, and Kolb’s Experiential Learning Model, and attempts to link them to trends and opportunities in online learning with emerging technologies. By intersecting such models with online technologies, it offers instructors and instructional designers across educational sectors and situations new ways to think about addressing diverse learner needs, backgrounds, and expectations. Learning technologies are important for effective teaching, as are theories and models and theories of learning. We argue that more immense power can be derived from connections between the theories, models and learning technologies. Résumé : Cet article passe en revue de manière critique les divers modèles et théories sur les préférences d’apprentissage et l’intelligence humaine, avec un accent particulier sur les implications qui en découlent pour l’apprentissage en ligne. L’article présente quelques-uns des principaux modèles (les intelligences multiples de Gardner, le modèle VAK de Fleming et Mills, les styles d’apprentissage de Honey et Mumford et le modèle d’apprentissage expérientiel de Kolb et tente de les relier à des tendances et occasions d’apprentissage en ligne qui utilisent les nouvelles technologies. En croisant ces modèles avec les technologies Web, les instructeurs et concepteurs pédagogiques dans les secteurs de l’éducation ou en situation éducationnelle se voient offrir de nouvelles façons de tenir compte des divers besoins, horizons et attentes des apprenants. Les technologies d’apprentissage sont importantes pour un enseignement efficace, tout comme les théories et les modèles d’apprentissage. Nous sommes d

  18. Research on application of intelligent computation based LUCC model in urbanization process

    Science.gov (United States)

    Chen, Zemin

    2007-06-01

    Global change study is an interdisciplinary and comprehensive research activity with international cooperation, arising in 1980s, with the largest scopes. The interaction between land use and cover change, as a research field with the crossing of natural science and social science, has become one of core subjects of global change study as well as the front edge and hot point of it. It is necessary to develop research on land use and cover change in urbanization process and build an analog model of urbanization to carry out description, simulation and analysis on dynamic behaviors in urban development change as well as to understand basic characteristics and rules of urbanization process. This has positive practical and theoretical significance for formulating urban and regional sustainable development strategy. The effect of urbanization on land use and cover change is mainly embodied in the change of quantity structure and space structure of urban space, and LUCC model in urbanization process has been an important research subject of urban geography and urban planning. In this paper, based upon previous research achievements, the writer systematically analyzes the research on land use/cover change in urbanization process with the theories of complexity science research and intelligent computation; builds a model for simulating and forecasting dynamic evolution of urban land use and cover change, on the basis of cellular automation model of complexity science research method and multi-agent theory; expands Markov model, traditional CA model and Agent model, introduces complexity science research theory and intelligent computation theory into LUCC research model to build intelligent computation-based LUCC model for analog research on land use and cover change in urbanization research, and performs case research. The concrete contents are as follows: 1. Complexity of LUCC research in urbanization process. Analyze urbanization process in combination with the contents

  19. Methods and Technologies of XML Data Modeling for IP Mode Intelligent Measuring and Controlling System

    International Nuclear Information System (INIS)

    Liu, G X; Hong, X B; Liu, J G

    2006-01-01

    This paper presents the IP mode intelligent measuring and controlling system (IMIMCS). Based on object-oriented modeling technology of UML and XML Schema, the innovative methods and technologies of some key problems for XML data modeling in the IMIMCS were especially discussed, including refinement for systemic business by means of use-case diagram of UML, the confirmation of the content of XML data model and logic relationship of the objects of XML Schema with the aid of class diagram of UML, the mapping rules from the UML object model to XML Schema. Finally, the application of the IMIMCS based on XML for a modern greenhouse was presented. The results show that the modeling methods of the measuring and controlling data in the IMIMCS involving the multi-layer structure and many operating systems process strong reliability and flexibility, guarantee uniformity of complex XML documents and meet the requirement of data communication across platform

  20. Artificial intelligence based models for stream-flow forecasting: 2000-2015

    Science.gov (United States)

    Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

    2015-11-01

    The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

  1. Goleman-Boyatzis Model of Emotional Intelligence for Dealing with Problems in Project Management

    Directory of Open Access Journals (Sweden)

    Peter Vincent Livesey

    2017-03-01

    Full Text Available As projects grow in size and complexity the sizes of teams needed to manage them also increases. This places greater emphasis on the need for the project manager to develop people management skills, commonly called soft skills, of which emotional intelligence (EI has been recognised as an important component. The objective of this research was to investigate the relevance of the Goleman-Boyatzis model of EI in dealing with the problems in large projects identified via a literature review. To achieve this end, a Delphi study using project managers who had been involved in the management of projects in excess of $500 million was used. The responses from the Delphi panel were analysed and the results showed that the competencies contained in the Goleman-Boyatzis model had a relevance of 95% or greater to the problems presented to the panel. A ranking of the various competencies contained within the model was also developed, some competencies being found to be more important than others. By confirming the importance of emotional intelligence, as described by the model, this research adds to the understanding of the necessary skills needed by a project manager to successfully manage large projects.

  2. Intelligent semantic interoperability: Integrating knowledge, terminology and information models to support stroke care.

    Science.gov (United States)

    Goossen, William T F

    2006-01-01

    Electronic patient record (EPR) systems for the continuity of care for stroke patient are under development. These systems are based on standards such as for clinical practice, vocabularies, and the HL7 information model. In order to achieve intelligent semantic interoperability, knowledge about evidence based patient care, vocabulary and information models need to be integrated. A format was developed in which the clinical knowledge, clinical terminology, and standard information models are integrated as specification for the technical implementation of electronic health systems and electronic messages. This format is verified by clinicians and technicians. The document structure consists of meta-information such as version control and changes, purpose of the clinical content, evidence from the literature, variables and values, terminology used, guidelines for application and interpretation, HL7 message models, coding, and technical data specification. Further, XML message excerpts, archetypes and screen designs are developed from these documents to facilitate implementation. The combination of these aspects in one document creates valuable content for intelligent semantic interoperability by means of development of messages and systems.

  3. Optimizing bi-objective, multi-echelon supply chain model using particle swarm intelligence algorithm

    Science.gov (United States)

    Sathish Kumar, V. R.; Anbuudayasankar, S. P.; Rameshkumar, K.

    2018-02-01

    In the current globalized scenario, business organizations are more dependent on cost effective supply chain to enhance profitability and better handle competition. Demand uncertainty is an important factor in success or failure of a supply chain. An efficient supply chain limits the stock held at all echelons to the extent of avoiding a stock-out situation. In this paper, a three echelon supply chain model consisting of supplier, manufacturing plant and market is developed and the same is optimized using particle swarm intelligence algorithm.

  4. Multiple Intelligences and quotient spaces

    OpenAIRE

    Malatesta, Mike; Quintana, Yamilet

    2006-01-01

    The Multiple Intelligence Theory (MI) is one of the models that study and describe the cognitive abilities of an individual. In [7] is presented a referential system which allows to identify the Multiple Intelligences of the students of a course and to classify the level of development of such Intelligences. Following this tendency, the purpose of this paper is to describe the model of Multiple Intelligences as a quotient space, and also to study the Multiple Intelligences of an individual in...

  5. An extension of the technology acceptance model for business intelligence systems: project management maturity perspective

    Directory of Open Access Journals (Sweden)

    Mirjana Pejić Bach

    2017-01-01

    Full Text Available Business intelligence systems (BISs refer to wide range of technologies and applications useful for retrieving and analyzing the large amount of information with the goal to generate knowledge useful for making effective business decision. In order to investigate adoption of BISs in companies, we propose a model based on the technology acceptance model (TAM that is expanded by variables representing the concept of a project management maturity (PMM. The survey on the sample of USA companies has been conducted with the chief information officer (CIO as the main informant. Structural equations model has been developed in order to test the research model. Results indicate that TAM expanded with the notion of PMM is useful in increasing understanding of BISs adoption in companies.

  6. Modeling an Optical and Infrared Search for Extraterrestrial Intelligence Survey with Exoplanet Direct Imaging

    Science.gov (United States)

    Vides, Christina; Macintosh, Bruce; Ruffio, Jean-Baptiste; Nielsen, Eric; Povich, Matthew Samuel

    2018-01-01

    Gemini Planet Imager (GPI) is a direct high contrast imaging instrument coupled to the Gemini South Telescope. Its purpose is to image extrasolar planets around young (~Intelligence), we modeled GPI’s capabilities to detect an extraterrestrial continuous wave (CW) laser broadcasted within the H-band have been modeled. By using sensitivity evaluated for actual GPI observations of young target stars, we produced models of the CW laser power as a function of distance from the star that could be detected if GPI were to observe nearby (~ 3-5 pc) planet-hosting G-type stars. We took a variety of transmitters into consideration in producing these modeled values. GPI is known to be sensitive to both pulsed and CW coherent electromagnetic radiation. The results were compared to similar studies and it was found that these values are competitive to other optical and infrared observations.

  7. Prediction of pork loin quality using online computer vision system and artificial intelligence model.

    Science.gov (United States)

    Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

    2018-06-01

    The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. Advanced Emergency Braking Control Based on a Nonlinear Model Predictive Algorithm for Intelligent Vehicles

    Directory of Open Access Journals (Sweden)

    Ronghui Zhang

    2017-05-01

    Full Text Available Focusing on safety, comfort and with an overall aim of the comprehensive improvement of a vision-based intelligent vehicle, a novel Advanced Emergency Braking System (AEBS is proposed based on Nonlinear Model Predictive Algorithm. Considering the nonlinearities of vehicle dynamics, a vision-based longitudinal vehicle dynamics model is established. On account of the nonlinear coupling characteristics of the driver, surroundings, and vehicle itself, a hierarchical control structure is proposed to decouple and coordinate the system. To avoid or reduce the collision risk between the intelligent vehicle and collision objects, a coordinated cost function of tracking safety, comfort, and fuel economy is formulated. Based on the terminal constraints of stable tracking, a multi-objective optimization controller is proposed using the theory of non-linear model predictive control. To quickly and precisely track control target in a finite time, an electronic brake controller for AEBS is designed based on the Nonsingular Fast Terminal Sliding Mode (NFTSM control theory. To validate the performance and advantages of the proposed algorithm, simulations are implemented. According to the simulation results, the proposed algorithm has better integrated performance in reducing the collision risk and improving the driving comfort and fuel economy of the smart car compared with the existing single AEBS.

  9. Process Materials Scientific Data for Intelligent Service Using a Dataspace Model

    Directory of Open Access Journals (Sweden)

    Yang Li

    2016-07-01

    Full Text Available Nowadays, materials scientific data come from lab experiments, simulations, individual archives, enterprise and internet in all scales and formats. The data flood has outpaced our capability to process, manage, analyze, and provide intelligent services. Extracting valuable information from the huge data ocean is necessary for improving the quality of domain services. The most acute information management challenges today stem from organizations relying on amounts of diverse, interrelated data sources, but having no way to manage the dataspaces in an integrated, user-demand driven and services convenient way. Thus, we proposed the model of Virtual DataSpace (VDS in materials science field to organize multi-source and heterogeneous data resources and offer services on the data in place without losing context information. First, the concept and theoretical analysis are described for the model. Then the methods for construction of the model is proposed based on users’ interests. Furthermore, the dynamic evolution algorithm of VDS is analyzed using the user feedback mechanism. Finally, we showed its efficiency for intelligent, real-time, on-demand services in the field of materials engineering.

  10. Artificial intelligence-based modeling and control of fluidized bed combustion

    Energy Technology Data Exchange (ETDEWEB)

    Ikonen, E.; Leppaekoski, K. (Univ. of Oulu, Dept. of Process and Environmental Engineering (Finland)). email: enso.ikonen@oulu.fi

    2009-07-01

    AI-inspired techniques have a lot to offer when developing methods for advanced identification, monitoring, control and optimization of industrial processes, such as power plants. Advanced control methods have been extensively examined in the research of the Power Plant Automation group at the Systems Engineering Laboratory, e.g., in fuel inventory modelling, combustion power control, modelling and control of flue gas oxygen, drum control, modelling and control of superheaters, or in optimization of flue-gas emissions. Most engineering approaches to artificial intelligence (AI) are characterized by two fundamental properties: the ability to learn from various sources and the ability to deal with plant complexity. Learning systems that are able to operate in uncertain environments based on incomplete information are commonly referred to as being intelligent. A number of other approaches exist, characterized by these properties, but not easily categorized as AI-systems. Advanced control methods (adaptive, predictive, multivariable, robust, etc.) are based on the availability of a model of the process to be controlled. Hence identification of processes becomes a key issue, leading to the use of adaptation and learning techniques. A typical learning control system concerns a selection of learning techniques applied for updating a process model, which in turn is used for the controller design. When design of learning control systems is complemented with concerns for dealing with uncertainties or vaguenesses in models, measurements, or even objectives, particularly close connections exist between advanced process control and methods of artificial intelligence and machine learning. Needs for advanced techniques are typically characterized by the desire to properly handle plant non-linearities, the multivariable nature of the dynamic problems, and the necessity to adapt to changing plant conditions. In the field of fluidized bed combustion (FBC) control, the many promising

  11. State and Local Intelligence Fusion Centers: An Evaluative Approach in Modeling a State Fusion Center

    National Research Council Canada - National Science Library

    Forsyth, William A

    2005-01-01

    .... Effective terrorism prevention, however, requires information and intelligence fusion as a cooperative process at all levels of government so that the flow of intelligence can be managed to support...

  12. Decay of Iconic Memory Traces Is Related to Psychometric Intelligence: A Fixed-Links Modeling Approach

    Science.gov (United States)

    Miller, Robert; Rammsayer, Thomas H.; Schweizer, Karl; Troche, Stefan J.

    2010-01-01

    Several memory processes have been examined regarding their relation to psychometric intelligence with the exception of sensory memory. This study examined the relation between decay of iconic memory traces, measured with a partial-report task, and psychometric intelligence, assessed with the Berlin Intelligence Structure test, in 111…

  13. Towards An Intelligent Model-Based Decision Support System For An Integrated Oil Company (EGPC)

    International Nuclear Information System (INIS)

    Khorshid, M.; Hassan, H.; Abdel Latife, M.A.

    2004-01-01

    Decision Support System (DSS) is an interactive, flexible and adaptable computer-based support system specially developed for supporting the solution of unstructured management problems [31] DSS has become widespread for oil industry domain in recent years. The computer-based DSS, which were developed and implemented in oil industry, are used to address the complex short-term planning and operational issues associated with downstream industry. Most of these applications concentrate on the data-centered tools, while the model-centered applications of DSS are still very limited up till now [20]. This study develops an Intelligent Model-Based DSS for an integrated oil company, to help policy makers and petroleum planner in improving the effectiveness of the strategic planning in oil sector. This domain basically imposes semi-structured or unstructured decisions and involves a very complex modeling process

  14. The use of emotional intelligence models in the assessment of management practices

    Directory of Open Access Journals (Sweden)

    L M Polyanova

    2014-12-01

    Full Text Available In the face of constant changes and instability, every organization feels an increasing demand to develop personal qualities of the staff for the effective functioning. As a result of the formation of intangible assets management, we have obtained methods for managing emotions as a part of the intellectual capital. The article discusses the basic models and techniques for measuring emotional intelligence of the head of a company, in particular, J. Mayer, D. Caruso and P. Salovey model of abilities (test MSCEIT V2.0 and mixed model (D. Lusin EmIn test and R. Bar-On EQ-i test, their advantages, limitations and heuristic value when applied to the study of organizations.

  15. Ontology, Epistemology, and Teleology for Modeling and Simulation Philosophical Foundations for Intelligent M&S Applications

    CERN Document Server

    2013-01-01

    In this book, internationally recognized experts in philosophy of science, computer science, and modeling and simulation are contributing to the discussion on how ontology, epistemology, and teleology will contribute to enable the next generation of intelligent modeling and simulation applications. It is well understood that a simulation can provide the technical means to display the behavior of a system over time, including following observed trends to predict future possible states, but how reliable and trustworthy are such predictions? The questions about what we can know (ontology), how we gain new knowledge (epistemology), and what we do with this knowledge (teleology) are therefore illuminated from these very different perspectives, as each experts uses a different facet to look at these challenges. The result of bringing these perspectives into one book is a challenging compendium that gives room for a spectrum of challenges: from general philosophy questions, such as can we use modeling and simulation...

  16. Transformational leadership and organizational citizenship behavior: Modeling emotional intelligence as mediator

    Directory of Open Access Journals (Sweden)

    Majeed Nauman

    2017-12-01

    Full Text Available Leadership and organizational citizenship behavior (OCB stayed at pinnacle in the arena of organizational behavior research since decades and has attained significant consideration of scholars pursuing to define multifaceted dynamics of leadership and their influence on follower’s behavior at work. The voluntary behavior of Organizational citizenship improves organizational effectiveness, and it goes beyond formal job duties. This study attempts to explore the association amongst transformational leadership and organizational citizenship behavior of teachers in public sector higher education institutions in Pakistan. Study of organizational citizenship behavior in educational organizations and academicians is of high value that definitely requires attention. This study examines the direct and indirect influence of transformational leadership through exploring the mediating role of emotional intelligence. The model was tested by employing structural equation modelling technique on survey responses collected from academicians. Results from 220 responses indicated that relationship between transformational leadership and Organizational Citizenship Behavior is statistically significant where Emotional Intelligence plays an important role as a mediator. The results support and add to the positive effects of transformational leadership style interconnected with extra role behavior at work making it more meaningful. The findings make a significant contribution to leadership and organizational behavior literature in higher education sector and propose that organizations should implement practices that help in enhancing the level of organizational citizenship behavior in organizations.

  17. Back Analysis of Rock Hydraulic Fracturing by Coupling Numerical Model and Computational Intelligent Technology

    Directory of Open Access Journals (Sweden)

    Shaojun Li

    2017-01-01

    Full Text Available Hydraulic fracturing is widely used to determine in situ stress of rock engineering. In this paper we propose a new method for simultaneously determining the in situ stress and elastic parameters of rock. The method utilizing the hydraulic fracturing numerical model and a computational intelligent method is proposed and verified. The hydraulic fracturing numerical model provides the samples which include borehole pressure, in situ stress, and elastic parameters. A computational intelligent method is applied in back analysis. A multioutput support vector machine is used to map the complex, nonlinear relationship between the in situ stress, elastic parameters, and borehole pressure. The artificial bee colony algorithm is applied in back analysis to find the optimal in situ stress and elastic parameters. The in situ stress is determined using the proposed method and the results are compared with those of the classic breakdown formula. The proposed method provides a good estimate of the relationship between the in situ stress and borehole pressure and predicts the maximum horizontal in situ stress with high precision while considering the influence of pore pressure without the need to estimate Biot’s coefficient and other parameters.

  18. Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios

    Science.gov (United States)

    Sanikhani, Hadi; Kisi, Ozgur; Maroufpoor, Eisa; Yaseen, Zaher Mundher

    2018-02-01

    The establishment of an accurate computational model for predicting reference evapotranspiration (ET0) process is highly essential for several agricultural and hydrological applications, especially for the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this research, six artificial intelligence (AI) models were investigated for modeling ET0 using a small number of climatic data generated from the minimum and maximum temperatures of the air and extraterrestrial radiation. The investigated models were multilayer perceptron (MLP), generalized regression neural networks (GRNN), radial basis neural networks (RBNN), integrated adaptive neuro-fuzzy inference systems with grid partitioning and subtractive clustering (ANFIS-GP and ANFIS-SC), and gene expression programming (GEP). The implemented monthly time scale data set was collected at the Antalya and Isparta stations which are located in the Mediterranean Region of Turkey. The Hargreaves-Samani (HS) equation and its calibrated version (CHS) were used to perform a verification analysis of the established AI models. The accuracy of validation was focused on multiple quantitative metrics, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R 2), coefficient of residual mass (CRM), and Nash-Sutcliffe efficiency coefficient (NS). The results of the conducted models were highly practical and reliable for the investigated case studies. At the Antalya station, the performance of the GEP and GRNN models was better than the other investigated models, while the performance of the RBNN and ANFIS-SC models was best compared to the other models at the Isparta station. Except for the MLP model, all the other investigated models presented a better performance accuracy compared to the HS and CHS empirical models when applied in a cross-station scenario. A cross-station scenario examination implies the

  19. Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

    Science.gov (United States)

    Kong, Changduk; Lim, Semyeong

    2011-12-01

    Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

  20. Machine listening intelligence

    Science.gov (United States)

    Cella, C. E.

    2017-05-01

    This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.

  1. An extended car-following model accounting for the average headway effect in intelligent transportation system

    Science.gov (United States)

    Kuang, Hua; Xu, Zhi-Peng; Li, Xing-Li; Lo, Siu-Ming

    2017-04-01

    In this paper, an extended car-following model is proposed to simulate traffic flow by considering average headway of preceding vehicles group in intelligent transportation systems environment. The stability condition of this model is obtained by using the linear stability analysis. The phase diagram can be divided into three regions classified as the stable, the metastable and the unstable ones. The theoretical result shows that the average headway plays an important role in improving the stabilization of traffic system. The mKdV equation near the critical point is derived to describe the evolution properties of traffic density waves by applying the reductive perturbation method. Furthermore, through the simulation of space-time evolution of the vehicle headway, it is shown that the traffic jam can be suppressed efficiently with taking into account the average headway effect, and the analytical result is consistent with the simulation one.

  2. Diagnostic models of intelligent tutor system for teaching skills to solve algebraic equations

    Directory of Open Access Journals (Sweden)

    Andrey Grigoriyevich Chukhray

    2007-10-01

    Full Text Available In this paper one solution for teaching skills to solve n-power algebraic equation by Lobachevsky-Greffe-Dandelen method is described. Student’s mistakes are discovered and classified. Based on signal-parametric approach to fault diagnosis in dynamic systems mathematical diagnostic models which allow detecting mistake classes by comparing student calculated results and system calculated results are created. Features of proposed diagnostic models application are presented. Intelligent tutor system is developed and used on “Automatic Control Theory” practical training by third year students of National Aerospace University.

  3. Cognitive Tutoring based on Intelligent Decision Support in the PENTHA Instructional Design Model

    Science.gov (United States)

    dall'Acqua, Luisa

    2010-06-01

    The research finality of this paper is how to support Authors to develop rule driven—subject oriented, adaptable course content, meta-rules—representing the disciplinary epistemology, model of teaching, Learning Path structure, and assessment parameters—for intelligent Tutoring actions in a personalized, adaptive e-Learning environment. The focus is to instruct the student to be a decision manager for himself, able to recognize the elements of a problem, select the necessary information with the perspective of factual choices. In particular, our research intends to provide some fundamental guidelines for the definition of didactical rules and logical relations, that Authors should provide to a cognitive Tutoring system through the use of an Instructional Design method (PENTHA Model) which proposes an educational environment, able to: increase productivity and operability, create conditions for a cooperative dialogue, developing participatory research activities of knowledge, observations and discoveries, customizing the learning design in a complex and holistic vision of the learning / teaching processes.

  4. Recent developments in spatial analysis spatial statistics, behavioural modelling, and computational intelligence

    CERN Document Server

    Getis, Arthur

    1997-01-01

    In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.

  5. Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

    Science.gov (United States)

    Luo, Zhonghui; Peng, Bin; Xiao, Qijun; Bai, Lu

    2018-03-01

    Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.

  6. Forensic intelligence applied to questioned document analysis: A model and its application against organized crime.

    Science.gov (United States)

    De Alcaraz-Fossoul, Josep; Roberts, Katherine A

    2017-07-01

    The capability of forensic sciences to fight crime, especially against organized criminal groups, becomes relevant in the recent economic downturn and the war on terrorism. In view of these societal challenges, the methods of combating crime should experience critical changes in order to improve the effectiveness and efficiency of the current resources available. It is obvious that authorities have serious difficulties combating criminal groups of transnational nature. These are characterized as well structured organizations with international connections, abundant financial resources and comprised of members with significant and diverse expertise. One common practice among organized criminal groups is the use of forged documents that allow for the commission of illegal cross-border activities. Law enforcement can target these movements to identify counterfeits and establish links between these groups. Information on document falsification can become relevant to generate forensic intelligence and to design new strategies against criminal activities of this nature and magnitude. This article discusses a methodology for improving the development of forensic intelligence in the discipline of questioned document analysis. More specifically, it focuses on document forgeries and falsification types used by criminal groups. It also describes the structure of international criminal organizations that use document counterfeits as means to conduct unlawful activities. The model presented is partially based on practical applications of the system that have resulted in satisfactory outcomes in our laboratory. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

  7. Implementation of Business Intelligence in An IT Organization - The Concept of An Evaluation Model

    Directory of Open Access Journals (Sweden)

    Sitek Tomasz

    2014-08-01

    Full Text Available This paper presents the issue of assessing the validity and effectiveness of implementing a Business Intelligence system in an IT Support Organization. This entity provides IT services to external clients involving, in particular, the storage and processing of large amounts of data. The vast amount of realized projects and also incidents reported in connection with those projects prevented effective decisions from being made without the support of dedicated technologies. The authors present the problems encountered by the studied entity and describe the tool that was selected to improve the situation. The aim of this study is to measure and describe the key processes in the organization on the basis of prepared aggregated measures, first prior to the implementation of the BI system and then a year after its implementation. The evaluation model developed by the authors allowed the assessment of the key aspects of the company’s operation over 2 years. It thus helped decision makers to establish whether the decision to implement the Business Intelligence system was correct or not.

  8. Learning vector quantization neural network–based model reference adaptive control method for intelligent lower-limb prosthesis

    Directory of Open Access Journals (Sweden)

    Jia-Qiang Yang

    2016-04-01

    Full Text Available This article focuses on the design of a control system for intelligent prostheses. Learning vector quantization neural network–based model reference adaptive control method is employed to implement real-time trajectory tracking and damp torque control of intelligent lower-limb prosthesis. The method is then analyzed and proposed. A model reference control system is first built with two learning vector quantization neural networks. One neural network is used for output prediction, and the other is used for input control. The angle information of the prosthetic knee joint is utilized to train these two neural networks with the given learning algorithm. The testing results of different movement patterns verify the effectiveness of the proposed method and its suitability for intelligent lower-limb prostheses.

  9. Towards intelligent diagnostic system employing integration of mathematical and engineering model

    Science.gov (United States)

    Isa, Nor Ashidi Mat

    2015-05-01

    The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well

  10. Towards intelligent diagnostic system employing integration of mathematical and engineering model

    International Nuclear Information System (INIS)

    Isa, Nor Ashidi Mat

    2015-01-01

    The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well

  11. Towards intelligent diagnostic system employing integration of mathematical and engineering model

    Energy Technology Data Exchange (ETDEWEB)

    Isa, Nor Ashidi Mat [Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang (Malaysia)

    2015-05-15

    The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well

  12. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Science.gov (United States)

    Pathak, Lakshmi; Singh, Vineeta; Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C K M; Mishra, B N

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  13. Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

    Science.gov (United States)

    Krishnan, Govindarajapuram Subramaniam

    1997-12-01

    The National Aeronautics & Space Administration (NASA), the European Space Agency (ESA), and the Canadian Space Agency (CSA) missions involve the performance of scientific experiments in Space. Instruments used in such experiments are fabricated using electronic parts such as microcircuits, inductors, capacitors, diodes, transistors, etc. For instruments to perform reliably the selection of commercial parts must be monitored and strictly controlled. The process used to achieve this goal is by a manual review and approval of every part used to build the instrument. The present system to select and approve parts for space applications is manual, inefficient, inconsistent, slow and tedious, and very costly. In this dissertation a computer based decision support model is developed for implementing this process using artificial intelligence concepts based on the current information (expert sources). Such a model would result in a greater consistency, accuracy, and timeliness of evaluation. This study presents the methodology of development and features of the model, and the analysis of the data pertaining to the performance of the model in the field. The model was evaluated for three different part types by experts from three different space agencies. The results show that the model was more consistent than the manual evaluation for all part types considered. The study concludes with the cost and benefits analysis of implementing the models and shows that implementation of the model will result in significant cost savings. Other implementation details are highlighted.

  14. Plant intelligence

    Science.gov (United States)

    Lipavská, Helena; Žárský, Viktor

    2009-01-01

    The concept of plant intelligence, as proposed by Anthony Trewavas, has raised considerable discussion. However, plant intelligence remains loosely defined; often it is either perceived as practically synonymous to Darwinian fitness, or reduced to a mere decorative metaphor. A more strict view can be taken, emphasizing necessary prerequisites such as memory and learning, which requires clarifying the definition of memory itself. To qualify as memories, traces of past events have to be not only stored, but also actively accessed. We propose a criterion for eliminating false candidates of possible plant intelligence phenomena in this stricter sense: an “intelligent” behavior must involve a component that can be approximated by a plausible algorithmic model involving recourse to stored information about past states of the individual or its environment. Re-evaluation of previously presented examples of plant intelligence shows that only some of them pass our test. “You were hurt?” Kumiko said, looking at the scar. Sally looked down. “Yeah.” “Why didn't you have it removed?” “Sometimes it's good to remember.” “Being hurt?” “Being stupid.”—(W. Gibson: Mona Lisa Overdrive) PMID:19816094

  15. Meta-modelling, visualization and emulation of multi-dimensional data for virtual production intelligence

    Science.gov (United States)

    Schulz, Wolfgang; Hermanns, Torsten; Al Khawli, Toufik

    2017-07-01

    Decision making for competitive production in high-wage countries is a daily challenge where rational and irrational methods are used. The design of decision making processes is an intriguing, discipline spanning science. However, there are gaps in understanding the impact of the known mathematical and procedural methods on the usage of rational choice theory. Following Benjamin Franklin's rule for decision making formulated in London 1772, he called "Prudential Algebra" with the meaning of prudential reasons, one of the major ingredients of Meta-Modelling can be identified finally leading to one algebraic value labelling the results (criteria settings) of alternative decisions (parameter settings). This work describes the advances in Meta-Modelling techniques applied to multi-dimensional and multi-criterial optimization by identifying the persistence level of the corresponding Morse-Smale Complex. Implementations for laser cutting and laser drilling are presented, including the generation of fast and frugal Meta-Models with controlled error based on mathematical model reduction Reduced Models are derived to avoid any unnecessary complexity. Both, model reduction and analysis of multi-dimensional parameter space are used to enable interactive communication between Discovery Finders and Invention Makers. Emulators and visualizations of a metamodel are introduced as components of Virtual Production Intelligence making applicable the methods of Scientific Design Thinking and getting the developer as well as the operator more skilled.

  16. Modeling of steam distillation mechanism during steam injection process using artificial intelligence.

    Science.gov (United States)

    Daryasafar, Amin; Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.

  17. Efficient drilling problem detection. Early fault detection by the combination of physical models and artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Nyboe, Roar

    2009-09-15

    The drilling of an oil or gas well is an expensive undertaking. Hence, it is not surprising that mistakes and accidents during drilling incur a high cost. Accidents could result in the loss of expensive equipment and subsequent delays setting back the operation for days or weeks and thus running up large bills on rig-time and personnel hours. Some types of accidents also pose a risk to the personnel or the environment. In this dissertation we study alarm systems which could give the driller an early warning of upcoming problems, and thus provide time to avoid these accidents. We explore alarm systems which combine advanced physical models of the well and drilling process with artificial intelligence and time series analysis. Finally, we determine the advantages as well as the challenges of this approach. It is our hope that this dissertation is accessible to both practitioners in machine learning and control engineering, as well as to petroleum engineers with a passing familiarity with machine learning. Hence this dissertation starts with a quick introduction to drilling problems and some terms from time series analysis and machine learning. We then briefly describe the theory of observer-based fault detection and isolation. Theories of supervisory control systems are also introduced, as these concern both the choice of algorithms and how AI-based alarm systems integrate with the rest of the operation. From chapter 6 and onward, the challenges to fault detection in drilling are discussed. We focus on clarifying what restrictions the available training data put on our choice of machine learning methods. In chapter 8 and 9, we propose ways to combine machine learning and observer-based fault detection. Experimental results are presented in chapter 10, before we end with concluding remarks in chapter 11. Our main conclusion, reflected in our experimental results, is that physical models and artificial intelligence can be combined to produce hybrid alarm systems that

  18. Computational intelligence models to predict porosity of tablets using minimum features

    Directory of Open Access Journals (Sweden)

    Khalid MH

    2017-01-01

    Full Text Available Mohammad Hassan Khalid,1 Pezhman Kazemi,1 Lucia Perez-Gandarillas,2 Abderrahim Michrafy,2 Jakub Szlęk,1 Renata Jachowicz,1 Aleksander Mendyk1 1Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland; 2Centre National de la Recherche Scientifique, Centre RAPSODEE, Mines Albi, Université de Toulouse, Albi, France Abstract: The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD practices. Computational intelligence (CI offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs, and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC (in percentage, granule size fraction (in micrometers, and die compaction force (in kilonewtons as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1% and symbolic regression (NRMSE =4% as the best-performing methods, also exhibiting reliable predictive

  19. A model for the role of emotional intelligence in patient safety

    Directory of Open Access Journals (Sweden)

    Estelle Codier

    2015-01-01

    Full Text Available Medical errors are the third leading cause of death in the USA, resulting in over 440,000 deaths/year. Although over a decade has passed since the first Institute of Medicine study that documented such horrific statistics and despite significant safety improvement efforts, serious progress has yet to be achieved. It is estimated that 80% of medical errors result from miscommunication among health care providers and between providers and patients. There is preliminary research evidence that communication skills programs can improve safety outcomes, but a systematic theoretical framework for such programs has not been identified. Because of the connection between emotional intelligence (EI ability and communication effectiveness, EI has been called by some "one of the largest drivers of patient safety." Little literature has explored this relationship. The purpose of this article was to present a theoretical model for the relationship between EI, communication and patient safety, with conceptual and clinical illustrations used to describe such a relationship.

  20. The NIST Real-Time Control System (RCS): A Reference Model Architecture for Computational Intelligence

    Science.gov (United States)

    Albus, James S.

    1996-01-01

    The Real-time Control System (RCS) developed at NIST and elsewhere over the past two decades defines a reference model architecture for design and analysis of complex intelligent control systems. The RCS architecture consists of a hierarchically layered set of functional processing modules connected by a network of communication pathways. The primary distinguishing feature of the layers is the bandwidth of the control loops. The characteristic bandwidth of each level is determined by the spatial and temporal integration window of filters, the temporal frequency of signals and events, the spatial frequency of patterns, and the planning horizon and granularity of the planners that operate at each level. At each level, tasks are decomposed into sequential subtasks, to be performed by cooperating sets of subordinate agents. At each level, signals from sensors are filtered and correlated with spatial and temporal features that are relevant to the control function being implemented at that level.

  1. A Fuzzy ANP Model Integrated with Benefits, Opportunities, Costs, and Risks to Prioritize Intelligent Power Grid Systems

    Directory of Open Access Journals (Sweden)

    Hsing Hung Chen

    2013-01-01

    Full Text Available Although growth of renewable energy is envisaged, many concerns are critical like the ability to maintain the balance between demands and supply and the variability, noncontrollability, and flexibility of the sources. Then, what will be the future concerns about the main composition of intelligent power grid systems in the future? There is no such research tackled before. Thus, this paper first finds critical success criteria of intelligent power grid systems and then constructs a multiple criteria and decision making model to help in identifying the suitable trends under complex economic performance, environmental impacts, and rapid technological and marketing changes. After empirical demonstration, the paper summarizes that the most suitable composition of future intelligent power grid systems should be constituted by “DHT” P2P grid, “C&D workflow” P2P scheduling, “GARCM” trustworthy P2P grid, and “multipurpose” grid applications in the future.

  2. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  3. Learning How to Construct Models of Dynamic Systems: An Initial Evaluation of the Dragoon Intelligent Tutoring System

    Science.gov (United States)

    VanLehn, Kurt; Wetzel, Jon; Grover, Sachin; van de Sande, Brett

    2017-01-01

    Constructing models of dynamic systems is an important skill in both mathematics and science instruction. However, it has proved difficult to teach. Dragoon is an intelligent tutoring system intended to quickly and effectively teach this important skill. This paper describes Dragoon and an evaluation of it. The evaluation randomly assigned…

  4. Teaching Culture and Language through the Multiple Intelligences Film Teaching Model in the ESL/EFL Classroom

    Science.gov (United States)

    Yeh, Ellen

    2014-01-01

    This paper will demonstrate how to enhance second language (L2) learners' linguistic and cultural competencies through the use of the Multiple Intelligences Film Teaching (MIFT) model. The paper will introduce two ideas to teachers of English as a Second/Foreign Language (ESL/EFL). First, the paper shows how L2 learners learn linguistic and…

  5. Strategic Management Model with Lens of Knowledge Management and Competitive Intelligence: A Review Approach

    OpenAIRE

    Shujahat, Muhammad; Hussain, Saddam; Javed, Sammar; Muhammad, Imran Malik; Thursamy, Ramayah; Ali, Junaid

    2017-01-01

    Purpose:\\ud First purpose of this study is to discuss the synergic and separate use of knowledge and\\ud intelligence, via knowledge management and competitive intelligence, in each stage of strategic\\ud management process. Second purpose is to discuss the implications of each stage of strategic\\ud management process for knowledge management and competitive intelligence and vice versa.\\ud Methodology/Design/Approach:\\ud A systematic literature review was performed within timeframe of 2000 to 2...

  6. Enabling intelligent copernicus services for carbon and water balance modeling of boreal forest ecosystems - North State

    Science.gov (United States)

    Häme, Tuomas; Mutanen, Teemu; Rauste, Yrjö; Antropov, Oleg; Molinier, Matthieu; Quegan, Shaun; Kantzas, Euripides; Mäkelä, Annikki; Minunno, Francesco; Atli Benediktsson, Jon; Falco, Nicola; Arnason, Kolbeinn; Storvold, Rune; Haarpaintner, Jörg; Elsakov, Vladimir; Rasinmäki, Jussi

    2015-04-01

    The objective of project North State, funded by Framework Program 7 of the European Union, is to develop innovative data fusion methods that exploit the new generation of multi-source data from Sentinels and other satellites in an intelligent, self-learning framework. The remote sensing outputs are interfaced with state-of-the-art carbon and water flux models for monitoring the fluxes over boreal Europe to reduce current large uncertainties. This will provide a paradigm for the development of products for future Copernicus services. The models to be interfaced are a dynamic vegetation model and a light use efficiency model. We have identified four groups of variables that will be estimated with remote sensed data: land cover variables, forest characteristics, vegetation activity, and hydrological variables. The estimates will be used as model inputs and to validate the model outputs. The earth observation variables are computed as automatically as possible, with an objective to completely automatic estimation. North State has two sites for intensive studies in southern and northern Finland, respectively, one in Iceland and one in state Komi of Russia. Additionally, the model input variables will be estimated and models applied over European boreal and sub-arctic region from Ural Mountains to Iceland. The accuracy assessment of the earth observation variables will follow statistical sampling design. Model output predictions are compared to earth observation variables. Also flux tower measurements are applied in the model assessment. In the paper, results of hyperspectral, Sentinel-1, and Landsat data and their use in the models is presented. Also an example of a completely automatic land cover class prediction is reported.

  7. Social intelligence, human intelligence and niche construction.

    Science.gov (United States)

    Sterelny, Kim

    2007-04-29

    This paper is about the evolution of hominin intelligence. I agree with defenders of the social intelligence hypothesis in thinking that externalist models of hominin intelligence are not plausible: such models cannot explain the unique cognition and cooperation explosion in our lineage, for changes in the external environment (e.g. increasing environmental unpredictability) affect many lineages. Both the social intelligence hypothesis and the social intelligence-ecological complexity hybrid I outline here are niche construction models. Hominin evolution is hominin response to selective environments that earlier hominins have made. In contrast to social intelligence models, I argue that hominins have both created and responded to a unique foraging mode; a mode that is both social in itself and which has further effects on hominin social environments. In contrast to some social intelligence models, on this view, hominin encounters with their ecological environments continue to have profound selective effects. However, though the ecological environment selects, it does not select on its own. Accidents and their consequences, differential success and failure, result from the combination of the ecological environment an agent faces and the social features that enhance some opportunities and suppress others and that exacerbate some dangers and lessen others. Individuals do not face the ecological filters on their environment alone, but with others, and with the technology, information and misinformation that their social world provides.

  8. Intelligence Quotient and Intelligence Grade of Artificial Intelligence

    OpenAIRE

    Liu, Feng; Shi, Yong; Liu, Ying

    2017-01-01

    Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat, this study proposes a standard intelligence model that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann archi...

  9. Resilience moderates the relationship between emotional intelligence and clinical communication ability among Chinese practice nursing students: A structural equation model analysis.

    Science.gov (United States)

    Kong, Linghua; Liu, Yun; Li, Guopeng; Fang, Yueyan; Kang, Xiaofei; Li, Ping

    2016-11-01

    To examine the positive association between emotional intelligence and clinical communication ability among practice nursing students, and to determine whether resilience plays a moderating role in the relationship between emotional intelligence and clinical communication ability among Chinese practice nursing students. Three hundred and seventy-seven practice nursing students from three hospitals participated in this study. They completed questionnaires including the Emotional Intelligence Inventory (EII), Connor-Davidson Resilience Scale (CD-RISC-10), and Clinical Communication Ability Scale (CCAS). Structural equation modeling was used to analyze the relationships among emotional intelligence, resilience, and clinical communication ability. Emotional intelligence was positively associated with clinical communication ability (Pintelligence and clinical communication ability (Pintelligence is positively related to clinical communication ability among Chinese practice nursing students, and resilience moderates the relationship between emotional intelligence and clinical communication ability, which may provide scientific evidence to aid in developing intervention strategies to improve clinical communication ability. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks

    International Nuclear Information System (INIS)

    Ahmadi, Rouhollah; Khamehchi, Ehsan

    2013-01-01

    Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data

  11. Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks

    Energy Technology Data Exchange (ETDEWEB)

    Ahmadi, Rouhollah, E-mail: rouhollahahmadi@yahoo.com [Amirkabir University of Technology, PhD Student at Reservoir Engineering, Department of Petroleum Engineering (Iran, Islamic Republic of); Khamehchi, Ehsan [Amirkabir University of Technology, Faculty of Petroleum Engineering (Iran, Islamic Republic of)

    2013-12-15

    Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data.

  12. Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models.

    Science.gov (United States)

    Allawi, Mohammed Falah; Jaafar, Othman; Mohamad Hamzah, Firdaus; Abdullah, Sharifah Mastura Syed; El-Shafie, Ahmed

    2018-04-03

    Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

  13. Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: methodology and preliminary report.

    Science.gov (United States)

    Fu, Jicheng; Jones, Maria; Jan, Yih-Kuen

    2014-01-01

    Wheelchair tilt and recline functions are two of the most desirable features for relieving seating pressure to decrease the risk of pressure ulcers. The effective guidance on wheelchair tilt and recline usage is therefore critical to pressure ulcer prevention. The aim of this study was to demonstrate the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance to individuals with spinal cord injury (SCI). The motivation stems from the clinical evidence that the requirements of individuals vary greatly and that no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI. We explored all aspects involved in constructing the intelligent model and proposed approaches tailored to suit the characteristics of this preliminary study, such as the way of modeling research participants, using machine learning techniques to construct the intelligent model, and evaluating the performance of the intelligent model. We further improved the intelligent model's prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes. Experimental results demonstrated that our approaches held the promise: they could effectively construct the intelligent model, evaluate its performance, and refine the participant model so that the intelligent model's prediction accuracy was significantly improved.

  14. Structural Equation Modeling of Classification Managers Based on the Communication Skills and Cultural Intelligence in Sport Organizations

    Directory of Open Access Journals (Sweden)

    Rasool NAZARI

    2015-03-01

    Full Text Available The purpose of this research is to develop structural equation model category managers on communication skills and cultural intelligence agencies had Isfahan Sports. Hence study was of structural equation modeling. The statistical population of this research formed the provincial sports administrators that according formal statistical was 550 people. Research sample size the sample of 207subjects was randomly selected. Cochran's sample size formula was used to determine. Measuring research and Communication Skills (0.81, Cultural Intelligence Scale (0.85 category manager's questionnaire (0.86, respectively. For analysis descriptive and inferential statistics SPSS and LISREL was used. Model results, communication skills, cultural intelligence and athletic directors classification of the fit was good (RMSEA=0.037, GFI= 0.902, AGFI= 0.910, NFT= 0.912. The prerequisite for proper planning to improve communication skills and cultural intelligence managers as influencing exercise essential while the authorial shave the right to choose directors analyst and intuitive strategies for management position shave because it looks better with the managers can be expected to exercise a clearer perspective.

  15. Calculating the Contribution Rate of Intelligent Transportation System in Improving Urban Traffic Smooth Based on Advanced DID Model

    Directory of Open Access Journals (Sweden)

    Ming-wei Li

    2015-01-01

    Full Text Available Recent years have witnessed the rapid development of intelligent transportation system around the world, which helps to relieve urban traffic congestion problems. For instance, many mega-cities in China have devoted a large amount of money and resources to the development of intelligent transportation system. This poses an intriguing and important issue: how to measure and quantify the contribution of intelligent transportation system to the urban city, which is still a puzzle. This paper proposes a matching difference-in-difference model to calculate the contribution rate of intelligent transportation system on traffic smoothness. Within the model, the main effect indicators of traffic smoothness are first identified, and then the evaluation index system is built, and finally the ideas of the matching pool are introduced. The proposed model is illustrated in Guangzhou, China (capital city of Guangdong province. The results show that introduction of ITS contributes 9.25% to the improvement of traffic smooth in Guangzhou. Also, the research explains the working mechanism of how ITS improves urban traffic smooth. Eventually, some strategy recommendations are put forward to improve urban traffic smooth.

  16. Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

    Science.gov (United States)

    Nourani, Vahid; Mousavi, Shahram

    2016-05-01

    Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

  17. Intelligent Model Building and GPC-PID Based Temperature Curve Control Strategy for Metallurgical Industry

    Directory of Open Access Journals (Sweden)

    Shuanghong Li

    2016-01-01

    Full Text Available Laminar cooling process is a large-scale, nonlinear system, so the temperature control of such system is a difficult and complex problem. In this paper, a novel modeling method and a GPC-PID based control strategy for laminar cooling process are proposed to control the global temperature curve to produce high quality steel. First, based on the analysis of the cooling process of laminar flow, a new TS fuzzy model which possesses intelligence and self-learning ability is established to improve the temperature prediction accuracy. Second, the target temperature curve can be divided into several subgoals and each subgoal can be described by a CARIMA type of model. Then, by the decentralized predictive control method, GPC-PID based control strategy is introduced to guarantee the laminar cooling process to achieve subtargets, respectively; in that way the steel plate temperature will drop along the optimal temperature curve. Moreover, by employing the dSPACE control board into the process control system, the matrix process ability is added to the production line without large-scale reconstruction. Finally, the effectiveness and performance of the proposed modeling and control strategy are demonstrated by the industrial data and metallography detection in one steel company.

  18. HCCI Intelligent Rapid Modeling by Artificial Neural Network and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    AbdoulAhad Validi

    2012-01-01

    Full Text Available A Dynamic model of Homogeneous Charge Compression Ignition (HCCI, based on chemical kinetics principles and artificial intelligence, is developed. The model can rapidly predict the combustion probability, thermochemistry properties, and exact timing of the Start of Combustion (SOC. A realization function is developed on the basis of the Sandia National Laboratory chemical kinetics model, and GRI3.0 methane chemical mechanism. The inlet conditions are optimized by Genetic Algorithm (GA, so that combustion initiates and SOC timing posits in the desired crank angle. The best SOC timing to achieve higher performance and efficiency in HCCI engines is between 5 and 15 degrees crank angle (CAD after top dead center (TDC. To achieve this SOC timing, in the first case, the inlet temperature and equivalence ratio are optimized simultaneously and in the second case, compression ratio is optimized by GA. The model’s results are validated with previous works. The SOC timing can be predicted in less than 0.01 second and the CPU time savings are encouraging. This model can successfully be used for real engine control applications.

  19. A Study on Intelligent User-Centric Logistics Service Model Using Ontology

    Directory of Open Access Journals (Sweden)

    Saraswathi Sivamani

    2014-01-01

    Full Text Available Much research has been undergone in the smart logistics environment for the prompt delivery of the product in the right place at the right time. Most of the services were based on time management, routing technique, and location based services. The services in the recent logistics environment aim for situation based logistics service centered around the user by utilizing various information technologies such as mobile devices, computer systems, and GPS. This paper proposes a smart logistics service model for providing user-centric intelligent logistics service by utilizing smartphones in a smart environment. We also develop an OWL based ontology model for the smart logistics for the better understanding among the context information. In addition to basic delivery information, the proposed service model makes use of the location and situation information of the delivery vehicle and user, to draw the route information according to the user’s requirement. With the increase of internet usage, the real-time situations are received which helps to create a more reliable relationship, owing to the Internet of Things. Through this service model, it is possible to engage in the development of various IT and logistics convergence services based on situation information between the deliverer and user which occurs in real time.

  20. Better Equipping Reserve Military Intelligence Analyst to Meet the Needs of the Commander by Championing a Process-Driven Training Model

    Science.gov (United States)

    2013-06-14

    BETTER EQUIPPING RESERVE MILITARY INTELLIGENCE ANALYST TO MEET THE NEEDS OF...TITLE AND SUBTITLE Better Equipping Reserve Military Intelligence Analyst To Meet The Needs Of The Commander by Championing A Process-Driven Training...Analyst to Meet the Needs of the Commander by Championing a Process-Driven Training Model Approved by: , Thesis Committee Chair Jack D. Kem, Ph.D

  1. Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

    Science.gov (United States)

    Gupta, Shikha; Basant, Nikita; Rai, Premanjali; Singh, Kunwar P

    2015-11-01

    Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The

  2. Computational intelligence models to predict porosity of tablets using minimum features.

    Science.gov (United States)

    Khalid, Mohammad Hassan; Kazemi, Pezhman; Perez-Gandarillas, Lucia; Michrafy, Abderrahim; Szlęk, Jakub; Jachowicz, Renata; Mendyk, Aleksander

    2017-01-01

    The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs), and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE) scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC) (in percentage), granule size fraction (in micrometers), and die compaction force (in kilonewtons) as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1%) and symbolic regression (NRMSE =4%) as the best-performing methods, also exhibiting reliable predictive behavior when presented with a challenging external validation data set (best achieved symbolic regression: NRMSE =3%). Symbolic regression demonstrates the transition from the black box modeling paradigm to more transparent predictive models. Predictive performance and feature selection behavior of CI models hints at the most important variables within this factor space.

  3. Intelligence and Metacognition as Predictors of Foreign Language Achievement: A Structural Equation Modeling Approach

    Science.gov (United States)

    Pishghadam, Reza; Khajavy, Gholam Hassan

    2013-01-01

    This study examined the role of metacognition and intelligence in foreign language achievement on a sample of 143 Iranian English as a Foreign Language (EFL) learners. Participants completed Raven's Advanced Progressive Matrices as a measure of intelligence, and Metacognitive Awareness Inventory as a measure of metacognition. Learners' scores at…

  4. Does Reading Cause Later Intelligence? Accounting for Stability in Models of Change

    Science.gov (United States)

    Bailey, Drew H.; Littlefield, Andrew K.

    2017-01-01

    This study reanalyzes data presented by Ritchie, Bates, and Plomin (2015) who used a cross-lagged monozygotic twin differences design to test whether reading ability caused changes in intelligence. The authors used data from a sample of 1,890 monozygotic twin pairs tested on reading ability and intelligence at five occasions between the ages of 7…

  5. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Directory of Open Access Journals (Sweden)

    Lakshmi Pathak

    Full Text Available Cholesterol oxidase (COD is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM, artificial neural network (ANN and genetic algorithm (GA have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  6. Student Modelling in an Intelligent Tutoring System for the Passive Voice of English Language

    Directory of Open Access Journals (Sweden)

    Dimitris Maras

    2000-01-01

    Full Text Available This paper describes an intelligent multimedia tutoring system for the passive voice of the English grammar. The system may be used to present theoretical issues about the passive voice and to provide exercises that the student may solve. The main focus of the tutor is on the student's error diagnosis process, which is performed by the student modelling component. When the student types the solution to an exercise, the system examines the correctness of the answer. If the student's answer has been erroneous it attempts to diagnose the underlying misconception of the mistake. In order to provide individualised help, the system holds a profile for every student, the long term student model. The student’s progress and his/her usual mistakes are recorded to this long term student model. This kind of information is used for the individualised error diagnosis of the student in subsequent sessions. In addition, the information stored about the student can also be used for the resolution of an arising ambiguity, as to what the underlying cause of a student error has been.

  7. The Comparison of Think Talk Write and Think Pair Share Model with Realistic Mathematics Education Approach Viewed from Mathematical-Logical Intelligence

    Directory of Open Access Journals (Sweden)

    Himmatul Afthina

    2017-12-01

    Full Text Available The aims of this research to determine the effect of Think Talk Write (TTW and Think Pair Share (TPS model with Realistic Mathematics Education (RME approach viewed from mathematical-logical intelligence. This research employed the quasi experimental research. The population of research was all students of the eight graders of junior high school in Karangamyar Regency in academic year 2016/2017. The result of this research shows that (1 TTW with RME approach gave better mathematics achievement than TPS with RME approach, (2 Students with high mathematical-logical intelligence can reach a better mathematics achievement than those with average and low, whereas students with average mathematical-logical intelligence can reach a better achievement than those with low one, (3 In TTW model with RME approach, students with high mathematical-logical intelligence can reach a better mathematics achievement than those with average and low, whereas students with average and low mathematical-logical intelligence gave same mathematics achievement, and  in TPS model with RME approach students with high mathematical-logical intelligence can reach a better mathematics achievement than those with average and low, whereas students with average mathematical-logical intelligence can reach a better achievement than those with low one (4 In each category of  mathematical-logical intelligence, TTW with RME approach and TPS with RME approach gave same mathematics achievement.

  8. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  9. Intelligent mechatronics; Intelligent mechatronics

    Energy Technology Data Exchange (ETDEWEB)

    Hashimoto, H. [The University of Tokyo, Tokyo (Japan). Institute of Industrial Science

    1995-10-01

    Intelligent mechatronics (IM) was explained as follows: a study of IM essentially targets realization of a robot namely, but in the present stage the target is a creation of new values by intellectualization of machine, that is, a combination of the information infrastructure and the intelligent machine system. IM is also thought to be constituted of computers positively used and micromechatronics. The paper next introduces examples of IM study, mainly those the author is concerned with as shown below: sensor gloves, robot hands, robot eyes, tele operation, three-dimensional object recognition, mobile robot, magnetic bearing, construction of remote controlled unmanned dam, robot network, sensitivity communication using neuro baby, etc. 27 figs.

  10. NEPHRUS: model of intelligent multilayers expert system for evaluation of the renal system based on scintigraphic images analysis

    International Nuclear Information System (INIS)

    Silva, Jose W.E. da; Schirru, Roberto; Boasquevisque, Edson M.

    1997-01-01

    This work develops a prototype for the system model based on Artificial Intelligence devices able to perform functions related to scintigraphic image analysis of the urinary system. Criteria used by medical experts for analysis images obtained with 99m Tc+DTPA and/or 99m Tc+DMSA were modeled and a multi resolution diagnosis technique was implemented. Special attention was given to the programs user interface design. Human Factor Engineering techniques were considered so as to ally friendliness and robustness. Results obtained using Artificial Neural Networks for the qualitative image analysis and the knowledge model constructed shows the feasibility of Artificial Intelligence implementation that use 'inherent' abilities of each technique in the resolution of diagnosis image analysis problems. (author). 12 refs., 2 figs., 2 tabs

  11. Application of artificial intelligent tools to modeling of glucosamine preparation from exoskeleton of shrimp.

    Science.gov (United States)

    Valizadeh, Hadi; Pourmahmood, Mohammad; Mojarrad, Javid Shahbazi; Nemati, Mahboob; Zakeri-Milani, Parvin

    2009-04-01

    The objective of this study was to forecast and optimize the glucosamine production yield from chitin (obtained from Persian Gulf shrimp) by means of genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural networks (ANNs) as tools of artificial intelligence methods. Three factors (acid concentration, acid solution to chitin ratio, and reaction time) were used as the input parameters of the models investigated. According to the obtained results, the production yield of glucosamine hydrochloride depends linearly on acid concentration, acid solution to solid ratio, and time and also the cross-product of acid concentration and time and the cross-product of solids to acid solution ratio and time. The production yield significantly increased with an increase of acid concentration, acid solution ratio, and reaction time. The production yield is inversely related to the cross-product of acid concentration and time. It means that at high acid concentrations, the longer reaction times give lower production yields. The results revealed that the average percent error (PE) for prediction of production yield by GA, PSO, and ANN are 6.84, 7.11, and 5.49%, respectively. Considering the low PE, it might be concluded that these models have a good predictive power in the studied range of variables and they have the ability of generalization to unknown cases.

  12. Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

    Science.gov (United States)

    Taylan, Osman

    2017-02-01

    High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes.

  13. Artificial intelligence for the modeling and control of combustion processes: a review

    Energy Technology Data Exchange (ETDEWEB)

    Kalogirou, S.A. [Higher Technical Inst., Nicosia, Cyprus (Greece). Dept. of Mechanical Engineering

    2003-07-01

    Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. Al systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how Al techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of Al as a design tool in many areas of combustion engineering. (author)

  14. Artificial intelligence for the modeling and control of combustion processes: a review

    Energy Technology Data Exchange (ETDEWEB)

    Soteris A. Kalogirou, [Higher Technical Institute, Nicosia (Cyprus). Department of Mechanical Engineering

    2003-07-01

    Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. AI systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how AI techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of AI as a design tool in many areas of combustion engineering. 109 refs., 31 figs., 11 tabs.

  15. Towards an intelligent learning management system under blended learning trends, profiles and modeling perspectives

    CERN Document Server

    Dias, Sofia B; Hadjileontiadis, Leontios J

    2013-01-01

    This book offers useful information that evokes initiatives towards rethinking of the value, efficiency, inclusiveness, effectiveness and personalization of the intelligent learning management systems-based blended-learning environment.

  16. Task modelling for ambient intelligent environments: design support for situated task executions

    OpenAIRE

    LUYTEN, Kris; VANDERVELPEN, Chris; CONINX, Karin

    2005-01-01

    The design of interactive systems for an ambient intelligent environment poses many challenges because of the great diversity in devices the user has control of and the user's situation imposed by the environment. Although task-centered interface design is an established approach for traditional form-based and even for multi-device user interfaces, this design approach is, in its current form, not ready for the design of user interfaces for ambient intelligent environments. In this paper we p...

  17. An Intelligent Body Posture Analysis Model Using Multi-Sensors for Long-Term Physical Rehabilitation.

    Science.gov (United States)

    Lai, Chin-Feng; Hwang, Ren-Hung; Lai, Ying-Hsun

    2017-04-01

    Sensors can be installed on various body parts to provide information for computer diagnosis to identify the current body state. However, as human posture is subject to gravity, the direction of the force on each limb differs. For example, the directions of gravitational force on legs and trunk differ. In addition, each person's height and structure of limbs differs, hence, the acceleration and rotation resulted from such differences on force and length of the limbs of a person in motion would be different, and be presented by cases of different postures. Thus, how to present body postures through skeleton system equations, and achieve an long-term physical rehabilitation, according to the different limb characteristics of each person, is a challenging research issue. This paper proposes a novel scheme named as "Intelligent Body Posture Analysis Model", which uses multiple acceleration sensors and gyroscopes to detect body motion patterns. The effectiveness of the proposed scheme is proved by conducting a large number of practical experiments and tests.

  18. Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

    Directory of Open Access Journals (Sweden)

    Juhwan Kim

    2018-01-01

    Full Text Available Recent developments in artificial intelligence (AI have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology.

  19. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes

    OpenAIRE

    Igor Vyacheslavovich Buzaev; Vladimir Vyacheslavovich Plechev; Irina Evgenievna Nikolaeva; Rezida Maratovna Galimova

    2016-01-01

    Objective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. Method: aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry dat...

  20. Do narcissism and emotional intelligence win us friends? : modeling dynamics of peer popularity using inferential network analysis

    OpenAIRE

    Czarna, Anna; Leifeld, Philip; Śmieja-Nęcka, Magdalena; Dufner, Michael; Salovey, Peter

    2016-01-01

    This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study, we examined the dynamics of popularity in 15 peer groups in two waves (N = 273). We measured narcissism, ability EI, and explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and 3 months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting...

  1. The intelligent customer: considerations around build-own-operate business and licensing models for small modular reactors in Canada

    International Nuclear Information System (INIS)

    Jones, K.

    2014-01-01

    An organization planning a proposal for a build-own-operate business model needs to address expanded licensee responsibilities under this model, associated regulatory impacts and how this affects their role as an 'intelligent customer'. This is particularly important for cases where builder-owner-operators plan to manufacture factory-fuelled designs and ship them to a site for installation and operation. The primary responsibility for safe conduct of licensed activities rests with the licensee. A build-own-operate model expands the scope of licensed activities to include design, manufacturing, transport, construction, and operation. The licensee must be able to demonstrate they are qualified to conduct all licensed activities including sufficient competent resources within the licensee's organization to oversee('Intelligent Customer') any work it commissions externally and the subsequent flow down through of the supply chain. This paper examines aspects that organizations need to assess the suitability of approaches that it may take to maintain in-house expertise for the control and oversight of licensed activities at all times. It considers the approach to identification of: core capabilities the licensee would need to understand its safety case under a build-own-operate model to manage licensed activities in accordance with requirements under the Nuclear Safety and Control Acta licensee's 'intelligent customer' capabilities in particular around understanding, specifying, overseeing and accepting work undertaken on its behalf by contractors. While this paper is focused on small modular reactors, being an intelligent customer applies to large commercial or research reactors equally; the size of reactor is immaterial.

  2. The intelligent customer: considerations around build-own-operate business and licensing models for small modular reactors in Canada

    Energy Technology Data Exchange (ETDEWEB)

    Jones, K., E-mail: kenneth.jones@cnsc-ccsn.gc.ca [Canadian Nuclear Safety Commission, Ottawa, Ontario (Canada)

    2014-07-01

    An organization planning a proposal for a build-own-operate business model needs to address expanded licensee responsibilities under this model, associated regulatory impacts and how this affects their role as an 'intelligent customer'. This is particularly important for cases where builder-owner-operators plan to manufacture factory-fuelled designs and ship them to a site for installation and operation. The primary responsibility for safe conduct of licensed activities rests with the licensee. A build-own-operate model expands the scope of licensed activities to include design, manufacturing, transport, construction, and operation. The licensee must be able to demonstrate they are qualified to conduct all licensed activities including sufficient competent resources within the licensee's organization to oversee('Intelligent Customer') any work it commissions externally and the subsequent flow down through of the supply chain. This paper examines aspects that organizations need to assess the suitability of approaches that it may take to maintain in-house expertise for the control and oversight of licensed activities at all times. It considers the approach to identification of: core capabilities the licensee would need to understand its safety case under a build-own-operate model to manage licensed activities in accordance with requirements under the Nuclear Safety and Control Acta licensee's 'intelligent customer' capabilities in particular around understanding, specifying, overseeing and accepting work undertaken on its behalf by contractors. While this paper is focused on small modular reactors, being an intelligent customer applies to large commercial or research reactors equally; the size of reactor is immaterial.

  3. Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation

    International Nuclear Information System (INIS)

    Jiang, He; Dong, Yao; Wang, Jianzhou; Li, Yuqin

    2015-01-01

    Highlights: • CS-hard-ridge-RBF and DE-hard-ridge-RBF are proposed to forecast solar radiation. • Pearson and Apriori algorithm are used to analyze correlations between the data. • Hard-ridge penalty is added to reduce the number of nodes in the hidden layer. • CS algorithm and DE algorithm are used to determine the optimal parameters. • Proposed two models have higher forecasting accuracy than RBF and hard-ridge-RBF. - Abstract: Due to the scarcity of equipment and the high costs of maintenance, far fewer observations of solar radiation are made than observations of temperature, precipitation and other weather factors. Therefore, it is increasingly important to study several relevant meteorological factors to accurately forecast solar radiation. For this research, monthly average global solar radiation and 12 meteorological parameters from 1998 to 2010 at four sites in the United States were collected. Pearson correlation coefficients and Apriori association rules were successfully used to analyze correlations between the data, which provided a basis for these relative parameters as input variables. Two effective and innovative methods were developed to forecast monthly average global solar radiation by converting a RBF neural network into a multiple linear regression problem, adding a hard-ridge penalty to reduce the number of nodes in the hidden layer, and applying intelligent optimization algorithms, such as the cuckoo search algorithm (CS) and differential evolution (DE), to determine the optimal center and scale parameters. The experimental results show that the proposed models produce much more accurate forecasts than other models

  4. Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modeling

    Directory of Open Access Journals (Sweden)

    Pooya Najaf

    2013-07-01

    Full Text Available Road freight transportation between provinces of a country has an important effect on the traffic flow of intercity transportation networks. Therefore, an accurate estimation of the road freight transportation for provinces of a country is so crucial to improve the rural traffic operation in a large scale management. Accordingly, the focused case study database in this research is the information related to Iran’s provinces in the year 2008. Correlation between road freight transportation with variables such as transport cost and distance, population, average household income and Gross Domestic Product (GDP of each province is calculated. Results clarify that the population is the most effective factor in the prediction of provinces’ transported freight. Linear Regression Model (LRM is calibrated based on the population variable, and afterwards Fuzzy Regression Algorithm (FRA is generated on the basis of the LRM. The proposed FRA is an intelligent modified algorithm with an accurate prediction and fitting ability. This methodology can be significantly useful in macro-level planning problems where decreasing prediction error values is one of the most important concerns for decision makers. In addition, Back-Propagation Neural Network (BPNN is developed to evaluate the prediction capability of the models and to be compared with FRA. According to the final results, the modified FRA estimates road freight transportation values more accurately than the BPNN and LRM. Finally, in order to predict the road freight transportation values, the reliability of the calibrated models is analyzed using the information of the year 2009. Results show higher reliability for the proposed modified FRA.

  5. The Life Cycle Application of Intelligent Software Modeling for the First Materials Science Research Rack

    Science.gov (United States)

    Rice, Amanda; Parris, Frank; Nerren, Philip

    2000-01-01

    Marshall Space Flight Center (MSFC) has been funding development of intelligent software models to benefit payload ground operations for nearly a decade. Experience gained from simulator development and real-time monitoring and control is being applied to engineering design, testing, and operation of the First Material Science Research Rack (MSRR-1). MSRR-1 is the first rack in a suite of three racks comprising the Materials Science Research Facility (MSRF) which will operate on the International Space Station (ISS). The MSRF will accommodate advanced microgravity investigations in areas such as the fields of solidification of metals and alloys, thermo-physical properties of polymers, crystal growth studies of semiconductor materials, and research in ceramics and glasses. The MSRR-1 is a joint venture between NASA and the European Space Agency (ESA) to study the behavior of different materials during high temperature processing in a low gravity environment. The planned MSRR-1 mission duration is five (5) years on-orbit and the total design life is ten (IO) years. The MSRR-1 launch is scheduled on the third Utilization Flight (UF-3) to ISS, currently in February of 2003). The objective of MSRR-1 is to provide an early capability on the ISS to conduct material science, materials technology, and space product research investigations in microgravity. It will provide a modular, multi-user facility for microgravity research in materials crystal growth and solidification. An intelligent software model of MSRR-1 is under development and will serve multiple purposes to support the engineering analysis, testing, training, and operational phases of the MSRR-1 life cycle development. The G2 real-time expert system software environment developed by Gensym Corporation was selected as the intelligent system shell for this development work based on past experience gained and the effectiveness of the programming environment. Our approach of multi- uses of the simulation model and

  6. Comparison of intelligent systems, artificial neural networks and neural fuzzy model for prediction of gas hydrate formation rate

    Directory of Open Access Journals (Sweden)

    Mohammad Javad Jalalnezhad

    2014-05-01

    Full Text Available The main objective of this study was to present a novel approach for predication of gas hydrate formation rate based on the Intelligent Systems. Using a data set including about 470 data obtained from flow tests in a mini-loop apparatus, different predictive models were developed. From the results predicted by these models, it can be pointed out that the developed models can be used as powerful tools for prediction of gas hydrate formation rate with total errors of less than 4%.

  7. Simulation Modeling of Intelligent Control Algorithms for Constructing Autonomous Power Supply Systems with Improved Energy Efficiency

    Directory of Open Access Journals (Sweden)

    Gimazov Ruslan

    2018-01-01

    Full Text Available The paper considers the issue of supplying autonomous robots by solar batteries. Low efficiency of modern solar batteries is a critical issue for the whole industry of renewable energy. The urgency of solving the problem of improved energy efficiency of solar batteries for supplying the robotic system is linked with the task of maximizing autonomous operation time. Several methods to improve the energy efficiency of solar batteries exist. The use of MPPT charge controller is one these methods. MPPT technology allows increasing the power generated by the solar battery by 15 – 30%. The most common MPPT algorithm is the perturbation and observation algorithm. This algorithm has several disadvantages, such as power fluctuation and the fixed time of the maximum power point tracking. These problems can be solved by using a sufficiently accurate predictive and adaptive algorithm. In order to improve the efficiency of solar batteries, autonomous power supply system was developed, which included an intelligent MPPT charge controller with the fuzzy logic-based perturbation and observation algorithm. To study the implementation of the fuzzy logic apparatus in the MPPT algorithm, in Matlab/Simulink environment, we developed a simulation model of the system, including solar battery, MPPT controller, accumulator and load. Results of the simulation modeling established that the use of MPPT technology had increased energy production by 23%; introduction of the fuzzy logic algorithm to MPPT controller had greatly increased the speed of the maximum power point tracking and neutralized the voltage fluctuations, which in turn reduced the power underproduction by 2%.

  8. The experimentation of LC7E learning model on the linear program material in terms of interpersonal intelligence on Wonogiri Vocational School students

    Science.gov (United States)

    Antinah; Kusmayadi, T. A.; Husodo, B.

    2018-03-01

    This study aimed to determine the effect of learning model on student achievement in terms of interpersonal intelligence. The compared learning models are LC7E and Direct learning model. This type of research is a quasi-experimental with 2x3 factorial design. The population in this study is a Grade XI student of Wonogiri Vocational Schools. The sample selection had done by stratified cluster random sampling. Data collection technique used questionnaires, documentation and tests. The data analysis technique used two different unequal cell variance analysis which previously conducted prerequisite analysis for balance test, normality test and homogeneity test. he conclusions of this research are: 1) student learning achievement of mathematics given by LC7E learning model is better when compared with direct learning; 2) Mathematics learning achievement of students who have a high level of interpersonal intelligence is better than students with interpersonal intelligence in medium and low level. Students’ mathematics learning achievement with interpersonal level of intelligence is better than those with low interpersonal intelligence on linear programming; 3) LC7E learning model resulted better on mathematics learning achievement compared with direct learning model for each category of students’ interpersonal intelligence level on linear program material.

  9. Model of facilitation of emotional intelligence to promote wholeness of neophyte critical care nurses in South Africa

    Directory of Open Access Journals (Sweden)

    A. Towell

    2015-10-01

    Full Text Available This study was undertaken in order to develop a model of facilitation of emotional intelligence to promote wholeness in neophyte critical care nurses in South Africa. A theory generative, explorative, descriptive, contextual research design was used. The model was developed utilising the four steps of theory generation as proposed by Dickoff, James, and Wiedenbach (1968, Chinn and Kramer (2011 and Walker and Avant (2011. Step one dealt with the empirical phase in which the concepts were distilled. The facilitation of inherent affective and mental resourcefulness and resilience was the main concept of the model. Step two comprised the definition and classification of central and related concepts. Step three provides a description of the model. The model operates in three phases namely the dependent phase, partially dependent phase and the independent phase. Step four entailed the description of guidelines for operationalizing the model. During the three phases of the model a new nurse who starts to work in critical care moves from a latent ability to develop an inherent affective and mental resourcefulness and resilience to a state of developing an inherent affective and mental resourcefulness and resilience. This model provides a structured framework for the facilitation of emotional intelligence (EI to promote wholeness in nurses who commence to work in critical care units.

  10. Intelligence analysis – the royal discipline of Competitive Intelligence

    Directory of Open Access Journals (Sweden)

    František Bartes

    2011-01-01

    Full Text Available The aim of this article is to propose work methodology for Competitive Intelligence teams in one of the intelligence cycle’s specific area, in the so-called “Intelligence Analysis”. Intelligence Analysis is one of the stages of the Intelligence Cycle in which data from both the primary and secondary research are analyzed. The main result of the effort is the creation of added value for the information collected. Company Competiitve Intelligence, correctly understood and implemented in business practice, is the “forecasting of the future”. That is forecasting about the future, which forms the basis for strategic decisions made by the company’s top management. To implement that requirement in corporate practice, the author perceives Competitive Intelligence as a systemic application discipline. This approach allows him to propose a “Work Plan” for Competitive Intelligence as a fundamental standardized document to steer Competitive Intelligence team activities. The author divides the Competitive Intelligence team work plan into five basic parts. Those parts are derived from the five-stage model of the intelligence cycle, which, in the author’s opinion, is more appropriate for complicated cases of Competitive Intelligence.

  11. Eye gaze in intelligent user interfaces gaze-based analyses, models and applications

    CERN Document Server

    Nakano, Yukiko I; Bader, Thomas

    2013-01-01

    Remarkable progress in eye-tracking technologies opened the way to design novel attention-based intelligent user interfaces, and highlighted the importance of better understanding of eye-gaze in human-computer interaction and human-human communication. For instance, a user's focus of attention is useful in interpreting the user's intentions, their understanding of the conversation, and their attitude towards the conversation. In human face-to-face communication, eye gaze plays an important role in floor management, grounding, and engagement in conversation.Eye Gaze in Intelligent User Interfac

  12. Investigation of an artificial intelligence technology--Model trees. Novel applications for an immediate release tablet formulation database.

    Science.gov (United States)

    Shao, Q; Rowe, R C; York, P

    2007-06-01

    This study has investigated an artificial intelligence technology - model trees - as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R(2). Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators.

  13. Analysis and Modeling of the Galvanic Skin Response Spontaneous Component in the context of Intelligent Biofeedback Systems Development

    Science.gov (United States)

    Unakafov, A.

    2009-01-01

    The paper presents an approach to galvanic skin response (GSR) spontaneous component analysis and modeling. In the study a classification of biofeedback training methods is given, importance of intelligent methods development is shown. The INTENS method, which is perspective for intellectualization, is presented. An important problem of biofeedback training method intellectualization - estimation of the GSR spontaneous component - is solved in the main part of the work. Its main characteristics are described; results of GSR spontaneous component modeling are shown. Results of small research of an optimum material for GSR probes are presented.

  14. Intelligence Ethics:

    DEFF Research Database (Denmark)

    Rønn, Kira Vrist

    2016-01-01

    .e., the search for an ideal ethical framework for intelligence actions). The article concludes that there are many holes to fill for future studies on intelligence ethics both in external and internal discussions. Thus, the article is an invitation – especially, to moral philosophers and political theorists......Questions concerning what constitutes a morally justified conduct of intelligence activities have received increased attention in recent decades. However, intelligence ethics is not yet homogeneous or embedded as a solid research field. The aim of this article is to sketch the state of the art...... of intelligence ethics and point out subjects for further scrutiny in future research. The review clusters the literature on intelligence ethics into two groups: respectively, contributions on external topics (i.e., the accountability of and the public trust in intelligence agencies) and internal topics (i...

  15. The role of empathy and emotional intelligence in nurses' communication attitudes using regression models and fuzzy-set qualitative comparative analysis models (fsQCA).

    Science.gov (United States)

    Del Carmen Giménez-Espert, María; Prado-Gascó, Vicente Javier

    2018-03-01

    To analyse link between empathy and emotional intelligence (EI) as a predictor of nurses' attitudes towards communication while comparing the contribution of emotional aspects and attitudinal elements on potential behaviour. Nurses' attitudes towards communication, empathy and emotional intelligence are key skills for nurses involved in patient care. There are currently no studies analysing this link, and its investigation is needed because attitudes may influence communication behaviours. Correlational study. To attain this goal, self-reported instruments (attitudes towards communication of nurses (ACO), trait emotional intelligence (TMMS24), and Jefferson-scale empathy (JSNE)) were collected from 460 nurses between September 2015 and February 2016. Two different analytical methodologies were used: traditional regression models and fuzzy-set qualitative comparative analysis models (fsQCA). The results of the regression model suggest that cognitive dimensions of attitude are a significant and positive predictor of the behavioural dimension. The perspective-taking dimension of empathy and the emotional-clarity dimension of emotional intelligence were significant positive predictors of the dimensions of attitudes towards communication, except for the affective dimension (for which the association was negative). The results of the fsQCA models confirm that the combination of high levels of cognitive dimension of attitudes, perspective-taking and emotional clarity explained high levels of the behavioural dimension of attitude. Empathy and EI are predictors of nurses' attitudes towards communication, and the cognitive dimension of attitude is a good predictor of the behavioural dimension of ACO in both regression models and fsQCA. In general, the fsQCA models appear to be better predictors than the regression models are. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  16. Emotional Intelligence: Theory and Description--A Competency Model for Interpersonal Effectiveness

    Science.gov (United States)

    Kunnanatt, James Thomas

    2008-01-01

    Purpose: Despite the crucial role that emotional intelligence (EI) could play in improving individuals' performance and career prospects in organizations, employees, executives and career professionals across the world are still in search of practical frameworks for understanding the concept. This is because EI research outputs from academics…

  17. Personality Traits and General Intelligence as Predictors of Academic Performance: A Structural Equation Modelling Approach

    Science.gov (United States)

    Rosander, Pia; Backstrom, Martin; Stenberg, Georg

    2011-01-01

    The aim of the present study was to investigate the extent to which personality traits, after controlling for general intelligence, predict academic performance in different school subjects. Upper secondary school students in Sweden (N=315) completed the Wonderlic IQ test (Wonderlic, 1992) and the IPIP-NEO-PI test (Goldberg, 1999). A series of…

  18. Methods and models for quantative assessment of speech intelligibility in cross-language communication

    NARCIS (Netherlands)

    Wijngaarden, S.J. van; Steeneken, H.J.M.; Houtgast, T.

    2001-01-01

    To deal with the effects of nonnative speech communication on speech intelligibility, one must know the magnitude of these effects. To measure this magnitude, suitable test methods must be available. Many of the methods used in cross-language speech communication research are not very suitable for

  19. An Ambient Intelligent Agent Model for Relapse and Recurrence Monitoring in Unipolar Depression

    NARCIS (Netherlands)

    Aziz, A.A.; Klein, M.C.A.; Treur, J.; Combi, C.; Shahar, Y.; Abu-Hanna, A.

    2009-01-01

    Mental healthcare is a prospective area for applying AI techniques. For example, a computerized system could support individuals with a history of depression in maintaining their well-being throughout their lifetime. In this paper, the design of an ambient intelligent agent to support these

  20. Research contributions to the modelling and design of Intelligent Manufacturing Systems

    DEFF Research Database (Denmark)

    Langer, Gilad; Sørensen, Christian; Stylios, C.

    1999-01-01

    This joint paper is the result of the work of cluster 3-4 within the Esprit WG no. 21955 on Intelligent Manufacturing Systems (IMS) working group. The paper conveys the results of a co-operative research effort between LAR Patras (Greece), DTU (Denmark), CRAN/GSIP (France) and Aachen WZL (Germany...

  1. Joint Intelligence Operations Center (JIOC) Baseline Business Process Model & Capabilities Evaluation Methodology

    Science.gov (United States)

    2012-03-01

    Targeting Review Board OPLAN Operations Plan OPORD Operations Order OPSIT Operational Situation OSINT Open Source Intelligence OV...Analysis Evaluate FLTREPs MISREPs Unit Assign Assets Feedback Asset Shortfalls Multi-Int Collection Political & Embasy Law Enforcement HUMINT OSINT ...Embassy Information OSINT Manage Theater HUMINT Law Enforcement Collection Sort Requests Platform Information Agency Information M-I Collect

  2. A context-aware preference model for database querying in an Ambient Intelligent environment

    NARCIS (Netherlands)

    van Bunningen, A.H.; Feng, L.; Apers, Peter M.G.

    2006-01-01

    Users' preferences have traditionally been exploited in query personalization to better serve their information needs. With the emerging ubiquitous computing technologies, users will be situated in an Ambient Intelligent (AmI) environment, where users' database access will not occur at a single

  3. Using and Evaluating Differential Modeling in Intelligent Tutoring and Apprentice Learning Systems.

    Science.gov (United States)

    1987-01-01

    University of South Carolina Training Command (N-5) Dr. Natalie Dehn Colombia , SC 29208 NAS Pensacola, FL 32508 Department of Computer and Information...DACS- DPM ) ’ 50 Moulton Street Washington, DC 20310 %A Cambridge, MA 02138 Dr. Gerald F. DeJong Artificial Intelligence Grou Dr. Susan Epstein Or. john

  4. Commitment to the Study of International Business and Cultural Intelligence: A Multilevel Model

    Science.gov (United States)

    Ramsey, Jase R.; Barakat, Livia L.; Aad, Amine Abi

    2014-01-01

    Adopting a multilevel theoretical framework, we examined how metacognitive and motivational cultural intelligence influence an individual's commitment to the study of international business (IB). Data from 292 undergraduate and graduate business students nested in 12 U.S. business school classes demonstrated that individuals' metacognitive and…

  5. An Agent-Based Model for the Development of Intelligent Mobile Services

    NARCIS (Netherlands)

    Koch, F.L.

    2009-01-01

    The next generation of mobile services must invisible, convenient, and useful. It requires new techniques to design and develop mobile computing applications, based on user-centred, environment-aware, adaptive behaviour. I propose an alternative technology for the development of intelligent mobile

  6. Intelligence Naturelle et Intelligence Artificielle

    OpenAIRE

    Dubois, Daniel

    2011-01-01

    Cet article présente une approche systémique du concept d’intelligence naturelle en ayant pour objectif de créer une intelligence artificielle. Ainsi, l’intelligence naturelle, humaine et animale non-humaine, est une fonction composée de facultés permettant de connaître et de comprendre. De plus, l'intelligence naturelle reste indissociable de la structure, à savoir les organes du cerveau et du corps. La tentation est grande de doter les systèmes informatiques d’une intelligence artificielle ...

  7. Nephrus: expert system model in intelligent multilayers for evaluation of urinary system based on scintigraphic image analysis

    International Nuclear Information System (INIS)

    Silva, Jorge Wagner Esteves da; Schirru, Roberto; Boasquevisque, Edson Mendes

    1999-01-01

    Renal function can be measured noninvasively with radionuclides in a extremely safe way compared to other diagnosis techniques. Nevertheless, due to the fact that radioactive materials are used in this procedure, it is necessary to maximize its benefits, therefore all efforts are justifiable in the development of data analysis support tools for this diagnosis modality. The objective of this work is to develop a prototype for a system model based on Artificial Intelligence devices able to perform functions related to cintilographic image analysis of the urinary system. Rules used by medical experts in the analysis of images obtained with 99m Tc+DTPA and /or 99m Tc+DMSA were modeled and a Neural Network diagnosis technique was implemented. Special attention was given for designing programs user-interface. Human Factor Engineering techniques were taking in account allowing friendliness and robustness. The image segmentation adopts a model based on Ideal ROIs, which represent the normal anatomic concept for urinary system organs. Results obtained using Artificial Neural Networks for qualitative image analysis and knowledge model constructed show the feasibility of Artificial Neural Networks for qualitative image analysis and knowledge model constructed show feasibility of Artificial Intelligence implementation that uses inherent abilities of each technique in the medical diagnosis image analysis. (author)

  8. Approaching Career Criminals With An Intelligence Cycle

    Science.gov (United States)

    2015-12-01

    adventures. The expected time remaining is a criminal career is five years for active offenders in their late teens while the expected time remaining...intelligence community tool—the intelligence cycle—to deal with career criminals effectively? This thesis studies serious- offender programs and the...use of the intelligence cycle by American intelligence agencies in order to create a model merging serious offender programs and intelligence cycles

  9. Toward intelligent flight control

    Science.gov (United States)

    Stengel, Robert F.

    1993-01-01

    Flight control systems can benefit by being designed to emulate functions of natural intelligence. Intelligent control functions fall in three categories: declarative, procedural, and reflexive. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are more-or-less spontaneous and are similar to inner-loop control and estimation. Intelligent flight control systems will contain a hierarchy of expert systems, procedural algorithms, and computational neural networks, each expanding on prior functions to improve mission capability to increase the reliability and safety of flight and to ease pilot workload.

  10. Modeling Mental Speed: Decomposing Response Time Distributions in Elementary Cognitive Tasks and Correlations with Working Memory Capacity and Fluid Intelligence

    Directory of Open Access Journals (Sweden)

    Florian Schmitz

    2016-10-01

    Full Text Available Previous research has shown an inverse relation between response times in elementary cognitive tasks and intelligence, but findings are inconsistent as to which is the most informative score. We conducted a study (N = 200 using a battery of elementary cognitive tasks, working memory capacity (WMC paradigms, and a test of fluid intelligence (gf. Frequently used candidate scores and model parameters derived from the response time (RT distribution were tested. Results confirmed a clear correlation of mean RT with WMC and to a lesser degree with gf. Highly comparable correlations were obtained for alternative location measures with or without extreme value treatment. Moderate correlations were found as well for scores of RT variability, but they were not as strong as for mean RT. Additionally, there was a trend towards higher correlations for slow RT bands, as compared to faster RT bands. Clearer evidence was obtained in an ex-Gaussian decomposition of the response times: the exponential component was selectively related to WMC and gf in easy tasks, while mean response time was additionally predictive in the most complex tasks. The diffusion model parsimoniously accounted for these effects in terms of individual differences in drift rate. Finally, correlations of model parameters as trait-like dispositions were investigated across different tasks, by correlating parameters of the diffusion and the ex-Gaussian model with conventional RT and accuracy scores.

  11. Computational Intelligence in Intelligent Data Analysis

    CERN Document Server

    Nürnberger, Andreas

    2013-01-01

    Complex systems and their phenomena are ubiquitous as they can be found in biology, finance, the humanities, management sciences, medicine, physics and similar fields. For many problems in these fields, there are no conventional ways to mathematically or analytically solve them completely at low cost. On the other hand, nature already solved many optimization problems efficiently. Computational intelligence attempts to mimic nature-inspired problem-solving strategies and methods. These strategies can be used to study, model and analyze complex systems such that it becomes feasible to handle them. Key areas of computational intelligence are artificial neural networks, evolutionary computation and fuzzy systems. As only a few researchers in that field, Rudolf Kruse has contributed in many important ways to the understanding, modeling and application of computational intelligence methods. On occasion of his 60th birthday, a collection of original papers of leading researchers in the field of computational intell...

  12. Artificial intelligence versus statistical modeling and optimization of continuous bead milling process for bacterial cell lysis

    Directory of Open Access Journals (Sweden)

    Shafiul Haque

    2016-11-01

    Full Text Available AbstractFor a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD was studied in a continuous bead milling process. A full factorial Response Surface Model (RSM design was employed and compared to Artificial Neural Networks coupled with Genetic Algorithm (ANN-GA. Significant process variables, cell slurry feed rate (A, bead load (B, cell load (C and run time (D, were investigated and optimized for maximizing COD recovery. RSM predicted an optimum of feed rate of 310.73 mL/h, bead loading of 79.9% (v/v, cell loading OD600 nm of 74, and run time of 29.9 min with a recovery of ~3.2 g/L. ANN coupled with GA predicted a maximum COD recovery of ~3.5 g/L at an optimum feed rate (mL/h: 258.08, bead loading (%, v/v: 80%, cell loading (OD600 nm: 73.99, and run time of 32 min. An overall 3.7-fold increase in productivity is obtained when compared to a batch process. Optimization and comparison of statistical vs. artificial intelligence techniques in continuous bead milling process has been attempted for the very first time in our study. We were able to successfully represent the complex non-linear multivariable dependence of enzyme recovery on bead milling parameters. The quadratic second order response functions are not flexible enough to represent such complex non-linear dependence. ANN being a summation function of multiple layers are capable to represent complex non-linear dependence of variables in this case; enzyme recovery as a function of bead milling parameters. Since GA can even optimize discontinuous functions present study cites a perfect example of using machine learning (ANN in combination with evolutionary optimization (GA for representing undefined biological functions which is the case for common industrial processes involving biological moieties.

  13. Development of a Techno-economic Model of Intelligent Transportation System (ITS) for Deployment in Ghana

    DEFF Research Database (Denmark)

    Adjin, Daniel Michael Okwabi; Tadayoni, Reza

    2011-01-01

    The concept of Intelligent Transportation System (ITS) is about the development and deployment of advanced Traffic Management Systems, Traveler Information Systems, Commercial Vehicle Operations, Public and Private Transportation Systems, and Rural Transportation Systems. Several key technologies......, the paper looks at how these modern technologies can be deployed in developing countries, with emphasis on wireless communications applications which will enable developing countries to take off smoothly and progress into their emerging economies successfully. In this paper we have looked at the key....... The results show that deployment of Intelligent Vehicle Tracking Technology (IVTT) will address the problems of inefficiencies experienced in the Ghanaian road transport haulage tracking industry. Research for ITS development and eployment in these countries should be cost effective....

  14. Progressions of Qualitative Models as a Foundation for Intelligent Learning Environments

    Science.gov (United States)

    1986-05-01

    J. (1979). Causal and teleological reasoning in circuit recognition. TR-529. MIT’ Artificial Intelligence Laboratory. Cambridge. MA. deler. J. (1995...Soloway. E. (1984). Intention-based diagnosis of progranming errors. In Procedings of the National Conference on Artificial jn~ience. Austin. Texas: NCAI...examples. Cognitive Psychology 17, 26-65. O’Shea, T. (1982). A sell-improving quadratic tutor. in Sleeman. D., & Brown. 3. S. (Eds.). Inteligent Tutoring

  15. Mathematic Modeling and Performance Analysis of an Adaptive Congestion Control in Intelligent Transportation Systems

    OpenAIRE

    Naja, Rola; Université de Versailles

    2015-01-01

    In this paper, we develop a preventive congestion control mechanism applied at highway entrances and devised for Intelligent Transportation Systems (ITS). The proposed mechanism provides a vehicular admission control, regulates input traffic and performs vehicular traffic shaping. Our congestion control mechanism includes two classes of vehicles and is based on a specific priority ticket pool scheme with queue-length threshold scheduling policy, tailored to vehicular networks. In an attempt t...

  16. Developing a fluid intelligence scale through a combination of Rasch modeling and cognitive psychology.

    Science.gov (United States)

    Primi, Ricardo

    2014-09-01

    Ability testing has been criticized because understanding of the construct being assessed is incomplete and because the testing has not yet been satisfactorily improved in accordance with new knowledge from cognitive psychology. This article contributes to the solution of this problem through the application of item response theory and Susan Embretson's cognitive design system for test development in the development of a fluid intelligence scale. This study is based on findings from cognitive psychology; instead of focusing on the development of a test, it focuses on the definition of a variable for the creation of a criterion-referenced measure for fluid intelligence. A geometric matrix item bank with 26 items was analyzed with data from 2,797 undergraduate students. The main result was a criterion-referenced scale that was based on information from item features that were linked to cognitive components, such as storage capacity, goal management, and abstraction; this information was used to create the descriptions of selected levels of a fluid intelligence scale. The scale proposed that the levels of fluid intelligence range from the ability to solve problems containing a limited number of bits of information with obvious relationships through the ability to solve problems that involve abstract relationships under conditions that are confounded with an information overload and distraction by mixed noise. This scale can be employed in future research to provide interpretations for the measurements of the cognitive processes mastered and the types of difficulty experienced by examinees. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  17. Development of a semantic-enabled cybersecurity threat intelligence sharing model

    CSIR Research Space (South Africa)

    Mtsweni, Jabu

    2016-03-01

    Full Text Available complex sets of information” that is continuously increasing in volume, velocity, and variety (Khan et al., 2014; Zikopoulos, Eaton, & DeRoos, 2012). Although, Big Data presents various opportunities for organisations (Kaisler, Armour, Espinosa, & Money... strategy. In the context of the government and military environments, intelligence is a well-understood concept and involves the collection, analysis, and interpretation of information for battlespace awareness (Waltz, 1998), and eventually for decision...

  18. Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

    OpenAIRE

    Daryasafar, Amin; Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulat...

  19. Distributed Model Predictive Control over Multiple Groups of Vehicles in Highway Intelligent Space for Large Scale System

    Directory of Open Access Journals (Sweden)

    Tang Xiaofeng

    2014-01-01

    Full Text Available The paper presents the three time warning distances for solving the large scale system of multiple groups of vehicles safety driving characteristics towards highway tunnel environment based on distributed model prediction control approach. Generally speaking, the system includes two parts. First, multiple vehicles are divided into multiple groups. Meanwhile, the distributed model predictive control approach is proposed to calculate the information framework of each group. Each group of optimization performance considers the local optimization and the neighboring subgroup of optimization characteristics, which could ensure the global optimization performance. Second, the three time warning distances are studied based on the basic principles used for highway intelligent space (HIS and the information framework concept is proposed according to the multiple groups of vehicles. The math model is built to avoid the chain avoidance of vehicles. The results demonstrate that the proposed highway intelligent space method could effectively ensure driving safety of multiple groups of vehicles under the environment of fog, rain, or snow.

  20. Assessing Intelligent Models in Forecasting Monthly Rainfall by Means of Teleconnection Patterns (Case Study: Khorasan Razavi Province

    Directory of Open Access Journals (Sweden)

    Farzaneh Nazarieh

    2016-02-01

    Full Text Available Introduction: Rainfall is affected by changes in the global sea level change, especially changes in sea surface temperature SST Sea Surface Temperature and sea level pressure SLP Sea level Pressure. Climate anomalies being related to each other at large distance is called teleconnection. As physical relationships between rainfall and teleconnection patterns are not defined clearly, we used intelligent models for forecasting rainfall. The intelligent models used in this study included Fuzzy Inference Systems, neural network and Neuro-fuzzy. In this study, first the teleconnection indices that could affect rainfall in the study area were identified. Then intelligent models were trained for rainfall forecasting. Finally, the most capable model for forecasting rainfall was presented. The study area for this research is the Khorasan Razavi Province. In order to present a model for rainfall forecasting, rainfall data of seven synoptic stations including Mashhad, Golmakan, Nishapur, Sabzevar, Kashmar, Torbate and Sharks since 1991 to 2010 were used. Materials and Methods: Based on previous studies about Teleconnection Patterns in the study area, effective Teleconnection indexes were identified. After calculating the correlation between the identified teleconnection indices and rainfall in one, two and three months ahead for all stations, fourteen teleconnection indices were chosen as inputs for intelligent models. These indices include, SLP Adriatic , SLP northern Red Sea, SLP Mediterranean Sea, SLP Aral sea, SST Sea surface temperature Labrador sea, SST Oman Sea, SST Caspian Sea, SST Persian Gulf, North Pacific pattern, SST Tropical Pacific in NINO12 and NINO3 regions, North Pacific Oscillation, Trans-Nino Index, Multivariable Enso Index. Inputs of the intelligent models include fourteen teleconnection indices, latitude and altitude of each station and their outputs are the prediction of rainfall for one, two and three months ahead. For calibration of

  1. Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods.

    Science.gov (United States)

    Ahmad, Jamal; Javed, Faisal; Hayat, Maqsood

    2017-05-01

    Golgi is one of the core proteins of a cell, constitutes in both plants and animals, which is involved in protein synthesis. Golgi is responsible for receiving and processing the macromolecules and trafficking of newly processed protein to its intended destination. Dysfunction in Golgi protein is expected to cause many neurodegenerative and inherited diseases that may be cured well if they are detected effectively and timely. Golgi protein is categorized into two parts cis-Golgi and trans-Golgi. The identification of Golgi protein via direct method is very hard due to limited available recognized structures. Therefore, the researchers divert their attention toward the sequences from structures. However, owing to technological advancement, exploration of huge amount of sequences was reported in the databases. So recognition of large amount of unprocessed data using conventional methods is very difficult. Therefore, the concept of intelligence was incorporated with computational model. Intelligence based computational model obtained reasonable results, but the gap of improvement is still under consideration. In this regard, an intelligent automatic recognition model is developed in order to enhance the true classification rate of sub-Golgi proteins. In this approach, discrete and evolutionary feature extraction methods are applied on the benchmark Golgi protein datasets to excerpt salient, propound and variant numerical descriptors. After that, an oversampling technique Syntactic Minority over Sampling Technique is employed to balance the data. Hybrid spaces are also generated with combination of these feature spaces. Further, Fisher feature selection method is utilized to reduce the extra noisy and redundant features from feature vector. Finally, k-nearest neighbor algorithm is used as learning hypothesis. Three distinct cross validation tests are used to examine the stability and efficiency of the proposed model. The predicted outcomes of proposed model are better

  2. Genes, evolution and intelligence.

    Science.gov (United States)

    Bouchard, Thomas J

    2014-11-01

    I argue that the g factor meets the fundamental criteria of a scientific construct more fully than any other conception of intelligence. I briefly discuss the evidence regarding the relationship of brain size to intelligence. A review of a large body of evidence demonstrates that there is a g factor in a wide range of species and that, in the species studied, it relates to brain size and is heritable. These findings suggest that many species have evolved a general-purpose mechanism (a general biological intelligence) for dealing with the environments in which they evolved. In spite of numerous studies with considerable statistical power, we know of very few genes that influence g and the effects are very small. Nevertheless, g appears to be highly polygenic. Given the complexity of the human brain, it is not surprising that that one of its primary faculties-intelligence-is best explained by the near infinitesimal model of quantitative genetics.

  3. Soft computing in artificial intelligence

    CERN Document Server

    Matson, Eric

    2014-01-01

    This book explores the concept of artificial intelligence based on knowledge-based algorithms. Given the current hardware and software technologies and artificial intelligence theories, we can think of how efficient to provide a solution, how best to implement a model and how successful to achieve it. This edition provides readers with the most recent progress and novel solutions in artificial intelligence. This book aims at presenting the research results and solutions of applications in relevance with artificial intelligence technologies. We propose to researchers and practitioners some methods to advance the intelligent systems and apply artificial intelligence to specific or general purpose. This book consists of 13 contributions that feature fuzzy (r, s)-minimal pre- and β-open sets, handling big coocurrence matrices, Xie-Beni-type fuzzy cluster validation, fuzzy c-regression models, combination of genetic algorithm and ant colony optimization, building expert system, fuzzy logic and neural network, ind...

  4. Illusory Intelligences?

    Science.gov (United States)

    White, John

    2008-01-01

    Howard Gardner's theory of Multiple Intelligences has had a huge influence on school education. But its credentials lack justification, as the first section of this paper shows via a detailed philosophical analysis of how the intelligences are identified. If we want to make sense of the theory, we need to turn from a philosophical to a historical…

  5. Spanish Intelligence

    OpenAIRE

    García Sanz, Carolina

    2014-01-01

    [EN]Spain became a centre of international espionage in 1914. Its strategic location in terms of the Anglo-French trade soon attracted German attention. The Germans waged an economic and propaganda war against the Allies from Spain, while the British took advantage of the Gibraltar Intelligence Centre. The French and Italian intelligence networks also spread out around Spain.

  6. Intelligent Design

    DEFF Research Database (Denmark)

    Hjorth, Poul G.

    2005-01-01

    Forestillingen om at naturen er designet af en guddommelig 'intelligens' er et smukt filosofisk princip. Teorier om Intelligent Design som en naturvidenskabeligt baseret teori er derimod helt forfærdelig.......Forestillingen om at naturen er designet af en guddommelig 'intelligens' er et smukt filosofisk princip. Teorier om Intelligent Design som en naturvidenskabeligt baseret teori er derimod helt forfærdelig....

  7. Artificial Intelligence.

    Science.gov (United States)

    Wash, Darrel Patrick

    1989-01-01

    Making a machine seem intelligent is not easy. As a consequence, demand has been rising for computer professionals skilled in artificial intelligence and is likely to continue to go up. These workers develop expert systems and solve the mysteries of machine vision, natural language processing, and neural networks. (Editor)

  8. Modelling public transport passenger flows in the era of intelligent transport systems COST Action TU1004 (TransITs)

    CERN Document Server

    Noekel, Klaus

    2016-01-01

    This book shows how transit assignment models can be used to describe and predict the patterns of network patronage in public transport systems. It provides a fundamental technical tool that can be employed in the process of designing, implementing and evaluating measures and/or policies to improve the current state of transport systems within given financial, technical and social constraints. The book offers a unique methodological contribution to the field of transit assignment because, moving beyond “traditional” models, it describes more evolved variants that can reproduce: • intermodal networks with high- and low-frequency services; • realistic behavioural hypotheses underpinning route choice; • time dependency in frequency-based models; and • assumptions about the knowledge that users have of network conditions that are consistent with the present and future level of information that intelligent transport systems (ITS) can provide. The book also considers the practical perspective of practit...

  9. Mindfulness facets, trait emotional intelligence, emotional distress, and multiple health behaviors: A serial two-mediator model.

    Science.gov (United States)

    Jacobs, Ingo; Wollny, Anna; Sim, Chu-Won; Horsch, Antje

    2016-06-01

    In the present study, we tested a serial mindfulness facets-trait emotional intelligence (TEI)-emotional distress-multiple health behaviors mediation model in a sample of N = 427 German-speaking occupational therapists. The mindfulness facets-TEI-emotional distress section of the mediation model revealed partial mediation for the mindfulness facets Act with awareness (Act/Aware) and Accept without judgment (Accept); inconsistent mediation was found for the Describe facet. The serial two-mediator model included three mediational pathways that may link each of the four mindfulness facets with multiple health behaviors. Eight out of 12 indirect effects reached significance and fully mediated the links between Act/Aware and Describe to multiple health behaviors; partial mediation was found for Accept. The mindfulness facet Observe was most relevant for multiple health behaviors, but its relation was not amenable to mediation. Implications of the findings will be discussed. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

  10. D&T: An Euclidean Distance Optimization based Intelligent Donation System Model for Solving the Community’s Problem

    Science.gov (United States)

    Utama, D. N.; Fitroh; Nuryasin; Rustamaji, E.; Nurbojatmiko; Qoyim, I.

    2017-01-01

    The trust is a main difficulty to propose a donation system to the community. A specific information system is scientifically estimated able to escalate the trust level of one community in donating; where, their donation can reinforce them to solve the socioeconomic problem in one region. The concept of fuzzy-logic has been practically embedded in measuring an inequality index of socioeconomic aspect, particularly for health and education sectors. Moreover, the concept of the Euclidean distance measurement is operated to measure the distance value of two parameters (geographical location and inequality). The hill-climbing optimization method that can recommend the most recommended donation recipient is embedded into system model to meet donor and recipient of donation. Here the intelligent donation system model is scientifically constructed. The proposed system model undoubtedly can solve the socioeconomic problem in one community. In this study, the urban village Sawah, Ciputat, Indonesia was taken as an object of the research where the empirical data coming from.

  11. The Internal Structure of Responses to the Trait Emotional Intelligence Questionnaire-Short Form: An Exploratory Structural Equation Modeling Approach.

    Science.gov (United States)

    Perera, Harsha N

    2015-01-01

    Notwithstanding the wide use of the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) as a brief assessment of trait emotional intelligence (TEI), the psychometric properties of this measure have not been systematically examined. This article reports on research conducted to evaluate the latent structure underlying TEIQue-SF item data and test the gender invariance of scores as critical initial steps in determining the psychometric robustness of the inventory. In doing so, the article demonstrates an application of exploratory structural equation modeling as an alternative to the more restrictive independent clusters model of confirmatory factor analysis for examining factorially complex personality data. On the basis of 476 responses to the TEIQue-SF, evidence was obtained for the multidimensionality of the inventory reflected in a retained correlated traits solution. Tests of gender invariance revealed equivalence of item factor loadings, intercepts, uniquenesses, correlated uniquenesses, and the factor variance-covariance matrix, but not latent means. Men were found to be moderately higher on self-control and sociability than women, whereas women scored marginally higher on emotionality than men. No significant gender differences were found on mean levels of well-being. The benefits of the multidimensionality of the TEIQue-SF, limitations of the study, and directions for future research are discussed.

  12. Artificial Intelligence in Civil Engineering

    OpenAIRE

    Lu, Pengzhen; Chen, Shengyong; Zheng, Yujun

    2012-01-01

    Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applicati...

  13. Intention recognition, commitment and their roles in the evolution of cooperation from artificial intelligence techniques to evolutionary game theory models

    CERN Document Server

    Han, The Anh

    2013-01-01

    This original and timely monograph describes a unique self-contained excursion that reveals to the readers the roles of two basic cognitive abilities, i.e. intention recognition and arranging commitments, in the evolution of cooperative behavior. This book analyses intention recognition, an important ability that helps agents predict others’ behavior, in its artificial intelligence and evolutionary computational modeling aspects, and proposes a novel intention recognition method. Furthermore, the book presents a new framework for intention-based decision making and illustrates several ways in which an ability to recognize intentions of others can enhance a decision making process. By employing the new intention recognition method and the tools of evolutionary game theory, this book introduces computational models demonstrating that intention recognition promotes the emergence of cooperation within populations of self-regarding agents. Finally, the book describes how commitment provides a pathway to the evol...

  14. Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.

    Science.gov (United States)

    Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid

    2016-07-01

    Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  15. An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems

    Energy Technology Data Exchange (ETDEWEB)

    El Hariri, Mohamad [Florida Intl Univ., Miami, FL (United States); Faddel, Samy [Florida Intl Univ., Miami, FL (United States); Mohammed, Osama [Florida Intl Univ., Miami, FL (United States)

    2017-11-01

    Decentralized and hierarchical microgrid control strategies have lain the groundwork for shaping the future smart grid. Such control approaches require the cooperation between microgrid operators in control centers, intelligent microcontrollers, and remote terminal units via secure and reliable communication networks. In order to enhance the security and complement the work of network intrusion detection systems, this paper presents an artificially intelligent physical model-checking that detects tampered-with circuit breaker switching control commands whether, due to a cyber-attack or human error. In this technique, distributed agents, which are monitoring sectionalized areas of a given microgrid, will be trained and continuously adapted to verify that incoming control commands do not violate the physical system operational standards and do not put the microgrid in an insecure state. The potential of this approach has been tested by deploying agents that monitor circuit breakers status commands on a 14-bus IEEE benchmark system. The results showed the accuracy of the proposed framework in characterizing the power system and successfully detecting malicious and/or erroneous control commands.

  16. Modeling and simulation of adaptive Neuro-fuzzy based intelligent system for predictive stabilization in structured overlay networks

    Directory of Open Access Journals (Sweden)

    Ramanpreet Kaur

    2017-02-01

    Full Text Available Intelligent prediction of neighboring node (k well defined neighbors as specified by the dht protocol dynamism is helpful to improve the resilience and can reduce the overhead associated with topology maintenance of structured overlay networks. The dynamic behavior of overlay nodes depends on many factors such as underlying user’s online behavior, geographical position, time of the day, day of the week etc. as reported in many applications. We can exploit these characteristics for efficient maintenance of structured overlay networks by implementing an intelligent predictive framework for setting stabilization parameters appropriately. Considering the fact that human driven behavior usually goes beyond intermittent availability patterns, we use a hybrid Neuro-fuzzy based predictor to enhance the accuracy of the predictions. In this paper, we discuss our predictive stabilization approach, implement Neuro-fuzzy based prediction in MATLAB simulation and apply this predictive stabilization model in a chord based overlay network using OverSim as a simulation tool. The MATLAB simulation results present that the behavior of neighboring nodes is predictable to a large extent as indicated by the very small RMSE. The OverSim based simulation results also observe significant improvements in the performance of chord based overlay network in terms of lookup success ratio, lookup hop count and maintenance overhead as compared to periodic stabilization approach.

  17. Design, Modelling, and Implementation of a Fuzzy Controller for an Intelligent Road Signaling System

    Directory of Open Access Journals (Sweden)

    José Manuel Lozano Domínguez

    2018-01-01

    Full Text Available Crossing points are not always 100% visible for drivers due to different factors (e.g., poor road maintenance, occlusion of vertical signs, and adverse weather conditions. USA estimated in 2015 the number of traffic accidents involving pedestrians and vehicles in 70,000 of whom 5,376 resulted in deceased people. To contribute in this field, this paper presents the design, implementation, and testing of a smart prototype system applied to pedestrian crossings—not regulated by semaphores—which try to reduce the accident rate on roads. The hardware and software system consists of a set of autonomous, intelligent, and wireless low-cost devices that generate a visual warning barrier perceived by drivers from a suitable distance when pedestrians traverse a crosswalk. In this way, drivers can reduce the speed of their vehicles and stop safely. The system’s intelligence is carried out by a fuzzy controller that performs sensory fusion at both low level and high level with various types of sensors from local and neighboring devices. The tests conducted have determined an average success of 94.64% and a precision of 100%, thus corresponding with a very good test according to a ROC analysis. As a result, the system proposed has been patented and extended to international PCT.

  18. The location of the Trait Emotional Intelligence in the Zuckerman's Personality Model space and the role of General Intelligence and social status.

    Science.gov (United States)

    Blanco, Eduardo; García, Luis Francisco; Aluja, Anton

    2016-10-01

    The aim of this study was to investigate the relationships between Emotional Intelligence (EI) measured by the Trait Emotional Intelligence Questionnaire (TEIQue) and personality measured by the Zuckerman-Kuhlman-Aluja Personality Questionnaire (ZKA-PQ) with the purpose of analyzing similarities and differences of both psychological constructs. Additionally, we studied the relationship among EI, personality, General Intelligence (GI) and a social position index (SPI). Results showed that the ZKA-PQ predicts the 66% (facets) and the 64% (factors) of the TEIQue. High scores in EI correlated negatively with Neuroticism (r: -0.66) and Aggressiveness (r: -0.27); and positively with Extraversion (r: 0.62). Oblique factorial analyses demonstrated that TEIQue scales were located basically in the Neuroticism and Extraversion factors. The SPI and GI no loaded in any factor. These findings showed that EI is a not a distinct construct of personality and it cannot be isolated in the ZKA-PQ personality space. GI is related with the SPI (r: 0.26), and EI correlated with GI (r: 0.18) and SPI (r: 0.16). Nevertheless, we found differences between GI high groups and the TEIQue and ZKA-PQ factors when controlling age and sex. These findings are discussed in the individual differences context. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

  19. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes

    Directory of Open Access Journals (Sweden)

    Igor Vyacheslavovich Buzaev

    2016-09-01

    Full Text Available Objective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. Method: aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG and percutaneous coronary intervention (PCI in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. Results: The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient (r of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679 vs. 20.3% (87/428, P = 0.065]. Conclusion: The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina. Keywords: Coronary artery bypass grafting, Percutaneous coronary intervention, Artificial intelligence, Decision making

  20. A model of strategic intelligence of industrial property information and in the field of clean energy; Un modelo de inteligencia estrategica de la informacion y propiedad industrial en el campo de las energias limpias

    Energy Technology Data Exchange (ETDEWEB)

    Fuente O' Conor, J. L. de la

    2011-07-01

    In this article, the model Intelligence Cell, as an Internet based strategic intelligence element for the management of interim key information of a company, is presented. The model has been profusely tested for technology watch, economic intelligence, and competitive intelligence related purposes within a general framework of technology deployment in a clean and renewable focused innovation department. As a practical example, is proposed, step by step, how to launch a fully operative specialized cell for analyzing how to deal with electric mobility, battery technologies and electric car issues so that challenges, opportunities, and threats can be tracked and anticipated so that a business strategy might be established. (Author) 8 refs.

  1. Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

    Science.gov (United States)

    Munteanu, Cristian R; Gonzalez-Diaz, Humberto; Garcia, Rafael; Loza, Mabel; Pazos, Alejandro

    2015-01-01

    The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.

  2. Artificial intelligence

    CERN Document Server

    Ennals, J R

    1987-01-01

    Artificial Intelligence: State of the Art Report is a two-part report consisting of the invited papers and the analysis. The editor first gives an introduction to the invited papers before presenting each paper and the analysis, and then concludes with the list of references related to the study. The invited papers explore the various aspects of artificial intelligence. The analysis part assesses the major advances in artificial intelligence and provides a balanced analysis of the state of the art in this field. The Bibliography compiles the most important published material on the subject of

  3. Artificial Intelligence

    CERN Document Server

    Warwick, Kevin

    2011-01-01

    if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined whether machines can 'think' sensory

  4. Intelligent playgrounds

    DEFF Research Database (Denmark)

    Larsen, Lasse Juel

    2009-01-01

    This paper examines play, gaming and learning in regard to intelligent playware developed for outdoor use. The key questions are how does these novel artefacts influence the concept of play, gaming and learning. Up until now play and game have been understood as different activities. This paper...... examines if the sharp differentiation between the two can be uphold in regard to intelligent playware for outdoor use. Play and game activities will be analysed and viewed in conjunction with learning contexts. This paper will stipulate that intelligent playware facilitates rapid shifts in contexts...

  5. Effect of roll compaction on granule size distribution of microcrystalline cellulose–mannitol mixtures: computational intelligence modeling and parametric analysis

    Directory of Open Access Journals (Sweden)

    Kazemi P

    2017-01-01

    Full Text Available Pezhman Kazemi,1 Mohammad Hassan Khalid,1 Ana Pérez Gago,2 Peter Kleinebudde,2 Renata Jachowicz,1 Jakub Szlęk,1 Aleksander Mendyk1 1Department of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy, Jagiellonian University Medical College, Krakow, Poland; 2Institute of Pharmaceutics and Biopharmaceutics, Heinrich-Heine-University, Düsseldorf, Germany Abstract: Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of

  6. Effect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures: computational intelligence modeling and parametric analysis.

    Science.gov (United States)

    Kazemi, Pezhman; Khalid, Mohammad Hassan; Pérez Gago, Ana; Kleinebudde, Peter; Jachowicz, Renata; Szlęk, Jakub; Mendyk, Aleksander

    2017-01-01

    Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination ( R 2 ) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R 2 =0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.

  7. Intelligent indexing

    International Nuclear Information System (INIS)

    Farkas, J.

    1992-01-01

    In this paper we discuss the relevance of artificial intelligence to the automatic indexing of natural language text. We describe the use of domain-specific semantically-based thesauruses and address the problem of creating adequate knowledge bases for intelligent indexing systems. We also discuss the relevance of the Hilbert space ι 2 to the compact representation of documents and to the definition of the similarity of natural language texts. (author). 17 refs., 2 figs

  8. Artificial intelligence for automotive applications - noise modelling; Kuenstliche Intelligenz fuer Fahrzeuganwendungen - Modellierung des Motorgeraeusches

    Energy Technology Data Exchange (ETDEWEB)

    Suchar, R. [nodes GmbH, Muenchen (Germany)

    2000-12-01

    Reducing the noise level in the interior of vehicles is both a comfort necessity and a challenging technical task. Reliable identification of the accessories responsible for high or low engine-noise levels in the interior of cars classically requires costly, time-consuming, extensive measurement sessions. Artificial intelligence tools efficiently solve the mentioned system-identification problem, and provide a principled way of extracting the information of interest from reduced size databases. (orig.) [German] Kuenstliche neuronale Netze, Fuzzy-Systeme, genetische Algorithmen - vor einem Jahrzehnt haette man gesagt, diese Begriffe stammen aus einem Science-Fiction-Roman. Heute verwenden sie Forscher an Hochschulen und in der Industrie mit dem gemeinsamen Ziel, kuenstliche Intelligenz (KI) in Real-Life-Projekte zu integrieren. Waehrend das Spektrum technischer Applikationen und kommerzieller Produkte sehr weit ist, konzentriert sich der vorliegende Beitrag auf eine spezielle Fahrzeuganwendung, die auf kuenstlichen neuronalen Netzen (KNN) basiert: die Ermittlung der Ausstattungsmerkmale, die fuer hohen Motorgeraeuschpegel im Fahrzeuginneren verantwortlich sind. (orig.)

  9. Artificial Intelligence and Science Education.

    Science.gov (United States)

    Good, Ron

    1987-01-01

    Defines artificial intelligence (AI) in relation to intelligent computer-assisted instruction (ICAI) and science education. Provides a brief background of AI work, examples of expert systems, examples of ICAI work, and addresses problems facing AI workers that have implications for science education. Proposes a revised model of the Karplus/Renner…

  10. Stupid Tutoring Systems, Intelligent Humans

    Science.gov (United States)

    Baker, Ryan S.

    2016-01-01

    The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development,…

  11. Openness as a buffer against cognitive decline: The Openness-Fluid-Crystallized-Intelligence (OFCI) model applied to late adulthood.

    Science.gov (United States)

    Ziegler, Matthias; Cengia, Anja; Mussel, Patrick; Gerstorf, Denis

    2015-09-01

    Explaining cognitive decline in late adulthood is a major research area. Models using personality traits as possible influential variables are rare. This study tested assumptions based on an adapted version of the Openness-Fluid-Crystallized-Intelligence (OFCI) model. The OFCI model adapted to late adulthood predicts that openness is related to the decline in fluid reasoning (Gf) through environmental enrichment. Gf should be related to the development of comprehension knowledge (Gc; investment theory). It was also assumed that Gf predicts changes in openness as suggested by the environmental success hypothesis. Finally, the OFCI model proposes that openness has an indirect influence on the decline in Gc through its effect on Gf (mediation hypothesis). Using data from the Berlin Aging Study (N = 516, 70-103 years at T1), these predictions were tested using latent change score and latent growth curve models with indicators of each trait. The current findings and prior research support environmental enrichment and success, investment theory, and partially the mediation hypotheses. Based on a summary of all findings, the OFCI model for late adulthood is suggested. (c) 2015 APA, all rights reserved).

  12. Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.

    Directory of Open Access Journals (Sweden)

    Jorge Gago

    Full Text Available Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale. Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology.In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2 s(-1.Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

  13. Co-evolution of intelligent socio-technical systems modelling and applications in large scale emergency and transport domains

    CERN Document Server

    2013-01-01

    As the interconnectivity between humans through technical devices is becoming ubiquitous, the next step is already in the making: ambient intelligence, i.e. smart (technical) environments, which will eventually play the same active role in communication as the human players, leading to a co-evolution in all domains where real-time communication is essential. This topical volume, based on the findings of the Socionical European research project, gives equal attention to two highly relevant domains of applications: transport, specifically traffic, dynamics from the viewpoint of a socio-technical interaction and evacuation scenarios for large-scale emergency situations. Care was taken to investigate as much as possible the limits of scalability and to combine the modeling using complex systems science approaches with relevant data analysis.

  14. Model Predictive Controller for Active Demand Side Management with PV Self-consumption in an Intelligent Building

    DEFF Research Database (Denmark)

    Zong, Yi; Mihet-Popa, Lucian; Kullmann, Daniel

    2012-01-01

    This paper presents a Model Predictive Controller (MPC) for electrical heaters’ predictive power consumption including maximizing the use of local generation (e.g. solar power) in an intelligent building. The MPC is based on dynamic power price and weather forecast, considering users’ comfort set...... for Active Demand Side Management (ADSM) can dramatically save energy and improve grid reliability, when there is a high penetration of Renewable Energy Sources (RESs) in the power system. © 2012 IEEE...... settings to meet an optimization objective such as minimum cost and minimum reference temperature error. It demonstrates that this MPC strategy can realize load shifting, and maximize the PV self-consumption in the residential sector. With this demand side control study, it is expected that MPC strategy...

  15. Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Cédric Beaulac

    2017-01-01

    Full Text Available We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

  16. Calculating the Contribution Rate of Intelligent Transportation System in Improving Urban Traffic Smooth Based on Advanced DID Model

    OpenAIRE

    Li, Ming-wei; Yun, Jun; Liu, Na

    2015-01-01

    Recent years have witnessed the rapid development of intelligent transportation system around the world, which helps to relieve urban traffic congestion problems. For instance, many mega-cities in China have devoted a large amount of money and resources to the development of intelligent transportation system. This poses an intriguing and important issue: how to measure and quantify the contribution of intelligent transportation system to the urban city, which is still a puzzle. This paper pro...

  17. J. Piaget's theory of intelligence: operational aspect

    Directory of Open Access Journals (Sweden)

    Xenia Naidenova

    2001-08-01

    Full Text Available The Piaget's theory of intelligence is considered from the point of view of genesis and gradual development of human thinking operations. Attention is given to operational aspects of cognitive structures and knowledge. The significance of the Piaget's theory of intelligence is revealed for modeling conceptual reasoning in the framework of artificial intelligence.

  18. Simulation of National Intelligence Process with Fusion

    National Research Council Canada - National Science Library

    Lupa, Joseph

    2008-01-01

    ...) and fusing the collected data into one new piece of intelligence. One fusion method is the one suggested by Whaley, which simply takes the best intelligence collected, while the other method captures the synergy of intelligence fusion. The two methods are compared to each other and to a baseline model where no fusion takes place.

  19. Management of competitive intelligence in Latvia enterprises

    OpenAIRE

    Cekuls, Andrejs

    2012-01-01

    The Doctoral Theses are dedicated to analyzes of competitive intelligence management. The author works out the qualitative study and quantitative study, analysing the practice of competitive intelligence in Latvian enterprises. When analysing the study results, the author evaluates the competitive intelligence process in the business environment of Latvia, works out the competitive intelligence model at the large and medium Latvian enterprises, as well as on the basis of the study resul...

  20. A condensed review of the intelligent user modeling of information retrieval system

    International Nuclear Information System (INIS)

    Choi, Kwang

    2001-10-01

    This study discussed theoretical aspects of user modeling, modeling cases of commecial systems and elements that need consideration when constructing user models. The results of this study are 1) Comprehensive and previous analysis of system users is required to bulid user model. 2) User information is collected from users directly and inference. 3) Frame structure is compatible to build user model. 4) Prototype user model is essential to bulid a user model and based on previous user analysis. 5) User model builder has interactive information collection, inference, flexibility, model updating functions. 6) User model builder has to reflect user's feedback

  1. The Spatio-Temporal Modeling of Urban Growth Using Remote Sensing and Intelligent Algorithms, Case of Mahabad, Iran

    Directory of Open Access Journals (Sweden)

    Alì Soltani

    2013-06-01

    Full Text Available The simulation of urban growth can be considered as a useful way for analyzing the complex process of urban physical evolution. The aim of this study is to model and simulate the complex patterns of land use change by utilizing remote sensing and artificial intelligence techniques in the fast growing city of Mahabad, north-west of Iran which encountered with several environmental subsequences. The key subject is how to allocate optimized weight into effective parameters upon urban growth and subsequently achieving an improved simulation. Artificial Neural Networks (ANN algorithm was used to allocate the weight via an iteration approach. In this way, weight allocation was carried out by the ANN training accomplishing through time-series satellite images representing urban growth process. Cellular Automata (CA was used as the principal motor of the model and then ANN applied to find suitable scale of parameters and relations between potential factors affecting urban growth. The general accuracy of the suggested model and obtained Fuzzy Kappa Coefficient confirms achieving better results than classic CA models in simulating nonlinear urban evolution process.

  2. Intelligent system for statistically significant expertise knowledge on the basis of the model of self-organizing nonequilibrium dissipative system

    Directory of Open Access Journals (Sweden)

    E. A. Tatokchin

    2017-01-01

    Full Text Available Development of the modern educational technologies caused by broad introduction of comput-er testing and development of distant forms of education does necessary revision of methods of an examination of pupils. In work it was shown, need transition to mathematical criteria, exami-nations of knowledge which are deprived of subjectivity. In article the review of the problems arising at realization of this task and are offered approaches for its decision. The greatest atten-tion is paid to discussion of a problem of objective transformation of rated estimates of the ex-pert on to the scale estimates of the student. In general, the discussion this question is was con-cluded that the solution to this problem lies in the creation of specialized intellectual systems. The basis for constructing intelligent system laid the mathematical model of self-organizing nonequilibrium dissipative system, which is a group of students. This article assumes that the dissipative system is provided by the constant influx of new test items of the expert and non-equilibrium – individual psychological characteristics of students in the group. As a result, the system must self-organize themselves into stable patterns. This patern will allow for, relying on large amounts of data, get a statistically significant assessment of student. To justify the pro-posed approach in the work presents the data of the statistical analysis of the results of testing a large sample of students (> 90. Conclusions from this statistical analysis allowed to develop intelligent system statistically significant examination of student performance. It is based on data clustering algorithm (k-mean for the three key parameters. It is shown that this approach allows you to create of the dynamics and objective expertise evaluation.

  3. How Intelligent is your Intelligent Robot?

    OpenAIRE

    Winfield, Alan F. T.

    2017-01-01

    How intelligent is robot A compared with robot B? And how intelligent are robots A and B compared with animals (or plants) X and Y? These are both interesting and deeply challenging questions. In this paper we address the question "how intelligent is your intelligent robot?" by proposing that embodied intelligence emerges from the interaction and integration of four different and distinct kinds of intelligence. We then suggest a simple diagrammatic representation on which these kinds of intel...

  4. The Relationship of Business Intelligence Systems to Organizational Performance Benefits: A Structural Equation Modeling of Management Decision Making

    Science.gov (United States)

    Sparks, Betsy H.

    2014-01-01

    Business Intelligence is a major expenditure in many organizations and necessary for competitive advantage. These expenditures do not result in maximum benefits for the organization if the information obtained from the Business Intelligence System (BIS) is not used in the management decision-making process. This quantitative research study used an…

  5. Do Sex Differences in a Faceted Model of Fluid and Crystallized Intelligence Depend on the Method Applied?

    Science.gov (United States)

    Steinmayr, Ricarda; Beauducel, Andre; Spinath, Birgit

    2010-01-01

    Recently, different methodological approaches have been discussed as an explanation for inconsistencies in studies investigating sex differences in different intelligences. The present study investigates sex differences in manifest sum scores, factor score estimates, and latent verbal, numerical, figural intelligence, as well as fluid and…

  6. The five factor model of personality and intelligence: A twin study on the relationship between the two constructs

    NARCIS (Netherlands)

    Bartels, M.; van Weegen, F.I.; van Beijsterveldt, C.E.M.; Carlier, M.; Polderman, T.J.C.; Hoekstra, R.A.; Boomsma, D.I.

    2012-01-01

    We assessed the association and underlying genetic and environmental influences among intelligence (IQ) and personality in adolescent and young adult twins. Data on intelligence were obtained from psychometric IQ tests and personality was assessed with the short form of the NEO five factor inventory

  7. Intelligent Potroom Operation

    Energy Technology Data Exchange (ETDEWEB)

    Jan Berkow; Larry Banta

    2003-07-29

    The Intelligent Potroom Operation project focuses on maximizing the performance of an aluminum smelter by innovating components for an intelligent manufacturing system. The Intelligent Potroom Advisor (IPA) monitors process data to identify reduction cells exhibiting behaviors that require immediate attention. It then advises operational personnel on those heuristic-based actions to bring the cell back to an optimal operating state in order to reduce the duration and frequency of substandard reduction cell performance referred to as ''Off-Peak Modes'' (OPMs). Techniques developed to identify cells exhibiting OPMs include the use of a finite element model-based cell state estimator for defining the cell's current operating state via advanced cell noise analyses. In addition, rule induction was also employed to identify statistically significant complex behaviors that occur prior to OPMs. The intelligent manufacturing system design, concepts and formalisms developed in this project w ere used as a basis for an intelligent manufacturing system design. Future research will incorporate an adaptive component to automate continuous process improvement, a technology platform with the potential to improve process performance in many of the other Industries of the Future applications as well.

  8. Synthetic collective intelligence.

    Science.gov (United States)

    Solé, Ricard; Amor, Daniel R; Duran-Nebreda, Salva; Conde-Pueyo, Núria; Carbonell-Ballestero, Max; Montañez, Raúl

    2016-10-01

    Intelligent systems have emerged in our biosphere in different contexts and achieving different levels of complexity. The requirement of communication in a social context has been in all cases a determinant. The human brain, probably co-evolving with language, is an exceedingly successful example. Similarly, social insects complex collective decisions emerge from information exchanges between many agents. The difference is that such processing is obtained out of a limited individual cognitive power. Computational models and embodied versions using non-living systems, particularly involving robot swarms, have been used to explore the potentiality of collective intelligence. Here we suggest a novel approach to the problem grounded in the genetic engineering of unicellular systems, which can be modified in order to interact, store memories or adapt to external stimuli in collective ways. What we label as Synthetic Swarm Intelligence defines a parallel approach to the evolution of computation and swarm intelligence and allows to explore potential embodied scenarios for decision making at the microscale. Here, we consider several relevant examples of collective intelligence and their synthetic organism counterparts. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  9. Artificial intelligence

    International Nuclear Information System (INIS)

    Perret-Galix, D.

    1992-01-01

    A vivid example of the growing need for frontier physics experiments to make use of frontier technology is in the field of artificial intelligence and related themes. This was reflected in the second international workshop on 'Software Engineering, Artificial Intelligence and Expert Systems in High Energy and Nuclear Physics' which took place from 13-18 January at France Telecom's Agelonde site at La Londe des Maures, Provence. It was the second in a series, the first having been held at Lyon in 1990

  10. Artificial Intelligence in Civil Engineering

    Directory of Open Access Journals (Sweden)

    Pengzhen Lu

    2012-01-01

    Full Text Available Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

  11. Computational Foundations of Natural Intelligence.

    Science.gov (United States)

    van Gerven, Marcel

    2017-01-01

    New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.

  12. Computational Foundations of Natural Intelligence

    Directory of Open Access Journals (Sweden)

    Marcel van Gerven

    2017-12-01

    Full Text Available New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence.

  13. Intelligent Integrated System Health Management

    Science.gov (United States)

    Figueroa, Fernando

    2012-01-01

    Intelligent Integrated System Health Management (ISHM) is the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system (Management: storage, distribution, sharing, maintenance, processing, reasoning, and presentation). Presentation discusses: (1) ISHM Capability Development. (1a) ISHM Knowledge Model. (1b) Standards for ISHM Implementation. (1c) ISHM Domain Models (ISHM-DM's). (1d) Intelligent Sensors and Components. (2) ISHM in Systems Design, Engineering, and Integration. (3) Intelligent Control for ISHM-Enabled Systems

  14. Corneal Intelligence

    African Journals Online (AJOL)

    Murdoch3

    Corneal Intelligence. Ian Murdoch. Institute of Ophthalmology, Bath Street, London. In 2002, the ocular hypertension treatment study (OHTS) published their results. This study had taken 1636 ocular hypertensives. 1, 2. (IOP 24-32mmHg) and randomized them to receive therapy or no therapy. The primary outcome of the ...

  15. Speech Intelligibility

    Science.gov (United States)

    Brand, Thomas

    Speech intelligibility (SI) is important for different fields of research, engineering and diagnostics in order to quantify very different phenomena like the quality of recordings, communication and playback devices, the reverberation of auditoria, characteristics of hearing impairment, benefit using hearing aids or combinations of these things.

  16. 3Es System Optimization under Uncertainty Using Hybrid Intelligent Algorithm: A Fuzzy Chance-Constrained Programming Model

    Directory of Open Access Journals (Sweden)

    Jiekun Song

    2016-01-01

    Full Text Available Harmonious development of 3Es (economy-energy-environment system is the key to realize regional sustainable development. The structure and components of 3Es system are analyzed. Based on the analysis of causality diagram, GDP and industrial structure are selected as the target parameters of economy subsystem, energy consumption intensity is selected as the target parameter of energy subsystem, and the emissions of COD, ammonia nitrogen, SO2, and NOX and CO2 emission intensity are selected as the target parameters of environment system. Fixed assets investment of three industries, total energy consumption, and investment in environmental pollution control are selected as the decision variables. By regarding the parameters of 3Es system optimization as fuzzy numbers, a fuzzy chance-constrained goal programming (FCCGP model is constructed, and a hybrid intelligent algorithm including fuzzy simulation and genetic algorithm is proposed for solving it. The results of empirical analysis on Shandong province of China show that the FCCGP model can reflect the inherent relationship and evolution law of 3Es system and provide the effective decision-making support for 3Es system optimization.

  17. Heuristic Analysis Model of Nitrided Layers’ Formation Consisting of the Image Processing and Analysis and Elements of Artificial Intelligence

    Science.gov (United States)

    Wójcicki, Tomasz; Nowicki, Michał

    2016-01-01

    The article presents a selected area of research and development concerning the methods of material analysis based on the automatic image recognition of the investigated metallographic sections. The objectives of the analyses of the materials for gas nitriding technology are described. The methods of the preparation of nitrided layers, the steps of the process and the construction and operation of devices for gas nitriding are given. We discuss the possibility of using the methods of digital images processing in the analysis of the materials, as well as their essential task groups: improving the quality of the images, segmentation, morphological transformations and image recognition. The developed analysis model of the nitrided layers formation, covering image processing and analysis techniques, as well as selected methods of artificial intelligence are presented. The model is divided into stages, which are formalized in order to better reproduce their actions. The validation of the presented method is performed. The advantages and limitations of the developed solution, as well as the possibilities of its practical use, are listed. PMID:28773389

  18. Heuristic Analysis Model of Nitrided Layers' Formation Consisting of the Image Processing and Analysis and Elements of Artificial Intelligence.

    Science.gov (United States)

    Wójcicki, Tomasz; Nowicki, Michał

    2016-04-01

    The article presents a selected area of research and development concerning the methods of material analysis based on the automatic image recognition of the investigated metallographic sections. The objectives of the analyses of the materials for gas nitriding technology are described. The methods of the preparation of nitrided layers, the steps of the process and the construction and operation of devices for gas nitriding are given. We discuss the possibility of using the methods of digital images processing in the analysis of the materials, as well as their essential task groups: improving the quality of the images, segmentation, morphological transformations and image recognition. The developed analysis model of the nitrided layers formation, covering image processing and analysis techniques, as well as selected methods of artificial intelligence are presented. The model is divided into stages, which are formalized in order to better reproduce their actions. The validation of the presented method is performed. The advantages and limitations of the developed solution, as well as the possibilities of its practical use, are listed.

  19. Modeling and simulation in inquiry learning: Checking solutions and giving intelligent advice

    NARCIS (Netherlands)

    Bravo, C.; van Joolingen, W.R.; de Jong, T.

    2006-01-01

    Inquiry learning is a didactic approach in which students acquire knowledge and skills through processes of theory building and experimentation. Computer modeling and simulation can play a prominent role within this approach. Students construct representations of physical systems using modeling.

  20. An approach to modeling operator's cognitive behavior using artificial intelligence techniques in emergency operating event sequences

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Sur, Sang Moon; Lee, Yong Hee; Park, Young Taeck; Moon, Sang Joon

    1994-01-01

    Computer modeling of an operator's cognitive behavior is a promising approach for the purpose of human factors study and man-machine systems assessment. In this paper, the states of the art in modeling operator behavior and the current status in developing an operator's model (MINERVA - NPP) are presented. The model is constructed as a knowledge-based system of a blackboard framework and is simulated based on emergency operating procedures

  1. Socio-psychological characteristics of the leaders of today's schools: the role of emotional intelligence in building a model of an effective leader

    Directory of Open Access Journals (Sweden)

    Mironova S.G.

    2017-10-01

    Full Text Available The article presents the data of study of expression of emotional intelligence in school leaders. Emotional intelligence, as one of the socio-psychological characteristics of personality, showed the closest relationship with the components of attitude of heads of schools towards his subordinates. In turn, these components of the relationship, in our opinion, represent a modern model of the head of school. The study surveyed 101 head of school from the Moscow region in age from 26 to 65 years males - 8.9 per cent; the Director of schools is 57, the position of Deputy Director of school on teaching and educational work of 44 people, a complex of six methods. One of which is the Author's questionnaire, the study of socio-psychological personality characteristics and components of attitude of heads of schools to subordinates-teachers. The rest EMIN questionnaire D. V. Lyusina, allowing to identify the level of emotional intelligence, the scale of personal anxiety CH. D. Spielberger, L. Y. Hanin, diagnosis of Machiavellianism personality of V. V. Znakov, the scale measure the level of sociability of the individual L.N. Lutoshkina, diagnosis of the tendency to stress G. Jackson. On the basis of obtained results it is concluded that the most important socio-psychological characteristics of personality is the emotional intelligence that allows a supervisor not only to understand their own and others ' emotions, to manage them successfully, but also contribute to the ability to arouse certain feelings in the people around them. Model the relationship of the heads of educational institutions to the staff, includes three components: emotional, behavioral and cognitive. It is suggested that such socio-psychological characteristics of personality as emotional intelligence, manipulative, sociability, anxiety and stress have a close relationship with all components of the attitude of heads of schools for their employees.

  2. Intelligence Past, Present, and Possible: The Theory of Multiple Intelligences in Dance Education.

    Science.gov (United States)

    Warburton, Edward C.

    2003-01-01

    Reviews the contributions of Gardner's Theory of Multiple Intelligences (MI) to dance education by placing MI theory in the context of historical perspectives on intelligences and examining the assumptions behind traditional models of intelligence and some of the more recent pluralistic approaches. The paper reviews the principal tenets of MI…

  3. Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes.

    Science.gov (United States)

    Buzaev, Igor Vyacheslavovich; Plechev, Vladimir Vyacheslavovich; Nikolaeva, Irina Evgenievna; Galimova, Rezida Maratovna

    2016-09-01

    The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG) and percutaneous coronary intervention (PCI) in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient ( r ) of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679) vs. 20.3% (87/428), P  = 0.065)]. The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina.

  4. Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Amin Daryasafar

    2014-01-01

    neurofuzzy interference system (ANFIS are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.

  5. An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube

    Science.gov (United States)

    2009-01-01

    Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input–output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input–output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down. PMID:20596382

  6. Improving the all-hazards homeland security enterprise through the use of an emergency management intelligence model

    OpenAIRE

    Schulz, William N.

    2013-01-01

    CHDS State/Local As the all-hazards approach takes hold in our national Emergency Management and Homeland Security efforts and continues to seek greater collaboration between these two fields, an area that has yet to be explored to its fullest extent is the utilization of an intelligence process to enhance EM operations. Despite the existence of multiple Federal-level policies that outline the importance of intelligence and information sharing across the all-hazards community, EM is still ...

  7. A Multi-Scale Energy Demand Model suggests sharing Market Risks with Intelligent Energy Cooperatives

    NARCIS (Netherlands)

    G. Methenitis (Georgios); M. Kaisers (Michael); J.A. La Poutré (Han)

    2015-01-01

    textabstractIn this paper, we propose a multi-scale model of energy demand that is consistent with observations at a macro scale, in our use-case standard load profiles for (residential) electric loads. We employ the model to study incentives to assume the risk of volatile market prices for

  8. A Procedure for Building Product Models in Intelligent Agent-based OperationsManagement

    DEFF Research Database (Denmark)

    Hvam, Lars; Riis, Jesper; Malis, Martin

    2003-01-01

    This article presents a procedure for building product models to support the specification processes dealing with sales, design of product variants and production preparation. The procedure includes, as the first phase, an analysis and redesign of the business processes that are to be supported...... by product models. The next phase includes an analysis of the product assortment, and the set up of a so-called product master. Finally the product model is designed and implemented by using object oriented modelling. The procedure is developed in order to ensure that the product models constructed are fit...... for the business processes they support, and properly structured and documented in order to facilitate the maintenance and further development of the systems. The research has been carried out at the Centre for Industrialisation of Engineering, Department of Manufacturing Engineering, Technical University...

  9. Examination of mitral regurgitation with a goat heart model for the development of intelligent artificial papillary muscle.

    Science.gov (United States)

    Shiraishi, Y; Yambe, T; Yoshizawa, M; Hashimoto, H; Yamada, A; Miura, H; Hashem, M; Kitano, T; Shiga, T; Homma, D

    2012-01-01

    Annuloplasty for functional mitral or tricuspid regurgitation has been made for surgical restoration of valvular diseases. However, these major techniques may sometimes be ineffective because of chamber dilation and valve tethering. We have been developing a sophisticated intelligent artificial papillary muscle (PM) by using an anisotropic shape memory alloy fiber for an alternative surgical reconstruction of the continuity of the mitral structural apparatus and the left ventricular myocardium. This study exhibited the mitral regurgitation with regard to the reduction in the PM tension quantitatively with an originally developed ventricular simulator using isolated goat hearts for the sophisticated artificial PM. Aortic and mitral valves with left ventricular free wall portions of isolated goat hearts (n=9) were secured on the elastic plastic membrane and statically pressurized, which led to valvular leaflet-papillary muscle positional change and central mitral regurgitation. PMs were connected to the load cell, and the relationship between the tension of regurgitation and PM tension were measured. Then we connected the left ventricular specimen model to our hydraulic ventricular simulator and achieved hemodynamic simulation with the controlled tension of PMs.

  10. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

    Science.gov (United States)

    Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

    2015-10-01

    Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

  11. Reactive Search and Intelligent Optimization

    CERN Document Server

    Battiti, Roberto; Mascia, Franco

    2008-01-01

    Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive search self-tunes and adapts, effectively learning by doing until a solution is found. Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics. Reactive Search and Intelligent Optimization is an exc

  12. Intelligence is what the intelligence test measures. Seriously

    NARCIS (Netherlands)

    van der Maas, H.L.J.; Kan, K.-J.; Borsboom, D.

    2014-01-01

    The mutualism model, an alternative for the g-factor model of intelligence, implies a formative measurement model in which "g" is an index variable without a causal role. If this model is accurate, the search for a genetic of brain instantiation of "g" is deemed useless. This also implies that the

  13. Community Options Model): Using Artificial Intelligence for Transportation Planning and Community Decision Making

    Science.gov (United States)

    1997-01-01

    This paper describes the Community Options Model for Transportation-Related Issues (COMTRI) designed to estimate the social and economic impacts of highway realignments on rural Michigan communities for the Michigan Department of Transportation (MDOT...

  14. Artificial Intelligence.

    Science.gov (United States)

    Lawrence, David R; Palacios-González, César; Harris, John

    2016-04-01

    It seems natural to think that the same prudential and ethical reasons for mutual respect and tolerance that one has vis-à-vis other human persons would hold toward newly encountered paradigmatic but nonhuman biological persons. One also tends to think that they would have similar reasons for treating we humans as creatures that count morally in our own right. This line of thought transcends biological boundaries-namely, with regard to artificially (super)intelligent persons-but is this a safe assumption? The issue concerns ultimate moral significance: the significance possessed by human persons, persons from other planets, and hypothetical nonorganic persons in the form of artificial intelligence (AI). This article investigates why our possible relations to AI persons could be more complicated than they first might appear, given that they might possess a radically different nature to us, to the point that civilized or peaceful coexistence in a determinate geographical space could be impossible to achieve.

  15. Swarm intelligence.

    Science.gov (United States)

    Chambers, David W

    2011-01-01

    The standard view of how things work is that an outside force impacts a group of individuals and causes outcomes they are interested in. The outside force may not affect all individuals to the same extent, but we can summarize the effect by taking the average. Effective influence is thought to come from the top, not from the group that is being led. The alternative considered here is that a substantial degree of intelligence resided in the individuals or elements that someone wants to study or change. And these elements of the system interact with each other. This phenomenon goes by many names, but will be called swarm intelligence here. There are many cases where simple rules followed at the local level trump or outperform understanding or control from above. Five examples will be given: (a) ethics; (b) the progression of periodontal diseases; (b) dental continuing education; (c) leadership from within; and (d) the wisdom of group decision making.

  16. Comparing the Performance of Artificial Intelligence Models in Estimating Water Quality Parameters in Periods of Low and High Water Flow

    Directory of Open Access Journals (Sweden)

    majid montaseri

    2017-03-01

    it suggests that the model fits the data well. On the other hand, if non- random distribution is evident in the residuals, the model does not fit the data adequately. On the base of these results, we propose ANFIS-SC and ANN (LM methods as effective tools for the computation of total dissolved solids in river water, respectively. Conclusion: It can be concluded that the ANN with Levenberg-Marquardt training algorithm and ANFIS-SC models can be considered as promising tools for forecasting TDS values, based on water quality parameters. With attention to the aim of current research that is presenting the feasibility of artificial intelligence techniques for modeling TDS values, it is notable that the results presented in this paper are for research purpose and applying the abstained results for real-world needs some complicated steps and building artificial intelligences methods, based on complete data and parameters maybe affected the TDS values

  17. Cooperation and the evolution of intelligence.

    Science.gov (United States)

    McNally, Luke; Brown, Sam P; Jackson, Andrew L

    2012-08-07

    The high levels of intelligence seen in humans, other primates, certain cetaceans and birds remain a major puzzle for evolutionary biologists, anthropologists and psychologists. It has long been held that social interactions provide the selection pressures necessary for the evolution of advanced cognitive abilities (the 'social intelligence hypothesis'), and in recent years decision-making in the context of cooperative social interactions has been conjectured to be of particular importance. Here we use an artificial neural network model to show that selection for efficient decision-making in cooperative dilemmas can give rise to selection pressures for greater cognitive abilities, and that intelligent strategies can themselves select for greater intelligence, leading to a Machiavellian arms race. Our results provide mechanistic support for the social intelligence hypothesis, highlight the potential importance of cooperative behaviour in the evolution of intelligence and may help us to explain the distribution of cooperation with intelligence across taxa.

  18. Extraordinary intelligence and the care of infants

    Science.gov (United States)

    Piantadosi, Steven T.; Kidd, Celeste

    2016-01-01

    We present evidence that pressures for early childcare may have been one of the driving factors of human evolution. We show through an evolutionary model that runaway selection for high intelligence may occur when (i) altricial neonates require intelligent parents, (ii) intelligent parents must have large brains, and (iii) large brains necessitate having even more altricial offspring. We test a prediction of this account by showing across primate genera that the helplessness of infants is a particularly strong predictor of the adults’ intelligence. We discuss related implications, including this account’s ability to explain why human-level intelligence evolved specifically in mammals. This theory complements prior hypotheses that link human intelligence to social reasoning and reproductive pressures and explains how human intelligence may have become so distinctive compared with our closest evolutionary relatives. PMID:27217560

  19. Recent Advances on Hybrid Intelligent Systems

    CERN Document Server

    Melin, Patricia; Kacprzyk, Janusz

    2013-01-01

    This book presents recent advances on hybrid intelligent systems using soft computing techniques for intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain groups of papers around a similar subject. The first part consists of papers with the main theme of hybrid intelligent systems for control and robotics, which are basically state of the art papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of hybrid intelligent systems for pattern recognition and time series prediction, which are basically papers using nature-inspired techniques, like evolutionary algo...

  20. 1726-IJBCS-Article-Mathias Pouya+

    African Journals Online (AJOL)

    hp

    Au Burkina Faso, la production cotonnière est assurée par un travail du sol à traction animale ou motorisée. Les sols assurant l'essentiel de cette production sont soit ferralitiques (à l'Ouest) soit ferrugineux tropicaux (au Centre). Pour évaluer l'impact socio-économique et agro-pédologique du type d'exploitation,.

  1. 2367-IJBCS-Article-Koffi Mathias Yao

    African Journals Online (AJOL)

    hp

    incapacité dans le monde (OMS, 2008). L'abus d'alcool est la principale cause de décès et d'incapacité dans les pays en développement à faible mortalité, le troisième facteur de risque de décès dans les pays ... Bien avant cette définition.

  2. 1898-IJBCS-Article-Mathias Koffi

    African Journals Online (AJOL)

    hp

    Cette étude a pour objectif d'évaluer le niveau de contamination en éléments traces métalliques (ETM) toxiques (Cd, Hg, Pb) dans les viandes et abats importés en vue de calculer l'exposition à long terme de la population ivoirienne. Pour ce faire, 192 échantillons prélevés sur la viande et les abats importés ont été.

  3. 750-IJBCS-Article-Dr Danho Mathias

    African Journals Online (AJOL)

    DR GATSING

    périurbaines du Bénin, du Togo, du Ghana et de la Côte d'Ivoire, à l'utilisation des insecticides botaniques (biopesticides) comme alternatives aux insecticides chimiques dans le contrôle des ravageurs et /ou vecteurs de virus de leurs cultures. C'est ainsi que nous avons évalué l'efficacité des extraits de feuilles et de ...

  4. 1997-IJBCS-Article-Danho Mathias

    African Journals Online (AJOL)

    hp

    afro.who.int. Efficacité des néonicotinoïdes et des pyréthrinoïdes utilisés contre le foreur des tiges du cacaoyer (Eulophonotus myrmeleon Felder : Lepidoptera, Cossidae). Implications dans la stratégie de protection de la cacaoculture en Côte d' ...

  5. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

    Science.gov (United States)

    Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

    2016-09-01

    This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

  6. Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence

    Science.gov (United States)

    Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

    2018-04-01

    Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

  7. Model Checking Artificial Intelligence Based Planners: Even the Best Laid Plans Must Be Verified

    Science.gov (United States)

    Smith, Margaret H.; Holzmann, Gerard J.; Cucullu, Gordon C., III; Smith, Benjamin D.

    2005-01-01

    Automated planning systems (APS) are gaining acceptance for use on NASA missions as evidenced by APS flown On missions such as Orbiter and Deep Space 1 both of which were commanded by onboard planning systems. The planning system takes high level goals and expands them onboard into a detailed of action fiat the spacecraft executes. The system must be verified to ensure that the automatically generated plans achieve the goals as expected and do not generate actions that would harm the spacecraft or mission. These systems are typically tested using empirical methods. Formal methods, such as model checking, offer exhaustive or measurable test coverage which leads to much greater confidence in correctness. This paper describes a formal method based on the SPIN model checker. This method guarantees that possible plans meet certain desirable properties. We express the input model in Promela, the language of SPIN and express the properties of desirable plans formally.

  8. Trading strategies modeling in Colombian power market using artificial intelligence techniques

    Energy Technology Data Exchange (ETDEWEB)

    Moreno, Julian [Escuela de Sistemas, Universidad Nacional de Colombia, Carrera 80 No. 65-223 Bloque M8A Medellin (Colombia)

    2009-03-15

    The aim of this paper is to present a model based on fuzzy logic and machine learning in order to maximize the profits of Colombian energy trade agents according to their risk profile. The model has two parts, the first one is a fuzzy expert system that gives a recommendation about the trade strategy these agents should follow, and whose definition depends mainly on market conditions. The second one is a reinforced learning mechanism with which the agents 'learn' when they perceive the consequences of their actions, so they modify such actions looking for a reward not just in short but also in long-term. The whole model is validated using actual data as well as a simulation approach using synthetic time series for some relevant variables as hydraulic availability, energy pool price and bilateral contracts price. (author)

  9. Trading strategies modeling in Colombian power market using artificial intelligence techniques

    International Nuclear Information System (INIS)

    Moreno, Julian

    2009-01-01

    The aim of this paper is to present a model based on fuzzy logic and machine learning in order to maximize the profits of Colombian energy trade agents according to their risk profile. The model has two parts, the first one is a fuzzy expert system that gives a recommendation about the trade strategy these agents should follow, and whose definition depends mainly on market conditions. The second one is a reinforced learning mechanism with which the agents 'learn' when they perceive the consequences of their actions, so they modify such actions looking for a reward not just in short but also in long-term. The whole model is validated using actual data as well as a simulation approach using synthetic time series for some relevant variables as hydraulic availability, energy pool price and bilateral contracts price

  10. Development of a non-contextual model for determining the autonomy level of intelligent unmanned systems

    Science.gov (United States)

    Durst, Phillip J.; Gray, Wendell; Trentini, Michael

    2013-05-01

    A simple, quantitative measure for encapsulating the autonomous capabilities of unmanned systems (UMS) has yet to be established. Current models for measuring a UMS's autonomy level require extensive, operational level testing, and provide a means for assessing the autonomy level for a specific mission/task and operational environment. A more elegant technique for quantifying autonomy using component level testing of the robot platform alone, outside of mission and environment contexts, is desirable. Using a high level framework for UMS architectures, such a model for determining a level of autonomy has been developed. The model uses a combination of developmental and component level testing for each aspect of the UMS architecture to define a non-contextual autonomous potential (NCAP). The NCAP provides an autonomy level, ranging from fully non- autonomous to fully autonomous, in the form of a single numeric parameter describing the UMS's performance capabilities when operating at that level of autonomy.

  11. Models of signal validation using artificial intelligence techniques applied to a nuclear reactor

    International Nuclear Information System (INIS)

    Oliveira, Mauro V.; Schirru, Roberto

    2000-01-01

    This work presents two models of signal validation in which the analytical redundancy of the monitored signals from a nuclear plant is made by neural networks. In one model the analytical redundancy is made by only one neural network while in the other it is done by several neural networks, each one working in a specific part of the entire operation region of the plant. Four cluster techniques were tested to separate the entire operation region in several specific regions. An additional information of systems' reliability is supplied by a fuzzy inference system. The models were implemented in C language and tested with signals acquired from Angra I nuclear power plant, from its start to 100% of power. (author)

  12. Organizing intelligence: development of behavioral science and the research based model of business education.

    Science.gov (United States)

    Bottom, William P

    2009-01-01

    Conventional history of the predominant, research-based model of business education (RBM) traces its origins to programs initiated by the Ford Foundation after World War II. This paper maps the elite network responsible for developing behavioral science and the Ford Foundation agenda. Archival records of the actions taken by central nodes in the network permit identification of the original vision statement for the model. Analysis also permits tracking progress toward realizing that vision over several decades. Behavioral science was married to business education from the earliest stages of development. The RBM was a fundamental promise made by advocates for social science funding. Appraisals of the model and recommendations for reform must address its full history, not the partial, distorted view that is the conventional account. Implications of this more complete history for business education and for behavioral theory are considered.

  13. Predicting Defects Using Information Intelligence Process Models in the Software Technology Project.

    Science.gov (United States)

    Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

    2015-01-01

    A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%-80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects.

  14. The Intelligent Ventilator Project: Application of Physiological Models in Decision Support

    DEFF Research Database (Denmark)

    Rees, Stephen Edward; Karbing, Dan Stieper; Allerød, Charlotte

    2011-01-01

    Management of mechanical ventilation in intensive care patients is complicated by conflicting clinical goals. Decision support systems (DSS) may support clinicians in finding the correct balance. The objective of this study was to evaluate a computerized model-based DSS for its advice on inspired...... in cardiac output (CO) was evaluated. Compared to the baseline ventilator settings set as part of routine clinical care, the system suggested lower tidal volumes and inspired oxygen fraction, but higher frequency, with all suggestions and the model simulated outcome comparing well with the respiratory goals...

  15. HISTORIC BUILDING INFORMATION MODELLING – ADDING INTELLIGENCE TO LASER AND IMAGE BASED SURVEYS

    Directory of Open Access Journals (Sweden)

    M. Murphy

    2012-09-01

    Full Text Available Historic Building Information Modelling (HBIM is a novel prototype library of parametric objects based on historic data and a system of cross platform programmes for mapping parametric objects onto a point cloud and image survey data. The HBIM process begins with remote collection of survey data using a terrestrial laser scanner combined with digital photo modelling. The next stage involves the design and construction of a parametric library of objects, which are based on the manuscripts ranging from Vitruvius to 18th century architectural pattern books. In building parametric objects, the problem of file format and exchange of data has been overcome within the BIM ArchiCAD software platform by using geometric descriptive language (GDL. The plotting of parametric objects onto the laser scan surveys as building components to create or form the entire building is the final stage in the reverse engin- eering process. The final HBIM product is the creation of full 3D models including detail behind the object's surface concerning its methods of construction and material make-up. The resultant HBIM can automatically create cut sections, details and schedules in addition to the orthographic projections and 3D models (wire frame or textured.

  16. The Intelligent Ventilator Project: Application of Physiological Models in Decision Support

    DEFF Research Database (Denmark)

    Rees, Stephen Edward; Karbing, Dan Stieper; Allerød, Charlotte

    2011-01-01

    Management of mechanical ventilation in intensive care patients is complicated by conflicting clinical goals. Decision support systems (DSS) may support clinicians in finding the correct balance. The objective of this study was to evaluate a computerized model-based DSS for its advice on inspired...

  17. Algorithms for Efficient Intelligence Collection

    Science.gov (United States)

    2013-09-01

    Software Implementation . . . . . . . . . . . . . . . . . . . . . . . 11 3 Creating Sample Intelligence Networks 15 3.1 The Enron Corpus...graphical model. . . . 10 Figure 3.1 A distribution of the edge pe values in the complete Enron network. . . 18 Figure 3.2 A histogram showing the...distribution of the number of total items avail- able for screening on the edges of the complete Enron network. . . . . 18 Figure 3.3 A small intelligence

  18. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    Directory of Open Access Journals (Sweden)

    Pudjo Sukarno

    2007-05-01

    Full Text Available Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields.

  19. Intelligent simulation of aquatic environment economic policy coupled ABM and SD models.

    Science.gov (United States)

    Wang, Huihui; Zhang, Jiarui; Zeng, Weihua

    2018-03-15

    Rapid urbanization and population growth have resulted in serious water shortage and pollution of the aquatic environment, which are important reasons for the complex increase in environmental deterioration in the region. This study examines the environmental consequences and economic impacts of water resource shortages under variant economic policies; however, this requires complex models that jointly consider variant agents and sectors within a systems perspective. Thus, we propose a complex system model that couples multi-agent based models (ABM) and system dynamics (SD) models to simulate the impact of alternative economic policies on water use and pricing. Moreover, this model took the constraint of the local water resources carrying capacity into consideration. Results show that to achieve the 13th Five Year Plan targets in Dianchi, water prices for local residents and industries should rise to 3.23 and 4.99 CNY/m 3 , respectively. The corresponding sewage treatment fees for residents and industries should rise to 1.50 and 2.25 CNY/m 3 , respectively, assuming comprehensive adjustment of industrial structure and policy. At the same time, the local government should exercise fine-scale economic policy combined with emission fees assessed for those exceeding a standard, and collect fines imposed as punishment for enterprises that exceed emission standards. When fines reach 500,000 CNY, the total number of enterprises that exceed emission standards in the basin can be controlled within 1%. Moreover, it is suggested that the volume of water diversion in Dianchi should be appropriately reduced to 3.06×10 8 m 3 . The reduced expense of water diversion should provide funds to use for the construction of recycled water facilities. Then the local rise in the rate of use of recycled water should reach 33%, and 1.4 CNY/m 3 for the price of recycled water could be provided to ensure the sustainable utilization of local water resources. Copyright © 2017 Elsevier B

  20. Intelligent Design and Intelligent Failure

    Science.gov (United States)

    Jerman, Gregory

    2015-01-01

    Good Evening, my name is Greg Jerman and for nearly a quarter century I have been performing failure analysis on NASA's aerospace hardware. During that time I had the distinct privilege of keeping the Space Shuttle flying for two thirds of its history. I have analyzed a wide variety of failed hardware from simple electrical cables to cryogenic fuel tanks to high temperature turbine blades. During this time I have found that for all the time we spend intelligently designing things, we need to be equally intelligent about understanding why things fail. The NASA Flight Director for Apollo 13, Gene Kranz, is best known for the expression "Failure is not an option." However, NASA history is filled with failures both large and small, so it might be more accurate to say failure is inevitable. It is how we react and learn from our failures that makes the difference.

  1. Trends in Artificial Intelligence.

    Science.gov (United States)

    Hayes, Patrick

    1978-01-01

    Discusses the foundations of artificial intelligence as a science and the types of answers that may be given to the question, "What is intelligence?" The paradigms of artificial intelligence and general systems theory are compared. (Author/VT)

  2. Model of Artificial Intelligence Medium and its Use in Treating Disorders of the Nervous System

    Directory of Open Access Journals (Sweden)

    Shumilov Vladimir

    2016-01-01

    Full Text Available The work is devoted to modeling of the nervous system, the brain. The article considers the mechanism of formation of event traces fixed in the brain in the form of connections between neurons and the effect of these traces on the later passage of signals through the brain. That is, the influence of experience (traces of previous events on the organism’s reaction by forecasting upcoming events. This «forecast» and anticipatory avoidance of dangers based on traces of previous events makes the nervous system, the brain useful for the organism, for its survival and expansion. The author proposes to use a computational model of the brain to study disorders of the nervous system, brain, and make recommendations for their prevention.

  3. Intelligent design of mechanical parameters of the joint in vehicle body concept design model

    Science.gov (United States)

    Hou, Wen-bin; Zhang, Hong-zhe; Hou, Da-jun; Hu, Ping

    2013-05-01

    In order to estimate the mechanical properties of the overall structure of the body accurately and quickly in conceptual design phase of the body, the beam and shell mixing elements was used to build simplified finite element model of the body. Through the BP neural network algorithm, the parameters of the mechanical property of joints element which had more affection on calculation accuracy were calculated and the joint finite element model based on the parameters was also constructed. The case shown that the method can improve the accuracy of the vehicle simulation results, while not too many design details were needed, which was fit to the demand in the vehicle body conceptual design phase.

  4. Aspects of intelligent electronic device based switchgear control training model application

    Science.gov (United States)

    Bogdanov, Dimitar; Popov, Ivaylo

    2018-02-01

    The design of the protection and control equipment for electrical power sector application was object of extensive advance in the last several decades. The modern technologies offer a wide range of multifunctional flexible applications, making the protection and control of facilities more sophisticated. In the same time, the advance of technology imposes the necessity of simulators, training models and tutorial laboratory equipment to be used for adequate training of students and field specialists

  5. GOLD: Integration of model-based control systems with artificial intelligence and workstations

    International Nuclear Information System (INIS)

    Lee, M.; Clearwater, S.

    1987-08-01

    Our experience with model based accelerator control started at SPEAR. Since that time nearly all accelerator beam lines have been controlled using model-based application programs, for example, PEP and SLC at SLAC. In order to take advantage of state-of-the-art hardware and software technology, the design and implementation of the accelerator control programs have undergone radical change with time. Consequently, SPEAR, PEP, and SLC all use different control programs. Since many of these application programs are imbedded deep into the control system, they had to be rewritten each time. Each time this rewriting has occurred a great deal of time and effort has been spent on training physicists and programmers to do the job. Now, we have developed these application programs for a fourth time. This time, however, the programs we are developing are generic so that we will not have to do it again. We have developed an integrated system called GOLD (Generic Orbit and Lattice Debugger) for debugging and correcting trajectory errors in accelerator lattices. The system consists of a lattice modeling program (COMFORT), a beam simulator (PLUS), a graphical workstation environment (micro-VAX) and an expert system (ABLE). This paper will describe some of the features and applications of our integrated system with emphasis on the automation offered by expert systems. 5 refs

  6. Integration of model-based control systems with artificial intelligence and workstations

    International Nuclear Information System (INIS)

    Lee, M.; Clearwater, S.

    1987-01-01

    Experience with model based accelerator control started at SPEAR. Since that SPEAR. Since that time nearly all accelerator beam lines have been controlled using model-based application programs, for example, PEP and SLC at SLAC. In order to take advantage of state-of-the-art hardware and software technology, the design and implementation of the accelerator control programs have undergone radical changes with time. Consequently, SPEAR, PEP, and SLC all use different control programs. Since many of these application programs are imbedded deep into the control system, they had to be rewritten each time. Each time this rewriting has occurred a great deal of time and effort has been spent on training physicists and programmers to do the job. Now, these application programs have been developed for a fourth time. This time, however, the programs being developed are generic so that they will not have to be done again. An integrated system called GOLD (Generic Orbit ampersand Lattice Debugger) has been developed for debugging and correcting trajectory errors in accelerator lattices. The system consists of a lattice modeling program (COMFORT), a beam simulator (PLUS), a graphical workstation environment (micro-VAX) and an expert system (ABLE). This paper will describe some of the features and applications of our integrated system with emphasis on the automation offered by expert systems. 5 refs., 4 figs

  7. GOLD: Integration of model-based control systems with artificial intelligence and workstations

    International Nuclear Information System (INIS)

    Lee, M.; Clearwater, S.

    1987-08-01

    Our experience with model-based accelerator control started at SPEAR. Since that time nearly all accelerator beamlines have been controlled using model-based application programs, for example, PEP and SLC at SLAC. In order to take advantage of state-of-the-art hardware and software technology, the design and implementation of the accelerator control programs have undergone radical changes with time. Consequently, SPEAR, PEP and SLC all use different control programs. Since many of these application programs are embedded deep into the control system, they had to be rewritten each time. Each time this rewriting has occurred a great deal of time and effort has been spent on training physicists and programmers to do the job. Now, we have developed an integrated system called GOLD (Genetic Orbit and Lattice Debugger) for debugging and correcting trajectory errors in accelerator lattices. The system consists of a lattice modeling program (COMFORT), a beam simulator (PLUS), a graphical workstation environment (micro-VAX) and an expert system (ABLE). This paper will describe some of the features and applications of our integrated system with emphasis on the automation offered by expert systems. 5 refs

  8. Computational Intelligence Based Data Fusion Algorithm for Dynamic sEMG and Skeletal Muscle Force Modelling

    Energy Technology Data Exchange (ETDEWEB)

    Chandrasekhar Potluri,; Madhavi Anugolu; Marco P. Schoen; D. Subbaram Naidu

    2013-08-01

    In this work, an array of three surface Electrography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test subjects. The skeletal muscle force is estimated using the acquired sEMG signals and a Non-linear Wiener Hammerstein model, relating the two signals in a dynamic fashion. The model is obtained from using System Identification (SI) algorithm. The obtained force models for each sensor are fused using a proposed fuzzy logic concept with the intent to improve the force estimation accuracy and resilience to sensor failure or misalignment. For the fuzzy logic inference system, the sEMG entropy, the relative error, and the correlation of the force signals are considered for defining the membership functions. The proposed fusion algorithm yields an average of 92.49% correlation between the actual force and the overall estimated force output. In addition, the proposed fusionbased approach is implemented on a test platform. Experiments indicate an improvement in finger/hand force estimation.

  9. Levy-like behaviour in deterministic models of intelligent agents exploring heterogeneous environments

    International Nuclear Information System (INIS)

    Boyer, D; Miramontes, O; Larralde, H

    2009-01-01

    Many studies on animal and human movement patterns report the existence of scaling laws and power-law distributions. Whereas a number of random walk models have been proposed to explain observations, in many situations individuals actually rely on mental maps to explore strongly heterogeneous environments. In this work, we study a model of a deterministic walker, visiting sites randomly distributed on the plane and with varying weight or attractiveness. At each step, the walker minimizes a function that depends on the distance to the next unvisited target (cost) and on the weight of that target (gain). If the target weight distribution is a power law, p(k) ∼ k -β , in some range of the exponent β, the foraging medium induces movements that are similar to Levy flights and are characterized by non-trivial exponents. We explore variations of the choice rule in order to test the robustness of the model and argue that the addition of noise has a limited impact on the dynamics in strongly disordered media.

  10. Lévy-like behaviour in deterministic models of intelligent agents exploring heterogeneous environments

    Science.gov (United States)

    Boyer, D.; Miramontes, O.; Larralde, H.

    2009-10-01

    Many studies on animal and human movement patterns report the existence of scaling laws and power-law distributions. Whereas a number of random walk models have been proposed to explain observations, in many situations individuals actually rely on mental maps to explore strongly heterogeneous environments. In this work, we study a model of a deterministic walker, visiting sites randomly distributed on the plane and with varying weight or attractiveness. At each step, the walker minimizes a function that depends on the distance to the next unvisited target (cost) and on the weight of that target (gain). If the target weight distribution is a power law, p(k) ~ k-β, in some range of the exponent β, the foraging medium induces movements that are similar to Lévy flights and are characterized by non-trivial exponents. We explore variations of the choice rule in order to test the robustness of the model and argue that the addition of noise has a limited impact on the dynamics in strongly disordered media.

  11. Modelling and Development of Linear and Nonlinear Intelligent Controllers for Anti-lock Braking Systems (ABS

    Directory of Open Access Journals (Sweden)

    Mohammad Najeh Nemah

    2018-02-01

    Full Text Available Antilock braking systems (ABS are utilized as a part of advanced autos to keep the vehicle’s wheels from deadlocking when the brakes are connected. The control performance of ABS utilizing linear and nonlinear controls are cleared up in this research. In order to design the control system of ABS a nonlinear dynamic model of the antilock braking systems is derived relying upon its physical system. The dynamic model contains set of equations valid for simulation and control of the mechanical framework. Two different controllers technique is proposed to control the behaviors of ABS. The first one utilized the PID controller with linearized technique around specific point to control the nonlinear system, while the second one used the nonlinear discrete time controller to control the nonlinear mathematical model directly. This investigation contributes to more additional information for the simulation of the two controllers, and demonstrate a clear and reasonable advantage of the classical PID controller on the nonlinear discrete time controller in control the antilock braking system.

  12. Fostering collective intelligence education

    Directory of Open Access Journals (Sweden)

    Jaime Meza

    2016-06-01

    Full Text Available New educational models are necessary to update learning environments to the digitally shared communication and information. Collective intelligence is an emerging field that already has a significant impact in many areas and will have great implications in education, not only from the side of new methodologies but also as a challenge for education. This paper proposes an approach to a collective intelligence model of teaching using Internet to combine two strategies: idea management and real time assessment in the class. A digital tool named Fabricius has been created supporting these two elements to foster the collaboration and engagement of students in the learning process. As a result of the research we propose a list of KPI trying to measure individual and collective performance. We are conscious that this is just a first approach to define which aspects of a class following a course can be qualified and quantified.

  13. Artificial intelligence and the future.

    Science.gov (United States)

    Clocksin, William F

    2003-08-15

    We consider some of the ideas influencing current artificial-intelligence research and outline an alternative conceptual framework that gives priority to social relationships as a key component and constructor of intelligent behaviour. The framework starts from Weizenbaum's observation that intelligence manifests itself only relative to specific social and cultural contexts. This is in contrast to a prevailing view, which sees intelligence as an abstract capability of the individual mind based on a mechanism for rational thought. The new approach is not based on the conventional idea that the mind is a rational processor of symbolic information, nor does it require the idea that thought is a kind of abstract problem solving with a semantics that is independent of its embodiment. Instead, priority is given to affective and social responses that serve to engage the whole agent in the life of the communities in which it participates. Intelligence is seen not as the deployment of capabilities for problem solving, but as constructed by the continual, ever-changing and unfinished engagement with the social group within the environment. The construction of the identity of the intelligent agent involves the appropriation or 'taking up' of positions within the conversations and narratives in which it participates. Thus, the new approach argues that the intelligent agent is shaped by the meaning ascribed to experience, by its situation in the social matrix, and by practices of self and of relationship into which intelligent life is recruited. This has implications for the technology of the future, as, for example, classic artificial intelligence models such as goal-directed problem solving are seen as special cases of narrative practices instead of as ontological foundations.

  14. Ethical Artificial Intelligence

    OpenAIRE

    Hibbard, Bill

    2014-01-01

    This book-length article combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence (AI). The behavior of future AI systems can be described by mathematical equations, which are adapted to analyze possible unintended AI behaviors and ways that AI designs can avoid them. This article makes the case for utility-maximizing agents and for avoiding infinite sets in agent definitions. It shows how to avoid agent self-delusion using model-based ut...

  15. Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres—Focus on Feature Selection

    Science.gov (United States)

    Zawbaa, Hossam M.; Szlȩk, Jakub; Grosan, Crina; Jachowicz, Renata; Mendyk, Aleksander

    2016-01-01

    Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven. PMID:27315205

  16. Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres-Focus on Feature Selection.

    Directory of Open Access Journals (Sweden)

    Hossam M Zawbaa

    Full Text Available Poly-lactide-co-glycolide (PLGA is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP, multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR. The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven.

  17. Computational Intelligence Modeling of the Macromolecules Release from PLGA Microspheres-Focus on Feature Selection.

    Science.gov (United States)

    Zawbaa, Hossam M; Szlȩk, Jakub; Grosan, Crina; Jachowicz, Renata; Mendyk, Aleksander

    2016-01-01

    Poly-lactide-co-glycolide (PLGA) is a copolymer of lactic and glycolic acid. Drug release from PLGA microspheres depends not only on polymer properties but also on drug type, particle size, morphology of microspheres, release conditions, etc. Selecting a subset of relevant properties for PLGA is a challenging machine learning task as there are over three hundred features to consider. In this work, we formulate the selection of critical attributes for PLGA as a multiobjective optimization problem with the aim of minimizing the error of predicting the dissolution profile while reducing the number of attributes selected. Four bio-inspired optimization algorithms: antlion optimization, binary version of antlion optimization, grey wolf optimization, and social spider optimization are used to select the optimal feature set for predicting the dissolution profile of PLGA. Besides these, LASSO algorithm is also used for comparisons. Selection of crucial variables is performed under the assumption that both predictability and model simplicity are of equal importance to the final result. During the feature selection process, a set of input variables is employed to find minimum generalization error across different predictive models and their settings/architectures. The methodology is evaluated using predictive modeling for which various tools are chosen, such as Cubist, random forests, artificial neural networks (monotonic MLP, deep learning MLP), multivariate adaptive regression splines, classification and regression tree, and hybrid systems of fuzzy logic and evolutionary computations (fugeR). The experimental results are compared with the results reported by Szlȩk. We obtain a normalized root mean square error (NRMSE) of 15.97% versus 15.4%, and the number of selected input features is smaller, nine versus eleven.

  18. Active load management in an intelligent building using model predictive control strategy

    DEFF Research Database (Denmark)

    Zong, Yi; Kullmann, Daniel; Thavlov, Anders

    2011-01-01

    shifting in PowerFlexHouse heaters' power consumption scheme. With this demand side control study, it is expected that this method of demand response can dramatically raise energy efficiencies and improve grid reliability, when there is a high penetration of intermittent energy resources in the power......This paper introduces PowerFlexHouse, a research facility for exploring the technical potential of active load management in a distributed power system (SYSLAB) with a high penetration of renewable energy and presents in detail on how to implement a thermal model predictive controller for load...

  19. Object as a model of intelligent robot in the virtual workspace

    Science.gov (United States)

    Foit, K.; Gwiazda, A.; Banas, W.; Sekala, A.; Hryniewicz, P.

    2015-11-01

    The contemporary industry requires that every element of a production line will fit into the global schema, which is connected with the global structure of business. There is the need to find the practical and effective ways of the design and management of the production process. The term “effective” should be understood in a manner that there exists a method, which allows building a system of nodes and relations in order to describe the role of the particular machine in the production process. Among all the machines involved in the manufacturing process, industrial robots are the most complex ones. This complexity is reflected in the realization of elaborated tasks, involving handling, transporting or orienting the objects in a work space, and even performing simple machining processes, such as deburring, grinding, painting, applying adhesives and sealants etc. The robot also performs some activities connected with automatic tool changing and operating the equipment mounted on the wrist of the robot. Because of having the programmable control system, the robot also performs additional activities connected with sensors, vision systems, operating the storages of manipulated objects, tools or grippers, measuring stands, etc. For this reason the description of the robot as a part of production system should take into account the specific nature of this machine: the robot is a substitute of a worker, who performs his tasks in a particular environment. In this case, the model should be able to characterize the essence of "employment" in the sufficient way. One of the possible approaches to this problem is to treat the robot as an object, in the sense often used in computer science. This allows both: to describe certain operations performed on the object, as well as describing the operations performed by the object. This paper focuses mainly on the definition of the object as the model of the robot. This model is confronted with the other possible descriptions. The

  20. Object as a model of intelligent robot in the virtual workspace

    International Nuclear Information System (INIS)

    Foit, K; Gwiazda, A; Banas, W; Sekala, A; Hryniewicz, P

    2015-01-01

    The contemporary industry requires that every element of a production line will fit into the global schema, which is connected with the global structure of business. There is the need to find the practical and effective ways of the design and management of the production process. The term “effective” should be understood in a manner that there exists a method, which allows building a system of nodes and relations in order to describe the role of the particular machine in the production process. Among all the machines involved in the manufacturing process, industrial robots are the most complex ones. This complexity is reflected in the realization of elaborated tasks, involving handling, transporting or orienting the objects in a work space, and even performing simple machining processes, such as deburring, grinding, painting, applying adhesives and sealants etc. The robot also performs some activities connected with automatic tool changing and operating the equipment mounted on the wrist of the robot. Because of having the programmable control system, the robot also performs additional activities connected with sensors, vision systems, operating the storages of manipulated objects, tools or grippers, measuring stands, etc. For this reason the description of the robot as a part of production system should take into account the specific nature of this machine: the robot is a substitute of a worker, who performs his tasks in a particular environment. In this case, the model should be able to characterize the essence of 'employment' in the sufficient way. One of the possible approaches to this problem is to treat the robot as an object, in the sense often used in computer science. This allows both: to describe certain operations performed on the object, as well as describing the operations performed by the object. This paper focuses mainly on the definition of the object as the model of the robot. This model is confronted with the other possible

  1. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

    Science.gov (United States)

    Barton, Alan J; Valdés, Julio J; Orchard, Robert

    2009-01-01

    Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

  2. An Integral Model to Provide Reactive and Proactive Services in an Academic CSIRT Based on Business Intelligence

    Directory of Open Access Journals (Sweden)

    Walter Fuertes

    2017-11-01

    Full Text Available Cyber-attacks have increased in severity and complexity. That requires, that the CERT/CSIRT research and develops new security tools. Therefore, our study focuses on the design of an integral model based on Business Intelligence (BI, which provides reactive and proactive services in a CSIRT, in order to alert and reduce any suspicious or malicious activity on information systems and data networks. To achieve this purpose, a solution has been assembled, that generates information stores, being compiled from a continuous network transmission of several internal and external sources of an organization. However, it contemplates a data warehouse, which is focused like a correlator of logs, being formed by the information of feeds with diverse formats. Furthermore, it analyzed attack detection and port scanning, obtained from sensors such as Snort and Passive Vulnerability Scanner, which are stored in a database, where the logs have been generated by the systems. With such inputs, we designed and implemented BI systems using the phases of the Ralph Kimball methodology, ETL and OLAP processes. In addition, a software application has been implemented using the SCRUM methodology, which allowed to link the obtained logs to the BI system for visualization in dynamic dashboards, with the purpose of generating early alerts and constructing complex queries using the user interface through objects structures. The results demonstrate, that this solution has generated early warnings based on the level of criticality and level of sensitivity of malware and vulnerabilities as well as monitoring efficiency, increasing the level of security of member institutions.

  3. Perturbation and Stability Analysis of the Multi-Anticipative Intelligent Driver Model

    Science.gov (United States)

    Chen, Xi-Qun; Xie, Wei-Jun; Shi, Jing; Shi, Qi-Xin

    This paper discusses three kinds of IDM car-following models that consider both the multi-anticipative behaviors and the reaction delays of drivers. Here, the multi-anticipation comes from two ways: (1) the driver is capable of evaluating the dynamics of several preceding vehicles, and (2) the autonomous vehicles can obtain the velocity and distance information of several preceding vehicles via inter-vehicle communications. In this paper, we study the stability of homogeneous traffic flow. The linear stability analysis indicates that the stable region will generally be enlarged by the multi-anticipative behaviors and reduced by the reaction delays. The temporal amplification and the spatial divergence of velocities for local perturbation are also studied, where the results further prove this conclusion. Simulation results also show that the multi-anticipative behaviors near the bottleneck will lead to a quicker backwards propagation of oscillations.

  4. An extended car-following model based on intelligent transportation system application

    Science.gov (United States)

    Ge, H. X.; Dai, S. Q.; Dong, L. Y.

    2006-06-01

    The jams in the congested traffic reveal various density waves. Some of them are described by the nonlinear wave equations: the Korteweg-de-Vries (KdV) equation, the Burgers equation and the modified KdV equation. An extended car following model are proposed in previous work, and the kink-antikink solution has been obtained from the mKdV equation. We continue to derive the KdV equation near the neutral stability line by applying the reductive perturbation method. The traffic jam could be thus described by the soliton solution, and the analysis result is consistent with the previous one. From the numerical simulations results, the soliton waves are found, and traffic jam is suppressed efficiently as encounter big disturbances.

  5. Soft computing in intelligent control

    CERN Document Server

    Jung, Jin-Woo; Kubota, Naoyuki

    2014-01-01

    Nowadays, people have tendency to be fond of smarter machines that are able to collect data, make learning, recognize things, infer meanings, communicate with human and perform behaviors. Thus, we have built advanced intelligent control affecting all around societies; automotive, rail, aerospace, defense, energy, healthcare, telecoms and consumer electronics, finance, urbanization. Consequently, users and consumers can take new experiences through the intelligent control systems. We can reshape the technology world and provide new opportunities for industry and business, by offering cost-effective, sustainable and innovative business models. We will have to know how to create our own digital life. The intelligent control systems enable people to make complex applications, to implement system integration and to meet society’s demand for safety and security. This book aims at presenting the research results and solutions of applications in relevance with intelligent control systems. We propose to researchers ...

  6. Before and Beyond Anticipatory Intelligence: Assessing the Potential for Crowdsourcing and Intelligence Studies

    Directory of Open Access Journals (Sweden)

    Alexander Halman

    2015-10-01

    Full Text Available Crowdsourcing is a new tool for businesses, academics, and now intelligence analysts. Enabled by recent technology, crowdsourcing allows researchers to harness the wisdom of crowds and provide recommendations and insight into complex problems. This paper examines the potential benefits and limitations of crowdsourcing for intelligence analysis and the intelligence community beyond its primary use: anticipatory intelligence. The author constructs a model and compares it to existing crowdsourcing theories in business, information science, and public policy. Finally, he offers advice for intelligence analysis and public policy.

  7. ICT-Supported Gaming for Competitive Intelligence

    NARCIS (Netherlands)

    Achterbergh, J.M.I.M.

    2005-01-01

    Collecting and processing competitive intelligence for the purpose of strategy formulation are complex activities requiring deep insight in and models of the “organization in its environment.” These insights and models need to be not only shared between CI (competitive intelligence) practitioners

  8. Fault identification using piezoelectric impedance measurement and model-based intelligent inference with pre-screening

    Science.gov (United States)

    Shuai, Q.; Zhou, K.; Zhou, Shiyu; Tang, J.

    2017-04-01

    While piezoelectric impedance/admittance measurements have been used for fault detection and identification, the actual identification of fault location and severity remains to be a challenging topic. On one hand, the approach that uses these measurements entertains high detection sensitivity owing to the high-frequency actuation/sensing nature. On the other hand, high-frequency analysis requires high dimensionality in the model and the subsequent inverse analysis contains a very large number of unknowns which often renders the identification problem under-determined. A new fault identification algorithm is developed in this research for piezoelectric impedance/admittance based measurement. Taking advantage of the algebraic relation between the sensitivity matrix and the admittance change measurement, we devise a pre-screening scheme that can rank the likelihoods of fault locations with estimated fault severity levels, which drastically reduces the fault parameter space. A Bayesian inference approach is then incorporated to pinpoint the fault location and severity with high computational efficiency. The proposed approach is examined and validated through case studies.

  9. Modeling level change in Lake Urmia using hybrid artificial intelligence approaches

    Science.gov (United States)

    Esbati, M.; Ahmadieh Khanesar, M.; Shahzadi, Ali

    2017-06-01

    The investigation of water level fluctuations in lakes for protecting them regarding the importance of these water complexes in national and regional scales has found a special place among countries in recent years. The importance of the prediction of water level balance in Lake Urmia is necessary due to several-meter fluctuations in the last decade which help the prevention from possible future losses. For this purpose, in this paper, the performance of adaptive neuro-fuzzy inference system (ANFIS) for predicting the lake water level balance has been studied. In addition, for the training of the adaptive neuro-fuzzy inference system, particle swarm optimization (PSO) and hybrid backpropagation-recursive least square method algorithm have been used. Moreover, a hybrid method based on particle swarm optimization and recursive least square (PSO-RLS) training algorithm for the training of ANFIS structure is introduced. In order to have a more fare comparison, hybrid particle swarm optimization and gradient descent are also applied. The models have been trained, tested, and validated based on lake level data between 1991 and 2014. For performance evaluation, a comparison is made between these methods. Numerical results obtained show that the proposed methods with a reasonable error have a good performance in water level balance prediction. It is also clear that with continuing the current trend, Lake Urmia will experience more drop in the water level balance in the upcoming years.

  10. Investment Cost Model in Business Process Intelligence in Banking And Electricity Company

    Directory of Open Access Journals (Sweden)

    Arta Moro Sundjaja

    2016-06-01

    Full Text Available Higher demand from the top management in measuring business process performance causes the incremental implementation of BPM and BI in the enterprise. The problem faced by top managements is how to integrate their data from all system used to support the business and process the data become information that able to support the decision-making processes. Our literature review elaborates several implementations of BPI on companies in Australia and Germany, challenges faced by organizations in developing BPI solution in their organizations and some cost model to calculate the investment of BPI solutions. This paper shows the success in BPI application of banks and assurance companies in German and electricity work in Australia aims to give a vision about the importance of BPI application. Many challenges in BPI application of companies in German and Australia, BPI solution, and data warehouse design development have been discussed to add insight in future BPI development. And the last is an explanation about how to analyze cost associated with BPI solution investment.

  11. Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence.

    Science.gov (United States)

    Aoun, Bachir

    2016-05-05

    A new Reverse Monte Carlo (RMC) package "fullrmc" for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group. © 2016 Wiley Periodicals, Inc.

  12. Ethical Leadership, Leader-Member Exchange and Feedback Seeking: A Double-Moderated Mediation Model of Emotional Intelligence and Work-Unit Structure

    Science.gov (United States)

    Qian, Jing; Wang, Bin; Han, Zhuo; Song, Baihe

    2017-01-01

    This research elucidates the role of ethical leadership in employee feedback seeking by examining how and when ethical leadership may exert a positive influence on feedback seeking. Using matched reports from 64 supervisors and 265 of their immediate employees from a hotel group located in a major city in China, we proposed and tested a moderated mediation model that examines leader-member exchange (LMX) as the mediator and emotional intelligence as well as work-unit structure as double moderators in the relationships between ethical leadership and followers’ feedback-seeking behavior from supervisors and coworkers. Our findings indicated that (1) LMX mediated the positive relationship between ethical leadership and feedback seeking from both ethical leaders and coworkers, and (2) emotional intelligence and work-unit structure served as joint moderators on the mediated positive relationship in such a way that the relationship was strongest when the emotional intelligence was high and work-unit structure was more of an organic structure rather than a mechanistic structure. PMID:28744251

  13. Ethical Leadership, Leader-Member Exchange and Feedback Seeking: A Double-Moderated Mediation Model of Emotional Intelligence and Work-Unit Structure

    Directory of Open Access Journals (Sweden)

    Jing Qian

    2017-07-01

    Full Text Available This research elucidates the role of ethical leadership in employee feedback seeking by examining how and when ethical leadership may exert a positive influence on feedback seeking. Using matched reports from 64 supervisors and 265 of their immediate employees from a hotel group located in a major city in China, we proposed and tested a moderated mediation model that examines leader-member exchange (LMX as the mediator and emotional intelligence as well as work-unit structure as double moderators in the relationships between ethical leadership and followers’ feedback-seeking behavior from supervisors and coworkers. Our findings indicated that (1 LMX mediated the positive relationship between ethical leadership and feedback seeking from both ethical leaders and coworkers, and (2 emotional intelligence and work-unit structure served as joint moderators on the mediated positive relationship in such a way that the relationship was strongest when the emotional intelligence was high and work-unit structure was more of an organic structure rather than a mechanistic structure.

  14. Ethical Leadership, Leader-Member Exchange and Feedback Seeking: A Double-Moderated Mediation Model of Emotional Intelligence and Work-Unit Structure.

    Science.gov (United States)

    Qian, Jing; Wang, Bin; Han, Zhuo; Song, Baihe

    2017-01-01

    This research elucidates the role of ethical leadership in employee feedback seeking by examining how and when ethical leadership may exert a positive influence on feedback seeking. Using matched reports from 64 supervisors and 265 of their immediate employees from a hotel group located in a major city in China, we proposed and tested a moderated mediation model that examines leader-member exchange (LMX) as the mediator and emotional intelligence as well as work-unit structure as double moderators in the relationships between ethical leadership and followers' feedback-seeking behavior from supervisors and coworkers. Our findings indicated that (1) LMX mediated the positive relationship between ethical leadership and feedback seeking from both ethical leaders and coworkers, and (2) emotional intelligence and work-unit structure served as joint moderators on the mediated positive relationship in such a way that the relationship was strongest when the emotional intelligence was high and work-unit structure was more of an organic structure rather than a mechanistic structure.

  15. Business Intelligence

    OpenAIRE

    Petersen, Anders

    2001-01-01

    Cílem této bakalářské práce je seznámení s Business Intelligence a zpracování vývojového trendu, který ovlivňuje podobu řešení Business Intelligence v podniku ? Business Activity Monitoring. Pro zpracování tohoto tématu byla použita metoda studia odborných pramenů, a to jak v českém, tak v anglickém jazyce. Hlavním přínosem práce je ucelený, v českém jazyce zpracovaný materiál pojednávající o Business Activity Monitoring. Práce je rozdělena do šesti hlavních kapitol. Prvních pět je věnováno p...

  16. Web Intelligence and Artificial Intelligence in Education

    Science.gov (United States)

    Devedzic, Vladan

    2004-01-01

    This paper surveys important aspects of Web Intelligence (WI) in the context of Artificial Intelligence in Education (AIED) research. WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web-related products, systems, services, and…

  17. Assessing Intelligence in Children and Youth Living in the Netherlands

    Science.gov (United States)

    Hurks, Petra P. M.; Bakker, Helen

    2016-01-01

    In this article, we briefly describe the history of intelligence test use with children and youth in the Netherlands, explain which models of intelligence guide decisions about test use, and detail how intelligence tests are currently being used in Dutch school settings. Empirically supported and theoretical models studying the structure of human…

  18. A Rasch Rating Scale Modeling of the Schutte Self-Report Emotional Intelligence Scale in a Sample of International Students

    Science.gov (United States)

    Kim, Do-Hong; Wang, Chuang; Ng, Kok-Mun

    2010-01-01

    This study examined the psychometric properties of the Schutte Self-Report Emotional Intelligence (SSREI) scale in a sample of international students studying in the U.S. universities using Rasch analysis. The results indicated that the original five-category rating structure may not function effectively for the international student sample. The…

  19. The Intention to Quit Smoking: The Impact of Susceptibility, Self-Efficacy, Social Norms and Emotional Intelligence Embedded Model

    Science.gov (United States)

    Rahman, Muhammad Sabbir; Mannan, Mahafuz; Rahman, Mohammad Mahboob

    2018-01-01

    Purpose: From the perspective of developing countries, studies regarding the behavioral effects of quitting tobacco consumption on emerging psychological determinants are limited. The purpose of this paper is to examine the influence of emotional intelligence (EI), social norms, susceptibility and self-efficacy on the behavioral effects of…

  20. DEVELOPING A CONCEPTUAL INFORMATION SYSTEMS (IS) SUCCESS MODEL FOR INTELLIGENT VEHICLE TRACKING SYSTEMS USED IN DEVELOPING COUNTRIES – THE CASE OF GHANA

    DEFF Research Database (Denmark)

    Adjin, Daniel Michael Okwabi

    This research developed a conceptual Information Systems (IS) success model to address problems of Intelligent Vehicle Tracking Systems (IVTS) in developing countries – the, case of Ghana. The study was based on existing IS Success Models used in measuring the performance, usefulness...... core theoretical IS concepts required to develop the conceptual IS success model. Research Outcome: the proposed conceptual IS Success Model is developed; has considerably extended the updated DeLone & McLean (2003) IS success model; making it possible to apply IS success model to evaluate...... and successfulness of information systems applicable to IVTS success evaluation. Problems addressed: users of IVTS in developing countries, especially in Ghana, are dissatisfied with the performances of IVTS and services, regarding system & service qualities, information quality, low user-perceptions, low system...