Relevant bibliographies by topics / Systems intelligence

Academic literature on the topic 'Systems intelligence'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Systems intelligence"

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Sauter, Vicki L. "Competitive intelligence systems." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 36, no. 2 (June 7, 2005): 43–57. http://dx.doi.org/10.1145/1066149.1066154.

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Törmänen, Juha, Raimo P. Hämäläinen, and Esa Saarinen. "Systems intelligence inventory." Learning Organization 23, no. 4 (May 9, 2016): 218–31. http://dx.doi.org/10.1108/tlo-01-2016-0006.

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Purpose Systems intelligence (SI) (Saarinen and Hämäläinen, 2004) is a construct defined as a person’s ability to act intelligently within complex systems involving interaction and feedback. SI relates to our ability to act in systems and reason about systems to adaptively carry out productive actions within and with respect to systems such as organizations, family and everyday life. This paper aims to develop an inventory to measure the SI construct. Design/methodology/approach A combination of exploratory and confirmatory factor analyses were carried out using data from self-report questionnaires. Findings Eight factors labeled systemic perception, attunement, attitude, spirited discovery, reflection, wise action, positive engagement and effective responsiveness are identified as the main components of SI. SI has associations with emotional intelligence but also captures additional dimensions. People in supervisor positions are found to score higher in a number of the SI factors. Originality/value A new measure is developed to evaluate and develop our ability to succeed in systemic contexts. The new measure is suggested to be particularly applicable in organizational contexts. It is directly related to the original core disciplines of the learning organization as described by Senge (1990), in particular personal mastery and systems thinking.
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Allen, Louis. "Japanese Intelligence Systems." Journal of Contemporary History 22, no. 4 (October 1987): 547–62. http://dx.doi.org/10.1177/002200948702200401.

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Abdullayeva, O., and M. Engalichev. "Artificial intelligence systems." Значение цифровых технологий в изучении истории Узбекистана 1, no. 01 (October 26, 2022): 382–85. http://dx.doi.org/10.47689/.v1i01.13612.

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Today, artificial intelligence is becoming more and more popular in almost every aspect of our daily lives. Artificial intelligence is usually concerned withthe creation of computer systems with the capabilities of the human mind: understanding, learning, discussing, solving problems, translating, and so on. During the study of artificial intelligence, human-intelligent programming, technologies and tools for designing intelligent systems, databases, knowledge bases, database management systems, speech interfaces, computer linguistics, computer graphics, computer networks and web programming , information security in computer systems
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DONG, TIANSI. "MODELING HUMAN INTELLIGENCE WITH SLOW INTELLIGENCE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 22, no. 08 (December 2012): 1039–53. http://dx.doi.org/10.1142/s0218194012400141.

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Chang [S. K. Chang, A general framework for slow intelligence systems, International Journal of Software Engineering and Knowledge Engineering, 20(1) (2010) 1–18.] argues that slow intelligence is a property shared by a large number of intelligent systems, and proposes a general framework, which has quick/slow decision cycles. The aim of this paper is to show that the general framework is applicable for modeling human intelligence. We show that the quick/slow decision cycles model the procedural/declarative memory system, and collect supports from adults' second language acquisition and aphasic bilinguals. As spatial cognition is fundamental in human intelligence, we show in detail how object tracing can be simulated within slow intelligence framework. We introduce a psychological experiment on object tracing, and present two rules in psychology which govern object tracing results under certain conditions, namely, the rule of minimal spatial transformation, and the rule of categorical continuity. We review related works in spatial knowledge representation and reduce object tracing problems into the problem of object mapping. We show that the following capabilities can be simulated in the slow intelligence framework: the capability of acquiring knowledge about one scene, and the capability of mapping object between scenes. An experimental result is presented.
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Krapivny, Jurij N., and Aleksandr O. Kryvonos. "HYBRID INTELLIGENCE SYSTEMS ARCHITECTURE." ELECTRICAL AND COMPUTER SYSTEMS 22, no. 98 (May 25, 2016): 260–66. http://dx.doi.org/10.15276/eltecs.22.98.2016.43.

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Chen, Z. "ANALOGY, SYSTEMS, AND INTELLIGENCE." Cybernetics and Systems 22, no. 6 (November 1991): 611–16. http://dx.doi.org/10.1080/01969729108902302.

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Adlassnig, Klaus-Peter. "Artificial-intelligence-augmented systems." Artificial Intelligence in Medicine 24, no. 1 (January 2002): 1–4. http://dx.doi.org/10.1016/s0933-3657(01)00102-6.

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Vickery, Brian, and Alina Vickery. "Intelligence and information systems." Journal of Information Science 16, no. 1 (February 1990): 65–70. http://dx.doi.org/10.1177/016555159001600111.

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Tunowski, Remigiusz. "Organization Effectiveness and Business Intelligence Systems. Literature Review." Management and Business Administration. Central Europe 23, no. 4 (December 15, 2015): 55–73. http://dx.doi.org/10.7206/mba.ce.2084-3356.157.

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Dissertations / Theses on the topic "Systems intelligence"

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MCINTYRE, SCOTT CAMPBELL. "PLEXPLAN: AN INTEGRATED INTELLIGENT ENVIRONMENT FOR INFORMATION SYSTEMS PLANNING (ARTIFICIAL INTELLIGENCE)." Diss., The University of Arizona, 1986. http://hdl.handle.net/10150/183897.

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PLEXPLAN, an automated software system and methodology for Information Systems (IS) planning, is described. The PLEXPLAN methodology and tools are used to integrate information from multiple and dynamic perspectives on the organization. A conceptual and technical software architecture is described which permits storage, integration and manipulation of such information. The PLEXPLAN methodology is consistent with the PLEXPLAN software architecture inasmuch as specific techniques in the methodology are represented in the automated environment as tools. A case is presented where PLEXPLAN was used to address an Information Systems planning task. Use of the PLEXPLAN methodology and tools is specifically described as they relate to the case.
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Anderson, C. D. P. "Intelligent computer systems : Philosophical objections to the project of artificial intelligence." Thesis, Queen's University Belfast, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.374202.

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Brunelli, Davide <1977&gt. "Electronic systems for ambient intelligence." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2007. http://amsdottorato.unibo.it/388/.

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Wong, Kwok Wai Johnny. "Development of selection evaluation and system intelligence analytic models for the intelligent building control systems." Thesis, The Hong Kong Polytechnic University, 2007. https://eprints.qut.edu.au/20343/1/c20343.pdf.

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With the availability of innumerable ‘intelligent’ building products and the dearth of inclusive evaluation tools, design teams are confronted with the quandary of choosing the apposite building control systems to suit the needs of a particular intelligent building project. The paucity of measures that represent the degree of system intelligence and indicate the desirable goal in intelligent building control systems design further inhibits the consumers from comparing numerous products from the viewpoint of intelligence. This thesis is organised respectively to develop models for facilitating the selection evaluation and the system intelligence analysis for the seven predominant building control systems in the intelligent building. To achieve these objectives, systematic research activities are conducted to first develop, test and refine the general conceptual models using consecutive surveys; then, to convert the developed conceptual frameworks to the practical models; and, finally, to evaluate the effectiveness of the practical models by means of expert validations.----- The findings of this study, on one hand, suggest that there are different sets of critical selection criteria (CSC) affecting the selection decision of the intelligent building control systems. Service life, and operating and maintenance costs are perceived as two common CSC. The survey results generally reflect that an ‘intelligent’ building control system does not necessarily need to be technologically advanced. Instead, it should be the one that can ensure efficiency and enhance user comfort and cost effectiveness. On the other hand, the findings of the research on system intelligence suggest that each building control system has a distinctive set of intelligence attributes and indicators. The research findings also indicate that operational benefits of the intelligent building exert a considerable degree of influence on the relative importance of intelligence indicators of the building control systems in the models. This research not only presents a systematic and structured approach to evaluate candidate building control systems against the CSC, but it also suggests a benchmark to measure the degree of intelligence of one control system candidate against another.
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Tebbutt, Colin Dean. "Control system design using artificial intelligence." Doctoral thesis, University of Cape Town, 1991. http://hdl.handle.net/11427/14697.

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Includes bibliography.
Successful multivariable control system design demands knowledge, skill and creativity of the designer. The goal of the research described in this dissertation was to investigate, implement, and evaluate methods by which artificial intelligence techniques, in a broad sense, may be used in a design system to assist the user. An intelligent, interactive, control system design tool has been developed to fulfil this aim. The design tool comprises two main components; an expert system on the upper level, and a powerful CACSD package on the lower level. The expert system has been constructed to assist and guide the designer in using the facilities provided by the underlying CACSD package. Unlike other expert systems, the user is also aided in formulating and refining a comprehensive and achievable design specification, and in dealing with conflicts which may arise within this specification. The assistance is aimed at both novice and experienced designers. The CACSD package includes a synthesis program which attempts to find a controller that satisfies the design specification. The synthesis program is based upon a recent factorization theory approach, where the linear multivariable control system design problem is translated into, and techniques efficiency solved as, a quadratic programming problem, which significantly improve the time and space of this method have been developed, making it practical to solve substantial multivariable design problems using only a microcomputer. The design system has been used by students at the University of Cape Town. Designs produced using the expert system tool are compared against those produced using classical design methods.
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Unciano, Conrad Vance. "The systems engineering development of an intelligence information system." Master's thesis, Virginia Tech, 1996. http://hdl.handle.net/10919/46376.

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Hadjimichael, Basil. "Manufacturing execution systems integration and intelligence." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82491.

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In order to survive in today's competitive manufacturing markets, manufacturing systems need to adapt at an ever-increasing pace to incorporate new technology which can lower the cost of production, while maintaining quality and delivery schedules. The task of the manufacturing system becomes even more challenging in the quest to use a common approach for different manufacturing plants and ever evolving manufacturing processes for specific plants. This thesis introduces a reference architecture that enables such changes between plants and updates within plants. For this, we use the paradigm of Manufacturing Execution Systems (MES). A developed MES architecture by the National Institute of Standards and Technology (NIST) is used as the standard reference architecture. Its flexibility and scalability is applied to a specific steel melt-shop plant case study. In this case study the standard framework is specified through re-labeling standard data and modules to specifics tailored for the melt process of a generic steel plant. Since steel plants are faced with difficult scheduling and disturbance handling problems, specific intelligent algorithms are developed to deal with these issues through integrating some of the control into the MES. Conclusions as to the success of the algorithms along with supporting data and recommendations of further use for them are also included.
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Sian, Satwinder Singh. "Learning in distributed artificial intelligence systems." Thesis, Imperial College London, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282757.

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Xu, Xian Zhong. "Information systems for strategic intelligence support." Thesis, Open University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244368.

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Barkalow, Daniel J. (Daniel James) 1977. "On making modular artificial intelligence systems." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86648.

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Books on the topic "Systems intelligence"

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Artificial Intelligence and Intelligent Systems. Oxford: Oxford University Press, 2005.

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Silhavy, Radek, ed. Artificial Intelligence in Intelligent Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77445-5.

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Li, Kangshun, Wei Li, Zhangxing Chen, and Yong Liu, eds. Computational Intelligence and Intelligent Systems. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1648-7.

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Li, Kangshun, Wei Li, Zhangxing Chen, and Yong Liu, eds. Computational Intelligence and Intelligent Systems. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1651-7.

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Peng, Hu, Changshou Deng, Zhijian Wu, and Yong Liu, eds. Computational Intelligence and Intelligent Systems. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6473-0.

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Li, Zhenhua, Xiang Li, Yong Liu, and Zhihua Cai, eds. Computational Intelligence and Intelligent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34289-9.

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Li, Kangshun, Jin Li, Yong Liu, and Aniello Castiglione, eds. Computational Intelligence and Intelligent Systems. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0356-1.

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Cai, Zhihua, Hengjian Tong, Zhuo Kang, and Yong Liu, eds. Computational Intelligence and Intelligent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16388-3.

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Cai, Zhihua, Zhenhua Li, Zhuo Kang, and Yong Liu, eds. Computational Intelligence and Intelligent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04962-0.

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Silhavy, Radek, Roman Senkerik, Zuzana Kominkova Oplatkova, Petr Silhavy, and Zdenka Prokopova, eds. Artificial Intelligence Perspectives in Intelligent Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33625-1.

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Book chapters on the topic "Systems intelligence"

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Hulten, Geoff. "Intelligence Management." In Building Intelligent Systems, 157–69. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3432-7_14.

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Hulten, Geoff. "Representing Intelligence." In Building Intelligent Systems, 197–207. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3432-7_17.

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Hulten, Geoff. "Evaluating Intelligence." In Building Intelligent Systems, 225–44. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3432-7_19.

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Hulten, Geoff. "Organizing Intelligence." In Building Intelligent Systems, 263–78. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3432-7_21.

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Richter, Michael M. "Knowledge-Based Systems." In Intelligence and Artificial Intelligence, 109–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03667-9_7.

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Sauter, Vicki L. "Competitive Intelligence Systems." In Handbook on Decision Support Systems 2, 195–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-48716-6_10.

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Caserio, Carlo, and Sara Trucco. "Business Intelligence Systems." In Contributions to Management Science, 43–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77679-8_3.

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Chang, Shi-Kuo. "Slow Intelligence Systems." In Lecture Notes in Computer Science, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11301-7_1.

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Glenn, Jerome C. "Collective Intelligence Systems." In Handbook of Science and Technology Convergence, 1–10. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04033-2_7-1.

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Kale, Vivek. "Business Intelligence Systems." In Enterprise Performance Intelligence and Decision Patterns, 139–50. New York : CRC Press, [2017]: Auerbach Publications, 2017. http://dx.doi.org/10.4324/9781351228428-10.

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Conference papers on the topic "Systems intelligence"

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Dasgupta, Supratim, and Vamsi Krishna Vankayala. "Developing Real Time Business Intelligence Systems the Agile Way." In 2007 1st Annual IEEE Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/systems.2007.374652.

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Dagli, Cihan, and Nil Kilicay. "Understanding Behavior of System of Systems Through Computational Intelligence Techniques." In 2007 1st Annual IEEE Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/systems.2007.374658.

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Shweta A.S. "Intelligent refrigerator using ARTIFICIAL INTELLIGENCE." In 2017 11th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2017. http://dx.doi.org/10.1109/isco.2017.7856036.

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Naidu, Prakash C. R. J., and Andrew A. Goldenberg. "Design of Intelligent Systems: Measure of Information Content." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85575.

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Increasing intelligence in new products has necessitated a closer look at the application of design theory and methodology in the specific context of intelligent systems. In this paper, after reviewing the several prevalent notions of intelligent systems, some of the parameters and the measure of intelligence are discussed. The earlier work on evaluation of information content of automation systems is extended in the context of intelligent systems. The paper discusses a generic definition of intelligence more suitable for measurement in the realm of axiomatic design theory. It investigates the different issues pertaining to computation of information and its interpretation as a measure of intelligence considering an example of the basic maneuvers of an autonomous robot. The paper discusses the observations and elucidates the usefulness of information as a metric for measurement of intelligence.
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"Track 3: Computational intelligence & intelligent systems." In 2015 12th International Symposium on Programming and Systems (ISPS). IEEE, 2015. http://dx.doi.org/10.1109/isps.2015.7244981.

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Coker, L. O. Toriola, N. AYekini, H. Alaka, and A. O. Ajimo. "Artificial Intelligence Systems for Teaching and Learning in Civil Engineering: Conceptual Framework." In 9th International Conference on Artificial Intelligence and Applications (AIAP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120405.

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Artificial intelligence technology is based on design of machine or computer application that mimic human intelligent. Use of artificial intelligence in teaching and learning in civil engineering is a welcome development. This paper presents a conceptual framework of Artificial Intelligence Systems for Teaching, Learning, and administration of in Civil Engineering education. The proposed system is to be designed using the following tools: Extensible Markup Language (XML) to develop the GUI, Hypertext Pre-processor (PHP) for the web user interface (WUI), APACHE for middleware, MYSQL for database design, and UML will be used to visualize the design of the system. If the system is developed and implemented, it will go a long way to advance teaching and learning, and educational administration in civil engineering profession.
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Sena, James A., and A. B. (Rami) Shani. "Intelligence systems." In the 1999 ACM SIGCPR conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/299513.299623.

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Коростелев, Дмитрий Александрович, Dmitriy Aleksandrovich Korostelev, Алексей Радченко, Aleksey Radchenko, Никита Сильченко, Nikita Silchenko, Ростислав Крылов, Rostislav Krylov, Павел Мигаль, and Pavel Migal. "Software Platform for Designing and Running Artificial Intelligence Competitions with a Visualization Subsystem." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-295-299.

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The paper describes the solution to the problem of testing the efficiency of new ideas and algorithms for intelligent systems. Simulation of interaction of the corresponding intelligent agents in a competitive form implementing different algorithms is proposed to use as the main approach to the solution. To support this simulation, a specialized software platform is used. The paper describes the platform developed for running competitions in artificial intelligence and its subsystems: a server, a client and visualization. Operational testing of the developed system is also described which helps to evaluate the efficiency of various algorithms of artificial intelligence in relation to the simulation like "Naval Battle".
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Westermann, Thorsten, Harald Anacker, Roman Dumitrescu, Ursula Frank, and Axel Hessenkämper. "Interdisciplinary System Architecture for Intelligent Technical Systems." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50579.

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Nowadays, mechanical engineering products change from mechatronic products, characterized by the close interplay of mechanics, electronics, control and software engineering to Intelligent Technical Systems (ITS). Intelligent Technical Systems are mechatronic systems with inherent partial intelligence. Mechanical engineering products will be a symbiosis of information technology and mechanical parts. This will entail a rising complexity of technical systems and its development. Therefore we introduce an approach to improve system architectures of mechanical engineering products to realize Intelligent Technical Systems. Our approach meets the following challenges: creating a domain-spanning description of the system with Model-Based Systems Engineering (MBSE); designing an interdisciplinary and modular system architecture by using the technological concept of Intelligent Technical Systems; identifying and implementing solution elements as reusable modules. Validated by a separator, our results clarify the benefits of MBSE and the technological concept of ITS to improve system architectures of mechanical engineering products to realize Intelligent Technical Systems.
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Hiura, Takuya, and Shin Morishita. "Application of Swarm Intelligence to a Vibration Monitoring System." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3734.

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The technology of swarm intelligence has been applied to a mechanical vibration monitoring system composed of a network of units equipped with sensors and actuators. The expression of “swarm intelligence” was first used in 1988 in the context of cellular robotic systems, where lots of simple agents may generate self-organized patterns through mutual interactions. There are various examples of the swarm intelligence in the natural environment, a swarm of ants, birds or fish. In this sense, the network of agents in a swarm may have some kind of intelligence or higher function than those appeared in a simple agent, which is defined as the swarm intelligence. The concept of swarm intelligence may be applied in diverse engineering fields such as flexible pattern recognition, adaptive control system, or intelligent monitoring system, because some kind of intelligence may emerge on the network without any special control system. In this study, a simulation model of a five degree-of-freedom lumped mass-spring system was prepared as an example of a mechanical dynamic system. Five units composed of a displacement sensor and a variable damper as actuator were assumed to be placed on each mass of the system. Each unit was connected to each other to exchange the information of state variables measured by sensors on each unit. Because the network of units configured as a mutual connected neural network, a kind of artificial intelligence, the network of units may memorize the several expected vibration-controlled patterns and may produce the signal to the actuators on the unit to reduce the vibration of target system. The simulation results showed that the excited vibration was reduced autonomously by selecting the position where the damping should be applied.
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Reports on the topic "Systems intelligence"

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Stevenson, Charles A. Artificial Intelligence and Expert Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada436516.

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Core, Mark G., H. C. Lane, Michael van Lent, Dave Gomboc, Steve Solomon, and Milton Rosenberg. Building Explainable Artificial Intelligence Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada459166.

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Perdigão, Rui A. P., and Julia Hall. Conceptualizing System-of-Systems for Water Intelligence Networks (SoS4WIN). Meteoceanics, July 2021. http://dx.doi.org/10.46337/210726.

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Tong, Richard M., Neville D. Newman, Gary Berg-Cross, and Fred Rook. Performance Evaluation of Artificial Intelligence Systems. Fort Belvoir, VA: Defense Technical Information Center, August 1987. http://dx.doi.org/10.21236/ada184054.

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Sobes, Vladimir, Briana Hiscox, Emilian Popov, Marco Delchini, Richard Archibald, Cory Hauck, Paul Laiu, et al. Artificial Intelligence Design of Nuclear Systems. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1811425.

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Waters, H. Artificial Intelligence for VHSIC Systems Design (AIVD). Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada259511.

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Engel, David, and Thomas Malone. Measuring Collective Intelligence in Human-Machine Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada602979.

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Rovira, Ericka, and Michelle Harper-Sciarini. Exploratory Evaluation of Intelligence-Operations Collaborative Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada543955.

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Meystel, A. M., A. M. Meystel, and E. R. Messina. Measuring the performance and intelligence of systems. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.sp.970.

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Messina, E. R., E. R. Messina, and A. M. Meystel. Measuring the performance and intelligence of systems. Gaithersburg, MD: National Institute of Standards and Technology, 2002. http://dx.doi.org/10.6028/nist.sp.982.

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