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Yingxu Wang - One of the best experts on this subject based on the ideXlab platform.

  • Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing
    2011
    Co-Authors: Yingxu Wang
    Abstract:

    Cognitive informatics is a multidisciplinary field that acts as the bridge between Natural science and information science. Specifically, it investigates the potential applications of information processing and Natural Intelligence to science and engineering disciplines. Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing examines innovative research in the emerging, multidisciplinary field of cognitive informatics. Researchers, practitioners and students can benefit from discussions of the connections between Natural science and informatics that are investigated in this fundamental collection of cognitive informatics research. This book provides information on the interrelation of the multidisciplinary research area of Cognitive Informatics and the transdisciplinary study of Natural Intelligence.

  • Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence (Advances in Cognitive Informatics and Natural Intelligence (Acini) Book Series)
    2009
    Co-Authors: Yingxu Wang
    Abstract:

    Cognitive informatics is a multidisciplinary field that acts as the bridge between Natural science and information science. Specifically, it investigates the potential applications of information processing and Natural Intelligence to science and engineering disciplines. This collection, entitled Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence provides emerging research topics in cognitive informatics research with a focus on such topics as reducing cognitive overload, real-time process algebra, and neural networks for iris recognition, emotion recognition in speech, and the classification of musical chords.

  • IEEE ICCI - Cognitive Computing and machinable thought
    2009 8th IEEE International Conference on Cognitive Informatics, 2009
    Co-Authors: Yingxu Wang
    Abstract:

    Cognitive Computing (CC) is an emerging paradigm of intelligent computing methodologies and systems that implements computational Intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain [1, 3, 4, 5, 6, 12, 13, 15, 16, 18, 20, 22, 23]. CC is emerged and developed based on the transdisciplinary research in cognitive informatics and abstract Intelligence. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information science, cognitive science, and Intelligence science that investigates into the internal information processing mechanisms and processes of the brain and Natural Intelligence, as well as their engineering applications [1, 3, 6, 12, 13, 20, 22]. The theoretical framework of cognitive informatics [6] covers the Information-Matter-Energy (IME) model [5], the Layered Reference Model of the Brain (LRMB) [19], the Object-Attribute-Relation (OAR) model of information representation in the brain [7], the cognitive informatics model of the brain [17], Natural Intelligence (NI) [6], and neuroinformatics [6]. Recent studies on LRMB in cognitive informatics reveal an entire set of cognitive functions of the brain and their cognitive process models, which explain the functional mechanisms and cognitive processes of the Natural Intelligence with 43 cognitive processes at seven layers known as the sensation, memory, perception, action, meta-cognitive, meta-inference, and higher cognitive layers from the bottom up [19].

  • on abstract Intelligence toward a unifying theory of Natural artificial machinable and computational Intelligence
    International Journal of Software Science and Computational Intelligence, 2009
    Co-Authors: Yingxu Wang
    Abstract:

    Abstract Intelligence is a human enquiry of both Natural and artificial Intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. This paper describes the taxonomy and nature of Intelligence. It analyzes roles of information in the evolution of human Intelligence, and the needs for logical abstraction in modeling the brain and Natural Intelligence. A formal model of Intelligence is developed known as the Generic Abstract Intelligence Mode (GAIM), which provides a foundation to explain the mechanisms of advanced Natural Intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is comparatively studied in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of the GAIM model and the abstract Intelligence theories, the compatibility of Natural and machine Intelligence is revealed the compatibility of Natural and machine Intelligence is revealed in order to investigate into a wide range of paradigms of abstract Intelligence such as Natural, artificial, machinable Intelligence, and their engineering applications.

  • Novel Approaches in Cognitive Informatics and Natural Intelligence
    2008
    Co-Authors: Yingxu Wang
    Abstract:

    Creating a link between a number of Natural science and life science disciplines, the emerging field of cognitive informatics presents a transdisciplinary approach to the internal information processing mechanisms and processes of the brain and Natural Intelligence. Novel Approaches in Cognitive Informatics and Natural Intelligence penetrates the academic field to offer the latest advancements in cognitive informatics and Natural Intelligence. This book covers the five areas of cognitive informatics, Natural Intelligence, autonomic computing, knowledge science, and relevant development, to provide researchers, academicians, students, and practitioners with a ready reference to the latest findings.

Roman Kulikowski - One of the best experts on this subject based on the ideXlab platform.

N. Kiupel - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy supervision and application to lean production
    International Journal of Systems Science, 1993
    Co-Authors: Paul M. Frank, N. Kiupel
    Abstract:

    Abstract A novel philosophy of process supervision based on functional redundancy, i.e., analytical or knowledge based redundancy which may specifically be used for lean production, is suggested. The key idea is to replace the conventional residual evaluator of the fault diagnosis system based on crisp logic, by both a decision maker with fuzzy logic for residual pre-evaluation and the human operator to make the final decisions using his Natural Intelligence, experience and common sense. The purpose of the employment of fuzzy logic for residual pre-evaluation is to release only weighted alarms instead of yes-no decisions, so that (by definition) no false alarms can be produced; besides this, the man-machine interaction becomes much easier. In contrast to the conventional expert system approach, the proposed concept leaves the final yes-no decisions to the Natural Intelligence, capability and responsibility of the human operator which are still superior to the artificial Intelligence and decision making ca...

  • Process supervision with the aid of fuzzy logic
    Proceedings of IEEE Systems Man and Cybernetics Conference - SMC, 1
    Co-Authors: N. Kiupel, Paul M. Frank
    Abstract:

    In this paper we suggest a novel philosophy of process supervision based on functional redundancy: analytical or knowledge based redundancy. The key idea is to replace the conventional residual evaluator of the fault diagnosis system based on crisp logic, by both a decision maker with fuzzy logic for residual pre-evaluation and the human operator to make the final decisions using his Natural Intelligence, experience and common sense. The purpose of the employment of fuzzy logic for residual pre-evaluation is to release only weighted alarms instead of yes-no decisions, so that (by definition) no false alarms can be produced; besides this the man-machine interaction becomes much easier. In contrast to the conventional expert system approach, the proposed concept leaves the final yes-no decisions to the Natural Intelligence, capability and responsibility of the human operator which are still superior to the artificial Intelligence and decision making capabilities of an expert system. >

  • Fuzzy residual evaluation concept (FREC)
    1995 IEEE International Conference on Systems Man and Cybernetics. Intelligent Systems for the 21st Century, 1
    Co-Authors: N. Kiupel, Birgit Köppen-seliger, H.s. Kellinghaus, Paul M. Frank
    Abstract:

    Suggests a novel philosophy of process supervision based on knowledge based redundancy. The key idea is to replace the conventional residual evaluator of the fault diagnosis system based on crisp logic, by both a decision maker with fuzzy logic for residual evaluation and the human operator to make the final decisions using his Natural Intelligence, experience and common sense. The purpose of the employment of fuzzy logic for residual evaluation is to release only weighted alarms instead of yes-no decisions, such that (by definition) no false alarms can be produced; besides this the man-machine interaction becomes much easier. In contrast to the conventional expert system approach, the proposed concept leaves the final yes-no decisions to the Natural Intelligence, capability and responsibility of the human operator. In addition this method can be seen as an extension to the quantitative model-based techniques. Nevertheless the huge amount of information, which is normally given by most of the fault diagnosis schemes, should be both, filtered and reduced in the sense of the detectability and reliability of an FDI scheme. As an application example this concept has been applied to a part of a power plant in order to prove this theory.

Olga Popova - One of the best experts on this subject based on the ideXlab platform.

  • Entropy and Algorithm of Obtaining Decision Trees in a Way Approximated to the Natural Intelligence
    International Journal of Cognitive Informatics and Natural Intelligence, 2019
    Co-Authors: Olga Popova, Boris Popov, Vladimir Karandey, Alexander Gerashchenko
    Abstract:

    The classification of knowledge of a specified subject area is an actual task. The well-known methods of obtaining decision trees using entropy are not suitable for the classification of the subject area knowledge. So, a new algorithm of obtaining decision trees, whose way of obtaining is approximated to the Natural Intelligence, is suggested in the article. Here, the knowledge of a subject area is presented as a complex of answers to questions, which help to find the solution to a current task. The connection of entropy with the appearance of knowledge, the classification of previous knowledge, and the definitions used in decision trees are also analyzed in the article. The latter is necessary to compare the suggested algorithm approximated to the Natural Intelligence with the traditional method, using a small example. The article contains the analysis of solving a classification task for such a subject area as optimization methods.

  • IntelliSys (2) - Studying an Element of the Information Search System: The Choice Process Approximated to the Natural Intelligence
    Advances in Intelligent Systems and Computing, 2018
    Co-Authors: Olga Popova, Yury Shevtsov, Boris Popov, Vladimir Karandey, Vladimir Klyuchko
    Abstract:

    Nowadays the task of studying the information search as a system is relevant, one of its elements being the process of choice. The study is essential for automating and performing subsequent statistical analysis of the process, so the logical, structural and functional models of the process have been obtained and studied. In the study, the authors used the deductive system (developed by them) of the process of choosing the best alternative. First of all, the network modelling method was applied, alongside with the graphic way of calculation and formulae for defining the early and late event occurrence term. It has been found that the optimal time implies one cognitive work on selecting the best alternative from many known ones proceeding from a certain volume of knowledge, which needs automating. Next, Kolmogorov equations for the choice process were generated, with data obtained and a graph built which allowed making some useful conclusions. Having only a half of the amount of knowledge in a subject area enables one to get a simple intelligent system repeating the critical path 1-2-3-4-5. A smaller volume of knowledge can be used for a learning intelligent system, and a greater volume – in an intelligent system featuring error correction and check for compliance with the expected result. Then, an intelligent system conducting exploratory research has to possess the total volume of knowledge in order to maximize its cognitive power up to λ = 1, in which case the efficiency of this system will be comparable to that of the Natural Intelligence. This is why the authors looked for an approach which would be close to the Natural Intelligence in representing metaknowledge. The paper provides a specific representation of metaknowledge, with the decision tree being that of a multitude of precedents. All the rules are applied for obtaining the tree, in which a consequent is a corresponding antecedent, and the transition to it corresponds to the obtained tree structure. Such trees can be used for representing metaknowledge and searching for solutions while organizing the efficient choice process.

  • studying an element of the information search system the choice process approximated to the Natural Intelligence
    SAI Intelligent Systems Conference, 2018
    Co-Authors: Olga Popova, Yury Shevtsov, Boris Popov, Vladimir Karandey, Vladimir Klyuchko
    Abstract:

    Nowadays the task of studying the information search as a system is relevant, one of its elements being the process of choice. The study is essential for automating and performing subsequent statistical analysis of the process, so the logical, structural and functional models of the process have been obtained and studied. In the study, the authors used the deductive system (developed by them) of the process of choosing the best alternative. First of all, the network modelling method was applied, alongside with the graphic way of calculation and formulae for defining the early and late event occurrence term. It has been found that the optimal time implies one cognitive work on selecting the best alternative from many known ones proceeding from a certain volume of knowledge, which needs automating. Next, Kolmogorov equations for the choice process were generated, with data obtained and a graph built which allowed making some useful conclusions. Having only a half of the amount of knowledge in a subject area enables one to get a simple intelligent system repeating the critical path 1-2-3-4-5. A smaller volume of knowledge can be used for a learning intelligent system, and a greater volume – in an intelligent system featuring error correction and check for compliance with the expected result. Then, an intelligent system conducting exploratory research has to possess the total volume of knowledge in order to maximize its cognitive power up to λ = 1, in which case the efficiency of this system will be comparable to that of the Natural Intelligence. This is why the authors looked for an approach which would be close to the Natural Intelligence in representing metaknowledge. The paper provides a specific representation of metaknowledge, with the decision tree being that of a multitude of precedents. All the rules are applied for obtaining the tree, in which a consequent is a corresponding antecedent, and the transition to it corresponds to the obtained tree structure. Such trees can be used for representing metaknowledge and searching for solutions while organizing the efficient choice process.

  • Entropy and Algorithm of the Decision Tree for Approximated Natural Intelligence
    Advances in Intelligent Systems and Computing, 2018
    Co-Authors: Olga Popova, Yury Shevtsov, Boris Popov, Vladimir Karandey, Vladimir Klyuchko, Alexander Gerashchenko
    Abstract:

    An actual task is the classification of knowledge of a specified subject area, where it’s represented not as information coded in a certain manner, but in a way close to the Natural Intelligence, which structures obtained knowledge according to a different principle. The well-known answers to the questions should be classified so that the current task could be solved. Thus a new method of decision tree formation, which is approximated to the Natural Intelligence, is suitable for knowledge understanding. The article describes how entropy is connected to knowledge appearance, classification of previous knowledge and with definitions used in decision trees. The latter is necessary for comparing the traditional methods with the algorithm of the decision tree obtaining approximated to the Natural Intelligence. The dependency of entropy on the properties of element subsets of a set has been obtained.

  • A New Approximated To the Natural Intelligence Decision Tree
    International Journal Of Engineering And Computer Science, 2016
    Co-Authors: Olga Popova, Dmitry Romanov, Marina Evseeva
    Abstract:

    The problem of adjustment of modern Intelligence enhancement methods and automated data analysis methods to the problems that are still handled manually is fairly topical. For the solution of such problems, this study suggests a new DT representation which uses approximated to the NI knowledge structuring. The structuring is implemented by the authors' question-answer binary tree. This is a new DT with only most optimal decisions for all known situations excluding non-efficient cases. A set of 'the most effective' solutions are leaves of the tree. This new approach can be applied in intelligent decision support systems (IDSS) which enhance the Natural Intelligence of the scientist in the exploratory research. This tree was tested on the problem of selecting 'the most suitable' optimization method out of all known ones. First, detailed material on the main optimization methods was selected. The material was processed by new rules of deriving tree elements. The resulted tree has 127 nodes, 64 leaves are optimization methods (solution options). 63 intermediary nodes form a unique path from root to leaf, showing the progress to the most suitable method. Also, an IDSS was implemented in C#. The paper dwells on all stages of the DT construction with detailed illustrations, including video. The suggested DT allowed: simplify knowledge base designing; reduce system designing time; simplify decision search algorithm in the knowledge base; refer to the expert in case of contributing one's own developed knowledge to the subject field in the tree; obtain a new way of meta-knowledge representation.

Paul M. Frank - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy supervision and application to lean production
    International Journal of Systems Science, 1993
    Co-Authors: Paul M. Frank, N. Kiupel
    Abstract:

    Abstract A novel philosophy of process supervision based on functional redundancy, i.e., analytical or knowledge based redundancy which may specifically be used for lean production, is suggested. The key idea is to replace the conventional residual evaluator of the fault diagnosis system based on crisp logic, by both a decision maker with fuzzy logic for residual pre-evaluation and the human operator to make the final decisions using his Natural Intelligence, experience and common sense. The purpose of the employment of fuzzy logic for residual pre-evaluation is to release only weighted alarms instead of yes-no decisions, so that (by definition) no false alarms can be produced; besides this, the man-machine interaction becomes much easier. In contrast to the conventional expert system approach, the proposed concept leaves the final yes-no decisions to the Natural Intelligence, capability and responsibility of the human operator which are still superior to the artificial Intelligence and decision making ca...

  • Process supervision with the aid of fuzzy logic
    Proceedings of IEEE Systems Man and Cybernetics Conference - SMC, 1
    Co-Authors: N. Kiupel, Paul M. Frank
    Abstract:

    In this paper we suggest a novel philosophy of process supervision based on functional redundancy: analytical or knowledge based redundancy. The key idea is to replace the conventional residual evaluator of the fault diagnosis system based on crisp logic, by both a decision maker with fuzzy logic for residual pre-evaluation and the human operator to make the final decisions using his Natural Intelligence, experience and common sense. The purpose of the employment of fuzzy logic for residual pre-evaluation is to release only weighted alarms instead of yes-no decisions, so that (by definition) no false alarms can be produced; besides this the man-machine interaction becomes much easier. In contrast to the conventional expert system approach, the proposed concept leaves the final yes-no decisions to the Natural Intelligence, capability and responsibility of the human operator which are still superior to the artificial Intelligence and decision making capabilities of an expert system. >

  • Fuzzy residual evaluation concept (FREC)
    1995 IEEE International Conference on Systems Man and Cybernetics. Intelligent Systems for the 21st Century, 1
    Co-Authors: N. Kiupel, Birgit Köppen-seliger, H.s. Kellinghaus, Paul M. Frank
    Abstract:

    Suggests a novel philosophy of process supervision based on knowledge based redundancy. The key idea is to replace the conventional residual evaluator of the fault diagnosis system based on crisp logic, by both a decision maker with fuzzy logic for residual evaluation and the human operator to make the final decisions using his Natural Intelligence, experience and common sense. The purpose of the employment of fuzzy logic for residual evaluation is to release only weighted alarms instead of yes-no decisions, such that (by definition) no false alarms can be produced; besides this the man-machine interaction becomes much easier. In contrast to the conventional expert system approach, the proposed concept leaves the final yes-no decisions to the Natural Intelligence, capability and responsibility of the human operator. In addition this method can be seen as an extension to the quantitative model-based techniques. Nevertheless the huge amount of information, which is normally given by most of the fault diagnosis schemes, should be both, filtered and reduced in the sense of the detectability and reliability of an FDI scheme. As an application example this concept has been applied to a part of a power plant in order to prove this theory.

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