Development of a cognitive mnemonic scheme for an optical Smart-technology of remote learning of the Experions PKS distributed control system on the basis of Artificial Immune Systems

В статье рассматриваются актуальные вопросы, посвящённые разработке информационной оптической Smart-технологии дистанционного обучения распределённой системы управления Experion PKS фирмы Honeywell для нефтегазовой отрасли. Около 70 % аварий на производстве вызваны человеческим фактором. Работа операторов заключается в наблюдении за высокотехнологичными процессами и управлении ими посредством мнемосхем и характеризуется повышенным напряжением зрительного аппарата, а также общей утомляемостью и потерей концентрации внимания. Инновационная персонализированная технология дистанционного обучения учитывает особенности зрения обучающихся с помощью корректировки цветоподачи учебного материала и динамического представления информации в зависимости от психотипа человека и основана на применении когнитивной, оптической и мультиагентной технологий, а также онтологическом и иммунносетевом подходах. Разработка когнитивных мнемосхем осуществляется с учётом этих особенностей, что позволяет снизить нагрузку на зрительный аппарат и повысить эффективность обучения практическим навыкам при работе с мнемосхемами. Подход искусственных иммунных систем используется для прогноза и оценки обучения, а также оперативной корректировки процесса получения знаний. Разработан модифицированный алгоритм функционирования дистанционной системы обучения на основе применения оптимизационных алгоритмов искусственного интеллекта и алгоритма иммунносетевого моделирования. Рассмотрены общие принципы создания мнемосхем и существующие мнемосхемы фирмы Honeywell. Приведён пример реализации предложенной дистанционной технологии, и представлены результаты моделирования когнитивных мнемосхем для различных категорий обучающихся с особенностями зрения.Исследование выполнено при финансовой поддержке КН МОН РК в рамках научного проекта № АР09258508 на тему: «Разработка интеллектуальной технологии управления сложными объектами на основе унифицированной искусственной иммунной системы для промышленной автоматизации с использованием современной микропроцессорной техники» (2021 – 2023 гг.)

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

Empirically characterizing evolvability and changeability in engineering systems

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012."June 2012." Cataloged from PDF version of thesis.Includes bibliographical references (p. 205-212).The beginning phases of system development and conceptual design require careful consideration, as these decisions will have significant influence on system lifetime performance and are often made with incomplete system knowledge. Decision makers may improve their capacity to discriminate between system concepts and design choices by measuring a system's "ilities" such as changeability, evolvability, and survivability. These ilities may enable systems to respond to perturbations in the design space, context space, and needs space in order to ensure system functionality and adequate performance over time. A system may be designed to change in response to perturbations, or remain statically robust/survivable to perturbations in order to avoid deficiencies or failures. This research attempts to analyze the mechanisms that allow system changes to occur. More specifically, this research will further the characterization of system changeability and evolvability and ultimately provide a structured and meaningful way of classifying system characteristics often described as "ilities". Value sustainment is proposed as an ultimate goal of systems, providing value in spite of perturbations in design, context, or needs. The premise of value sustainment is investigated through four distinct research thrusts: 1) a basis for defining system changes and ilities; 2) a system change examples database with categorical cluster analysis case research; 3) epoch-shift, impact, response, outcome case research; and 4) expert interviews case research. Focusing on change-related ilities, this research proposes constructs for identifying and enabling vague, yet desirable, system properties. Evolvability is characterized as a subset of changeability and defined as the ability of an architecture to be inherited and changed across generations [over time], with a set of ten proposed design principles including decentralization, redundancy, targeted modularity, scalability, integrability, reconfigurability, mimicry, leverage ancestry, disruptive architectural overhaul, and resourceful exaptation.by Jay Clark Beesemyer, Jr.S.M

On engineering smart systems

A smart system exhibits the four important properties: (i) Interactive, collective, coordinated and efficient Operation (ii) Self -organization and emergence (iii) Power law scaling under emergence (iv) Adaptive. We describe the role of fractal and percolation models for understanding smart systems. A hierarchy based on metric entropy is suggested among the computational systems to differentiate ordinary system from the smart system. Engineering a general purpose smart system is not feasible, since emergence is a global behaviour (or a goal) that evolves from the local behaviour (goals) of components. This is due to the fact that the evolutionary rules for the global goal is non-computable, as it cannot be expressed as a finite composition of computable function of local goals for any arbitrary problem domain