Computational physics of the mind

In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures

Towards systems engineering approach applicable for small developing organisation

Includes bibliographical references.The purpose of this thesis was to create the model which could be used to optimise the operational effectiveness of the general type of small developing business/system. The small developing organisation is the system which has the same type of components as a big organisation but the structure is much simpler. These components develop a system in the Technical, Economic and Environmental areas. Each of these components has its own purpose and together these purposes optimise the entire system. The identification of the purpose of the components of the small developing organisation should be analysed by: • investigating the component's performance; • investigating the component's behavioural pattern; and • investigating the component's support requirement. Further, the analysis of the general characteristics of the small developing organisation is such that: * analysis of the performance considers only the main point of the component's operation but not the entire operational function; * the behavioural pattern (i.e. failure and repair occurrence) is random; * the evaluation of the support requirements is subjective where the statistical tools may not be applicable; • the relationship between the elements of the aforementioned three activities is often unpredictable; • the components are usually analysed according to the hierarchy of importance to the development of the entire system

One-Point Functions of Non-protected Operators in the SO(5) symmetric D3-D7 dCFT

We study tree level one-point functions of non-protected scalar operators in the defect CFT, based on N=4 SYM, which is dual to the SO(5) symmetric D3-D7 probe brane system with non-vanishing instanton number. Whereas symmetries prevent operators from the SU(2) and SU(3) sub-sectors from having non-vanishing one-point functions, more general scalar conformal operators, which in particular constitute Bethe eigenstates of the integrable SO(6) spin chain, are allowed to have non-trivial one-point functions. For a series of operators with a small number of excitations we find closed expressions in terms of Bethe roots for these one-point functions, valid for any value of the instanton number. In addition, we present some numerical results for operators with more excitations.Comment: 15 pages, 1 figure. Minor corrections & update

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Methods: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Results: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). Conclusion: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller

Expert supervision of conventional control systems

The objective of this paper is to outline a general concept for the design of supervising fuzzy controllers to back up or monitor a conventzonal control system. The use of fuzzy logic in an external, hierarchacal control structure provides a systematic approach to integrate heuristics in a conventional control loop. Supervising techniques become especially interesting, when the system to be controlled is highly non-linear (parameter variation, saturation of the control surfaces etc.). By the means of two application examples it will be shown, how this method can effectively be used to improve the performance of a conventional control system. Both examples are part of an extended research project that is being carried out at Akrospatiale and E.N.S.I.C.A. in France to study the role of fuzzy control for potential applications in aircraft control systems

Past, present and future mathematical models for buildings (i)

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems