Virtual Concept of a Symbiotic Environment for CBL and CBT Methods Based Education in Aircraft System

The contribution is informing the reader on the current potentials of improving efficiency in aviation education based on the use of available means of simulation and computer assisted methods of teaching such as the Computer Based Training a Computer Based Learning, which also enable realization of integrated systems assisted by modern educational methods and potentials for them to be implemented into the aircrew training systems. The authors are introducing a virtual concept of a symbiotic environment for education in aircraft systems. The concept makes use the mutual interrelation of three-dimensional dynamic models of aircraft instruments developed, which can be used for electronic training using simulation interface based on open standards in a virtual environment within a country and later with real onboard complex featuring the Instruments themselves. Attention is drawn to both the advantages and disadvantages of using such dynamic models of aircraft Instruments in aircrew training as well as the practical implementation of such a virtual concept into practice employing computer assisted progressive methods of teaching. The need of improving the quality of education in aviation is a reaction to the requirements coming from actual practice calling for the implementation of intelligent systems into the complex and dynamic systems of aircraft. At the present time, it is the very reason that requires shifting towards learning new ways and acquiring skills in applying progressive technologies to education exercising direct influence on the safety of air transportation

Modeling Tolerance in Dynamic Social Networks

The study of social networks has become increasingly important in recent years. Multi-agent systems research has proven to be an effective way of representing both static and dynamic social networks in order to model and analyze many different situations. Previous implementations of multi-agent systems have observed a phenomenon called tolerance between agents through simulation studies, which is defined as an agent maintaining an unrewarding connection. This concept has also arisen in the social sciences through the study of networks. We aim to bridge this gap between simulation studies in multi-agent systems and real-world observations. This project explores how local interactions of autonomous agents in a network relate to the development of tolerance. We have developed a new model for multi-agent system interactions based on these observations. We also claim that tolerance is directly observable in real dynamic social networks, and the parameters that govern tolerance of a system can be estimated using a Hidden Markov Model

Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System

Parametric Identification of State-Space Dynamic Systems: A Time-Domain Perspective

In this paper we have presented a time-domain approach to parametric identification of state-space dynamic models comprised both an equation of motion and a system potential (a performance measure). The proposed techniques have been elaborated in order to obtain high simulation and forecast properties and applied to systems of nonstationary accelerator, gradient systems, and linear-quadratic stationary systems. We have also demonstrated a new concept of system potential specification in case of linearquadratic stationary systems. It is based on the principle of its basis decomposition as an element of energy space. All models and algorithms have been approbated using real statistical data for models of macroeconomic dynamics. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/290

A black-box model for neurons

We explore the identification of neuronal voltage traces by artificial neural networks based on wavelets (Wavenet). More precisely, we apply a modification in the representation of dynamical systems by Wavenet which decreases the number of used functions; this approach combines localized and global scope functions (unlike Wavenet, which uses localized functions only). As a proof-of-concept, we focus on the identification of voltage traces obtained by simulation of a paradigmatic neuron model, the Morris-Lecar model. We show that, after training our artificial network with biologically plausible input currents, the network is able to identify the neuron's behaviour with high accuracy, thus obtaining a black box that can be then used for predictive goals. Interestingly, the interval of input currents used for training, ranging from stimuli for which the neuron is quiescent to stimuli that elicit spikes, shows the ability of our network to identify abrupt changes in the bifurcation diagram, from almost linear input-output relationships to highly nonlinear ones. These findings open new avenues to investigate the identification of other neuron models and to provide heuristic models for real neurons by stimulating them in closed-loop experiments, that is, using the dynamic-clamp, a well-known electrophysiology technique.Peer ReviewedPostprint (author's final draft

Minimum daylight autonomy: A new concept to link daylight dynamic metrics with daylight factors

Daylight metrics act as a useful tool to quantify the potential of natural light in an architectural space as well as the energy savings promoted by a suitable design of windows, atriums, and skylights. Accordingly, a new indoor lighting metric is proposed, minimum daylight autonomy, defined as the percentage of occupied time when an illuminance threshold can be met by daylight alone under continuous overcast sky conditions. This novel concept can determine an approximation of the maximum use of electric lighting and the quantification of minimum energy savings without the need for advanced calculation tools. Although daylight factor is the most widespread concept, it cannot forecast energy savings as accurately as dynamic metrics. In addition, daylight autonomy is the most usual dynamic definition, because it estimates the energy consumption of on–off electric lighting systems depending on weather conditions. However, there is no link between static and dynamic metrics, because both concepts are based on different variables. This research proposes the calculation procedure for minimum daylight autonomy, as well as the equations that serve to predict dynamic metrics based on static metrics, after confirming the accuracy of the simulation program that calculates the metrics using a test cell under real conditions

Robust detection, isolation and accommodation for sensor failures

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous technique