DEVELOPMENT OF A STRATEGIC MANAGEMENT SYSTEM IN GREEK PUBLIC SCHOOLS

The New Public Management, as a philosophy, focuses on providing more quality service to the citizen by delivering it to "the customer", a concept derived from the theory of Total Quality Management. Besides, it contributes to optimizing the decision-making process by linking costs - benefits and taking into account emerging efficiencies, as well as achieving predetermined results - objectives set in specific time frames. Concepts such as indicators, performance, evaluation, due to misuse and mistakes made at the political level in previous years, cause reactions to the educational community, which is distrustful of political leadership and shows the majority is reluctant to participate in processes that their outcome could be interpreted as perceived by the superior authority. There is no doubt that education has the most important contribution to the improvement of human capital from birth to university studies. The Greek Ministry of Interior is responsible for the supervision over decentralized administrations and local authorities (municipalities and regions), coordination and organization of recourse to the popular verdict (elections, referendums), legislation on Greek citizenship and expatriates, and legislation on registers, registries and demographics. So, it can be considered as the main stakeholder for Public Management issues. With a series of Legislation, the roadmap seems to be set. Recently, by the Greek Law 4692/2020, some of the basic concepts of Management are institutionalized, such as the process of planning and evaluation (internal and external) the school unit and Teachers evaluation are being defined. Article visualizations

State Feedback of Complex Systems using Fuzzy Cognitive Maps

Complex systems have become a research area with increasing interest over the last years. The emergence of new technologies, the increase in computational power with reduced resources and cost, the integration of the physical world with computer based systems has created the possibility of significantly improving the quality of life of humans. While a significant degree of automation within these systems exists and has been provided in the past decade with examples of the smart homes and energy efficient buildings, a paradigm shift towards autonomy has been noted. The need for autonomy requires the extraction of a model; while a strict mathematical formulation usually exists for the individual subsystems, finding a complete mathematical formulation for the complex systems is a near impossible task to accomplish. For this reason, methods such as the Fuzzy Cognitive Maps (FCM) have emerged that are able to provide with a description of the complex system. The system description results from empirical observations made from experts in the related subject – integration of expert’s knowledge – that provide the required cause-effect relations between the interacting components that the FCM needs in order to be formulated. Learning methods are employed that are able to improve the formulated model based on measurements from the actual system. The FCM method, that is able to inherently integrate uncertainties, is able to provide an adequate model for the study of a complex system. With the required system model, the next step towards the development of a autonomous systems is the creation of a control scheme. While FCM can provide with a system model, the system representation proves inadequate to be utilized to design classic model based controllers that require a state space or frequency domain representation. In state space representation, the state vector contains the variables of the system that can describe enough about the system to determine its future behavior in absence of external variables. Thus, within the components – the nodes of the FCM, ideally those can be identified that constitute the state vector of the system. In this work the authors propose the creation of a state feedback control law of complex systems via Fuzzy Cognitive Maps. Given the FCM representation of a system, initially the components-states of the system are identified. Given the identified states, a FCM representation of the controller occurs where the controller parameters are the weights of the cause-effect relations of the system. The FCM of the system then is augmented with the FCM of the controller. An example of the proposed methodology is given via the use of the cart-pendulum system, a common benchmark system for testing the efficiency of control systems

State Feedback of Complex Systems Using Fuzzy Cognitive Maps

Complex systems have become a research area with increasing interest over the last years. The emergence of new technologies, the increase in computational power with reduced resources and cost, the integration of the physical world with computer based systems has created the possibility of significantly improving the quality of life of humans. While a significant degree of automation within these systems exists and has been provided in the past decade with examples of the smart homes and energy efficient buildings, a paradigm shift towards autonomy has been noted. The need for autonomy requires the extraction of a model; while a strict mathematical formulation usually exists for the individual subsystems, finding a complete mathematical formulation for the complex systems is a near impossible task to accomplish. For this reason, methods such as the Fuzzy Cognitive Maps (FCM) have emerged that are able to provide with a description of the complex system. The system description results from empirical observations made from experts in the related subject – integration of expert’s knowledge – that provide the required cause-effect relations between the interacting components that the FCM needs in order to be formulated. Learning methods are employed that are able to improve the formulated model based on measurements from the actual system. The FCM method, that is able to inherently integrate uncertainties, is able to provide an adequate model for the study of a complex system. With the required system model, the next step towards the development of a autonomous systems is the creation of a control scheme. While FCM can provide with a system model, the system representation proves inadequate to be utilized to design classic model based controllers that require a state space or frequency domain representation. In state space representation, the state vector contains the variables of the system that can describe enough about the system to determine its future behavior in absence of external variables. Thus, within the components – the nodes of the FCM, ideally those can be identified that constitute the state vector of the system. In this work the authors propose the creation of a state feedback control law of complex systems via Fuzzy Cognitive Maps. Given the FCM representation of a system, initially the components-states of the system are identified. Given the identified states, a FCM representation of the controller occurs where the controller parameters are the weights of the cause-effect relations of the system. The FCM of the system then is augmented with the FCM of the controller. An example of the proposed methodology is given via the use of the cart-pendulum system, a common benchmark system for testing the efficiency of control systems

Partial Splenic Embolization: Successful treatment of hypersplenism, secondary to biliary cirrhosis and portal hypertension in cystic fibrosis

AbstractPartial Splenic Embolization (PSE) is a non-surgical treatment for hypersplenism. It has been reported only in a limited number of patients with Cystic Fibrosis (CF). We report a case of a female cystic fibrosis patient who developed hypersplenism at the age of 14 and underwent PSE. Long term results over a period of 14 years after the procedure are presented

A New Approach for Broken Rotor Bar Detection in Induction Motors Using Frequency Extraction in Stray Flux Signals

This paper offers a reliable solution to the detection of broken rotor bars in induction machines with a novel methodology, which is based on the fact that the fault-related harmonics will have oscillating amplitudes due to the speed ripple effect. The method consists of two main steps: Initially, a time-frequency transformation is used and the focus is given on the steady-state regime; thereupon, the fault-related frequencies are handled as periodical signals over time and the classical fast Fourier transform is used for the evaluation of their own spectral content. This leads to the discrimination of subcomponents related to the fault and to the evaluation of their amplitudes. The versatility of the proposed method relies on the fact that it reveals the aforementioned signatures to detect the fault, regardless of the spatial location of the broken rotor bars. Extensive finite element simulations on a 1.1 MW induction motor and experimental testing on a 1.1 kW induction motor lead to the conclusion that the method can be generalized on any type of induction motor independently from the size, power, number of poles, and rotor slot numbers

On the broken rotor bar diagnosis using time-frequency analysis:'Is one spectral representation enough for the characterisation of monitored signals?'

© 2019 Institution of Engineering and Technology. All rights reserved. This work enhances the knowledge of the diagnostic potential of the broken bar fault in induction motors. Since a series of studies have been published over the years regarding condition monitoring and fault diagnostics of these machines, it is essential to reach a common ground on why - sometimes - different techniques render different results. In this context, an investigation is provided with regards to the optimal window that should be adopted for the implementation of a proper time-frequency analysis of the monitored signals. On this agenda, this study attempts to set lower and upper bound limits for proper windowing from the digital signal processing point of view. This is done by proposing a formula for the lower limit, which is derived according to the specific frequencies one desires to put under inspection and which are the fault-related signatures. Finally, a discussion on the upper bound is put onwards; results from finite-element simulations are examined with the discussed approach in both the transient regime and the steady state, while experimental results verify the simulations with satisfying accuracy

FEM approach for diagnosis of induction machines' non-adjacent broken rotor bars by short-time Fourier transform spectrogram

Rotor electrical faults are an issue frequently encountered when applying condition monitoring and fault diagnosis on induction machines. The detection via the analysis of the stator current becomes challenging when the rotor cage suffers from multiple breakages at non-adjacent positions. In that case, electromagnetic asymmetries induced by the broken bars can be masked in such a way, that the diagnostic ability is highly likely to be obscured, thus leading to misinterpretation of the monitored signals’ signatures. A new approach is proposed in this work to overcome this problem while the motor is at steady state. In this paper, an industrial 6.6kV, 1.1MW induction motor is simulated with Finite Element Analysis (FEM) and its electromagnetic variables are analysed and studied under healthy state and several faulty conditions. The analysis of the stator current and stray flux waveforms is executed in both the transient and the steady state and aims to diagnose the challenging cases where the rotor breakages are nonconsecutive with regards to their spatial location. The results show the potential of flux analysis to fault severity regardless of the spatial position of the broken bars