MODELOWANIE STANÓW PRZEJŚCIOWYCH MASZYN PRĄDU PRZEMIENNEGO Z UWZGLĘDNIENIEM EFEKTÓW DRUGIEGO RZĘDU

In this paper, the transient simulation of AC machines considering spatial field harmonics and current displacement in rotor bars is examined. The first point of discussion is the sufficient order of the equivalent circuit of the current displacement model for its accurate co-simulation with the higher spatial field harmonics. In the second place, the problem of inversion of the inductance matrix considering the flux linkage through higher spatial harmonics is studied.W artykule przeanalizowano symulację stanów przejściowych maszyn prądu przemiennego z uwzględnieniem harmonicznych pola przestrzennego i przesunięcia prądu w prętach wirnika. Po pierwsze przeanalizowano jaki jest wystarczający rząd modelu równoważnego obwodu przesunięcia prądu dla jego dokładnej symulacji wraz z wyższymi harmonicznymi pola przestrzennego. Po drugie, badany jest problem inwersji macierzy induktancji z uwzględnieniem sprzężenia strumienia przez wyższe harmoniczne przestrzenne

The development of a power system simulator using multiple microprocessors

Imperial Users onl

Effective influences in neuronal networks : attentional modulation of effective influences underlying flexible processing and how to measure them

Selective routing of information between brain areas is a key prerequisite for flexible adaptive behaviour. It allows to focus on relevant information and to ignore potentially distracting influences. Selective attention is a psychological process which controls this preferential processing of relevant information. The neuronal network structures and dynamics, and the attentional mechanisms by which this routing is enabled are not fully clarified. Based on previous experimental findings and theories, a network model is proposed which reproduces a range of results from the attention literature. It depends on shifting of phase relations between oscillating neuronal populations to modulate the effective influence of synapses. This network model might serve as a generic routing motif throughout the brain. The attentional modifications of activity in this network are investigated experimentally and found to employ two distinct channels to influence processing: facilitation of relevant information and independent suppression of distracting information. These findings are in agreement with the model and previously unreported on the level of neuronal populations. Furthermore, effective influence in dynamical systems is investigated more closely. Due to a lack of a theoretical underpinning for measurements of influence in non-linear dynamical systems such as neuronal networks, often unsuited measures are used for experimental data that can lead to erroneous conclusions. Based on a central theorem in dynamical systems, a novel theory of effective influence is developed. Measures derived from this theory are demonstrated to capture the time dependent effective influence and the asymmetry of influences in model systems and experimental data. This new theory holds the potential to uncover previously concealed interactions in generic non-linear systems studied in a range of disciplines, such as neuroscience, ecology, economy and climatology

ASiMOV: Microservices-based verifiable control logic with estimable detection delay against cyber-attacks to cyber-physical systems

The automatic control in Cyber-Physical-Systems brings advantages but also increased risks due to cyber-attacks. This Ph.D. thesis proposes a novel reference architecture for distributed control applications increasing the security against cyber-attacks to the control logic. The core idea is to replicate each instance of a control application and to detect attacks by verifying their outputs. The verification logic disposes of an exact model of the control logic, although the two logics are decoupled on two different devices. The verification is asynchronous to the feedback control loop, to avoid the introduction of a delay between the controller(s) and system(s). The time required to detect a successful attack is analytically estimable, which enables control-theoretical techniques to prevent damage by appropriate planning decisions. The proposed architecture for a controller and an Intrusion Detection System is composed of event-driven autonomous components (microservices), which can be deployed as separate Virtual Machines (e.g., containers) on cloud platforms. Under the proposed architecture, orchestration techniques enable a dynamic re-deployment acting as a mitigation or prevention mechanism defined at the level of the computer architecture. The proposal, which we call ASiMOV (Asynchronous Modular Verification), is based on a model that separates the state of a controller from the state of its execution environment. We provide details of the model and a microservices implementation. Through the analysis of the delay introduced in both the control loop and the detection of attacks, we provide guidelines to determine which control systems are suitable for adopting ASiMOV. Simulations show the behavior of ASiMOV both in the absence and in the presence of cyber-attacks

Symbolic tolerance and sensitivity analysis of large scale electronic circuits

Available from British Library Document Supply Centre-DSC:DXN029693 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Effective influences in neuronal networks : attentional modulation of effective influences underlying flexible processing and how to measure them

Selective routing of information between brain areas is a key prerequisite for flexible adaptive behaviour. It allows to focus on relevant information and to ignore potentially distracting influences. Selective attention is a psychological process which controls this preferential processing of relevant information. The neuronal network structures and dynamics, and the attentional mechanisms by which this routing is enabled are not fully clarified. Based on previous experimental findings and theories, a network model is proposed which reproduces a range of results from the attention literature. It depends on shifting of phase relations between oscillating neuronal populations to modulate the effective influence of synapses. This network model might serve as a generic routing motif throughout the brain. The attentional modifications of activity in this network are investigated experimentally and found to employ two distinct channels to influence processing: facilitation of relevant information and independent suppression of distracting information. These findings are in agreement with the model and previously unreported on the level of neuronal populations. Furthermore, effective influence in dynamical systems is investigated more closely. Due to a lack of a theoretical underpinning for measurements of influence in non-linear dynamical systems such as neuronal networks, often unsuited measures are used for experimental data that can lead to erroneous conclusions. Based on a central theorem in dynamical systems, a novel theory of effective influence is developed. Measures derived from this theory are demonstrated to capture the time dependent effective influence and the asymmetry of influences in model systems and experimental data. This new theory holds the potential to uncover previously concealed interactions in generic non-linear systems studied in a range of disciplines, such as neuroscience, ecology, economy and climatology

Discovering phase and causal dependencies on manufacturing processes

Discovering phase and causal dependencies on manufacturing processes. Keyword machine learning, causality, Industry 4.

The ecology of distance learning : towards a framework for student communication at the University of South Africa

This autoethnographic journey started out as a qualitative research study to discover a new framework for student communication at Unisa. However, I found Unisa and myself reflecting each other, defenceless. Although autoethnography is defined as a research methodology that brings the story of the self into an ethnographic cultural description, it is much more than that. It is a way of being a researcher, where self and culture merge into one ecological unity to present the world with a story that is honest and reflective. The purpose of this study was to present Unisa with a new framework for student communication by exploring new epistemological perspectives. This journey took me from the beginnings of humanity where love and collaboration were our foundations, through our evolution into civilisation, competition, science and education. These contributed a great deal to our intellectual development through mechanistic thinking and a scientific approach but alienated us from each other, which could potentially lead to our destruction. I also explored cybernetics, complex and ecological thinking as new epistemological horizons to view distance learning from. Such a perspective requires a radical epistemological shift from hierarchical, mechanistic and reductionist thinking towards creating an ecology of learning, one that is more dynamic, living, vibrant, caring and empowering. Through my intimate reflective struggle with others, trying to understand how to create this vibrant student communication context I discovered in the quietness of reflection the patterns that connect us all, students, Unisa and myself. Our dialogical relationship emerged, one where there is an epistemological split between our reductionist and mechanistic thinking that requires us to be efficient on the one hand and ecologic and complex thinking that requires a caring, connected and collaborative ecology on the other hand. The question then is how we create this ecology where we reclaim our original humanness and co-evolve into an ecology of learning that is truly empowering. This can be done by co-evolving a new culture of learning grounded in passion, curiosity, openness and preparing us to be responsible and participating citizens of this most beautiful planet.Educational FoundationsD. Ed. (Philosophy of Education