This paper presents the current measurement issues in sensorless control of a permanent magnet (PM) machine based on the fluctuating high frequency voltage signal injection (HFI) method. In such a sensorless control o...
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This paper presents the current measurement issues in sensorless control of a permanent magnet (PM) machine based on the fluctuating high frequency voltage signal injection (HFI) method. In such a sensorless control of the PM machine, the accuracy of the rotor position estimation mainly depends on the accuracy of the current measurement of the drive system. And, the accuracy of current measurement mainly depends on the current scaling errors, offset currents, and the quantization errors of the analog-to-digital (A/D) converter. In this paper, the effects of the current measurement errors on the accuracy of the rotor position estimation in sensorless control of the PM machine based on the HFI method is analyzed using an example of current measurement system and sensorless control sch.me. The simulation and experimental results are shown in order to verify the validity of the analysis results.
Machine Learning has traditionally been a topic of research and instruction in computerscience and computerengineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expand...
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Machine Learning has traditionally been a topic of research and instruction in computerscience and computerengineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as elec.rical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently, many undergraduate and first-year graduate students in the aforementioned fields do not have exposure to recent research trends in Machine Learning. This paper reports on a project in progress, funded by the National science Foundation under the program Combined Research and Curriculum Development (CRCD), whose goal is to remedy this shortcoming. The project involves the development of a model for the integration of Machine Learning into the undergraduate curriculum of those engineering and science disciplines mentioned above. The goal is increased exposure to Machine Learning technology for a wider range of students in science and engineering than is currently available. Our approach of integrating Machine Learning research into the curriculum involves two components. The first component is the incorporation of Machine Learning modules into the first two years of the curriculum with the goal of sparking student interest in the field. The second is the development of new upper level Machine Learning courses for advanced undergraduate students. The paper will describe the first phase of the project, that of the integration of Machine Learning concepts into introductory engineering and science programming courses through appropriately designed programming projects.
The Gaussian mixture classifier with regularized covariance estimator for hyperspectral data classification was discussed. The results suggested that the nearest mean clustering and BIC were better choices to build a ...
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The Gaussian mixture classifier with regularized covariance estimator for hyperspectral data classification was discussed. The results suggested that the nearest mean clustering and BIC were better choices to build a Gaussian mixture classifier. The advantages of the proposed BIC mix were also discussed.
In this paper, a method for enhancing current QoS routing methods by means of QoS protection is presented. In an MPLS network, the segments (links) to be protected are predefined and an LSP request involves, apart fro...
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In this paper, a method for enhancing current QoS routing methods by means of QoS protection is presented. In an MPLS network, the segments (links) to be protected are predefined and an LSP request involves, apart from establishing a working path, creating a specific type of backup path (local, reverse or global). Different QoS parameters, such as network load balancing, resource optimization and minimization of LSP request rejection should be considered. QoS protection is defined as a function of QoS parameters, such as packet loss, restoration time, and resource optimization. A framework to add QoS protection to many of the current QoS routing algorithms is introduced. A Backup Decision Module to selec. the most suitable protection method is formulated and different case studies are analyzed.
The regularized feature extraction methods for hyperspectral data classification were studied. The regularization algorithms worked for both parametric and nonparametric within-class scatter matrix. Real data experime...
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The regularized feature extraction methods for hyperspectral data classification were studied. The regularization algorithms worked for both parametric and nonparametric within-class scatter matrix. Real data experiment and simulated results show that the nonparametric weighted feature extraction (NWFE) is better method than the nonparametric discriminant analysis (NDA) and discriminant analysis feature extraction (DAFE).
The elec.rical characteristics of biologically active points (BAPs) compared with those of the surrounding human skins are investigated. We confirm that BAPs have lower resistance and higher capacitance than the surro...
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The elec.rical characteristics of biologically active points (BAPs) compared with those of the surrounding human skins are investigated. We confirm that BAPs have lower resistance and higher capacitance than the surrounding skins have. We find that BAPs have higher characteristic frequency than surrounding skins and sometimes the impedance spectra of BAPs have two semicircles on the complex impedance plane. Therefore, we propose the skin impedance model that is proper to the BAPs. This model describes our experimental results sufficiently.
An effective compensation method for the IR lamp deterioration in NDIR (non-dispersive infrared) capnograph system is proposed. The optical chamber with two IR (infrared) lamps has been designed newly and an elec.roni...
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An effective compensation method for the IR lamp deterioration in NDIR (non-dispersive infrared) capnograph system is proposed. The optical chamber with two IR (infrared) lamps has been designed newly and an elec.ronic hardware for the control of lamp intensity has been implemented. After applying the proposed optical chamber and the reference lamp control circuit to the NDIR type capnograph system, it is identified that the proposed method can compensate the lamp deterioration effectively.
This research investigates the effects of a variable machine speed on machine vibration and the implications for bearing fault detection. These effects are important to understand because when ignored they can signifi...
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This research investigates the effects of a variable machine speed on machine vibration and the implications for bearing fault detection. These effects are important to understand because when ignored they can significantly hinder the ability to detect bearing faults. Experimental results verify that a variable machine speed can directly and nonlinearly alter the level of machine vibration. This is due to differences in mechanical damping and resonance at various machine speeds. While this effect is difficult to notice in healthy machines, it can become significant as bearing health degrades. An additional effect that speed can exert is on the rate of development of a bearing fault. Variations in speed can actually retard or temporarily mask the increase in machine vibration due to a bearing fault. This phenomenon is observed in experimental trials as the bearing fault enters an advanced and more deteriorated stage. This can inadvertently make a machine appear healthy even though a bearing failure is imminent. However, by understanding these effects, a more skillful application of the available condition monitoring tools in variable speed applications is achieved.
A novel computational sch.me has been used to predict the elec.ric potentials generated by arbitrary temperature gradients in semiconductor materials. Written in object-oriented code, the Discrete State Simulation (DS...
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A novel computational sch.me has been used to predict the elec.ric potentials generated by arbitrary temperature gradients in semiconductor materials. Written in object-oriented code, the Discrete State Simulation (DSS) is a coupled cellular automata simulator that builds upon the objects and rules of quantum mechanics. The DSS represents global non-equilibrium processes as patterns that emerge through an ensemble of scattering events that are localized at vibronic nodes. By tracking the energy-momentum-position coordinates of the individual particles that define the vibronic state at a node, the DSS undercuts equilibrium concepts such as temperature. Consequently, the DSS can represent physical systems that are described by more than one temperature or that contain physical features that defy definitions of temperature. Using modified bootstrap sampling algorithms, the DSS depicted (1) shifts in distribution functions induced by external fields and temperature gradients, (2) field-dependent transitions from linear mobility to non-linear mobility, (3) saturation velocities, (4) non-exponential decay functions generated by multiple phonon scattering modes, and (5) charge separations and elec.ric potentials generated by temperature gradients. Ensemble averages were sensitive to the structure of dispersion relations, to the energy of the system, and to quantum coupling strengths. Seebeck coefficients were sensitive to the features of the elec.ronic and the vibrational band structures, and their associated coupling coefficients.
In this paper, the dual heuristic programming (DHP) optimization algorithm is used for the design of a nonlinear optimal neurocontroller that replaces the proportional-integral (PI) based conventional linear controlle...
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In this paper, the dual heuristic programming (DHP) optimization algorithm is used for the design of a nonlinear optimal neurocontroller that replaces the proportional-integral (PI) based conventional linear controller (CONVC) in the internal control of a power elec.ronic converter based series compensator in the elec.ric power transmission system. The performance of the proposed DHP based neurocontroller is compared with that of the CONVC with respect to damping low frequency oscillations. Simulation results using the PSCAD/EMTDC software package are presented.
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