The theme of Master project of Energetics engineer is to analyze the damage of the sags and the dips of the voltage to going concerns, to find the ways of avoiding the sags and the dips of the voltage and to improve the quality of the electric power. The graphs which are included show the sags and the dips of the voltage. Some of the ways to avoid it are included also. An example introduces the best way how to supply the electric power without the sags and the dips of it.

Voice recognition technologies appeared in the period of general device miniaturization, when all technologies were commonly integrated into one lust. There is no space for buttons and displays anymore. To have a good system of Lithuanian language recognition, a number of throughout researches must be implemented. Only after selecting the most efficient speech recognition scheme, we can proceed to the development of software adapted to the contemporary time. The aim of this paper is to determine, how efficient speech recognition is possible using neuron networks. MFCC and LPC coefficients were chosen as the parameters characterizing the phonemes. The paper attempts at the determination of the coefficients, which lead to the most efficient recognition of phonemes. For testing, programs PRAAT and MatLab were used. After implementing a number of phoneme recognition experiments in the research work, the results were obtained, which lead to the following conclusions: 1. In case of using neuron network for the recognition of isolated sounds and characterizing the phonemes by MFCC or LPC coefficients, the possibility of recognition does not exceed 90 per cent. It is not enough for quality recognition of Lithuanian speech. 2. In case of using MFCC coefficients, separate phonemes are recognized better than using LPC coefficients. The difference is about 15 per cent. 3. The advantage of LPC coefficients in comparison with MFCC is the curve of recognition possibility, which is more even... [to full text]

In the contemporary world of techniques, voice technologies, such as speech recognition, synthesis of speech signals, and their combined versions, acquire more and more significance. If we had a good synthesizer, we could use it widely. An example could be the reading of electronic books in voice, etc. Speech synthesizers of older generation were quite primitive. A modern synthesis machine is not only able to read the text evenly, but to convey the emotionality as well. Speech synthesizer can raise a tone, dictate a question, and synthesize a voice of a desired timbre and speed. These features considerably enrich the speech synthesized. The articulatory speech synthesizer is based on a model of the physiology of the human speech production process. Articulatory synthesis usually consists of two separate components – articulatory model and acoustic model. In the articulatory model, the vocal tract is divided into numerous small sections and the corresponding cross – sectional areas are used as parameters to represent the vocal tract characteristics. In the acoustic model, each cross–sectional area is approximated by an electrical analog transmission line. To simulate the movement of the vocal tract, the area functions change time. The aim of the research paper is to analyze the consistent pattern of the vocal tract, generating the sound of Lithuanian language. The tasks are these: to look at the history of speech synthesis; to look at the architecture of speech; to overlook... [to full text]

<p> The purpose of this thesis is to ascertain the feasibility of using strain gauges attached to a Unmanned Aerial Vehicle (UAV) gripper to determine, upon impact, the hardness of a landing site. We design and fabricate a four finger gripper that uses a rotary component to convert the rotational motion of a servo to the linear motion of the finger assemblies. We functionally test a gripper prototype made from rapid-prototype material. We conduct three experiments to test the gripper's functionality. The first experiment tests the gripper's ability to grasp, lift, and release a centered payload, and the gripper performed with overall success rates of 91%, 100%, and 87% respectively. The second experiment tests the gripper's ability to self-align, lift and release the payload and the gripper performed with overall success rates of 99%, 100%, and 96% respectively. The third experiment tests the functional durability of the gripper, and it performed without error for 5000 open/close cycles. </p>

<p> A DC&ndash;thermal plasma jet is proposed as a reliable ignition source for reaction control system (RCS) thrusters employing oxygen with hydrocarbons, like methane. Industrial plasma torch systems are analyzed to understand the behavioral characteristics of DC&ndash;thermal plasmas. Nitrogen is used as a working gas for the source of plasma jet to understand the general mechanism of thermal plasma formation. DC&ndash;thermal plasmas require high electrical energy to maintain their arc discharge status which presents challenges in space systems. The purpose of this study is, therefore, to find a suitable configuration which minimizes power consumption.</p><p> Various physical and electrical conditions relate to a thermal plasma formation. In this study, the input voltage (221&ndash;332V) and pressure (5&ndash;15 psi) are applied as initial conditions. The DC&ndash;power module and starter module are designed as plasma drivers and a commercial off&ndash;the&ndash;shelf torch head is used for this research. The normalized method is developed to estimate the arc temperature. Test results show that the lowest power consumption and arc&ndash;starting voltage are 1,321W and 248.8 VDC, respectively. In addition, it is found that the current is a major factor for varying the mass flow rate.</p><p> Since the lowest power consumption is still high, future improvements and research should focus on integrating a high&ndash;power and lightweight energy source, developing a high&ndash;frequency and half&ndash;duty cycle power system, and incorporating a composite cathode. In addition, a new conceptual torch design is proposed to be considered as an igniter for RCS thrusters. The next step would be to repeat the plasma torch tests with the new configuration at ambient and vacuum conditions. These would be followed by combustion tests to verify the actual functionality of the plasma igniter for RCS thrusters with various oxidizer and fuel mixture ratios. In parallel, research should focus on miniaturization of the electrical system.</p>

No description available.

<p> Thermoelectric devices have been employed for many years as a reliable energy conversion technology for applications ranging from the cooling of sensors or charge coupled devices to the direct conversion of heat into electricity for remote power generation. However, its relatively low conversion efficiency has limited the implementation of thermoelectric materials for large scale cooling and waste heat recovery applications. Recent advances in semiconductor growth technology have enabled the precise and selective engineering of material properties to improve the thermoelectric figure of merit and thus the efficiency of thermoelectric devices. Accurate characterization at the intended operational temperature of novel thermoelectric materials is a crucial component of the optimization process in order to fundamentally understand material behavior and evaluate performance. </p><p> The objective of this work is to provide the tools necessary to characterize high efficiency bulk and thin-film materials for thermoelectric energy conversion. The techniques developed here are not bound to specific material or devices, but can be generalized to any material system. </p><p> Thermoreflectance imaging microscopy has proven to be invaluable for device thermometry owing to its high spatial and temporal resolutions. It has been utilized in this work to create two-dimensional temperature profiles of thermoelectric devices during operation used for performance analysis of novel materials, identification of defects, and visualization of high speed transients in a high-temperature imaging thermostat. We report the development of a high temperature imaging thermostat capable of high speed transient thermoelectric characterization. In addition, we present a noninvasive method for thermoreflectance coefficient calibration ideally suited for vacuum and thus high temperature employment. This is the first analysis of the thermoreflectance coefficient of commonly used metals at high-temperatures. </p><p> High temperature vacuum thermostats are designed and fabricated with optical imaging capability and interchangeable measurement stages for various electrical and thermoelectric characterizations. We demonstrate the simultaneous measurement of in-plane electrical conductivity and Seebeck coefficient of thin-film or bulk thermoelectric materials. Furthermore, we utilize high-speed circuitry to implement the transient Harman technique and directly determine the cross-plane figure of merit of thin film thermoelectric materials at high temperatures. </p><p> Transient measurements on thin film devices are subject to complications from the growth substrate, non-ideal contacts and other detrimental thermal and electrical effects. A strategy is presented for optimizing device geometry to mitigate the impact of these parasitics. This design enabled us to determine the cross-plane thermoelectric material properties in a single high temperature measurement of a 25&mu;m InGaAs thin film with embedded ErAs (0.2%) nanoparticles using the bipolar transient Harman technique in conjunction with thermoreflectance thermal imaging. This approach eliminates discrepancies and potential device degradation from the multiple measurements necessary to obtain individual material parameters. Finite element method simulations are used to analyze non-uniform current and temperature distributions over the device area and determine the three dimensional current path for accurate extraction of material properties from the thermal images. Results match with independent measurements of thermoelectric material properties for the same material composition, validating this approach. </p><p> We apply high magnification thermoreflectance imaging to create temperature maps of vanadium dioxide nanobeams and examine electro-thermal energy conversion along the nanobeam length. The metal to insulator transition of strongly correlated materials is subject to strong lattice coupling which brings about the unique one-dimensional alignment of metal-insulator domains along nanobeams. Many studies have investigated the effects of stress on the metal to insulator transition and hence the phase boundary, but few have directly examined the temperature profile across the metal-insulating interface. Here, thermoreflectance microscopy reveals the underlying behavior of single-crystalline VO₂ nanobeams in the phase coexisting regime. We directly observe highly localized alternating Peltier heating and cooling as well as Joule heating concentrated at the domain interfaces, indicating the significance of the domain walls and band offsets. Moreover, we are able to elucidate strain accumulation along the nanobeam and distinguish between two insulating phases of VO₂ through detection of the opposite polarity of their respective thermoreflectance coefficients.</p>

<p> Inverse optimal control is the problem of computing a cost function with respect to which observed state input trajectories are optimal. We present a new method of inverse optimal control based on minimizing the extent to which observed trajectories violate first-order necessary conditions for optimality. We consider continuous-time deterministic optimal control systems with a cost function that is a linear combination of known basis functions. We compare our approach with three prior methods of inverse optimal control. We demonstrate the performance of these methods by performing simulation experiments using a collection of nominal system models. We compare the robustness of these methods by analyzing how they perform under perturbations to the system. We consider two scenarios: one in which we exactly know the set of basis functions in the cost function, and another in which the true cost function contains an unknown perturbation. Results from simulation experiments show that our new method is computationally efficient relative to prior methods, performs similarly to prior approaches under large perturbations to the system, and better learns the true cost function under small perturbations. We then apply our method to three problems of interest in robotics. First, we apply inverse optimal control to learn the physical properties of an elastic rod. Second, we apply inverse optimal control to learn models of human walking paths. These models of human locomotion enable automation of mobile robots moving in a shared space with humans, and enable motion prediction of walking humans given partial trajectory observations. Finally, we apply inverse optimal control to develop a new method of learning from demonstration for quadrotor dynamic maneuvering. We compare and contrast our method with an existing state-of-the-art solution based on minimum-time optimal control, and show that our method can generalize to novel tasks and reject environmental disturbances. </p>

<p> With increasing penetrations of solar photovoltaic (PV) power in the electricity grid, the variability of the irradiance, and therefore power, is important to understand because variable resources can challenge grid operations. The smoothing of solar irradiance over the extent of PV power plants is examined using two methods: averaging measurements from many irradiance sensors, and using a model developed by Lave, Kleissl, and Stein (2013) called the Wavelet Variability Model. This thesis utilizes data from a network of 45 solar irradiance sensors which was deployed north of Flagstaff, Arizona. The results show the similarities and differences between two irradiance smoothing methods. These two models both show that the smoothing effect is significant for large PV power plants, which means the power plant output has less variability and is easier to integrate into the electricity grid than might have been expected using a single point sensor measurement to predict variability.</p>

<p> Magnetic Resonance Elastography (MRE) is a non-invasive phase contrast MR imaging method that captures the three-dimensional harmonic wave propagation introduced into subject by external actuators. This wave propagation vector field is processed into stiffness maps of various kinds that are used to assess the pathological changes that cannot be detected otherwise with non-invasive imaging methods. As in all other MR imaging methods, long acquisition duration is one of the important limiting factors for MRE. There are different approaches to reduce the scan time, such as reduced motion encoding MRE or fractional multi-frequency MRE; however, these methods are all at the cost of the reduced signal to noise ratio (SNR) or reduced phase to noise ratio (PNR). Recently we have introduced two accelerated MRE methods, which do not compromise SNR or PNR while reducing the acquisition time by a factor of three compared to the conventional MRE methods. The first one is Selective Spectral Displacement Projection (SDP) MRE method that can encode a mechanical motion of multiple frequency components at once. The second one is SampLe Interval Modulation (SLIM) MRE which can encode the mono-frequency motion in multiple directions concurrently. In this dissertation, I propose a final optimal method that integrates the technique developed in SLIM MRE into SDP MRE, namely Unified sampLing Time Interval ModulATion (ULTIMATe) MRE. This method is the optimal MRE method in the sense that it can reach the limit of time efficiency without sacrificing SNR and PNR. A new mathematical framework was introduced to accommodate all three methods while preventing any ambiguity which might otherwise can occur with the existing MRE notation.</p>