42 research outputs found
Mechanical properties of aluminium fused SiO2 particulate composites cast using metallic and non-metallic chills
No full textComparison of two ventilation modes in post-cardiac surgical patients
No full textBackground: The cardiopulmonary bypass (CPB)-associated atelectasis accounted for most of the marked post-CPB increase in shunt and hypoxemia. We hypothesized that pressure-regulated volume-control (PRVC) modes having a distinct theoretical advantage over pressure-controlled ventilation (PCV) by providing the target tidal volume at the minimum available pressure may prove advantageous while ventilating these atelactic lungs. Methods: In this prospective study, 36 post-cardiac surgical patients with a PaO 2 /FiO 2 (arterial oxygen tension/Fractional inspired oxygen) < 300 after arrival to intensive care unit (ICU), (n = 34) were randomized to receive either PRVC or PCV. Air way pressure (Paw ) and arterial blood gases (ABG) were measured at four time points [T1: After induction of anesthesia, T2: after CPB (in the ICU), T3: 1 h after intervention mode, T4: 1 h after T3]. Oxygenation index (OI) = [PaO 2 / {FiO 2 × mean airway pressure (Pmean )}] was calculated for each set of data and used as an indirect estimation for intrapulmonary shunt. Results: There is a steady and significant improvement in OI in both the groups at first hour [PCV, 27.5(3.6) to 43.0(7.5); PRVC, 26.7(2.8) to 47.6(8.2) (P = 0.001)] and second hour [PCV, 53.8(6.4); PRVC, 65.8(7.4) (P = 0.001)] of ventilation. However, the improvement in OI was more marked in PRVC at second hour of ventilation owing to significant low mean air way pressure compared to the PCV group [PCV, 8.6(0.8); PRVC, 7.7(0.5), P = 0.001]. Conclusions: PRVC may be useful in a certain group of patients to reduce intrapulmonary shunt and improve oxygenation after cardiopulmonary bypass-induced perfusion mismatch
Implementation and verification of energy efficient software for ADCS
No full textThe Delfi-n3Xt was the second nanosatellite developed at the Delft University of Technology, and launched in 2013. Its successor Delfi-PQ is expected to be launched in first half of 2019. The Attitude of a satellite can be referred to as its orientation in space with respect to inertial reference frame. The Delfi-n3Xt was the first satellite from Delft University of Technology, to include three-axis Attitude Determination and Control System/Subsystem (ADCS). It was designed with 5 modes of operation. Four of these were advanced modes. In addition, the Delfi-PQ is not intended to include advanced modes of operation. Hence, this thesis considers using the Delfi-n3Xt ADCS software. This software is extended as a baseline implementation on MSP-EXP432E401Y launchpad. Nearly, 32 % of total nominal power is assigned to ADCS. Hence, energy efficient design alternatives could be considered for future satellite missions. In addition, ADCS is a critical subsystem, failure of ADCS means failure of satellite mission. This thesis aims to improve performance and energy consumption of ADCS. This thesis considers study of three different Digital Signal Processing (DSP) alternatives: Double Precision (DP), Single Precision (SP) and Fixed Point (FxP) arithmetic. Study in this thesis concludes that FxP alternative provides approximately 6.7 times better performance, and approximately 7 times better energy efficiency over baseline. Hence, this thesis proposes the use of FxP DSP alternative. It was also concluded that, the SP arithmetic has equivalent accuracy compared with DP. Moreover, SP provides approximately 3 times better performance, and approximately 2.7 times better energy efficiency over baseline. Therfore, future implementations could benefit from an SP alternative. A major part of the ADCS power is allocated to sensor and actuator. This leaves only 10 % of the total nominal power assigned to ADCS software. Hence, the proposed alternative might not provide considerable improvements on total nominal power. However, for future satellite missions, if there exists a computationally intensive algorithm assigned with more significant amount of total nominal power, then, the proposed alternative could serve as an initial study. However, this does not guarantee that the suggested alternative could satisfy more accurate requirements. In such case, FxP implementation might result in accuracy violation. And use of SP alternative is proposed. In such case, a new study is suggested in order to benefit from FxP alternative.Delfi-PocketQubeElectrical Engineering | Embedded System
The Indian Ocean and Sri Lanka's foreign policy (1948 to 1977) : the development of the zone of Peace concept
No full textThis thesis was scanned from the print manuscript for digital preservation and is copyright the author.
Researchers can access this thesis by asking their local university, institution or public library to
make a request on their behalf. Monash staff and postgraduate students can use the link in the References field
High Frequency Power Converter with ZVT for Variable DC-link in Electric Vehicles
No full textabstract: The most important metrics considered for electric vehicles are power density, efficiency, and reliability of the powertrain modules. The powertrain comprises of an Electric Machine (EM), power electronic converters, an Energy Management System (EMS), and an Energy Storage System (ESS). The power electronic converters are used to couple the motor with the battery stack. Including a DC/DC converter in the powertrain module is favored as it adds an additional degree of freedom to achieve flexibility in optimizing the battery module and inverter independently. However, it is essential that the converter is rated for high peak power and can maintain high efficiency while operating over a wide range of load conditions to not compromise on system efficiency. Additionally, the converter must strictly adhere to all automotive standards.
Currently, several hard-switching topologies have been employed such as conventional boost DC/DC, interleaved step-up DC/DC, and full-bridge DC/DC converter. These converters face respective limitations in achieving high step-up conversion ratio, size and weight issues, or high component count. In this work, a bi-directional synchronous boost DC/DC converter with easy interleaving capability is proposed with a novel ZVT mechanism. This converter steps up the EV battery voltage of 200V-300V to a wide range of variable output voltages ranging from 310V-800V. High power density and efficiency are achieved through high switching frequency of 250kHz for each phase with effective frequency doubling through interleaving. Also, use of wide bandgap high voltage SiC switches allows high efficiency operation even at high temperatures.
Comprehensive analysis, design details and extensive simulation results are presented. Incorporating ZVT branch with adaptive time delay results in converter efficiency close to 98%. Experimental results from a 2.5kW hardware prototype validate the performance of the proposed approach. A peak efficiency of 98.17% has been observed in hardware in the boost or motoring mode.Dissertation/ThesisMasters Thesis Electrical Engineering 201
Deep Domain Fusion for Adaptive Image Classification
No full textabstract: Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data.
In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.Dissertation/ThesisMasters Thesis Computer Science 201
Characterizing Dysarthric Speech with Transfer Learning
No full textabstract: Speech is known to serve as an early indicator of neurological decline, particularly in motor diseases. There is significant interest in developing automated, objective signal analytics that detect clinically-relevant changes and in evaluating these algorithms against the existing gold-standard: perceptual evaluation by trained speech and language pathologists. Hypernasality, the result of poor control of the velopharyngeal flap---the soft palate regulating airflow between the oral and nasal cavities---is one such speech symptom of interest, as precise velopharyngeal control is difficult to achieve under neuromuscular disorders. However, a host of co-modulating variables give hypernasal speech a complex and highly variable acoustic signature, making it difficult for skilled clinicians to assess and for automated systems to evaluate. Previous work in rating hypernasality from speech relies on either engineered features based on statistical signal processing or machine learning models trained end-to-end on clinical ratings of disordered speech examples. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, while end-to-end methods tend to overfit to the small datasets on which they are trained. In this thesis, I present a set of acoustic features, models, and strategies for characterizing hypernasality in dysarthric speech that split the difference between these two approaches, with the aim of capturing the complex perceptual character of hypernasality without overfitting to the small datasets available. The features are based on acoustic models trained on a large corpus of healthy speech, integrating expert knowledge to capture known perceptual characteristics of hypernasal speech. They are then used in relatively simple linear models to predict clinician hypernasality scores. These simple models are robust, generalizing across diseases and outperforming comprehensive set of baselines in accuracy and correlation. This novel approach represents a new state-of-the-art in objective hypernasality assessment.Dissertation/ThesisMasters Thesis Electrical Engineering 202
