27 research outputs found
Effects of the injection timing on spray and combustion characteristics in a spray-guided DISI engine under the lean-stratified operation
No full textThe effect of split injection in a spray guided DISI engine under the lean stratified operation
No full text
Combustion Process and PM Emission Characteristics in a Stratified DISI Engine under Low Load Condition
No full textEffect of pintle lift on spray characteristics of piezo actuated outward opening injector for a direct injection spark ignition engine
No full textImpact of Ethanol Blended Fuel in Spray Characteristics of Piezo-actuated Outward-opening Injector for DISI Engine
No full textMachine-learning based prediction of injection rate and solenoid voltage characteristics in GDI injectors
No full textCurrent state-of-the-art gasoline direct-injection (GDI) engines use multiple injections as one of the key technologies to improve exhaust emissions and fuel efficiency. For this technology to be successful, secured adequate control of fuel quantity for each injection is mandatory. However, nonlinearity and variations in the injection quantity can deteriorate the accuracy of fuel control, especially with small fuel injections. Therefore, it is necessary to understand the complex injection behavior and to develop a predictive model to be utilized in the development process. This study presents a methodology for rate of injection (ROI) and solenoid voltage modeling using artificial neural networks (ANNs) constructed from a set of Zeuch-style hydraulic experimental measurements conducted over a wide range of conditions. A quantitative comparison between the ANN model and the experimental data shows that the model is capable of predicting not only general features of the ROI trend, but also transient and non-linear behaviors at particular conditions. In addition, the end of injection (EOI) could be detected precisely with a virtually generated solenoid voltage signal and the signal processing method, which applies to an actual engine control unit. A correlation between the detected EOI timings calculated from the modeled signal and the measurement results showed a high coefficient of determination.
Effect of the Multiple Injection on Stratified Combustion Characteristics in a Spray-Guided DISI Engine
No full textImpact of Ethanol in Ethanol Blended Gasoline on the Spray Characteristics of Piezo Actuated Outward-Opening Injector
No full textEnsuring security and privacy in a personalized mobile environment
No full textServices in a mobile environment are based on the locations of mobile users. Personalization, based on the profiles of mobile users, significantly increases the value of such services. However, they pose significant security and privacy challenges; ensuring security and privacy for a personalized mobile environment in an efficient manner is the primary objective of this dissertation. Often, access control requirements in a mobile environment are based on the spatiotemporal attributes of mobile users, resources to be protected, profiles of users, or all of these. Evaluating an access request incurs significant overhead as it requires searching for the relevant moving objects that satisfy the query as well as the applicable security policies. In this dissertation, we have developed a unified index structure capable of indexing mobile objects, security policies and profiles, in a single index. This enables the efficient enforcement of access control. Another contribution is to extend the enforcement of access control to the case where instead of the exact location, only the approximate location of moving objects is maintained. To this end, the dissertation proposes an authorization model that takes the uncertainty of location measures into consideration for specifying and evaluating access control policies. Another pressing issue in delivering mobile services is protecting the privacy of users. In this dissertation, we have proposed a comprehensive family of anonymity models, based on k-anonymity, that incorporates location, direction, as well as profile information. We have also developed anonymization algorithms that can constrain both the generalization of the location as well as that of profiles and direction, while meeting the quality of service requirements. In addition, we have proposed a partitioning method that can limit tracking of the service requestor while continuously receiving a service, thus achieving enhanced level of both privacy and quality of service.Ph.D.Includes bibliographical referencesIncludes vitaby Heechang Shi
