Computation for Design and Optimization - Master's degree
http://hdl.handle.net/1721.1/39117
Sun, 28 May 2017 14:37:28 GMT2017-05-28T14:37:28ZComputational tools for enabling longitudinal skin image analysis
http://hdl.handle.net/1721.1/107060
Computational tools for enabling longitudinal skin image analysis
Lee, Kang Qi Ian
We present a set of computational tools that enable quantitative analysis of longitudinally acquired skin images: the assessment and characterization of the evolution of skin features over time. A framework for time-lapsed skin imaging is proposed. A nonrigid registration algorithm based on multiple plane detection for landmark identification accurately aligns pairs of longitudinal skin images. If dense and thick hairs are present, then nonrigid registration is used to reconstruct the skin texture of occluded regions by recording multiple images from the same area. Realistic reconstruction of occluded skin texture is aided by an automatic hair segmentation algorithm and guided painting method based on image blending. We demonstrate that constituent algorithms in this framework are accurate and robust in a multitude of scenarios. In addition, a methodology for rigorous longitudinal analysis of skin microrelief structure is introduced. Following rigid registration, a microrelief junction point matching algorithm based on point pattern matching is shown to accurately match two sets of junction points. Immediate applications for these computational tools are change detection for pigmented skin lesions and deformation field computation of the skin surface under stress using only visual features of the skin. Prospective applications include new insights in skin physiology and diseases from the capability to precisely track movements of the microrelief structure over time and localization of skin images on the body.
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 165-174).
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/1721.1/1070602016-01-01T00:00:00ZMolecular dynamics-based approaches for mesoscale lubrication
http://hdl.handle.net/1721.1/107059
Molecular dynamics-based approaches for mesoscale lubrication
Chandramoorthy, Nisha
Classical lubrication theory is unable to describe nanoscale flows due to the failure of two of its constitutive components: a) the Newtonian stress-strain rate relationship and b) the no-slip boundary condition. In this thesis, we present a methodology for deriving a modified Reynolds equation (referred to as the Molecular Dynamics-based Equation for Lubrication, or the MODEL) which overcomes these limitations by introducing a Molecular Dynamics-based constitutive relationship for the flow rate through the lubrication gap, that is valid beyond the range of validity of the Navier-Stokes constitutive models. We demonstrate the proposed methodology for the flow of a simple lubricant, n-hexadecane, between smooth Iron walls and show that the MODEL is able to predict flow rates with good accuracy even in nanochannels that are only a few atomic layers wide. The MODEL constitutive relationship for the flow rate used in this work is a slip-corrected Poiseuille model with the slip length and viscosity derived from Molecular Dynamics (MD) simulations of pressure-driven flow in nanochannels sufficiently large that the Navier-Stokes description is valid. Although more general expressions for the flow rate can certainly be used, for the lubricant-solid system modeled here, the slip-corrected Poiseuille flow was surprisingly found to be sufficient. We validate the MODEL by comparing MD results for the pressure distribution in a barrel-drop lubrication configuration with the analytical solution for the pressure obtained by solving the MODEL. The excellent agreement obtained between the dynamic pressure in the fluid measured from these MD simulations and the MODEL results suggests that it is possible to extend pde-based hydrodynamic modelling of lubrication problems even to nanoscale films beyond the validity of the Navier-Stokes description. In other words, once the flow rate constitutive relation is obtained, lubrication problems in nanoscale films can be solved without resorting to expensive particle methods like MD. We demonstrate that slip cannot be neglected in the boundary lubrication regime by considering various lubrication problems of practical interest. Using a simple barrel-drop lubrication model for the top two rings in an internal combustion engine, we show that for lubrication gaps with a minimum thickness that is ten times the size of the slip length, the normal force and the frictional force are overestimated by a factor of 1.5 when assuming no-slip. By modifying the Twin Land Oil Control Ring (TLOCR)-liner interface model to include slip, we find significant reduction in the hydrodynamic pressure and the friction when compared to the original model; the oil flow rate does not change appreciably. Finally, we chalk out a procedure for the inclusion of slip in the methodology for developing correlations for the pressure, friction and the flow rate in the TLOCR-liner system.
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 101-109).
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/1721.1/1070592016-01-01T00:00:00ZUnderstanding and modeling human movement in cities using phone data
http://hdl.handle.net/1721.1/107058
Understanding and modeling human movement in cities using phone data
Alhasoun, Fahad
Cities today are strained by the exponential growth in population where they are homes to the majority of world's population. Understanding the complexities underlying the emerging behaviors of human travel patterns on the city level is essential toward making informed decision-making pertaining to urban transportation infrastructures This thesis includes several attempts towards modeling and understanding human mobility at the scales of individuals and the scale of aggregate population movement. The second chapter includes the development of a browser delivering visual insights of the aggregate behavior of populations in cities. The third chapter provides a computational framework for clustering regions in cities based on their attraction behavior and in doing so aids a predictive model in predicting inflows to newly developed regions. The fourth chapter investigates the patterns of individuals' movement at the city scale towards developing a predictive model for a persons' next visited location. The predictive accuracy is then increased by adding movement information of the population. The motivation behind the work of this thesis is derived from the demand of tools that provides fine-grained analysis of the complexity of human travel within cites. The approach takes advantage of the existing built infrastructures to sense the mobility of people eliminating the financial and temporal burdens of traditional methods. The outcomes of this work will assist both planners and the public in understanding the complexities of human mobility within their cities.
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016.; S.M. !c Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2016; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 83-88).
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/1721.1/1070582016-01-01T00:00:00ZEvaluating Intrusion Detection Systems for Energy Diversion Attacks
http://hdl.handle.net/1721.1/107021
Evaluating Intrusion Detection Systems for Energy Diversion Attacks
Sethi, Abhishek Rajkumar
The widespread deployment of smart meters and ICT technologies is enabling continuous collection of high resolution data about consumption behavior and health of grid infrastructure. This has also spurred innovations in technological solutions using analytics/machine learning methods that aim to improve efficiency of grid operations, implement targeted demand management programs, and reduce distribution losses. One one hand, the technological innovations can potentially lead large-scale adoption of analytics driven tools for predictive maintenance and anomaly detection systems in electricity industry. On the other hand, private profit-maximizing firms (distribution utilities) need accurate assessment of the value of these tools to justify investment in collection and processing of significant amount of data and buy/implement analytics tools that exploit this data to provide actionable information (e.g. prediction of component failures, alerts regarding fraudulent customer behavior, etc.) In this thesis, the focus on the value assessment of intrusion/fraud detection systems, and study the tradeoff faced by distribution utilities in terms of gain from fraud investigations (and deterrence of fraudulent customer) versus cost of investigation and false alarms triggered due to probabilistic nature of IDS. Our main contribution is a Bayesian inspection game framework, which models the interactions between a profit-maximizing distribution utility and a population of strategic customers. In our framework, a fraction of customers are fraudulent - they consume same average quantity of electricity but report less by strategically manipulating their consumption data. We consider two sources of information incompleteness: first, the distribution utility does not know the identity of fraudulent customers but only knows the fraction of these consumers, and second, the distribution utility does not know the actual theft level but only knows its distribution. We first consider situation in which only the first source of information incompleteness is present, i.e., the distribution utility has complete information about the actual theft level. We present two simultaneous game models, which have same assumption about customer preferences and fraud, but differ in the way in which the distribution utility operates the IDS. In the first model, the distribution utility probabilistically chooses to use IDS with a default (fixed) configuration. In the second model, the distribution utility can configure/tune the IDS to achieve an optimal operating point (i.e. combination of detection probability and false alarm rate). Throughout, we assume that the theft level is greater than cost of attack. Our results show that for, the game with default IDS configuration, the distribution utility does not use the IDS in equilibrium if the fraction of fraudulent customers is less than a critical fraction. Also the distribution utility realizes a positive "value of IDS" only if one or both have the following conditions hold: (a) the ratio of detection probability and false alarm probability is greater than a critical ratio, (b) the fraction of fraudulent customers is greater than the critical fraction. For the tunable IDS game, we show that the distribution utility always uses an optimal configuration with non-zero false alarm probability. Furthermore, the distribution utility does not tune the false alarm probability when the fraction of fraudulent customers is greater than a critical fraction. In contrast to the game with fixed IDS, in the game of tunable IDS, the distribution utility realizes a positive value from IDS, and the value increases in fraction of fraudulent customers. Next, we consider the situation in which both sources of information incompleteness are present. Specifically, we present a sequential game in which the distribution utility first chooses the optimal configuration of the IDS based on its knowledge of theft level distribution (Stage 1), and then optimally uses the configured IDS in a simultaneous interaction with the customers (Stage 2). This sequential game naturally enables estimation of the "value of information" about theft level, which represents the additional monetary benefit the distribution utility can obtain if the exact value of average theft level is available in choosing optimal IDS configuration in Stage 1. Our results suggest that the optimal configuration under lack of full information on theft level lies between the optimal configurations corresponding to the high and low theft levels. Interestingly enough, our analysis also suggests that for certain technical (yet realistic) conditions on the ROC curve that characterizes achievable detection probability and false alarm probability configurations, the value of information about certain combination of theft levels can attain negligibly small values.
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016.; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (pages 111-114).
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/1721.1/1070212016-01-01T00:00:00Z