Timely identification of undesirable pavement crack conditions has been a major task in pavement management. Up to date, myriads of pavement performance models have been developed for forecasting pavement crack condition with the traditional preferred techniques being the use of regression relationships developed from laboratory and/or field statistical data. However, it becomes difficult for regression techniques to predict the crack performance accurately and robustly in the presence of a variety of tributary factors, high nonlinearity, and uncertainty. With the advancement of modeling techniques, two innovative breeds of models, Artificial Neural Networks and Markov Chains, have drawn increasing attention from researchers for modeling complex phenomena like the pavement crack performance. In this study, two distinct models, a recurrent Markov chain, and an Artificial Neural Network (ANN), were developed for modeling the performance of pavement crack condition with time. A logistic model was used to establish a dynamic relationship between transition probabilities associated with the pavement crack condition and the applicable tributary variables. The logistic model was then used conveniently to construct a recurrent Markov chain for use in predicting the crack performance of asphalt pavements in Florida. Florida pavement condition survey database were utilized to perform a case study of the proposed methodologies. For comparison purpose, a currently popular static Markov chain was also developed based on a homogeneous transition probability matrix that was derived from the crack index statistics of Florida pavement survey database. To evaluate the model performance, two comparisons were made; (1) between the recurrent Markov chain and the static Markov chain; and (2) between the recurrent Markov chain and the ANN. It is shown that the recurrent Markov chain outperforms both the static Markov chain and the ANN in terms of one-year forecasting accuracy. Therefore, with high uncertainty typically experienced in the pavement condition deterioration process, the probabilistic dynamic modeling approach as embodied in the recurrent Markov chain provides a more appropriate and applicable methodology for modeling the pavement deterioration process with respect to cracks.

<p>This thesis is an approach to improve a two-mode hybrid electric vehicle, which is currently under development by GM, with respect to fuel consumption. The study is not only restricted to the specific two-mode HEV but also presents results regarding parallel as well as serial HEV’s. GM whishes to verify if the online-based controller in the prototype vehicle utilizes the most of the HEV ability and if there is more potential to lower the fuel consumption. The purpose is that the results and conclusions from this work are to be implemented in the controller to further improve the vehicle’s performance. To analyze the behavior of the two-mode HEV and to see where improvements can be made, models of its driveline and components are developed with a focuson losses and efficiency. The models are implemented in MATLAB together with an optimization algorithm based on Dynamic Programming. The models are validated against data retrieved from the prototype vehicle and various cases with different inputs is set up and optimized over the NEDC cycle. Compensation for cold starts and NOx emissions are also implemented in the final model. Deliberate simplifications are made regarding the modeling of the power split’s functionality due to the limited amount of time available for this thesis. The optimizations show that there is potential to lower the fuel consumptionfor the two-mode HEV. The results are further analyzed and the behavior of the engine, motors/generators and battery are compared with recorded data from a prototype vehicle and summarized to a list of suggestions to improve fuel economy.</p>

In recent years many researchers have investigated the use of Markov random fields (MRFs) for computer vision. The computational complexity of the implementation has been a drawback of MRFs. In this paper we derive deterministic approximations to MRFs models. All the theoretical results are obtained in the framework of the mean field theory from statistical mechanics. Because we use MRFs models the mean field equations lead to parallel and iterative algorithms. One of the considered models for image reconstruction is shown to give in a natural way the graduate non-convexity algorithm proposed by Blake and Zisserman.

Current geocasting algorithms for VANETs are being designed to enable either private or reliable communications, but not both. Existing algorithms preserve privacy by minimizing the information used for routing, and sacrifice message delivery success. On the other hand, reliable protocols often store node information that can be used to compromise a vehicle's privacy. We have designed two private and reliable geocasting protocols for VANETs that ensure confidentiality. One is a probabilistic algorithm that uses direction-based dissemination, while the other is a deterministic algorithm that uses transmission-coverage dissemination. To preserve privacy, we create unlinkable and pseudonymous channels of communication with geocasting. For encryption and authentication, we use a public key technique. Our probabilistic forwarding model depends on message rate and cumulative payload, as well as the value of the angle of spreading of the direction-based scheme. To reduce message duplication, we apply dynamic traffic restriction and probabilistic forwarding techniques. The deterministic forwarding algorithm delays forwarding messages based on its uncovered transmission area after neighbouring nodes have broadcast the message. We prove that both algorithms ensure node privacy with appropriate message encryption security, and we ran simulations to demonstrate that both meet the message delivery requirements. From the gathered data, we observe that both algorithms behave differently depending on the scenario, with node density affecting the deterministic algorithm, while the angle of spreading does have a significant impact on the probabilistic protocol.

A spinning reserve assessment technique for a deregulated system has been developed and presented in this thesis. The technique is based on direct search optimization approach. Computer programs have been developed to implement the optimization processes both for transmission loss and without transmission loss.<p> A system commits adequate generation to satisfy its load and export/import commitment. Additional generation known as spinning reserve is also required to satisfy unforeseen load changes or withstand sudden generation loss. In a vertically integrated system, a single entity generates, transmits and distributes electrical energy. As a part of its operational planning, the single entity decides the level of spinning reserve. The cost associated with generation, transmission, distribution including the spinning reserve is then passed on to the customers.<p> In a deregulated system, generation, transmission and distribution are three businesses. Generators compete with each other to sell their energy to the Independent System Operators (ISO). ISO coordinates the bids from the generation as well as the bids from the bulk customers. In order to ensure a reliable operation, ISO must also ensure that the system has adequate spinning reserve. ISO must buy spinning reserve from the spinning reserve market. A probabilistic method called the load forecast uncertainty (LFU)-based spinning reserve assessment (LSRA) is proposed to assess the spinning reserve requirements in a deregulated power system.<p> The LSRA is an energy cost- based approach that incorporates the load forecast uncertainty of the day-ahead market (DAM) and the energy prices within the system in the assessment process. The LSRA technique analyzes every load step of the 49-step LFU model and the probability that the hourly DAM load will be within that load step on the actual day. Economic and reliability decisions are made based on the analysis to determine and minimize the total energy cost for each hour subject to certain system constraints in order to assess the spinning reserve requirements. The direct search optimization approach is easily implemented in the determination of the optimal SR requirements since the objective function is a combination of linear and non-linear functions. This approach involves varying the amount of SR within the system from zero to the maximum available capacity. By varying the amount of SR within the system, the optimal SR for which the hourly total operating cost is minimum and all operating constraints are satisfied is evaluated.<p> One major advantage of the LSRA technique is the inclusion of all the major system variables like DAM hourly loads and energy prices and the utilization of the stochastic nature of the system components in its computation. The setback in this technique is the need to have access to historical load data and spot market energy prices during all seasons. The availability and reliability of these historical data has a huge effect on the LSRA technique to adequately assess the spinning reserve requirements in a deregulated system.<p> The technique, along with the effects of load forecast uncertainty, energy prices of spinning reserve and spot market and the reloading up and down limits of the generating zones on the spinning reserve requirements are illustrated in detail in this thesis work. The effects of the above stochastic components of the power system on the spinning reserve requirements are illustrated numerically by different graphs using a computer simulation of the technique incorporating test systems with and without transmission loss.

Traffic congestion is a serious problem with large economic and environmental impacts. To reduce congestion (as a city planner) or simply to avoid congested channels (as a road user), one might like to accurately know the flow on roads in the traffic network. This information can be obtained from traffic sensors, devices that can be installed on roads or intersections to measure traffic flow. The sensor location problem is the problem of efficiently locating traffic sensors on intersections such that the flow on the entire network can be extrapolated from the readings of those sensors. I build on current research concerning the sensor location problem to develop conditions on a traffic network and sensor configuration such that the flow can be uniquely extrapolated from the sensors. Specifically, I partition the network by a method described by Morrison and Martonosi (2010) and establish a necessary and sufficient condition for uniquely extrapolatable flow on a part of that network that has certain flow characteristics. I also state a different sufficient but not necessary condition and include a novel proof thereof. Finally, I present several results illustrating the relationship between the inputs to a general network and the flow solution.

No description available.

This thesis is an approach to improve a two-mode hybrid electric vehicle, which is currently under development by GM, with respect to fuel consumption. The study is not only restricted to the specific two-mode HEV but also presents results regarding parallel as well as serial HEV’s. GM whishes to verify if the online-based controller in the prototype vehicle utilizes the most of the HEV ability and if there is more potential to lower the fuel consumption. The purpose is that the results and conclusions from this work are to be implemented in the controller to further improve the vehicle’s performance. To analyze the behavior of the two-mode HEV and to see where improvements can be made, models of its driveline and components are developed with a focuson losses and efficiency. The models are implemented in MATLAB together with an optimization algorithm based on Dynamic Programming. The models are validated against data retrieved from the prototype vehicle and various cases with different inputs is set up and optimized over the NEDC cycle. Compensation for cold starts and NOx emissions are also implemented in the final model. Deliberate simplifications are made regarding the modeling of the power split’s functionality due to the limited amount of time available for this thesis. The optimizations show that there is potential to lower the fuel consumptionfor the two-mode HEV. The results are further analyzed and the behavior of the engine, motors/generators and battery are compared with recorded data from a prototype vehicle and summarized to a list of suggestions to improve fuel economy.

A spinning reserve assessment technique for a deregulated system has been developed and presented in this thesis. The technique is based on direct search optimization approach. Computer programs have been developed to implement the optimization processes both for transmission loss and without transmission loss.<p> A system commits adequate generation to satisfy its load and export/import commitment. Additional generation known as spinning reserve is also required to satisfy unforeseen load changes or withstand sudden generation loss. In a vertically integrated system, a single entity generates, transmits and distributes electrical energy. As a part of its operational planning, the single entity decides the level of spinning reserve. The cost associated with generation, transmission, distribution including the spinning reserve is then passed on to the customers.<p> In a deregulated system, generation, transmission and distribution are three businesses. Generators compete with each other to sell their energy to the Independent System Operators (ISO). ISO coordinates the bids from the generation as well as the bids from the bulk customers. In order to ensure a reliable operation, ISO must also ensure that the system has adequate spinning reserve. ISO must buy spinning reserve from the spinning reserve market. A probabilistic method called the load forecast uncertainty (LFU)-based spinning reserve assessment (LSRA) is proposed to assess the spinning reserve requirements in a deregulated power system.<p> The LSRA is an energy cost- based approach that incorporates the load forecast uncertainty of the day-ahead market (DAM) and the energy prices within the system in the assessment process. The LSRA technique analyzes every load step of the 49-step LFU model and the probability that the hourly DAM load will be within that load step on the actual day. Economic and reliability decisions are made based on the analysis to determine and minimize the total energy cost for each hour subject to certain system constraints in order to assess the spinning reserve requirements. The direct search optimization approach is easily implemented in the determination of the optimal SR requirements since the objective function is a combination of linear and non-linear functions. This approach involves varying the amount of SR within the system from zero to the maximum available capacity. By varying the amount of SR within the system, the optimal SR for which the hourly total operating cost is minimum and all operating constraints are satisfied is evaluated.<p> One major advantage of the LSRA technique is the inclusion of all the major system variables like DAM hourly loads and energy prices and the utilization of the stochastic nature of the system components in its computation. The setback in this technique is the need to have access to historical load data and spot market energy prices during all seasons. The availability and reliability of these historical data has a huge effect on the LSRA technique to adequately assess the spinning reserve requirements in a deregulated system.<p> The technique, along with the effects of load forecast uncertainty, energy prices of spinning reserve and spot market and the reloading up and down limits of the generating zones on the spinning reserve requirements are illustrated in detail in this thesis work. The effects of the above stochastic components of the power system on the spinning reserve requirements are illustrated numerically by different graphs using a computer simulation of the technique incorporating test systems with and without transmission loss.

One of the important factors to be considered in designing an engineering system is uncertainty, which emanates from natural randomness, limited data, or limited knowledge of systems. In this study, a robust design methodology is established in order to design multifunctional materials, employing multi-time and length scale analyses. The Robust Concept Exploration Method with Error Margin Index (RCEM-EMI) is proposed for design incorporating non-deterministic system behavior. The Inductive Design Exploration Method (IDEM) is proposed to facilitate distributed, robust decision-making under propagated uncertainty in a series of multiscale analyses or simulations. These methods are verified in the context of Design of Multifunctional Energetic Structural Materials (MESM). The MESM is being developed to replace the large amount of steel reinforcement in a missile penetrator for light weight, high energy release, and sound structural integrity. In this example, the methods facilitate following state-of-the-art design capabilities, robust MESM design under (a) random microstructure changes and (b) propagated uncertainty in a multiscale analysis chain. The methods are designed to facilitate effective and efficient materials design; however, they are generalized to be applicable to any complex engineering systems design that incorporates computationally intensive simulations or expensive experiments, non-deterministic models, accumulated uncertainty in multidisciplinary analyses, and distributed, collaborative decision-making.