Simulation algorithms for inductive effects

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 105-110).by Yehia Mahmoud Massoud.Ph.D

The use of simulation in the design of a road transport incident detection algorithm

Automatic incident detection is becoming one of the core tools of urban traffic management, enabling more rapid identification and response to traffic incidents and congestion. Existing traffic detection infrastructure within urban areas (often installed for traffic signal optimization) provides urban traffic control systems with a near continuous stream of data on the state of traffic within the network. The creation of a simulation to replicate such a data stream therefore provides a facility for the development of accurate congestion detection and warning algorithms. This paper describes firstly the augmentation of a commercial traffic model to provide an urban traffic control simulation platform and secondly the development of a new incident detection system (RAID-Remote Automatic Incident Detection), with the facility to use the simulation platform as an integral part of the design and calibration process. A brief description of a practical implementation of RAID is included along with summary evaluation results

Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines

Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods

Tradeoffs between AC power quality and DC bus ripple for 3-phase 3-wire inverter-connected devices within microgrids

Visions of future power systems contain high penetrations of inverters which are used to convert power from dc (direct current) to ac (alternating current) or vice versa. The behavior of these devices is dependent upon the choice and implementation of the control algorithms. In particular, there is a tradeoff between dc bus ripple and ac power quality. This study examines the tradeoffs. Four control modes are examined. Mathematical derivations are used to predict the key implications of each control mode. Then, an inverter is studied both in simulation and in hardware at the 10 kVA scale, in different microgrid environments of grid impedance and power quality. It is found that voltage-drive mode provides the best ac power quality, but at the expense of high dc bus ripple. Sinusoidal current generation and dual-sequence controllers provide relatively low dc bus ripple and relatively small effects on power quality. High-bandwidth dc bus ripple minimization mode works well in environments of low grid impedance, but is highly unsuitable within higher impedance microgrid environments and/or at low switching frequencies. The findings also suggest that the certification procedures given by G5/4, P29 and IEEE 1547 are potentially not adequate to cover all applications and scenarios

Using First Order Inductive Learning as an Alternative to a Simulator in a Game Artificial Intelligence

Currently many game artificial intelligences attempt to determine their next moves by using a simulator to predict the effect of actions in the world. However, writing such a simulator is time-consuming, and the simulator must be changed substantially whenever a detail in the game design is modified. As such, this research project set out to determine if a version of the first order inductive learning algorithm could be used to learn rules that could then be used in place of a simulator. By eliminating the need to write a simulator for each game by hand, the entire Darmok 2 project could more easily adapt to additional real-time strategy games. Over time, Darmok 2 would also be able to provide better competition for human players by training the artificial intelligences to play against the style of a specific player. Most importantly, Darmok 2 might also be able to create a general solution for creating game artificial intelligences, which could save game development companies a substantial amount of money, time, and effort.Ram, Ashwin - Faculty Mentor ; Ontañón, Santi - Committee Member/Second Reade

Towards a real-time microscopic emissions model

This article presents a new approach to microscopic road traffic exhaust emission modelling. The model described uses data from the SCOOT demand-responsive traffic control system implemented in over 170 cities across the world. Estimates of vehicle speed and classification are made using data from inductive detector loops located on every SCOOT link. This data feeds into a microscopic traffic model to enable enhanced modelling of the driving modes of vehicles (acceleration, deceleration, idling and cruising). Estimates of carbon monoxide emissions are made by applying emission factors from an extensive literature review. A critical appraisal of the development and validation of the model is given before the model is applied to a study of the impact of high emitting vehicles. The article concludes with a discussion of the requirements for the future development and benefits of the application of such a model