Baseline tests of the EPC Hummingbird electric passenger vehicle

The rear-mounted internal combustion engine in a four-passenger Volkswagen Thing was replaced with an electric motor made by modifying an aircraft generator and powered by 12 heavy-duty, lead-acid battery modules. Vehicle performance tests were conducted to measure vehicle maximum speed, range at constant speed, range over stop-and-go driving schedules, maximum acceleration, gradeability limit, road energy consumption, road power, indicated energy consumption, braking capability, battery charger efficiency, and battery characteristics. Test results are presented in tables and charts

Algebraic Attack on the Alternating Step(r,s)Generator

The Alternating Step(r,s) Generator, ASG(r,s), is a clock-controlled sequence generator which is recently proposed by A. Kanso. It consists of three registers of length l, m and n bits. The first register controls the clocking of the two others. The two other registers are clocked r times (or not clocked) (resp. s times or not clocked) depending on the clock-control bit in the first register. The special case r=s=1 is the original and well known Alternating Step Generator. Kanso claims there is no efficient attack against the ASG(r,s) since r and s are kept secret. In this paper, we present an Alternating Step Generator, ASG, model for the ASG(r,s) and also we present a new and efficient algebraic attack on ASG(r,s) using 3(m+n) bits of the output sequence to find the secret key with O((m^2+n^2)*2^{l+1}+ (2^{m-1})*m^3 + (2^{n-1})*n^3) computational complexity. We show that this system is no more secure than the original ASG, in contrast to the claim of the ASG(r,s)'s constructor.Comment: 5 pages, 2 figures, 2 tables, 2010 IEEE International Symposium on Information Theory (ISIT2010),June 13-18, 2010, Austin, Texa

Belt Driven Alternator and Starter with a Series Magnetized Synchronous Machine Drive

Electric Hybrid Vehicles, EHV, are under development to provide lower fuel consumption levels and minimize the environmental pollution compared to pure Internal Combustion Engine, ICE, driven vehicles. The EHV is more complex and thus carry many more extra parts than the pure ICE based vehicle. Competing against the pure ICE vehicle in the sense of nonexpensive mass production is hard. This thesis is a result of a research project with the goal to develop a complete Belt driven Alternator and Starter, BAS, system for a Stop&Go functionality as a cost-effective hybrid vehicle solution. BAS is based on a Series Magnetized Synchronous Machine, SMSM, which as an adjustable-speed drive system comprises power electronics but excludes permanent magnets. BAS is a rather old concept. It merges two functions, an electric starting motor and an generator, into one single electric machine. It thereby makes the total system lighter and smaller. Furthermore, it facilitates technology leaps on the road towards mass production of electric hybrid vehicles. The developed BAS system is suitable for a midrange passenger vehicle. The Stop&Go functionality provides an ICE turn-off at each vehicle stop. The SMSM is, in addition to generating electricity and starting the ICE, intended to support the ICE with an additional torque when it is assumed beneficial in the sense of reaching low fuel consumption. Topics in the field of power electronics and control of the SMSM that are covered in this thesis are: • Simulations on vehicle basis are performed for optimizing the rated power of the electric machine and its power electronics in the sense of low fuel consumption. • The Series Magnetized Synchronous Machine, SMSM, and the theory lying behind it are presented. The SMSM is carefully investigated both magnetically and electrically. • A simulation model for the SMSM is derived based on the theoretical model that describes the SMSM. • Based on the theoretical model of the SMSM, dedicated current controllers are derived. Other types, as standard PI controllers and a so-called field voltage vector feed forward controller are investigated and simulated for control of the SMSM. • The SMSM is tested in laboratory environment for confirming the behaviour of the derived model of the adjustable-speed drive system including its power electronics

Algorithms for randomness in the behavioral sciences: A tutorial

Simulations and experiments frequently demand the generation of random numbera that have specific distributions. This article describes which distributions should be used for the most cammon problems and gives algorithms to generate the numbers.It is also shown that a commonly used permutation algorithm (Nilsson, 1978) is deficient

Explainable and Advisable Learning for Self-driving Vehicles

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly

Selection of the Bookmobile

published or submitted for publicatio

Sizing and Energy Management of a Hybrid Locomotive Based on Flywheel and Accumulators

The French National Railways Company (SNCF) is interested in the design of a hybrid locomotive based on various storage devices (accumulator, flywheel, and ultracapacitor) and fed by a diesel generator. This paper particularly deals with the integration of a flywheel device as a storage element with a reduced-power diesel generator and accumulators on the hybrid locomotive. First, a power flow model of energy-storage elements (flywheel and accumulator) is developed to achieve the design of the whole traction system. Then, two energy-management strategies based on a frequency approach are proposed. The first strategy led us to a bad exploitation of the flywheel, whereas the second strategy provides an optimal sizing of the storage device. Finally, a comparative study of the proposed structure with a flywheel and the existing structure of the locomotive (diesel generator, accumulators, and ultracapacitors) is presented

A knowledge-based system with learning for computer communication network design

Computer communication network design is well-known as complex and hard. For that reason, the most effective methods used to solve it are heuristic. Weaknesses of these techniques are listed and a new approach based on artificial intelligence for solving this problem is presented. This approach is particularly recommended for large packet switched communication networks, in the sense that it permits a high degree of reliability and offers a very flexible environment dealing with many relevant design parameters such as link cost, link capacity, and message delay

Performance Evaluation of Components Using a Granularity-based Interface Between Real-Time Calculus and Timed Automata

To analyze complex and heterogeneous real-time embedded systems, recent works have proposed interface techniques between real-time calculus (RTC) and timed automata (TA), in order to take advantage of the strengths of each technique for analyzing various components. But the time to analyze a state-based component modeled by TA may be prohibitively high, due to the state space explosion problem. In this paper, we propose a framework of granularity-based interfacing to speed up the analysis of a TA modeled component. First, we abstract fine models to work with event streams at coarse granularity. We perform analysis of the component at multiple coarse granularities and then based on RTC theory, we derive lower and upper bounds on arrival patterns of the fine output streams using the causality closure algorithm. Our framework can help to achieve tradeoffs between precision and analysis time.Comment: QAPL 201