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This paper describes intra-layer architecture of a neuroprocessor organized in a SIMD configuration and the two challenging applications, misfire detection and engine idle speed control, that had served as the focus of this effort.
In Linear Temporal Logic (LTL) model checking, we check LTL formulas representing desired behaviors against a formal model of the system designed to exhibit these behaviors. To accomplish this task, the LTL formulas must be translated into automata [21]. We focus on LTL compilation by investigating LTL satisfiability checking via a reduction to model checking. Having shown that symbolic LTL compilation algorithms are superior to explicit automata construction algorithms for this task [16], we concentrate here on seeking a better symbolic algorithm.We present experimental data comparing algorithmic variations such as normal forms, encoding methods, and variable ordering and examine their effects on performance metrics including processing time and scalability. Safety critical systems, such as air traffic control, life support systems, hazardous environment controls, and automotive control systems, pervade our daily lives, yet testing and simulation alone cannot adequately verify their reliability [3]. Model checking is a promising approach to formal verification for safety critical systems which involves creating a formal mathematical model of the system and translating desired safety properties into a formal specification for this model. The complement of the specification is then checked against the system model. When the model does not satisfy the specification, model-checking tools accompany this negative answer with a counterexample, which points to an inconsistency between the system and the desired behaviors and aids debugging efforts.
Nanosized cerium oxide (CeO 2 ) has been extensively used as the oxygen storage component in automotive emission control systems. However, the possible influence of atomically dispersed Ce in these catalysts has not been recognized. Here, in this study, we demonstrate the controllable transformation of ceria nanoparticles into isolated cerium cations on γ-Al 2 O 3 via reductive atom trapping in 10% H 2 at 800 °C, achieving over half-monolayer coverage. Dispersed Ce 1 ions anchored by surface penta- and octa-coordinated Al sites exhibit outstanding thermal stability in air up to 500 °C, enabling further loading of active metals with well-defined catalyst structures. With this strategy, supported single-atom Rh 1 surrounded by dispersed Ce 1 is confirmed to exhibit much superior performance to Rh 1 on bare Al 2 O 3 or nanocrystalline CeO 2 in catalyzing NO reduction by CO, exhibiting a striking one-order-of-magnitude increase in activity. Dispersed Ce 1 exhibits greatly enhanced oxygen transfer capability compared to ceria nanoparticles and introduces a modified reaction mechanism that involves an adjacent Rh 1 –Ce 1 motif, resulting in a greatly decreased activation barrier (from 192 to 96 kJ/mol). The reactivity enhancements are also seen with Ce 1 -promoted Pt nanoparticles for oxidation of CO and hydrocarbons.
Photomultiplier detector of Canopus for spacecraft attitude control
Modeling guidelines constitute one of the fundamental cornerstones for Model Based Development. Their relevance is essential when dealing with code generation in the safety-critical domain. This article presents the experience of a railway signaling systems manufacturer on this issue. Introduction of Model-Based Development (MBD) and code generation in the industrial safety-critical sector created a crucial paradigm shift in the development process of dependable systems. While traditional software development focuses on the code, with MBD practices the focus shifts to model abstractions. The change has fundamental implications for safety-critical systems, which still need to guarantee a high degree of confidence also at code level. Usage of the Simulink/Stateflow platform for modeling, which is a de facto standard in control software development, does not ensure by itself production of high-quality dependable code. This issue has been addressed by companies through the definition of modeling rules imposing restrictions on the usage of design tools components, in order to enable production of qualified code. The MAAB Control Algorithm Modeling Guidelines (MathWorks Automotive Advisory Board)[3] is a well established set of publicly available rules for modeling with Simulink/Stateflow. This set of recommendations has been developed by a group of OEMs and suppliers of the automotive sector with the objective of enforcing and easing the usage of the MathWorks tools within the automotive industry. The guidelines have been published in 2001 and afterwords revisited in 2007 in order to integrate some additional rules developed by the Japanese division of MAAB [5]. The scope of the current edition of the guidelines ranges from model maintainability and readability to code generation issues. The rules are conceived as a reference baseline and therefore they need to be tailored to comply with the characteristics of each industrial context. Customization of these recommendations has been performed for the automotive control systems domain in order to enforce code generation [7]. The MAAB guidelines have been found profitable also in the aerospace/avionics sector [1] and they have been adopted by the MathWorks Aerospace Leadership Council (MALC). General Electric Transportation Systems (GETS) is a well known railway signaling systems manufacturer leading in Automatic Train Protection (ATP) systems technology. Inside an effort of adopting formal methods within its own development process, GETS decided to introduce system modeling by means of the MathWorks tools [2], and in 2008 chose to move to code generation. This article reports the experience performed by GETS in developing its own modeling standard through customizing the MAAB rules for the railway signaling domain and shows the result of this experience with a successful product development story.
A system for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This system can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.
A system and method for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This technology can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system and method can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.
Fault detection in Controller Area Network (CAN) systems is crucial for ensuring the reliability and safety of automotive and industrial applications. This study investigates and compares the effectiveness of time series classification models for supervised fault detection in CAN data. This repository contains the code and data for our benchmarking experiment aimed at detecting intermittent faults in automotive Controller Area Network (CAN) data. The goal of this project is to compare various machine learning (ML) and deep learning (DL) models using different Time Series Cross-Validation (TSCV) techniques to evaluate their effectiveness in a streaming environment for fault detection.
A multiple channel high data rate pressure sensing device is disclosed for use in wind tunnels, spacecraft, airborne, process control, automotive, etc., pressure measurements. Data rates in excess of 100,000 measurements per second are offered with inaccuracies from temperature shifts less than 0.25% (nominal) of full scale over a temperature span of 55 C. The device consists of thirty-two solid state sensors, signal multiplexing electronics to electronically address each sensor, and digital electronic circuitry to automatically correct the inherent thermal shift errors of the pressure sensors and their associated electronics.
Fluidic controls for automotive engine examined by Rankine cycle performance with water, CP-34, and freon TF and investigation for boiler and feed pump control criteria
Not provided.
Demands on the performance of vehicle control and diagnostic systems are steadily increasing as a consequence of stiff global competition and government mandates. In the United States, light trucks and passenger cars are required both to meet strict emission standards and to perform continuous diagnostics of all emissions systems operating in the vehicle.
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A fuel control system is reported for automotive-type gas turbines and particulary advanced gas turbines utilizing variable geometry components to improve mileage and reduce pollution emission. The fuel control system compensates for fuel density variations, inlet temperature variations, turbine vane actuation, acceleration, and turbine braking. These parameters are utilized to control various orifices, spool valves and pistons.
The Catalyst CRADA focused on lean-NO x trap (LNT) and selective catalytic reduction (SCR) automotive emission control catalyst technology. It particularly had the goal of developing and applying advanced diagnostics capable of elucidating the network and sequence of catalyst reactions, as well as how they are affected by real-world operation and aging. Another overarching goal and action of this CRADA section was to develop methods to quantify and monitor catalyst states and support the development of full and reduced models for design and in-use control applications. William Partridge was the ORNL principal investigator (PI) throughout the Catalyst CRADA; Neal Currier was the Cummins PI through circa 2017, and Saurabh Joshi was the subsequent Cummins PI.
High-temperature shape-memory alloys (HTSMAs) based on nickel-titanium (NiTi) with significant ternary additions of palladium (Pd), platinum (Pt), gold (Au), or hafnium (Hf) have been identified as potential high-temperature actuator materials for use up to 500 C. These materials provide an enabling technology for the development of "smart structures" used to control the noise, emissions, or efficiency of gas turbine engines. The demand for these high-temperature versions of conventional shape-memory alloys also has been growing in the automotive, process control, and energy industries. However these materials, including the NiPtTi alloys being developed at the NASA Glenn Research Center, will never find widespread acceptance unless they can be readily processed into useable forms.
The ability to measure emissions from aeronautic engines and in commercial applications such as automotive emission control and chemical process monitoring is a necessary first step if one is going to actively control those emissions. One single sensor will not give all the information necessary to determine the chemical composition of a high-temperature, harsh environment. Rather, an array of gas sensor arrays--in effect, a high-temperature electronic "nose"--is necessary to characterize the chemical constituents of a diverse, high-temperature environment, such as an emissions stream. The signals produced by this nose could be analyzed to determine the constituents of the emission stream. Although commercial electronic noses for near-room temperature applications exist, they often depend significantly on lower temperature materials or only one sensor type. A separate development effort necessary for a high-temperature electronic nose is being undertaken by the NASA Glenn Research Center, Case Western Reserve University, Ohio State University, and Makel Engineering, Inc. The sensors are specially designed for hightemperature environments. A first-generation high-temperature electronic nose has been demonstrated on a modified automotive engine. This nose sensor array was composed of sensors designed for hightemperature environments fabricated using microelectromechanical-systems- (MEMS-) based technology. The array included a tin-oxide-based sensor doped for nitrogen oxide (NOx) sensitivity, a SiC-based hydrocarbon (CxHy) sensor, and an oxygen sensor (O2). These sensors operate on different principles--resistor, diode, and electrochemical cell, respectively--and each sensor has very different responses to the individual gases in the environment. A picture showing the sensor head for the array is shown in the photograph on the left and the sensors installed in the engine are shown in the photograph on the right. Electronics are interfaced with the sensors for temperature control and signal conditioning, and packaging designed for high temperatures is necessary for the array to survive the engine environment.
In this simulation study, cycle-to-cycle fuel control was used to reduce CCV by injecting additional fuel in operating conditions with sporadic misfires and partial burns. An optimal control policy was proposed that utilizes 1) a physics-based model that tracks in-cylinder gas composition and 2) a one-step-ahead prediction of the combustion efficiency based on a kernel density estimator. The optimal solution, however, presents a tradeoff between the reduction in combustion CCV and the increase in fuel injection quantity required to stabilize the charge. Such a tradeoff can be ad- just by a single parameter embedded in the cost function.