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Operator training using a weapon in a real-world environment is risky, expensive, time-consuming, and restricted to the given environment. In addition, governments are under intense scrutiny to provide security, yet they must also strive for efficiency and reduce spending. In other words, they must do more with less. Virtual simulation, is usually employed to solve these limitations. As the operator is trained to maximize weapon effectiveness, the effectiveness-focused training can be completed in an economical manner. Unfortunately, the training is completed in limited scenarios without objective levels of training factors for an individual operator to optimize the weapon effectiveness. Thus, the training will not be effective. For overcoming this problem, we suggest a methodology on guiding effectiveness-focused training of the weapon operator through usability assessments, big data, and Virtual and Constructive (VC) simulations.

The Service Bay Diagnostic System (SBDS) will be designed to assist the dealership technician in diagnosing and repairing Ford Motor vehicles. The system hardware will be configured around a Service Bay Computer with mass storage capability and auxiliary service equipment. Major system features include: guided service writer/customer interaction, interactive vehicle diagnostics, information management. capabilities, and an additional aid to identifying intermittent failures through the use of a portable over-the-road data acquisition device. In order to assist the technician in properly diagnosing the causal factor, the Service Bay Computer System will also be enhanced through the use of an expert system knowledge base.

Complex aerospace engineering systems require innovative methods for performance monitoring and anomaly detection. The interface of a real-time data stream to a system for analysis, pattern recognition, and anomaly detection can require distributed system architectures and sophisticated custom programming. This paper presents a case study of a simplified interface between Programmable Logic Controller (PLC) real-time data output, signal processing, cloud computing, and tablet systems. The discussed approach consists of three parts: First, the connectivity of real-time data from PLCs to the signal processing algorithms, using standard communication technologies. Second, the interface of legacy routines, such as NASA's Inductive Monitoring System (IMS), with a hybrid signal processing system. Third, the connectivity and interaction of the signal processing system with a wireless and distributed tablet, (iPhone/iPad) in a hybrid system configuration using cloud computing.

An innovative nonlinear simulation approach for high power density synchronous generator systems is developed and implemented. Due to high power density, the generator operates in nonlinear region of the magnetic circuit. Magnetic Finite Element Analysis (FEA) makes nonlinear simulation possible. Neural network technique provides nonlinear functions for system level simulation. Dynamic voltage equation provides excellent mathematical model for system level simulations. Voltage, current, and flux linkage quantities are applied in Direct-Quadrature (DQ) rotating frame. The simulated system includes main machine, exciter, rectifier bridge, bang-bang control, and PI control circuitry, forming a closed loop system. Each part is modeled and then integrated into the system model.

When considering ride comfort and precision there are lots of components in the vehicle suspensions that have influence in this behavior and some ride occurrences (mainly higher frequencies) are rubber bushing responsibility but due their compliance, other vehicle attributes, steering and handling, can be affected. So the correct components tuning can maintain or improve vehicle attributes to address desired brand DNA and vehicle its specific needs. These studies were done considering the elastokinematics of front axle only due need of improve its comfort concerning higher frequencies (impacts and harshness). In addiction, correlation between subjective evaluation and objective data acquisition/post processing is desirable to optimize development time. Based in subjective directional, the activities time was reduced and final configuration reached faster.

This paper proposes a novel algorithm. This algorithm is called Self-Organizing Fuzzy Neural Network (SOFNN). SOFNN revolutionizes how researchers apply control theories, image/signal processing on control systems and other applications. In general, SOFNN is an identification technique that automatically initiates, builds and fine-tunes the required network parameters. SOFNN evaluates required structures without predefined parameters or expressions regarding systems. SOFNN sets out to learn and configure a system's characteristics. Self-constructing and self-tuning features enable SOFNN to handle complex, non-linear, and time-varying systems with higher accuracy, making systems identification easier. SOFNN constructs and fine-tunes the system parameter through two phases. The two phases are the construction and the parameter-tuning phase. The two phases run concurrently allowing SOFNN to identify systems on-line.

The application of Statistical Process Control and Design of Experiment methods in the research laboratory can lead to significant gains in the Powertrain development process. Empirical methods such as Design of Experiments, Regression, and Neural Network techniques can be applied to help researchers gain better understanding of the cause and effect relationships of emission, alternative fuel source, performance, fuel economy, and engine management system - calibration studies. The use of these empirical modeling techniques along with model based Genetic Algorithm, Gradient, or Constraint based solution search methods will help identify the “process settings” that improve fuel economy, improve performance, and reduce pollutants. Since empirical methods are fundamentally based on the acquired test data, it is vitally important that the laboratory measurements are repeatable, consistent, and void of sources of variance that have a significant effect on the acquired test data.

Calibrating the engine control unit to satisfy pollutant and performance objectives can be a challenging task. Due to the large number of variables and their interactive complexities, many firms apply design of experiment methods and modeling techniques to the acquired test data. This establishes a “black box” or “gray box” simulation model that predicts power and emissions as a function of the engine parameters. An offline optimization procedure on the fitted model(s) will identify the engine control strategy that best satisfies pollutant and performance objectives. A review of the literature reveals that the General Linear Modeling method and Neural Network modeling architectures are widely used in the development of “black box” or “gray box” simulation models. While Neural Network methods are “assumption free”, the General Linear Model method is limited to those problems in which the errors, ε, are normally distributed and have constant variance, σ2.

A flexible, easily configuration data acquisition system was designed and built for detailed studies of the steady state and dynamic properties of lean NOx traps for an engine dynamometer environment. The system is based on the industry standard VXI backplane. The overriding design philosophy was to design and develop a data acquisition system that was user friendly and could be operated easily by engine laboratory technicians, as well as by test engineers. The primary requirements guiding the design were the following: (1) the ability easily to configure, save, recall, modify, and print test configurations. (2) The ability to configure the gain, channel name, and engineering units for each analog channel. (3) The ability to trigger from one analog input channel. (4) A provision for numeric auto-incrementing of data file names. (5) The ability to save data in Excel™ compatible ASCII format. (6) The utilization of off-the shelf VXI hardware.

An experimental study of the performance of a reverse tumble, DISI engine is reported. Specific fuel consumption and engine-out emissions have been investigated for both homogeneous and stratified modes of fuel injection. Trends in performance with varying AFR, EGR, spark and injection timings have been explored. It is shown that neural networks can be trained to describe these trends accurately for even the most complex case of stratified charge operation with exhaust gas recirculation.

A number of advancements have been made in the coulometric sulfur trace instrumental technique for the real-time measurement of engine oil economy. These advancements include modification of the coulometric cell to improve reliability and reproducibility. The instrument has been interfaced with a microcomputer for instrument control as well as data acquisition, storage, and analysis. Studies were undertaken which demonstrate sufficient sensitivity and linearity for determination of engine oil economy at levels better than 10,000 miles/quart. Applications to steady-state engine oil consumption mapping and to instantaneous oil consumption during transient engine cycling are described. These instruments are being produced by an outside supplier for use in various company locations in both the engine production and engine research environments.

A real-time approach has been developed and assessed to control BMEP (brake mean effective pressure) and MFB50 (crank angle at which 50% of fuel mass has burnt) in a Euro 6 1.6L GM diesel engine. The approach is based on the use of feed-forward ANNs (artificial neural networks), which have been trained using virtual tests simulated by a previously developed low-throughput physical engine model. The latter is capable of predicting the heat release and the in-cylinder pressure, as well as the related metrics (MFB50, IMEP - indicated mean effective pressure) on the basis of an improved version of the accumulated fuel mass approach. BMEP is obtained from IMEP taking into account friction losses. The low-throughput physical model does not require high calibration effort and is also suitable for control-oriented applications. However, control tasks characterized by stricter demands in terms of computational time may require a modeling approach characterized by a further lower throughput.

This paper is the second in the series of documents designed to record the progress of a series of SAE documents - SAE J2836™, J2847, J2931, & J2953 - within the Plug-In Electric Vehicle (PEV) Communication Task Force. This follows the initial paper number 2010-01-0837, and continues with the test and modeling of the various PLC types for utility programs described in J2836/1™ & J2847/1. This also extends the communication to an off-board charger, described in J2836/2™ & J2847/2 and includes reverse energy flow described in J2836/3™ and J2847/3. The initial versions of J2836/1™ and J2847/1 were published early 2010. J2847/1 has now been re-opened to include updates from comments from the National Institute of Standards Technology (NIST) Smart Grid Interoperability Panel (SGIP), Smart Grid Architectural Committee (SGAC) and Cyber Security Working Group committee (SCWG).

The concept of autonomous driving is becoming increasingly familiar in the automotive and “in-door” automation systems fields. Furthermore, the industrial development is focusing its efforts on industry 4.0, whose some main features are data transfer, programming, systems interconnection and automation. The agricultural sector just recently has experienced the first examples of autonomous agricultural vehicles, although agricultural mechanization has reached a good level of automation. Indeed, many examples of automatic machineries are already present in the market such as little robots for the execution of some operations. This work focuses on modelling and simulation of a self-driving orchard tractor. The main goal was to reproduce the behaviour of the specialized vehicle, moving in an orchard or a vineyard and conducting automatic or semi-automatic operations.

The paper describes the design and construction of a two cylinder apparatus to measure heat transfer under conditions of oscillating pressure and oscillating flow such as found in Stirling-cycle machines. The apparatus consists of two large single stage air compressors joined by a rigid drive shaft between the two crank shafts. The compressors are 27.94 cm (11-in) diameter by 22.86 cm (9-in) stroke. The apparatus is powered by a 25 HP variable speed DC motor. Belts and a jack shaft provide wide speed ranges. The test section, which is connected between the compressor cylinders, is a 44.45 mm (1.75-in) diameter tube and about 254 cm (100-in) long. The test section is configured for measuring wall heat flux, and gas pressure as a function of time. An LDV system is being installed for measurement of gas velocity as a function of time and position. A fast response micro thermocouple measures gas temperature as a function of time and position.

This paper describes the design and development of the Ford Driving Simulator. The simulator is a fixed-base device which provides real-time, interactive feedback to the driver through a combination of visual, auditory and tactile cues. The system is comprised of a modular buck, 150° field-of-view visual scene, a steering torque controller, high fidelity dynamics models, and an interactive experimenters station. Data acquisition systems have been developed to capture a broad spectrum of driver performance metrics.

During the fall of 1992, the Michigan Roadside Study was conducted. During this study IM240 tests were conducted on vehicles that had also been emissions tested during on-road operation via two remote sensors that were separated by 100 feet. The use of two remote sensors provided an indication of the short-term real-world emissions variability of a large number of on-road vehicles. This data was used to determine the frequency of flippers, i.e. vehicles that are sometimes high emitters (>4% CO) and at other times low emitters (<2% CO). The data show that the flipper frequency increases for older model year vehicles. Also, the correlations between remote sensor readings of emissions concentrations and IM240 mass emissions rates were determined. The data show that the correlation between remote sensing and IM240 improves with increasing numbers of remote sensing readings. For three remote sensor readings, CO correlates with an r2 of 0.69 and HC correlates with an r2 of 0.54

A method for determining the flame contour based on the flame arrival time at the fiber optic (FO) spark plug and at the head gasket ionization probe (IP) locations has been developed. The experimental data were generated in a single-cylinder Ricardo Hydra spark-ignition engine. The head gasket IP, constructed from a double-sided copper-clad circuit board, detects the flame arrival time at eight equally spaced locations at the top of the cylinder liner. Three other IP's were also installed in the cylinder head to provide additional intermediate data on flame location and arrival time. The FO spark plug consists of a standard spark plug with eight symmetrically spaced optical fibers located in the ground casing of the plug. The cylinder pressure was recorded simultaneously with the eleven IP signals and the eight optical signals using a high-speed PC-based data acquisition system.

A highly adaptable fatigue testing computer system is presented for controlling single or multichannel test machines. The system imposes most common varieties of waveforms and also provides time synchronization between channels, such as in the case of variable amplitude biaxial load histories, and monitors various feedback signals for both data acquisition and alarm purposes. The program operates in a real-time Unix system as a separate stand-alone process. Communication with other users or the operator is done only through a reserved common block of shared memory. This feature allows control and monitoring of all tests over the computer network. A user can simply login remotely and check the test or start a data acquisition task from any workstation in the company, and then take the data files and analyze them on other computers. This paper describes the operation of the software, the methodology behind the hardware selection and the software structure.

A robotic sound intensity data acquisition system has been developed to overcome the problems of manual data acquistion. The system has the following performance: Accurate and repeatable positioning of the intensity probe Regular five and six sided rectangular measurement surfaces Irregular measurement surfaces around the complete powerplant, or individual components Continuous scanning or fixed point measurements Probe motion, data measurement, and storage of acoustic and geometric data integrated within the acquisition system database.