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Microprocessors and microcontrollers are now widely used in automobiles. Microprocessor systems contain sources of interrupt and interrupt service routines, which are software components executed in response to the assertion of an interrupt in hardware. A major problem in designing the software of microprocessor systems is the analytical treatment of interrupt latency. Because multiple interrupt service routines are executed on the same CPU, they compete for the CPU and interfere with each other's latency requirements. Here, interrupt latency is defined as the delay between the assertion of the interrupt in hardware and the start of execution of the associated interrupt service routine. It is estimated that 80% of intermittent bugs in small microprocessor software loads are due to improper treatment of interrupts. Until this work, there is no analytic method for analyzing a particular system to determine if it may violate interrupt latency requirements.

The paper focuses on various important decisions of verification and testing plans of the product during its design and production stages. In most of the product and process development projects, decisions on verification and testing are ad-hoc or based on traditions. Such decisions never guarantee the performance of the product as planned, during its whole life cycle. We propose an analytical approach to provide the concrete base for such crucial decisions of verification planning. Accordingly, a mathematical model is presented. Also, a case study of an automotive Electro-mechanical product is included to illustrate the application of the model.

The goal of optimization in vehicle design is often blurred by the myriads of requirements belonging to attributes that may not be quite related. If solutions are sought by optimizing attribute performance-related objectives separately starting with a common baseline design configuration as in a traditional design environment, it becomes an arduous task to integrate the potentially conflicting solutions into one satisfactory design. It may be thus more desirable to carry out a combined multi-disciplinary design optimization (MDO) with vehicle weight as an objective function and cross-functional attribute performance targets as constraints. For the particular case of vehicle body structure design, the initial design is likely to be arrived at taking into account styling, packaging and market-driven requirements.

A unified approach has been developed and applied to solder joint life prediction in this paper, which indicates a breakthrough for solder joint reliability simulation. It includes the material characterization of solder alloys, the testing of solder joint specimens, a unified viscoplastic constitutive framework with damage evolution, numerical algorithm development and implementation, and experimental validation. The emphasis of this report focuses on the algorithm development and experimental verification of proposed viscoplasticity with damage evolution.

A new algorithm for carrier removal, a key step in the Fourier transform method of fringe pattern analysis, is presented in this paper. The accuracy of frequency estimations is critical to carrier removal to avoid potential significant errors in the recovered phase. A new algorithm on Fourier transform and curve fitting technique is developed. To avoid an ill-conditioned result in solving the least-square problem, an orthogonal polynomial curve fitting algorithm is developed. A new system that combines projected grating moiré (PM) with shadow moiré (SM), recently designed and built with large dynamic range for both component level and board level warpage measurement for the reliability study of electronic packaging materials and structures, is presented and demonstrated.

This paper investigates the use of an adaptive proportional-integral-derivative (APID) controller to reduce a combustion engine crankshaft speed pulsation. Both computer simulations and engine test rig experiments are used to validate the proposed control scheme. The starter/alternator (S/A) is used as the actuator for engine speed control. The S/A is an induction machine. It produces a supplemental torque source to cancel out the fast engine torque variation. This machine is placed on the engine crankshaft. The impact of the slowly varying changes in engine operating conditions is accounted for by adjusting the APID controller parameters on-line. The APID control scheme tunes the PID controller parameters by using the theory of adaptive interaction. The tuning algorithm determines a set of PID parameters by minimizing an error function. The error function is a weighted combination of the plant states and the required control effort.

The Hotelling multivariate control chart and the sample generalized variance |S| are used to monitor the mean and dispersion of vehicle build vision data including the pallet information to identify the non-conforming pallets that are used in body shops of FCA US LLC assembly plants. An iterative procedure and the Gaussian mixture model (GMM) are used to rank the non-conforming or bad pallets in the order of severity. The Hotelling multivariate T2 test statistic along with Mason-Tracy-Young (MYT) signal decomposition method is used to identify the features that are affected by the bad pallets. These algorithms were implemented in the Advanced Pallet Analysis module of the FCA US software Body Shop Analysis Toolbox (BSAT). The identified bad pallets are visualized in a scatter plot with a different color for each of the top bad pallets. The run chart of an affected feature confirms the bad pallet by highlighting data points from the bad pallet.

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material.

In this paper, we propose a secure protocol for inter-vehicle communication (IVC) networks without the use of centralized roadside infrastructure. Future vehicles may use wireless IVC networks to exchange safety-critical information among each other. IVC networks do not have a centralized control, and instead rely on vehicles to coordinate with each other to exchange information. Because of the open medium, security is a concern in IVC networks. Vehicles need a mechanism to authenticate the safety-critical information that will be exchanged in IVC networks. A trusted third party Certificate Authority (CA) can provide such a mechanism through public-key certificates. However, the disadvantage of using public-key certificates is that drivers can identify each other. The certificate will allow drivers to trace each other's movements and will raise a privacy concern.

The present work is concerned with the objective of developing a process for practical multi-disciplinary design optimization (MDO). The main goal adopted here is to minimize the weight of a vehicle body structure meeting NVH (Noise, Vibration and Harshness), durability, and crash safety targets. Initially, for simplicity a square tube is taken for the study. The design variables considered in the study are width, thickness and yield strength of the tube. Using the Response Surface Method (RSM) and the Design Of Experiments (DOE) technique, second order polynomial response surfaces are generated for prediction of the structural performance parameters such as lowest modal frequency, fatigue life, and peak deceleration value. The optimum solution is then obtained by using traditional gradient-based search algorithm functionality “fmincon” in commercial Matlab package.

This paper details the Wayne State University development of the Hybrid Supervisory Controller strategies for the Year 3 of the EcoCAR 3 competition. Included in this paper are the processes for developing the strategies for the supervisory control system, which includes the torque distribution among the powertrain components, and the diagnostic strategies adopted to guarantee the safety critical functionalities of the vehicle. The EcoCAR 3 competition challenges sixteen North American universities to re-engineer the 2016 Chevrolet Camaro to reduce its environmental impact without compromising its performance and consumer acceptability. During the Year 3 of the competition the team has refined the control strategies designed in the previous years, to enable the powertrain full functionalities and achieve better energy consumption over pre-determined drive cycles.

A review of the existing mathematical models of a car occupant in a rear-end crash reveals that existing models inadequately describe the kinematics of the occupant and cannot demonstrate the injury mechanisms involved. Most models concentrate on head and neck motion and have neglected to study the interaction of the occupant with the seat back, seat cushion, and restraint systems. Major deficiencies are the inability to simulate the torso sliding up the seat back and the absence of the thoracic and lumbar spine as deformable, load transmitting members. The paper shows the results of a 78 degree-of-freedom model of the spine, head, and pelvis which has already been validated in +Gz and -Gx acceleration directions. It considers automotive-type restraint systems, seat back, and seat cushions, and the torso is free to slide up the seat back.

The performance of the Common Rail diesel injection system (CRS) is investigated experimentally in a single cylinder engine and a test rig to determine the cycle-to-cycle variation in the injection pressure and its effects on the needle opening and rate of fuel delivery. The engine used is a single cylinder, simulated-turbocharged diesel engine. Data for the different injection and performance parameters are collected under steady state conditions for 35 consecutive cycles. Furthermore, a mathematical model has been developed to calculate the instantaneous fuel delivery rate at various injection pressures. The experimental results supported with the model computations indicated the presence of cycle-to-cycle variations in the fuel injection pressure and needle lift. The variations in the peak-cylinder gas pressure, rate of heat release, cylinder gas temperature and IMEP are correlated with the variation in the injection rate.

Inverse kinematic solutions of six degree of freedom (DOF) robot manipulation is a challenging task due to complex kinematic structure and application conditions which affects and depend on the robot’s tool frame position, orientation and different possible configurations. The robot trajectory represents a series of connected points in three dimensional space. Each point is defined with its position and orientation related to the robot’s base frames or users teach pendant. The robot will move from point to point using the desired motion type (linear, arc, or joint). This motion requires inverse kinematic solution. This paper presents a detailed inverse kinematic solution for Fanuc 6R (Rotational) robot family using a geometrical method. Each joint angular position will be geometrically analyzed and all possible solutions will be included in the decision equations. The solution will be developed in a parametric manner to cover the complete Fanuc six DOF family.

Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate Ground Truth (GT) to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system.

This study investigates using driver prediction to anticipate energy usage over a 160-meter look-ahead distance for a series, plug-in, hybrid-electric vehicle to improve conventional thermostatic powertrain control. Driver prediction algorithms utilize a hidden Markov model to predict route and a regression tree to predict speed over the route. Anticipated energy consumption is calculated by integrating force vectors over the look-ahead distance using the predicted incline slope and vehicle speed. Thermostatic powertrain control is improved by supplementing energy produced by the series generator with regenerative braking during events where anticipated energy consumption is negative, typically associated with declines or decelerations.

Experiments were conducted on a single cylinder engine to measure the ionization current across the spark plug electrodes as a function of key operating parameters including air/fuel ratio. A unique ignition circuit was adapted to measure the ion current as early as 300 microseconds after the initiation of spark discharge. A strong relationship between air/fuel ratio and features of the measured ion current was observed. This relationship can be exploited via relatively simple algorithms in a wide range of electronic engine control strategies. Measurements of spark plug ion current for approximating air/fuel ratio may be especially useful for use with low cost mixture control in small engine applications. Cylinder-to-cylinder mixture balancing in conjunction with a global exhaust gas oxygen sensor is another promising application of spark plug ion current measurement.

An iterative learning control (ILC) algorithm has been developed for a test cell electro-hydraulic, fully flexible valve actuation system to track valve lift profile under steady-state and transient operation. A dynamic model of the plant was obtained from experimental data to design and verify the ILC algorithm. The ILC is implemented in a prototype controller. The learned control input for two different lift profiles can be used for engine transient tests. Simulation and bench test are conducted to verify the effectiveness and robustness of this approach. The simple structure of the ILC in implementation and low cost in computation are other crucial factors to recommend the ILC. It does not totally depend on the system model during the design procedure. Therefore, it has relatively higher robustness to perturbation and modeling errors than other control methods for repetitive tasks.

Normal motion of the lower limbs is discussed in this paper. The biomechanics of human gait has been studied experimentally using an instrumented walkway and analytically by means of mathematical models. Experimental methods for measuring ground reaction forces and limb kinematics are discussed. If limb kinematics are known, they can be used to compute the resultant joint forces and moments, using equations of motion which are algebraic in form. To obtain limb kinematics from the differential equations of motion, the problem is generally redundant, the degree of redundancy being equal to the number of unknown joint moments. The computation of muscle, ligament and bone contact forces from known resultant loads is also a redundant problem because there are more unknowns than there are available equations. For these there is no general consensus regarding the best objective function to be minimized.

Map matching determines which road a vehicle is on based on inaccurate measured locations, such as GPS points. Simple algorithms, such as nearest road matching, fail often. We introduce a new algorithm that finds a sequence of road segments which simultaneously match the measured locations and which are traversable in the time intervals associated with the measurements. The time constraint, implemented with a hidden Markov model, greatly reduces the errors made by nearest road matching. We trained and tested the new algorithm on data taken from a large pool of real drivers.