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This work presents a method for estimating human body orientation using a combination of convolutional neural network (CNN) and stereo camera in real time. The approach uses the CNN model to predict certain human body keypoints then transforms these points into a 3D space using the stereo vision system to estimate the body orientations. The CNN module is trained to estimate the shoulders, the neck and the nose positions, detecting of three points is required to confirm human detection and provided enough data to translate the points into 3D space.

Technological advancement in the automotive industry necessities a closer focus on the functional safety for higher automated driving levels. The automotive industry is transforming from conventional driving technology, where the driver or the human is a part of the control loop, to fully autonomous development and self-driving mode. The Society of Automotive Engineers (SAE) defines the level 4 of autonomy: “Automated driving feature will not require the driver to take over driving control.” Thus, more and more safety related electronic control units (ECUs) are deployed in the control module to support the vehicle. As a result, more complexity of system architecture, software, and hardware are interacting and interfacing in the control system, which increases the risk of both systematic and random hardware failures.

This research assesses the integration of different levels of electric vehicles (EVs) in the distribution system and observes its impacts on environmental emissions and power system operational costs. EVs can contribute to reducing the environmental emission from two different aspects. First, by replacing the traditional combustion engine cars with EVs for providing clean and environment friendly transportation and second, by integrating EVs in the distribution system through the V2G program, by providing power to the utility during peak hours and reducing the emission created by hydrocarbon dependent generators. The PG&E 69-bus distribution system (DS) is used to simulate the integration of EVs and to perform energy management to assess the operational costs and emissions. The uncertainty of driving patterns of EVs are considered in this research to get more accurate results.

This paper describes the scuffing tests performed to understand the effect of surface roughness and lubrication on scuffing behavior for austempered ductile iron (ADI) material. As the scuffing tendency is increased, metal-to-metal interaction between contacting surfaces is increased. Lubrication between sliding surfaces becomes the boundary or mixed lubrication condition. Oil film breakdown leads to scuffing failure with the critical load. Hence, the role of surface roughness and lubrication becomes prominent in scuffing study. There are some studies in which the influence of the surface roughness and lubrication on scuffing was evaluated. However, no comprehensive scuffing study has been found in the literature regarding the effect of surface roughness and lubrication on scuffing behavior of ADI material. The current research took into account the inferences of surface roughness and lubrication on scuffing for ADI.

Tribological performance of tungsten sulfide (WS2) nanoparticles, microparticles and mixtures of the two were investigated. Previous research showed that friction and wear reduction can be achieved with nanoparticles. Often these improvements were mutually exclusive, or achieved under special conditions (high temperature, high vacuum) or with hard-to-synthesize inorganic-fullerene WS2 nanoparticles. This study aimed at investigating the friction and wear reduction of WS2 of nanoparticles and microparticles that can be synthesized in bulk and/or purchased off the shelf. Mixtures of WS2 nanoparticles and microparticles were also tested to see if a combination of reduced friction and wear would be achieved. The effect of the mixing process on the morphology of the particles was also reported. The microparticles showed the largest reduction in coefficient of friction while the nanoparticles showed the largest wear scar area reduction.

In practice, the piston wrist pin is either fixed to the connecting rod or floats between the connecting rod and the piston. The tribological behavior of fixed wrist pins have been studied by several researchers, however there have been few studies done on the floating wrist pin. A new bench rig has been designed and constructed to investigate the tribological behavior between floating pins and pin bore bearings. The experiments were run using both fixed pins and floating pins under the same working conditions. It was found that for fixed pins there was severe damage on the pin bore in a very short time (5 minutes) and material transfer occurs between the wrist pin and pin bore; however, for the floating pin, even after a long testing time (60 minutes) there was minimal surface damage on either the pin bore or wrist pin.

An existing challenge in current state-of-the-art autonomous vehicles is the process of safely transferring control from autonomous driving mode to manual mode because the driver may be distracted with secondary tasks. Such distractions may impair a driver’s situational awareness of the driving environment which will lead to fatal outcomes during a handover. Current state-of-the-art vehicles notify a user of an imminent handover via auditory, visual, and physical alerts but are unable to improve a driver’s situational awareness before a handover is executed. The overall goal of our research team is to address the challenge of providing a driver with relevant information to regain situational awareness of the driving task. In this paper, we introduce a novel approach to estimating a driver’s visual focus of attention using a 2D RGB camera as input to a Multi-Input Convolutional Neural Network with shared weights. The system was validated in a realistic driving scenario.

Design of sheet metal drawing processes requires accurate information about the distribution of restraining forces, which is usually accomplished by a set of drawbeads positioned along the perimeter of the die cavity. This study is targeting bringing together the results of finite element analysis and experimental data in order to understand the most critical factors influencing the restraining force. The experimental study of the restraining force was performed using drawbead simulator tool installed into a tensile testing machine. Based upon the experimental results, it was observed that the restraining force of the given drawbead configuration is dependent upon the depth of bead penetration, friction between the drawbead surfaces as well as the clearance between the flanges of the drawbead simulator. This clearance is often adjusted during stamping operations to increase or decrease material inflow into the die cavity without any modification in the die.

Finite element analysis is a well-established methodology in structural dynamics. However, optimization and/or probabilistic studies can be prohibitively expensive because they require repeated FE analyses of large models. Various reanalysis methods have been proposed in order to calculate efficiently the dynamic response of a structure after a baseline design has been modified, without recalculating the new response. The parametric reduced-order modeling (PROM) and the combined approximation (CA) methods are two re-analysis methods, which can handle large model parameter changes in a relatively efficient manner. Although both methods are promising by themselves, they can not handle large FE models with large numbers of DOF (e.g. 100,000) with a large number of design parameters (e.g. 50), which are common in practice. In this paper, the advantages and disadvantages of the PROM and CA methods are first discussed in detail.

Inter-vehicle communication (IVC) is the next step in developing a coordinated vehicular transportation system. With today's concerns of security in Internet communications, it is critical that the security in IVC be considered. With its many life-depending implications it is crucial that an IVC system be tolerant of both natural and man-made interference. The goal of our research was to explore solutions to address the issues of security of IVC in spite of its vulnerability to Denial-of- Service (DoS) attacks, in which an attacker systematically or selectively jams the signals exchanged by the vehicles. We present proposed solutions to DoS attacks on IVC, as well as introduce additional concepts and ideas that help address this important problem.

Polymer Electrolyte Membrane (PEM) fuel cells have grown in research and development for many applications due to their high efficiency and humble operating condition requirements. Water management in the cathode region of the PEM fuel cell is an essential and sensitive phenomenon for cold environments and fuel cell’s performance. This paper investigates the behavior of water production by constructing a transparent-cathode PEM fuel cell. The effects of pressure, relative humidity, and cathode stoichiometric ratio on the production of water as a function of time were studied. Each test set is compared to a reference state. The images of water liquid accumulation inside the cathode bipolar plate channels are shown with the corresponding polarization curves.

Application of cervical spine range of motion data and related anthropometric measures of the head and neck include physical therapy, product design, and computational modeling. This study utilized the Cervical Range of Motion device (CROM) to define the normal range of motion of the cervical spine for subjects five (5) through ten (10) years of age. And, the data was collected and analyzed with respect to anatomical measures such as head circumference, face height, neck length, and neck circumference. This study correlates these static anthropometric measures to the kinematic measurement of head flexion, extension, lateral extension, and rotation.

Reliability and resiliency (R&R) definitions differ depending on the system under consideration. Generally, each engineering sector defines relevant R&R metrics pertinent to their system. While this can impede cross-disciplinary engineering projects as well as research, it is a necessary strategy to capture all the relevant system characteristics. This paper highlights the difficulties associated with defining performance of such systems while using smart microgrids as an example. Further, it develops metrics and definitions that are useful in assessing their performance, based on utility theory. A microgrid must not only anticipate load conditions but also tolerate partial failures and remain optimally operating. Many of these failures happen infrequently but unexpectedly and therefore are hard to plan for. We discuss real life failure scenarios and show how the proposed definitions and metrics are beneficial.

A multi-sensor Digital Image Correlation (DIC) system is employed to measure the deformation of metal specimens during tensile tests. The multi-sensor DIC system is capable of providing high quality contour and deformation data of a 3D object. Methodology and advantages of the multi-sensor DIC system is introduced. Tests have been done on steel and aluminum specimens to prove the performance of the system. With the help of the multi-sensor DIC system, we proposed our approaches to determine the forming limit based on shape change around the necking area instead of calculate the FLD based on the in-plane strains. With the employed system, all measurements are done post-deformation, no testing controlling mechanism, such as load force control or touching control, is required. The extracted data is analyzed and the result shows a possibility that we may be able to improve current technique for Forming Limit Diagram (FLD) measurement.

Proper valve angles and concentric valve seats are critical to performance of an engine. If the valve seat were not right, the valve is not going to seat properly resulting in reduced power output. Although the performance of CNC machines is accurate, unavoidable human errors at the part loading position have serious repercussions on engine performance. A solution algorithm presented in this paper employs the principles of inverse kinematics wherein a faulty compound-hole angle axis in space caused by the translational and rotational errors at the part loading position is identified with an imaginary true axis in space by enforcing identity through a modified machine axes.

Shearography has been proved to be highly effective for nondestructive testing (NDT), especially for NDT of composite materials used in the automotive and aerospace engineering. While its application in material testing and material research has already achieved more and more acceptance in research and industry, its applications are mainly limited to the inspection and testing of an object surface which can directly be observed by a shearographic camera. Its application is mainly limited to inspect and test an object surface which can directly be observed by a shearographic camera. It is impossible to inspect an internal surface of a container. If the reflected light of the surface, which has to be examined, can’t reach the shearographic camera there is still no inspection possible. This paper presents the development of a rigid shearographic endoscope. The development enabled shearographic inspection on both external and internal surfaces of objects.

Multicore microcontrollers are rapidly making their way into the automotive industry. We have adopted the Cilk approach (MIT 1994) to develop a pure ANSI C Fork-Join dynamic scheduling runtime middle-layer with a work-stealing scheduler targeted for automotive multicore embedded systems. This middle-layer could be running on top of any AUTOSAR compliant multicore RTOS. We recently have successfully integrated our runtime layer into parts of legacy Ford powertrain software at Ford Motor Company. We have used the 3-core AURIX multicore chip from Infineon and the multicore RTA-OS. For testing purposes, we have forked some parallelizable functions inside two periodic tasks in Ford legacy powertrain software to be dynamically scheduled and executed on the available cores. Our preliminary evaluation showed 1.3–1.4x speedups for these two forked tasks.

The number of EVs are increasing in power distribution systems every day. This research analyses different penetration levels of electric vehicles in power distribution systems to provide stable energy for smart devices and observes its impacts on operational costs and environmental emissions. The supply of EV power is determined based on smart device consumption by optimal energy management of EV batteries so that both the utilities and the car owner get benefits. Utilities can save energy by reducing system loss, while EV owners can earn money by selling it to utilities at their convenient time for smart device operations. The PG&E 69-bus distribution system is used for the simulation and case studies. Case studies in this research show how the power management of EV's batteries charging and discharging characteristics benefits both utilities and EV owners. The uncertainty of the driving pattern of EVs is also considered in the research to get more accurate results.

Engineering practice routinely involves decision making under uncertainty. Much of this decision making entails reconciling multiple pieces of information to form a suitable model of uncertainty. As more information is collected, one expectedly makes better and better decisions. However, conditional probability assessments made by human decision makers, as new information arrives does not always follow expected trends and instead exhibits inconsistencies. Understanding them is necessary for a better modeling of the cognitive processes taking place in their mind, whether it be the designer or the end-user. Doing so can result in better products and product features. Quantum probability has been used in the literature to explain many commonly observed deviations from the classical probability such as: question order effect, response replicability effect, Machina and Ellsberg paradoxes and the effect of positive and negative interference between events.

Digital Image Correlation (DIC) technology is a powerful tool in the field of experimental mechanics to obtain the full-field deformation/strain information of an object. It has been rapidly applied in industry in recent years. However, for the large-angle full-field strain measurement of small-sized cylindrical objects, it’s still a challenge to the DIC accurate measurement due to its small size and curved surface. In this paper, a measurement method based on the multi-camera DIC system is proposed to study the compressive performance of small-sized cylindrical materials. Three cameras form two stereo DIC measurement systems (1 and 2 cameras, and 2 and 3 cameras), each of which measures a part of the object. By calibrating three cameras at the same time, two stereos DIC coordinate systems can be unified to one coordinate system. Then match the two sets of DIC measurement data together to achieve large-angle measurement of the cylindrical surface.