Vukica Jovanovic, Ph.D., began her academic career in 2001 when she graduated with her dipl.ing.-M.S. degree at University of Novi Sad, majoring in industrial engineering and focusing on mechatronics, robotics, and automation. She lectured various courses at departments of Industrial Engineering, Mechanical Engineering, and Mechatronics from 2001 until 2006. She was an active member of a European organizing committee of the student robotic contest Eurobot and chief of the Eurobot organizing committee of the Serbian student national competition in robotics. In the summer of 2002, she had an internship in aircraft manufacturing company Aernnova Aerospace, Spain, where she worked in assembly of aircraft wings. Jovanovic subsequently continued to work towards her doctorate at Purdue University, Department of Mechanical Engineering Technology in Aug. 2006, as a Graduate Research Assistant in Product Lifecycle Management Centre of Excellence Laboratory. As a graduate student, she was involved in the following projects: Boeing PLM Certificate Program, Society of Manufacturing Engineers Education Foundation Project: Product Lifecycle Management Curriculum Modules, National Science Foundation project: Midwest Coalition for Comprehensive Design Education, and Department of Laborfunded project: Development of Integrated Digital Manufacturing Curriculum. She was also lecturing six different courses in the areas of mechanical engineering technology and computer graphics technology. She published chapters in three books, three journal articles, and presented 31 conference papers. Her dissertation research focused on environmental compliance, product lifecycle management, and engineering design of mechatronic products. She is working at the Design Engineering Technology Department at Trine University, where she teaches courses related to engineering graphics and design.
Light Imaging Detection and Ranging (LiDAR) systems generate point cloud imagery by using laser light to measure distance to a surface and then combine numerous points to create a three-dimensional (3-D) image. Since early adaptations, LiDAR is now common in aerial and subterranean geographical surveying and autonomous vehicle operations. The transportation industry uses LiDAR to monitor roadway quality, which can allow hazardous roadway corrosion to be spotted and repaired before endangering drivers. However, a leading issue with LiDAR availability is the respectively high price point for effective systems, therefore preventing widespread usage. Previous work at fabrication of a low-cost LiDAR system generated high resolution 3-D imagery but was faulted by limited portability and a long run-time while also finding issues with gimbal translation and C++ programming. This effort improves the prior work by combining a touchscreen Graphical User Interface (GUI) with a rangefinder (Garmin LiDAR-Lite v3HP) powered by Raspberry Pi 4 Model B hardware. The rangefinder is housed in a 3-D printed gimbal mount that translates via two stepper motors and driver board. The system runs via a Python script that allows the user to select varying levels of resolution on the GUI prior to data collection onto a Secure Digital card or a file accessible through an internet connection. Like the previous work, data output is in Cartesian coordinates through a .xyz file format with a MATLAB script used to create a point cloud and two-dimensional image with a depth gradient. Overall, a more efficient, easier to use, and accurate LiDAR system was created that offers various resolution levels for under the cost of $500.
As society moves into the digital age, the expectation of instantaneous electricity at the flip of a switch is more prominent than ever. The traditional electric grid has become outdated and Smart Grids are being developed to deliver reliable and efficient energy to consumers. However, the costs involved with implementing their infrastructure often limits research to theoretical models. As a result, an undergraduate capstone design team constructed a small-scale 12 VDC version to be used in conjunction with classroom and research activities. In this model Smart Grid, two houses act as residential consumers, an industrial building serves as a high-load demand device, and a lead-acid battery connected to a 120 VAC wall outlet simulates fossil fuel power plants. A smaller lead-acid battery provides a microgrid source while a photovoltaic solar panel adds renewable energy into the mix and can charge either lead-acid battery. All components are connected to a National Instruments CompactRIO system while being controlled and monitored via a LabVIEW software program. The resulting Smart Grid can run independently based on constraints related to energy demand, cost, efficiency, and environmental impact. Results are shown demonstrating choices based on these constraints, including a corresponding weighting according to controller objectives.
Climate change concerns are driving incentives to increase the fuel economy of passenger vehicles. Consequently, this has resulted in a growing prevalence of electrified vehicles (EVs) consisting of hybrid, plug-in hybrid, and fully electric vehicles. Unfortunately, EVs are often removed from the road when 70 to 80% of the original energy capacity remains in their battery pack. In order to maintain or increase the value of EV battery packs in an end-of-vehicle life scenario, there are three potential solutions: remanufacturing for re-use, recycling, or repurposing. However, the complexity of handling dissimilar battery chemistries makes both remanufacturing and recycling a significant challenge. Hence, repurposing may prove to be a more viable short-term goal of the industry. In order to explore this potential outcome, a team of undergraduate students studied the continuous cycling effects of used and refurbished Toyota® Prius nickel metal hydride battery packs. A Raspberry Pi 2 Model B microcomputer recorded relevant data, including battery pack voltage, energy input, and energy output. In combination, a Laboratory Virtual Instrument Engineering Workbench (LabVIEW™) control system used this logged information to regulate charging and discharging of the battery pack. In addition, to enhance the environmental sustainability of the project, this control system acquired solar information from a nearby weather station, subsequently ensuring that the battery pack only recharged during times of peak solar radiation. Analysis of the pack’s energy balance helped to characterize the cycle life of the pack and its potential in repurposing. Others can emulate the methodology employed as a way to instruct students about the potential left in used vehicular battery packs and their possible integration with the electrical grid.
Light Imaging Detection and Ranging (LIDAR) cameras and Light Detecting and Ranging (LiDAR) rangefinders were initially implemented in the 1960s as a higher-resolution and increased capability alternative to radar. Since then, LIDAR and LiDAR (hereto called lidar) have expanded into applications in aerial geographical surveying and collision-detection systems for autonomous vehicles. Current commercial systems are relatively expensive and potentially oversized for noncommercial applications. Consequently, this deters their use on consumer products like bicycles, where lidar systems can enable safety advancements that are necessary to counter the rising numbers of hazards affecting riders. In addition, widespread usage of inexpensive lidar systems can facilitate a more complete picture of our transportation infrastructure by delivering information (e.g., pavement quality) suited for U.S. Department of Transportation Highway Performance Monitoring System (HPMS) reports. This will aid in the creation of a safer infrastructure by highlighting critical areas in need of improvement and repair. As a result, this effort outlines the development of a compact and cost-effective lidar system. The constructed system includes the ability to generate a static image by collecting several hundred thousand distance signals measured by a lidar rangefinder. Since the rangefinder has no self-contained rotation or translation systems, an Arduino Mega 2560 v3 microcontroller operates a pair of stepper motors that adjusts its azimuthal angle and pitch. Coalescing these signals into an ASCII text file for viewing in MATLAB results in a reasonably accurate picture of the surroundings. While the current system takes 1–2 hours to complete a full sweep, it has the potential to provide sufficient accuracy for HPMS reports at a moderate expenditure: the entire system costs less than $300. Finally, upgrading to a more powerful microprocessor, implementing slip rings for enhanced electrical connectivity, and refining the code by including interpolation between points will enable faster point cloud generation while still maintaining an inexpensive device.
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