USMA Digital Commons (United States Military Academy, West Point)
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Radiometric and Geometric Calibration of an Inexpensive LED-Based Lidar Sensor
Get PDFRadiometric calibration of traditional lidar sensors that employ direct time of flight or phase-based ranging is well established. However, emerging inexpensive, lightweight, short-range lidar sensors that utilize non-traditional ranging methods report measurements that are not appropriate for existing radiometric calibration techniques. One such sensor, the TeraRanger Evo 60m by Terabee is a light emitting diode (instead of laser) lidar sensor with an automatically varying collection rate. This thesis investigates the performance of a new radiometric calibration model, one based on a neural network, applied to the Evo 60m. Application of the proposed radiometric calibration model resulted in performance similar to traditional lidar sensors, with mean differences in reflectance of no more than 5% and root mean square errors of no more than 6% for non-specular targets. The radiometric calibration model provides a generic approach that may be applicable to other low-cost lidar sensors and is a potential stepping stone toward development of a low-cost, multiple wavelength (multispectral) lidar sensor. The ranging performance of the Evo 60m was also evaluated in this work. Three of the four sensors evaluated fall below the manufacturer’s stated accuracy level of ±40 millimeters while one lies just above the threshold at ±43 millimeters
Dean\u27s Significant Activities Report 01-24-2020
Get PDFThe Dean’s Weekly Significant Activities Report lists all activities conducted within the Departments, Centers & Staff. The Report is provided to the Dean and Directorate personnel for situational awareness.https://digitalcommons.usmalibrary.org/sigact/1029/thumbnail.jp
Methodology for Choosing a Contractor for the Apollo Spacecraft Command and Service Module
No full textThe decision of selecting a contractor to design and build the Apollo spacecraft was a critical decision that would seemingly have a direct effect on the ultimate success or failure of the manned lunar landing program. This paper attempts to piece together the complete and official story of NASA’s selection of North American Aviation, Inc. (NAA) for the Apollo Command and Service Module (CSM) contract. By analyzing Webb, Dryden, and Seamans’ direct remarks it is possible to gain insight into one of the most crucial management, engineering, and historical decisions of the 20th century. The paper begins by covering the background on the methods used to source contracts as they evolved from the NACA into a relatively-new NASA of the era. A detailed breakdown from primary sources investigates the methods behind the NASA source evaluation board (SEB), including asking: (1) what weight was a company’s ability to do zero-defect, high reliability manufacturing; (2) what weight was the ability to maintain cost and schedule; (3) was flight simulation a critical factor; and (4) what other external factors were weighed subjectively? Upon reviewing the process in place, the paper analyzes the decision reasoning of the SEB results by each bidder and their decision to choose the Martin Company over the other major bidders. The paper then covers the NASA administrators’ reasoning to overrule the SEB by ultimately choosing North American Aviation, Inc. over the Martin Company. Next, the paper discusses North American in the well-established debate on the aftermath of the Apollo 1 fire in 1967. The paper concludes with lessons-learned by examining the immediate lessons of the decision to choose North American Aviation over the Martin Company, such as the low weight that actual design played in their final selection and the high weight that experience played. Additionally, the paper discusses the significant penalties that companies faced by forming joint ventures with industry partners regarding business organization and the inherently high costs that came with it, that ultimately played a large role in the final selection. Finally, the paper concludes with a cohesive discussion of these lessons and potential forward work
Tough Teams and Optimistic Individuals: The Intersecting Roles of Group and Individual Attributes in Helping to Predict Physical Performance
No full textThis study tested the effects of individual and group-level characteristics on performance during a mandatory and challenging physical education course at the United States Military Academy (USMA). We focused on attributes related to mental toughness, and examined both self-report and utilized an other-rating scale that measures mental toughness-related characteristics and is important to USMA generally. We examined course scores for 5,581 first-year students over five academic years, accounted for background physical fitness, and determined how mental toughness attributes at the group and individual-level contributed to overall course score and scores on constituent events (e.g. obstacle course, rope climbing). Self-reported optimism, self-reported resilience, and mental toughness items from a peer rating scale, but not self-reported grit, significantly improved course performance. The average score across class section on optimism or the peer rating scale also positively covaried with course score, over and above the individual-level impact of that attribute. Analyses of individual events demonstrated that “group-level character” was important for some events, whereas individual attributes were most important for others. Findings suggested an emergent group character capable of influencing individual physical performance scores. Being a member of a tough group may have comparable effects to individual mental toughness
Virtual Reality for Immersive Human Machine Teaming with Vehicles
No full textWe present developments in constructing a 3D environment and integrating a virtual reality headset in our Project Aquaticus platform. We designed Project Aquaticus to examine the interactions between human-robot teammate trust, cognitive load, and perceived robot intelligence levels while they compete in games of capture the flag on the water. Further, this platform will allows us to study human learning of tactical judgment under a variety of robot capabilities. To enable human-machine teaming (HMT), we created a testbed where humans operate motorized kayaks while the robots are autonomous catamaran-style surface vehicles. MOOS-IvP provides autonomy for the robots. After receiving an order from a human, the autonomous teammates can perform tasks conducive to capturing the flag, such as defending or attacking a flag. In the Project Aquaticus simulation, the humans control their virtual vehicle with a joystick and communicate with their robots via radio. Our current simulation is not engaging or realistic for participants because it presents a top-down, omniscient view of the field. This fully observable representation of the world is well suited for managing operations from the shore and teaching new players game mechanics and strategies; however, it does not accurately reflect the limited and almost chaotic view of the world a participant experiences while in their motorized kayak on the water. We present creating a 3D visualization through Unity that users experience through a virtual reality headset. Such a system allows us to perform experiments without the need for a significant investment in on-water experiment resources while also permitting us to gather data year-round through the cold winter months
The Five-Minute Adsorption Demonstration
No full textAdsorption is one of the most common physicochemical treatment processes in environmental engineering. Faculty typically teach this process by explaining figures and equations in texts, which can limit learning. The five minute classroom demonstration presented here replicates the adsorption experiment and data analysis, which may engage students and enhance learning without imposing substantial demands on student time. Students observe removal of Crystal violet dye or food coloring by activated carbon in real time in a column demonstration. Simultaneously, data from an adsorption experiment is collected in an accelerated video format and an animated PowerPoint presentation illustrates how experimental data is used to quantify Isotherm Model parameters. Results from the Crystal violet adsorption experiment and isotherm model parameters are presented along with an in-class example problem
A Gesture-Controlled Rehabilitation Robot to Improve Engagement and Quantify Movement Performance
No full textRehabilitation requires repetitive and coordinated movements for effective treatment, which are contingent on patient compliance and motivation. However, the monotony, intensity, and expense of most therapy routines do not promote engagement. Gesture-controlled rehabilitation has the potential to quantify performance and provide engaging, cost-effective treatment, leading to better compliance and mobility. We present the design and testing of a gesture-controlled rehabilitation robot (GC-Rebot) to assess its potential for monitoring user performance and providing entertainment while conducting physical therapy. Healthy participants (n = 11) completed a maze with GC-Rebot for six trials. User performance was evaluated through quantitative metrics of movement quality and quantity, and participants rated the system usability with a validated survey. For participants with self-reported video-game experience (n = 10), wrist active range of motion across trials (mean ± standard deviation) was 41.6 ± 13° and 76.8 ± 16° for pitch and roll, respectively. In the course of conducting a single trial with a time duration of 68.3 ± 19 s, these participants performed 27 ± 8 full wrist motion repetitions (i.e., flexion/extension), with a dose-rate of 24.2 ± 5 reps/min. These participants also rated system usability as excellent (score: 86.3 ± 12). Gesture-controlled therapy using the GC-Rebot demonstrated the potential to be an evidence-based rehabilitation tool based on excellent user ratings and the ability to monitor at-home compliance and performance
Use of X-Ray Fluorescence to Expedite Sampling to Evaluate and Visualize Soil Lead Concentrations at West Point, NY
No full textThe concentration of heavy metals, specifically lead, in soil may create unsafe environmental conditions. Unsafe conditions may occur based upon previous exposure to lead, such as particulate pollution from leaded gasoline. Accumulation of lead in the soil is especially concerning due to the detrimental physiological effects soil lead has on populations within residential neighborhoods. This study investigates the efficacy of an X-ray fluorescence (XRF) sensor compared to use of an inductively coupled plasma (ICP) laboratory instrument to measure soil lead concentration through a comparison of 87 soils samples. Findings note a strong correlation between both measurement methods. Additionally, 206 samples were evaluated to visualize soil lead concentrations throughout the residential West Point area. The highest soil lead concentrations are along the former route 9W, at locations associated with buildings that pre-date 1940
Bridges At Panmunjom
No full textFrom 2018 through 2020, United Nations Command Soldiers in Panmunjom had unprecedented access to North Korean Soldiers as the troops from the US, South Korea, and North Korea worked together to implement the Comprehensive Military Agreement while upholding the Armistice Agreement. This is the most in-depth look to date of those interactions, the challenges that UNC may still face, and the hope and opportunity that springs from talking to your enemy
The Spatially Conscious Machine Learning Model
No full textSuccessfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system comprised of intrinsic and extrinsic characteristics, perceived value, and market speculation. Using New York City real estate as our subject, we combine modern techniques of data science and machine learning with traditional spatial analysis to create robust real estate prediction models for both classification and regression tasks. We compare several cutting edge machine learning algorithms across spatial, semispatial, and nonspatial feature engineering techniques, and we empirically show that spatially conscious machine learning models outperform nonspatial models when married with advanced prediction techniques such as Random Forests, generalized linear models, gradient boosting machines, and artificial neural networks