USMA Digital Commons (United States Military Academy, West Point)
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Incorporation of Blade Twist and Non-Uniform Inflow Effects In Undergraduate Helicopter Aeronautics Whirl Stand Laboratory
No full textFlight and aerodynamics laboratory experiences have supported the aeronautical engineering courses in the United States Military Academy’s mechanical engineering program for over 50 years. Whirl stands are the rotary-wing equivalent of wind tunnels; they are used to generate experimental data on small- or full-scale rotor systems. The helicopter whirl stand laboratory is a cornerstone event in the program’s Helicopter Aeronautics course, used to reinforce students’ understanding for predicting and calculating hover performance data. The experimental apparatus includes a remote control (RC) helicopter mounted to a static test stand, instrumented with load cells to measure lift and torque. The helicopter is capable of varied revolutions per minute (RPM) and collective blade pitch. Control of the apparatus and measurement readings occur from behind a protective wall with an observation window. The objective of the laboratory is to compare the results of predictive analyses, conducted using Blade Element Theory (BET) and Blade Element Momentum Theory (BEMT), to experimental data. Students calculate the coefficient of thrust based on collective pitch angles and atmospheric conditions using an iterative approach in numerical analysis software. A recent effort appreciably improved the lab by adding two experimental twisted-blade cases in addition to the original untwisted blades. The ability to change between the original and updated (twisted) blades offers insight into the advantages and disadvantages of each in hover. The upgraded blades were designed internally by students to match the original rotor diameter, outsourced for precision manufacturing, and tested for incorporation into the laboratory. Overall, the upgraded laboratory offers a relevant, comprehensive application to deepen students’ conceptual understanding of rotorcraft aerodynamics, laboratory procedures, and modeling principles taught in the course
Towards A Framework for Preprocessing Analysis of Adversarial Windows Malware
No full textMachine learning for malware detection and classification has shown promising results. However, motivated adversaries can thwart such classifiers by perturbing the classifier’s input features. Feature perturbation can be realized by transforming the malware, inducing an adversarial drift in the problem space. Realizable adversarial malware is constrained by available software transformations that preserve the malware’s original semantics yet perturb its features enough to cross a classifier’s decision boundary. Further, transformations should be plausible and robust to preprocessing. If a defender can identify and filter the adversarial noise, then the utility of the adversarial approach is decreased. In this paper, we examine common adversarial techniques against a set of constraints that expose each technique’s realizability. Our observations indicate that most adversarial perturbations can be reduced through forensic preprocessing of the malware, highlighting the advantage of forensic analysis prior to classification
The Devil Is in the Data: Publicly Available Information and the Risks To Force Protection and Readiness
No full textThe commercialization of personal data and the practices of commercial data brokers have enabled companies and our adversaries to accumulate vast amounts of knowledge on US persons, included service members. We argue that failing to fully understand how these data systems impact military operational effectiveness will set the United States at a strategic disadvantage. While the wholesale removal of risk is not possible, a careful examination of risk can shape policy, open pathways for technical mitigation strategies, and define best data hygiene practices. We propose that commanders conceptualize these risks as stemming from the information dimension and manifesting in the cognitive and physical dimensions to assess the risks they face in the modern operating environment more accurately
Russia’s Imperial Farce
Get PDFRussia is going to war with Ukraine to defend the Motherland from gay parades. Russia is defending against an onslaught of transgender NATO satanist mercenaries of mixed ethnicity. Russian state television discusses whether it’s best to bomb Berlin first or more sensible to start with London and then move on to eradicate the rest of Western Europe, thus removing the sources of support for the queer-Nazi government in Kyiv. I must be honest and say it is hard to write about the Russian geopolitical situation when the Russian weltanschauung (worldview) is, to put it mildly, clinically interesting. For those of us who were active during the Cold War and spent time studying the-then Soviets, the war in Ukraine has gone from the predictable — e.g., the old-school Soviet doctrine of an initial air assault on the Antonov airfield in a bid to decapitate the Ukrainian government — to a humiliating exposure of Russian military shortcomings and failures, and thereafter a profoundly odd misalignment to reality. I don’t say Russia’s ability to kill and harm other people should be underestimated, but that now appears to be the only skill left in the toolbox while the narrative supporting the war has disintegrated
Mapping State-Sponsored Information Operations with Multi-View Modularity Clustering
Get PDFThis paper presents a new computational framework for mapping state-sponsored information operations into distinct strategic units. Utilizing a novel method called multi-view modularity clustering (MVMC), we identify groups of accounts engaged in distinct narrative and network information maneuvers. We then present an analytical pipeline to holistically determine their coordinated and complementary roles within the broader digital campaign. Applying our proposed methodology to disclosed Chinese state-sponsored accounts on Twitter, we discover an overarching operation to protect and manage Chinese international reputation by attacking individual adversaries (Guo Wengui) and collective threats (Hong Kong protestors), while also projecting national strength during global crisis (the COVID-19 pandemic). Psycholinguistic tools quantify variation in narrative maneuvers employing hateful and negative language against critics in contrast to communitarian and positive language to bolster national solidarity. Network analytics further distinguish how groups of accounts used network maneuvers to act as balanced operators, organized masqueraders, and egalitarian echo-chambers. Collectively, this work breaks methodological ground on the interdisciplinary application of unsupervised and multi-view methods for characterizing not just digital campaigns in particular, but also coordinated activity more generally. Moreover, our findings contribute substantive empirical insights around how state-sponsored information operations combine narrative and network maneuvers to achieve interlocking strategic objectives. This bears both theoretical and policy implications for platform regulation and understanding the evolving geopolitical significance of cyberspace
A Ranked Solution for Social Media Fact Checking Using Epidemic Spread Modeling
No full textWithin the past decade, social media has become a primary platform for consumption of information and current events. Unlike with traditional news sources, however, social media posts do not have to go through a rigorous validation process prior to publication. The 2019 Mueller Report illustrates how malicious actors have taken advantage of these lax requirements to sway public opinion on topics from the #blacklivesmatter movement to the 2016 U.S. Presidential election. Currently, social media companies rely primarily on communal-policing of misinformation; it is unlikely that this will happen with regularity. To counteract this, other literature on the topic is focused on using deep learning models to separate accurate from misleading content; however, the rapidly evolving nature of misinformation means that they will have to be retrained and redeployed on an iterative and time-consuming basis. This work, therefore, proposes a novel approach to the problem: treating misinformation as a virus. Specifically, we propose a ranking system that third-party fact checkers can utilize to prioritize posts for checking. This algorithm is then tested against multiple data sets with strong positive results, decreasing viral spread in a matter of minutes
Probabilistic Estimation of Posture Metrics using Novel Loadsols
No full textThis paper presents an original technique for estimating human posture metrics using Novel Loadsols®. Under the proposed technique, center of pressure (COP) metrics are derived by combining physics- and data-driven estimates to achieve reasonably high accuracy at relatively low cost. To develop a training set upon which the probabilistic data model was constructed, 79 trials were conducted in which participants stood comfortably still for 30 seconds at a time simultaneously on a force plate and a pair of Loadsols, where the force plate is considered to be the gold-standard of COP measurement. These data were then used to generate Gaussian mixture models (GMMs) of pairwise combinations of force plate and Loadsol metrics. The GMMs can then be conditioned on Loadsol measurements and fused using Bayesian inference. When the training set was re-processed by converting 12 Loadsol metrics into estimated force plate metrics, it was found that the converted metrics matched ground-truth more accurately on average than raw Loadsol metrics. Furthermore, there was improvement in the r2 values of the regression lines after conversion for 75% of the metrics. Given some experiment and algorithm refinement, the proposed probabilistic approach has potential to offer the accuracy of force plate COP estimation at a fraction of the cost
Euler-Cauchy Undetermined Coefficients Exception
Get PDFExceptions to the requirements that the nonhomogeneous terms have a finite set of derivatives, and that the coefficients be constant when utilizing the Method of Undetermined Coefficients, are presented. A simple method of solution is developed for Euler-Cauchy Differential Equations with nonhomogeneous terms that are exclusively powers of the independent variable. The pairing of the power of the variable coefficient with the order of the derivative in the homogeneous terms of the differential equation is shown to isolate each term of the solution
Applying Data Analytics as an Alternative to Subjective Rankings of Players in Fantasy Basketball
Get PDFThis paper demonstrates the ranking of players for fantasy basketball using one of the platforms of Multi Criteria Decision Making (MCDM), the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. Specially, it compares results of TOPSIS generated fantasy rankings from the 2016-2017 NBA Season against industry fantasy experts’ 2017-2018 NBA pre-season rankings. Fantasy experts combine various techniques to create their rankings. Frequently blending quantitative and qualitative factors in order to project bottom-up rankings, they incongruently mix subjective and objective criterion. Conversely, TOPSIS is a mathematical way of doing literally what its name describes, ranking by a predetermined preference. The best ranking should be closest to the positive ideal solution and be the furthest away from the least ideal, or negative, solution. This model allows a user to subjectively or objectively select a weighting criteria, determined by scarcity of statistics, and find the solutions that are most positively aligned to the ideal solution, or how the ideal player should perform. As a result, TOPSIS ranks players based on “super-player” attributes and selects them to identify the players with qualities that will most help and least hurt their fantasy basketball team. Notably, the comparison reveals TOPSIS as a better forecast of individual players’ statistics and rankings for the 2017-2018 NBA season and a superior option beyond the Top-100 players. The analysis and results demonstrate how the TOPSIS method can be incorporated in different fantasy basketball leagues and settings