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Metabolic Cost Adaptations During Training with a Soft Exosuit Assisting the Hip Joint
No full textDifferent adaptation rates have been reported in studies involving ankle exoskeletons designed to reduce the metabolic cost of their wearers. This work aimed to investigate energetic adaptations occurring over multiple training sessions, while walking with a soft exosuit assisting the hip joint. The participants attended five training sessions within 20 days. They walked carrying a load of 20.4 kg for 20 minutes with the exosuit powered and five minutes with the exosuit unpowered. Percentage change in net metabolic cost between the powered and unpowered conditions improved across sessions from -6.2 ± 3.9% (session one) to -10.3 ± 4.7% (session five), indicating a significant effect associated with training. The percentage change at session three (-10.5 ± 4.5%) was similar to the percentage change at session five, indicating that two 20-minute sessions may be sufficient for users to fully adapt and maximize the metabolic benefit provided by the exoskeleton. Retention was also tested measuring the metabolic reduction five months after the last training session. The percent change in metabolic cost during this session (-10.1 ± 3.2%) was similar to the last training session, indicating that the adaptations resulting in reduced metabolic cost are preserved. These outcomes are relevant when evaluating exoskeletons\u27 performance on naïve users, with a specific focus on hip extension assistance
Changes in Spatio-Temporal Gait Measures Throughout a Load-Bearing Military March
No full textThis research examined the changes in gait throughout loaded military march. Data on pace, step length, stance time, and cadence were collected in the field during a dismounted military movement. The results showed that all gait metrics tended to decline with fatigue until approximately 65% completion, when they improved toward the starting values. Pace demonstrated the most marked changes with time, which suggests that it may be the best measure of fatigue status
Integrating Data Science into a General Education Information Technology Course: An Approach to Developing Data Savvy Undergraduates
Get PDFThe National Academies recommend academic institutions foster a basic understanding of data science in all undergraduates. However, data science education is not currently a graduation requirement at most colleges and universities. As a result, many graduates lack even basic knowledge of data science. To address the shortfall, academic institutions should incorporate introductory data science into general education courses. A general education IT course provides a unique opportunity to integrate data science education. Modules covering databases, spreadsheets, and presentation software, already present in many survey IT courses, teach concepts and skills needed for data science. As a result, a survey IT course can provide comprehensive introductory data science education by adding a data science module focused on modeling and evaluation, two key steps in the data science process. The module should use data science software for application, avoiding the complexities of programming and advanced math, while enabling an emphasis on conceptual understanding. We implemented a course built around these ideas and found that the course helps develop data savvy in students
Integrate cyber maintenance into the US Army’s battle rhythm
No full textThe U.S. Army continually transforms over time, and the latest iteration is the transformation to support the concept of Multi-Domain Battle. This concept describes how the Army will operate, fight and campaign successfully across space, cyberspace, air, land and maritime domains. While cyberspace is defined as a domain, it is not separate and integrates across all other domains. Maintaining cyber physical systems is critical to succeed across all domains
Solving the Army’s Cyber Workforce Planning Problem using Stochastic Optimization and Discrete-Event Simulation Modeling
No full textThe U.S. Army Cyber Proponent (Office Chief of Cyber) within the Army\u27s Cyber Center of Excellence is responsible for making many personnel decisions impacting officers in the Army Cyber Branch (ACB). Some of the key leader decisions include how many new cyber officers to hire and/or branch transfer into the ACB each year, and how many cyber officers to promote to the next higher rank (grade) each year. We refer to this decision problem as the Army\u27s Cyber Workforce Planning Problem. We develop and employ a discrete-event simulation model to validate the number of accessions, branch transfers, and promotions (by grade and cyber specialty) prescribed by the optimal solution to a corresponding stochastic goal program that is formulated to meet the demands of the current force structure under conditions of uncertainty in officer retention. In doing so, this research provides effective decision-support to senior cyber leaders and force management technicians
Dean\u27s Significant Activities Report 10-25-2019
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/1016/thumbnail.jp
Optimal High Efficiency 3D Plasmonic Metasurface Elements Revealed by Lazy Ants
No full textRecent transmissive optical metamaterials that leverage a generalized form of Snell’s law to induce an anomalous refraction of light have garnered considerable interest in both optical and materials communities. However, most of these designs have primarily centered around parametric studies of planar canonical structures for their low profile and relative ease of manufacturing. In many of these cases, all-dielectric designs are preferred over metallodielectrics due to their low loss characteristics. Moreover, considering modern advances in nanofabrication techniques, these canonical structures represent only a small portion of the design space that is explorable. In this work, we exploit a generalized Multi-Objective Lazy Ant Colony Optimization (MOLACO) algorithm and a modified Pareto locus search mechanism to optimize arbitrary three-dimensional metamaterial unit cells in the optical regime based on the Membrane Projection Lithography technique. Our exploration has revealed unintuitive metallodielectric structures for phase-gradient metasurface applications in the midwave infrared (MWIR) regime that achieve transmission magnitudes comparable to the highest-performance all-dielectric designs found in the literature. As a proof-of-concept, a beam-steering metasurface is synthesized using these unintuitive unit cell geometries and is shown to achieve over 84% diffraction efficiency, which is among the highest performing metallodielectric metasurfaces in the MWIR reported to date
Cascaded Neural Networks for Identification and Posture-Based Threat Assessment of Armed People
Get PDFThis paper presents a near real-time, multi-stage classifier which identifies people and handguns in images, and then further assesses the threat-level that a person poses based on their body posture. The first stage consists of a convolutional neural network (CNN) that determines whether a person and a handgun are present in an image. If so, a second stage CNN is then used to estimate the pose of the person detected to have a handgun. Lastly, a feed-forward neural network (NN) makes the final threat assessment based on the joint positions of the person’s skeletal pose estimate from the previous stage. On average, this entire pipeline requires less than 1 second of processing time on a desktop computer. The model was trained using approximately 2,000 images and achieved a pistol and person detection rate of 22% and 55%, respectively. The final stage NN correctly identified the severity of the threat with 84% accuracy. The images used to train each stage of our multi-classifier model are available online. With an expanded dataset the accuracy of detecting people and pistols can likely be improved in the future
Towards a Heterogeneous Swarm for Object Classification
No full textObject classification capabilities and associated reactive swarm behaviors are implemented in a decentralized swarm of autonomous, heterogeneous unmanned aerial vehicles (UAVs). Each UAV possesses a separate capability to recognize and classify objects using the You Only Look Once (YOLO) neural network model. The UAVs communicate and share data through a swarm software architecture using an adhoc wireless network. When one UAV recognizes a particular object of interest, the entire swarm reacts with a pre-programmed behavior. Classification results of people and backpacks using our modified UAV detection platforms are provided, as well as a simulated demonstration of the reactive swarm behaviors with actual hardware and swarm software in the loop
Cyber Threat Report 03 January 2019
Get PDFArmy Cyber Institute Cyber Threat Report
Tech Trends: Stories and Highlights French data protection agency fines Uber €400k for a 2016 data breach FCC fines satellite startup Swarm Technologies $900k over unauthorized launch Amazon expands its fleet of Prime Air planes Dell Technologies, the largest privately held tech company, returns to public market