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The ERDC Library supports the mission-related research needs of ERDC scientists and engineers at three physical locations with a centralized library catalog and web site. It also hosts an online digital repository of ERDC-authored reports.

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Publication Notices

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  • PUBLICATION NOTICE: Isarithmic mapping of radio-frequency noise in the urban environment

    Abstract: Radio-frequency (RF) background noise is a spatially-varying and critical parameter for predicting radio communication system and electromagnetic sensor performance in urban environments. Previous studies have measured urban RF noise at fixed, representative locations. The Cold Regions Research and Engineering Laboratory (CRREL) has developed a tunable system for conducting mobile RF noise measurements in the VHF and UHF and shown that urban RF noise characteristics vary significantly and repeatably at a scale of tens of meters (Haedrich & Breton, 2019). CRREL also found high-powered regions in Boston, MA that are persistent over time. However, since previous studies conducted stationary measurements or measurements along linear transects, little is known about the 2-dimensional topography of urban noise and the spatial distribution and characteristics of these high-powered regions. In this paper, we present the results of a dense, block-grid survey of downtown Boston, MA at 142 and 246.5 MHz with measurements taken every meter along each street. We present isarithmic maps of median noise power and describe the spatial distribution, shape and other characteristics of the high-powered regions. We compare the rate of noise power decay around high-powered regions to losses predicted by a power law model of path loss.
  • PUBLICATION NOTICE: Seasonal Effects on Vehicle Mobility: High-Latitude Case Study

    Abstract: Seasonality plays a key role in altering the terrain of many military operating environments. Since seasonality has such a large impact on the terrain, it needs to be properly accounted for in vehicle dynamics models. This work outlines a variety of static and dynamic seasonal terrain conditions and their impacts on vehicle mobility in an austere region of Europe. Overall the vehicles performed the best in the dry season condition. The thaw season condition had the most drastic impact on mobility with all but the heavy tracked vehicle being almost completely NOGO in the region. Overall, the heavy tracked vehicle had the best performance in all terrain conditions. These results highlight the importance of incorporating seasonal impacts on terrain into NRMM or any vehicle dynamics model. Future work will focus on collecting more data to improve the empirical relationships between vehicles and seasonal terrain conditions, thereby allowing for more accurate speed predictions.
  • PUBLICATION NOTICE: Insights: An Update of the USACE Data Strategy Initiative; November 2019 Edition

    Abstract: The Data25 strategy was advanced in FY19 by the U.S. Army Corps of Engineers’ Chief Information Officer (USACE CIO) by funding pilots to show the power of data analytics on real USACE operations. This report details pilots that were conducted in three of USACEs Business Lines; Dredging, Hydropower, and Military Construction. The purposes for each of these pilots are listed below. 1. Enterprise value: Demonstrates the power of data analytics and its ability to generate business value by improving decision-making across the organization. 2. Technology value: Helps the CIO understand how cloud technology could support the overall data strategy. 3. Business value: Provides examples of data analytics in action. This view helps the Business Lines, Divisions, and Districts understand what it takes to supplement decision-making with insights generated from data. The main purpose of the pilots was to provide a glimpse of what could be gained from data analytics. From the initial business questions, the pilot Business Lines are seeking to use data to improve decisions through the automation of business processes, more rapid decision cycles, and the layering of previously siloed data on their own to reveal new insights.
  • PUBLICATION NOTICE: Insights: An Update on the USACE Data Strategy Initiative; April 2019 Edition

    Abstract: The U.S. Army Corps of Engineering (USACE) Commanding General, LTG Todd T. Semonite, announced 10 initiatives on May 2018 to improve execution through informed decision-making, enabling lower costs, and world-class engineering results today and tomorrow. Two of the initiatives were geared to transition USACE to data-informed decision-making through the use of data analytics. The first of these two initiatives sought to implement a data strategy that included a doctrine and governance, through creation of a data management plan. The plan implemented tools to aggregate data across the organization and improve reporting. Dr. Cary Butler, USACE Chief Data Scientist, led this effort. The second initiative sought to establish a dedicated USACE Innovations Team to build and recruit a skilled team to act as finders and enablers of innovative, enterprise solutions that enabled USACE to become a digital business. The USACE Chief Information Officer oversaw both initiatives. Because goals and objectives overlapped in many areas and gathered momentum for these initiatives, leaders from both teams came together and decided that showing the business value of data analytics to improve decision making across the Corps should be a top priority. This document was used to report on the progress of the initiatives and give a better understanding of the need for data-informed decisions throughout USACE.
  • PUBLICATION NOTICE: New York/New Jersey Harbor Sedimentation Study: Numerical Modeling of Hydrodynamics and Sediment Transport

    Abstract: The New York/New Jersey Harbor (NYNJH) is a vital economic resource for both the local economy and the entire US economy due to the vast quantity of imports and exports handled by the numerous ports in this waterway. As with most ports, there is a significant, recurring expense associated with dredging the navigation channels to the authorized depths. In an effort to determine the impact of channel enlargements (“the project”) on dredging volumes, a numerical model study was performed. The advantage of a numerical model study is the ability to isolate individual system modifications and associated impacts in terms of dredging volumes. Five years (1985, 1995, 1996, 2011, and 2012) were simulated for both the with- and without-project conditions to determine the impact of the channel deepening on the dredging requirements for a wide range of meteorological conditions including storm events. The numerical model results were analyzed to provide insight into which locations will experience increased/decreased deposition and quantify the amount of increase/decrease for a given channel reach. The model results indicate a relatively minor increase in the total dredge volumes for the NYNJH with the increase being insignificant in comparison to the natural variability in dredge volumes across years.
  • PUBLICATION NOTICE: Spatial and Temporal Variance in the Thermal Response of Buried Objects

    ABSTRACT:  Probability of detection and false alarm rates for current military sensor systems used for detecting buried objects are often unacceptable. One approach to increasing sensor performance and detection reliability is to better understand which physical processes are dominant under certain environmental conditions. Incorporating this understanding into detection algorithms will improve detection performance. Our approach involved studying a small, 3.05 × 3.05 m, test plot at the Engineer Research and Development Center’s Cold Regions Research and Engineering Laboratory (ERDC-CRREL) in Hanover, New Hampshire. There we monitored a number of environmental variables (soil temperature moisture, and chemistry as well as air temperature and humidity, cloud cover, and incoming solar radiation) coupled with thermal infrared and electro-optical image collection. Data collection occurred over 4 months with measurements made at 15 minute intervals. Initial findings show that significant spatial and thermal temporal variability is caused by incoming solar radiation; meteorologically driven surface heat exchange; and subsurface-soil temperatures, density, moisture content, and surface roughness.
  • PUBLICATION NOTICE: Shallow Water Seakeeping Tests with Columbia Class Submarine for Integration into the Environmental Monitoring and Operator Guidance System

    Abstract: The Environmental Monitoring and Operation Guidance System (EMOGS) tool was developed in 1989 to provide a real-time risk analysis for underkeel clearance for the Ohio class submarine while in transit to the Naval Submarine Base at Kings Bay, Georgia. The program computes expected submarine response for input water level, depth, speed, wave, and other input conditions using shallow-water motion transfer functions generated by the strip theory tool, Large Amplitude Motion Program (LAMP). The integration of the new Columbia class submarine into EMOGS required that new transfer functions be developed using LAMP. The LAMP results are to be validated using measured motions from physical model laboratory testing. This report summarizes a laboratory study of the Columbia class submarine response in shallow-water waves. The study was conducted at the US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, and was done in direct support of the Naval Surface Warfare Center, Carderock Division. These seakeeping tests were performed in a shallow basin with a multi-directional wave generator, with measured still water vessel motions and measured vessel motion in regular and irregular waves of varying height, period, and direction.
  • PUBLICATION NOTICE: Evaluating Collection Parameters for Mobile Lidar Surveys in Vegetated Beach-Dune Settings

    Purpose: The goal of this Coastal and Hydraulics Engineering Technical Note (CHETN) is to compare collection parameters and gridding techniques for mobile lidar surveys of beach-dune systems in the northern Outer Banks, NC.
  • PUBLICATION NOTICE: Three Rivers, Southeast Arkansas Navigation Study: Ship Simulation Report

    Abstract: The McClellan-Kerr Arkansas River System (MKARNS) is a major inland waterway that begins at the Port of Catoosa in Tulsa, OK, and travels to the confluence of the White and Mississippi Rivers. Over the years, many structures have been built to help control overland flow between the White, Arkansas, and Mississippi Rivers. These structures have required a significant amount of rehabilitation, which has resulted in high maintenance costs. The US Army Corps of Engineers and the Arkansas Waterways Commission conducted the Three Rivers Southeast Arkansas Feasibility Study (also known as the Three Rivers Study). The Three Rivers Study focused on providing long-term dependable navigation in the MKARNS. From this study, a proposal was developed that included a 1,000 ft reopening of the Historic Cutoff and a reinforcement of several areas near the White River. In 2019, the US Army Engineer Research and Development Center Ship/Tow Simulator was used to perform a navigation study to ensure the proposed modifications did not negatively impact navigation on the White River section of the MKARNS. Assessment of the proposed modifications was accomplished through analysis of ship simulations completed by experienced pilots, discussions, track plots, run sheets, and final pilot surveys.
  • PUBLICATION NOTICE: Understanding State-of-the-Art Material Classification through Deep Visualization

    Abstract: Neural networks (NNs) excel at solving several complex, non-linear problems in the area of supervised learning. A prominent application of these networks is image classification. Numerous improvements over the last few decades have improved the capability of these image classifiers. However, neural networks are still a black-box for solving image classification and other sophisticated tasks. A number of experiments conducted look into exactly how neural networks solve these complex problems. This paper dismantles the neural network solution, incorporating convolution layers, of a specific material classifier. Several techniques are utilized to investigate the solution to this problem. These techniques look at specifically which pixels contribute to the decision made by the NN as well as a look at each neuron’s contribution to the decision. The purpose of this investigation is to understand the decision-making process of the NN and to use this knowledge to suggest improvements to the material classification algorithm.

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