Coupled Environment and Munition Burial and Movement (UnMUMB) Model for Assessing Characteristics of Munitions Underwater and Their Environment

Prepared for: The DoD Strategic Environmental Research and Development Program (SERDP), Munition Response (MR), Project MR19-C1-1073 (Final Report)The objectives of this project were: (i) to develop a model for underwater munition's mobility and burial (UnMUMB), (ii) to use SERDP field experimental data to explore seafloor environment characteristics such as liquefaction, sand wave migration and deep scour, (iii) to develop new methodology for deep scour burial, (iv) to use Delft3D to predict complex seafloor environment, (v) to develop a coupled Delft3D and wave induced liquefaction model to predict sandy seafloor morphological change, (vi) to develop a coupled Delft3D-UnMUMB model to predict under water munitions’ mobility and burial as well as the change of the environment, and (vii) to provide the model formulations with User’s Guide to SERDP investigators such as to whom working on a more sophisticated Underwater Munitions Expert System (UnMES) as well as to the larger SERDP, DoD, coastal engineering, and scientific communities via six peer-reviewed journal articles and the User’s Guide for the coupled Delft3D-UnMUMB model.Approved for public release; distribution is unlimited.W74RDV90818446, W74RDV0080166Strategic Environmental Research and Development Program 4800 Mark Center Drive, Suite 17D03 Alexandria, VA 2000

User Guide: Coupled Delft3D-Underwater Munition Scour Burial (UnMUSB) Model

Prepared for: The DoD Strategic Environmental Research and Development Program (SERDP), Munition Responses (MR), Project MR19-C1-1073A coupled Delft3D and underwater munition scour burial (UnMUSB) model has been developed and evaluated to assess sea-floor environment as well as munitions’ migration and burial. The UnMUSB is a user-friendly physical based software written in MATLAB to help remediation management of the underwater munitions. This user guide documents the progress made in development of UnMUSB with coupling to the well-established Delft3D for nearshore environment during the project MR19-C1-1073 contract period (2019-2022), and includes a guide for detail and usage of UnMUSB with connection to Delft3D. The usage of coupled Deft3D-UnMUSB was demonstrated using the TREX13 data collected in the northern coastal region of the Gulf of Mexico near Panama City during the SERDP project MR-2320.Strategic Environmental Research and Development ProgramW74RDV90818446W74RDV00801666W74RDV03437239Approved for public release; distribution is unlimited

Research Naval Postgraduate School, v. 4. no. 1, November 2011

NPS Research is published by the Research and Sponsored Programs, Office of the Vice President and Dean of Research, in accordance with NAVSOP-35. Views and opinions expressed are not necessarily those of the Department of the Navy.Approved for public release; distribution is unlimited

Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

Risk Assessment of Urban Gas Pipeline Based on Different Unknown Measure Functions

Several risk factors threaten the safety of urban gas pipeline. How to effectively identify various risk factors affecting urban gas pipeline and put forward scientific risk assessment method is the focus in the field of urban safety research. To explore the uncertain factors in the process of gas pipeline risk assessment, and propose a practical assessment method, a three-layer index system for the risk assessment of urban gas pipeline was established using unascertained measure theory, which included 5 first-class evaluation factors and 34 second-class evaluation indexes. Four unascertained measure models (linear, parabolic, exponential and sinusoidal) were constructed, and the unascertained measure values of each evaluation index under four unknown measure function models were calculated. The weight of evaluation factors was determined by Analytic Hierarchy Process (AHP), and the confidence criterion was used for discriminant evaluation. Results demonstrate that the risk assessment models constructed with different measurement functions can effectively reduce the uncertainty of urban gas pipeline risk assessment, but for the same object, the risk level of the linear measurement model in 4# pipeline is lower than other measurement functions, and the risk level of sinusoidal measurement model in 8# pipeline is higher than other measurement functions. Therefore, considering the evaluation results under different measure functions and focusing on monitoring objects with different results is necessary when using unascertained measure theory for risk assessment. The conclusions obtained from this study clarify the application conditions of unascertained measure theory in urban gas pipeline risk assessment, which helps to reduce the uncertainty in the assessment process and improve the accuracy of the assessment results

Hydrodynamics of mine impact burial

A general physics based hydrodynamic flow model is developed that predicts the three-dimensional six degrees of freedom free fall time history of a circular cylinder through the water column to impact with an unspecified bottom. Accurate vertical impact velocity and impact angle parameters are required inputs to subsequent portions of any Impact Mine Burial Model. The model vertical impact velocity and impact angle are compared with experimental data, vertical impact velocities and impact angle to validate the model mechanics and accuracy. The three dimensional model results are compared through the experimental data with IMPACT28 vertical impact velocities and impact angle. Results indicate the three dimensional model mechanics are sound and marginal improvements are obtained in predicted vertical velocities. No improvement is gained using the three-dimensional model over IMPACT28 to predict impact angle. The observed stochastic nature of mine movement in experimental data suggests this three dimensional model be used to model the hydrodynamic flow phase in a statistical mine burial model that provides distributions for input parameters, and domain characteristics and present a probabilistic output for development of a relevant navy tactical decision aid.http://archive.org/details/hydrodynamicsofm109455310Lieutenant Commander, United States Nav

Independent Expert Scientific Panel – Report on Unconventional Oil and Gas

No abstract available

Development of a quantitative assessment approach for the coal and gas outbursts in coal mines using rock engineering systems

In this study, a new approach is proposed and developed for evaluating the comprehensive outburst index (range between 0 and 100), which is a quantitative assessment approach and will enable us to better understand the risk degree of coal and gas outburst in coal mines. By selecting some typical risk-free and high-risk outburst mines from China as the evaluation targets, we assessed their comprehensive outburst indexes with the developed approach. The assessment results are fully consistent with the actual situations, which indicates that our new developed approach is reliable and can be recommended for applying in more coal mines

Ensemble learning method for hidden markov models.

For complex classification systems, data are gathered from various sources and potentially have different representations. Thus, data may have large intra-class variations. In fact, modeling each data class with a single model might lead to poor generalization. The classification error can be more severe for temporal data where each sample is represented by a sequence of observations. Thus, there is a need for building a classification system that takes into account the variations within each class in the data. This dissertation introduces an ensemble learning method for temporal data that uses a mixture of Hidden Markov Model (HMM) classifiers. We hypothesize that the data are generated by K models, each of which reacts a particular trend in the data. Model identification could be achieved through clustering in the feature space or in the parameters space. However, this approach is inappropriate in the context of sequential data. The proposed approach is based on clustering in the log-likelihood space, and has two main steps. First, one HMM is fit to each of the N individual sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE) based discriminative, and the Variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the multiple models outputs using a decision level fusion method such as an artificial neural network or a hierarchical mixture of experts. Our approach was evaluated on two real-world applications: (1) identification of Cardio-Pulmonary Resuscitation (CPR) scenes in video simulating medical crises; and (2) landmine detection using Ground Penetrating Radar (GPR). Results on both applications show that the proposed method can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data

Complex inner shelf environments: Observations and modeling of morphodynamics and scour processes

The inner continental shelf is a complex environmental system marked by sharp variations in bed roughness. Such heterogeneous systems account for 80% of the non-rocky inner shelves worldwide. Interactions among forces (waves, tides, turbulence, and bioturbation) and roughness elements (bed forms, rocks, and anthropogenic objects) exert major controls on sedimentary processes. This study attempts to advance the knowledge and understanding of the morphodynamics of the inner shelf. This study investigates scour and morphodynamic processes at Tairua, New Zealand; Cedar Island, Virginia; Indian Rocks Beach, Florida; and Beaufort Inlet, North Carolina. Using data from the field, the study develops new conceptual models to characterize and quantify the hydrodynamics and morphology of the seabed. The overall dataset includes side-scan sonograms, sub-bottom profiles, grain-size analyses, suspended sediment concentrations and hydrodynamic measurements. Analysis of the morphological data yielded a six-type classification of bottom features previously termed Rippled Scour Depressions (RSDs). The observed stratigraphic signature of RSDs does not agree with the previous interpretation of their formation. Striking spatial and temporal variations in seabed roughness produce significant enhancements of hydraulic roughness and turbulence over different substrates resulting in a self-organized, feed-back system of erosion (scour), deposition, and modified bed forms. The study demonstrates that widely used ripple models inadequately predict bed form geometry and behavior, especially during storms. Improved understanding of scour processes developed in this study leads to a new model of scour and burial of sea-bed objects such as naval mines and archaeological artifacts. When using the model to predict scour and burial, the greatest errors result from the uncertainties in the available forecasts of wave conditions. The model includes vertical variations in sediment characteristics as field observations indicate abrupt changes in substrate substantially alter the scour process. The overall study makes substantial contributions to the general understanding of RSD behavior by tying together detailed field studies with applicable insights from the area of complexity research. A new conceptual model of complex phase-transition is developed, involving critical process factors (hydrodynamics, underlying geology, and depth), which contribute to the observed spatial complexity and temporal variability of different RSD types