4,838 research outputs found

    A multi-modal discrete-event simulation model for military deployment

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    Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Ph.D.) -- Bilkent University, 2009.Includes bibliographical references leaves 132-136.This study introduces a logistics and transportation simulation as a tool that can be used to provide insights into potential outcomes of proposed military deployment plans.    More specifically, we model a large‐scale real‐world military Deployment Planning Problem (DPP) that involves planning the movement of military units from their home bases to their final destinations using different transportation assets on a multimodal transportation network.    We apply, for the first time, the Event Graph methodology and Listener Event Graph Object framework to create a discrete event simulation (DES) model of the DPP.  We use and extend Simkit, an open‐source Java Application Programming Interface for creating DES models.    The high‐resolution approach that we take in most part, allows us to estimate whether a given plan of deployment will go as intended, and determine prospective problem areas in a relatively short time compared to other existing simulations because of the absence of the need to use several models of differing resolutions in succession as often done in literature.  For a typical deployment scenario for four battalions, run times are between 25 to 27 minutes for 60 runs of the model on a 1.6 GHz Pentium(R) M PC with 512 MB RAM.  That is less than 30 seconds per run.      To accurately incorporate real and detailed transportation network data into the simulation, we use GeoKIT, a state‐of‐the‐art, Java‐based Geographical Information System.    The component‐based approach adopted in development of our simulation model enables us to easily integrate future additions to our model.  The DES developed as part of this dissertation provides a test bed for currently existing deployment scenarios.    While our DES model is not a panacea for all, it allows for testing the feasibility and sensitivity of deployment plans under stochastic conditions prior to committing members of the military into harm’s way.    Our main contribution is to develop a comprehensive, multi‐modal, high‐ resolution, loosely‐coupled and modular, extendable, platform independent, state‐of‐ the‐art GIS based simulation environment that views the deployment operations as end‐to‐end processes.    Such a simulation environment for multi modal deployment planning and analysis does not exist.       Additionally, we simulate and analyze a typical real‐world case study by using conventional methods and the rather novice Nearly Orthogonal Latin Hypercube Sampling (NOLHS) technique.  We use a space‐filling nearly orthogonal design of 29 factors and 257 runs to determine the factors that impact most on a deployment plan.  We make 15 replications of each of the 257 runs (scenarios) to reach a total of 257x15=3855 computer runs compared to an experiment with 29 factors, each with only 2 levels and 15 replications per run, for a complete enumeration experiment (229 x15= 8,053,063,680 computer runs!).   Yıldırım, Uğur ZiyaPh.D

    A simulation model for military deployment

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    The Deployment Planning Problem (DPP) for military units may in general be defined as the problem of planning the movement of geographically dispersed military units from their home bases to their final destinations using different transportation assets and a multimodal transportation network while obeying the constraints of a time-phased force deployment data describing the movement requirements for troops and equipment. Our main contribution is to develop a GISbased, object-oriented, loosely-coupled, modular, platformindependent, multi-modal and medium-resolution discrete event simulation model to test the feasibility of deployment scenarios. While our simulation model is not a panacea for all, it allows creation and testing the feasibility of a given scenario under stochastic conditions and can provide insights into potential outcomes in a matter of a few hours. © 2007 IEEE

    A simulation system for WSNs as a digital eco-system approach considering goodput metric

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    Sensor networks are a sensing, computing and communication infrastructure that are able to observe and respond to phenomena in the natural environment and in our physical and cyber infrastructure. The sensors themselves can range from small passive micro-sensors to larger scale, controllable weather-sensing platforms. In order to simulate Wireless Sensor Networks (WSNs), we implemented a simulation system as a Digital Eco-System (DES) approach. We implement our system as a multi-modal system considering different topologies, radio models, routing protocols, MAC protocols, and different number of nodes. However, in this work, we consider the goodput metric and evaluate the performance of WSN for AODV and TwoRayGround model considering different topologies and number of nodes. To reduce the consumed energy of a large scale WSN network, we consider a mobile sink node in the observing area. We investigate how the sensor network performs in the case when the sink node moves. We compare the simulation results for two cases: when the sink node is mobile and stationary. The simulation results have shown that for the case of mobile sink, the goodput of random topology is better than the case of lattice. In the case of stationary sink, the goodput is unstable. In case of mobile sink, the goodput is stable and better than in case of stationary sinkPeer ReviewedPostprint (published version

    Human-Machine Collaborative Optimization via Apprenticeship Scheduling

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    Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

    Spiking Neural Network-based Structural Health Monitoring Hardware System

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    A Hybrid Simulation Methodology To Evaluate Network Centricdecision Making Under Extreme Events

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    Currently the network centric operation and network centric warfare have generated a new area of research focused on determining how hierarchical organizations composed by human beings and machines make decisions over collaborative environments. One of the most stressful scenarios for these kinds of organizations is the so-called extreme events. This dissertation provides a hybrid simulation methodology based on classical simulation paradigms combined with social network analysis for evaluating and improving the organizational structures and procedures, mainly the incident command systems and plans for facing those extreme events. According to this, we provide a methodology for generating hypotheses and afterwards testing organizational procedures either in real training systems or simulation models with validated data. As long as the organization changes their dyadic relationships dynamically over time, we propose to capture the longitudinal digraph in time and analyze it by means of its adjacency matrix. Thus, by using an object oriented approach, three domains are proposed for better understanding the performance and the surrounding environment of an emergency management organization. System dynamics is used for modeling the critical infrastructure linked to the warning alerts of a given organization at federal, state and local levels. Discrete simulations based on the defined concept of community of state enables us to control the complete model. Discrete event simulation allows us to create entities that represent the data and resource flows within the organization. We propose that cognitive models might well be suited in our methodology. For instance, we show how the team performance decays in time, according to the Yerkes-Dodson curve, affecting the measures of performance of the whole organizational system. Accordingly we suggest that the hybrid model could be applied to other types of organizations, such as military peacekeeping operations and joint task forces. Along with providing insight about organizations, the methodology supports the analysis of the after action review (AAR), based on collection of data obtained from the command and control systems or the so-called training scenarios. Furthermore, a rich set of mathematical measures arises from the hybrid models such as triad census, dyad census, eigenvalues, utilization, feedback loops, etc., which provides a strong foundation for studying an emergency management organization. Future research will be necessary for analyzing real data and validating the proposed methodology

    Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning

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    “This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv

    Long-Duration Space Exploration and Emotional Health: Recommendations for Conceptualizing and Evaluating Risk.

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Spaceflight to Mars will by far exceed the duration of any previous mission. Although behavioral health risks are routinely highlighted among the most serious threats to crew safety, understanding of specific emotional responses most likely to occur and interfere with mission success has lagged in comparison to other risk domains. Even within the domain of behavioral health, emotional constructs remain to be ‘unpacked’ to the same extent as other factors such as attention and fatigue. The current paper provides a review of previous studies that have examined emotional responses in isolated, confined, extreme environments (ICE) toward informing a needed research agenda. We include research conducted during space flight, long-duration space simulation analogs, and polar environments and utilize a widely-accepted and studied model of emotion and emotion regulation by Gross [6] to conceptualize specific findings. Lastly, we propose four specific directions for future research: (1) use of a guiding theoretical framework for evaluating emotion responses in ICE environments; (2) leveraging multi-method approaches to improve the reliability of subjective reports of emotional health; (3) a priori selection of precise emotional constructs to guide measure selection; and (4) focusing on positive in addition to negative emotion in order to provide a more complete understanding of individual risk and resilience

    Technology for large space systems: A bibliography with indexes (supplement 17)

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    This bibliography lists 512 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1987 and June 30, 1987. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems
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