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Virtual Computing Environment for Future Combat Systems Maps are as important to soldiers as guns Shooters Network Sensor Network HPGIS National Assets, e.g. Maps Commanders Network e.g. Situation Assessment Example Usage of Geographic Info. Systems (GIS) in Battlefield : •Rescue of pilots after their planes went down (recently in Kosovo) •Precision targeting e.g. avoid civilian casualities (e.g. friendly embassies) •Logistics of Troop movements, avoid friendly fires Motivating Example – Urban Warfare “Black Hawk Down” Mogadishu, Somalia, 10/3/1993 Soldiers trapped by roadblocks No alternate evacuation routes Rescue team got lost in alleys having no planned route to crash site 18 Army Rangers and elite Delta Force soldiers killed, 73 wounded. ( Mark Bowden, Black Hawk Down: A Story of Modern War ) Motivating Example – Chem-Bio Portfolio • Examples • • Weather, Terrain, Base map Chem-Bio portfolio project (Dr. Alibadi) Scenario – managing a (say chem-bio) attack • Components of the system • • • • • Gathering initial conditions • Weather data from NWS or JSU • Terrain maps (State of federal Govt.) • Building geometry (City Govt.) Plume simulation using supercomputers Visualizing results – map, 3D graphics Response planning Plume Modeling Q? What happens after plume simulation, visualization? Demographics, Transportation ( Images from www.fortune.com ) Homeland Defense: Chem-Bio Portfolio "We packed up Morgan City residents to evacuate in the a.m. on the day that Andrew hit coastal Louisiana, but in early afternoon the majority came back home. The traffic was so bad that they couldn't get through Lafayette." - Morgan City, Louisiana Mayor Tim Mott ( http://i49south.com/hurricane.htm ) ( National Weather Services) Hurrican Andrew, 1992 Traffic congestions on all highways Great confusions and chaos ( www.washingtonpost.com) Problem Statement Given • Transportation network (e.g. building floor map, city roadmap) with capacity constraints • Initial number of people to be evacuated and their initial location • Evacuation destinations Output • Scheduling of people to be evacuated and the routes to be taken Objective • Minimize total time needed for evacuation • Minimize computational overhead Constraints • Capacity constraints: evacuation plan meets capacity of the network Route Algorithm - Related Works • Dynamic network flow (Ford and Fulkerson, 1960’s) – • Simple algorithms for multiple source and destination (1970’s-1980’s) – • Quickest Flow Problem: Only apply to single source and single destination node Algorithms have exponential running time, e.g. EVACNET(University of Florida) Improved algorithms (1990’s) – Klinz: • Polynomial time algorithm • Can only find required time, not the evacuation plan – Tardos(1994): • • • • Polynomial time algorithm to find optimal plan for fixed number of sources Cannot apply to variable number of sources Cannot apply to variable arc capacity, e.g. arc capacity changed over time May produce fractional solution, e.g. “5.2 people go to …”, feasible evacuation plan requires integer solution Route Algorithm - Our Approach • Algorithm Design – Extend shortest path algorithms (e.g. A*) To honor capacity contraints – Attach a time-series with each node and edge • Edge capacity • Node occupancy – Start single-source routing between all (source, dst) pairs • First route found is used to reduce edge and node attributes • Process repeats till node capacities are reduced to zero • Evaluation – Much faster than the current approaches – Solution quality is comparable on hand tested examples • Problems with little interference across routes, ;arge edge capacities – Detailed evaluation in progress Example Map Node N1, 50 (10) Node ID, Max Capacity (Initial Occupancy) (7,1) N4, 8 (3,3) First Floor EXIT #1 N13 (14,4) N9, 25 N5, 6 (3,4) N7, 8 (6,4) (2,5) (6,4) (8,1) N10, 30 (6,3) N8, 65 (15) (3,3) N11, 8 (3,1) (6,4) N12, 18 Edge (Max Capacity, Travel time) Exit N2, 50 (5) N6, 10 (5,5) (3,3) (7,1) (5,4) Second Floor N3, 30 (3,3) N14 EXIT #2 Node ID Result: Routes, Schedules Group of People Start time Route Exit time ID Origin No. of people A N8 6 0 N8-N10-N13 4 B N8 6 1 N8-N10-N13 5 C N8 3 0 N8-N11-N14 4 D N1 3 0 N1-N3-N4-N6-N10-N13 14 E N1 3 1 N1-N3-N4-N6-N10-N13 15 F N1 3 2 N1-N3-N4-N6-N10-N13 16 G N1 1 0 N1-N3-N5-N7-N11-N14 14 H N2 1 0 N2-N3-N5-N7-N11-N14 14 I N2 2 1 N2-N3-N5-N7-N11-N14 15 J N2 2 2 N2-N3-N5-N7-N11-N14 16 Result – Checking edge capacity constraints Number of people move though each edge starting from each time interval N8N10 N1-N3 N2-N3 6 7 5 6 3 N4-N6 N5-N7 N6N10 N7N11 3 2 5 3 2 6 3 2 8 3 2 9 3 2 10 3 2 0 1 N8N11 3 N11N14 N10N13 2 3 3 4 6 6 N3-N4 N3-N5 3 2 3 2 3 2 7 11 12 13 2 3 14 2 3 15 2 3 Routing – Next Phase (S. Shekhar) • AHPCRC Relevance – Projectile Target Interaction Portfolio – Increase lethality of weapons such as guided missiles – Pre-lauch routing – stealth route avoiding enemy sensor network – In-route routing • to correct drifts from planned trajectory • To route route unanticipated obstacles • Possible Extensions in 2002-2003 – Focus on relevance to AHPCRC Portfolios – Complete design and implementation of routing algorithm with capacity constraints – Performance evaluation with real datasets SPIRAL NATURE OF THE PRECISION ENGAGEMENT PROCESS Locate Assess ISR Locate Assess TST Locate Defer Assess Attack ID ID ID Assess Re-attack Detect Decide Attack Decide Employ wpns Decide TST Status Detect Detect Detect • Process timeline compresses for TSTs ID Locate Target • Iterative process driven by effort to refine data about target ID, location, and status • Process necessarily balances timeliness, lethality, and accuracy Decide Guidance and Objectives Location Prediction and Spatial Data Mining (S. Shekhar) • Specific Project in 2001-2002 – Evaluation of location prediction techniques – Towards high performance parallel implementation • AHPCRC Relevance – Projectile Target Interaction Portfolio – Increase lethality of weapons such as guided missiles – Location prediction for map matching • to check correctness of missile trajectory • To identify unanticipated obstacle – Towards possible rerouting • Army Relevance in general – – – – – – – Predicting global hot spots (FORMID) Army land management endangered species vs. training and war games Search for local trends in massive simulation data Critical infra-structure defense (threat assessment) Inferring enemy tactics (e.g. flank attack) from blobology Locating enemy (e.g. sniper in a haystack, sensor networks) Locating friends to avoid friendly fire Accomplishments • Formal Results • • • SAR - parametric statistics, provides confidence measures in model MRF from non-parametric statistics SAR : MRF-BC :: linear regression : Bayesian Classifier • Rewrite SAR as y = (QX) + Q, where Q = (I- W)-1 • SAR has linear class boundaries in transformed space (QX, y) • MRF-BC can represent non-linear class boundaries • Experimental results • • MRF-BC can provide better classification accuracies than SAR • But solution procedure is very slow Details in Recent paper in IEEE Transactions on Multimedia Location Prediction • Problem Definition: Nest locations Given: 1. Spatial Framework S {s1 ,...sn } 2. Explanatory functions: f X : S R 3. A dependent function: fY : S {0,1} 4. A family of function mappings: Find: A function fˆy R ... R {0,1} Objective: maximize classification accuracy ( fˆy , f y ) Constraints: Spatial Autocorrelation in dependent function Distance to open water k • Past Approaches: Non-spatial: logistic regression, decision trees, Bayesian – Assume independent distribution for learning samples – Auto-correlation => poor prediction performance Spatial: Spatial auto-regression (SAR), Markov random field Bayesian classifier (MRF) – No literature comparing the two! – Learning algorithms for SAR are slow (took 3 hours for 5000 data points)! Vegetation durability Water depth Accomplishments • Formal Results • • • SAR - parametric statistics, provides confidence measures in model MRF from non-parametric statistics SAR : MRF-BC :: linear regression : Bayesian Classifier • Rewrite SAR as y = (QX) + Q, where Q = (I- W)-1 • SAR has linear class boundaries in transformed space (QX, y) • MRF-BC can represent non-linear class boundaries • Experimental results • • MRF-BC can provide better classification accuracies than SAR • But solution procedure is very slow Details in Recent paper in IEEE Transactions on Multimedia Past Accomplishments • Scaleable parallel methods for GIS Querying for Battlefield Visualization • A spatial data model for directions for querying battlefield information • Spatial data mining: Predicting Locations Using Maps Similarity (PLUMS) •An efficient indexing method, CCAM, for spatial graphs, e.g. Road Maps GIS Research at AHPCRC • • • High Performance Geographic Information Systems (HPGIS) – Parallel formulations for terrain visualization – Efficient storage (e.g. CCAM), join-index More expressive GIS - Query languages, Data models – Mobile objects, Direction and Orientation – Processing direction based queries Smarter GIS - Spatial Data Mining – Spatial prediction, classification – Association among spatial features – Spatial outlier detection