KEYWORDS: Sensor networks, Intelligence systems, Control systems, Information fusion, Analytical research, Telecommunications, Weapons, Control systems design, Automatic control, Sensor fusion
This research is intended to contribute to the development of automated and human-in-the-loop systems for higher level fusion to respond to the information requirements of command decision making. In tactical situations with short time constraints, the analysis of information requirements may take place in advance for certain classes of problems, and provided to commanders and their staff as part of the control and communications systems that come with sensor networks. In particular, it may be possible that certain standing orders can assume the role of Priority Intelligence Requirements. Standing orders to a sensor network are analogous to standing orders to Soldiers. Trained Soldiers presumably don't need to be told to report contact with hostiles, for example, or to report any sighting of civilians with weapons. Such standing orders define design goals and engineering requirements for sensor networks and their control and inference systems. Since such standing orders can be defined in advance for a class of situations, they minimize the need for situation-specific human analysis. Thus, standing orders should be able to drive automatic control of some network functions, automated fusion of sensor reports, and automated dissemination of fused information. We define example standing orders, and outline an algorithm for responding to one of them based on our experience in the field of multisensor fusion.
KEYWORDS: Systems modeling, Computer simulations, Modeling and simulation, Defense technologies, Decision support systems, Visual analytics, Visualization, Data analysis
This research is part of a proposed shift in emphasis in decision support from optimality to robustness. Computer simulation is emerging as a useful tool in planning courses of action (COAs). Simulations require domain models, but there is an inevitable gap between models and reality - some aspects of reality are not represented at all, and what is represented may contain errors. As models are aggregated from multiple sources, the decision maker is further insulated from even an awareness of model weaknesses. To realize the full power of computer simluations to support decision making, decision support systems should support the planner in exporing the robustness of COAs in the face of potential weaknesses in simulation models.
This paper demonstrates a method of exploring the robustness of a COA with respect to specific model assumptions about whose accuracy the decision maker might have concerns. The domain is that of peacekeeping in a country where three differenct demographic groups co-exist in tension. An external peacekeeping force strives to achieve stability, an improved economy, and a higher degree of democracy in the country. A proposed COA for such a force is simluated multiple times while varying the assumptions. A visual data analysis tool is used to explore COA robustness. The aim is to help the decision maker choose a COA that is likely to be successful even in the face of potential errors in the assumptions in the models.
The article presents the results of a large scale design space exploration for the hybridization of two off-road vehicles,
part of the Future Tactical Truck System (FTTS) family: Maneuver Sustainment Vehicle (MSV) and Utility Vehicle (UV). Series hybrid architectures are examined.
The objective of the paper is to illustrate a novel design methodology that allows for the choice of the optimal values of several vehicle parameters. The methodology consists in an extensive design space exploration, which involves running a large number of computer simulations with systematically varied vehicle design parameters, where each variant is paced through several different mission profiles, and multiple attributes of performance are measured. The resulting designs are filtered to choose the design tradeoffs that better satisfy the performance and fuel economy requirements. At the end, few promising vehicle configuration designs will be selected that will need additional detailed investigation including neglected metrics like ride and drivability.
Several powertrain architectures have been simulated. The design parameters include the number of axles in the vehicle (2 or 3), the number of electric motors per axle (1 or 2), the type of internal combustion engine, the type and quantity of energy storage system devices (batteries, electrochemical capacitors or both together).
An energy management control strategy has also been developed to provide efficiency and performance. The control parameters are tunable and have been included into the design space exploration.
The results show that the internal combustion engine and the energy storage system devices are extremely important for the vehicle performance.
The ability of contemporary military commanders to estimate and understand complicated situations already suffers from information overload, and the situation can only grow worse. We describe a prototype application that uses abductive inferencing to fuse information from multiple sensors to evaluate the evidence for higher-level hypotheses that are close to the levels of abstraction needed for decision making (approximately JDL levels 2 and 3). Abductive inference (abduction, inference to the best explanation) is a pattern of reasoning that occurs naturally in diverse settings such as medical diagnosis, criminal investigations, scientific theory formation, and military intelligence analysis. Because abduction is part of common-sense reasoning, implementations of it can produce reasoning traces that are very human understandable. Automated abductive inferencing can be deployed to augment human reasoning, taking advantage of computation to process large amounts of information, and to bypass limits to human attention and short-term memory.
We illustrate the workings of the prototype system by describing an example of its use for small-unit military operations in an urban setting. Knowledge was encoded as it might be captured prior to engagement from a standard military decision making process (MDMP) and analysis of commander's priority intelligence requirements (PIR). The system is able to reasonably estimate the evidence for higher-level hypotheses based on information from multiple sensors. Its inference processes can be examined closely to verify correctness. Decision makers can override conclusions at any level and changes will propagate appropriately.
KEYWORDS: Roads, Computer simulations, Simulink, Capacitors, Systems modeling, Instrument modeling, Control systems, Resistance, Fluctuations and noise, Energy efficiency
A large scale design space exploration can provide valuable insight into vehicle design tradeoffs being considered for the U.S. Army’s FMTV (Family of Medium Tactical Vehicles). Through a grant from TACOM (Tank-automotive and Armaments Command), researchers have generated detailed road, surface, and grade conditions representative of the performance criteria of this medium-sized truck and constructed a virtual powertrain simulator for both conventional and hybrid variants. The simulator incorporates the latest technology among vehicle design options, including scalable ultracapacitor and NiMH battery packs as well as a variety of generator and traction motor configurations. An energy management control strategy has also been developed to provide efficiency and performance.
A design space exploration for the family of vehicles involves running a large number of simulations with systematically varied vehicle design parameters, where each variant is paced through several different mission profiles and multiple attributes of performance are measured. The resulting designs are filtered to remove dominated designs, exposing the multi-criterial surface of optimality (Pareto optimal designs), and revealing the design tradeoffs as they impact vehicle performance and economy. The results are not yet definitive because ride and drivability measures were not included, and work is not finished on fine-tuning the modeled dynamics of some powertrain components. However, the work so far completed demonstrates the effectiveness of the approach to design space exploration, and the results to date suggest the powertrain configuration best suited to the FMTV mission.
We start with a vision of an integrated decision architecture to assist in the various stages and subtasks of decisionmaking.
We briefly describe how the Seeker-Filter-Viewer (S-F-V) architecture for multi-criterial decision support
helps realize many components of that vision. The rest of the paper is devoted to one of the components: developing
insights about the course of action (COA) decision space from COA simulations. We start with data obtained from
multiple simulation executions of an urban combat COA in a specified scenario, where the stochastic nature of different
executions produce a range of intermediate events and final outcomes. The Viewer in the S-F-V decision architecture is
used to make and visually test hypotheses about how sensitive different events and outcomes are to different aspects of
the COA and to various intermediate events. The analyst engages in a cycle of hypothesis making, visually evaluating
the hypothesis, and making further hypotheses. A set of snapshots illustrates an investigational sequence of abstractions
in an example of iterating on hypotheses. The synergy of data mining tools, high performance computing, and advanced
high-resolution combat simulation has the potential to assist battle planners to make better decisions for imminent
combat.
Noise is typically present in the input signal for perception problems. Noise arises in speech recognition due to both background sounds, and unintentional derivations from the intended utterance on the part of the speaker. The task of speech recognition is to correctly identify the words (or meaning) carried by the speech signal. Thus the speech recognizer must be able to successfully handle noise. We describe here a method of explicitly identifying and labeling noise elements in a speech signal. NOISE hypotheses are generated, and considered for acceptance, as part of an abductive inference strategy for speech processing. An abductive problem solver is able to treat noise within a unified inferential framework, treating noise hypotheses similarly to other hypotheses, weighing the explanatory alternatives in a context-sensitive manner, and with no need to resort to indirect methods to achieve noise tolerance.
KEYWORDS: Image processing, Signal processing, Artificial intelligence, Instrument modeling, Tolerancing, Digital signal processing, Data processing, Control systems, Process control, Prototyping
In the presented work, we combine structure-based models with functional representation in one common framework. The combined model explicitly represents the causal process descriptions (cpds) by which functions are achieved, and these cpds are used to guide simulation based on a set of simulation goals. We demonstrate on some examples how this functional approach of simulation can lead to savings in simulation time.
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