The allocation of functions in man-machine systems
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Assortments analyzed for cost effectiveness and effects on system functionality. Eisenberger-Maiocco algorithm (EMA) is efficient Markov algorithm to aid in provision of spare parts. Two calculations performed: forecasting availability of system with given pool of spare parts and determining most cost-effective assortment of spares. EMA written in interpreter PC-BASIC.
Over the past decade considerable strides were made in explicating the antecedant conditions necessary for the elicitation, and the modulation of the amplitude and latency, of a number of components of the event-related brain potential (ERP). The focus of this report is on P300. The degree to which the psychophysiological measures contribute to issues in two real-world domains (communication devices for the motor impaired and the assessment of mental workload of aircraft pilots) are examined.
The increasing load density in the LC-39 area of Kennedy Space Center (KSC) can be met by either modifying the existing substation and increasing its capacity or by planning an additional new substation. Evidence that the later approach is more economical, enhances the system reliability, and would produce more satisfactory performance indices is provided. Network theory is the basis for the optimal location determination of the proposed substation. A load reallocation plan which minimizes investment cost and power losses and meets other desirable system features is drafted. The report should be useful to the system designer and can be a useful guideline for future facility planners.
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Dumas, Briggs, Reid and Smith (1989) describe the need for identifying mutually acceptable methodologies for developing standard agreements for the exchange of tracking time or facility use among international components. One possible starting point is the current process used at the Jet Propulsion Laboratory (JPL) in planning the use of tracking resources. While there is a significant promise of better resource utilization by international cooperative agreements, there is a serious challenge to provide convenient user participation given the separate project and network locations. Coordination among users and facility providers will require a more decentralized communication process and a wider variety of automated planning tools to help users find potential exchanges. This paper provides a framework in which international cooperation in the utilization of ground based space communication systems can be facilitated.
A hybrid genetic algorithm is used to schedule tasks for 8 satellites, which can be modelled as a robot whose task is to retrieve objects from a two dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable genetic algorithm.
ACARA analyzes availability, life-cycle cost, and scheduling of resources. Uses statistical Monte Carlo method to simulate capacity states of system as well as failure and repair of components. Failures of components modeled mathematically by use of combination of exponential and Weibull probability distributions. Schedules replacement of components to optimize performance of system. Made to comply with any constraints on production of components, capacities of resupply vehicles, spares kept on site, crews, and/or equipment. Written in APL2.
The Dynamic Planner (DP) has been designed, implemented, and integrated into the Center-TRACON Automation System (CTAS) to assist Traffic Management Coordinators (TMCs), in real time, with the task of planning and scheduling arrival traffic approximately 35 to 200 nautical miles from the destination airport. The TMC may input to the DP a series of current and future scheduling constraints that reflect the operation and environmental conditions of the airspace. Under these constraints, the DP uses flight plans, track updates, and Estimated Time of Arrival (ETA) predictions to calculate optimal runway assignments and arrival schedules that help ensure an orderly, efficient, and conflict-free flow of traffic into the terminal area. These runway assignments and schedules can be shown directly to controllers or they can be used by other CTAS tools to generate advisories to the controllers. Additionally, the TMC and controllers may override the decisions made by the DP for tactical considerations. The DP will adapt to computations to accommodate these manual inputs.
Estimating tight resource level is a fundamental problem in the construction of flexible plans with resource utilization. In this paper we describe an efficient algorithm that builds a resource envelope, the tightest possible such bound. The algorithm is based on transforming the temporal network of resource consuming and producing events into a flow network with noises equal to the events and edges equal to the necessary predecessor links between events. The incremental solution of a staged maximum flow problem on the network is then used to compute the time of occurrence and the height of each step of the resource envelope profile. The staged algorithm has the same computational complexity of solving a maximum flow problem on the entire flow network. This makes this method computationally feasible for use in the inner loop of search-based scheduling algorithms.
Computing tight resource-level bounds is a fundamental problem in the construction of flexible plans with resource utilization. In this paper we describe an efficient algorithm that builds a resource envelope, the tightest possible such bound. The algorithm is based on transforming the temporal network of resource consuming and producing events into a flow network with nodes equal to the events and edges equal to the necessary predecessor links between events. A staged maximum flow problem on the network is then used to compute the time of occurrence and the height of each step of the resource envelope profile. Each stage has the same computational complexity of solving a maximum flow problem on the entire flow network. This makes this method computationally feasible and promising for use in the inner loop of flexible-time scheduling algorithms.
As aerospace systems continue to evolve in addressing newer challenges in air and space transportation, there exists a heightened priority for significant improvement in system performance, cost effectiveness, reliability, and safety. Tools, which synthesize multidisciplinary integration, probabilistic analysis, and optimization, are needed to facilitate design decisions allowing trade-offs between cost and reliability. This study investigates tools for probabilistic analysis and probabilistic optimization in the multidisciplinary design of aerospace systems. A probabilistic optimization methodology is demonstrated for the low-fidelity design of a reusable launch vehicle at two levels, a global geometry design and a local tank design. Probabilistic analysis is performed on a high fidelity analysis of a Navy missile system. Furthermore, decoupling strategies are introduced to reduce the computational effort required for multidisciplinary systems with feedback coupling.
Upon resupply of exogenous nitrogen to nitrogen-stressed plants, uptake rate of nitrogen is enhanced relative to nonstressed plants. Absorption of nitrogen presumably is dependent on availability of carbohydrates in the roots. A buildup in soluble carbohydrates thus should occur in roots of nitrogen-stressed plants, and upon resupply of exogenous nitrogen the increased uptake rate should be accompanied by a rapid decline in carbohydrates to prestress levels. To evaluate this relationship, three sets of tobacco plants growing in a complete hydroponic solution containing 1.0 mM NO3- were either continued in the complete solution for 21 d, transferred to a minus-nitrogen solution for 21 d, or transferred to a minus-nitrogen solution for 8-9 d and then returned to the 1.0 mM NO3- solution. These nitrogen treatments were imposed upon plants growing at photosynthetic photon flux densities of 700 and 350 micromoles m-2 s-1. Soluble carbohydrate levels in roots increased during onset of nitrogen stress to levels that were fourfold greater than in roots of non-stressed plants. Following resupply of external nitrogen, a rapid resumption of nitrogen uptake was accompanied by a decline in soluble carbohydrates in roots to levels characteristic of nonstressed plants. This pattern of soluble carbohydrate levels in roots during onset of and recovery from nitrogen stress occurred at both irradiance levels. The response of net photosynthetic rate to nitrogen stress could be expressed as a nonlinear function of concentration of reduced nitrogen in leaves. The net photosynthetic rate at a given concentration of reduced nitrogen, however, averaged 10% less at the lower than at the higher irradiance. The decline in net photosynthetic rate per unit of reduced nitrogen in leaves at the lower irradiance was accompanied by an increase in the nitrate fraction of total nitrogen in leaves from 20% at the higher irradiance to 38% at the lower irradiance.
We developed a dynamic model to optimize irrigation application in three major crops (corn, cotton and peanuts) grown in the Southeast USA. Water supply amount is generated from an engineering model which is then combined with economic models to find the optimal amount of irrigation water to apply on each crop field during the six critical water deficit weeks in summer. Results indicate that water is applied on the crop with the highest marginal value product of irrigation. Decision making tool such as the one developed here would help farmers and policy makers to find the maximum profitable solution when water shortage is a serious concern.
Contents include the following: 1. Funded instruments in development. Advanced technology microwave sounder (ATMS) on NPOESS preperatory mission (NPP). Aquarius microwave radiometer. Global precipitation measurement (GPM) microwave imager (GMI). Hydros microwave radiometer. 2. Proposed/notional instruments. Cirrus clouds submmw radiometer. Cold-lends microwave radiometer. Geostationary millimeterwave radiometer (GeoSTAR). Geostationary soil moisture and salinity radiometer.
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This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning cost and show that the problem of efficient learning can be cast as a resource optimization problem. Solutions found in this way can be significantly more efficient than the best solutions that do not account for these factors. We introduce a heuristic learning algorithm that approximately solves this optimization problem and document its performance improvements on synthetic and real-world problems.