2,332 research outputs found
PBL + IMM = PBL²: problem based learning and interactive multimedia development
No full textProblem-based learning (PBL) is a powerful instructional design for professional education, which may be used to guide the design of interactive multimedia (IMM). An IMM package incorporating PBL principles has been developed to assist teachers in learning to integrate information and communications technologies (ICT) into their teaching. The development process became a PBL experience for the author as design and implementation issues were met and resolved. This article describes how the learning that occurred has influenced the final form of the multimedia materials and discusses how the PBL principles were implemented in IMM
Variable- and Fixed-Structure Augmented Interacting Multiple Model Algorithms for Manoeuvring Ship Tracking Based on New Ship Models
No full textThe real-world tracking applications meet a number of difficulties caused by the presence of different kinds of uncertainty - unknown or not precisely known system model and random processes’ statistics or due to abrupt changes in the system modes of functioning. These problems are especially complicated in the marine navigation practice, where the commonly used simple models of rectilinear or curvilinear target motions do not match to the highly non-linear dynamics of the manoeuvring ship motion. A solution of these problems is to derive more adequate descriptions of the real ship dynamics and to design adaptive estimation algorithms. After analysis of basic hydrodynamic models, new ship models are derived in the paper. They are implemented in two versions of the recently very popular Interacting Multiple Model (IMM) algorithm. The first one is a standard IMM version using preliminary defined fixed structure (FS) of models. They represent various modes of ship motion, distinguished by their rate of turns. The same rate of turn is additionally adjusted in the proposed new augmented versions of the IMM (AIMM) algorithm by using FS and variable structure (VS) of adaptive models estimating the current change of the system control parameters. The obtained Monte Carlo simulation results show that the VS AIMM algorithm outperforms the FS AIMM and FS IMM algorithms with respect to accuracy and adaptability
Integrated Medical Model (IMM) 4.0 Enhanced Functionalities
No full textThe Integrated Medical Model is a probabilistic simulation model that uses input data on 100 medical conditions to simulate expected medical events, the resources required to treat, and the resulting impact to the mission for specific crew and mission characteristics. The newest development version of IMM, IMM v4.0, adds capabilities that remove some of the conservative assumptions that underlie the current operational version, IMM v3. While IMM v3 provides the framework to simulate whether a medical event occurred, IMMv4 also simulates when the event occurred during a mission timeline. This allows for more accurate estimation of mission time lost and resource utilization. In addition to the mission timeline, IMMv4.0 features two enhancements that address IMM v3 assumptions regarding medical event treatment. Medical events in IMMv3 are assigned the untreated outcome if any resource required to treat the event was unavailable. IMMv4 allows for partially treated outcomes that are proportional to the amount of required resources available, thus removing the dichotomous treatment assumption. An additional capability IMMv4 is to use an alternative medical resource when the primary resource assigned to the condition is depleted, more accurately reflecting the real-world system. The additional capabilities defining IMM v4.0the mission timeline, partial treatment, and alternate drug result in more realistic predicted mission outcomes. The primary model outcomes of IMM v4.0 for the ISS6 mission, including mission time lost, probability of evacuation, and probability of loss of crew life, are be compared to those produced by the current operational version of IMM to showcase enhanced prediction capabilities
Carlo Scarpa. Casa Zentner a Zurigo_Electa_Errata corrige imm.109
No full textErrata corrige of imm. 109, Carlo Scarpa. Cara Zentner a Zurigo: una villa italiana in Svizzera, D. Fornari, G. Jean, R. Martinis, Electa 2020, p.91
Integrated Medical Model (IMM) Optimization Version 4.0 Functional Improvements
No full textThe IMMs ability to assess mission outcome risk levels relative to available resources provides a unique capability to provide guidance on optimal operational medical kit and vehicle resources. Post-processing optimization allows IMM to optimize essential resources to improve a specific model outcome such as maximization of the Crew Health Index (CHI), or minimization of the probability of evacuation (EVAC) or the loss of crew life (LOCL). Mass and or volume constrain the optimized resource set. The IMMs probabilistic simulation uses input data on one hundred medical conditions to simulate medical events that may occur in spaceflight, the resources required to treat those events, and the resulting impact to the mission based on specific crew and mission characteristics. Because IMM version 4.0 provides for partial treatment for medical events, IMM Optimization 4.0 scores resources at the individual resource unit increment level as opposed to the full condition-specific treatment set level, as done in version 3.0. This allows the inclusion of as many resources as possible in the event that an entire set of resources called out for treatment cannot satisfy the constraints. IMM Optimization version 4.0 adds capabilities that increase efficiency by creating multiple resource sets based on differing constraints and priorities, CHI, EVAC, or LOCL. It also provides sets of resources that improve mission-related IMM v4.0 outputs with improved performance compared to the prior optimization. The new optimization represents much improved fidelity that will improve the utility of the IMM 4.0 for decision support
Qualitative Validation of the IMM Model for ISS and STS Programs
No full textTo validate and further improve the Integrated Medical Model (IMM), medical event data were obtained from 32 ISS and 122 STS person-missions. Using the crew characteristics from these observed missions, IMM v4.0 was used to forecast medical events and medical resource utilization. The IMM medical condition incidence values were compared to the actual observed medical event incidence values, and the IMM forecasted medical resource utilization was compared to actual observed medical resource utilization. Qualitative comparisons of these parameters were conducted for both the ISS and STS programs. The results of these analyses will provide validation of IMM v4.0 and reveal areas of the model requiring adjustments to improve the overall accuracy of IMM outputs. This validation effort should result in enhanced credibility of the IMM and improved confidence in the use of IMM as a decision support tool for human space flight
Three Model IMM-EKF for Tracking Targets Executing Evasive Maneuvers
No full textA constant jerk model consisting of third order derivative of target position is included in IMM estimator along with constant velocity and constant acceleration models to track a maneuvering target. The motivation for including a higher order model is that agile target maneuvers are likely to have more significant higher order derivatives which lower order tracking models such as constant velocity and constant acceleration models currently in use cannot adequately handle. This has been demonstrated through numerical simulation studies. Inclusion of jerk model in the IMM algorithm shows overall better performance of the tracking filter
S.L.I.M., a small linear interdependent model of eight EU-member states, the USA and Japan
No full textInternational Economics;EU
Extended target tracking using an IMM based nonlinear Kalman filters
No full textDimirovski, Georgi M. (Dogus Author) -- Conference full title: 2010 American Control Conference, ACC 2010; Baltimore, MD; United States; 30 June 2010 through 2 July 2010The unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) are developed to extended target tracking problem for high resolution sensors. The nonlinear Kalman filters are based on an ellipsoidal model, which is proposed to exploit sensor measurement of target extent. The ellipsoidal model can provide extra information to enhance tracking accuracy, data association performance, and target identification. In contrast to the most commonly used extended Kalman filter (EKF), the UKF and EnKF provide more accurate and reliable estimation performance, due to the presence of high nonlinearity of the model. Correspondingly, the EnKF has lower computational complexity than the UKF. An interacting multiple model (IMM) technique is combined with the filters to adapt the target maneuver and motion mode switching problem which is vital for nonlinear filtering. The developed IMM-UKF and IMM-EnKF algorithms on extended target tracking problem are validated and evaluated by computer simulations.AIAA, AIChE, AIST, ASCE, IEEE -- This work is supported by the Forestry Projects in the National Science & Technology Pillar Program 2006BAD18130805 and Key Research Projects for the Forestry Science & Technology 2006-60, P. R. of China
Three Model IMM-EKF for Tracking Targets Executing Evasive Maneuvers
No full textA constant jerk model consisting of third order derivative of target position is included in IMM estimator along with constant velocity and constant acceleration models to track a maneuvering target. The motivation for including a higher order model is that agile target maneuvers are likely to have more significant higher order derivatives which lower order tracking models such as constant velocity and constant acceleration models currently in use cannot adequately handle. This has been demonstrated through numerical simulation studies. Inclusion of jerk model in the
IMM algorithm shows overall better performance of the tracking filter
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