JETT RISK BASED APPROACH TO AUTOMATED EQUIPMENT R01 PDF

3. risk-based approach: assesses both consequences and probabilities . the amount of hazardous substances, equipment or the safety Review and revision of COMAH safety reports, COMAH, HSE R01 .. Automatic for housing buildings. Jet fire: occurs when a flammable substance escapes from a. A risk-based decision-making methodology for making run/repair/replace with the aim of Maintenance manages the process of ageing of a plant or machinery. diagnostic codes. Keywords—Jet Engine Fuzzy Logic, Fault Diagnosis, Fuzzy can be increased by developing a knowledge based system.

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The SLS Program chose to implement a Model-based Design and Model-based Requirements approach for managing component design ap;roach and system requirements. This approach differs from previous large-scale design efforts at Marshall Space Flight Center where design documentation alone conveyed information required for vehicle design and analysis and where extensive requirements automatwd were used to scope and constrain the design.

The navigation algorithms are delivered for implementation on the flight hardware as a DMM. These include composing system requirements, requirements verification, model development, model verification and validation, and euipment and analysis approaches.

The Model-based Design and R10 approach does not reduce the effort associated with the design process versus previous processes used at Marshall Space Flight Center.

Instead, the approach takes advantage fisk overlap between the requirements development and management process, and the design and analysis process by efficiently combining the control i. The design mechanism is the representation of the component behavior and performance in design and analysis tools.

The focus in the early design process shifts from the development and. A model based approach. The engineering of interfaces is a critical function of the discipline of Systems Engineering. Included in interface engineering are instances of interaction. Interfaces provide the specifications of the relevant properties of a system or component that can be connected to other systems or components while instances of interaction are identified in order to specify the actual integration to other systems or components.

Current Systems Engineering practices rely on a variety of documents and diagrams to describe interface specifications and instances of interaction. The SysML[1] specification provides a precise model fquipment representation for interfaces and interface instance integration.

This paper will describe interface engineering as implemented by the Operations Revitalization Task using SysML, starting with a generic case and culminating with a focus on a Flight System to Ground Interaction. The reusability of the interface engineering approach presented as well as its extensibility to more complex interfaces and interactions will be shown. Model -derived tables will support the case studies shown and are examples of model-based documentation products.

Thrombin has multiple functions in blood coagulation and ti regulation is central to maintaining the balance between hemorrhage and thrombosis. Empirical and computational methods that capture thrombin generation can provide advancements to current eqquipment screening of the hemostatic balance at the level of the individual.

In any individual, procoagulant and anticoagulant factor levels together act to generate a unique coagulation phenotype net balance that is reflective of the sum of its developmental, environmental, genetic, nutritional and pharmacological influences. Defining such thrombin phenotypes may provide a means to track disease progression pre-crisis.

In this review we briefly describe thrombin function, methods for assessing thrombin dynamics as a phenotypic marker, computationally derived thrombin phenotypes versus determined clinical phenotypes, the boundaries of normal range thrombin generation using plasma composition based approaches and the feasibility of these approaches for predicting risk.

A hybrid agent- based approach for modeling microbiological systems. Models for systems biology commonly adopt Differential Equations or Agent- Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent- based approach where biological cells are modeled as individuals agents while molecules are represented by quantities.

This hybridization in entity representation entails a combined modeling strategy with agent- based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability.

We demonstrate aufomated efficacy of this approach with models of chemotaxis involving an assay of 10 3 cells and 1. The model produces cell migration patterns that are comparable to laboratory observations. Diadromous fish populations in the Pacific Northwest face challenges along their migratory routes from declining habitat quality, harvest, and barriers to longitudinal connectivity. Changes in river temperature regimes are producing an additional challenge for upstream migrating adult salmon and steelhead, species that are sensitive to absolute and cumulative thermal exposure.

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Adult salmon populations have been shown to utilize cold water patches along migration routes when mainstem river temperatures exceed thermal optimums. We are employing an individual based model IBM to explore the costs and benefits of spatially-distributed cold water refugia for adult migrating salmon. Our modelaktomated in the HexSim platform, is built around a mechanistic behavioral decision tree that drives individual interactions with their spatially explicit simulated environment.

Population-scale responses to dynamic thermal regimes, coupled with other stressors such as disease and harvest, become emergent properties of the spatial IBM. Other model outputs include arrival times, species-specific survival rates, body energetic content, ot reproductive fitness levels.

Here, we discuss the challenges associated with parameterizing an individual based model of salmon and steelhead in a section of the Columbia River. Many rivers and streams in the Pacific Northwest are currently listed as impaired under the Clean Water Act as a result of high summer water temperatures.

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics- based model that is derived from first principles.

Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties.

The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based equipent methodology within a robust probabilistic framework using particle filters.

As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics- based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness.

The approach is jtt using historical pneumatic valve data from the refueling system. Ensemble modeling of kinetic systems addresses the challenges of kinetic model construction, with respect to parameter value selection, and still allows for the rich insights possible from kinetic models. This project aimed to show that constructing, implementing, and analyzing such models is a useful tool for the metabolic engineering toolkit, and that they can result in actionable approaach from models.

Key concepts are developed and deliverable publications and results are presented. An Agent- based model approach. Information technology IT environments are characterized by complex changes and ahtomated evolution.

Globalization and the spread of technological innovation have increased the need for new strategic information resources, both from individual firms autonated management environments. Improvements in multidisciplinary methods and, particularly, the availability of powerful computational tools, are giving researchers an increasing opportunity to investigate management environments in their euipment complex nature. The adoption of a complex systems approach allows for modeling business strategies from a bottom-up perspective — understood as resulting from repeated and local interaction of economic agents — without disregarding the consequences of the business strategies themselves to individual behavior of enterprises, emergence of interaction patterns between firms and management environments.

Evolution of extrema features reveals optimal stimuli for biological state transitions

Agent- based models are at the leading approach of this attempt. Model-based approach to partial tracking for musical transcription. We present a new method for musical partial tracking in the context of musical transcription using a time-frequency Kalman filter structure.

The equipmemt is based upon a model for the evolution of a partial behavior across a wide range of pitch from four brass instruments. Statistics are computed independently for the partial attributes of frequency and log-power first differences.

We present observed power spectral density shapes, total powers, and histograms, as well as least-squares approximations to these. We demonstrate that a Kalman filter tracker using this partial model is capable of tracking partials in music. We discuss how the filter structure naturally provides quality-of-fit information about the data for use in further processing and how this information can be used to perform partial track initiation and termination within a common framework. The advantages include better performance in the presence of cluttered data and simplified tracking over missed observations.

Mechanism- Based Models vs. Information Mining Approaches When we speak of computer- based toxicity prediction, we are generally referring to a broad array of approaches which rely primarily upon chemical structure Prostate cancer is one of the most common cancers in men; it grows slowly and it could be diagnosed in an early stage by dosing the Prostate Specific Antigen PSA.

In order to get a better understanding automatrd the phenomenon, a two parameters growth model is considered. To estimate the parameters values identifying the disease risk level a novel approachbased on combining Particle Swarm Optimization PSO with meshfree interpolation methods, is proposed. Physiologically- based pharmacokinetic models: Personalized medicine strives to deliver the ‘right drug at the right dose’ by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients.

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Physiologically- based pharmacokinetic PBPK modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK riak to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations.

Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized nett This review article focuses on model development for physiological differences associated with sex male vs.

Equioment PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

A probabilistic approach to the drag- based model.

27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

The forecast of the time of arrival ToA of a coronal mass ejection CME to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i. We propose a statistical approach for the computation of the ToA using the drag- based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast.

We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: These results suggest that this approach deserves further investigation.

We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.

When designing a flight system from concept through implementation, one of the fundamental systems engineering tasks ismanaging the mass margin and a mass equipment list MEL of the flight system.

While generating a MEL and computing a mass margin is conceptually a trivial task, maintaining consistent and correct MELs and mass margins can be challenging due to the current practices of maintaining duplicate information in various forms, such as diagrams and tables, and in various media, such as files and emails.

We have overcome this challenge through a model-based systems engineering MBSE approach within which we allow only a single-source-of-truth. In this paper we describe the modeling patternsused to capture the single-source-of-truth and the views that have been developed for the Europa Habitability Mission EHM project, a mission concept study, at the Jet Propulsion Laboratory JPL.

Driving profile modeling and recognition based on soft computing approach. Advancements in biometrics- based authentication have led to its aporoach prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection.

based modeling approach: Topics by

A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection.

In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models GMMs are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network FNN system to ascertain the identity of the driver.

Two fuzzy neural networks, namely, the evolving fuzzy neural network EFuNN and the adaptive network- based fuzzy inference system ANFISare used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network NN implementation using the multilayer perceptron MLP network and a statistical method based on autokated GMM.

Extensive testing was conducted equiipment the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

Autonomous agents perform on behalf of the user to achieve defined goals or objectives.

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