A Detection and Warning System for Unintended Acceleration

2015 ◽  
Author(s):  
Hongtao Yu ◽  
Reza Langari
Keyword(s):  
Mathematical Models ◽  
Diagnostic System ◽  
Warning System ◽  
Diagnostic Algorithm ◽  
Data Driven ◽  
Simulation Results ◽  
Major Factors

This paper presents a data-driven method to detect vehicle problems related to unintended acceleration (UA). A diagnostic system is formulated by analyzing several specific vehicle events such as acceleration peaks and generating corresponding mathematical models. The diagnostic algorithm was implemented in the Simulink/dSpace environment for validation. Major factors that affect vehicles’ acceleration (e.g., changes of road grades and gear shifting) were included in the simulation. UA errors were added randomly when human drivers drove virtual cars. The simulation results show that the algorithm succeeds in detecting abnormal acceleration.

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

scholarly journals A Comparison of Different Data-driven Procedures to Determine the Bunching Window

2021 ◽  
Vol 49 (2) ◽  
pp. 262-293
Author(s):  
Vincent Dekker ◽  
Karsten Schweikert
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Income Distribution ◽  
Prior Information ◽  
Structural Breaks ◽  
Data Driven ◽  
Taxable Income ◽  
Counterfactual Models ◽  
Simulation Results ◽  
Second Procedure

In this article, we compare three data-driven procedures to determine the bunching window in a Monte Carlo simulation of taxable income. Following the standard approach in the empirical bunching literature, we fit a flexible polynomial model to a simulated income distribution, excluding data in a range around a prespecified kink. First, we propose to implement methods for the estimation of structural breaks to determine a bunching regime around the kink. A second procedure is based on Cook’s distances aiming to identify outlier observations. Finally, we apply the iterative counterfactual procedure proposed by Bosch, Dekker, and Strohmaier which evaluates polynomial counterfactual models for all possible bunching windows. While our simulation results show that all three procedures are fairly accurate, the iterative counterfactual procedure is the preferred method to detect the bunching window when no prior information about the true size of the bunching window is available.

Hybrid Model-Based and Data-Driven Diagnostic Algorithm for Gas Turbine Engines

2020 ◽  
Author(s):  
Amare Fentaye ◽  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Mikael Stenfelt ◽  
Konstantinos Kyprianidis
Keyword(s):  
High Pressure ◽  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Diagnostic Algorithm ◽  
Data Driven ◽  
Intermediate Pressure ◽  
Path Component ◽  
Fault Diagnostic ◽  
Component Faults ◽  
Pressure Compressor

Abstract Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.

An Episode in the History of PreCrime

2018 ◽  
Vol 48 (5) ◽  
pp. 637-647
Author(s):  
Rebecca Lemov
Keyword(s):  
Mathematical Models ◽  
Crime Prevention ◽  
Scientific Study ◽  
Behavioral Data ◽  
Data Driven ◽  
Special Issue ◽  
Predictive Policing ◽  
The Core ◽  
Current Spread ◽  
History Of

This article traces the rise of “predictive” attitudes to crime prevention. After a brief summary of the current spread of predictive policing based on person-centered and place-centered mathematical models, an episode in the scientific study of future crime is examined. At UCLA between 1969 and 1973, a well-funded “violence center” occasioned great hopes that the quotient of human “dangerousness”—potential violence against other humans—could be quantified and thereby controlled. At the core of the center, under the direction of interrogation expert and psychiatrist Louis Jolyon West, was a project to gather unprecedented amounts of behavioral data and centrally store it to identify emergent crime. Protesters correctly seized on the violence center as a potential site of racially targeted experimentation in psychosurgery and an example of iatrogenic science. Yet the eventual spectacular failure of the center belies an ultimate success: its data-driven vision itself predicted the Philip K. Dick–style PreCrime policing now emerging. The UCLA violence center thus offers an alternative genealogy to predictive policing. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

Fractal analysis and control of the competition model

2016 ◽  
Vol 09 (03) ◽  
pp. 1650045 ◽  
Author(s):  
Mianmian Zhang ◽  
Yongping Zhang
Keyword(s):  
Feedback Control ◽  
Mathematical Models ◽  
Fractal Analysis ◽  
Fractal Geometry ◽  
Julia Set ◽  
Julia Sets ◽  
Competition Model ◽  
Simulation Results ◽  
Population Competition ◽  
And Control

Lotka–Volterra population competition model plays an important role in mathematical models. In this paper, Julia set of the competition model is introduced by use of the ideas and methods of Julia set in fractal geometry. Then feedback control is taken on the Julia set of the model. And synchronization of two different Julia sets of the model with different parameters is discussed, which makes one Julia set change to be another. The simulation results show the efficacy of these methods.

scholarly journals Mathematical Models for Assessing Vaccination Scenarios in Several Provinces in Indonesia

2020 ◽  
Author(s):  
N. Nuraini ◽  
K. Khairudin ◽  
P. Hadisoemarto ◽  
H. Susanto ◽  
A. Hasan ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Age Groups ◽  
Transmission Model ◽  
West Java ◽  
Dynamic Transmission Model ◽  
Simulation Results ◽  
Age Structured

AbstractTo mitigate more casualties from the COVID-19 outbreak, this study assessed optimal vaccination scenarios, considering some existing healthcare conditions and some assumptions, by developing SIQRD (Susceptible-Infected-Quarantine-Recovery-Death) models for Jakarta, West Java, and Banten, in Indonesia. The models included an age-structured dynamic transmission model that naturally could give different treatments among age groups of population. The simulation results show that the timing and period’s length of the vaccination should be well planned and prioritizing particular age groups will give significant impact on the total number of casualties.

scholarly journals Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4642
Author(s):  
Li Dai ◽  
Dahai You ◽  
Xianggen Yin
Keyword(s):  
Robust Optimization ◽  
Dirichlet Process ◽  
Forecast Error ◽  
Optimization Methods ◽  
Optimization Method ◽  
Gaussian Mixture ◽  
Data Driven ◽  
Uncertainty Sets ◽  
Uncertainty Set ◽  
Simulation Results

Traditional robust optimization methods use box uncertainty sets or gamma uncertainty sets to describe wind power uncertainty. However, these uncertainty sets fail to utilize wind forecast error probability information and assume that the wind forecast error is symmetrical and independent. This assumption is not reasonable and makes the optimization results conservative. To avoid such conservative results from traditional robust optimization methods, in this paper a novel data driven optimization method based on the nonparametric Dirichlet process Gaussian mixture model (DPGMM) was proposed to solve energy and reserve dispatch problems. First, we combined the DPGMM and variation inference algorithm to extract the GMM parameter information embedded within historical data. Based on the parameter information, a data driven polyhedral uncertainty set was proposed. After constructing the uncertainty set, we solved the robust energy and reserve problem. Finally, a column and constraint generation method was employed to solve the proposed data driven optimization method. We used real historical wind power forecast error data to test the performance of the proposed uncertainty set. The simulation results indicated that the proposed uncertainty set had a smaller volume than other data driven uncertainty sets with the same predefined coverage rate. Furthermore, the simulation was carried on PJM 5-bus and IEEE-118 bus systems to test the data driven optimization method. The simulation results demonstrated that the proposed optimization method was less conservative than traditional data driven robust optimization methods and distributionally robust optimization methods.

scholarly journals Integration of Single-Center Data-Driven Vital Sign Parameters into a Modified Pediatric Early Warning System

2017 ◽  
Vol 18 (5) ◽  
pp. 469-476 ◽  
Author(s):  
Catherine E. Ross ◽  
Iliana J. Harrysson ◽  
Veena V. Goel ◽  
Erika J. Strandberg ◽  
Peiyi Kan ◽  
...  
Keyword(s):  
Early Warning ◽  
Vital Sign ◽  
Early Warning System ◽  
Warning System ◽  
Data Driven ◽  
Single Center

scholarly journals Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways

2006 ◽  
Vol 3 (9) ◽  
pp. 515-526 ◽  
Author(s):  
Fei Hua ◽  
Sampsa Hautaniemi ◽  
Rayka Yokoo ◽  
Douglas A Lauffenburger
Keyword(s):  
Decision Tree ◽  
Mathematical Models ◽  
Large Scale ◽  
Therapeutic Interventions ◽  
Data Driven ◽  
Signalling Pathways ◽  
Apoptotic Pathway ◽  
Tree Analysis ◽  
Modelling Framework ◽  
Novel Approach

Mathematical models of highly interconnected and multivariate signalling networks provide useful tools to understand these complex systems. However, effective approaches to extracting multivariate regulation information from these models are still lacking. In this study, we propose a data-driven modelling framework to analyse large-scale multivariate datasets generated from mathematical models. We used an ordinary differential equation based model for the Fas apoptotic pathway as an example. The first step in our approach was to cluster simulation outputs generated from models with varied protein initial concentrations. Subsequently, decision tree analysis was applied, in which we used protein concentrations to predict the simulation outcomes. Our results suggest that no single subset of proteins can determine the pathway behaviour. Instead, different subsets of proteins with different concentrations ranges can be important. We also used the resulting decision tree to identify the minimal number of perturbations needed to change pathway behaviours. In conclusion, our framework provides a novel approach to understand the multivariate dependencies among molecules in complex networks, and can potentially be used to identify combinatorial targets for therapeutic interventions.

scholarly journals Data-driven selection and parameter estimation for DNA methylation mathematical models

2019 ◽  
Vol 467 ◽  
pp. 87-99 ◽  
Author(s):  
Karen Larson ◽  
Loukas Zagkos ◽  
Mark Mc Auley ◽  
Jason Roberts ◽  
Nikos I. Kavallaris ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Parameter Estimation ◽  
Mathematical Models ◽  
Data Driven
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