4,691 research outputs found
Optimizing fire station locations for the Istanbul metropolitan municipality
Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire stationâs receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbulâs road network, and solve two location modelsâset-covering and maximal-coveringâas what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the cityâs fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years
Aerodynamic Parameters Estimation Using Radial Basis Function Neural Partial Differentiation Method
Aerodynamic parameter estimation involves modelling of force and moment coefficients and computation of stability and control derivatives from recorded flight data. This problem is extensively studied in the past using classical approaches such as output error, filter error and equation error methods. An alternative approach to these model based methods is the machine learning such as artificial neural network. In this paper, radial basis function neural network (RBF NN) is used to model the lateral-directional force and moment coefficients. The RBF NN is trained using k-means clustering algorithm for finding the centers of radial basis function and extended Kalman filter for obtaining the weights in the output layer. Then, a new method is proposed to obtain the stability and control derivatives. The first order partial differentiation is performed analytically on the radial basis function neural network approximated output. The stability and control derivatives are computed at each training data point, thus reducing the post training time and computational efforts compared to hitherto delta method and its variants. The efficacy of the identified model and proposed neural derivative method is demonstrated using real time flight data of ATTAS aircraft. The results from the proposed approach compare well with those from the other
Bibliography and summary of methods related to the error analysis of hybrid computers technical note no. 4
Bibliography and summary of methods used in error analysis of hybrid computer
Optimizing fire station locations for the Istanbul metropolitan municipality
Copyright @ 2013 INFORMSThe Istanbul Metropolitan Municipality (IMM) seeks to determine locations for additional fire stations to build in Istanbul; its objective is to make residences and historic sites reachable by emergency vehicles within five minutes of a fire stationâs receipt of a service request. In this paper, we discuss our development of a mathematical model to aid IMM in determining these locations by using data retrieved from its fire incident records. We use a geographic information system to implement the model on Istanbulâs road network, and solve two location modelsâset-covering and maximal-coveringâas what-if scenarios. We discuss 10 scenarios, including the situation that existed when we initiated the project and the scenario that IMM implemented. The scenario implemented increases the cityâs fire station coverage from 58.6 percent to 85.9 percent, based on a five-minute response time, with an implementation plan that spans three years
Reversible Computation: Extending Horizons of Computing
This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
One of the central elements of any causal inference is an object called
structural causal model (SCM), which represents a collection of mechanisms and
exogenous sources of random variation of the system under investigation (Pearl,
2000). An important property of many kinds of neural networks is universal
approximability: the ability to approximate any function to arbitrary
precision. Given this property, one may be tempted to surmise that a collection
of neural nets is capable of learning any SCM by training on data generated by
that SCM. In this paper, we show this is not the case by disentangling the
notions of expressivity and learnability. Specifically, we show that the causal
hierarchy theorem (Thm. 1, Bareinboim et al., 2020), which describes the limits
of what can be learned from data, still holds for neural models. For instance,
an arbitrarily complex and expressive neural net is unable to predict the
effects of interventions given observational data alone. Given this result, we
introduce a special type of SCM called a neural causal model (NCM), and
formalize a new type of inductive bias to encode structural constraints
necessary for performing causal inferences. Building on this new class of
models, we focus on solving two canonical tasks found in the literature known
as causal identification and estimation. Leveraging the neural toolbox, we
develop an algorithm that is both sufficient and necessary to determine whether
a causal effect can be learned from data (i.e., causal identifiability); it
then estimates the effect whenever identifiability holds (causal estimation).
Simulations corroborate the proposed approach.Comment: 10 pages main body (53 total pages with references and appendix), 5
figures in main body (20 total figures including appendix
Flipping Biological Switches: Solving for Optimal Control: A Dissertation
Switches play an important regulatory role at all levels of biology, from molecular switches triggering signaling cascades to cellular switches regulating cell maturation and apoptosis. Medical therapies are often designed to toggle a system from one state to another, achieving a specified health outcome. For instance, small doses of subpathologic viruses activate the immune systemâs production of antibodies. Electrical stimulation revert cardiac arrhythmias back to normal sinus rhythm. In all of these examples, a major challenge is finding the optimal stimulus waveform necessary to cause the switch to flip. This thesis develops, validates, and applies a novel model-independent stochastic algorithm, the Extrema Distortion Algorithm (EDA), towards finding the optimal stimulus. We validate the EDAâs performance for the Hodgkin-Huxley model (an empirically validated ionic model of neuronal excitability), the FitzHugh-Nagumo model (an abstract model applied to a wide range of biological systems that that exhibit an oscillatory state and a quiescent state), and the genetic toggle switch (a model of bistable gene expression). We show that the EDA is able to not only find the optimal solution, but also in some cases excel beyond the traditional analytic approaches. Finally, we have computed novel optimal stimulus waveforms for aborting epileptic seizures using the EDA in cellular and network models of epilepsy. This work represents a first step in developing a new class of adaptive algorithms and devices that flip biological switches, revealing basic mechanistic insights and therapeutic applications for a broad range of disorders
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