Uncertain Observations Research Articles

Event-based robust state estimator for linear time-varying system with uncertain observations and randomly occurring uncertainties

This paper is concerned with the event-based robust state estimation problem for discrete time-varying system with uncertain observations and randomly occurring uncertainties. Norm-bounded uncertainty occurring in a random way is introduced in the state matrix. A Bernoulli distributed random variable is employed to characterize uncertain observations phenomenon. To efficiently utilize the network resources, an event-triggered scheme is adopted here to transmit the measurements to the estimator. The purpose of this paper is to propose two-stage event-based estimators including predictor and filter such that, in the presence of uncertain observations, randomly occurring uncertainties and event-based scheme, upper bounds on the estimation error covariance can be established and estimator׳s parameters are obtained. By minimizing the trace of estimation error covariances, estimator׳s parameters are derived, which are the solutions to discrete Riccati difference equations. The proposed estimation algorithm, expressed in a recursive form, involves the occurrence probabilities of uncertain observations and randomly occurring uncertainties, the threshold and an event indictor variable caused by the event-triggered scheme. Finally, numerical simulation results are provided to demonstrate the effectiveness of the developed estimators.

Harnessing ontology and machine learning for RSO classification.

Classification is an important part of resident space objects (RSOs) identification, which is a main focus of space situational awareness. Owing to the absence of some features caused by the limited and uncertain observations, RSO classification remains a difficult task. In this paper, an ontology for RSO classification named OntoStar is built upon domain knowledge and machine learning rules. Then data describing RSO are represented by OntoStar. A demo shows how an RSO is classified based on OntoStar. It is also shown in the demo that traceable and comprehensible reasons for the classification can be given, hence the classification can be checked and validated. Experiments on WEKA show that ontology-based classification gains a relatively high accuracy and precision for classifying RSOs. When classifying RSOs with imperfect data, ontology-based classification keeps its performances, showing evident advantages over classical machine learning classifiers who either have increases of 5 % at least in FP rate or have decreases of 5 % at least in indexes such as accuracy, precision and recall.

ELECTROMAGNETIC SPINDOWN OF A TRANSIENT ACCRETING MILLISECOND PULSAR DURING QUIESCENCE

ABSTRACT The measured spindown rates in quiescence of the transient accreting millisecond pulsars IGR J00291+5934, XTE J1751–305, SAX J1808.4–3658, and Swift J1756.9–2508 have been used to estimate the magnetic moments of these objects assuming standard magnetic dipole braking. It is shown that this approach leads to an overestimate if the amount of residual accretion is enough to distort the magnetosphere away from a force-free configuration through magnetospheric mass loading or crushing, so that the lever arm of the braking torque migrates inside the light cylinder. We derive an alternative spindown formula and calculate the residual accretion rates where the formula is applicable. As a demonstration we apply the alternative spindown formula to produce updated magnetic moment estimates for the four objects above. We note that based on current uncertain observations of quiescent accretion rates, magnetospheric mass loading and crushing are neither firmly indicated nor ruled out in these four objects. Because quiescent accretion rates are not measured directly (only upper limits are placed), without more data it is impossible to be confident about whether the thresholds for magnetospheric mass loading or crushing are reached or not.

Maintenance planning using continuous-state partially observable Markov decision processes and non-linear action models

The signs of deterioration in worldwide infrastructure and the associated socio-economic and environmental losses call for sustainable resource management and policy-making. To this end, this work presents an enhanced variant of partially observable Markov decision processes (POMDPs) for the life cycle assessment and maintenance planning of infrastructure. POMDPs comprise a method, commonly employed in the field of robotics, for decision-making on the basis of uncertain observations. In the work presented herein, a continuous-state POMDP formulation is presented which is adapted to the problem of decision-making for optimal management of civil structures. The aforementioned problem may comprise non-linear and non-deterministic action and observation models. The continuous-state POMDP is herein coupled with a normalised unscented transform (NUT) in order to deliver a framework able to tackle non-linearities that likely characterise action models. The capabilities of this enhanced framework and its applicability to the maintenance planning problem are presented via two applications. In a first illustrative example, the use of the NUT is demonstrated within the framework of the value iteration algorithm. Next, the proposed continuous-state framework is compared against a discrete-state formulation for implementation on a life cycle assessment problem.

A mathematical programming approach to sample coefficient of variation with interval-valued observations

The coefficient of variation is a useful statistical measure, which has long been widely used in many areas. In real-world applications, there are situations where the observations are inexact and imprecise in nature and they have to be estimated. This paper investigates the sample coefficient of variation (CV) with uncertain observations, which are represented by interval values. Since the observations are interval-valued, the derived CV should be interval-valued as well. A pair of mathematical programs is formulated to calculate the lower bound and upper bound of the CV. Originally, the pair of mathematical programs is nonlinear fractional programming problems, which do not guarantee to have global optimum solutions. By model reduction and variable substitutions, the mathematical programs are transformed into a pair of quadratic programs. Solving the pair of quadratic programs produces the global optimum solutions and constructs the interval of the CV. The given example shows that the proposed model is indeed able to help the manufacturer select the most suitable manufacturing process with interval-valued observations.

Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

Catastrophic floods cause significant socio-economical losses. Non-structural measures, such as real-time flood forecasting, can potentially reduce flood risk. To this end, data assimilation methods have been used to improve flood forecasts by integrating static ground observations, and in some cases also remote sensing observations, within water models. Current hydrologic and hydraulic research works consider assimilation of observations coming from traditional, static sensors. At the same time, low-cost, mobile sensors and mobile communication devices are becoming also increasingly available. The main goal and innovation of this study is to demonstrate the usefulness of assimilating uncertain streamflow observations that are dynamic in space and intermittent in time in the context of two different semi-distributed hydrological model structures. The developed method is applied to the Brue basin, where the dynamic observations are imitated by the synthetic observations of discharge. The results of this study show how model structures and sensors locations affect in different ways the assimilation of streamflow observations. In addition, it proves how assimilation of such uncertain observations from dynamic sensors can provide model improvements similar to those of streamflow observations coming from a non-optimal network of static physical sensors. This can be a potential application of recent efforts to build citizen observatories of water, which can make the citizens an active part in information capturing, evaluation and communication, helping simultaneously to improvement of model-based flood forecasting.

Control of dynamical systems with discrete and uncertain observations

In this paper we provide a design methodology to compute control strategies for a primary dynamical system which is operating in a domain where other dynamical systems are present and the interactions between these systems and the primary one are of interest or are being pursued in some sense. The information from the other systems is available to the primary dynamical system only at discrete time instances and is assumed to be corrupted by noise. Having available only this limited and somewhat corrupted information which depends on the noise, the primary system has to make a decision based on the estimated behavior of other systems which may range from cooperative to noncooperative. This decision is reflected in a design of the most appropriate action, that is, control strategy of the primary system. The design is illustrated by considering some particular collision avoidance problem scenarios.

Qualitative Event-Based Fault Isolation under Uncertain Observations


 
 
 For many systems, automatic fault diagnosis is critical to ensuring safe and efficient operation. Fault isolation is performed by analyzing measured signals from the system, and reasoning over the system behavior to determine which faults have occurred, based on models of predicted faulty behavior. For dynamic systems, reasoning may be performed using qualitative analysis of the differences between measured signals and their predicted values, in which observations take the form of qualitative symbols. Such an approach is quick to isolate faults, but depends critically on correct generation of the qualitative symbols from the signals. In this paper, we develop an approach to qualitative event-based fault isolation for dynamic systems that is robust to incorrect qualitative observations. Observations are treated as uncertain, where multiple interpretations of an observation, each with its own probability, are considered. By interpreting observed symbols in a probabilistic manner, the approach degrades gracefully as the number of incorrectly-generated symbols increases. The approach is demonstrated on an electrical power system testbed, and experiments using real data obtained from the hardware demonstrate the improved fault isolation performance in the presence of incorrect symbol generation.
 
 

In silico model-based inference: a contemporary approach for hypothesis testing in network biology.

Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics.

Distributed fusion estimation in networked systems with uncertain observations and Markovian random delays

This paper addresses the least-squares linear estimation problem in networked systems with uncertain observations and one-step random delays in the measurements. The uncertainties in the observations and the delays are modeled by sequences of Bernoulli random variables with different characteristics for each sensor; the uncertainties are described by independent random variables whereas the delays are modeled by homogeneous Markov chains. The estimators are obtained by a distributed fusion method; specifically, for each sensor, local estimation algorithms are derived by using the information provided by the covariance functions of the processes involved in the observation model, as well as the probability distributions of the variables modeling the uncertainties and delays. The distributed fusion filter and fixed-point smoother are then obtained as the linear combination of the corresponding local linear estimators verifying that the mean squared error is minimum.

English

Mobile ad-hoc network technology has gained popularity in recent years by researchers on account of its flexibility, low cost and eases of deployment. The objective of proposed R2M2 model is to evaluate the performance of MANET (Mobile ad hoc Network ) operating with DSR( Dynamic Source Routing) as routing protocol. The R2M2 model for MANET is simulated and implemented using network simulator ns 2.34 and validated using Bayesian rules. The R2M2 model is able to find out the probability of having certain behavior (able to decide reliable or not reliable) of routes, in uncertain observations (not able to decide whether the route is reliable or not) in presence of drop and delay. For performance evaluation of MANET using the proposed R2M2 model input variables (like node density, number of active connections, duration of communication, node movement speed, pause time & data transfer rate etc. ) and output variables drop rate and delay have been taken as output variables. The R2M2 model helps in deciding the certain and uncertain behavior of routes and consequently the nodes and is found satisfactory.

Rejoinder on “Likelihood-based belief function: Justification and some extensions to low-quality data”

This note is a rejoinder to comments by Dubois and Moral about my paper “Likelihood-based belief function: justification and some extensions to low-quality data” published in this issue. The main comments concern (1) the axiomatic justification for defining a consonant belief function in the parameter space from the likelihood function and (2) the Bayesian treatment of statistical inference from uncertain observations, when uncertainty is quantified by belief functions. Both issues are discussed in this note, in response to the discussants' comments.

Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation

The overall objective of this two part study is to highlight the advanced attributes, capabilities and use of stochastic control techniques, and especially Partially Observable Markov Decision Processes (POMDPs), that can address the conundrum of planning optimum inspection/monitoring and maintenance policies based on stochastic models and uncertain structural data in real time. In this second part of the study a distinct, advanced, infinite horizon POMDP formulation with 332 states is cast and solved, related to a corroding reinforced concrete structure and its minimum life-cycle cost. The formation and solution of the problem modernize and extend relevant approaches and motivate the use of POMDP methods in challenging practical applications. Apart from uncertain observations the presented framework can also support uncertain action outcomes, non-periodic inspections and choice availability of inspection/monitoring types and intervals, as well as maintenance actions and action times. It is thus no surprise that the estimated optimum policy consists of a complex combination of a variety of actions, which cannot be achieved by any other method. To be able to solve the problem we resort to a point-based value iteration solver and we evaluate its performance and solution quality for this type of applications. Simpler approximate solvers based on MDPs are also used and compared and the important notions of observation gathering actions and the value of information are briefly discussed.

Adaptive exponential-reaching sliding-mode control for antilock braking systems

Most commercial antilock braking system (ABS) is based on a look-up table. The table is calibrated through laboratory experiments and engineering field tests under specified road conditions, but it is not adaptive. To attack this problem, this paper proposes an adaptive exponential-reaching sliding-mode control (AERSMC) system for an ABS. The proposed AERSMC system is composed of an equivalent controller and an exponential compensator. The equivalent controller uses a functional-linked wavelet neural network (FWNN) to online approximate the system uncertainties and the exponential compensator is designed to eliminate the effect of the approximation error introduced by the FWNN uncertain observer with an exponential-reaching law. In addition, the adaptive laws online-tune the controller parameters in the sense of Lyapunov function to guarantee the system stability. Finally, the simulation results verify that the proposed AERSMC system can achieve favorable slip tracking performance and is robust against parameter variations in the plant.

Retrospective analysis of uncertain eruption precursors at La Soufrière volcano, Guadeloupe, 1975–77: volcanic hazard assessment using a Bayesian Belief Network approach

Scientists monitoring active volcanoes are increasingly required to provide decision support to civil authorities during periods of unrest. As the extent and resolution of monitoring improves, the process of jointly interpreting multiple strands of indirect evidence becomes increasingly complex. Similarities with uncertainties in medical diagnosis suggest a formal evidence-based approach, whereby monitoring data are analysed synoptically to provide probabilistic hazard forecasts. A statistical tool to formalize such inferences is the Bayesian Belief Network (BBN). By explicitly representing conditional dependencies between the volcanological model and observations, BBNs use probability theory to treat uncertainties in a rational and auditable manner, as warranted by the strength of the scientific evidence. A retrospective analysis is given for the 1976 Guadeloupe crisis, using a BBN to provide inferential assessment of the state of the evolving magmatic system and probability of incipient eruption. Conditional dependencies are characterized quantitatively by structured expert elicitation. Analysis of the available monitoring data suggests that at the height of the crisis the probability of magmatic intrusion was high, in accordance with scientific thinking at the time. The corresponding probability of magmatic eruption was elevated in July and August 1976 and signs of precursory activity were justifiably cause for concern. However, collective uncertainty about the future course of the crisis was also substantial. Of all the possible scenarios, the most likely outcome evinced by interpretation of observations on 31 August 1976 was 'no eruption’ (mean probability 0.5); the chance of a magmatic eruption/blast had an estimated mean probability of ~0.4. There was therefore no evidential basis for asserting one scenario to be significantly more likely than another. Our analysis adds objective probabilistic expression to the volcanological narrative at the time of the 1976 crisis, and demonstrates that a formal evidential case could have supported the authorities' concerns about public safety and decision to evacuate. Revisiting the episode highlights many challenges for modern, contemporary decision making under conditions of considerable uncertainty, and suggests the BBN is a suitable framework for marshalling multiple, uncertain observations, model results and interpretations. The formulation presented here can be developed as a tool for ongoing use in the volcano observatory.

Dealing with Observation Outages within Navigation Data using Gaussian Process Regression

Gaussian process regression (GPR) is used in a Spare-grid Quadrature Kalman filter (SGQKF) for Strap-down Inertial Navigation System (SINS)/odometer integrated navigation to bridge uncertain observation outages and maintain an estimate of the evolving SINS biases. The SGQKF uses nonlinearized dynamic models with complex stochastic nonlinearities so the performance degrades significantly during observation outages owing to the uncertainties and noise. The GPR calculates the residual output after factoring in the contributions of the parametric model that is used as a nonlinear SINS error predictor integrated into the SGQKF. The sensor measurements and SINS output deviations from the odometer are collected in a data set during observation availability. The GPR is then applied to predict SINS deviations from the odometer and then the predicted SINS deviations are fed to the SGQKF as an actual update to estimate all SINS biases during observation outages. We demonstrate our method's effectiveness in bridging uncertain observation outages in simulations and in real road tests. The results agree with the theoretical analysis, which demonstrate that SGQKF using GPR can maintain an estimate of the evolving SINS biases during signal outages.

Evidence equilibrium: Nash equilibrium in judgment processes

The purpose of evidence inference is to judge truth values of the environment states under uncertain observations. This has been modeled as mathematical problems, using Bayesian inference, Dempster-Shafer theory, etc. After formalizing judgment processes in evidence inference, we found that the judgment process under uncertainty can be modeled as a Bayesian game of subjective belief and objective evidence. Another, the rational judgment involves a perfect Nash equilibrium. Evidence equilibrium is the Nash equilibrium in judgment processes. It helps us to maximize the possibility to avoid bias, and minimize the requirement for evidence. This will be helpful for the dynamic analysis of uncertain data. In this paper, we provide an Expected k-Conviction (EkC) algorithm for the dynamic data analysis based on evidence equilibrium. The algorithm uses dynamic evidence election and combination to resolve the estimation of uncertainty with time constraint. Our experimental results demonstrate that the EkC algorithm has better efficiency compared with the static evidence combination approach, which will benefit realtime decision making and data fusion under uncertainty.

Quadratic extended filtering in nonlinear systems with uncertain observations

Quadratic extended filtering in nonlinear systems with uncertain observations

向きに依存した不確実性を考慮したスイッチング回帰モデルと太陽光発電データの分析への応用

Switching regression is a data mining tool for revealing intrinsic non-linear dependencies among exploratory and objective variables, and Fuzzy c-Regression Models (FCRM) is an FCM-type switching regression algorithm. In this paper, FCRM is applied to a residential solar electric power analysis. Because meteorological observation of actual sunshine duration is often underestimated from the actual quantity of solar radiation, we often fail to reveal intrinsic relation among solar electric power and sunshine duration. In this paper, FCRM is modied for handling direction-dependent uncertain observations in an element-wise manner and its applicability is demonstrated with several experimental results.

Application of endovascular stent placement as a remedy for spontaneous isolated superior mesenteric artery dissection

To present our experience regarding endovascular stent placement for the treatment of spontaneous isolated superior mesenteric artery dissection (SISMAD) and to evaluate the safety and feasibility of the endovascular therapy. The clinical data from six patients with SISMAD who underwent endovascular stent placement in two institutions from March 2010 to May 2012 were analyzed retrospectively. Four patients were implanted a self-expanding stent, and an additional stent was deployed in two of these patients after the first stent was implanted. One patient was subjected to a self-expanding stent implantation combined with coil embolization. The remaining patient underwent thrombectomy plus partial intimectomy 2 months before a balloon-expandable stent was implanted. All of the patients recuperated uneventfully without any reoccurrence of the symptoms in the follow-up period (range 12-38 months). Contrast-enhanced computer tomography scanning was performed 3 months after the procedure in all of the patients, and the images showed that the false lumen was nearly thrombosed and that the true lumen was maintained patent. Endovascular stent placement is a simple and safe alternative to aggressive surgery or uncertain observation.