Various Types Of Uncertainty Research Articles

Uncertainty in Structural Engineering

Structural engineering design is replete with uncertainties, some of which are obvious and some of which many engineers may never have considered. This paper is an examination of the uncertainties facing structural engineers and the ways that engineers have developed to handle those uncertainties. Uncertainty can be separated into two categories: Aleatory, related to luck or chance, and epistemic, related to knowledge. This breakdown has an impact on how we handle the various types of uncertainty. Uncertainty has a range of sources; we consider five broad sources: time, statistical limits, model limits, randomness, and human error. These five sources will be examined using examples from structural engineering, particularly with respect to the allowable stress design and LRFD code formats. Some uncertainties are explicitly dealt with in design codes, some are dealt with through quality control measures, and some are dealt with in implicit ways that we often do not think much about, e.g., heuristics. Design codes can deal with uncertainties caused by randomness, statistical limits, some aspects of time, and modeling. Other uncertainties such as human error must be dealt with using quality control methods, such as peer reviews and construction inspection.

Evaluating structural and topological consistency of complex regions with broad boundaries in multi-resolution spatial databases

Multi-resolution or multi-scale spatial databases store and manage multiple representations of spatial objects in the same area, so consistency among multiple representations of the same objects should be evaluated and maintained. Although many approaches have been proposed to check inconsistencies in multi-resolution databases, there is still a lack of effective approaches working for complex objects, especially for regions with broad boundaries which is a general model for representing various types of uncertainties. This paper presents approaches for evaluating structural and topological consistency among multiple representations of complex regions with broad boundaries (CBBRs) based on map generalization operators: merging, dropping, and hybrid of these two. For evaluation of structural consistency, all possible multiple representations of a CBBR are generated automatically and organized into a structured neighborhood graph, and then correspondences and equivalences among the multiple representations are defined to determine whether two representations at different levels of detail are structurally consistent. For evaluation of topological consistency, the topological relations between all pairs of regions in two CBBRs are considered, and their variation with change of spatial scale is analyzed. Since the approaches in this paper are built on a hiearchical representation of CBBRs with arbitrarily complex structure, they will also work well for evaluating consistency among multiple representations of complex objects.

Performance assessments for the geological storage of carbon dioxide: Learning from the radioactive waste disposal experience

The geological storage of carbon dioxide is currently being considered as a possible technology for reducing emissions to atmosphere. Although there are several operational sites where carbon dioxide is stored in this way, methods for assessing the long-term performance and safety of geological storage are at an early stage of development. In this paper the similarities and differences between this field and the geological disposal of radioactive wastes are considered. Priorities are suggested for the development of performance assessment methods for carbon dioxide storage based on areas where experience from radioactive waste disposal can be usefully applied. These include, inter alia, dealing with the various types of uncertainty, using systematic methodologies to ensure an auditable and transparent assessment process, developing whole system models and gaining confidence to model the long-term system evolution by considering information from natural systems. An important area of data shortage remains the potential impacts on humans and ecosystems.

MO‐D‐M100J‐03: Reducing the Sensitivity of IMPT Treatment Plans to Setup Errors and Range Variations

Purpose: Intensity modulated proton therapy (IMPT) has the ability to deliver highly conformal dose distributions to tumors of complex shape. However, the accuracy of IMPT treatment plans is potentially compromised by various uncertainties, including setup errors and variations of the range of a proton beam in the patient. Method and Materials: We present two treatment planning concepts for IMPT which incorporate uncertainties into the optimization. The first approach minimizes the expectation value of the deviation of delivered dose and prescribed dose, assuming the delivered dose depends on several random variables that model the uncertainty. The second approach minimizes the maximal dose deviation that can occur. Results: It is shown that these methods can utilize the physical characteristics of the proton beam to make treatment plans relatively insensitive to a particular type of uncertainty.A treatment plan optimized while accounting for range uncertainties avoids placing a bragg peak directly in front of the OAR. Instead, the lateral fall‐off of the pencil beam is utilized in order to avoid the risk of overdosing the OAR. In addition, the dose distributions delivered by individual beams are relatively homogeneous in beam direction. This ensures tumor coverage and homogeneity.On the other hand, treatment plans optimized by accounting for setup errors (modeled as a rigid translation of the entire patient) utilize the distal fall‐off at a transition of OAR and tumor. In addition, the dose distributions delivered by individual beams avoid steep dose gradients perpendicular to the beam direction to ensure tumor homogeneity.Robustness of a treatment plan with respect to both setup error and range variations is possible to a limited degree only, since both types or uncertainty favor conflictive treatment plans. Conclusion: The presented methods are valuable to make IMPT treatment plans insensitive to various types of uncertainty.

SENORA: A P2P Service-Oriented Framework for Collaborative Multirobot Sensor Networks

SENORA is an open hardware and software architecture for the cooperative coordination of multiple heterogeneous mobile robots operating in a common environment. It is designed to meet the stringent requirements of modern loosely coupled multirobot architectures, such as flexibility, reliability, and fault tolerance. As such, the proposed architecture enables the robots to cope with the ubiquitous presence of various types of uncertainties in their operating environments. SENORA is a fully autonomous and scalable sensory-based peer-to-peer (P2P) framework. It also offers a real-time inter-robot communication protocol and it is based on the state-of-the-art P2P technology, which is specifically designed to satisfy the requirements of physical sensory data publishing and fusion. This architecture is implemented and evaluated on a team of indoor mobile robots. The test results manifest the architecture's distinguished features and capabilities

A financial model of flexible manufacturing systems planning under uncertainty: identification, valuation and applications of real options

Flexible manufacturing systems (FMS) have been considered to be essential for manufacturers to succeed in the uncertain market place. Flexibility typically comes at a price and is only valuable as a hedge against environmental uncertainty. It is important to determine an appropriate level of flexibility in the production system while considering the tradeoffs between its costs and benefits. This paper proposes to apply a real option theoretic approach to the modeling and analysis of various types of uncertainty involved in an FMS's operational environment. In comparison with financial options, real options in the context of an FMS, namely, production options, are identified and accordingly a pricing model for production options is proposed based on the option theory. Based on the valuation of production options, a general framework of flexibility planning is formulated. The real option approach surmounts traditional discounted cash flow analysis based valuation methods that tend to ignore the upside potentials to an investment from management flexibility. The proposed model is tested in a refrigerator company that deals with high product variety and uncertain demand.

Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means

In many pattern recognition applications, it may be impossible in most cases to obtain perfect knowledge or information for a given pattern set. Uncertain information can create imperfect expressions for pattern sets in various pattern recognition algorithms. Therefore, various types of uncertainty may be taken into account when performing several pattern recognition methods. When one performs clustering with fuzzy sets, fuzzy membership values express assignment availability of patterns for clusters. However, when one assigns fuzzy memberships to a pattern set, imperfect information for a pattern set involves uncertainty which exist in the various parameters that are used in fuzzy membership assignment. When one encounters fuzzy clustering, fuzzy membership design includes various uncertainties (e.g., distance measure, fuzzifier, prototypes, etc.). In this paper, we focus on the uncertainty associated with the fuzzifier parameter m that controls the amount of fuzziness of the final C-partition in the fuzzy C-means (FCM) algorithm. To design and manage uncertainty for fuzzifier m, we extend a pattern set to interval type-2 fuzzy sets using two fuzzifiers m1 and m2 which creates a footprint of uncertainty (FOU) for the fuzzifier m. Then, we incorporate this interval type-2 fuzzy set into FCM to observe the effect of managing uncertainty from the two fuzzifiers. We also provide some solutions to type-reduction and defuzzification (i.e., cluster center updating and hard-partitioning) in FCM. Several experimental results are given to show the validity of our method

Uncertainty analysis via aggregation operators for cost estimation in a large-scale nuclear project

Cost estimation for large projects can be difficult because of many complex factors and uncertainties involved. It is often the case that only very rough guesses of actual costs may be provided. In this paper, we will examine the various types of uncertainty in the cost estimation problem of a large-scale nuclear project, namely, the MYRRHA project at SCK_CEN. We will then use a few examples from the conceptual phase of the project to illustrate the practical use of a software package, the IDS Cost Estimator, in cost analysis of nuclear systems. It is demonstrated that the IDS Cost Estimator can take care of several types of uncertainties in cost estimation and provide user friendly interfaces for data collection, reporting and display. Moreover, several common aggregation operators are extended under the ordered 3-tuple space to achieve aggregation of any items and uncertainty analysis by output of the IDS cost estimator.

A Bayesian analysis of NSW eastern king prawn stocks ( Melicertus plebejus) using multiple model structures

The eastern king prawn ( Melicertus plebejus) is a valuable target species for commercial fisheries operating on the Australian east coast. The Bayesian analysis presented here aims to determine the current state and productivity of the NSW component of the eastern king prawn stock and analyse the possible consequences of altering commercial catches in the future. The Bayesian approach is well suited to both these aims, particularly given the significant uncertainty about the true population dynamics of the stock, and the multiple sources of information available. The sampling/importance resampling method was applied as it is numerically robust and straightforward to implement. Various types of uncertainty were incorporated into this analysis including: process and observation error, uncertainty in model structure, and uncertainty associated with the parameter values (captured with prior probability distribution functions). A delay–difference model was used with four different representations of recruitment. Each of the four models examined provided differing results for stock depletion since 1984/1985. Despite this uncertainty, none of the models suggested that the stock has been heavily depleted since 1984/1985. The analysis also identifies 2003/2004 as a particularly poor year for production (as was 1984/1985) but that such events lie within the limits of historically observed variability. Projections of the modelled stock dynamics into future years indicate that the stock does not appear to be at high-risk in the near future. Finally, the results of the decision analysis suggest that significant changes in the future catch are not expected to have a large impact on catch rates or the stock depletion ratio. These results, however, are dependent upon the assumption of continued and robust recruitment from Queensland.

Waste load allocation modeling with fuzzy goals; simulation-optimization approach

Presence of various types of uncertainties in water quality management problems has been recognized as one of the major challenges in water quality modeling. Vagueness, lack of adequate data and nonlinearity of cost and/or benefit functions in most of water quality and waste load allocation management problems have reduced the capability of direct inclusion of uncertainty analysis in the management models. This study presents a fuzzy waste load allocation model in which cost function and the water quality standards or the goals of dischargers and pollution control agencies are expressed with appropriate linear and/or nonlinear and nondecreasing and/or nonincreasing membership functions. QUAL2E and Classified Population Genetic Algorithm (CPGA) were coupled to develop the optimum strategy resulting in maximum value of the minimum nonzero membership values, which represent the optimum satisfaction level of the conflicting goals. Number of constraint violations was used to penalize the fitness function in order to eliminate the infeasible solutions at the final results. The model was applied to a hypothetical case example. Results show a very suitable convergence of the proposed algorithm to good of possibility to the near global optima. Effects of linear and nonlinear membership functions are examined and the results are analyzed.

The effects of behavioral and outcome feedback on prudent decision-making under conditions of present and future uncertainty

AbstractOne of the largest reasons decision-makers make bad decisions (act imprudently) is that the world is full of uncertainty, we feel uncertain about the consequences of our actions. Participants played a repeated game in which decisions were made under various types of uncertainty (either no uncertainty, uncertainty about the present consequences of behaviors, uncertainty about the future consequences of behavior, or both types of uncertainty). The game required prudent decision making for success. While playing the game one of three types of feedback was placed between trials, either no feedback, behavioral feedback, or behavioral plus outcome feedback. Prudent decision-making decreased when both types of uncertainty were added. Further, the addition of feedback increased prudent decision-making when future uncertainty was present. The increase in prudent decisions appears to be from feedback’s ability to allow us to create probabilities associated with behaviors and their consequences, implying that anything that reduces the uncertainty people feel in a world full of uncertainty will increase their ability to make prudent decisions.

Least squares estimation of a linear regression model with LR fuzzy response

The problem of regression analysis in a fuzzy setting is discussed. A general linear regression model for studying the dependence of a LR fuzzy response variable on a set of crisp explanatory variables, along with a suitable iterative least squares estimation procedure, is introduced. This model is then framed within a wider strategy of analysis, capable to manage various types of uncertainty. These include the imprecision of the regression coefficients and the choice of a specific parametric model within a given class of models. The first source of uncertainty is dealt with by exploiting the implicit fuzzy arithmetic relationships between the spreads of the regression coefficients and the spreads of the response variable. Concerning the second kind of uncertainty, a suitable selection procedure is illustrated. This consists in maximizing an appropriately introduced goodness of fit index, within the given class of parametric models. The above strategy is illustrated in detail, with reference to an application to real data collected in the framework of an environmental study. In the final remarks, some critical points are underlined, along with a few indications for future research in this field.

Evaluating the ecological and operational basis of vegetation condition assessments

Evaluating the ecological and operational basis of vegetation condition assessments. Emma Gorrod School of Botany, University of Melbourne, Parkville, Vic. 3010, Australia, and NSW Department of Environment & Conservation, PO Box 1967, Hurstville, NSW 2220, Australia. Email: e.gorrod@pgrad.unimelb.edu.au. Key words: habitat, management, uncertainty, vegetation condition. Vegetation condition assessments as management tools. Vegetation condition can be defined as the capacity of a site to provide habitat for all the indigenous species that may reasonably be expected to use it. Quantitative information on vegetation condition has recently been incorporated into policies and legislation for native vegetation management in Australia (ANZECC 1999; Natural Resources and Environment 2003; NSW Native Vegetation Regulation 2005). The general aim of each of these policies is to achieve a ‘net gain’ in the quality and extent of native vegetation. Indices of vegetation condition have been developed in a number of jurisdictions for this purpose, including Habitat Hectares (Parkes et al. 2003) in Victoria and BioMetric (Gibbons et al. 2005) in New South Wales. Habitat Hectares aims to assess how ‘natural’ a site is by comparing it to the same vegetation type in the absence of major ecosystem changes that have occurred following European settlement of Australia (Parkes et al. 2003). BioMetric aims to assess the value of native vegetation for terrestrial biodiversity at a range of scales (Gibbons et al. 2005). Both indices score a similar set of site and landscape context attributes, with different scoring systems and index construction. Human value judgements are embedded in the selection of attributes and scoring methods of these indices. Evaluating the ecological basis of vegetation condition assessments. A number of theoretical and practical methods will be used to evaluate the ecological basis of vegetation condition assessments, commencing with an analysis of the ecological theory and assumptions that underlie these indices. The properties of the indices will be investigated by constructing a matrix to assess the correspondence between the attributes scored in condition assessments and habitat requirements for a range of species that may be expected to use a patch of grassy woodland. This will help to identify groups of species and groups of habitat attributes for which vegetation condition assessments theoretically perform both well and poorly. I will assess vegetation condition assessments in patches of grassy woodland in a range of condition states on the Cumberland Plain. I will also record presence of groups of organisms including plants, birds and invertebrates and additional habitat information. These data will be used to validate the predictions of the matrix analysis and potentially lead to recommendations for improving the indices’ sensitivity to habitat suitability for groups of organisms. In addition, the relationship between vegetation condition as measured by these indices and the contribution of a patch to population viability of will be examined for selected species. Evaluating the operational basis of vegetation condition assessments. The various types of uncertainty associated with vegetation condition assessments, including both epistemic and linguistic uncertainties, will be evaluated. Operational uncertainties will be examined by comparing conducting vegetation condition assessments carried out by different assessors in the field. I will investigate how uncertainty may be incorporated into these indices through relatively simple methods. Management implications. This research is intended to improve understanding of the strengths, weaknesses and sensitivities of indices of vegetation condition. The implications for achieving management objectives, particularly ‘net gain’ in native vegetation quality, will be considered.

The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties

Many multiple attribute decision analysis (MADA) problems are characterised by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) approach has been developed in the 1990s and in the recent years to support the solution of MADA problems with ignorance, a kind of probabilistic uncertainty. In this paper, the ER approach is further developed to deal with MADA problems with both probabilistic and fuzzy uncertainties. In this newly developed ER approach, precise data, ignorance and fuzziness are all modelled under the unified framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A utility-based grade match method is proposed to transform both numerical data and qualitative (fuzzy) assessment information of various formats into the fuzzy belief structure. A new fuzzy ER algorithm is developed to aggregate multiple attributes using the information contained in the fuzzy belief matrix, resulting in an aggregated fuzzy distributed assessment for each alternative. Different from the existing ER algorithm that is of a recursive nature, the new fuzzy ER algorithm provides an analytical means for combining all attributes without iteration, thus providing scope and flexibility for sensitivity analysis and optimisation. A numerical example is provided to illustrate the detailed implementation process of the new ER approach and its validity and wide applicability.

A Partially Observed Markov Decision Process for Dynamic Pricing

In this paper, we develop a stylized partially observed Markov decision process (POMDP) framework to study a dynamic pricing problem faced by sellers of fashion-like goods. We consider a retailer that plans to sell a given stock of items during a finite sales season. The objective of the retailer is to dynamically price the product in a way that maximizes expected revenues. Our model brings together various types of uncertainties about the demand, some of which are resolvable through sales observations. We develop a rigorous upper bound for the seller’s optimal dynamic decision problem and use it to propose an active-learning heuristic pricing policy. We conduct a numerical study to test the performance of four different heuristic dynamic pricing policies in order to gain insight into several important managerial questions that arise in the context of revenue management.

The epistemological challenge of self-modifying systems: Governance and sustainability in the post-normal science era

In this paper, we conceptualize the difference among risk, uncertainty and ignorance and focus on a theoretical analysis of “modeling relation” applied to self-modifying systems within the framework of post-normal science. We provide an overview of the concept of risk and uncertainty elaborating on the work of F. Knight. We extend R. Rosen's theory of “modeling relation” originally aimed in natural science to the issue of science for governance. Knight's analysis of risk and uncertainty can be used to generate a list of various types of uncertainty involved in every stage to model self-modifying systems. Such a list is relevant for gaining insight into the issue of science for governance. It is important for those attempting to model sustainability, especially in the field of ecological economics, to be aware of the theoretical difference between models and similes for self-modifying systems.

Uncertainty Analysis in Atmospheric Dispersion Modeling

The concentration of a pollutant in the atmosphere is a random variable that cannot be predicted accurately, but can be described using quantities such as ensemble mean, variance, and probability distribution. There is growing recognition that the modeled concentrations of hazardous contaminants in the atmosphere should be described in a probabilistic framework. This paper discusses the various types of uncertainties in atmospheric dispersion models, and reviews sensitivity/uncertainty analysis methods to characterize and/or reduce them. Evaluation and quantification of the range of uncertainties in predictions yield a deeper insight into the capabilities and limitations of atmospheric dispersion models, and increase our confidence in decision-making based on models.

Fuzzy Rule-Based Evidential Reasoning Approach for Safety Analysis

This paper aims at proposing a framework for modelling the safety of an engineering system with various types of uncertainties using a fuzzy rule-based evidential reasoning (FURBER) approach. In the framework, parameters used to define the safety level, including failure rate, failure consequence severity and failure consequence probability, are described using fuzzy linguistic variables; a fuzzy rule-base designed on the basis of a belief structure is used to capture uncertainty and non-linear relationships between these parameters and the safety level; and the inference of the rule-based system is implemented using the evidential reasoning algorithm. A numerical study of collision risk analysis for a kind of offshore platform is used to illustrate the application of the proposed approach.

Robust sliding mode control of an electrorheological suspension system with parameter perturbations

This paper presents a robust feedback control of an electrorheological (ER) vehicle suspension system with parameter uncertainties. There exist various types of uncertainties in practical vibration control of an ER suspension system, for example, the yield stress of ER fluid that is varied with the operation temperatures, and the sprung mass is to be changed by the number of riding persons and payload. Therefore, it is necessary to design a robust controller, which compensates parameter uncertainties for a vehicle featuring ER suspension system. After identifying the yield stress variation of the employed ER fluid with respect to temperatures, a quarter-vehicle ER suspension system with parameter uncertainties is constructed. A robust sliding mode controller, which has inherent robustness against system uncertainties, is then formulated by treating the sprung mass and the yield stress of the ER fluid as uncertain parameters. For the demonstration of robustness and performance of the proposed sliding mode controller, control responses such as vertical displacement and acceleration are presented in time and frequency domains.

A TAXONOMY AND TREATMENT OF UNCERTAINTY FOR ECOLOGY AND CONSERVATION BIOLOGY

Uncertainty is pervasive in ecology where the difficulties of dealing with sources of uncertainty are exacerbated by variation in the system itself. Attempts at classifying uncertainty in ecology have, for the most part, focused exclusively on epistemic uncertainty. In this paper we classify uncertainty into two main categories: epistemic uncertainty (uncertainty in determinate facts) and linguistic uncertainty (uncertainty in language). We provide a classification of sources of uncertainty under the two main categories and demonstrate how each impacts on applications in ecology and conservation biology. In particular, we demonstrate the importance of recognizing the effect of linguistic uncertainty, in addition to epistemic uncertainty, in ecological applications. The significance to ecology and conservation biology of developing a clear understanding of the various types of uncertainty, how they arise and how they might best be dealt with is highlighted. Finally, we discuss the various general strategies for dealing with each type of uncertainty and offer suggestions for treating compounding uncertainty from a range of sources.