Kinds Of Uncertainties Research Articles

A stochastic network equilibrium model for electric power markets with uncertain demand

ABSTRACT We use the theory of stochastic variational inequalities to develop a network equilibrium model of the whole supply chain of electricity markets: power generation, supply, transmission, and consumption. In particular, we take into account the case where the market demand functions are not exactly known but are affected by some kind of uncertainty. A discretization and truncation procedure is used to numerically solve the stochastic variational inequality model. Monotonicity properties of the operator are investigated and the affine case is analysed in detail. Finally, numerical experiments show the impact of different probability densities of the random variables on the approximated solutions and the scalability of the proposed numerical method for real-world sized problems.

Intelligent decision support system for the control of complex technical systems

This article discusses the design of the control system of complex technical objects. A model of the system for intelligent decision support in the performance of control tasks, characterized by multi-criteria and the presence of various kinds of uncertainties in the source information and expertise. The description of criteria, models and algorithms allowing to expand functionality of hardware of process of parametric admission control of a condition of technical systems is given.

Explore and evaluate innovative value propositions for smart product service system: A novel graphics-based rough-fuzzy DEMATEL method

Abstract The rapid development of advanced information and communication technology enables the mainstream trend of manufacturing value propositions towards smart product service system (PSS). Prospect innovative value propositions (IVPs) emerge in the context of smart PSS. The IVPs analysis plays a critical role in the successful planning and design of smart PSS business model, which includes two key steps: identification and evaluation. However, the existing research rarely systematically explore what value propositions can be identified in the emerging field of smart PSS. Thus, this study conducts a comprehensive IVPs identification through literature review and prioritize these IVPs to provide support for later smart PSS concept design. Compared to other evaluation methods, the Decision Making Trial and Evaluation Laboratory (DEMATEL) presents higher effectiveness to evaluate the interrelationship and importance degree of the identified IVPs. Moreover, the previous fuzzy-based DEMATEL is feasible to handle intrapersonal uncertainty (individual linguistic vagueness), and the previous rough-based DEMATEL can effectively manipulate the interpersonal uncertainty (group preference diversity). Nevertheless, there are few researches on simultaneous manipulation of the two kinds of uncertainty which may lead to inaccurate evaluation results. Therefore, this study develops a novel graphics-based rough-fuzzy DEMATEL method for IVPs evaluation. The effectiveness and accuracy of the proposed evaluation method are illustrated through the application in the smart vehicle service system and the comparisons with several different methods. The proposed evaluation model can provide more accurate and informative results, because it integrates the merits of fuzzy set in coping with the intrapersonal uncertainty and the strength of rough set in handling interpersonal uncertainty, and supports to present decision inconsistency along with the final weights based on a proposed graphics-based operator.

Discrete Adaptive Control with Multiple-Step-Guess Estimation for Brushless DC Motor

The brushless DC motor (BLDCM) speed control system has various kinds of uncertainties, such as reference speed mutation, noise and parameters change, etc. However, proportional integral (PI) control method used widely cannot handle the uncertainties in the control system well. A novel discrete adaptive control with Multiple-Step-Guess (MSG) estimation for BLDCM speed control system is proposed in this contribution. MSG estimation is firstly developed and applied in BLDCM speed control system, which estimate the BLDCM model parameters online with only five steps history information sampled from the input signal and output signal. The tracking adaptive control law is designed to ensure the speed can track reference speed rapidly and accurately. Compared with PI control and recursive least square adaptive control (RLSAC), extensive simulations verify that the BLDCM speed response under MSG adaptive control (MSGAC) has better dynamic and steady state performance in the case of reference speed mutation and BLDCM parameters change. Simulation results illustrate that the novel proposed method is effective and robust for uncertainties of BLDCM speed control system.

Updating for externalists

AbstractThe externalist says that your evidence could fail to tell you what evidence you do or not do have. In that case, it could be rational for you to be uncertain about what your evidence is. This is a kind of uncertainty which orthodox Bayesian epistemology has difficulty modeling. For, if externalism is correct, then the orthodox Bayesian learning norms of conditionalization and reflection are inconsistent with each other. I recommend that an externalist Bayesian reject conditionalization. In its stead, I provide a new theory of rational learning for the externalist. I defend this theory by arguing that its advice will be followed by anyone whose learning dispositions maximize expected accuracy. I then explore some of this theory's consequences for the rationality of epistemic akrasia, peer disagreement, undercutting defeat, and uncertain evidence.

Estimating the Probability Density Function of Remaining Useful Life for Wiener Degradation Process with Uncertain Parameters

The effective prediction of remaining useful life is essential to realize system failure diagnosis and health management. The existing researches often assume that the degradation model is constant or the degradation process is measurable. The accurate degradation model, however, usually can not be established, and the parametric variation and measurement error of the degradation process are unavoidable, which makes it hard to obtain the exact value for predicting the remaining useful life. Regarding this problem, on basis of the concept of first failure time, a real-time probability density function is derived for the Wiener degradation process with the uncertainty of parameters, the stochasticity of degradation process and the randomness of measurement error. The main steps are as follows: firstly, the degradation model with three kinds of uncertainties is established, and then the stochastic degradation state and the parameters of the uncertainty model are estimated by fusion Kalman/UFIR filter; then, the analytical expression of the probability density function of remaining useful life is deduced. Finally, the correctness and effectiveness of the proposed method are verified by a group of comparison experiments and Monte Carlo simulations.

The effects of foreign uncertainty shocks on China’s macro-economy: Empirical evidence from a nonlinear ARDL model

This paper investigates the impacts of uncertainty on China’s macro-economy by a nonlinear cointegrating autoregressive distributed lag (NARDL) model. The three widely used uncertainty proxies—economic policy (EPU), the financial markets (VIX), and energy market (OVX) are considered, and the short-run and long-run nonlinear asymmetric relationship between these three kinds of uncertainty and macroeconomic variables are examined. The empirical results provide significant evidence of short-run relationship between all types of uncertainty shocks and the China’s macro-economy except the effects of OVX on China’s output. Furthermore, in the long run, the results show that the asymmetric VIX seems to be the most relevant determinative uncertainty of China’s macro-economy; the economic policy uncertainty has a long-run asymmetric impact on China’s inflation; the increases in oil price uncertainty may damage China’s output and the decreases in the uncertainty of the energy market may lead to the rises in money supply. The findings of this study have significant implications for investors and policy-makers who are interested in uncertainty risks and who look forward to making more effective decisions.

Interval Energy Flow Analysis in Integrated Electrical and Natural-Gas Systems Considering Uncertainties

As integrated electrical and natural-gas systems (IENGS) are popularized, the uncertainties brought by variation of electrical load, power generation, and gas load should not be ignored. The aim of this paper is to analyze the impact of those uncertain variables on the steady-state operation of the whole systems. In this paper, an interval energy flow model considering uncertainties was built based on the steady-state energy flow. Then, the Krawczyk–Moore interval iterative method was used to solve the proposed model. To obtain precise results of the interval model, interval addition and subtraction operations were performed by affine mathematics. The case study demonstrated the effectiveness of the proposed approach compared with Monte Carlo simulation. Impacts of uncertainties brought by the variation of electrical load, power generation, and gas load were analyzed, and the convergence of energy flow under different uncertainty levels of electrical load was studied. The results led to the conclusion that each kind of uncertainties would have an impact on the whole system. The proposed method could provide good insights into the operating of IENGS with those uncertainties.

Reliability analysis of multi‐state series systems with performance sharing mechanism under epistemic uncertainty

AbstractFor real engineering systems, it is sometimes difficult to obtain sufficient data to estimate the precise values of some parameters in reliability analysis. This kind of uncertainty is called epistemic uncertainty. Because of the epistemic uncertainty, traditional universal generating function (UGF) technique is not appropriate to analyze the reliability of systems with performance sharing mechanism under epistemic uncertainty. This paper proposes a belief UGF (BUGF)‐based method to evaluate the reliability of multi‐state series systems with performance sharing mechanism under epistemic uncertainty. The proposed BUGF‐based reliability analysis method is validated by an illustrative example and compared with the interval UGF (IUGF)‐based methods with interval arithmetic or affine arithmetic. The illustrative example shows that the proposed BUGF‐based method is more efficient than the IUGF‐based methods in the reliability analysis of multi‐state systems (MSSs) with performance sharing mechanism under epistemic uncertainty.

Shortest path problem using Bellman algorithm under neutrosophic environment

An elongation of the single-valued neutrosophic set is an interval-valued neutrosophic set. It has been demonstrated to deal indeterminacy in a decision-making problem. Real-world problems have some kind of uncertainty in nature and among them; one of the influential problems is solving the shortest path problem (SPP) in interconnections. In this contribution, we consider SPP through Bellman’s algorithm for a network using interval-valued neutrosophic numbers (IVNNs). We proposed a novel algorithm to obtain the neutrosophic shortest path between each pair of nodes. Length of all the edges is accredited an IVNN. Moreover, for the validation of the proposed algorithm, a numerical example has been offered. Also, a comparative analysis has been done with the existing methods which exhibit the advantages of the new algorithm.

Probability/Possibility Systems for Modeling of Random/Fuzzy Information with Parallelization Consideration

As a potential tool for handling fuzziness, possibility theory has been proposed for decades but is still far beyond mature. The fundamental reason is that we lack a clear understanding of the nature of randomness/fuzziness and then the connotation of probability/possibility. This work presented a clear definition of randomness/fuzziness and an intuitive definition of possibility as reasonable physical interpretation for existing axiomatic definition of possibility. The concepts of random/fuzzy sample spaces were introduced, upon which the axiomatic definition of possibility was properly reformulated in a structure parallel to the axiomatic definition of probability. Possibility update equation as well as possibility operators of disjunction/conjunction is discussed and justified. Though a simple rule of probability/possibility transformation is available, the whole systems of probability/possibility are found to be not coherent yet still comparable. In situations where both kinds of uncertainties are involved, fusion of one kind of uncertainty into another uncertain inference system is workable, which was further illustrated by an application example of recognizing noncooperative target using feature observations of radar cross section. Parallel computing of probability/possibility is also discussed to cope with the intensive computation challenge of practical problems of high dimension and/or with big data. We conclude that probability/possibility systems are complementary methods for handling of random/fuzzy information.

Distributed adaptive fuzzy tracking algorithms for multiple uncertain mechanical systems

PurposeThis paper aims to investigate the distributed coordinated fuzzy tracking problems for multiple mechanical systems with nonlinear model uncertainties under a directed communication topology.Design/methodology/approachThe dynamic leader case is considered while only a subset of the follower mechanical systems can obtain the leader information. First, this paper approximates the system uncertainties with finite fuzzy rules and proposes a distributed adaptive tracking control scheme. Then, this paper makes a detailed classification of the system uncertainties and uses different fuzzy systems to approximate different kinds of uncertainties. Further, an improved distributed tracking strategy is proposed. Closed-loop systems are investigated using graph theory and Lyapunov theory. Numerical simulations are performed to verify the effectiveness of the proposed methods.FindingsBased on fuzzy control and adaptive control theories, the desired distributed coordinated tracking control strategies for multiple uncertain mechanical systems are developed.Originality/valueCompared with most existing literature, the proposed distributed tracking algorithms use fuzzy control and adaptive control techniques to cope with system nonlinear uncertainties of multiple mechanical systems. Moreover, the improved control strategy not only reduces fuzzy rules but also has higher control accuracy.

A fuzzy approach for the estimation of foreign investment risk based on values of rating indices

The paper discusses the uncertainty resulting from vagueness. Within this topic we present an original version of the fuzzy approach to a foreign investment risk estimation based on values of rating indices. The transition from the basic point values of rating indices into the linguistic values within intervals of linguistic variables of fuzzy logic enables us to take into account the diverse kinds of uncertainty. The theoretical and methodological part submits fundamentals of the general fuzzy model of the vaguely defined problem, which is applied to the problem of the foreign investment “risk” estimation of selected countries of Europe and Asia. The inclusion of a country into one of the categories (“high risk countries”, “conditional risk countries” and “non-risk countries”) is based on a vector of value indexes of the sub-components of business environment quality (corrupt environment, economic stability and political stability).

Uncertainty of the second order

We study the stochastic dynamics of the quasispecies model with inverse Bayesian inference under environmental uncertainty. Inverse Bayesian inference is introduced through the correspondence between Bayesian inference and the replicator equation. We consider environmental uncertainty that is not modeled as the stochastic fitness called uncertainty of the second order. This is in contrast to uncertainty of the first order that can be subsumed by the stochastic fitness. The difference between these two kinds of uncertainty is discussed in the framework of categorical Bayesian probability theory. We analytically show that if the time scale of inverse Bayesian inference is sufficiently larger than that of Bayesian inference, then the quasispecies model exhibits a noise-induced transition. The theoretical result is verified by a numerical simulation.

Pitfalls of Global Harmonization of Systemic Risk Regulation in a World of Financial Innovation

The working hypothesis of international financial regulation is that it should be globally harmonized. This paper contends, to the contrary, that we should be wary about the efficacy of global harmonization, and in particular, harmonization of systemic risk measurement and regulation. The thesis is informed by what I consider two key lessons from the recent global financial crisis. The first lesson is that, when business strategies that internationally-harmonized regulation induces banks to follow go seriously awry, the adverse consequences will spread globally and not be limited to one regulator’s domain. The second lesson is that, innovations in financial technology that have been engines of prosperity across the globe also may contain the seeds of financial calamity with imprudent use and regulatory inattention. In addition, three kinds of uncertainty operate in this context: i) uncertainty regarding how best to define and measure systemic risk; (ii), dynamic uncertainty, that financial institutions respond to regulation in unpredictable ways that tend to undermine regulatory effectiveness; and (iii) radical uncertainty, that we do not know all possible future states of the financial system and therefore cannot compute the probabilities of outcomes that would be necessary for informing rules regarding systemic risk measures. The uncertainty in the regulatory context, in conjunction with the lessons from the crisis, suggest that a value-added international regulatory strategy would foster at least a modicum of diversity across national regulatory regimes, along with periodic updating of global standards. At the national level, they suggest adopting a dual-pronged regulatory approach that focuses regulators’ attention on monitoring developments in short-term debt markets, leverage levels, and the impact of new financial products and services, as well as on promoting experimentation, to better inform regulatory decisionmaking.

A new interval differential equation for edge detection and determining breast cancer regions in mammography images

ABSTRACTBreast cancer is the most common form of cancer in women. The importance of diagnosing breast cancer is one of the important issues in medical science. Diagnosis of benign or malignant cancer is of great importance in addition to reducing costs in the direction of treatment. A non-destructive test method for early detection of breast cancer is image processing. Image processing has various uncertainties which are generated by different reasons such as sampling to noise, initial digitalization, intensity, and special domain. In this study, a strong image segmentation method based on interval uncertainties is proposed for the breast cancer images diagnosis. The main purpose of this paper is to improve the ordinary Sobel filter based on interval analysis by considering the intensity uncertainties. Simulation results have been implemented on MIAS which is an applicable database for breast cancer detection. The results of the proposed method have been compared with some state of the art methods such as LoG, Prewitt and canny filters. Final results showed that using the proposed method gives better achievement than the others by considering some kinds of uncertainties like Gaussian noise and salt and pepper noise.

Degradation Data Analysis Using a Wiener Degradation Model With Three-Source Uncertainties

The degradation data have been widely applied in reliability analysis for deteriorating systems. However, the degradation data are usually contaminated by measurement errors that can severely affect the life estimation performance. This paper presents a degradation modeling and life estimation method using a Wiener degradation model by taking temporal uncertainty, measurement uncertainty, and unit-to-unit heterogeneity into account. The truncated normal distribution is employed to characterize the unit-to-unit heterogeneity in a population due to the fact that the degradation rates of many systems often manifest as positive values. The exact and explicit expressions of the life distribution are derived in the sense of the first hitting time by considering three kinds of uncertainties. The expectation maximization algorithm is used to estimate the model parameters because the resulting likelihood function includes hidden variables, which improves the estimation efficiency compared with the existing maximum likelihood estimation procedure. The effectiveness of the proposed approach is validated through a simulation example and a case study involving the degradation dataset of the LED.

Managing Measurement and Occurrence Uncertainty in Complex Event Processing Systems

Complex event processing (CEP) is a powerful technology for analyzing streams of real-time events, coming from different sources, and for extracting conclusions from them. In many situations, these events are not free from uncertainty, due to either unreliable data sources and networks, measurement uncertainty, or inability to determine whether an event has actually happened or not. This paper presents a proposal for incorporating and managing different kinds of uncertainty that may happen in both events and rules of the CEP systems. We provide a library that enables the representation and propagation of uncertain values, which can be efficiently integrated with the existing CEP languages and engines to deal with uncertainty, and we show how the treatment of uncertainty can be smoothly added to two of them: Esper and Apache Flink. Five applications coming from various domains serve to evaluate the proposal and to analyze its performance and accuracy. The results show that the overhead introduced by the treatment of uncertainty is not high and good precision and recall are achieved.

Lip image segmentation based on a fuzzy convolutional neural network

Research has shown that the human lip and its movements are a rich source of information related to speech content and speaker's identity. Lip image segmentation, as a fundamental step in many lip-reading and visual speaker authentication systems, is of vital importance. Because of variations in lip color, lighting conditions and especially the complex appearance of an open mouth, accurate lip region segmentation is still a challenging task. To address this problem, this article proposes a new fuzzy deep neural network having an architecture that integrates fuzzy units and traditional convolutional units. The convolutional units are used to extract discriminative features at different scales to provide comprehensive information for pixel-level lip segmentation. The fuzzy logic modules are employed to handle various kinds of uncertainties and to provide a more robust segmentation result. An end-to-end training scheme is then used to learn the optimal parameters for both the fuzzy and the convolutional units. A dataset containing more than 48 000 images of various speakers, under different lighting conditions, was used to evaluate lip segmentation performance. According to the experimental results, the proposed method achieves state-of-the-art performance when compared with other algorithms.

A Multi-Model Combined Filter with Dual Uncertainties for Data Fusion of MEMS Gyro Array.

The gyro array is a useful technique in improving the accuracy of a micro-electro-mechanical system (MEMS) gyroscope, but the traditional estimate algorithm that plays an important role in this technique has two problems restricting its performance: The limitation of the stochastic assumption and the influence of the dynamic condition. To resolve these problems, a multi-model combined filter with dual uncertainties is proposed to integrate the outputs from numerous gyroscopes. First, to avoid the limitations of the stochastic and set-membership approaches and to better utilize the potentials of both concepts, a dual-noise acceleration model was proposed to describe the angular rate. On this basis, a dual uncertainties model of gyro array was established. Then the multiple model theory was used to improve dynamic performance, and a multi-model combined filter with dual uncertainties was designed. This algorithm could simultaneously deal with stochastic uncertainties and set-membership uncertainties by calculating the Minkowski sum of multiple ellipsoidal sets. The experimental results proved the effectiveness of the proposed filter in improving gyroscope accuracy and adaptability to different kinds of uncertainties and different dynamic characteristics. Most of all, the method gave the boundary surrounding the true value, which is of great significance in attitude control and guidance applications.