Different Kinds Of Uncertainty Research Articles

A study and development of high‐order fuzzy time series forecasting methods for air quality index forecasting

AbstractThe endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high‐order fuzzy TSF methods, TFS‐multilayer perceptron (MLP) and HFS‐MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long‐short term memory (LSTM), and convolutional neural network (CNN). The “Wilcoxon Signed‐Rank test” and “Friedman and Nemenyi hypothesis test” are applied to the results obtained by employing different ratios in the train‐validation‐test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS‐MLP method over all other crisp and fuzzy TSF methods employed in this paper.

Uncertainty, politics, and crises: The case for cash

We analyze the repercussions of different kinds of uncertainty on cash demand, including uncertainty of cashless infrastructures, confidence crises of the financial system, natural disasters, political uncertainties, and inflationary crises. Based on a comprehensive literature survey and complemented by case studies, we derive a classification scheme how cash holdings typically evolved in each of these types of uncertainty over the past 30 years by separating between demand for domestic and international cash as well as between transaction and store of value balances. Hereby, we focus on the stabilizing macroeconomic properties of cash and recommend guidelines for cash supply by central banks and the banking system. Finally, we exemplify our analysis with five case studies from the developing world, namely Venezuela, Zimbabwe, Afghanistan, Iraq, and Libya.

From the detection of monitoring anomalies to the probabilistic forecast of the evolution of volcanic unrest: an entropy-based approach

Owing to the current lack of plausible and exhaustive physical pre-eruptive models, often volcanologists rely on the observation of monitoring anomalies to track the evolution of volcanic unrest episodes. Taking advantage from the work made in the development of Bayesian Event Trees (BET), here we formalize an entropy-based model to translate the observation of anomalies into probability of a specific volcanic event of interest. The model is quite general and it could be used as a stand-alone eruption forecasting tool or to set up conditional probabilities for methodologies like the BET and of the Bayesian Belief Network (BBN). The proposed model has some important features worth noting: (i) it is rooted in a coherent logic, which gives a physical sense to the heuristic information of volcanologists in terms of entropy; (ii) it is fully transparent and can be established in advance of a crisis, making the results reproducible and revisable, providing a transparent audit trail that reduces the overall degree of subjectivity in communication with civil authorities; (iii) it can be embedded in a unified probabilistic framework, which provides an univocal taxonomy of different kinds of uncertainty affecting the forecast and handles these uncertainties in a formal way. Finally, for the sake of example, we apply the procedure to track the evolution of the 1982–1984 phase of unrest at Campi Flegrei.

An Information Security Engineering Framework for Modeling Packet Filtering Firewall Using Neutrosophic Petri Nets

Due to the Internet’s explosive growth, network security is now a major concern; as a result, tracking network traffic is essential for a variety of uses, including improving system efficiency, fixing bugs in the network, and keeping sensitive data secure. Firewalls are a crucial component of enterprise-wide security architectures because they protect individual networks from intrusion. The efficiency of a firewall can be negatively impacted by issues with its design, configuration, monitoring, and administration. Recent firewall security methods do not have the rigor to manage the vagueness that comes with filtering packets from the exterior. Knowledge representation and reasoning are two areas where fuzzy Petri nets (FPNs) receive extensive usage as a modeling tool. Despite their widespread success, FPNs’ limitations in the security engineering field stem from the fact that it is difficult to represent different kinds of uncertainty. This article details the construction of a novel packet-filtering firewall model that addresses the limitations of current FPN-based filtering methods. The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area of firewall packet filtering that are characterized by imprecise knowledge. Because of SNPNs’ symbolic ability, the packet filtration model can be quickly and easily established, examined, enhanced, and maintained. Based on the idea that the ambiguity of a packet’s movement can be described by if–then fuzzy production rules realized by the truth-membership function, the indeterminacy-membership function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. In addition, we simulate the dynamic behavior of the tracking system in light of the ambiguity inherent in packet filtering by presenting a two-level filtering method to improve the ranking of the filtering rules list. Results from experiments on a local area network back up the efficacy of the proposed method and illustrate how it can increase the firewall’s susceptibility to threats posed by network traffic.

Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya

Abstract Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset.

Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and flexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum offer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model different kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fluctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets, reserve regulations, and risk preferences.

On the Tremaine–Weinberg method: how much can we trust gas tracers to measure pattern speeds?

ABSTRACT Pattern speeds are a fundamental parameter of the dynamical features (e.g. bars, spiral arms) of a galaxy, setting resonance locations. Pattern speeds are not directly observable, so the Tremaine–Weinberg (TW) method has become the most common method used to measure them in galaxies. However, it has not been tested properly whether this method can straightforwardly be applied to gas tracers, despite this being widely done in the literature. When applied to observations, the TW method may return invalid results, which are difficult to diagnose due to a lack of ground truth for comparison. Although some works applying the TW method to simulated galaxies exist, only stellar populations have been tested. Therefore, here we explore the applicability of the TW method for gas tracers, by applying it to hydrodynamical simulations of galaxies, where we know the true value of the bar pattern speed. We perform some simple tests to see if the TW method has a physically reasonable output. We add different kinds of uncertainties (e.g. in position angle or flux) to the data to mock observational errors based on the magnitude of uncertainty present in the observations. Secondly, we test the method on 3D simulations with chemical networks. We show that in general, applying TW to observations of gas will not recover the true pattern speed. These results have implications for many ‘pattern speeds’ reported in the literature, and based on these tests we also give some best practices for measuring pattern speeds using gas tracers going forwards.

Nine Degrees of Uncertainty in Negotiations

Joint decision‐making processes such as negotiations play a vital role in diverse societal contexts spanning from business and politics to sustainability‐related negotiations. One of the most prominent examples of how negotiations play an important role in overcoming societal challenges was the COVID‐19 vaccine supply negotiations. These negotiations have put the spotlight on an aspect of joint decision‐making that always has been of great interest to both negotiation researchers and practitioners yet remains empirically understudied—the effect of uncertainty. In the present article, we develop a framework of uncertainty in negotiation using the COVID‐19 vaccine supply negotiations between the European Union and pharmaceutical companies as an example. More specifically, we categorize different kinds of uncertainty based both on mathematical considerations (i.e., differentiation between risk, ambiguity, and uncertainty) as well as on more circumstantial factors. To do so, we adapted the nine kinds of uncertainty in environmental governance proposed by Dewulf and Biesbroek to the more general context of negotiations. We first differentiate between three natures of uncertainty (i.e., lack of knowledge, unpredictability, and interpretations) and three objects of uncertainty (i.e., issue‐based, strategy‐based, and context‐based). Second, we illustrate the psychological barriers that negotiators face when handling uncertainty, before concluding with proposals for practitioners on how to manage different kinds of uncertainty. Overall, we aim at stimulating investigations of the effects of uncertainty in mixed‐motive decision‐making while simultaneously helping negotiation teachers and practitioners better cope with the additional demands created by specific kinds of uncertainty.

Graph Convolutional Network-Based Interpretable Machine Learning Scheme in Smart Grids

Smart grid is a typical application of industrial cyber-physical systems (ICPS) in the electric power industry. Due to the exposure to different kinds of uncertainties and unpredictable faults, how to reliably assess the short-term voltage stability (SVS) of smart grids to prevent the occurrence of large-scale blackouts is still of primary concern. To tackle this challenging problem, this article develops a novel machine learning scheme to achieve accurate and interpretable online SVS assessment in two steps. First, it utilizes time-series shapelet transform to extract key dynamics and convert the postfault time series into flat features. Second, it designs a graph convolutional network (GCN) to incorporate these features with topology information for SVS assessment. The GCN explores the spatial-temporal dynamics of power system via graph convolution and introduces a system layer to derive the final assessment result. Compared with conventional methods, this novel scheme makes full use of the spatial-temporal information in SVS dynamics, resulting in higher assessment accuracy and stronger adaptability. Besides, it is capable of discovering certain valuable underlying rules and patterns related to SVS. Test results on the IEEE 39-bus system and real-world Guangdong Power Grid in South China verify the effectiveness of the proposed scheme. Note to Practitioners—To achieve accurate and interpretable online short-term voltage stability (SVS) assessment in the challenging environment of smart grids, this article develops a novel machine learning scheme with full consideration of the spatial-temporal information in SVS dynamics. First, it utilizes the time series shapelet transform to convert the postfault time series into flat features. Second, it designs a graph convolutional network (GCN) to incorporate these features with topology information. The full consideration of spatial-temporal information in SVS dynamics can improve the assessment accuracy, and the integration of topology in the scheme can promote its adaptability to topology changes. Apart from the decent performances under changeable environments, the proposed scheme can provide certain valuable underlying rules and patterns related to SVS. Therefore, not only the proposed scheme for SVS assessment can work well in the practical challenging environment of smart grids, but also it helps the dispatchers in smart grids better understand and trust the proposed SVS assessment scheme.

Survival Function Estimation for Fuzzy Gompertz Distribution with neutrosophic data

A relatively recent area of research known as neutrosophic statistics deals with data that are ambiguous, indeterminate, and inconsistent. By embracing the idea of neutrosophy, which denotes the existence of three components in a statement: truth, falsity, and indeterminacy, it broadens the application of classical statistics. One of the significant offshoots of statistics is life-time data analysis. Traditional statistical methods only account for variation within the data and calculate lifetime observations as accurate numbers. Actually, there are two different kinds of uncertainty in data: fluctuation between observations and fuzziness. As a result, analysis techniques that solely employ precise lifetime data and ignore fuzziness use incomplete information and produce false results. This paper sought to generalize hazard rates, survival functions, and parameter estimates for fuzzy Gompertz Distribution. Simulation studies are implemented to examine the performance of the fuzzy Gompertz Distribution. The results show that the fuzzy Gompertz Distribution has better flexibility in handling over the standard Gompertz Distribution.

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

Intelligent mobile robots need to deal with different kinds of uncertainties in order to perform their tasks, such as tracking predefined paths and avoiding static and dynamic obstacles until reaching their destination. In this research, a Robotino® from Festo Company was used to reach a predefined target in different scenarios, autonomously, in a static and dynamic environment. A Type-2 fuzzy logic controller was used to guide and help Robotino® reach its predefined destination safely. The Robotino® collects data from the environment. The rules of the Type-2 fuzzy logic controller were built from human experience. They controlled the Robotino® movement, guiding it toward its goal by controlling its linear and angular velocities, preventing it from colliding obstacles at the same time, as well. The Takagi–Sugeno–Kang (TSK) algorithm was implemented. Real-time and simulation experimental results showed the capability and effectiveness of the proposed controller, especially in dealing with uncertainty problems.

Topological Data Analysis of m-Polar Spherical Fuzzy Information with LAM and SIR Models

The concept of m-polar spherical fuzzy sets (mPSFS) is a combination of m-polar fuzzy sets (mPFS) and spherical fuzzy sets (SFS). An mPSFS is an optimal strategy for addressing multipolarity and fuzziness in terms of ordered triples of positive membership grades (PMGs), negative membership grades (NMGs), and neutral grades (NGs). In this study, the innovative concept of m-polar spherical fuzzy topology (mPSF-topology) is proposed for data analysis and information aggregation. We look into the characteristics and results of mPSF-topology with the help of several examples. Topological structures on mPSFSs help with both the development of new artificial intelligence (AI) tools for different domain strategies and the study of different kinds of uncertainty in everyday life problems. These strategies make it possible to recognise and look into a situation early on, which helps professionals to reduce certain risks. In order to address various group decision-making issues in the m-polar spherical fuzzy domain, one suggestion has been to apply an extended linear assignment model (LAM) along with the SIR method known as superiority and inferiority ranking methodology in order to analyze road accident issues and dispute resolution. In addition, we examine the symmetry of optimal decision and perform a comparative study between the research carried out using the suggested methodology and several existing methods.

Path Learning in Human–Robot Collaboration Tasks Using Iterative Learning Methods

In a repetitive human–robot collaboration (HRC) task, robots typically are required to learn the intended motion of the human user to improve the collaboration efficiency. However, the human user’s trajectory is of uncertainty when repeating the same task (e.g., human hand tremor and uncertain movement speed), which may directly deteriorate the learning performance. To address this issue, a path characterized by spatial correlation parameters, is of necessity to be learned by robots so that the aforementioned time-related uncertainty will be avoided. In this article, based on the path parameterization, a gradient-based iterative path learning (IPL) strategy is designed for the robot to learn the desired path of human. The proposed IPL strategy draws on the iterative learning methods with a properly designed performance index. Since the gradient of the performance index is hard to directly obtain in HRC, two learning methods with gradient search (GS) and gradient estimation (GE) are developed. The GS estimates the gradient online and has an advantage of easy implementation. By contrast, the advantage of GS is more obvious when the number of learned parameters is considerable due to its high learning efficiency. With these two methods, the unknown path parameters can be iteratively updated toward the desired values. To verify the effectiveness of the proposed IPL algorithm, experiments are carried out. In the experiment, a comparison between GS and GE methods is made to display their respective advantages. Besides, the proposed IPL is compared with an existing trajectory learning method subject to two different kinds of uncertainties and its better learning performance verifies its stability and capability in dealing with uncertainty.

An Investigation of Uncertainty Propagation in Non-equilibrium Flows

ABSTRACT Considerable uncertainties can exist between the field solutions of coarse-grained fluid models and the real-world flow physics. To study the emergence, propagation, and evolution of uncertainties poses great opportunities and challenges to develop both sound theories and reliable numerical methods. In this paper, we study the stochastic behaviour of multi-scale gaseous flows from molecular to hydrodynamic level, especially focussing on the non-equilibrium effects. The theoretical analysis is presented based on the gas kinetic model and its upscaling macroscopic system with random inputs. A newly developed stochastic kinetic scheme is employed to conduct numerical simulation of multi-scale and non-equilibrium flows. Different kinds of uncertainties are involved in the gas evolutionary processes. New physical observations, such as the synchronous travel pattern between mean fields and uncertainties, sensitivity of different orders of uncertainties and the influence of boundary effects from continuum to rarefied regimes, are identified and analysed.

An agent-based infrastructure for assessing the performance of planning approaches for semiconductor supply chains

Semiconductor supply chains that manufacture integrated circuits consist of dozens of semiconductor wafer fabrication, assembly and testing and storage facilities. The sheer size of the geographically distributed facilities and the involved supply chains, the pervasive presence of different kinds of uncertainties and the rapid pace of change lead to large-scale, distributed planning problems. However, the interactions between the different planning functions in semiconductor supply chains are not well understood. Systematic study of the interplay between the various planning algorithms in a real-world setting is extremely difficult due to the different software systems and decision-makers involved. In this paper, we discuss the design and implementation of a software agent-based experimental infrastructure for assessing the performance of different planning functions in a risk-free simulation environment, using web services to implement parts of the planning functionality. The environment is highly scalable since the planning functionality is provided by cloud computing mechanisms. Discrete-event simulation is used to emulate the underlying supply chain and execute the planning functions in a rolling horizon setting. We illustrate the benefits of the experimental environment and the benefits of using distributed computing techniques by studying the interaction between master planning and demand fulfillment, and between master planning and production planning in simplified semiconductor supply chains.

The asymmetric effects of oil price shocks and uncertainty on non-ferrous metal market: Based on quantile regression

With the intensifying of competition among big countries and trade friction, prices of non-ferrous metal fluctuate more and more fiercely. This paper examines how asymmetric oil price shocks and different kinds of uncertainty affect non-ferrous metal market under different market conditions over the period from April 2nd, 1990 to April 7th, 2021. The results show that the nexus of non-ferrous metal prices with oil price shocks and uncertainty are distinct under different market conditions. First of all, the effects caused by oil price shocks are the strongest under the bearish market, then the bullish market. Then, with the increase of quantiles, the impact strength of positive oil price shocks is intensified, while the impact strength of negative oil price shocks decreased with the increase of quantiles. Moreover, effects of VIX are more significant than EPU. At last, effects of oil price shocks and uncertainty varies during the financial crisis in 2008, the European sovereign debt crisis, and the COVID-19 pandemic. Considering the asymmetries and heterogeneous of effects from oil price shocks and uncertainty under different market conditions, it's of great necessary for policy makers to formulate targeted policies according to different market expectations.

A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions

While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case.

Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model

Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.

Modelling and an improved NSGA-II algorithm for sustainable manufacturing systems with energy conservation under environmental uncertainties: a case study

ABSTRACT This study addresses a flexible job shop scheduling problem considering the energy tax regulations. We introduce two strategies as reactive scheduling to deal with two different kinds of uncertainty. The first uncertainty is about the start and processing times of the jobs with a known probability distribution. The second kind is related to machine breakdowns, modification or cancellation of the orders, and receive new orders without any known probability distribution. Two conflict objective functions are considered as minimising tax cost on surplus energy consumption and minimising total cost of jobs lateness based on soft time-windows. A bi-objective mathematical model is developed to formulate the problem. Since the problem is well-known strongly NP-hard, a new solution approach is introduced based on the NSGA-II algorithm to solve the problem in a reasonable computational time. Some test problems based on a real case study are used to evaluate the performance of the proposed solution approach. The result analysis confirms the effectiveness of the proposed algorithm and its superiority comparing with another algorithm based on the classic NSGA-II. Moreover, sensitivity analysis is done on the main parameters to provide proper managerial suggestions based on the obtained Pareto front solutions.

Fuzzy numerical solution to the unconfined aquifer problem under the Boussinesq equation

Abstract In this article, the fuzzy numerical solution of the linearized one-dimensional Boussinesq equation of unsteady flow in a semi-infinite unconfined aquifer bordering a lake is examined. The equation describing the problem is a partial differential parabolic equation of second order. This equation requires knowledge of the initial and boundary conditions as well as the various soil parameters. The above auxiliary conditions are subject to different kinds of uncertainty due to human and machine imprecision and create ambiguities for the solution of the problem, and a fuzzy method is introduced. Since the physical problem refers to a partial differential equation, the generalized Hukuhara (gH) derivative is used, as well as the extension of this theory regarding partial derivatives. The objective of this paper is to compare the fuzzy numerical and analytical results, for two different cases of the physical problem of an aquifer's unsteady flow, in order to prove the reliability and efficiency of the proposed fuzzy numerical scheme (fuzzy Crank–Nicolson scheme). The comparison of the methods is based on the transformed Haussdorff metric, which shows that the distances between the analytical and numerical results tend to zero.