Representation Of Uncertainty Research Articles

Flow‐ and scale‐dependent spatial predictability of convective precipitation combining different model uncertainty representations

AbstractConsidering a whole summer season in central Europe, we find that the operational, convection‐permitting ICON‐D2 ensemble prediction system is spatially underdispersive in convective precipitation forecasts. The spatial spread of hourly precipitation is insufficient to capture the inherent error adequately across all scales (up to 300 km) and forecast times (up to 24 h). This lack of spread becomes more pronounced in the weak convective forcing regime. Using physically based stochastic perturbations in the planetary boundary layer is beneficial and leads to a reduction in spatial error at scales larger than 20 km and increases the spread at scales less than 50 km during weak forcing of convection, whereas the effect is almost neutral during strong forcing. Complementing the stochastic perturbations by perturbed parameters in the microphysics scheme shows an additive effect on spatial error and spread for a characteristic case study. Assessing the practical predictability of convective precipitation in a flow‐dependent manner is crucial, and our approach of combining multiple sources of uncertainty proves beneficial in mitigating the spatial underdispersion across scales, particularly during weak convective forcing.

Adaptive Uncertainty-Based Learning for Text-Based Person Retrieval

Text-based person retrieval aims at retrieving a specific pedestrian image from a gallery based on textual descriptions. The primary challenge is how to overcome the inherent heterogeneous modality gap in the situation of significant intra-class variation and minimal inter-class variation. Existing approaches commonly employ vision-language pre-training or attention mechanisms to learn appropriate cross-modal alignments from noise inputs. Despite commendable progress, current methods inevitably suffer from two defects: 1) Matching ambiguity, which mainly derives from unreliable matching pairs; 2) One-sided cross-modal alignments, stemming from the absence of exploring one-to-many correspondence, i.e., coarse-grained semantic alignment. These critical issues significantly deteriorate retrieval performance. To this end, we propose a novel framework termed Adaptive Uncertainty-based Learning (AUL) for text-based person retrieval from the uncertainty perspective. Specifically, our AUL framework consists of three key components: 1) Uncertainty-aware Matching Filtration that leverages Subjective Logic to effectively mitigate the disturbance of unreliable matching pairs and select high-confidence cross-modal matches for training; 2) Uncertainty-based Alignment Refinement, which not only simulates coarse-grained alignments by constructing uncertainty representations but also performs progressive learning to incorporate coarse- and fine-grained alignments properly; 3) Cross-modal Masked Modeling that aims at exploring more comprehensive relations between vision and language. Extensive experiments demonstrate that our AUL method consistently achieves state-of-the-art performance on three benchmark datasets in supervised, weakly supervised, and domain generalization settings. Our code is available at https://github.com/CFM-MSG/Code-AUL.

Extended EDAS Analysis for Multi-Criteria Decision-Making Based on Distributed Generation (DG) Technologies System

Recently, there has been a growing interest in distributed generation (DG) technologies, driven by various factors such as fuel price uncertainties, environmental constraints, and increasing power consumption along with transmission capacity shortages, in modern power systems. DG, which involves utilizing clean and renewable energy sources for power generation within the distribution system, has gained significant attention globally. Many developing countries, including Libya, are considering the adoption of DG technologies as part of their energy system expansion plans. Libya, located in North Africa and characterized by vast desert lands, has abundant solar radiation, making solar energy a promising and sustainable source of power. However, despite this energy potential, the southern part of Libya faces frequent power outages. In order to effectively maintain service quality, it is essential to conduct quantitative evaluation of wireless sensor networks. the evaluation of wireless sensor networks involves addressing the multiple attribute group decision-making (MAGDM) problem. To tackle the challenges of MAGDM, an extension of the classical EDAS (Evaluation based on Distance from Average Solution) method is proposed in this paper. The proposed method incorporates interval-valued intuitionistic fuzzy sets (IVIFSs), which provide a more flexible and comprehensive representation of uncertainty, to handle the complexities of MAGDM. The paper begins with a brief review of essential concepts related to IVIFSs. Then, the weights of attributes are determined using the CRITIC method. Subsequently, the IVIF-EDAS method is established by integrating the EDAS method with IVIFSs, and all the calculation procedures are described.

Arousal coherence, uncertainty, and well-being: an active inference account.

Here we build on recent findings which show that greater alignment between our subjective experiences (how we feel) and physiological states (measurable changes in our body) plays a pivotal role in the overall psychological well-being. Specifically, we propose that the alignment or 'coherence' between affective arousal (e.g. how excited we 'feel') and autonomic arousal (e.g. heart rate or pupil dilation) may be key for maintaining up-to-date uncertainty representations in dynamic environments. Drawing on recent advances in interoceptive and affective inference, we also propose that arousal coherence reflects interoceptive integration, facilitates adaptive belief updating, and impacts our capacity to adapt to changes in uncertainty, with downstream consequences to well-being. We also highlight the role of meta-awareness of arousal, a third level of inference, which may permit conscious awareness, learning about, and intentional regulation of lower-order sources of arousal. Practices emphasizing meta-awareness of arousal (like meditation) may therefore elicit some of their known benefits via improved arousal coherence. We suggest that arousal coherence is also likely to be associated with markers of adaptive functioning (like emotional awareness and self-regulatory capacities) and discuss mind-body practices that may increase coherence.

A robust data-model dual-driven fusion with uncertainty estimation for LiDAR–IMU localization system

Accurate and robust localization is a critical requirement for autonomous driving and intelligent robots, particularly in complex dynamic environments and various motion scenarios. However, existing LiDAR odometry methods often struggle to promptly respond to changes in the surroundings and motion conditions with fixed parameters through execution, hindering their ability to adaptively adjust system model parameters. Additionally, current localization techniques frequently overlook the confidence level associated with their pose results, leading the autonomous systems to unconditionally accept estimated outputs, even when they may be erroneous. In this paper, we propose a robust data-model dual-driven fusion with uncertainty estimation for the LiDAR–IMU localization system, which integrates the advantages of data-driven and model-driven approaches. We introduce data-driven feature encoder modules for LiDAR and IMU raw data, enabling the system to detect changes in the environment and motion status. Subsequently, these data-driven findings are incorporated into a filtering based model, allowing for the adaptive refinement of system model parameters. Furthermore, we refine the representation of uncertainty based on the Extended-Kalman-Filter model covariance, integrating uncertainty from sensor data and model parameters, which helps to evaluate the confidence of fusion system results. We conducted extensive experiments on two publicly available datasets and one dataset we collected with three sequences, verifying the accuracy of our method. In addition, we have demonstrated the robustness of our method in different motion states and scenarios through comparative experiments, as well as the effectiveness of our refined uncertainty estimation, compared with existing great methods, such as Fast-LIO2 and LIO-SAM, the localization accuracy has been improved by 8.3%.

AI-enhanced forecasting of Indian primary energy demand: Fuzzy auto-regressive distributed lag models

As the demand for energy in India continues to surge, accurate forecasting becomes paramount for efficient resource allocation and sustainable development. This study proposes an innovative approach to forecasting Indian primary energy demand by integrating Artificial Intelligence (AI) techniques with Fuzzy Auto-regressive Distributed Lag (FADL) models. FADL models, incorporating fuzzy logic, allow for a nuanced representation of uncertainties and complexities within the energy demand dynamics. In this research, historical energy consumption data is analysed using FADL models with both symmetric and non-symmetric triangular coefficients, enhancing the model’s adaptability to the inherent uncertainties associated with energy forecasting. This study addresses the urgent need for enhanced energy planning models in the context of sustainable development. Our research aims to provide a comprehensive framework for predicting future Total Final Consumption (TFC) in alignment with the Indian National Energy Plan’s net-zero emissions target by 2035. Recognizing the limitations of current models, our research introduces a novel approach that integrates advanced algorithms and methodologies, offering a more flexible and realistic assessment of TFC trends. The primary objective of this study is to develop an improved energy planning model that surpasses existing projections by incorporating sophisticated algorithms. We aim to refine

Mathematical analysis of big data analytics under bipolar complex fuzzy soft information

The aim of this study is to develop new and efficient theories for handling complex and unreliable data in real-world scenarios. The proposed approach integrates two distinct theories: the Bipolar Complex Fuzzy Set (BCFS) and the Soft Set (SS), resulting in a novel and superior method compared to existing solutions. Furthermore, the value of big data analytics cannot be overstated as it provides businesses with the opportunity to use their data to find areas for development and progress. To gain insights from raw data, it must first be collected and organized. Data modeling represents these large, complex data sets in a visual format, such as a chart or diagram. However, the visualization process often results in uncertainties and inaccuracies, highlighting the need for a more robust approach to data analysis. To address this issue, this paper introduces the concept of Bipolar Complex Fuzzy Soft Relations (BCFSRs), a fuzzy decision-making method within a complex bipolar fuzzy environment that can have the advantages of both a Bipolar Complex fuzzy relation and a soft relation at the same time. In BCFSRs, an element can have degrees of membership in both the positive and negative directions, reflecting a more nuanced and versatile representation of uncertainty or ambiguity. These mathematical ideas are explained through the Cartesian product of two Bipolar Complex Fuzzy Soft Sets (BCFSSs) and serve to simplify the decision-making process by presenting the BCFSSs in a clear and concise manner. The inventive notion of the BCFSRs elucidates the combined positive and negative effects of anything with parameterization. To support organizations in making data-driven choices and obtain insights for strategic planning and inventiveness, the analysis of big data is essential in several industries, including financial services, marketing, and medical treatment, among others. To choose the optimal big data analytics for efficient operation, this research presents modeling methodologies based on BCFSRs, including the formulation and analysis of scoring functions. The validity of the proposed work is demonstrated through a comparison study with existing methods, providing valuable insights for effective data analysis.

Effects of aging on externally cued and internally driven uncertainty representations.

The Hick-Hyman law states that response time (RT) increases linearly with increasing information uncertainty. The effects of aging on uncertainty representations in choice RT paradigms remain unclear, including whether aging differentially affects processes mediating externally cued versus internally driven uncertainty. This study sought to characterize age-related differences in uncertainty representations using a card-sorting task. The task separately manipulated internally driven uncertainty (i.e., probability of each stimulus type with fixed number of response piles) and externally cued uncertainty (i.e., number of response piles with fixed probability of each stimulus type). Older adults (OA) showed greater RT slowing than younger adults in response to uncertainty load, an effect that was stronger in the externally cued than internally driven condition. While both age groups showed lower accuracy and greater RTs in response to unexpected (surprising) stimuli in the internally driven condition at low uncertainty loads, OA were unable to distinguish between expected and nonexpected stimuli at higher uncertainty loads when the probability of each stimulus type was close to equal. Among OA, better performance on the internally driven, but not externally cued, condition was associated with better global cognitive performance and verbal fluency. Collectively, these findings provide behavioral evidence of age-related disruptions to bottom-up (externally cued) and top-down (supporting internally driven mental representations) resources to process uncertainty and coordinate task-relevant action. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An evidential linguistic ELECTRE method for selection of emergency shelter sites

In many circumstances, decisions are based on subjective experience. However, some views can be vague, meaning that policymakers do not know exactly how they should express their opinions. Therefore, it is necessary for researchers to provide scientific decision frameworks, among which the multi-criteria decision making (MCDM) method in the linguistic environment is gradually favored by scholars. A large body of literature reports relevant approaches with regard to linguistic term sets, but existing approaches are insufficient to express the subjective thoughts of policymakers in a complex and uncertain environment. In this paper, we address this problem by introducing the concept of evidential linguistic term set (ELTS). ELTS generalizes many other uncertainty representations under linguistic context, such as fuzzy sets, probabilities, or possibility distributions. Measures on ELTS, such as uncertainty measure, dissimilarity measure and expectation function, provide general frameworks to handle uncertain information. Modeling and reasoning of information expressed by ELTSs are realized by the proposed aggregation operators. Subsequently, this paper presents a novel MCDM approach called evidential linguistic ELECTRE method, and applies it to the case of selection of emergency shelter sites. The findings demonstrate the effectiveness of the proposed method for MCDM problems under linguistic context and highlight the significance of the developed ELTS.

Cover Feature: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests (Batteries & Supercaps 3/2024)

Cover Feature: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests (Batteries & Supercaps 3/2024)

A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease.

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study's aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.

Metacognition and Confidence: A Review and Synthesis.

Determining the psychological, computational, and neural bases of confidence and uncertainty holds promise for understanding foundational aspects of human metacognition. While a neuroscience of confidence has focused on the mechanisms underpinning subpersonal phenomena such as representations of uncertainty in the visual or motor system, metacognition research has been concerned with personal-level beliefs and knowledge about self-performance. I provide a road map for bridging this divide by focusing on a particular class of confidence computation: propositional confidence in one's own (hypothetical) decisions or actions. Propositional confidence is informed by the observer's models of the world and their cognitive system, which may be more or less accurate-thus explaining why metacognitive judgments are inferential and sometimes diverge from task performance. Disparate findings on the neural basis of uncertainty and performance monitoring are integrated into a common framework, and a new understanding of the locus of action of metacognitive interventions is developed.

Uncertainty in humanities network visualization

Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated/pinto the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations.

Experimental Assessment of Chance-Constrained Motion Planning for Small Uncrewed Aircraft

This work extends the experimental evaluation of chance-constrained motion planning algorithms to fielded fixed-wing small uncrewed aircraft systems (sUAS). Despite advances in planning algorithms, certain challenges remain to producing trajectories for nonholonomic mobile robotic systems such as sUAS. These challenges include nonlinear dynamics which create a complex mapping from inputs to outputs, initialization uncertainty in online motion planning due to compute time of planners and latency in data transfer, environmental uncertainty that has non-Gaussian impact on the robot’s trajectory, and system uncertainty that arises from incomplete models of complex systems. Small UAS often have proprietary components such as commercial, off-the-shelf autopilots which prevent motion planning models from accurately capturing system behavior in all regions of the state space. These challenges can be addressed by leveraging probabilistic motion planners that accurately represent and reason over dynamics and uncertainty. Chance-constrained motion planning offers a method of reasoning over uncertainty as feasibility constraints, and Monte Carlo sampling within a motion planning algorithm offers a method of representing complex uncertainty that may not have a closed form representation. This work extends the chance-constrained motion planning problem to reason over constraint on the trajectory and constraints on the state which ensure that the system model remains valid. Dynamical and systems models are formulated to extend to a broad class of fixed-wing UAS through the experimental derivation of input distributions and system parameters. Uncertainty is quantified using a data-driven approach to modeling input distributions, and Monte Carlo uncertainty sampling deployed within a Rapidly-exploring Random Tree motion planning algorithm is used to plan a trajectory containing a representation of uncertainty. The motion planning algorithm’s ability to accurately reason over layered chance constraints is evaluated experimentally in 61 fielded missions. The results show that when the flight conditions fall within the domain of the uncertainty distributions, the motion planning system is able to accurately reason over the chance constraints.

Map-Based Ensemble Forecasts for Maritime Operations: An Interactive Usability Assessment with Decision Scenarios

Abstract Previous research indicates that forecast uncertainty can, in certain formats and decision contexts, provide actionable insights that help users in their decision-making. However, how to best disseminate forecast uncertainty, which factors affect successful uptake, and how forecast uncertainty transforms into better decision-making remains an ongoing topic for discussion in both academic and operational contexts. Interpreting and using visualizations of forecast uncertainty are not straightforward, and choosing how to represent uncertainty in forecast products should be dependent on the specific audience in mind. We present findings from an interactive stakeholder workshop that aimed to advance context-based insights on the usability of graphical representations of forecast uncertainty in the field of maritime operations. The workshop involved participants from various maritime sectors, including cruise tourism, fisheries, government, private forecast service providers, and research/academia. Geographically situated in Norway, the workshop employed sea spray icing as a use case for various decision scenario exercises, using both fixed probability and fixed threshold formats, supplemented with temporal ensemble diagrams. Accumulated operational expertise and characteristics of the forecast information itself, such as color coding and different forms of forecast uncertainty visualizations, were found to affect perceptions of decision-making quality. Findings can inform codesign processes of translating ensemble forecasts into usable and useful public and commercial forecast information services. The collaborative nature of the workshop facilitated knowledge sharing and coproduction between forecast providers and users. Overall, the study highlights the importance of incorporating methodological approaches that consider the complex and dynamic operational contexts of ensemble-based forecast provision, communication, and use. Significance Statement We wanted to understand how maps showing uncertainty in weather forecasts can help maritime users in their operational decisions. We organized a workshop with Norwegian maritime stakeholders and forecasters, who interpreted maps that combined layers of maritime operational activities and the likelihood of sea spray icing (an important hazard for ships operating on higher latitudes). The results show that contextual knowledge, and the use visual formats such as traffic light colors may help users to understand the maps. The results will help to better communicate weather forecasts to maritime users and gives suggestions about how to involve users in codesigning forecast products. Follow-up research could use our approach to investigate other hazardous conditions, such as wind, waves and sea ice.

Simulation workflow for fault detection and localization in sandwich panels

Sandwich panels in civil engineering have the advantage of a high flexural strength and thermal insulation while providing a very low density. They are made of two steel sheets and a core of polyurethane or polyisocyanurate foam. Due to the manufacturing process, the panels occasionally have defects, that are not visible from the outside. These may cause visible damages after installation in building facades. This paper presents a workflow for the detection and localization of the defects exploiting their influence on the response to a dynamic excitation. It uses an experimentally validated finite element model, feature engineering, and a neural network. The workflow initiates with the development and validation of a mechanical finite element model using experimental data. The model serves as the foundation for a simulation campaign, examining both defect-free and models with defects. The simulation process incorporates variations in material parameters based on experimentally obtained standard deviations, ensuring a robust representation of real-world uncertainties. The resultant simulation dataset is subjected to a feature engineering process encompassing time and frequency domain features. This multi-domain feature extraction enhances the dataset’s informativeness. The feature dataset is employed to train a neural network for two primary use cases: Firstly, to differentiate between faulty and fault-free models, providing an accurate fault detection mechanism. And secondly, to localize faults within the model. The effectiveness of this approach is demonstrated using various testing datasets. The proposed methodology represents a combination of a synthetically created database from a validated finite element model, multi-domain feature engineering, and neural network capabilities. It offers a methodology for fault detection and localization in sandwich panels. Using a dataset of several hundred samples, a detection rate of 94.8 % and a localization rate of 81.3 % are achieved in this work. In practical use, this approach can lead to significant reduction of customer complaints, and savings in replacement costs and hence carbon footprint.

Battery Energy Storage System Planning Based on Super-Resolution Source-Load Uncertainty Reconstruction

Low-time resolution electricity data have been used to drive battery energy storage system (BESS) planning due to data barriers. However, the coarse-resolution time series cannot reflect real power variation, and the planning results may be inappropriate due to the unrealistic representation of source-load uncertainties. To this end, this paper proposes a BESS planning method based on super-resolution (SR) source-load uncertainty reconstruction. The Gramian angular field (GAF) is employed to transform the 1D time series into 2D images to enrich the time dependency of signals. With advanced computer vision (CV), the entropy decrease generative adversarial network (EDGAN) based on information-growth attention is designed to recover the source-load profile from low resolution to high resolution. To depict source-load uncertainties in BESS sizing, a two-stage multi-distributionally robust optimization (2S-MDRO) is established under SR ambiguity sets. Case studies validated the accuracy of the proposed SR method and proved the effectiveness of the proposed sizing model. The recommended time resolution for BESS planning is presented in the comparative results.

Enhancing Decision-Making in Complex Environments: Integrating AHP, Delphi, and Neutrosophic Logic

The integration of the Analytic Hierarchy Process (AHP), the Delphi method, and neutrosophic logic provides a powerful framework for complex decision-making, allowing for an enhanced handling of uncertainties and multiple criteria that characterizes many strategic planning and policy formulation scenarios. AHP’s structured approach helps decompose decision-making into manageable sub-problems, while the Delphi method facilitates expert consensus through iterative rounds, enriching the decision-making process with diverse expert insights. The inclusion of neutrosophic logic allows for better representation and processing of uncertainty, offering a flexible way to handle indeterminate and contradictory information. This robust methodology not only improves the precision of decisions but also adapts to the nuanced requirements of multifaceted decision environments. Future research could benefit from integrating these methods with technological advancements like artificial intelligence to automate and optimize the decision-making process further. Applying this integrated approach in various sectors such as healthcare, environmental management, and urban planning could also provide valuable insights into its effectiveness and scalability.

Dynamic bipolar fuzzy aggregation operators: A novel approach for emerging technology selection in enterprise integration

<abstract> <p>Emerging technology selection is crucial for enterprise integration, driving innovation, competitiveness, and streamlining operations across diverse sectors like finance and healthcare. However, the decision-making process for technology adoption is often complex and fraught with uncertainties. Bipolar fuzzy sets offer a nuanced representation of uncertainty, allowing for simultaneous positive and negative membership degrees, making them valuable in decision-making and expert systems. In this paper, we introduce dynamic averaging and dynamic geometric operators under bipolar fuzzy environment. We also establish some of the fundamental crucial features of these operators. Moreover, we present a step by step mechanism to solve MADM problem under bipolar fuzzy dynamic aggregation operators. In addition, these new techniques are successfully applied for the selection of the most promising emerging technology for enterprise integration. Finally, a comparative study is conducted to show the validity and practicability of the proposed techniques in comparison to existing methods.</p> </abstract>

Decision Making under Uncertainty for Reconfigurable Manufacturing Systems: a framework for uncertainty representation

Traditional manufacturing paradigms cannot deal with the current pace of uncertain events in demand, supply and beyond. Reconfigurable Manufacturing Systems (RMS) are designed to adapt to these challenges in a rapid and cost-effective way. In order to decide when and how to reconfigure an RMS, it is necessary to identify the external events which trigger change in the system. This paper proposes a framework for uncertainty representation in RMS based on three levels of uncertainty and decision horizons. An illustrative example shows how such framework can be used by researchers and practitioners to better understand RMS and its context.