Uncertainty Model Research Articles

Adaptive Characteristic Modeling of Long-Period Uncertainties: A Multi-Stage Robust Energy Storage Planning Approach Based on the Finite Covering Theorem

Adaptive Characteristic Modeling of Long-Period Uncertainties: A Multi-Stage Robust Energy Storage Planning Approach Based on the Finite Covering Theorem

Developing models of environment uncertainty, incoterms on strategic alliance and competitive advantage

This study aims to analyze the impact of environmental uncertainty and Incoterm on strategic alliances and competitive advantage in the Freight Forwarder Industry in Indonesia. The measuring method examines how variables, including environmental uncertainty, Incoterm, and strategic alliances, affect competitive advantage using SmartPLS technology and structural equation modeling (SEM) analysis. The questionnaire was created using a 5-point Likert scale. Through a simple random selection procedure, study participants selected freight forwarder companies. Validity, reliability, and significance tests—often along with hypothesis testing—are all included in the sequential data analysis. The study has identified the following relationships based on an evaluation of the data processing: Strategic alliances are positively impacted by environmental uncertainty, Incoterm is positively affected by environmental uncertainty, Incoterm is positively influenced by competitive advantage, and strategic alliances are positively affected by competitive advantage. The novelty of this study is the influence of environmental uncertainty and Incoterm on Strategic alliance and competitive advantage in the Freight Forwarder Industry in Indonesia. The theoretical implication supports previous theories on environmental uncertainty and Incoterm to increase competitive advantage through strategic alliances. The practical implication is for the management of a Freight forwarding company to scan the uncertainty of the logistics environment and implement the selection of the right Incoterm to encourage strategic alliances to encourage competitive advantage. This research makes a significant contribution to the advancement of scientific principles, particularly within the field of strategic management. Furthermore, it provides a valuable reference and comparative material for future studies focusing on the impact of environmental uncertainty and Incoterm on strategic alliances and competitive advantage within the freight forwarder industry in Indonesia.

Continuous-Time Stochastic State-Space Modeling of Non-Stationary Power System Uncertainty: A Data-Driven Systematic Realization Method

Continuous-Time Stochastic State-Space Modeling of Non-Stationary Power System Uncertainty: A Data-Driven Systematic Realization Method

A Resource Allocation Model for Deep Uncertainty (RAM-DU) Based on Nonlinear Optimization

A Resource Allocation Model for Deep Uncertainty (RAM-DU) Based on Nonlinear Optimization

Uncertainty in tuberculosis clinical decision-making: An umbrella review with systematic methods and thematic analysis.

Tuberculosis is a major infectious disease worldwide, but currently available diagnostics have suboptimal accuracy, particularly in patients unable to expectorate, and are often unavailable at the point-of-care in resource-limited settings. Test/treatment decision are, therefore, often made on clinical grounds. We hypothesized that contextual factors beyond disease probability may influence clinical decisions about when to test and when to treat for tuberculosis. This umbrella review aimed to identify such factors, and to develop a framework for uncertainty in tuberculosis clinical decision-making. Systematic reviews were searched in seven databases (MEDLINE, CINAHL Complete, Embase, Scopus, Cochrane, PROSPERO, Epistemonikos) using predetermined search criteria. Findings were classified as barriers and facilitators for testing or treatment decisions, and thematically analysed based on a multi-level model of uncertainty in health care. We included 27 reviews. Study designs and primary aims were heterogeneous, with seven meta-analyses and three qualitative evidence syntheses. Facilitators for decisions to test included providers' advanced professional qualification and confidence in tests results, availability of automated diagnostics with quick turnaround times. Common barriers for requesting a diagnostic test included: poor provider tuberculosis knowledge, fear of acquiring tuberculosis through respiratory sampling, scarcity of healthcare resources, and complexity of specimen collection. Facilitators for empiric treatment included patients' young age, severe sickness, and test inaccessibility. Main barriers to treatment included communication obstacles, providers' high confidence in negative test results (irrespective of negative predictive value). Multiple sources of uncertainty were identified at the patient, provider, diagnostic test, and healthcare system levels. Complex determinants of uncertainty influenced decision-making. This could result in delayed or missed diagnosis and treatment opportunities. It is important to understand the variability associated with patient-provider clinical encounters and healthcare settings, clinicians' attitudes, and experiences, as well as diagnostic test characteristics, to improve clinical practices, and allow an impactful introduction of novel diagnostics.

Fractional generalized cumulative residual entropy: properties, testing uniformity, and applications to Euro Area daily smoker data

<abstract><p>The fractional generalized cumulative residual entropy, a broader version of the cumulative residual entropy, holds significance in assessing the uncertainty model of random variables and maintains straightforward connections with reliability models and crucial information. This article represents and modifies some novel features of the fractional generalized cumulative residual entropy and discusses the weak convergence. Additionally, the measure is utilized to assess uniformity, involving the derivation of the limit distribution and an approximation of the test statistic's distribution. Furthermore, the concept of stability is addressed. Moreover, the presentation includes the critical points and power analysis against alternative distributions of this test statistic. Furthermore, a simulation study is carried out to compare the power value of the proposed test with that of other tests of uniformity. Moreover, the uniformity test utilizes real data on daily smokers in the countries of the Euro Area. Finally, our model's exponential distribution is applied to our model's empirical form.</p></abstract>

Day-ahead Generation Scheduling with Information Gap Decision Models

Recently, because of global warming countermeasures, interest in renewable energy has increased, particularly photovoltaic power generation (PV). On the other hand, when PV, whose output varies greatly depending on weather conditions, is introduced into the power systems in a large amount, several issues and problems are raised. It becomes difficult to adjust the voltage of the distribution systems and secure adjustment power to absorb PV output fluctuations, which affects the system frequency. In contrast, the authors have proposed the concept of robust power system security, which can apply the conventional N-1 reliability to uncertain environments. Furthermore, we formulated and developed a robust dynamic feasible (RDF) region and proposed a computation method for the evaluation index that indicates the size of the RDF region. However, executing this index calculation in a large-scale system can take a huge amount of time. Moreover, the calculation is expected to become complicated depending on the future development of a robust power system security index. In this paper, we formulate a novel method for assessing uncertainty using the Information Gap Decision Theory (IGDT). Based on the concept of the robust confidence region developed in our previous research, the authors have proposed an uncertainty model and are studying the application of IGDT theory for robust confidence evaluation using the proposed model. The simulation results confirm and validate the effectiveness of this proposed model.

DebSDF: Delving into the Details and Bias of Neural Indoor Scene Reconstruction.

In recent years, the neural implicit surface has emerged as a powerful representation for multi-view surface reconstruction due to its simplicity and state-of-the-art performance. However, reconstructing smooth and detailed surfaces in indoor scenes from multi-view images presents unique challenges. Indoor scenes typically contain large texture-less regions, making the photometric loss unreliable for optimizing the implicit surface. Previous work utilizes monocular geometry priors to improve the reconstruction in indoor scenes. However, monocular priors often contain substantial errors in thin structure regions due to domain gaps and the inherent inconsistencies when derived independently from different views. This paper presents DebSDF to address these challenges, focusing on the utilization of uncertainty in monocular priors and the bias in SDF-based volume rendering. We propose an uncertainty modeling technique that associates larger uncertainties with larger errors in the monocular priors. High-uncertainty priors are then excluded from optimization to prevent bias. This uncertainty measure also informs an importance-guided ray sampling and adaptive smoothness regularization, enhancing the learning of fine structures. We further introduce a bias-aware signed distance function to density transformation that takes into account the curvature and the angle between the view direction and the SDF normals to reconstruct fine details better. Our approach has been validated through extensive experiments on several challenging datasets, demonstrating improved qualitative and quantitative results in reconstructing thin structures in indoor scenes, thereby outperforming previous work.

Uncertainty Modeling for Gaze Estimation.

Gaze estimation is an important fundamental task in computer vision and medical research. Existing works have explored various effective paradigms and modules for precisely predicting eye gazes. However, the uncertainty for gaze estimation, e.g., input uncertainty and annotation uncertainty, have been neglected in previous research. Existing models use a deterministic function to estimate the gaze, which cannot reflect the actual situation in gaze estimation. To address this issue, we propose a probabilistic framework for gaze estimation by modeling the input uncertainty and annotation uncertainty. We first utilize probabilistic embeddings to model the input uncertainty, representing the input image as a Gaussian distribution in the embedding space. Based on the input uncertainty modeling, we give an instance-wise uncertainty estimation to measure the confidence of prediction results, which is critical in practical applications. Then, we propose a new label distribution learning method, probabilistic annotations, to model the annotation uncertainty, representing the raw hard labels as Gaussian distributions. In addition, we develop an Embedding Distribution Smoothing (EDS) module and a hard example mining method to improve the consistency between embedding distribution and label distribution. We conduct extensive experiments, demonstrating that the proposed approach achieves significant improvements over baseline and state-of-the-art methods on two widely used benchmark datasets, GazeCapture and MPIIFaceGaze, as well as our collected dataset using mobile devices.

Uncertainty Modeling for Plane and Line Features to Improve Consistency in RTK/INS/LiDAR Integrated Navigation

Uncertainty Modeling for Plane and Line Features to Improve Consistency in RTK/INS/LiDAR Integrated Navigation

Probabilistic Forecasting-Based Reserve Determination Considering Multi-Temporal Uncertainty of Renewable Energy Generation

Operating reserve, among the most important ancillary services, is a powerful prescription for mitigating increasing uncertainty of renewable generation. Traditional reserve determination neglects uncertainty of renewable generation variations within the dispatch interval, which cannot gurantee sufficient reserve deliverability in real-time operation. This paper proposes a probabilistic forecasting-based reserve determination method to efficiently deal with multi-temporal uncertainty of renewable energy in power systems. A novel probabilistic forecasting approach is proposed by integrating Dirichlet process Gaussian mixture model into bootstrap-based extreme learning machine. A unified uncertainty model is constructed for depicting both interval-averaged uncertainty and intra-interval uncertainty by combing the probabilistic forecasts and linearized Itô-process model. In current business practices of many independent system operators, the coordinated determination of two well-designed reserve products, including regulating reserve and ramp capability reserve, is formulated in a two-stage robust optimization framework. A Bernstein polynomial-based model reformulation approach is then employed to handle the existing heterogeneity in the sub-models of each stage. Consequently, the integrated reserve determination is embedded in a generic two-stage robust optimization problem, which can be efficiently solved by the adopted modified column-and-constraint generation method. Finally, numerical simulations are implemented to validate the effectiveness and profitability of the proposed approach.

Gaussian Mixture Model Uncertainty Modeling for Power Systems Considering Mutual Assistance of Latent Variables

Gaussian Mixture Model Uncertainty Modeling for Power Systems Considering Mutual Assistance of Latent Variables

Multiple Joint Chance Constraints Approximation for Uncertainty Modeling in Dispatch Problems

Multiple Joint Chance Constraints Approximation for Uncertainty Modeling in Dispatch Problems

Development of a novel uncertainty model for interval-valued Q-fuzzy soft sets: Application in design-making

In actual life, dealing with uncertain information has become a challenge for researchers who strive day after day to develop more accurate mathematical tools for better dealing with this information. The Q-Fuzzy soft model can process uncertain information in two dimensions by dealing with the subjective judgments of users effectively. Therefore, this article aims to increase the effectiveness of the Q-fuzzy soft model and address the challenges of design-making under uncertain information by proposing a new model called the interval-valued Q-fuzzy soft (IV-Q-FSS) model. Under the IV-Q-FSSs, we discuss strongly set-theory operations such as subset, union of two IV-Q-FSSs, intersection of two IV-Q-FSSs, complement of IV-Q-FSS, AND operation, and OR operation for IV-Q-FSSs, and some operations like the possibility and necessity operations of an IV-Q-FSS. In addition, we hand over numerous properties held up by numerical examples that describe how they toil. Finally, this recently developed model has been successfully trying out in dealing with one of the design-making problems based on hypothetical data for a respiratory disease. This algorithm is built based on the aggregation operator for IV-Q-FSS data to break this issue (i.e., selecting the optimal alternative).

Introduction to the Special Issue on Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures

Introduction to the Special Issue on Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess.

Seasonal-trend decomposition based on loess (STL) is a powerful tool to explore time series data visually. In this paper, we present an extension of STL to uncertain data, named uncertainty-aware STL (UASTL). Our method propagates multivariate Gaussian distributions mathematically exactly through the entire analysis and visualization pipeline. Thereby, stochastic quantities shared between the components of the decomposition are preserved. Moreover, we present application scenarios with uncertainty modeling based on Gaussian processes, e.g., data with uncertain areas or missing values. Besides these mathematical results and modeling aspects, we introduce visualization techniques that address the challenges of uncertainty visualization and the problem of visualizing highly correlated components of a decomposition. The global uncertainty propagation enables the time series visualization with STL-consistent samples, the exploration of correlation between and within decomposition's components, and the analysis of the impact of varying uncertainty. Finally, we show the usefulness of UASTL and the importance of uncertainty visualization with several examples. Thereby, a comparison with conventional STL isperformed.

Uncertainty Modeling of the Transmission Map for Single Image Dehazing

Uncertainty Modeling of the Transmission Map for Single Image Dehazing

Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing

Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing

Establishing the Influence of Methanol Fuelled Power Propulsion and Energy Systems on Ship Design

The adoption of alternative energy carriers is one of the key ways to meet the increasingly stricter emission regulations faced by shipping vessels from the international maritime organisation (IMO) and European Commission. To support this objective, this study examines the challenges and uncertainties associated with implementing a methanol power propulsion and energy (PPE) system on the design of a vessel. This paper argues that new fuels, such as methanol, should be treatedas disruptive innovations due, in part, to the uncertainties surrounding their implementation. Their integration causes challenges regarding systems selection, layout design, and maintaining strict safety measures. In the case of methanol, current research treats the fuel as a system conversion based on diesel fuel. This paper provides a review of the state-of-the-art on the design of methanol fuelled vessels, and identifies a research gap related to the need for a new suitable design method for the design of ships integrating future alternatively fuelled PPE systems. A design approach inspired by model-based systems engineering integrating uncertainty modelling is proposed to examine the influence of uncertainty on the design of the vessels. The impact of uncertainty on the design is investigated through a case study of a simplified engine room layout utilizing a genetic algorithm to produce layouts for variable PPE systems dimensions within a MonteCarlo simulation.

Impact assessment of shared storage and peer-to-peer trading on industrial buildings in the presence of electric vehicle parking lots: A hybrid robust-CVaR analysis

Local power markets and peer-to-peer (P2P) power transaction plans between the clients have lately received attractions as a cost-effective approach to encourage flexible energy options and sharing the internal power resources. For the integration of industrial consumers (ICs) in the presence of electric vehicle parking lot, this study modeled local power markets to share the surplus power of ICs between each other. Other hands, shared storage is modeled in the proposed system to benefit consumers. In order to investigate the proposed system under uncertain conditions, a hybrid optimization method is proposed for uncertainty modeling. In this paper, the well-known uncertainty modeling tools, including the robust optimization and Conditional Value at Risk (CVaR) embedded stochastic optimization approach, are proposed to model the uncertainties. The markets price uncertainty is one of the important uncertain parameters assumed as an unknown-distribution parameter due to insufficient historical data or not following a specific distribution; thus, the risks of this parameter are modeled by robust optimization, which has no need for big historical data of uncertain parameter. The other uncertain parameter is solar irradiation. The corresponding risks of solar irradiation is modeled by CVaR modeling because of the following Beta distribution. Thus, the financial risks of different uncertain parameters can be modeled effectively using the proposed hybrid optimization framework. According to obtained results, using peer-to-peer trading reduces the operation cost by 7.94%, while using the shared storage along with peer-to-peer trading reduces the operation cost by 21.49%.