Uncertainty Model Research Articles

Incremental robust predictive control for anti-pitching in catamarans with appendage constraints

To solve the problem that high-speed catamarans have excessive amplitude of vertical motion in rough sea conditions, an anti-pitching method based on incremental robust predictive control of catamarans is proposed, which takes into account the model uncertainty and input rate of appendage requirements. Thus, a high-speed catamaran vertical control model is established with T-foils and flaps serving as anti-pitching appendages, and the hydrodynamic coefficient uncertainties is analyzed, which is converted into a state space model with norm-bounded uncertainty to facilitate control system design; moreover, transforming the norm-bounded uncertainty model of a high-speed catamaran into an incremental model, which can increase the integral action to improve the anti-pitching performance. On this basis, the hybrid H2/H∞ robust predictive control method is proposed to achieve wave disturbance resistance capability and robust stability of the catamaran system, taking the rate of input of the anti-pitching appendages as the input constraints, and the linear matrix inequality (LMI) receding-horizon optimization is used to solve the incremental control input of appendages. Ultimately, the effectiveness of the proposed algorithm is verified by digital simulation.

Integrated Deep Learning Architectures for Perception, Control, and Decision-making in Robotics: a Framework for Sensing, Cognition, and Transparent Decision-making

The increasing complexity of robotic applications demands innovative approaches for addressing problems that lack analytical solutions, with deep learning (DL) emerging as a key tool for enabling robots to learn and adapt in dynamic environments. This survey reviews existing DL techniques in robotics, categorizing the major challenges and exploring successful solutions that leverage DL for perception, control, and decision-making. We discuss the use of modular versus end-to-end DL architectures, providing guidelines for selecting appropriate model structures and training strategies. The review also examines advancements in neuro-robotics systems (NRS), where the convergence of neuroscience and robotics is driving the development of robots with embodied intelligence, enabling more natural human-robot interactions. Recent progress in neural mechanisms for perception, cognition, learning, and control is highlighted, offering insights into creating future neuro-robots. Furthermore, we address the challenge of uncertainty estimation in neural networks, crucial for reliable robotic decision-making, and evaluate existing frameworks such as Bayesian belief networks and Monte Carlo sampling that improve uncertainty modeling without requiring architectural changes. Lastly, we explore efforts to enhance transparency in robotic systems through integrated reasoning and learning methods, focusing on architectures that combine logical reasoning with deep learning to provide explainable decision-making. This survey aims to offer a structured overview of current research and guide future developments in neuro-robotics.

Economic Burden of Foodborne Illnesses Acquired in the United States.

Foodborne disease burden estimates inform public health priorities and can help the public understand disease impact. This article provides new estimates of the cost of U.S. foodborne illness. Our research updated disease modeling underlying these cost estimates with a focus on enhancing chronic sequelae modeling and enhancing uncertainty modeling. Our cost estimates were based on U.S. Centers for Disease Control and Prevention estimates of the numbers of foodborne illnesses, hospitalizations, and deaths caused by 31 known foodborne pathogens and unspecified foodborne agents. We augmented these estimates of illnesses, hospitalizations, and deaths with more detailed modeling of health outcomes, including chronic sequelae. For health outcomes, we relied on U.S. data and research where possible, supplemented by the use of non-U.S. research where necessary and scientifically appropriate. Cost estimates were developed from large insurance or hospital charge databases, public data sources, and existing literature and were adjusted to 2023 dollars. We estimated the cost of foodborne illness in the United States circa 2023 to be $75 billion. Deaths accounted for 56% and chronic outcomes for 31% of the mean cost. The costliest pathogen was nontyphoidal Salmonella at $17.1 billion followed by Campylobacter at $11.3 billion. Toxoplasma ($5.7 billion) and Listeria ($4 billion) followed due primarily to deaths and chronic outcomes from pregnancy-associated cases. Per-case cost ranged from $196 for Bacillus cereus to $4.6 million for Vibrio vulnificus. Unspecified agents accounted for 38% of the total cost of foodborne illness, but these illnesses were generally mild (per-case cost $781). These cost estimates can help inform food safety priorities. Our pathogen-specific per-case cost estimates can also help inform benefit-cost analysis required for new federal food safety regulations.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

Co-optimization of distributed generation, flexible load, and energy storage for promoting renewable energy consumption and power balancing in distribution networks

As more and more DGs are built and connected to the DN, the DN exhibits a notable degree of stochasticity and volatility. This stochasticity and volatility can result in an imbalance of active power in real-time, which may ultimately lead to a collapse of the DN. Therefore, in order to achieve a balance between carbon emissions and economic efficiency, this study introduces a strategy aimed at enhancing the PBC of the DN by employing a collaborative optimization approach involving DG, FL, and ES. The main contribution of this paper is to find a methodology for the co-optimization of DG, FL, and ES, which optimizes the PBC of the DN and the absorption rate of DG to the best conditions. Firstly, a model of DG-FL uncertainty in multiple scenarios is developed, and typical operational scenarios are extracted by effectively quantifying the DG-FL uncertainty of the distribution network. Secondly, a quantitative evaluation method for the balance degree of DG-FL based on IET is proposed. Finally, a two-stage optimization model for DG-FL-ES collaboration is established. The scientific rigor and practical applicability of the proposed method for improving the PBC and facilitating the integration of renewable energy sources are demonstrated through a case study of the power grid in a specific region of Zhejiang Province. The results demonstrate that the method proposed in this paper can enhance the BDODF by 43.44 % and achieve a 100 % absorption rate of renewable energy.

Towards a new model-independent calibration of Gamma-Ray Bursts

Current data on baryon acoustic oscillations and Supernovae of Type Ia (SNIa) cover up to z∼2.5. These low-redshift observations play a very important role in the determination of cosmological parameters and have been widely used to constrain the ΛCDM and models beyond the standard, such as the ones with open curvature. To extend this investigation to higher redshifts, Gamma-Ray Bursts (GRBs) stand out as one of the most promising observables. In spite of being transient, they are extremely energetic and can be used to probe the universe up to z∼9.4. They exhibit characteristics that suggest they are potentially standardizable candles and this allows their use to extend the distance ladder beyond SNIa. The use of GRB correlations is still a challenge due to the spread in their intrinsic properties. One of the correlations that can be employed for the standardization is the fundamental plane relation between the peak prompt luminosity, the rest-frame end time of the plateau phase, and its corresponding luminosity, also known as the three-dimensional Dainotti correlation. In this work, we propose an innovative method of calibration of the Dainotti relation which is independent of cosmology. We employ state-of-the-art data on Cosmic Chronometers (CCH) at z≲2 and use the Gaussian Processes Bayesian reconstruction tool. To match the CCH redshift range, we select 20 long GRBs in the range 0.553≤z≤1.96 from the Platinum sample, which consists of well-defined GRB plateau properties that obey the fundamental plane relation. To ensure the generality of our method, we verify that the choice of priors on the parameters of the Dainotti relation and the modelling of CCH uncertainties and covariance have negligible impact on our results. Moreover, we consider the case in which the redshift evolution of the physical features of the plane is accounted for. We find that the use of CCH allows us to identify a sub-sample of GRBs that adhere even more closely to the fundamental plane relation, with an intrinsic scatter of σint=0.20−0.05+0.03 obtained in this analysis when evolutionary effects are considered. In an epoch in which we strive to reduce uncertainties on the variables of the GRB correlations in order to tighten constraints on cosmological parameters, we have found a novel model-independent approach to pinpoint a sub-sample that can thus represent a valuable set of standardizable candles. This allows us to extend the cosmic distance ladder presenting a new catalogue of calibrated luminosity distances up to z=5.

Trajectory optimization of spacecraft autonomous far-distance rapid rendezvous based on deep reinforcement learning

This paper investigates the application of Deep Reinforcement Learning (DRL) in the trajectory optimization of spacecraft far-distance rapid rendezvous, and uses the most advanced DRL method Proximal Policy Optimization (PPO) to solve the continuous high-thrust minimum-fuel trajectory optimization problem. The space J2 perturbation was considered, its impact on the spacecraft’s on-orbit operation and trajectory design was analyzed, and the effectiveness and accuracy of the proposed method were verified in two far-distance rapid rendezvous missions. In order to ensure the safety of the subsequent close-range operation phase, a safe area reward framework is proposed, and sparse and dense safe area reward functions are designed. The dense safe area reward function significantly improves the training efficiency of the algorithm on the basis of ensuring terminal performance. In addition, the modeling and analysis of possible uncertainties in the spacecraft’s orbit operation, including observation uncertainty, state uncertainty and control uncertainty, is carried out to verify the performance of the proposed method through simulation. For uncertainties, the closed-loop performance of the policy is also evaluated by performing Monte Carlo simulations. The results show that the PPO algorithm can effectively deal with the rendezvous problem in uncertainty environments. These preliminary results demonstrate the great potential of the DRL method in achieving autonomous guidance of spacecraft.

Combating Misinformation on Social Media Using Social Noise and Social Entropy as a Measure of Uncertainty

ABSTRACTRecent events around the world, including the war in Ukraine and the conflict in Gaza have highlighted the effective use of social media as a tool to voice concerns about social issues to create awareness. At the same time, social media has become a fertile ground for spreading misinformation, fake news, and conspiracy theories. Misinformation and conspiracy theories have existed since the existence of mankind. What is new today is the speed by which misinformation can be created, magnified and spread using social media. Efforts to regulate social media and control the widespread spread of misinformation are still lacking due to rapid advances in technology and concerns regarding free speech. One approach to minimizing the impact of misinformation is to focus on social noise as an important factor in magnifying and spreading misinformation. In this paper, we investigate methods of identifying and quantifying social noise using social entropy as a measure of uncertainty and topic modeling. Results from the study have shown a direct relationship between social noise and social entropy. The results have also shown that social noise and social entropy decrease with the use of URLs and rich content (sematic information). Further studies will include the use of machine learning and AI techniques to improve the definition of social news and social entropy.

Influence of Noise Uncertainty Source and Model on the SNR Wall of Energy Detection

Influence of Noise Uncertainty Source and Model on the SNR Wall of Energy Detection

An interval uncertainty modeling method based on information granulation and improved multidimensional parallelepiped

Interval uncertainty modeling is essential for non-probabilistic reliability analysis of uncertain structures in the context of small samples, which confronts the difficulties of obtaining marginal intervals and characterizing variables correlation. A novel method for interval uncertainty modeling based on information granulation and improved multidimensional parallelepiped is proposed in this paper. Firstly, the method for constructing marginal intervals of uncertain variables is presented by integrating the up-sampling method based on kernel density estimation and information granulation theory (KDE-IG). Secondly, a relatively simple correlation coefficient expression based on the correlation angle is defined, and an improved multidimensional parallelepiped modeling method is proposed by optimizing the correlation coefficient within the marginal intervals. Additionally, two evaluation indexes are proposed to evaluate the robustness and accuracy of the constructed model. Finally, the applicability and effectiveness of the proposed method are demonstrated through two examples.

Hail hazard modeling with uncertainty analysis and roof damage estimation of residential buildings in North America

This research presents a statistical approach for hail risk modeling that incorporates the uncertainties of hail model prediction to provide insight into assessing the roof damage of a residential house in hail events. By quantifying the inherent uncertainties in evaluating hailstorm characteristics, this study extends the current existing hail models. The hail data are sourced from the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) in the U.S. In the modeling process, the largest hail diameter reported in the CoCoRaHS database serves as a primary input variable to estimate the number of observations for the largest hail diameter, hailstorm duration, and hit rate. The assessment of hail risk in this study focuses on the probability of hail damage and resultant repair costs for five types of roofs in North America (unrated roof and impact-resistant roofs with UL 2218 rating classes 1 to 4). The probability of hail damage is calculated as the failure probability by integrating all individual hailstone hits having variable diameters during a hailstorm with fragility curves, which estimate the probability that hailstones will fracture asphalt shingles (allowing water infiltration) or that they dislodge enough granules to cause visible damage requiring replacement for aesthetic reasons. The results reveal that an impact-resistant roof (impact-resistant rating classes 1 to 4) is associated with lower hail risks, with 60 % to 98 % reduction on average compared to unrated roofs. This study provides a comprehensive uncertainty modeling approach for hail hazard and risk, enabling better-informed decision-making and risk management strategies.

Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

A deeper look at switching intention to electric moped: Magnitude vs Uncertainty

While behavioral intention is often considered the immediate predictor of actions, the actual realization of a stated intention is often affected by the intention-behavior gap. This research investigates motorcyclists’ switching intention to an electric moped from a fossil-fuel moped, examining two key components of self-reported intentions: magnitude and uncertainty. Using the judgement uncertainty and magnitude parameters (JUMP) model within the Theory of Planned Behavior framework, we differentiate the two components of stated intention and explore their determinants. Our analysis of data from 293 Taiwanese moped users reveals that while attitude is the major determinant of intention magnitude, intention uncertainty is determined by perceived control, subjective norms and the interaction between attitude and perceived behavioral control. We observed high intention uncertainty across all responses, with negative intentions showing greater uncertainty. Conflicts between attitude and perceived control relate to higher intention uncertainty.

4D flow MRI velocity uncertainty quantification.

An automatic method is presented for estimating 4D flow MRI velocity measurement uncertainty in each voxel. The velocity distance (VD) metric, a statistical distance between the measured velocity and local error distribution, is introduced as a novel measure of 4D flow MRI velocity measurement quality. The method uses mass conservation to assess the local velocity error variance and the standardized difference of means (SDM) velocity to estimate the velocity error correlations. VD is evaluated as the Mahalanobis distance between the local velocity measurement and the local error distribution. The uncertainty model is validated synthetically and tested in vitro under different flow resolutions and noise levels. The VD's application is demonstrated on two in vivo thoracic vasculature 4D flow datasets. Synthetic results show the proposed uncertainty quantification method is sensitive to aliased regions across various velocity-to-noise ratios and assesses velocity error correlations in four- and six-point acquisitions with correlation errors at or under 3.2%. In vitro results demonstrate the method's sensitivity to spatial resolution, venc settings, partial volume effects, and phase wrapping error sources. Applying VD to assess in vivo 4D flow MRI in the aorta demonstrates the expected increase in measured velocity quality with contrast administration and systolic flow. The proposed 4D flow MRI uncertainty quantification method assesses velocity measurement error owing to sources including noise, intravoxel phase dispersion, and velocity aliasing. This method enables rigorous comparison of 4D flow MRI datasets obtained in longitudinal studies, across patient populations, and with different MRI systems.

Eco-environmental regret-aware optimization of networked multi-energy microgrids with fully carbon elimination and electric vehicles' promotion

With the escalating concern of global warming propelled by the rise in Earth's temperature, the need for effective CO2 management has become crucial. This paper presents an innovative CO2 elimination approach, wherein a multiple integrated system of energies (MISEs) incorporating sustainable resources, including renewable resources (RENs), plug-in electric vehicles (PEVs), and demand response programs, is optimized. The proposed carbon elimination framework begins by modeling the onsite carbon capturing and recycling within each MISE. To effectively utilize the onsite carbon recycling facilities and achieve carbon neutrality, the proposed model also incorporates carbon transfer capability between MISEs, thereby enhancing the efficiency of overall carbon recycling. Furthermore, a stochastic p-robust optimization technique is proposed to effectively manage uncertainties by combining the advantages of stochastic programming and robust optimization. This uncertainty modeling approach promotes greater utilization of sustainable resources like PEVs and RENs due to their lower operational regrets from economic and environmental perspectives. Based on the simulation results, implementing the p-robust-based regret assessment technique led to the total operation cost increasing by only 2.75 %, while achieving a significant 44.5 % reduction in maximum relative regret. These results underscore the effectiveness of the proposed framework in enhancing both the economic and environmental performance of MISEs.

A data-driven underground gas storage production system string failure prediction model for time-varying reliability analysis

In underground gas storage (UGS) systems, the production tubing string system undergoes complex thermo-mechanical loading conditions, leading to fatigue damage accumulation and potential failure risks. To accurately assess the fatigue reliability and mitigate risks, it is crucial to develop a data-driven physics-informed uncertainty modeling approach that captures the coupled thermo-mechanical effects and accounts for various uncertainties. This study proposes a data-driven physics-informed modeling method based on thermo-mechanical coupling for fatigue reliability assessment and risk mitigation in critical UGS systems. A quantitative analysis method is introduced, which relies on small-scale multi-stage loading experiments and combines the stochastic degradation process with time-series reliability theory. To analyze the fatigue life of joints under complex loading conditions, a modified S-N curve model is developed. This model takes into account the influences of the dimension coefficient, stress concentration factors, surface finish coefficients, and thermal load. It considers the uncertainties associated with different temperature loads and surface finish on fatigue life. By integrating physics-informed modeling, uncertainty quantification, and fatigue damage accumulation analysis, this approach provides a comprehensive framework for fatigue reliability assessment and risk mitigation in critical UGS systems. It enables informed decision-making for safe and reliable operations, as well as optimized maintenance strategies, ultimately enhancing the overall system performance and mitigating potential failures.

Treatment of epistemic uncertainty in conjunction analysis with Dempster-Shafer theory

The paper presents an approach to the modelling of epistemic uncertainty in Conjunction Data Messages (CDM) and the classification of conjunction events according to the confidence in the probability of collision. The approach proposed in this paper is based on Dempster-Shafer Theory (DSt) of evidence and starts from the assumption that the observed CDMs are drawn from a family of unknown distributions. The Dvoretzky–Kiefer–Wolfowitz (DKW) inequality is used to construct robust bounds on such a family of unknown distributions starting from a time series of CDMs. A DSt structure is then derived from the probability boxes constructed with DKW inequality. The DSt structure encapsulates the uncertainty in the CDMs at every point along the time series and allows the computation of the belief and plausibility in the realisation of a given probability of collision. The methodology proposed in this paper is tested on a number of real events and compared against existing practices in the European and French Space Agencies. We will show that the classification system proposed in this paper is more conservative than the approach taken by the European Space Agency but provides an added quantification of uncertainty in the probability of collision.

A Hybrid Interactive Decision-making Model Based on Multi-objective Optimization: An Application to Sustainable Supplier Selection

Sustainable development is crucial for conserving resources and ensuring smooth supply chain operations. In the context of the complex task of supplier selection, the traditional assumptions of attribute independencies are frequently found to be inadequate. The effective interaction between attributes and their weights plays a crucial role in navigating the decision outcomes. This paper presents a Choquet integral-based hybrid interactive decision-making approach to address the two important issues regarding the weights and interaction of criteria. To achieve this aim, first a hybrid weight determination method is proposed, combining the merits of multi-objective optimization and clustering techniques. To comprehensively assess the interactions among attributes, a non-additive measure determination method is introduced, leveraging concepts such as standard deviation and correlation. This approach effectively captures the interdependencies among attributes, leading to a more accurate decision-making process. Then, the Choquet integral incorporates a logarithmic function, which serves to enhance the modeling of uncertainty and effectively address the complexities present in intricate decision-making situations. Furthermore, a comprehensive interactive MCDM model is constructed, serving as a framework for supplier selection. Finally, experimental and sensitivity analysis demonstrate the effectiveness and stability of the proposed approach.

Fuzzy uncertainty modeling of generalized Hirota–Satsuma coupled Korteweg–de Vries equation

This article explores the solitary wave solutions of a generalized Hirota–Satsuma Coupled Korteweg–de Vries (HSCKdV) equation. The HSCKdV equation is a mathematical model that describes certain types of long waves, particularly those found in shallow water. The generalized HSCKdV equation is solved exactly using the Homotopy Perturbation Transform Method (HPTM). By applying this technique, the authors obtain solutions in the form of a convergent power series. These solutions offer an understanding of the characteristics of solitary waves within the domain of shallow water waves. The HSCKdV equation has been solved using the adomian decomposition method, and the results have been compared with those obtained from the HPTM. This comparison demonstrates the effectiveness of the HPTM in solving such nonlinear equations. Further, the HSCKdV equation is extended to a fuzzy version considering the initial condition as a fuzzy parameter. Uncertainty in the initial condition is addressed by representing it using triangular and trapezoidal fuzzy numbers. The generalized fuzzy HSCKdV equation is subsequently tackled using the fuzzy HPTM (FHPTM) providing fuzzy bound solutions. Using the FHPTM, we explain the fuzzy results, highlighting how the solitary wave splits into two solitary waves and noting that the lower and upper bound solutions are interchanged due to negative fuzzy results.

Exploring the Potential of Neutrosophic Topological Spaces in Computer Science

Neutrosophic topological spaces (NTS) offer a novel framework for uncertainty modeling by incorporating degrees of truth, indeterminacy, and falsity. This paper investigates the potential applications of NTS in computer science. We provide background on neutrosophic sets and their extension to topological spaces. We then explore how NTS could be used for uncertainty modeling in data analysis (e.g., handling noisy data in sensor networks), pattern recognition (e.g., improving image classification with imprecise features), and information retrieval (e.g., enhancing search results by considering relevance uncertainty). We discuss the challenges associated with applying NTS and highlight promising areas for future research, such as developing efficient algorithms for NTS operations. Overall, this paper aims to stimulate further exploration of how neutrosophic topological spaces can contribute to advancements in various computer science domains.