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Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially in challenging coastal environments, are lacking. This study introduces a novel likelihood-based approach for through-water photogrammetry, focusing on uncertainties associated with camera pose—a key factor affecting depth mapping accuracy. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation to provide realistic estimates of uncertainty in Water Column Depth (WCD) and Water–Air Interface (WAI) height. Using simulated scenarios for both drone and airborne surveys, we demonstrate that viewing geometry and camera pose quality significantly influence resulting uncertainties, often overshadowing the impact of depth itself. Our results reveal the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Notably, drone-based applications show particular promise, achieving decimeter-level WCD precision and WAI height estimations comparable to high-quality GNSS measurements when using large samples. These findings highlight the potential of drone-based surveys in producing more accurate bathymetric charts for shallow coastal waters. This research contributes to the refinement of uncertainty quantification in bathymetric charting and sets a foundation for future advancements in through-water surveying methodologies. [ABSTRACT FROM AUTHOR]

Aiming at the contract period of Shared Water Saving Management Contract (WSMC), this paper establishes a contract period optimization model based on win-win. Firstly, the water-saving investment and revenue are modeled as stochastic processes considering the fluctuation of uncertain variables such as operation and maintenance cost, water-saving amount and water price. Secondly, the profits of the water owner and Water-Saving Service Company (WSCO) are portrayed separately based on the WSMC market service mechanism. Then, the contract period model is constructed by minimizing the difference between the NPV of the two parties involved in the project as a constraint. Finally, the model is applied to two real cases, where the optimized contract period is 9 years in Case 1 and 12 years in Case 2. In addition, several variables affecting the model are analyzed, allowing the model to expand its applicability. [ABSTRACT FROM AUTHOR]

The speed of using renewable resources is expanding day by day. Renewable energy systems have many benefits for energy supply that do not include diesel, natural gas, or coal. Despite the many advantages, the use of renewable resources also includes basic challenges. With the presence of these sources, many technical issues must be considered in the network, the most important of which are voltage quality and network losses. The presence of these power plants can reduce fossil fuel costs and help reduce emissions. However, the high‐capacity connection of these types of power plants in the transmission networks despite the uncertainty may cause the congestion of transmission lines, increase losses and decrease voltage quality. Therefore, to reduce the need to build transmission lines, energy storage devices can be installed and energy can be stored and returned to the network in certain hours. The purpose of this paper is to build the maximum capacity of wind power plants in the transmission network in such a way that its profitability is guaranteed. For this purpose, in addition to considering the costs related to the power plant, the costs of storage devices and the construction of possible new lines have been considered. Also, improving the technical conditions of the network and reducing the maximum emission after installing these units is considered as a multiobjective function. The problem tested on the standard IEEE test transmission network and the results show that it is possible to determine the maximum profitable capacity of wind power plants. [ABSTRACT FROM AUTHOR]

Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs. [ABSTRACT FROM AUTHOR]

This paper discusses the influence of major disruptions, especially their ripple effects, on strategic and tactical decisions from the perspective of quantitative analysis. To mitigate the adverse consequence, we introduce proactive and reactive strategies and investigate the performance of these strategies. Considering the risk preference of decision-makers, we formulate the above problem as uncertain programming models under three different criteria (minimizing the expected cost, optimistic cost, and pessimistic cost). In the presented models, the side consequences and ripple effects of major disruptions are quantified by the facility capacity loss, production cost variation, and demand shift using uncertainty theory since disruption-related data are limited. Then, the developed models are converted into crisp deterministic ones with the aid of inverse distributions. Finally, we conduct a series of numerical experiments based on a real-world geographic data set of China to illustrate the validity of the presented models. The comparison between resilient solutions for the resilient supply portfolios and the non-resilient solutions with no measures available demonstrates the effectiveness of developed strategies in mitigating the ripple effects. Confronted with uncertainty of production cost, decision-makers are advised to adopt the reactive strategy, rather than the proactive strategy. [ABSTRACT FROM AUTHOR]

Abstract Microgrids (MGs) can enhance the consumers’ reliability. Nevertheless, besides significant outcomes, some challenges arise. Regarding the intermittent nature of Renewable Energy Resources (RESs), MGs are not operated radially. Accordingly, the reliable protection of MGs considering uncertainty in RESs is crucial for planners and operators. This paper uses data analysis to extract knowledge from locally available measurements using RMS values of symmetrical components. The learning-based characteristic of the suggested technique with a low computational burden exempts the need for an available communication infrastructure in the MG. The Support Vector Machine (SVM) technique is applied to train the Intelligent Electronic Devices to have a reliable MG protection scheme. The proposed method, which performs fault detection and classification together, just requires local information and functions effectively to discriminate faulty from normal conditions considering different uncertainty of resistance faults. Digital simulations on an MV test network were conducted to construct an appropriate database to consider some aspects of uncertainty in the network. The various faults considering their uncertainty, the different modes of operation, the uncertainty of RESs generation, and the load levels are combined to produce myriad scenarios. The simulation results confirm the effectiveness of the proposed adaptive protection approach in accurately distinguishing different system modes and consistently protecting the MG, achieving an accuracy rate of 99.75%. Furthermore, it offers the MG an optimal protection scheme that is not limited by selectivity constraints across diverse conditions.

Abstract The speed of using renewable resources is expanding day by day. Renewable energy systems have many benefits for energy supply that do not include diesel, natural gas, or coal. Despite the many advantages, the use of renewable resources also includes basic challenges. With the presence of these sources, many technical issues must be considered in the network, the most important of which are voltage quality and network losses. The presence of these power plants can reduce fossil fuel costs and help reduce emissions. However, the high‐capacity connection of these types of power plants in the transmission networks despite the uncertainty may cause the congestion of transmission lines, increase losses and decrease voltage quality. Therefore, to reduce the need to build transmission lines, energy storage devices can be installed and energy can be stored and returned to the network in certain hours. The purpose of this paper is to build the maximum capacity of wind power plants in the transmission network in such a way that its profitability is guaranteed. For this purpose, in addition to considering the costs related to the power plant, the costs of storage devices and the construction of possible new lines have been considered. Also, improving the technical conditions of the network and reducing the maximum emission after installing these units is considered as a multiobjective function. The problem tested on the standard IEEE test transmission network and the results show that it is possible to determine the maximum profitable capacity of wind power plants.

Reactive power dispatch (RPD) in electric power systems, integrated with renewable energy sources, is gaining popularity among power engineers because of its vital importance in the planning, designing, and operation of advanced power systems. The goal of RPD is to upgrade the power system performance by minimizing the transmission line losses, enhancing voltage profiles, and reducing the total operating costs by tuning the decision variables such as transformer tap setting, generator's terminal voltages, and capacitor size. But the complex, non-linear, and dynamic characteristics of the power networks, as well as the presence of power demand uncertainties and non-stationary behavior of wind generation, pose a challenging problem that cannot be solved efficiently with traditional numerical techniques. In this study, a new fractional computing strategy, namely, fractional hybrid particle swarm optimization (FHPSO), is proposed to handle RPD issues in electric networks integrated with wind power plants (WPPs) while incorporating the power demand uncertainties. To improve the convergence characteristics of the Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the proposed FHPSO incorporates the concepts of Shannon entropy inside the mathematical model of traditional PSOGSA. Extensive experimentation validates FHPSO effectiveness by computing the best value of objective functions, namely, voltage deviation index and line loss minimization in standard power systems. The proposed FHPSO shows an improvement in percentage of 61.62%, 85.44%, 86.51%, 93.15%, 84.37%, 67.31%, 61.64%, 61.13%, 8.44%, and 1.899%, respectively, over ALC_PSO, FAHLCPSO, OGSA, ABC, SGA, CKHA, NGBWCA, KHA, PSOGSA, and FPSOGSA in case of traditional optimal reactive power dispatch(ORPD) for IEEE 30 bus system. Furthermore, the stability, robustness, and precision of the designed FHPSO are determined using statistical interpretations such as cumulative distribution function graphs, quantile-quantile plots, boxplot illustrations, and histograms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Groundwater is a vital factor affecting the stability of water-rich slope of open-pit mine, and the distribution of groundwater inside water-rich slope is always uncertain. To investigate the uncertainty of groundwater distribution inside the water-rich slope, based on the engineering background of DEZIWA open-pit mine, this paper adopts the research method of field investigation and test (single and multiple borehole pumping tests), numerical simulation (Visual MODFOW), and theoretical analysis (nonlinear fitting theory, distribution fitting theory, and one-sample Kolmogorov–Smirnov test) to study the uncertainty of the distribution of groundwater table line within the water-rich slope of open-pit mine. Results obtained from the above research indicate that, Visual MODFLOW is an effective tool for obtaining groundwater distribution information inside the water-rich slope of open-pit mine. When the open-pit mine excavated to the final boundary, it is found that within multiple cross-sections of the southern and western regions of water-rich slope of the open-pit mine, groundwater table line can all be delineated by a series of 3-term Fourier equations, which can be characterized by identical equation forms but varying fitting coefficients. Furthermore, based on the findings of distribution fitting and one-sample Kolmogorov–Smirnov test, it becomes evident that the probability distribution of the fitting coefficients of the aforementioned 3-term Fourier equations can all be described by the normal distribution models. Use the established normal distribution models in this paper, uncertainty of the groundwater distribution within the water-rich slope of DEZIWA open-pit mine can be described indirectly and quantitatively, and the research methods of this paper can provide a meaningful reference to the slope engineering with similar conditions. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive seismic hazard assessment for Büyükçekmece, a district in Istanbul, Turkey, situated near the seismically active North Anatolian Fault (NAF). The study utilizes stochastic ground motion simulations with the validated EXSIM algorithm to understand the potential impact of medium to large-magnitude earthquakes (ranging from MW 6.3 to 7.42) on this vulnerable community. The research employs a site-specific approach, considering unique amplification factors for each location. By conducting 50 scenario-based simulations, the study assesses the seismic hazard, highlighting the importance of comprehending variations in ground motion, even when they are small, for a more precise hazard assessment. Furthermore, this study addresses the critical issue of uncertainty, particularly concerning stress parameters and hypocenter locations. The researchers demonstrate that variability in these factors can introduce substantial uncertainty in ground motion predictions. The study provides insights into the range of potential ground motion outcomes through probabilistic assessments involving multiple scenarios and stress drop values. Notably, the results indicate that ground motion levels vary with earthquake magnitudes and underscore the significance of accounting for this variability. This research emphasizes the seismic vulnerability of Büyükçekmece and the importance of accurate ground motion simulations, offering valuable insights for earthquake preparedness and mitigation efforts in the region. [ABSTRACT FROM AUTHOR]

The assessment of system performance and identification of failure mechanisms in complex engineering systems often requires the use of computation-intensive finite element software or physical experiments, which are both costly and time-consuming. Moreover, when accounting for uncertainties in the manufacturing process, material properties, and loading conditions, the process of reliability-based design optimization (RBDO) for complex engineering systems necessitates the repeated execution of expensive tasks throughout the optimization process. To address this problem, this paper proposes a novel methodology for RBDO. First, a multi-fidelity surrogate modeling strategy is presented, leveraging partially observed information (POI) from diverse sources with varying fidelity and dimensionality to reduce computational cost associated with evaluating expensive high-dimensional complex systems. Second, a multi-task surrogate modeling framework is proposed to address the concurrent evaluation of multiple constraints for each design point. The multi-task framework aids in the development of surrogate models and enhances the effectiveness of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly accurate and efficient framework for reliability-based design optimization through knowledge sharing. The proposed method was applied to two design case studies. By incorporating POI from various sources, the proposed approach improves the accuracy and efficiency of system performance prediction, while simultaneously addressing the cost and complexity associated with the design of complex systems. [ABSTRACT FROM AUTHOR]

Though there are several studies on uncertain single-period portfolio selection, the uncertain multiperiod portfolio selection literature is still in an exploration phase. Besides, the effects and influences of investors' attitudes have not been extensively investigated in a multiperiod framework. Further, the possible application of the contingent and borrowing and lending constraints in an uncertain multiperiod portfolio selection framework has not been explored. In this paper, we propose an uncertain multiobjective multiperiod portfolio selection model that handles the uncertainty using the Me operator. The Me operator integrates the investor's attitude (conservative, neutral, or aggressive) into the portfolio selection model. The proposed model maximizes the terminal wealth and minimizes the cumulative risk of the portfolio subject to several realistic constraints, such as minimum return threshold, borrowing, and lending of the capital, value-at-risk, liquidity, cardinality, minimal and maximal fraction, no short selling, and contingent constraints, for each period. These realistic constraints adequately address the practical concerns of the investors and aptly mimic the investment market conditions concerning multiperiod investment over a long investment horizon. The weighted goal programming then solves the proposed model. Finally, a detailed empirical illustration is presented to demonstrate the efficacy of the proposed model. The proposed approach is also substantiated through comparison with the existing research works. The proposed approach effectively integrates the investor's attitude and aptly simulates the real-world investment market conditions to incorporate the investor's preferences into the portfolio selection. [ABSTRACT FROM AUTHOR]

Along with the rapid advancement of additive manufacturing technology, 3D-printed structures and materials have been successfully employed in various applications. Computer simulations of these structures and materials are often characterized by a vast number of spatial-varied parameters to predict the structural response of interest. Direct Monte Carlo methods are infeasible for uncertainty quantification and reliability assessment of such systems as they require a large number of forward model evaluations to obtain convergent statistics. To alleviate this difficulty, this paper presents a convolutional dimension-reduction method with knowledge reasoning-based loss regularization for surrogate modeling and uncertainty quantification of structures with high-dimensional spatial uncertainties. To manage the inherent high-dimensionality, a deep convolutional dimension-reduction network (ConvDR) is constructed to transform the spatial data into a low-dimensional latent space. In the latent space, knowledge reasoning is formulated as a form of loss regularization, and evolutionary algorithms are employed to train both the ConvDR network and a linear regression model as surrogate models for predicting the response of interest. 2D structures with spatial-variated material compositions are used to demonstrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Motivation: The need to improve the accuracy and reliability of market valuation and risk assessment in real estate markets, especially under conditions of uncertainty. Aim: To integrate theoretical foundations and methodological approaches for modeling aleatoric and epistemic uncertainties in real estate markets using credal networks and confidence boxes (c-boxes). Approach: This paper presents a comprehensive theoretical and methodological framework for uncertainty modeling in real estate markets, focusing on the application of credal networks and confidence boxes. It does not include empirical validation or practical case studies, instead providing a detailed conceptual and me - thodological discussion. Results: The proposed method demonstrates significant improvements in uncertainty quantification and market analysis accuracy in theoretical terms, offering valuable insights for investors, urban planners, and policymakers. However, empirical validation is suggested for future research to confirm practical applicability. [ABSTRACT FROM AUTHOR]

The moment method is commonly used in reliability analysis, in which the maximum entropy method (MEM) and polynomial fitting (PF) have been widely used due to their advantages in accuracy and efficiency, respectively. In this paper, we propose a novel reliability analysis method by combining MEM and PF. The probability density function is preliminarily estimated using the fractional moment maximum entropy method (FM-MEM), based on which PF is then used to further improve the accuracy. The proposed method can avoid the phenomenon of the negative probability density and function oscillations in PF effectively. Moreover, the order of the exponential polynomial in the FM-MEM is adaptively selected in the preliminary solution calculation process. An iterative process for the number of exponential polynomial terms is also proposed, using the integral of the moment error function and the integrals of the local and global negative probability density as the convergence criteria. Four numerical examples and one engineering example are tested, and the results are compared with those of the Monte Carlo simulation and the classical FM-MEM results, respectively, demonstrating the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Energy demand is surging with the rise in population, economic development, and ever-increasing living standards. Due to sustainability and environmental issues, renewable energy sources have emerged as a credible option to meet this increased energy demand. However, it is plagued with the issue of variability and intermittency. Hybrid energy systems are proposed as a possible solution to this problem. The optimal sizing of hybrid energy systems ensures a reliable, efficient, and cost-effective power supply. Therefore, this paper discusses different hybrid energy systems in both on-grid and off-grid configurations, followed by the review of various sizing methodologies. The article also discusses various multi-criteria design indicators acting as decision variables, sensitivity variables, and constraints in different capacities while preparing the mathematical model of hybrid energy systems. As renewable resources and their based systems are inherently uncertain, it becomes imperative to characterize and model the uncertainty associated with such systems. Sincere efforts were made to understand various sources of uncertainty and how to characterize and model these uncertainties using different methodologies. The existing uncertainty modeling approaches were studied, compared, and analyzed. Further, the need for conducting sensitivity analysis and its usage in hybrid energy system design considering different sensitive parameters were also studied. [ABSTRACT FROM AUTHOR]

Machine learning is gaining prominence in mechanical design, offering cost-effective surrogate models to replace computationally expensive models. Nevertheless, concerns persist regarding the accuracy of these models, especially when applied to safety-critical products. To address this challenge, this study investigates methods to account for model prediction errors by incorporating epistemic uncertainty within surrogate models while managing aleatory uncertainty in input variables. The paper clarifies key aspects of modeling coupled epistemic and aleatory uncertainty when using surrogate models derived from noise-free training data. Specifically, the study concentrates on quantifying the impacts of coupled uncertainty in mechanical design through the development of numerical methods based on the concept of the most probable point. This method is particularly relevant for mechanical component design, where failure prevention holds paramount importance, and the probability of failure is low. It is applicable to design problems characterized by probability distributions governing aleatory and epistemic uncertainties in model inputs and predictions. The proposed method is demonstrated using shaft and beam designs as two illustrative examples. The results demonstrate the method's effectiveness in quantifying and mitigating the influence of coupled uncertainty in the design process. [ABSTRACT FROM AUTHOR]

Unmanned engineering systems that execute various operations are becoming increasingly complex relying on a large number of components and their interactions. The reliability, maintainability, and performance optimization of these systems are critical due to their intricate nature and inaccessibility during operations. This paper introduces a new reliability-based optimization framework for planning operational profiles for unmanned systems. The proposed method employs deep learning techniques for subsystem health monitoring, dynamic Bayesian networks for system reliability analysis, and multi-objective optimization schemes for optimizing system performance. The proposed framework systematically integrates these schemes to enable their application to a wide range of tasks, including offline reliability-based optimization of system operational profiles. This framework is the first in the literature that incorporates health monitoring of multi-component systems with causal relationships. Using this hybrid scheme on unmanned systems can improve their reliability, extend their lifespan, and enable them to execute more challenging missions. The proposed framework is implemented and executed using a simulation model for the engine cooling and control system of an unmanned surface vessel. [ABSTRACT FROM AUTHOR]

Kinematic multi-sensor systems (MSS) are widely used for various applications, like mobile mapping or for autonomous systems. Depending on the application, insufficient knowledge of a system, like wrong assumptions about the accuracy of calibrations, might lead to inaccurate maps for mapping tasks or it might endanger humans in the context of autonomous driving. Uncertainty modeling can help to gain knowledge about the data captured by a system. Usually, uncertainty estimations for MSSs are done as backward modeling based on a comparison to reference datasets. In this paper, a forward modeling approach for the uncertainty modeling of a LiDAR-based kinematic MSS is chosen to estimate the uncertainty of an acquired point cloud. The MSS consists of a Leica Absolute Tracker and a platform with a 6-DoF sensor and Velodyne VLP-16 LiDAR. Results of multiple calibrations are used as the source for the uncertainty information for a Monte Carlo (MC) variance propagation of the point uncertainties. The deviations of the acquired point clouds in comparison to a ground truth can be decreased by an ensemble referencing process using the MC samples. Furthermore, the predicted uncertainties for the point clouds are well representing the actual deviations for reference panels closer to the system. Panels farther away indicate remaining distance depending effects. [ABSTRACT FROM AUTHOR]

Purpose: Trigeminal neuralgia (TN) can be treated on the CyberKnife system using two different treatment delivery paths: the general‐purpose full path corrects small rotations, while the dedicated trigeminal path improves dose fall‐off but does not allow rotational corrections. The study evaluates the impact of uncorrected rotations on brainstem dose and the length of CN5 (denoted as Leff) covered by the prescription dose. Methods and materials: A proposed model estimates the delivered dose considering translational and rotational delivery errors for TN treatments on the CyberKnife system. The model is validated using radiochromic film measurements with and without rotational setup error for both paths. Leff and the brainstem dose is retrospectively assessed for 24 cases planned using the trigeminal path. For 15 cases, plans generated using both paths are compared for the target coverage and toxicity to the brainstem. Results: In experimental validations, measured and estimated doses agree at 1%/1 mm level. For 24 cases, the treated Leff is 5.3 ± 1.7 mm, reduced from 5.9 ± 1.8 mm in the planned dose. Constraints for the brainstem are met in 23 cases for the treated dose but require frequent treatment interruption to maintain rotational corrections <0.5° using the trigeminal path. The treated length of CN5, and plan quality metrics are similar for the two paths, favoring the full path where rotations are corrected. Conclusions: We validated an analytical model that can provide patient‐specific tolerances on rotations to meet plan objectives. Treatment using the full path can reduce treatment time and allow for rotational corrections. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.

Electric vehicle (EV) drivers aim to charge their vehicles cost-effectively and with minimal charging time. Meanwhile, the ever-increasing number of EVs without charging control strategies could result in a massive surge in peak demand, potentially overloading distribution equipment and violating voltage constraints. To tackle this challenge, this paper introduces a transactive energy market (TEM) framework for an EV parking lot (EVPL) equipped with photovoltaic (PV) panels and battery systems (BSs), considering the preferences of both EV drivers and the EVPL operator. In this framework, EVs parked in the EVPL participate in the TEM by submitting their charging flexibility through response curves, which indicate the compensation required for different values of flexibility. Furthermore, the proposed model allows the EVPL operator to utilize the flexibility of EVs in the vehicle-to-grid (V2G) program by incentivizing EV drivers, considering their preferences and the degradation cost of EV batteries. The study employs the stochastic programming method to model uncertainties in PV output, electricity prices, and EV availability. It also incorporates BS degradation costs and carbon emissions constraints into the EVPL scheduling problem. Linearization techniques are then applied to transform the non-linear optimization problem into a mixed-integer linear programming (MILP) model. Finally, applying the model to a case study validates its superiority in satisfying the preferences of both EV drivers and EVPL.

In this paper, the class of uncertain multi-dimensional fractional control problems with the first-order PDE constraints is investigated. The robust approach and the parametric method are applied for solving such control problems. Then, robust optimality is analyzed for the considered PDE-constrained multi-dimensional fractional control problem with uncertainty. Further, the exact absolute penalty function method is used for solving control problems created in both the aforementioned approaches. Then, under appropriate convexity hypotheses, exactness of the penalization of this exact penalty function method is investigated in the case when it is used for solving the considered control problem with uncertainty. Further, an algorithm based on the used method is presented, the main goal of which is to illustrate the precise steps to solve the unconstrained multi-dimensional non-fractional control problem with uncertainty associated with the constrained fractional control problem. [ABSTRACT FROM AUTHOR]

Cognitive Reliability and Error Analysis Method (CREAM) is widely used in human reliability analysis (HRA). It defines nine common performance conditions (CPCs), which represent the factors that may affect human reliability and are used to modify the cognitive failure probability (CFP). However, the levels of CPCs are usually determined by domain experts, which may be subjective and uncertain. What's more, the classic CREAM assumes that the CPCs are independent, which is unrealistic. Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation. To address the issue of uncertain information modeling and processing, this paper introduces evidence theory to evaluate the CPC levels in specific scenarios. To address the issue of dependence modeling, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to process the dependence among CPCs and calculate the relative weights of each CPC, thus modifying the multiplier of the CPCs. The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable. [ABSTRACT FROM AUTHOR]

The rapid growth and development in DC microgrids (DCmGs), led system-level operation and control to be reformed continuously imbibing constraints and concerns that improve system operational performance. Focusing only on islanded DCmGs, quite a literature/work has been attempted in various perspectives to solve energy management problems. In light of this aspect, an optimal operation/control of standalone DCmGs, composed of the droop-regulated dispatchable type distributed generators (DGs) considering generation cost, voltage deviation, and current-sharing objectives has been proposed. The proposed work includes the uncertainties in network variables such as load demand, wind, and solar power generations. The scenario-based analytical method has been employed for modeling the uncertainties stated. For optimal operation, the droop parameters were solved by means of the heuristic-based multi-objective optimization technique, Dragonfly Algorithm (DA). The proposed methodology has been implemented on modified 6-bus and 33-bus test systems operated as DCmGs. Furthermore, the applicability of the proposed approach has been presented for the case study with battery energy storage system (BESS) scheduled DCmG network operation on a 24-hour time horizon. The obtained results have been compared with other optimization algorithms to validate the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

A priori variance–covariance matrix (VCM) estimation of global navigation satellite systems (GNSS) double difference observations in relative positioning is challenging. Existing methods have been limited to estimate variances only or present variables challenging to acquire a priori, unfeasible for observation planning. Ignoring the covariances produces misleading results and compromises reliance on GNSS positioning design. In this study, we propose models to estimate the VCM a priori for planning, based on simple variables accessible to any professional: observation time span, vector length, and ephemeris type. Using a database of over 140,000 GNSS vectors with double difference (DD) observations, we group the data by time span and length range and extract standard deviations and covariances for the linear regression process. The Isolation Forest algorithm is employed to filter outlying observations. Our models provide standard deviations and root square covariances in a local coordinate system, requiring only vector length, observation time span, and ephemeris type as input. Additionally, the equations can be easily implemented in a simple spreadsheet. The results show high coefficients of determination (R2 > 0.8). We tested the models in a simulated GNSS network and verified broadcast ephemeris resulted in 6.5 to 16.7 times larger error ellipsoids compared to the precise ephemeris, indicating higher uncertainty. Ellipsoids differed in flattening and orientation when compared to the null covariance (variance only) approach. Although VCM models better reflect the precision of relative positioning observations, they did not affect the number of non-controllable observations in the observation's reliability tests. [ABSTRACT FROM AUTHOR]

Uncertainty is an essential feature of many concepts, data sets, and operations encountered in various applications related to computer science and engineering, and medical diagnostics. This paper introduces an extension to the Simpful library, enhancing it with capabilities for interval-valued fuzzy sets (IVFSs) and generalized fuzzy inference. Addressing the need for comprehensive uncertainty modeling in various applications, this extension offers intuitive definitions and operations for interval-valued fuzzy sets, facilitating advanced reasoning in uncertain environments.

Reactive power dispatch (RPD) in electric power systems, integrated with renewable energy sources, is gaining popularity among power engineers because of its vital importance in the planning, designing, and operation of advanced power systems. The goal of RPD is to upgrade the power system performance by minimizing the transmission line losses, enhancing voltage profiles, and reducing the total operating costs by tuning the decision variables such as transformer tap setting, generator’s terminal voltages, and capacitor size. But the complex, non-linear, and dynamic characteristics of the power networks, as well as the presence of power demand uncertainties and non-stationary behavior of wind generation, pose a challenging problem that cannot be solved efficiently with traditional numerical techniques. In this study, a new fractional computing strategy, namely, fractional hybrid particle swarm optimization (FHPSO), is proposed to handle RPD issues in electric networks integrated with wind power plants (WPPs) while incorporating the power demand uncertainties. To improve the convergence characteristics of the Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the proposed FHPSO incorporates the concepts of Shannon entropy inside the mathematical model of traditional PSOGSA. Extensive experimentation validates FHPSO effectiveness by computing the best value of objective functions, namely, voltage deviation index and line loss minimization in standard power systems. The proposed FHPSO shows an improvement in percentage of 61.62%, 85.44%, 86.51%, 93.15%, 84.37%, 67.31%, 61.64%, 61.13%, 8.44%, and 1.899%, respectively, over ALC_PSO, FAHLCPSO, OGSA, ABC, SGA, CKHA, NGBWCA, KHA, PSOGSA, and FPSOGSA in case of traditional optimal reactive power dispatch(ORPD) for IEEE 30 bus system. Furthermore, the stability, robustness, and precision of the designed FHPSO are determined using statistical interpretations such as cumulative distribution function graphs, quantile-quantile plots, boxplot illustrations, and histograms.

Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data augmentation methods cannot effectively enhance the generalization performance of the deep model. To solve this problem, we improve modeling Domain Shifts with Uncertainty (DSU) and propose a new brain-inspired computer vision image data augmentation method which consists of two key components, namely, using Robust statistics and controlling the Coefficient of variance for DSU (RCDSU) and Feature Data Augmentation (FeatureDA). RCDSU calculates feature statistics (mean and standard deviation) with robust statistics to weaken the influence of outliers, making the statistics close to the real values and improving the robustness of deep learning models. By controlling the coefficient of variance, RCDSU makes the feature statistics shift with semantic preservation and increases shift range. FeatureDA controls the coefficient of variance similarly to generate the augmented features with semantics unchanged and increase the coverage of augmented features. RCDSU and FeatureDA are proposed to perform style transfer and content transfer in the feature space, and improve the generalization ability of the model at the style and content level respectively. On Photo, Art Painting, Cartoon, and Sketch (PACS) multi-style classification task, RCDSU plus FeatureDA achieves competitive accuracy. After adding Gaussian noise to PACS dataset, RCDSU plus FeatureDA shows strong robustness against outliers. FeatureDA achieves excellent results on CIFAR-100 image classification task. RCDSU plus FeatureDA can be applied as a novel brain-inspired semantic data augmentation method with implicit robot automation which is suitable for datasets with large style differences between training and testing data.

This paper addresses a linear programming problem with interval right-hand sides, forming a family of linear programs associated with each realization of the interval data. The paper focuses on the outcome range problem, which seeks the range of an additional function—termed the outcome function—over all possible optimal solutions of such linear programs. We explore the problem's applicability in diverse contexts, discuss its connections to certain existing problems, and analyze its computational complexity and theoretical foundations. Given the inherent computational challenges, we propose three heuristics to solve the problem. The first heuristic employs a reformulation–linearization technique (RLT) to obtain an outer approximation of the range of the outcome function. We also present two algorithms—a gradient-restoration-based approach (GI) and a bases inspection method (BI)—for computing an inner approximation of the range. Computational experiments illustrate the competitive advantage of our proposed approaches versus off-the-shelf solvers. The GI and BI methods present promising results in finding a cheap but tight inner approximation, while the performance of the RLT technique decreases as problem size and uncertainty increase. [ABSTRACT FROM AUTHOR]

In this paper, the operation of electrical, heat and gas networks with flexible energy hubs is presented, where hubs is contributed in energy market that is based on market clearing price. This scheme maximizes the difference between energy hubs profit and operation cost of generation units subjected to operation model of sources, storages and responsible load in hub format, flexibility limits of hubs, and optimal power flow model of energy networks. This formulation is based on market clearing price model, so that it obtain value of energy price according to balancing between the generation and demand in different buses if energy networks. Penalty function method in this paper uses to calculate of primal variables and some dual variables (energy prices). In the following, the stochastic optimization uses to model the uncertainties of load and renewable power. Finally, the obtained numerical results show the capabilities of the proposed scheme in the improving flexibility and economic situations of hubs. Also, optimal operation of hubs is able to improve the economic and operation induces of energy networks in comparison to load flow study, and it improves the social welfare based on reducing of energy price. So that 100% flexibility condition obtains for Hubs. In this condition, the operation cost, energy loss, voltage drop and temperature drop are reduced about to 19.8%, 7.1%, 19.5% and 16.7%, respectively, with respect to load flow analysis, but the pressure drop increases about to 4.9%. [ABSTRACT FROM AUTHOR]

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation. [ABSTRACT FROM AUTHOR]

Methods for modeling the uncertainty in laser tracker angle measurements vary within the metrology industry, leading to confusion and questionable stochastic modeling for survey network adjustments and error propagation analysis. Interpreting the published laser tracker manufacturer performance specifications to determine an a priori sigma value for weighting azimuth and zenith angle measurements can be confusing and has led to differing implementations. This paper proposes a unique way to model survey network a priori laser tracker angular uncertainties based on laser tracker manufacturers' published maximum permissible error (MPE) values referenced to current standards for weighting survey network measurements. This paper's proposed model takes into account the disparate effects that pointing errors, target centering errors, and leveling errors have on azimuth and zenith angular uncertainties for measurements with steep sightings and at near ranges. [ABSTRACT FROM AUTHOR]

This paper introduces a recommendation system aimed at enhancing the sustainable process of risk management within airport operations, with a special focus on Occupational Stress Risks (OSRs). The recommendation system is implemented via a flexible Python code that offers seamless integration into various operational contexts. It leverages Fuzzy Cognitive Maps (FCMs) to conduct comprehensive risk assessments, subsequently generating prioritized recommendations for predefined risk management measures aimed at preventing and/or reducing the most critical OSRs. The system's reliability has been validated by iterating the procedure with diverse input data (i.e., matrices of varying sizes) and measures. This confirms the system's effectiveness across a broad spectrum of engineering scenarios. [ABSTRACT FROM AUTHOR]

Conflict analysis in intelligent decision making has received increasing attention in recent years. However, few researchers have analyzed conflicts by considering trustworthiness from the perspective of common agreement and common opposition. Since L-fuzzy three-way concept lattice is able to describe both the attributes that objects commonly possess and the attributes that objects commonly do not possess, this paper introduces an L-fuzzy three-way concept lattice to capture the issues on which agents commonly agree and the issues which they commonly oppose, and proposes a hybrid conflict analysis model. In order to resolve conflicts identified by the proposed model, we formulate the problem as a knapsack problem and propose a method for selecting the optimal attitude change strategy. This strategy takes into account the associated costs and aims to provide the decision maker with the most favorable decision in terms of resolving conflicts and reaching consensus. To validate the effectiveness and feasibility of the proposed model, a case study is conducted, providing evidence of the model's efficacy and viability in resolving conflicts. [ABSTRACT FROM AUTHOR]