Search Results (1,347)

Several attempts have been made to replace the critical raw material platinum (Pt) with other metals, mainly focusing on its functional performance, while safety and sustainability criteria are often overlooked. Here, the substitution of Pt by nickel-based coatings is addressed for water electrolysis applications. Risk assessment and life cycle assessment are iteratively performed at the laboratory scale and after upscaling metal coating protocols. The challenges for the transition towards an integrated safe and sustainable by design (SSbD) approach are identified, and strategies are proposed to resolve them. Valuable insights emerge from the individual assessments (e.g., hotspots, trade-offs, and recommendations for sustainability and safety), as well as regarding the transition towards an integrated SSbD (e.g., dealing with data gaps and uncertainties). Full article

In this study, we applied the fuzzy analytic hierarchy process (FAHP) to the multi-objective optimization of the performance of a solar-electromagnetic energy heating system (SEHS). Optimizing the performance of SEHS as a sustainable heating solution in rural areas is crucial for improving energy efficiency and reducing environmental impacts. To achieve the optimal balance between economy, system performance, energy efficiency, and comfort, we developed a FAHP-based optimization model using system simulation data from the experimentally validated TRNSYS model. The results show that the optimal decision scheme improved the overall performance by 38% compared to the original design scheme. This work confirms the effectiveness of FAHP in dealing with uncertainty and multi-objective decision-making in SEHS and provides valuable scientific support for engineering practice. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Aiming to solve optimization scheduling difficulties caused by the double uncertainty of source-side photovoltaic (PV) output and load-side demand response in active distribution networks, this paper proposes a two-stage distribution robust optimization method. First, the first-stage model with the objective of minimizing power purchase cost and the second-stage model with the co-optimization of active loss, distributed power generation cost, PV abandonment penalty, and load compensation cost under the worst probability distribution are constructed, and multiple constraints such as distribution network currents, node voltages, equipment outputs, and demand responses are comprehensively considered. Secondly, the second-order cone relaxation and linearization technique is adopted to deal with the nonlinear constraints, and the inexact column and constraint generation (iCCG) algorithm is designed to accelerate the solution process. The solution efficiency and accuracy are balanced by dynamically adjusting the convergence gap of the main problem. The simulation results based on the improved IEEE33 bus system show that the proposed method reduces the operation cost by 5.7% compared with the traditional robust optimization, and the cut-load capacity is significantly reduced at a confidence level of 0.95. The iCCG algorithm improves the computational efficiency by 35.2% compared with the traditional CCG algorithm, which verifies the effectiveness of the model in coping with the uncertainties and improving the economy and robustness. Full article

A rational exchange protocol is a type of e-commerce protocol that aims to maximize the participants’ own interests. The Buttyan model is commonly used to analyze the security of such protocols. However, this model has limitations in dealing with uncertainties and false messages in rational exchanges. To address these shortcomings, this paper proposes a formal analysis method based on Bayesian games. By incorporating participants’ types and beliefs, the Buttyan model is extended to enhance its ability to express uncertainties. Additionally, attack messages are introduced to simulate the potential fraudulent behaviors that participants may exploit through the security vulnerabilities in the protocol. Finally, the improved model is applied to conduct a formal analysis of a rational electronic contract signing protocol, and it is found that the protocol meets the usability requirements. The results show that this method can be effectively applied to the security analysis of rational exchange protocols, thereby enhancing the security of the e-commerce transaction process. Full article

The purpose of this paper is to present a novel optimization framework that enhances Wasserstein Distributionally Robust Optimization (WDRO) for chance-constrained facility location problems under demand uncertainty. Traditional methods often rely on predefined probability distributions, limiting their flexibility in adapting to real-world demand fluctuations. To overcome this limitation, the proposed approach integrates two methodologies, specifically a Genetic Algorithm to search for the optimal decision about facility opening, inventory, and allocation, and a constrained Jordan–Kinderlehrer–Otto (cJKO) scheme for dealing with robustness in the objective function and chance-constraint with respect to possible unknown fluctuations in demand. Precisely, cJKO is used to construct Wasserstein ambiguity sets around empirical demand distributions (historical data) to achieve robustness. As a result, computational experiments demonstrate that the proposed hybrid approach achieves over 90% demand satisfaction with limited violations of probabilistic constraints across various demand scenarios. The method effectively balances operational cost efficiency with robustness, showing superior performance in handling demand uncertainty compared to traditional approaches. Full article

Structure-from-motion (SfM) is a foundational technology that facilitates 3D scene understanding and visual localization. However, bundle adjustment (BA)-based SfM is usually very time-consuming, especially when dealing with numerous unknown focal length cameras. To address these limitations, we proposed a novel SfM system based on pose-only adjustment (PA) for intrinsic and extrinsic joint optimization to accelerate computing. Firstly, we propose a base frame selection method based on depth uncertainty, which integrates the focal length and parallax angle under a multi-camera system to provide more stable depth estimation for subsequent optimization. We explicitly derive a global PA of joint intrinsic and extrinsic parameters to reduce the high dimensionality of the parameter space and deal with cameras with unknown focal lengths, improving the efficiency of optimization. Finally, a novel pose-only re-triangulation (PORT) mechanism is proposed for enhanced reconstruction completeness by recovering failed triangulations from incomplete point tracks. The proposed framework has been demonstrated to be both faster and comparable in accuracy to state-of-the-art SfM systems, as evidenced by public benchmarking and analysis of the visitor photo dataset. Full article

A conflict is a situation where multiple stakeholders have different evaluations over possible scenarios or states. Conflict analysis is an essential tool for understanding and resolving complex conflicts, especially in scenarios involving multiple stakeholders and uncertainties. Confrontation analysis (ConAna) and graph model for conflict resolution (GMCR) have been integrated for dilemma reasoning and conflict resolution in region crisis analysis. This paper discusses the application of decision-theoretic rough sets (DTRS) to three-way decisions (3WD) in dilemma reasoning and conflict resolution. Three-way decisions are a strategy for making decisions under uncertain conditions, which compensates for the shortcomings of traditional two-way decisions (such as accept or reject) by introducing a “delayed decision” option. In terms of dilemma reasoning, we try to address incomplete or conflicting information and provide a more reasonable decision path for decision-makers through comprehensive evaluation of multi-criteria. In terms of conflict resolution, the DTRS model seeks a compromising solution that is acceptable to all parties by analyzing the game relationship between different stakeholders. The DTRS model combines decision-making theory and rough set theory to determine the balanced decision region by constructing a game between multiple criteria. This dynamic integration is of great significance for the study of complex international conflicts, providing a cross-disciplinary perspective for related research. In this paper, we demonstrate the application of DTRS in 3WD and discuss the relationship between DTRS and probabilistic rough sets. The research shows that the DTRS model has significant advantages in dealing with complex decision problems and can effectively deal with the conflicts and uncertainties in multi-criteria decision-making. Full article

This paper addresses the challenges of broadband impedance identification in wind farms connected to the power grid, where broadband oscillations can compromise grid stability. Traditional impedance modeling approaches, including white-box and black/grey-box methods, face limitations in real-world applications, particularly when dealing with commercial new energy units with unknown control structures. To overcome these challenges, a novel real-time impedance identification method is proposed for PMSGs(Permanent Magnet Synchronous Generators). The method, called ASSA (Attention-based Shared and Specific Architecture), utilizes a multi-task neural network model combined with an attention mechanism to improve the accuracy of impedance fitting across different frequency bands. A broadband impedance dataset is constructed offline under various operating conditions, incorporating uncertainties like wind speed. The proposed approach offers an efficient solution for impedance identification, enhancing the stability and reliability of grid-connected renewable energy systems. Full article

A significant advancement in Neural Network (NN) research is the integration of domain-specific knowledge through custom loss functions. This approach addresses a crucial challenge: How can models utilize physics or mathematical principles to enhance predictions when dealing with sparse, noisy, or incomplete data? Physics-Informed Neural Networks (PINNs) put this idea into practice by incorporating a forward model, such as Partial Differential Equations (PDEs), as soft constraints. This guidance helps the networks find solutions that align with established laws. Recently, researchers have expanded this framework to include Bayesian NNs (BNNs) which allow for uncertainty quantification. However, what happens when the governing equations of a system are not completely known? In this work, we introduce methods to automatically select PDEs from historical data in a parametric family. We then integrate these learned equations into three different modeling approaches: PINNs, Bayesian-PINNs (B-PINNs), and Physical-Informed Bayesian Linear Regression (PI-BLR). To assess these frameworks, we evaluate them on a real-world Multivariate Time Series (MTS) dataset related to electrical power energy management. We compare their effectiveness in forecasting future states under different scenarios: with and without PDE constraints and accuracy considerations. This research aims to bridge the gap between data-driven discovery and physics-guided learning, providing valuable insights for practical applications. Full article

With the rapid development of global renewable energy, the virtual power plant (VPP), as an emerging power management model, has attracted increasing attention. Traditional manual management is difficult to effectively deal with because of the complexity and uncertainty of the VPP. The application of artificial intelligence (AI) technology provides new solutions for the VPP to cope with these problems. This review presents the research of AI technology in the VPP. Firstly, the basic concepts and theoretical framework of the VPP are presented. Then, the application of AI technology in VPP functional modules is discussed. Finally, the challenges of the VPP in coping with uncertainty, improving algorithmic interpretability and ensuring data security are pointed out, which provides theoretical support for subsequent research in the field of VPPs. Full article

In this article, we introduce and investigate two classes of fuzzy fractional two-dimensional continuous-time (FFTDCT) linear systems to deal with uncertainty and fuzziness in system parameters. First, we analyze FFTDCT linear systems based on the Roesser model, incorporating fuzzy parameters into the state-space equations. The potential solution of the fuzzy fractional system is obtained using a two-dimensional granular Laplace transform approach. Second, we examine FFTDCT linear systems described by the second Fornasini–Marchesini (FM) model, where the state-space equations involve two-dimensional and one-dimensional partial fractional-order granular Caputo derivatives. We determine the fuzzy solution for this model by applying the two-dimensional granular Laplace transform. To enhance the validity of the proposed approaches, real-world applications, including signal processing systems and wireless sensor network data fusion, are solved to support the theoretical framework and demonstrate the impact of uncertainty on the system’s behavior. Full article

An important mathematical way to deal with ambiguity and uncertainty in knowledge is rough set (RS) theory. It is believed that a grill is a necessary addition to this idea. Since it expands the approximate of RSs, it is a helpful technique for removing ambiguity and uncertainty. One of the key and important issues for developing rough sets, which subsequently aim to maximize the accuracy measure, is minimization of the boundary region (BR). One of the most practical and successful ways to accomplish this is with a grill. Thus, the goal of this work is to introduce novel grill-based approaches for rough sets (RSs). A few important aspects of these techniques are examined and illustrated to indicate that they produce accuracy measures that are higher and more significant than those of the previous methods. In the end, a medical application is shown to emphasize the need of using grills as instructed. Full article

A Bayesian network (BN) is an uncertainty processing model that simulates human cognitive thinking on the basis of probability theory and graph theory. Its network topology is a directed acyclic graph (DAG) that can be manually constructed through expert knowledge or automatically generated through data learning. However, the acquisition of expert knowledge faces problems such as excessively high labor costs, limited expert experience, and the inability to solve large-scale and highly complex DAGs. Moreover, the current data learning methods also face the problems of low computational efficiency or decreased accuracy when dealing with large-scale and highly complex DAGs. Therefore, we consider mining fragmented knowledge from the data to alleviate the bottleneck problem of expert knowledge acquisition. This generated fragmented knowledge can compensate for the limitations of data learning methods. In our work, we propose a new binary stochastic fractal search (SFS) algorithm to learn DAGs. Moreover, a new feature selection (FS) method is proposed to mine fragmented knowledge. This fragmented prior knowledge serves as a soft constraint, and the acquired expert knowledge serves as a hard constraint. The combination of the two serves as guidance and constraints to enhance the performance of the proposed SFS algorithm. Extensive experimental analysis reveals that our proposed method is more robust and accurate than other algorithms. Full article

The traditional finite element method deals with the temperature field around the cooling tube due to the computational efficiency problems caused by grid division and the uncertainty of the convective heat transfer coefficient, resulting in inaccurate calculation results around the cooling tube. We conducted experiments to study the thermal stress and temperature gradient caused by various factors such as different materials of cooling pipes, pipe diameters, cooling water temperatures, and flow rates. The results showed that aluminum alloy pipes had the highest cooling efficiency but also produced a large temperature gradient. Pipe diameter had the most significant impact on cooling efficiency. Additionally, it is recommended that the cooling water flow velocity is not less than 0.6 m/s to achieve the best efficiency for the cooling pipe of any pipe diameter. The influence range of the cooling pipe on concrete could vary with pipe material, flow rate, and ambient factors. Our experimental results were compared with other heat transfer formulas (the Dittus–Boelter formula and the Yang Joo-Kyoung formula). According to the measured results, the formula is modified). The modified formula can estimate the heat transfer coefficient more accurately according to the flow rate and pipeline characteristics. Finally, the applicability of the formula is further verified by comparing the concrete on the bottom plate of a dam. The proposed heat transfer prediction model can estimate the heat transfer coefficient according to the flow rate and pipeline characteristics, The accuracy of the convection coefficient under different working conditions is improved by 10–25%. It is convenient to predict concrete temperature in practical engineering. Full article