Probabilistic Approach Research Articles

Extended Certainty Factors utilizing Neutrosophic Logic

Addressing uncertainty is a significant challenge in the field of artificial intelligence (AI). AI systems often encounter uncertainty due to incomplete data, ambiguous information, or inherent unpredictability in specific situations. To tackle this uncertainty, various solutions have been developed. These solutions range from probabilistic approaches like Bayesian networks and Monte Carlo methods to fuzzy logic and neural networks. These strategies enable AI systems to model and make judgments amid ambiguity by assigning probabilities, dealing with imprecise data, or utilizing learning processes that adapt to changing and uncertain contexts. However, probabilistic methods have limitations when it comes to handling uncertainty. These limitations include assumptions of independence, computational complexity, difficulty in capturing subjectivity, and interpretability. To address these limitations, other methods have been proposed, such as the model of certainty factors. This model offers a framework for reasoning under uncertainty by assigning a numerical value to statements or propositions. Additionally, neutrosophic logic extends classical logic by incorporating the concept of indeterminacy through truth/indeterminacy/falsity-membership functions. In this paper, we propose a method for investigating how certainty factors adapt to a neutrosophic environment. This method contributes to the development of more robust and adaptable decision support systems capable of dealing with diverse uncertainty. Our method, introduced for the first time in related literature, addresses issues such as the limited handling of indeterminacy, the inability to address contradictions, and the limited binary representation of uncertainty that characterize the model of certainty factors. We will provide a clear illustration of these implications through an example that demonstrates the superiority of our suggested method over the traditional technique of certainty factors.

A multiple-input fluid queue model for performance evaluation of fog server in an intelligent vehicular network

Nowadays, there is a growing trend towards fog computing which is a relatively new concept that has been introduced as an extension of cloud computing. It is considered as a promising paradigm for intelligent vehicular networks due to its ability to reduce delays and enhance network efficiency. The utilization of fog computing plays a crucial role in tackling the distinct obstacles encountered in vehicular computing, including issues like real-time data processing, and limitations on bandwidth. By relocating computational resources closer to the network’s edge, it enhances the effectiveness, dependability, and safety of vehicular applications while simultaneously boosting privacy and security measures. In addition, fog offloading has played a pivotal role in fog computing and edge computing structures. Its objective is to efficaciously allocate the data and assign the processing of tasks among edge devices, fog nodes, and cloud resources. Efficient fog offloading strategies are adaptable, scalable, and are able to handle faults, making them indispensable for optimizing these architectures. Although efficiency and quality of service are crucial objectives in a fog computing based intelligent vehicular network environment, performance remains a significant concern that cannot be overlooked. In light of this, to evaluate the efficacy of the probabilistic offloading approach on a fog server, a thorough performance evaluation is necessary. This research proposes a fluid queue approach that accounts for the constant flow of data packets while evaluating a fog server’s performance. For a fog computing-based intelligent vehicular network (FCIVN) with numerous heterogeneous smart vehicles (SVs), we construct a multiple-input fluid queue to model the tasks handed over to the fog server in order to evaluate its performance. In an FCIVN, the arrivals are drawn from various sources at the fog server. Accordingly, in the proposed model the fluid queue is modulated by more than one independent and distinct finite-state birth–death processes (BDPs) which control the variable inflow, and another BDP which controls the variable outflow. We present the details concerning an intermediate fog server’s buffer occupancy distribution within an FCIVN. Further, we assess the performance measures in terms of the offered load, expected buffer-level, average throughput and average latency of the tasks in an FCIVN. Finally, quantitative illustrations are presented to demonstrate the appropriateness of the fluid queue model developed in this study. The results are found to be in consistence with the expected behaviour of these performance indicators.

Optimal renewable distributed generation planning in radial distribution systems: a probabilistic and multi-objective approach with enhanced Young’s double-slit experiment optimizer

Optimal renewable distributed generation planning in radial distribution systems: a probabilistic and multi-objective approach with enhanced Young’s double-slit experiment optimizer

A Probabilistic Approach to Correct for Oblique Biases in Sea Ice Parametrization from Shipborne Cameras

Abstract This work investigates the perception bias caused by ice floes occluding open water regions behind them when analyzing sea ice from shipborne cameras. We propose a method to model this effect by predicting an occlusion probability for a given surface region based on a stochastic representation of the sea ice fields. We further show how the proposed framework can be used to design an unbiased estimator for image-based sea ice concentration by correcting for these occlusions. The occlusion model and proposed sea ice concentration estimator have been verified through simulated experiments. Results from the experiments showed that predictions from the occlusion model align with the simulated observations and that the proposed estimator remains unbiased for all environments tested. The contributions of this study can be used in algorithms for automated identification and characterization of ice environments from surface ships and are thus an essential step towards improved autonomy in Arctic navigation.

INTERRELATION AND INTERPRETATION OF EFFECTS IN QUANTUM MECHANICS AND CLASSICAL PHYSICS

Quantum mechanics based on the probabilistic approach provides a powerful tool for accurate prediction and interpretation of quantum phenomena, allowing statistically sound predictions about the behavior of microparticles and quantum systems. This statement emphasizes the probabilistic nature of quantum mechanics, its applicability to quantum phenomena and microparticles, as well as the statistical nature of its predictions when applied to the macro effects of classical physics. In addition, the role of statistics and probability in various fields of science, such as particle physics, thermodynamics, biology, sociology, psychology, economics and finance, is discussed. The philosophical implications of the probabilistic approach and the associated limitations and challenges are also considered.

On modeling seismicity in seismic hazard assessment problems

Seismicity modeling is an important part of creating General Seismic Zoning maps within the framework of a probabilistic approach. We consider the main disadvantages of individual elements of the recent seismicity models. A variant of the methodology is proposed, which, due to the improvements of those elements, should provide more accurate estimates of the future seismicity. For the first time, a stochastic seismicity model has been proposed in the form of a synthetic earthquake catalog, generated for an arbitrary conditional period and reproducing the properties of the catalog of actual earthquakes, including spatiotemporal grouping. A methodology for verifying seismicity models is proposed to check the compliance of the models with the initial data, to assess the predictive efficiency of the models, and to compare efficiency of different models.

Fragility analysis of existing RCC frame structure

This In recent years, number of studies have been carried out for the evaluation of vulnerability of structure during seismic events. Fragility analysis is one of the important probabilistic approach to estimate the damage data at different damage state during seismic events. The G+7 Reinforced Concrete frame structure is considered for analysis. The structure is analyzed in ETABS by Non-linear Pushover Analysis. Fragility curve developed for Non-linear Pushover Analysis from results obtained by capacity spectrum method for different damage states. The fragility curves are derived from analytical method. The fragility points for different damage states are derived from analytical formula. Fragility curve is plotted for probability in Y-axis and spectral displacement in X-axis for 4 different damage states from Non-linear Pushover Analysis. From Incremental Dynamic Analysis fragility curve plotted for probability in Y-axis and peak ground acceleration in X-axis for five different damage state. The behavior of fragility curve after achieving the 100% probability for different damage state is constant.

Seismic quantitative interpretation of diagenetic processes: A study in Brazilian pre-salt carbonate reservoirs

The Brazilian pre-salt carbonate province is a scientific frontier that has been subject of extensive research and academic discussion. Santos basin carbonates show that reservoir quality is strongly conditioned by the characteristics of the depositional environment and its associated diagenetic history and alterations. Dolomitization and silicification are two of the most common diagenetic processes in Brazilian carbonates, and the combination of sedimentary and diagenetic complexity leads to a much more heterogeneous reservoir, especially complicated for geoscientists trying to mimic these geological features in terms of static and dynamic modelling. This study proposes a methodology for seismic derived diagenetic properties calculation through seismic quantitative interpretation and classification of inverted P- and S-impedance volumes applied to Brazilian Pre-Salt carbonate reservoirs. Our proposition is to establish relations between petroelastic properties and diagenetic features, first in the well-log domain and, later, in the seismic domain through different quantitative probabilistic techniques. Using the available seismic data and probabilistic approaches through stochastic inversion and Bayesian facies classification, we can interpret the occurrence of diagenetic silica and dolomite in 3D volumes. The volumetric analysis generated through the presented methodology can provide much more insights into the sedimentary history of the field, especially for static and dynamic porosity and permeability modelling.

The instant probability of object with deterministic motion. Another way to understand quantum mechanics

Usually, the idea of probability is applied only to the random motion in physics. Here, the instant probability concept is obtained by applying the probability approach to objects with deterministic motion trajectories via a coin falling to the ground as an example. Applied this concept to understand quantum mechanics, the following conclusions are inferred: The wave function/quantum state represents an instant probability distribution of physical quantities after taking into account Planck's constant; the collapse of the wave function is a special case of any function on time-dependent probability prediction has to collapse when performing measurement. The arrangement of measurements will affect the observed results; Quantum mechanics reveals the existence of non-local effects, which have nothing to do with the interaction in classical physics. Quantum mechanics cannot answer questions such as whether the cat is dead or alive before opening the box. The correct answer to this kind of question has to be from classic physics.

A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response

Secure operation of a DC microgrid with high penetration of renewables and electric vehicle load is challenging. This paper proposes a decentralized energy management scheme for a grid-connected renewable integrated community DC microgrid considering the water-energy nexus. Uncertainties of renewable generation, plug-in electric vehicle load, electricity demand, electricity price in the day-ahead wholesale market, ambient temperature, and water demand are modelled using a probabilistic approach. Correlation between the input random variables is modelled using Copula theory. Flexibilities on the consumer side across multiple entities (temperature-dependent loads, electric vehicles, and water supply system storage) are coordinated to form an “integrated demand response entity”, which is further coordinated with the DC microgrid operator side flexibility (electrochemical storage) to support system operation. A distributed dynamic pricing scheme is used to implement the integrated demand response program. The objectives of the energy management scheme are to minimize the DC microgrid operator’s operating cost and consumers’ electricity cost. The decentralized algorithm is solved by the “alternating direction method of multipliers”. Simulation studies on a six-bus DC microgrid test system demonstrate that the proposed strategy reduces the operating cost of the DC microgrid operator by ∼19.28% and the electricity cost of the consumers by ∼13.82%.

Using removable sensors in structural health monitoring: A Bayesian methodology for attachment-to-attachment uncertainty quantification

Structural responses are vulnerable to perturbations when subjected to variable environmental and operational conditions, thereby contributing to the slow uptake of Structural Health Monitoring (SHM) beyond academic research. More importantly, SHM systems using removable sensors face additional sensing variability due to the inherent uncertainty in sensor reattachment compared to permanently attached sensors. In this study, a novel probabilistic approach is developed for output-only damage detection utilizing ultrasonic guided waves. Experimental uncertainties and sensor attachment factors are simultaneously considered for the first time in a Bayesian context. The uncertainty introduced by reattached sensors is incorporated into the likelihood function in a comprehensive way. Bayesian model class selection using an adaptive sampling scheme is adopted to evaluate the evidence of discrete damage classes. A reference case to an aluminum plate with progressive notches is utilized to demonstrate the significant effects of attachment uncertainty on guided wave signals. The optimal data normalization model for guided wave processing under different attachment states is evaluated using the Mahalanobis distance. The notch length is identified accurately along with the uncertainty, even though the latent adhesive parameters cannot be estimated directly.

A Problem as Much of Economic Theory as of Statistical Technique? The Walsh-Edgeworth Debate on Index Numbers, 1921–25

Abstract Correa Moylan Walsh (1862–1936) was a monetary economist of the early twentieth century, mostly remembered for his contributions to the theory of index numbers. Francis Ysidro Edgeworth (1845–1926) was a renowned mathematical economist, remembered as one of the great masters of the discipline. Walsh was an adamant defender of a deterministic, test approach to the theory of index numbers, whereas Edgeworth favored probabilistic approaches and thought index-number theorists should not close themselves to alternative methods. Holding contrasting views about the making of index numbers, they engaged in a fierce debate in the first half of the 1920s—a period marked by several quarrels on the subject. The main points in dispute between Walsh and Edgeworth were the existence of an ideal formula for index numbers and the use of probability in the measurement of price variations. The goal of this article is to explore this debate and its underpinnings. It is argued that the main points of contention between them relate to their conflicting perspectives on the nature of index numbers.

Intrusion detection systems for the internet of things: a probabilistic anomaly detection approach

The expansion of IoT domains presents new cybersecurity challenges. In this paper, we propose an enhanced Intrusion Detection System (IDS) designed to tackle these emerging security issues. Our approach harnesses the power of unsupervised learning techniques, particularly the Variational Autoencoder (VAE), to reduce data dimensionality while preserving essential features. By integrating advanced clustering algorithms–including K-means, Gaussian Mixture Model (GMM), and Variational Bayesian Gaussian Mixture Model (VBGMM)–we facilitate precise data categorization within IoT networks. Our conducted experiments using the IoT Network Intrusion Dataset demonstrate that VAE-based methods consistently outperform traditional Autoencoders (AE), achieving superior performance across critical metrics such as False Alarm Rate (FAR), Detection Rate (DR), Accuracy, Precision, and Area Under the ROC Curve (AUC).

Optimal surrender policy for reverse mortgage loans

Abstract This study conducts an optimal surrender analysis of reverse mortgage (RM) loans offered to elderly homeowners as a financing option. Recent market evidence on borrower early surrenders has raised concerns about the marketability of RM products and their impact on the program viability. In this article, we derive the borrower optimal surrender strategy as a function of the underlying value of the home used as collateral for RM contracts with tenure payment option. Using a probabilistic approach to American option pricing, we present a decomposition result for the value of the contract as the sum of its European counterpart without the surrendering provision and an early exercise premium. The methodology allows policymakers to assess the financial incentive of their policy design, from which we explain the existing market evidence about borrower rational lapse by means of the resulting surrender boundary and reference probabilities.

Probabilistic, scenario-based hazard assessment for pyroclastic density currents at Tungurahua volcano, Ecuador

We assess the volcanic hazard posed by pyroclastic density currents (PDCs) at Tungurahua volcano, Ecuador, using a probabilistic approach based on the analysis of calibrated numerical simulations. We address the expected variability of explosive eruptions at Tungurahua volcano by adopting a scenario-based strategy, where we consider three cases: violent Strombolian to Vulcanian eruption (VEI 2), sub-Plinian eruption (VEI 3), and sub-Plinian to Plinian eruption (VEI 4–5). PDCs are modeled using the branching energy cone model and the branching box model, considering reproducible calibration procedures based on the geological record of Tungurahua volcano. The use of different calibration procedures and reference PDC deposits allows us to define uncertainty ranges for the inundation probability of each scenario. Numerical results indicate that PDCs at Tungurahua volcano propagate preferentially toward W and NW, where a series of catchment ravines can be recognized. Two additional valleys of channelization are observed in the N and NE flanks of the volcano, which may affect the city of Baños. The mean inundation probability calculated for Baños is small (6 ± 3%) for PDCs similar to those emplaced during recent VEI 2 eruptions (July 2006, February 2008, May 2010, July 2013, February 2014, and February 2016), and on the order of 13 ± 4% for a PDC similar to that produced during the sub-Plinian phase of the August 2006 eruption (VEI 3). The highest intensity scenario (VEI 4–5), for which we present and implement a novel calibration procedure based on a few control points, produces inundation areas that nearly always include inhabited centers such as Baños, Puela, and Cotaló, among others. This calibration method is well suited for eruptive scenarios that lack detailed field information, and could be replicated for poorly known active volcanoes around the world.

The effects of mechanical imperfections on the stability behavior of double-layer Scallop domes

Mechanical imperfections are almost inevitable in large-span spatial domes with hundreds of members and joints. The lack of fit in members is the most common form of imperfections in double-layer domes which influences the behavior of these structures under gravitational or seismic loads. In this study, the probabilistic effect of mechanical imperfections (lack of fit) on the stability behavior of double-layer Scallop domes with sinusoidal arching style under symmetric and asymmetric snow-loading is investigated. On some double-layer scallop domes, nonlinear static and dynamic analyses are carried out using Abaqus/CAE and MATLAB. The behavior of the structures with randomly distributed imperfect members is compared with those of the ideally perfect structures. A Monte Carlo simulation approach is used to statistically assess the effects of uncertainties on the response parameters and the ultimate capacity of the structures. In the first approach, named here as the “rigorous probabilistic approach”, the amount and the distribution of imperfect members are all treated as random variables, and in the second approach, named as “concise approach”, a fixed amount of imperfections are applied to a number of randomly selected critical members. No simplification method is used in the rigorous probabilistic approach to achieve a reliable assessment of the effect of uncertain parameters. Results show that the collapse behavior of double-layer Scallop domes is sensitive to random imperfections and a considerable decrease is seen in their load-bearing capacity.

Probabilistic Approach for Q-based Ground Support Design

For tunnel, cavern, and shaft design, the inherent variability in a given rock mass domain makes accurately estimating rock mass quality and support requirements difficult. To capture the variability in rock mass properties when using the Q system, a methodology incorporating a statistical analysis of measured Q input parameters and Monte Carlo Simulation was developed to perform a probabilistic ground support design approach. Probability and cumulative density function curves were then developed using the mathematical program MATLAB to guide in estimating ground support based on all potential rock mass conditions. To illustrate the proposed approach, two hypothetical tunnels were designed based on real data from two previous projects. Finite Element Modelling was used to evaluate the suggested Q rock support performance in a range of rock conditions for one of the hypothetical excavations to validate the proposed approach. This method demonstrated that associating a range of potential ground conditions instead of a single deterministic value for each input parameter can provide a quantifiable measurement of uncertainty within a given rock mass domain. Additionally, the approach provides insight into the design criteria for ground support in underground excavations to potentially reduce overly conservative and costly recommendations.

SMRF: a new class-based probabilistic approach for season-ahead monthly rainfall forecasting

SMRF: a new class-based probabilistic approach for season-ahead monthly rainfall forecasting

A probabilistic approach for the levelized cost of energy of floating offshore wind farms

This paper aims to analyze the levelized cost of energy (LCOE) of floating offshore wind farms from a probabilistic point of view. Understanding and addressing the uncertainty associated with the main parameters that influence the wind energy generated by a wind farm during its lifetime is crucial for the economic evaluation of offshore wind energy in the broader energy landscape. The methodology for probabilistic assessment of LCOE is introduced, and the uncertainty in input parameters are discussed. In a base case study, an assumed Floating Offshore Wind Farm (FOWT) consisting of 250 5-MW wind turbines is considered. The use of bias and randomness in key random variables is discussed and studied in detail. Results indicate that LCOE estimates of 15 EURc/kWh for offshore wind turbines are achievable with reasonable confidence, while estimates of 5 EURc/kWh require careful consideration of uncertainty in the wind farm’s parameters. The feasibility analysis showed that techno-economic parameters are more influenced by wind characteristics and efficient use of wind turbines than by the cost of the wind farm. This paper provides general guidance on how to carry out early-stage techno-economic analysis of FOWFs.

Parameter identification and uncertainty evaluation in quasi-dynamic test of solar thermal collectors with Monte Carlo method

This work presents a comparison between two methods used for parameter identification and calculation of parameter uncertainty applied to the measured data of solar thermal collectors when tested according to ISO 9806:2017. One method is using a weighted least square (WLS) fit and the partial derivative approach described in the GUM (the Guide to the Expression of Uncertainty in Measurement). The second is using the Monte Carlo (MC) method, also described in GUM. Uncertainty evaluation by Monte Carlo method is based on a probabilistic approach and is an alternative way for identification of parameters and determination of the uncertainties. In this work the results were obtained according to Quasi-Dynamic Test (QDT) method for a flat plate collector and an evacuated tubular collector. The least squares (LS) method and a nonlinear regression method (MPFit) are used in the identification of parameters for each iteration of the MC method. For the implemented MC method computation times are also discussed. One disadvantage of the MC method is the computation time which depends on the number of samples in the experimental test quantity files, however with this study we think that the advantages of the MC method outweigh the disadvantages, and it is useful even as a complementary tool in QDT testing of collectors.