Probabilistic Analysis Techniques Research Articles

Lagrange-enhanced GRA framework for probabilistic simplified neutrosophic multiple-attributes decision-making and applications to painting teaching quality evaluation

Universities are important talent training bases in China and the main driving force for achieving the strategic layout of “revitalizing the country through science and education” and “strengthening the country through talent". Oil painting is a global art with rich humanistic and artistic value. Most art colleges in China have set up oil painting courses. Analyze the current situation and value of oil painting course teaching in local art (teacher training) majors, and leverage the educational role of oil painting courses by enriching course offerings, emphasizing the integration of humanistic innovation, improving teacher literacy, and striving to further improve the quality and efficiency of oil painting course teaching. The quality evaluation of oil painting teaching in universities is viewed as multiple-attribute decision-making (MADM). The grey relational analysis (GRA) is a useful tool to cope with the MADM issue. The probabilistic simplified Neutrosophic set (PSNSs) is easy to characterize uncertain information during the quality evaluation of oil painting teaching in universities. In this paper, in order to obtain the weight information, an optimization model implemented to obtain a simple and exact formula which can be employed to derive the attribute weights values based on the Lagrange function and the probabilistic simplified neutrosophic number grey relational analysis (PSNN-GRA) technique is implemented for MADM to rank the alternatives. Finally, a numerical example for quality evaluation of oil painting teaching in universities is used to verify the practicability of the PSNN-GRA technique and compares it with other techniques.

Probabilistic analysis of agent-based opinion formation models

When agent-based models are developed to capture opinion formation in large-scale populations, the opinion update equations often need to embed several complex psychological traits. The resulting models are more realistic, but also challenging to assess analytically, and hence numerical analysis techniques have an increasing importance in their study. Here, we propose the Qualitative Outcome Likelihood (QOL) analysis, a novel probabilistic analysis technique aimed to unravel behavioural patterns and properties of agent-based opinion formation models, and to characterise possible outcomes when only limited information is available. The QOL analysis reveals which qualitative categories of opinion distributions a model can produce, brings to light their relation to model features such as initial conditions, agent parameters and underlying digraph, and allows us to compare the behaviour of different opinion formation models. We exemplify the proposed technique by applying it to four opinion formation models: the classical Friedkin-Johnsen model and Bounded Confidence model, as well as the recently proposed Backfire Effect and Biased Assimilation model and Classification-based model.

Evaluating uncertainty in particle roughness of coated sand and its implication to coating abrasion

Coated soils have recently been spotlighted for their prospective applications in geotechnical engineering. Prior studies have shown that Polydimethylsiloxane (PDMS) coatings can substantially mitigate surface roughness and have suggested that such coatings may experience abrasion under stress conditions. However, comprehensive studies exploring the impacts of these coatings on particle surface attributes have been scarce. In this work, we scrutinize the surface properties of coated sands with varying mass ratios (MRs), applying probabilistic analysis to account for inherent uncertainty. According to the uncertainty quantification investigation via experimental data, the Gamma distribution is optimal to examine increasing MRs, highlighting a steady decline in surface roughness and corresponding dispersion. Furthermore, a probabilistic assessment of a specific coated sand sample (3% MR) exposed to different stress levels are conducted. The findings suggest that polymer coatings may endure abrasion as stress levels rise, with the average roughness values incrementally converging towards those of natural sands. The detailed examination of particle surface characteristics also unveils the existence of more pronounced peaks and valleys at elevated stress levels, implying significant height variations and evident heterogeneity in surface profiles. Overall, the use of probabilistic analysis techniques provides a more precise understanding of how particle surface properties depend on increasing MRs and their notable dispersion under escalating stress levels. This methodology could be instrumental in probing the mechanical behavior of coated sands, known for their considerable uncertainty.

A Case Study of Safety Evaluation Using Probabilistic Analysis Techniques - Application to the Survival Rate of Passengers in Tunnel Fire –

A Case Study of Safety Evaluation Using Probabilistic Analysis Techniques - Application to the Survival Rate of Passengers in Tunnel Fire –

A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems

In pursuit of identifying the most accurate and efficient uncertainty modelling (UM) techniques, this paper provides an extensive review and classification of the available UM techniques for probabilistic power system stability analysis. The increased penetration of system uncertainties related to renewable energy sources, new types of loads and their fluctuations, and deregulation of the electricity markets necessitates probabilistic power system analysis. The abovementioned factors significantly affect the power system stability, which requires computationally intensive simulation, including frequency, voltage, transient, and small disturbance stability. Altogether 40 UM techniques are collated with their characteristics, advantages, disadvantages, and application areas, particularly highlighting their accuracy and efficiency (as both are crucial for power system stability applications). This review recommends the most accurate and efficient UM techniques that could be used for probabilistic stability analysis of renewable-rich power systems.

Statistical analysis of PV penetration impact on residential distribution grids

This paper presents a comprehensive approach to the probabilistic analysis of residential distribution grid injected by distributed PV sources. The approach is data-driven and is able to deal with the general scenario that includes the uncertainty of correlated PV sources and of statistically independent consumer loads. The novel method adopts a Gaussian-Mixture-Model for correctly representing PV sources correlation and a fresh probabilistic analysis technique employing Multi-Expansion polynomial chaos. Numerical experiments carried out on the Non-Synthetic European low voltage test system, highlight the importance of the comprehensive modeling strategy in order to realistically quantify uncertainty impact on the grid.

Life-Cycle Assessment of Apartment Buildings Based on Standard Quantities of Building Materials Using Probabilistic Analysis Technique.

Given the increasingly serious nature of environmental problems, many countries have recently declared carbon neutrality policies and expended efforts to implement them. The domestic building industry aims to reduce its environmental impact using life-cycle assessments (LCAs) of buildings according to the Green Standard for Energy and Environmental Design. However, it is difficult to perform efficient LCAs because the required quantity takeoff process is complex, and the quantity takeoff sheet may not exist during the building’s design phase. In this study, 21 building LCAs were used to simplify and improve the efficiency of the proposed method and enable building LCAs even when there was no quantity takeoff sheet. Furthermore, a standard quantity database of building materials was constructed based on the analysis of the input quantities of building materials per unit area, and the apartment buildings LCA method was proposed using this database. The input quantities of building materials were analyzed using the probabilistic analysis technique. The probability distribution was derived using Monte Carlo simulations, and the goodness-of-fit was verified. Finally, the reliability of the proposed building LCA method was verified using a case study.

Probabilistic Flood Assessment Methodology for Nuclear Power Plants Considering Extreme Rainfall

Abnormal weather conditions due to climate change are currently increasing on both global and local scales. It is therefore important to ensure the safety of the areas where major national facilities are located by analyzing risk quantitatively and re-evaluating the existing major facilities, such as nuclear power plants, considering the load and capacity of extreme flood conditions. In this study, a risk analysis method is developed that combines flood hazard curves with fragility curves using hydraulic and hydrological models by GIS tools and the @RISK model for the probabilistic flood analysis of nuclear power plant sites. A two-dimensional (2D) analysis is first carried out to estimate flood depths in various watershed scenarios, and a representative hazard curve for both external and internal flooding is made by applying a verified probability distribution type for the flood watersheds. For the analysis of flooding within buildings, an internal grid is constructed using GIS with related design drawings, and based on the flood depth results of the 2D analysis, a hazard curve for the representative internal inundation using a verified probability distribution type is presented. In the present study, walkdowns with nuclear experts are conducted around the nuclear power plant area to evaluate the fragile structures and facilities under possible flooding. After reviewing the 2D inundation analysis results based on the selected major equipment and facilities, the zones requiring risk assessment are re-assigned. A fragility curve applying probability distribution for the site’s major equipment and facilities is also presented. Failure risk analysis of the major facilities is then conducted by combining the proposed hazard and fragility curves. Results in the form of quantitative values are obtained, and the indicators for risks as well as the reliability and optimal measures to support decision-making are also presented. Through this study, it is confirmed that risk assessment based on the proposed probabilistic flood analysis technique is possible for flood events occurring at nuclear power plant sites.

Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R2) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

Active distribution network operation management with large penetration of hybrid electric vehicles and sustainable distributed energy generation

The unprecedented growth of technological advances, industrialization and sophisticated urbanization have contributed to a staggering proliferation of plug-in hybrid electric vehicles (PHEVs) and renewable energy sources (RESs) in transportation and electric power distribution systems. As a result, the nature of optimal distribution network operation is ever-changing due to the dramatic uncertainty in the increased energy supply and demand caused by large integration of these emerging technologies into the systems. This study proposes a synergistic approach for boosting power system operation performance in sustainable distribution networks considering renewable power generation and high integration of PHEVs, using multi-objective stochastic optimization and probabilistic analysis techniques. To this end, distribution network reconfiguration and evolutionary optimization schemes are deployed, and a probabilistic multi-criteria decision making (MCDM) system based on point estimate method is developed, considering stochastic correlation between distributed energy generation sources. The results obtained along with the performance appraisal observations, indicate the effectiveness of the proposed method in sustainable active distribution network operation management in their entirety.

Probabilistic latent semantic analysis of composite excitation-emission matrix fluorescence spectra of multicomponent system.

In the present work, a simple and fast analytical procedure involving minimum user intervention was developed by combining the excitation-emission matrix fluorescence (EEMF) spectroscopy with Probabilistic latent semantic analysis (pLSA) technique. Akaike Information Criterion (AIC) was used to enable the user to automatically select the optimum model for analysing the mixtures of fluorescent components. The utility of the present work was successfully evaluated by analysing the dilute aqueous mixtures of certain fluorescent molecule such as Catechol, Hydroquinone, Indole, Tryptophan and Tyrosine of biological relevance. The developed AIC assisted pLSA model of five components explained >90% variance of spectral data sets. The identity between the pLSA retrieved spectral profiles was established using similarity index (SI) parameter in automatic manner. The SI values were found to be close to unit values for each of the five analyzed molecules. The regression parameter between the actual and pLSA predicted concentrations were found to be well within acceptable limits. Both root mean square of calibration and predictions for each of the five fluorescent molecules were found to be <1%, whereas, the square of the correlation coefficient (R2) value was found to be >0.98 suggesting the developed pLSA model was quite precise in analysing both calibration and validation set samples. The uniqueness of the developed pLSA model for EEMF spectroscopic data was successfully tested using the sequential quadratic programming (SQP) algorithm. The differences between the upper and lower bands in SQP were found to be ≤0.005. In summary, the proposed approach serve as swift and simple analytical tool for the analysis of fluorescent mixtures without involving pre-separation step.

A Framework for Quantitative Modeling and Analysis of Highly (Re)configurable Systems

This paper presents our approach to the quantitative modeling and analysis of highly (re)configurable systems, such as software product lines. Different combinations of the optional features of such a system give rise to combinatorially many individual system variants. We use a formal modeling language that allows us to model systems with probabilistic behavior, possibly subject to quantitative feature constraints, and able to dynamically install, remove or replace features. More precisely, our models are defined in the probabilistic feature-oriented language QFLan, a rich domain specific language (DSL) for systems with variability defined in terms of features. QFLan specifications are automatically encoded in terms of a process algebra whose operational behavior interacts with a store of constraints, and hence allows to separate system configuration from system behavior. The resulting probabilistic configurations and behavior converge seamlessly in a semantics based on discrete-time Markov chains, thus enabling quantitative analysis. Our analysis is based on statistical model checking techniques, which allow us to scale to larger models with respect to precise probabilistic analysis techniques. The analyses we can conduct range from the likelihood of specific behavior to the expected average cost, in terms of feature attributes, of specific system variants. Our approach is supported by a novel Eclipse-based tool which includes state-of-the-art DSL utilities for QFLan based on the Xtext framework as well as analysis plug-ins to seamlessly run statistical model checking analyses. We provide a number of case studies that have driven and validated the development of our framework.

A fuzzy‐clustering‐based hierarchical i‐vector/probabilistic linear discriminant analysis system for text‐dependent speaker verification

AbstractIn the i‐vector/probabilistic linear discriminant analysis (PLDA) technique, the PLDA backend classifier is modelled on i‐vectors. PLDA defines an i‐vector subspace that compensates the unwanted variability and helps to discriminate among speaker‐phrase pairs. The channel or session variability manifested in i‐vectors are known to be nonlinear in nature. PLDA training, however, assumes the variability to be linearly separable, thereby causing loss of important discriminating information. Besides, the i‐vector estimation, itself, is known to be poor in case of short utterances. This paper attempts to address these issues using a simple hierarchy‐based system. A modified fuzzy‐clustering technique is employed to divide the feature space into more characteristic feature subspaces using vocal source features. Thereafter, a separate i‐vector/PLDA model is trained for each of the subspaces. The sparser alignment owing to subspace‐specific universal background model and the relatively reduced dimensions of variability in individual subspaces help to train more effective i‐vector/PLDA models. Also, vocal source features are complementary to mel frequency cepstral coefficients, which are transformed into i‐vectors using mixture model technique. As a consequence, vocal source features and i‐vectors tend to have complementary information. Thus using vocal source features for classification in a hierarchy tree may help to differentiate some of the speaker‐phrase classes, which otherwise are not easily discriminable based on i‐vectors. The proposed technique has been validated on Part 1 of RSR2015 database, and it shows a relative equal error rate reduction of up to 37.41% with respect to the baseline i‐vector/PLDA system.

A probabilistic analysis technique for single event transient sensitivity evaluation of phase-lock-loops

This paper proposes a probabilistic analysis technique to evaluate the single event transient sensitivity. Using the technique in both the time domain and spatial domain, Single-Event Transients (SET) sensitivity of a Phase-Locked-Loop (PLL) designed for SET mitigation has been analyzed as an example. Statistical results of simulation can figure out the SET sensitive nodes, which are validated by laser experiment of PLL chip fabricated with high voltage process (3.3 V) of a 90 nm PDSOI baseline.

A Survey of Probabilistic Schedulability Analysis Techniques for Real-Time Systems

This survey covers schedulability analysis techniques for probabilistic real-time systems. It reviews the key results in the field from its origins in the late 1980s to the latest research published up to the end of August 2018. The survey outlines fundamental concepts and highlights key issues. It provides a taxonomy of the different methods used, and a classification of existing research. A detailed review is provided covering the main subject areas as well as research on supporting techniques. The survey concludes by identifying open issues, key challenges and possible directions for future research.

A Survey of Probabilistic Timing Analysis Techniques for Real-Time Systems

This survey covers probabilistic timing analysis techniques for real-time systems. It reviews and critiques the key results in the field from its origins in 2000 to the latest research published up to the end of August 2018. The survey provides a taxonomy of the different methods used, and a classification of existing research. A detailed review is provided covering the main subject areas: static probabilistic timing analysis, measurement-based probabilistic timing analysis, and hybrid methods. In addition, research on supporting mechanisms and techniques, case studies, and evaluations is also reviewed. The survey concludes by identifying open issues, key challenges and possible directions for future research.

Dynamic Reliability Design of Multicomponent Structure with Improved Weighted Regression Distributed Collaborative Surrogate Model Method

For dynamic reliability design of complex structures with multiple components, an improved weighted regression distributed collaborative surrogate model method (IWRDCSMM) is developed from the extremum response surface method (ERSM), decomposed‐coordinated thought, and improved weighted regression principle. The ERSM is used to address the dynamic reliability and sensitivity analyses of multicomponent structures and enhance the computing efficiency. The decomposed‐coordinated thought is applied to handle the relationship among multiple components. The improved weighted regression method is used to find the efficient samples with smaller errors to improve the modeling accuracy. The proposed method is first introduced for dynamic probabilistic analysis (including reliability analysis and sensitivity analysis) of multicomponent structures. The method is then mathematically modeled by adopting the efficient samples selected based on the improved weighted regression method. Finally, the radial deformation dynamic probabilistic analysis of an aeroengine turbine blisk assembled by blade and disk is accomplished, in respect of the IWRDCSMM, fluid‐thermal‐structure interaction, and the randomness of input parameters within the time domain [0, T]. The results illustrate that the reliability degree of turbine blisk radial deformation is 0.9951 when the allowable value uallow is 2.30 × 10−3 m, and all the input parameters affecting the turbine blisk radial deformation are gas temperature, angular speed, inlet velocity, outlet pressure, material density, and inlet pressure, successively. As revealed by the comparison of different methods, the IWRDCSMM has high fitting speed and simulation efficiency with the guarantee of accuracy. The efforts of this study provide a promising dynamic probabilistic analysis technique for complex structures with multiple components and enrich mechanical reliability theory.

Improving PLDA speaker verification performance using domain mismatch compensation techniques

The performance of state-of-the-art i-vector speaker verification systems relies on a large amount of training data for probabilistic linear discriminant analysis (PLDA) modeling. During the evaluation, it is also crucial that the target condition data is matched well with the development data used for PLDA training. However, in many practical scenarios, these systems have to be developed, and trained, using data that is often outside the domain of the intended application, since the collection of a significant amount of in-domain data is often difficult. Experimental studies have found that PLDA speaker verification performance degrades significantly due to this development/evaluation mismatch. This paper introduces a domain-invariant linear discriminant analysis (DI-LDA) technique for out-domain PLDA speaker verification that compensates domain mismatch in the LDA subspace. We also propose a domain-invariant probabilistic linear discriminant analysis (DI-PLDA) technique for domain mismatch modeling in the PLDA subspace, using only a small amount of in-domain data. In addition, we propose the sequential and score-level combination of DI-LDA, and DI-PLDA to further improve out-domain speaker verification performance. Experimental results show the proposed domain mismatch compensation techniques yield at least 27% and 14.5% improvement in equal error rate (EER) over a pooled PLDA system for telephone-telephone and interview-interview conditions, respectively. Finally, we show that the improvement over the baseline pooled system can be attained even when significantly reducing the number of in-domain speakers, down to 30 in most of the evaluation conditions.

Probabilistic Multiobjective Transmission Expansion Planning Incorporating Demand Response Resources and Large-Scale Distant Wind Farms

Transmission expansion planning (TEP) is a critical issue, particularly coping with the new challenges of smart grids. Under the smart grid environment, demand response resources (DRRs) are contemplated as virtual power plants in energy policy decisions. DRRs affect the controllability of power systems, ranging from short-term to long-term scheduling. In this paper, a nonlinear economic model of responsive loads is presented based on price elasticity of demand and customers benefit function. In order to investigate the impacts of DRRs on TEP, a probabilistic multiobjective TEP incorporating demand response programs (DRPs) is introduced (PMO-TEP $^\mathrm{DRPs}$ ). IC, congestion costs, risk costs, and total incentive costs for participating in DRPs are considered different objectives of PMO-TEP $^\mathrm{DRPs}$ . Here, a probabilistic analysis technique, the so-called two-point estimation method, is applied to handle the uncertainty of wind-farm generation in the TEP problem. Furthermore, the maximum achievable potential and realistic achievable potential for participating customers in DRPs are derived. Due to the problem's nonconvex formulation, a nondominated sorting genetic algorithm II is utilized to evaluate PMO-TEP $^\mathrm{DRPs}$ . Several analyses are carried out on the IEEE Reliability Test System and Iran 400-kV transmission grid to confirm the capability of the proposed framework.

Invariant probabilistic sensitivity analysis for building energy models

One of the key challenges in the field of building-performance analysis is to identify the key source of input parameter/s that has/have largest effect on the energy efficiency of the buildings. Critical input parameters can be identified through the use of sensitivity analysis models. In this paper, we outline an invariant probabilistic sensitivity analysis technique to enable screening of the most important parameters in the building energy models. The proposed probabilistic sensitivity analysis method measures the average distance between unconditional probability distribution and conditional (on an input) probability distribution of an output. The distribution's description is based on the scale-invariant heat kernel signature (SI-HKS). In this paper, SI-HKS method is extended for invariant probabilistic sensitivity analysis. We demonstrate the application of the proposed method through the use of Energy Plus building-performance modelling tool.