Variation In Probability Research Articles

RETRACTED ARTICLE: Application of AI in image recognition technology for power line inspection

Artificial Intelligence (AI) is an energetic consideration in the electric power company for the scrutiny of the power lines in the forest area. The inspection is escorted to reduce input resources, less cost, rehabilitate electricity, and avoid overhead transmission and distribution lines. The imperfection in the electric companies is deliberate outage management, safety hazards, and diversified system, expensive and not flexible, which disturb the inspection in the forest areas. To recognize the images and status of the power lines, the Naive Bayesian classifier with Automatic Classification (NBC-AC) algorithms is implemented to classify the observed probabilistic variables of inspection accurately. To monitor the images in long-distance, an Unmanned Aerial Vehicle–Hunter (UAV-H) is proposed to predict the different features of power lines. Hence NBC-AC algorithms with UAV-H systems are used to recognize and classify the characteristics of the power line images with 95% accuracy in the forest area for the electric power company. This encouraging technique contributes to high efficiency and accuracy with the best quality of images and the results were obtained for many AI applications.

Effect of structural uncertainties on seismic performance of moment frame rehabilitated with steel shear wall

In performance-based engineering, conservatism in acceptance criteria at structural performance levels has increased the cost of retrofitting. When simulating structure response in the presence of uncertainties, seismic demand and structural capacity cannot be expressed certainly, though the possible range of these can be predicted. In the seismic rehabilitation of structures, uncertainties have been studied on existing structures and rehabilitation guidelines have been implemented on these uncertainties by reliability index. Adding a secondary system to rehabilitate existing structure may increase uncertainties and may be effective for results of reliability. Therefore, in this study, reliability of rehabilitated a structure with steel shear wall has been discussed with the aim of quantifying uncertainties. Also, the parametric study of reliability index has been performed on probabilistic variables considered for steel shear wall. The selected structure is a three-storey structure of SAC steel moment frame, which was rehabilitated by steel shear wall. For modeling and analyzing the structure, OpenSees software was used. The structure was subject to incremental dynamic analysis before and after of rehabilitation, with probabilistic variables considered for steel shear wall.

Probabilistic time‐driven activity‐based costing

AbstractThis article proposes a Probabilistic Time‐driven Activity‐based costing model. Time consumption is represented using probabilistic variables and Monte Carlo simulation technique is used to forecast total cost. The simulation experiments provide data to estimate a confidence interval for the total cost of a cost object. Hence, simulation can easily address variations in time forecasts and provide a more realistic characterization of total cost. The definition of the number of resources (e.g., employees) as decision variables allows analyzing alternatives and answer “what‐if questions”. The proposal would be very useful for an operation manager in analyzing uncertain scenarios and providing information indicating under what conditions different cost estimates are feasible.

The Effects of Word Frequency and Word Probability on Speech Rhythm in Dysarthria.

Purpose In healthy speakers, the more frequent and probable a word is in its context, the shorter the word tends to be. This study investigated whether these probabilistic effects were similarly sized for speakers with dysarthria of different severities. Method Fifty-six speakers of New Zealand English (42 speakers with dysarthria and 14 healthy speakers) were recorded reading the Grandfather Passage. Measurements of word duration, frequency, and transitional word probability were taken. Results As hypothesized, words with a higher frequency and probability tended to be shorter in duration. There was also a significant interaction between word frequency and speech severity. This indicated that the more severe the dysarthria, the smaller the effects of word frequency on speakers' word durations. Transitional word probability also interacted with speech severity, but did not account for significant unique variance in the full model. Conclusions These results suggest that, as the severity of dysarthria increases, the duration of words is less affected by probabilistic variables. These findings may be due to reductions in the control and execution of muscle movement exhibited by speakers with dysarthria.

Application of probabilistic random variables and their digital characteristics

Application of probabilistic random variables and their digital characteristics

Análisis de riesgo financiero en el sector de fabricación de otros productos minerales no metálicos del Ecuador

En la presente investigación se analiza el riesgo financiero (insolvencia, liquidez y de mercado), en el sector de la fabricación de otros productos minerales no metálicos en el periodo 2007 – 2017. Con la aplicación de metodologías como Ohlson y Altman, en el caso de Insolvencia, un modelo Logit y Probit en liquidez y para el riesgo de mercado el modelo matemático Capital Asset Pricing Model CAPM, se tendrá como resultado tendencias y comportamientos de los riesgos financieros, así como información de variables estadísticas y probabilísticas. Para este análisis se tomó de la Superintendencia de Compañías su base datos como fuente de información; el 2008 y 2016 presentan los indicadores con un mayor riesgo: en Altman una puntuación de 5,646 (zona gris) y en Ohlson 10,06% coincidiendo que son los años más riesgoso. En el análisis de riego de mercado el sector de fabricación de productos minerales no metálicos obtuvo 1,2806 el mismo que expresa que con una variación de 1% en el rendimiento de mercado este variaría en 1,2806%. Con respecto a la liquidez de sector existe un alto riesgo en años como el 2008,2010 y encabezando 2015, siendo las microempresas las más afectadas. Por lo cual esta investigación ofrece información relevante a los distintos grupos de interés, incentivando la inversión y fomentando el apoyo gubernamental en situaciones de riesgo.

Supply chain management and market responsiveness: a simulation study

PurposeThis study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement).Design/methodology/approachSimulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist.FindingsThe demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research.Originality/valueThis study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

New hybrid reliability-based topology optimization method combining fuzzy and probabilistic models for handling epistemic and aleatory uncertainties

This study presents a hybrid reliability-based topology optimization (RBTO) method for handling epistemic and aleatory uncertainties. First, we establish a new triple-nested RBTO model based on fuzzy and probabilistic theory for describing the multi-source uncertainties. Subsequently, an efficient single-loop optimization method is proposed to degrade the triple-nested optimization problem into a deterministic optimization problem using the Karush–Kuhn–Tucker optimality condition. Furthermore, the sensitivities of the hybrid reliability constraint with respect to the random probabilistic variables, fuzzy variables, and deterministic design variables are derived using the adjoint variable method. Finally, a cantilever beam example, an L-shape beam design and a 3D example are tested to verify the validity of the proposed single-loop method.

Probability analysis of an embedded water tank

The design of building structures must fulfil specific regulations, in our case, different building standards, among them Eurocodes. In addition to deterministic procedures in structural design, these standards also allow probabilistic procedures. The embedded tank loaded with soil and liquid is solved by the probability analysis using ANSYS, which contains several probabilistic methods. The reinforced concrete tank is solved by the RSM probabilistic method, which uses the well-known Monte-Carlo method in the background. Input parameters (material properties of soil and reinforced concrete, load - pressure from water, geometric data - change of both wall and tank bottom thickness) are entered into the calculation with certain aberrances allowed by standards in the construction and loading of structures. The results are also sets of probabilistic variables with a certain variance, as opposed to a deterministic calculation, where only one value results. These procedures, which use statistical methods, have been at the forefront in recent decades. At the end of the paper, some results of the analysis of embedded reinforced concrete water tank (deterministic and probabilistic procedure) in state of tank failure on the second limit state are presented.

Improvements on Evaluation Functions of a Probabilistic Fracture Mechanics Analysis Code for Reactor Pressure Vessels

Abstract In Japan, a probabilistic fracture mechanics (PFM) analysis code PASCAL was developed for structural integrity assessment of reactor pressure vessels (RPVs) considering neutron irradiation embrittlement and pressurized thermal shock (PTS) events. By reflecting the latest knowledge and findings, the evaluation functions are continuously improved and have been incorporated into PASCAL4 which is the most recent version of the PASCAL code. In this paper, the improvements made in PASCAL4 are explained in detail, such as the evaluation model of warm prestressing (WPS) effect, evaluation function of confidence levels for PFM analysis results by considering the epistemic and aleatory uncertainties in probabilistic variables, the recent stress intensity factor (KI) solutions, and improved methods for KI calculations when considering complicated stress distributions. Moreover, using PASCAL4, PFM analysis examples considering these improvements are presented.

Probabilistic approximation of runtime quantitative verification in self-adaptive systems

Cyber-physical systems (CPS) are expected to continuously monitor the physical components to autonomously calculate appropriate runtime reactions to deal with the uncertain environmental conditions. Self-adaptation, as a promising concept to fulfill a set of provable rules, majorly needs runtime quantitative verification (RQV). Taking a few probabilistic variables into account to represent the uncertainties, the system configuration will be extremely large. Thus, efficient approaches are needed to reduce the model state-space, preferably with certain bounds on the approximation error. In this paper, we propose an approximation framework to efficiently approximate the entire model of a self-adaptive system. We split up the large model into strongly-connected components (SCCs), apply the approximation algorithm separately on each SCC, and integrate the result of each part using a centralized algorithm. Due to a number of changes in probabilistic variables, it is not possible to use static models. Addressing this issue, we have deployed parametric Markov decision process. In order to apply approximation on the model, the notion of ε-approximate probabilistic bisimulation is utilized that introduces the approximation level ε. We show that our approximation framework offers a certain error bound on each level of approximation. Then, we denote that the approximation framework is appropriate to be applied in decision-making process of self-adaptive systems where the models are relatively large. The results reveal that we can achieve up to 50% size reduction in the approximate model while maintaining the accuracy about 95%. In addition, we discuss about the trade-off between efficiency and accuracy of our approximation framework.

Quantification of parametric uncertainty in k–ω–γ transition model for hypersonic flow heat transfer

The uncertain empirical values of closure coefficients in k–ω–γ transition model flaws the predictive capability. The current work aims to examine the uncertainty of k–ω–γ transition model due to uncertain closure coefficients for hypersonic flow over a flat plate and a straight cone, with introducing the stochastic expansions based on the point-collocation non-intrusive polynomial chaos method into computational fluid dynamics simulations for efficient and accurate uncertainty propagation. Specifically, five model coefficients are regarded as epistemic uncertain probabilistic variables on intervals. The output quantities of interests include the surface heat flux, skin friction coefficient, transition onsets and transition lengths. To better identify the key uncertainty contributor, Sobol indices are utilized as a variable-based global sensitivity analysis approach. Results show that the performance of the k–ω–γ transition model is highly susceptible to its closure coefficients evidenced by that small changes (10%) of model coefficients could yield more than 30% uncertainty in the transition onsets and roughly 17% uncertainty in the transition lengths. The largest uncertainty source contributing to the uncertainty in heat flux, Cf and transition onsets is C3 for hypersonic transitional flows. Besides, C4 dominates the uncertainty in transition lengths. In the future model improvement, attention should be paid to improve apprehension of rational values or constitutions of C2, C3 and C4.

Random projectors with continuous resolutions of the identity in a finite-dimensional Hilbert space

Random sets are used to get a continuous partition of the cardinality of the union of many overlapping sets. The formalism uses Möbius transforms and adapts Shapley’s methodology in cooperative game theory, into the context of set theory. These ideas are subsequently generalized into the context of finite-dimensional Hilbert spaces. Using random projectors into the subspaces spanned by states from a total set, we construct an infinite number of continuous resolutions of the identity, that involve Hermitian positive semi-definite operators. The simplest one is the diagonal continuous resolution of the identity, and it is used to expand an arbitrary vector in terms of a continuum of components. It is also used to define the function on the ‘probabilistic quadrant’ , which is analogous to the Wigner function for the harmonic oscillator, on the phase-space plane. Systems with finite-dimensional Hilbert space (which are naturally described with discrete variables) are described here with continuous probabilistic variables.

The relationship between Iranian maternal verbal responsiveness styles and child's communication acts with expressive and receptive vocabulary in 13–18 months old typically developing children

ABSTRACT This observational study examined the Iranian mothers’ interaction with their typically developing children aged 13–18 months during free play at home (n = 40). The first aim was to determine the main type of Iranian maternal verbal responsiveness. Another aim was to investigate the impact of concurrent prediction of maternal verbal responsiveness and children's communication acts on children's vocabulary. The results showed that the frequency of follow-in directive responsiveness was significantly higher than those of the other two types of responses. Among the various types of probabilistic variables predicting the status of children’s vocabulary, the follow-in directive responses of the mothers and the level of communicative acts of children had a significant predictive role in children’s vocabulary status. These results suggest that in non-western cultures, a directive interactional style that follows the child attentional focus and the child’s own communication acts have a transactional influence on the child language status.

A Stochastic Assessment of PV Hosting Capacity Enhancement in Distribution Network Utilizing Voltage Support Techniques

The low voltage (LV) network's constraints are prone to violations with the introduction of distributed energy resources at the customer vicinity. This study assesses the technical aspects of the realistic Finnish distribution network with varying photovoltaic (PV) penetration and quantifies the hosting capacities (HCs) of different regions. Moreover, to maximize the network's HC, having the overvoltage issue, different voltage control strategies are employed comprising on-load tap changer (OLTC), reactive power control (RPC) of inverters, network reinforcement (NR), and hybrid approaches. HC relative to different country-specific over-voltage limits are defined and the best voltage control technique is assessed, utilizing the stochastic approach of Monte Carlo simulations to model the uncertainty in the LV network's probabilistic variables. Moreover, combined medium voltage (MV)/LV network simulations are conducted to compare with the LV only simulations results. Technically, NR is the best approach to maximize the HC of a particular region but economically it is not proven a feasible one. The results presented that the OLTC employment will maximize the PV penetration if the overvoltage limit derived from EN 50160 is implemented and doubles the HC in the stricter-voltage-limits' scenarios, which can be maximized by the RPC inclusion in cohesion with OLTC. Moreover, RPC only strategy can be employed in the cases of low PV penetration scenarios, when OLTC will be proven the more expensive option. As the voltage violation limit becomes stricter, the hybrid approach of OLTC and RPC will be the best strategy.

Probabilistic-Interval Energy Flow Analysis of Regional Integrated Electricity and Gas System Considering Multiple Uncertainties and Correlations

This paper studies the uncertainty analysis of the regional integrated electricity and gas system (IEGS) composed of a three-phase unbalanced power distribution system and a gas distribution network. The well-known probabilistic energy flow (PEF) analysis of IEGS is extended to a comprehensive probabilistic-interval energy flow (PIEF) problem by modeling uncertainties in pipeline parameters as interval variables and spatial dependency between them as correlation angles. The polynomial chaos expansion (PCE) method is introduced to IEGS analysis and an improved PCE-based method combining the technique of dimension-reduction and the sparse-PCE is proposed to address the numerous uncertainties involved in a regional IEGS. A methodological framework considering probabilistic/interval uncertainties, linear/rank correlations among probabilistic variables, and dependency between interval variables is then developed for both PEF and PIEF analysis. Numerical simulations of a sample regional IEGS are used to verify the accuracy and efficiency of the proposed method, as well as the superiority of the proposed PIEF analysis over the existing PEF analysis.

An engineer-based methodology to perform Explosion Risk Analyses

Explosion Risk Analyses (ERA) are usually performed as part of the Quantitative Risk Assessment (QRA). The combination of frequencies and associated consequences allow to get a risk picture of the facility and provides decision support to the risk owner. The outcomes of this study allow also to provide, after adequate interpretation, Design Explosion Loads (DEL) to engineering disciplines (e.g. structures, piping and equipment) according to a given Risk Acceptance Criterion (RAC). For most of the offshore applications, the consequence part of the ERA is done with Computational Fluids Dynamics (CFD) to properly handle congestion and confinement effects as simple models cannot. With the increase of computational power, thanks to Moore's Law, there is an increasing trend to perform more and more CFD simulations with the expectation to improve confidence in results while taking more and more probabilistic variables into account. In the early 2000s, it was 10's of simulations, in the 2010s, it was 100's and now it is common to reach 1000's. However, one should remark that there is still a lot of uncertainties behind these studies since the geometry maturity is generally not enough especially at the early stage of detailed engineering when the preliminary Design Explosion Loads (pDEL) should be provided to disciplines. Anticipated congestion is normally put in the model, but it usually put a bias at the beginning of the consequence modelling part. In the risk-based approach, the frequency part is also of major importance. One need to keep in mind that consequence refinement should be done in close relation with the frequency refinement to ensure consistency in the approach. The practical methodology presented in this paper was developed to provide reliable inputs to engineering disciplines, taking into consideration uncertainties and potential spread of results while using a reasonable number of CFD scenarios. Finally, the safety engineers are still the key contributor in the performance of the ERA, and hence brain-based design is kept in the loop while minimizing computer-based design.

Assessment of Overhaul Effectiveness and Usage-based Inference using Bayesian Networks

The process of assessment of effectiveness of the existing overhaul practices determined that the historical usage of assets provides valuable contextual information. Usage data is typically highly reliable, but not in legacy fleets, featuring older vehicles with missing, incomplete, inconsistent, and contradictory data. This paper describes two methods for usage estimation from noisy data by exploiting two data sources: 1) unreliable, manually-entered usage data and 2) part replacements. The first method employs a probabilistic model to reconcile missing and inconsistent data entries; the second is based on the replacement of consumable components. The probabilistic model, fully and uniquely specified by the probabilistic variables (with their distributions) and deterministic variables, is validated using synthetic datasets because the real ground truth associated with the field data does not exist. Disproportional impact of an incorrect initial data point is mitigated by training the model in both forward and reverse directions. The motivating hypothesis for usage estimation from part replacements is based on a plausible assumption that specific consumables, e.g. brake pads, have reasonably repeatable replacement patterns which can be related to usage. For many vehicles mean time between failures of a component was even longer than the average data collection time span. But for assets with sufficiently longer data records, the cumulative replacements of components are well-correlated with the probabilistic usage estimates, providing additional reinforcement for the inference.

Studying the factors related to refractive error regression after PRK surgery

BackgtoundPhotorefractive keratectomy (PRK) is used for a wide range of refractive errors such as low to moderate myopia, hyperopia and astigmatism. While many improvements have been made in laser application and accuracy as well as the modes of corneal flap removal, and although the results are somewhat predictable, regression of refractive errors is still a common complaint among the patients undergoing refractive surgery with Excimer Laser. We aimed to determine related factors of regression following photorefractive keratectomy (PRK) in different types of refractive errors.MethodsThis cross-sectional study included patients who had undergone PRK more than 6 months previously and investigated refractive error regression and related factors. The participants were those who had PRK eye surgery for the first time from 2013 to 2016 using Technolas 217z100. A refraction value of spherical equivalent > 0.75 D after cycloplegic refraction was defined as refractive error regression.ResultsA total of 293 eyes on 150 subjects were studied. The preoperative refractive error of the eyes were as follows: 5.5% were myopic, 1% were hyperopic, 4.8% had astigmatism, 76% had myopic astigmatism and 12.6% had hyperopic astigmatism. Regressed and non-regressed eyes were assessed using the generalized estimating equations for the probabilistic variables of demographic characteristics, topography and eye refraction. The variables of simulated keratometry astigmatism (simK) (OR = 2.8; p = 0.04), 5 mm irregularity (OR = 3.56; p = 0.01) and sphere value (OR = 1.98; p = 0.01) were significantly related to refractive error regression. There was no significant relationship between the regressed and non-regressed eyes of the same person (p ≥ 0.05).ConclusionThere was a positive relationship between the increase of 5 mm irregularity, simK, sphere value before surgery and refractive error regression. Age, sex and type of refraction error of the patient and the expertise of the PRK surgeon could change the general results; therefore, not all cases should be dealt with identically.

Load flow balancing and transient stability analysis in renewable integrated power grids

Due to random patterns of demand response from the consumer side and the unreliable nature of renewable energy resources, load flow balancing and transient stability become challenging issues in power systems. They are even more challenging in cases of multiple interval three phase (L-L-L) faults (MITPFs), which arise in power systems due to power quality disturbances. The intent of this work is to examine the influence of MITPFs on renewable energy resources (RERs) for load flow balancing and transient stability analysis. Wind turbine power dispatchability and uncertainty have a significant impact on load flow balancing and transient stability, especially in cases of occurrence of MITPFs. Probabilistic modeling is performed in this paper to formulate the complexity of randomness for load flow balancing through a smart node and transient stability analysis through a unified power flow controller. The proposed probabilistic algorithm is based on the deviations between generation and demand response patterns due to an MITPF. An autocorrelation expansion is applied to approximate the randomness of probabilistic variables between the forecast generation and actual response pattern. Future contingencies can be predicted before disruptive changes arise due to the occurrence of an MITPF using the above probabilistic analysis. Simulation results show that the proposed algorithm outperforms existing alternatives and can achieve near optimal performance for a wide range of load variations and power quality disturbances in renewable-integrated power grids.