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This paper investigates intelligent reconfigurable surface‐assisted non‐orthogonal multiple access (NOMA) networks for short packet communications where the source with multiple antennas utilizes beamforming technology to serve two end users including a near user and a far user. The analysis system, closed‐form expressions of the average block error rates (BLERs), latency and reliability at all users are derived under assuming perfect channel state information over quasi‐static Rayleigh fading channels. For evaluating the improvement of the network system, a deep neural network (DNN) framework is designed to predict the performance parameters in two cases: (i) BLERs prediction of the users, (ii) transmit power allocation under the constraints of BLERs at each user which satisfy the extremely high reliability requirements of uRLLCs. The extensive results demonstrate that first, the DNN‐based estimation results are more accurate than Monte‐Carlo simulation results with low execution time. Second, the allocated power for each user can be accurately predicted at the high layers. Finally, two methods of evaluating the effectiveness of the DNN model, i.e. root mean square error and mean absolute percentage error, reach low error which verify the power of the designed DNN model for future analysis. [ABSTRACT FROM AUTHOR]

The level of ground settlement under the effect of certain factors is one of the markers indicating the adequacy of adopted design solutions. By comprehending settlement values, it is possible to infer the degree of risk associated with a project and, if necessary, introduce changes to the design. The concept of reliability management here manifests itself in the timely adjustment of the decisions made, while it is necessary to use the procedure of the quantitative assessment of the probability of the occurrence of a particular limit state. A qualitative assessment indicates only the limits of an area, which approximately characterizes the effects of changes in the geotechnical situation. In this article, a method for determining the probability of exceeding the limit deformation of a building-base massif using the Monte Carlo statistical method is developed, and an algorithm and computer program implementing the proposed calculation method are created. [ABSTRACT FROM AUTHOR]

Shear wave velocity (Vs) offers engineers a promising alternative means to evaluate liquefaction resistance of sandy soils. However, the Vs-based probabilistic evaluation of soil liquefaction using machine learning method or Bayesian network method is limited. To this end, an attractive probability framework incorporating grey wolf optimization optimized kernel extreme learning machine (GWO–KLEM), and Bayesian theorem was developed for Vs-based probabilistic liquefaction model. The framework mainly includes two components: (1) an GWO–KLEM-based metamodel is developed to approximate the limit state function and identify the uncertain location of the boundary between the liquefaction and the non-liquefaction points; (2) the Bayesian theorem along with total probability theorem is used to integrate the prior knowledge with the site-specific Vs data for the updated distributions of input parameters. Then, a large number of equivalent samples from the above integrated knowledge can be generated based on the Markov chain Monte Carlo simulation; thus the probability of liquefaction can be calculated. Based on searching technique, perspective under simplified framework is also developed. The results show that limit state function searched by GWO–KELM is reliable and the proposed Vs-based probabilistic liquefaction model has a very good performance. Details on the development of the proposed system are presented, along with comparisons of the results obtained by this system with existing methods. Overall, the proposed method is advantageous and reliable for probabilistic assessment of liquefaction potential. [ABSTRACT FROM AUTHOR]

Strainburst poses significant risks to the safety of workers in deep underground engineering. Assessing strainburst potential plays a crucial role in disaster prevention. Due to the anisotropy and heterogeneity of rock mass, it is essential to quantify the strainburst potential from the perspective of probability. This study aims to propose a methodology that integrates Monte Carlo simulation (MCS) and machine learning (ML) algorithms to evaluate the probability of strainburst potential. First, a numerical model based on damage initiation and spalling limit (DISL) method is established. Then, a novel indicator, absolute local energy release rate (ALERR), is proposed to assess strainburst potential, which describes the absolute value of elastic strain energy released after rock mass failure. The location of the maximum value of ALERR is used to determine the depth of outburst pit, which is adopted to evaluate the strainburst potential level. So, the quantitative relationship between strainburst potential and ALERR can be established. Thereafter, a strainburst dataset including six variables and the depth of outburst pit is obtained based on the MCS method and numerical model. To improve the computational efficiency, seven different ML algorithms are integrated with MCS to calculate the probability of strainburst potential, respectively. Finally, the proposed methodology is used for the strainburst potential evaluation in the 3# headrace tunnel of Jinping II hydropower station. Results show that random forest (RF) and gradient boosting (GB) algorithms have better evaluation performance and can be combined with MCS to calculate the probability of strainburst potential reliably. The proposed methodology can provide valuable guidance for the probability assessment of strainburst potential. Highlights: A new methodology that integrates Monte Carlo simulation (MCS) and machine learning (ML) algorithms is proposed to evaluate the probability of strainburst potential. A novel indicator, absolute local energy release rate (ALERR), is proposed to assess strainburst potential. A numerical model, which combines MCS and damage initiation and spalling limit (DISL) approaches, is established to simultaneously simulate the brittle failure behavior of hard rock and variability of rock mechanics parameters. [ABSTRACT FROM AUTHOR]

Context-dependent sensitivities of parameters and reliability-based design (RBD) of rock slopes are the subjects of this paper. The similarities and differences between the design points in RBD and those of partial factor design methods are discussed. It is demonstrated that partial factors provided by the design point of the first-order reliability method (FORM) can provide case-specific insights and guidance to partial factor design methods like Eurocode 7 (EC7) and the load and resistance factor design (LRFD). It is suggested that conducting RBD-via-FORM in tandem with partial factor designs is more illuminating and meaningful than calibration of partial factors of parameters which can be sensitive in one case but insensitive in another case. Three cases are analysed probabilistically with respect to plane sliding in rock slopes with one or more discontinuities. In the first two cases, different deterministic solution procedures are used for the single block and two-block mechanisms, for comparison with stereographic projection method and closed form equation, respectively, prior to extending the cases into RBD. The third case involves a failed slope in a limestone quarry, analysed using FORM in this paper, for comparison with Monte Carlo simulation. Highlights: Alternative deterministic computations obtain same solutions as Goodman's deterministic results for single-block slide and two-block slide. Extend Goodman's deterministic single-block and two-block slides to reliability analysis accounting for input uncertainties. Reliability-based design (RBD) via the first-order reliability method (FORM) provides enlightening information at the FORM design point on parameter sensitivities. RBD-via-FORM automatically reflects context-dependent sensitivities of parameters at the design point of FORM, and can provide insights for Eurocode 7 and LRFD. FORM analysis of a failed slope confirms inadequate reliability. Failure probability from reliability index compares well with Monte Carlo simulation. An efficient procedure for rapid convergence of RBD-via-FORM using the Excel Forecast function. [ABSTRACT FROM AUTHOR]

As an efficient and interference-resistant coding technique, multiple description coding (MDC) is proposed with the aim to solve the transmission unreliability problem caused by packet errors, loss or blocking delays. The integration of MDC with non-orthogonal multiple access (NOMA) scheme (MDC-NOMA) can effectively boost the robustness and throughput of the underlying system. Additionally, space-time block coding (STBC) can further enhance the system reliability with increased diversity gain, as well as reduced decoding complexity. In this paper, we propose a novel framework referred to as MDC-NOMA-STBC, in which we apply Alamouti STBC integrated with MDC and NOMA to implement more reliable transmission. Specifically, NOMA signals are constructed by superimposing the descriptions from different users, then transmitted by the base station (BS) using Alamouti STBC. In order to substantiate the performance of the proposed framework, first, we derive closed-form expressions of both the outage probability and ergodic rate for each user in a two-antenna BS scenario. Second, for multi-antenna BS, we investigate different antenna selection strategies to further enhance the outage performance under the considered framework, whose analytical expression is provided wherever possible. Third, Monte Carlo simulation results are presented to validate our theoretical framework and to corroborate the superiority of the proposed MDC-NOMA-STBC over state-of-the-art MDC-NOMA scheme. Moreover, under realistic contexts with image transmission, it is confirmed that MDC-NOMA-STBC outperforms its counterpart MDC-NOMA in terms of the peak signal-to-noise ratio and the bit error rate. [ABSTRACT FROM AUTHOR]

We study the problem of off-policy evaluation in the multi-armed bandit model with bounded rewards, and develop minimax rate-optimal procedures under three settings. First, when the behavior policy is known, we show that the Switch estimator, a method that alternates between the plug-in and importance sampling estimators, is minimax rate-optimal for all sample sizes. Second, when the behavior policy is unknown, we analyze performance in terms of the competitive ratio, thereby revealing a fundamental gap between the settings of known and unknown behavior policies. When the behavior policy is unknown, any estimator must have mean-squared error larger—relative to the oracle estimator equipped with the knowledge of the behavior policy— by a multiplicative factor proportional to the support size of the target policy. Moreover, we demonstrate that the plug-in approach achieves this worst-case competitive ratio up to a logarithmic factor. Third, we initiate the study of the partial knowledge setting in which it is assumed that the minimum probability taken by the behavior policy is known. We show that the plug-in estimator is optimal for relatively large values of the minimum probability, but is sub-optimal when the minimum probability is low. In order to remedy this gap, we propose a new estimator based on approximation by Chebyshev polynomials that provably achieves the optimal estimation error. Numerical experiments on both simulated and real data corroborate our theoretical findings. [ABSTRACT FROM AUTHOR]

Line-of-sight (LoS) path is essential for the reliability of air-to-ground (A2G) communications, but the existence of the LoS path is difficult to predict due to random obstacles on the ground. Based on the statistical geographic information and Fresnel clearance zone, a general stochastic LoS probability model for 3-D A2G channels under urban scenarios is developed. By considering the factors, that is, building height distribution, building width, building space, carrier frequency, and transceiver’s heights, the proposed model is suitable for different frequencies and altitudes. Moreover, in order to get a closed-form expression and reduce the computational complexity, an approximate parametric model is also built with the machine-learning (ML) method to estimate the model parameters. The simulation results show that the proposed model has good consistency with existing models at low altitude. When the altitude increases, it has a better performance by comparing with that of the ray-tracing (RT) Monte-Carlo simulation data. The analytical results of the proposed model are helpful for the channel modeling and performance analyses such as cell coverage, outage probability, and bit error rate in A2G communications. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is considered as a multiple access technique for supporting massive number of devices with high spectral efficiency. Simultaneous wireless information and power transfer (SWIPT) is an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in networks employing cooperative NOMA (CNOMA) is investigated with imperfect channel state information (CSI). Performance of the CNOMA network with SWIPT is investigated in a multi-tier downlink scenario with macro-base stations (MBSs) and femto-base stations (FBSs). The distribution of MBSs and FBSs follow Poisson point process model. Unlike solution in the existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating node/user employs EH using interference from the adjacent base stations. Expressions for outage probability and throughput are derived at the user pair (comprising a cell-center user and a cell-edge user) served using NOMA. Comparison of the proposed EH system with the existing solutions is highlighted. Monte Carlo simulations are carried out to validate the analytical results. [ABSTRACT FROM AUTHOR]

In this paper, a novel statistical application of large deviation principle (LDP) to the robot trajectory tracking problem is presented. The exit probability of the trajectory from stability zone is evaluated, in the presence of small-amplitude Gaussian and Poisson noise. Afterward, the limit of the partition function for the average tracking error energy is derived by solving a fourth-order system of Euler–Lagrange equations. Stability and computational complexity of the proposed approach is investigated to show the superiority over the Lyapunov method. Finally, the proposed algorithm is validated by Monte Carlo simulations and on the commercially available Omni bundleTM robot. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a new framework for low-outage downlink non-orthogonal multiple access (NOMA) using a coordinated direct and relay transmission (CDRT) scheme with direct links to both the near-user (NU) and the far-user (FU). Both amplify-and-forward and decode-and-forward relays are considered. In this framework, the NU combines the signals from base-station and relay at each stage of the successive interference cancellation (SIC) to attain good outage performance. For both NU and FU, the expressions for outage probability and throughput are derived in closed form. We also derive the high-SNR expressions for the outage probability to demonstrate that with the proposed framework, both users harness a diversity of two without feedback bits (this is the only framework to achieve this). We demonstrate that the choice of power allocation coefficient and target symbol rates is crucial to maximizing the NU throughput while ensuring a desired target FU throughput. We demonstrate that CDRT with the proposed framework outperforms known schemes in terms of outage probability, sum throughput, and energy efficiency. Moreover, we also show that optimal rate selection is important to maximize the energy efficiency. Monte Carlo simulations validate the accuracy of the derived analytical expressions. [ABSTRACT FROM AUTHOR]

Accurately locating and quantifying carbon dioxide (CO2) leakage to the atmosphere is important for diffuse degassing studies in volcanic / geothermal areas and for safety monitoring and/or carbon credit auditing of Carbon Capture and Storage (CCS) sites. This is typically conducted by measuring CO2 flux at numerous points over a large area and applying statistics or geostatistical interpolation. Accuracy of the results will depend on many factors related to survey/data-processing choices and site characteristics, and thus uncertainties can be difficult to quantify. To address this issue, we have developed a Monte Carlo-based program (MC-Flux) that repeatedly subsamples a high-resolution synthetic or real dataset using a choice of different sampling strategies (one random and four grid types) at multiple user-defined sample densities. The program keeps track of the anomalies found and estimates total flux using two statistical and two geostatistical approaches from the literature. This paper describes the use of MC-Flux to assess the potential impact of various sampling and interpretation decisions on the accuracy of the final results. Simulations show that an offset grid sample distribution yields the best results, however relatively dense sampling is required to obtain a high probability of an accurate flux estimate. For the test dataset used, ordinary kriging interpolation produces a range of flux estimates that are centered on the true value while sequential Gaussian simulation tends to slightly overestimate values at intermediate sample spacings and is sensitive to input parameters. These results point to the need for developing new approaches that decrease uncertainty, such as integration with high-resolution co-kriging datasets that complement the more accurate point flux measurements. [ABSTRACT FROM AUTHOR]

In this paper, a new method is developed to consider the effect of pipe roughness, diameter and nodal demand uncertainty on the overall reliability of the Water Distribution networks (WDNs) throughout the day as well as the planning horizon. For this purpose, probabilistic series for each of the mentioned parameters is generated by Monte Carlo Simulation (MCS). Network Pressure Reliability Index (NPRI) is then used to evaluate the overall reliability of the system. The study is performed on the benchmark Two-loop sample network and a real-world one. The comparison of deterministic and stochastic results reveal that the stochastic method tends to capture subtleties missed by deterministic method such as probability of failing the reliability in various locations at the same time. Also, the stochastic method seems to have an undoubted advantage in prediction of undesirable results prior to deterministic method, making it an essential component in long-term planning. [ABSTRACT FROM AUTHOR]

In this work, we study the outage probability (OP) at the destination of an intelligent reflecting surface (IRS) assisted communication system in a $\kappa -\mu $ fading environment. A practical system model that takes into account the presence of phase error due to quantization at the IRS when a) source-destination (SD) link is present and b) SD link is absent is considered. First, an exact expression is derived, and then we derive three simple approximations for the OP using the following approaches: (i) uni-variate dimension reduction, (ii) moment matching and, (iii) Kullback–Leibler (KL) divergence minimization. The resulting expressions for OP are simple to evaluate and quite tight even in the tail region. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We also study the impact of the number of bits available for quantization, the position of IRS with respect to the source and destination and the number of IRS elements on the OP for systems with and without an SD link. We also demonstrate how the method of moment matching and KL divergence minimization can be used to analyze systems experiencing spatial correlation between the IRS elements. [ABSTRACT FROM AUTHOR]

Cumulative damage processes for discrete degrading systems caused by stochastic shocks have received increasing attention. However, the degradation data is often subject to incomplete observation or censoring due to imperfect monitoring instruments or inspection strategies. In such situations, the arrival time of stochastic shocks cannot be accurately recorded. This makes a challenging task for parameter estimation and remaining useful lifetime (RUL) prognosis based on the cumulative damage process. Motivated by this fact, this article proposed a new Bayesian data augmentation approach to overcome the obstacles caused by incomplete observation. First, the proposal distribution for the occurrence number of shocks in the cumulative damage process subject to interval censoring is derived. Second, an effective data augmentation for the missing shock time is proposed by integrating rejection sampling and the order statistics technique. On this basis, multiple imputation is extended to discrete non-Gaussian stochastic processes subject to interval censoring. Finally, an RUL prognosis method of a system subject to the cumulative damage process is derived based on the Monte Carlo method. And a real case study on the head wear problem is applied to illustrate the superiority of the proposed approach, and the results show that the proposed approach can improve the estimation accuracy of RUL compared with the existing method. [ABSTRACT FROM AUTHOR]

Uncertainties in parameters of landslide susceptibility models often hinder them from providing accurate spatial and temporal predictions of landslide occurrences. Substantial contribution to the uncertainties in landslide assessment originates from spatially variable geotechnical and hydrological parameters. These input parameters may often vary significantly through space, even within the same geological deposit, and there is a need to quantify the effects of the uncertainties in these parameters. This study addresses this issue with a new three-dimensional probabilistic landslide susceptibility model. The spatial variability of the model parameters is modeled with the random field approach and coupled with the Monte Carlo method to propagate uncertainties from the model parameters to landslide predictions (i.e., factor of safety). The resulting uncertainties in landslide predictions allow the effects of spatial variability in the input parameters to be quantified. The performance of the proposed model in capturing the effect of spatial variability and predicting landslide occurrence has been compared with a conventional physical-based landslide susceptibility model that does not account for three-dimensional effects on slope stability. The results indicate that the proposed model has better performance in landslide prediction with higher accuracy and precision than the conventional model. The novelty of this study is illustrating the effects of the soil heterogeneity on the susceptibility of shallow landslides, which was made possible by the development of a three-dimensional slope stability model that was coupled with random field model and the Monte Carlo method. [ABSTRACT FROM AUTHOR]

For the widespread fatigue problems on orthotropic steel decks (OSDs), the failure criteria in the fatigue analyses and assessments should be improved with respect to the crack behaviors and consequences specifically. This paper presents a study on the fatigue failure criteria for OSDs considering the penetrating cracks, which is the most unfavorable case by resulting in premature damage on pavements and the consequent serviceability loss. The fatigue test results on different types of penetrating cracks are analyzed, showing similar fatigue strength in terms of S N curves, yet with different behaviors in crack growth. Further analysis indicates that the failure evaluation should not only focus on the crack growth rates of penetrating cracks, as it may stay at a rather low level and hardly reach the unstable propagation region. Hence, it is proposed to evaluate the damage evolution and life reduction of pavements caused by penetrating cracks. It shows that the penetrating crack with the depths of 0.5 t and 0.75 t will considerably reduce the fatigue life of the pavements on the cracked OSD, and the equivalent crack area is then proposed to determine the fatigue failure criteria considering serviceability. Finally, a study based on Monte-Carlo simulation and probabilistic fracture mechanics is conducted to obtain the time-dependent failure probabilities and reliability indexes. Different target reliability indexes are also considered, which help the management and maintenance of the bridge and benefit its life-cycle performance. • The fatigue failure of OSDs by penetrating cracks are analyzed and compared. • The damage evolution of pavements on a cracked OSD is investigated. • The reliability based failure criteria are proposed considering OSD crack growth and pavement life reduction. [ABSTRACT FROM AUTHOR]

In this work, we provide a framework analysis of dual-hop hybrid Millimeter Wave Radio Frequency (RF)/Free Space Optical (FSO) MIMO relaying system. The source is equipped with multiple antennas and employs conjugate beamforming while the destination consists of multiple apertures with selection combining. The system also consists of a relay operating at amplify-and-forward mode. The RF channels are subject to Nakagami-m fading while the optical links experience the Málaga distribution. In addition, we introduce the impairments to the relay and receiver. In fact, the relay is impaired by the High Power Amplifier (HPA) nonlinearities while the receiver suffers from the In phase and Quadrature Imbalance. Moreover, we assume two types of HPA nonlinearities impairments called Soft Envelope Limiter (SEL) and Traveling Wave Tube Amplifier (TWTA). Closed-forms of the outage probability, the bit error probability, and the ergodic capacity are derived. Capitalizing on these performances, we derive the high SNR asymptotes to unpack insightful metrics such as the diversity gain. We also address the impacts of some key factors on the system performance such as the impairments, the interferers, the number of antennas and apertures and the pointing errors, etc. Finally, the analytical expressions are confirmed by Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Aging asset management requires careful consideration of end-of-life of any asset. This is particularly serious with aging infrastructure which may require complete replacement of a large asset which has a long lead time. In many current End-of-Life analysis approaches to calculate unavailability, the period of interest is sub-divided into yearly cycles with the assumption that the asset is available at the start of the year. Although this assumption is adequate if the replacement time for the asset occurs within the year, with longer lead times this can create excessively optimistic availabilities. This paper presents an improved probabilistic tool for risk assessment due to End-of-Life failure unavailability which overcomes this deficiency. The End-of-Life methodology proposed in this paper is required when aging assets have long lead times to replacement and so the capital investment decision must be made several years ahead. The method is applied to evaluate the End-of-Life unavailability of Manitoba Hydro Bipole II HVdc converters. The results are corroborated using Monte Carlo simulation. Finally, the risk of End-of-Life is incorporated into an economic cost-benefit analysis corresponding to the presented unavailability evaluation method. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is considered as a potential multiple access technique for supporting massive number of devices with high spectral efficiency for the 5 G and beyond networks. Simultaneous wireless information and power transfer (SWIPT) is considered as an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in heterogeneous networks (HetNets) employing cooperative NOMA (CNOMA) is investigated. The HetNets consists of macro-base stations (MBS) under-laid by small-base stations (SBS). The distribution of MBSs follow Poisson point process model, while the SBS tier supports NOMA and carrier sensing for its transmission. Carrier sensing helps in reducing interference by blocking the base stations within a specific range from transmitting. In this work, the analysis at SBS tier is carried out in two phases. In the direct phase transmission, the SBS transmits the superimposed signal to a user pair, comprising of a cell-centre user (CCU) and a cell-edge user (CEU), using NOMA principle. The CCU acts as a cooperative relay to forward information to the CEU in the cooperative phase. Unlike existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating user (i.e. CCU) employs EH using interference from the adjacent base stations along with the EH from the superimposed signal. Expressions for outage probability and throughput are derived at the user pair served using NOMA. Comparison of the proposed EH system with existing solutions are highlighted and useful insights are drawn. Monte Carlo simulations are carried out to validate the analytical results. [ABSTRACT FROM AUTHOR]

In this study, the probability of occurrence of fumonisins in corn in the states of greatest production in Brazil was determined. The data were analyzed through quantitative risk analysis using the Monte Carlo simulation. The results indicated that there is a strong correlation between fumonisins contamination levels and the geographical region due to the influence of climatic characteristics, with temperature having the main influence. The Southern states presented higher risks of occurrence and concentration levels of fumonisins in corn due to the temperate climate with lower average temperature and higher relative humidity and precipitation indices. Cultivation in the best season indicates a significant reduction in the production of fumonisins when this period was evaluated, with average concentration levels up to 42% lower. The generated data are important for regulatory agencies and the agricultural sector, which needs to be aware that the chance of success in grain production depends on efficient planning of the growing season, mainly concerning the climatic conditions to which it is subject to minimize the risks. • Risk of fumonisins in corn was assessed using quantitative risk assessment; • Temperature plays a critical role in fumonisin incidence and level in corn; • Reduction in fumonisin of up to 42% when cultivation at the best time was assessed; • Strong correlation between fumonisins levels and the geographical region was found; • Low temperatures and high humidity increased the probability of fumonisin in corn. [ABSTRACT FROM AUTHOR]

Performance failure has become a major threat for various memory and analog circuits. It is challenging to estimate the extremely small failure probability when failed samples are distributed in multiple disjoint regions. In this article, we propose an adaptive importance sampling (AIS) algorithm. AIS has several iterations of sampling region adjustments, while existing methods predecide a static sampling distribution. We design two adaptive frameworks based on resampling and population Metropolis–Hastings (MH) to iteratively search for failure regions. The experimental results of the AIS method exhibit better efficiency and higher accuracy. For SRAM bit cell with single failure region, the AIS method uses 2– $27{\times }$ fewer samples and reaches better accuracy when compared to several recent methods. For a two-stage amplifier circuit with multiple failure regions, the AIS method is $90{\times }$ faster than Monte Carlo and 7–23 ${\times }$ over other methods. For charge pump circuit and $C^{2}MOS$ master–slave latch circuit, the AIS method can reach 6– $18{\times }$ and 4– $6{\times }$ speedup over other methods, respectively. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) communication are two important technologies considered for the fifth generation (5G) of mobile networks. In this paper, we propose a machine-to-machine (M2M) communication underlaying heterogeneous network (HetNet) with mmWave-NOMA transmission, where small cell users (SCUs) and machine type communication (MTC) devices share the same resource block (RB) to increase the network capacity. We formulate an optimization problem to select efficient users and allocate transmission powers to SCUs and MTC devices. The object of optimization is to maximize the energy efficiency (EE) subject to the minimum spectral efficiency (SE) requirements and maximum transmission powers. We present a low computational complexity sub-optimum solution based on the non-cooperative game. An iterative algorithm with fast convergence speed and a reduced complexity search-based algorithm are respectively presented to solve the power control and user selection problems. The comparison between the proposed solution and optimum solution for different number of transmit antennas indicate that there is a small gap between SEs and EEs of the proposed and optimum solutions. Further, the results of user selection scheme demonstrate that the location of strong user affects the performance. We also derive analytical expressions for the outage probability which confirm Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

We motive and calculate Newton–Cotes quadrature integration variance and compare it directly with Monte Carlo (MC) integration variance. We find an equivalence between deterministic quadrature sampling and random MC sampling by noting that MC random sampling is statistically indistinguishable from a method that uses deterministic sampling on a randomly shuffled (permuted) function. We use this statistical equivalence to regularize the form of permissible Bayesian quadrature integration priors such that they are guaranteed to be objectively comparable with MC. This leads to the proof that simple quadrature methods have expected variances that are less than or equal to their corresponding theoretical MC integration variances. Separately, using Bayesian probability theory, we find that the theoretical standard deviations of the unbiased errors of simple Newton–Cotes composite quadrature integrations improve over their worst case errors by an extra dimension independent factor ∝ N - 1 2 {\propto N^{-\frac{1}{2}}}. This dimension independent factor is validated in our simulations. [ABSTRACT FROM AUTHOR]

Gold, used for jewelry and ornaments at times, is now considered a safe metal for investment. In addition to buying and selling gold physically, some markets offer electronic trade in gold. Trade volume is on surge in these bullion markets with each passing day. Daily opening and closing gold prices are used for analysis for 7740 days. Data series of daily return on investment in gold is split into two data series; first when the price goes up and second when the price goes down, at the day's end. Statistical properties of three data series are analyzed and probabilities to earn profits are calculated during any day regardless of what types of decisions are going to be made that day. Probabilities of daily return, for both long and short positions, are presented in the form of tables. The expected daily profit of a trader with associated probability is explained in an illustrative example. [ABSTRACT FROM AUTHOR]

The combination between non-orthogonal multiple access (NOMA) and hybrid automatic repeat request (HARQ) is capable of realizing ultra-reliability, high throughput and many concurrent connections particularly for emerging communication systems. This paper focuses on characterizing the asymptotic scaling law of the outage probability of HARQ-aided NOMA systems with respect to the transmit power, i.e., diversity order. The analysis of diversity order is carried out for three basic types of HARQ-aided downlink NOMA systems, including Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR). The diversity orders of three HARQ-aided downlink NOMA systems are derived in closed-form, where an integration domain partition trick is developed to obtain the bounds of the outage probability specially for HARQ-CC and HARQ-IR-aided NOMA systems. The analytical results show that the diversity order is a decreasing step function of transmission rate, and full time diversity can only be achieved under a sufficiently low transmission rate. It is also revealed that HARQ-IR-aided NOMA systems have the largest diversity order, followed by HARQ-CC-aided and then Type I HARQ-aided NOMA systems. Additionally, the users’ diversity orders follow a descending order according to their respective average channel gains. Furthermore, we expand discussions on the cases of power-efficient transmissions and imperfect channel state information (CSI). Monte Carlo simulations finally confirm our analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-way cooperative relay scheme based on non-orthogonal multiple access (NOMA) where bidirectional communication is supported for two users in three phases. We jointly optimize power and time allocations to minimize system outage probability whose exact closed-form expression is derived and verified by Monte Carlo simulations. For performance comparison, four schemes based on power allocation and time allocation as well as the conventional one-way cooperative NOMA (OW-CNOMA) scheme are provided. It is shown that the proposed scheme can improve outage probability and ergodic rate performances over a conventional OW-CNOMA system. [ABSTRACT FROM AUTHOR]

We propose a new opportunistic non-orthogonal multiple access (NOMA) scheme under wireless backhaul unreliability and fronthaul channel uncertainty, where fronthaul represents regular radio access link. In particular, we propose two opportunistic methods for the proposed NOMA, which allow the best transmitter selection approaches based on either a near or a far-away receiver, considering the wireless backhaul unreliability and the fronthaul fading impairment. For the performance analysis, new closed-form expressions of exact and approximated outage probabilities of the grouped receivers are derived. The theoretical analysis provides an insight into the impact of wireless backhaul unreliability and imperfect channel estimation on the behaviour of outage probabilities at NOMA receivers. Furthermore, we analytically investigate how the number of multiple transmitters in the proposed opportunistic NOMA can determine the outage floors. We show that under unreliable wireless backhauls, a dominant receiver in the proposed NOMA scheme can achieve more than 3 dB gain in outage performance, compared to the orthogonal multiple access. In addition, the outage probability at a dominant receiver is less influenced by imperfect channel information, while the outage probability at a non-dominant receiver is significantly sensitive. The analytical expressions and asymptotic results have been validated through Monte Carlo simulations, thus verifying the derived impact analysis of NOMA under wireless backhaul unreliability and imperfect channel estimation. [ABSTRACT FROM AUTHOR]

The statistical size effect has generally been explained by the weakest-link model, which is valid if the failure of one representative volume element (RVE) of material, corresponding to one link, suffices to cause failure of the whole structure under the controlled load. As shown by the recent formulation of fishnet statistics, this is not the case for some architectured materials, such as nacre, for which one or several microstructural links must fail before reaching the maximum load or the structure strength limit. Such behavior was shown to bring about major safety advantages. Here, we show that it also alters the size effect on the median nominal strength of geometrically scaled rectangular specimens of a diagonally pulled fishnet. To derive the size effect relation, the geometric scaling of a rectangular fishnet is split into separate transverse and longitudinal scalings, for each of which a simple scaling rule for the median strength is established. Proportional combination of both then yields the two-dimensional geometric scaling and its size effect. Furthermore, a method to infer the material failure probability (or strength) distribution from the median size effect obtained from experiments or Monte Carlo simulations is formulated. Compared to the direct estimation of the histogram, which would require more than ten million test repetitions, the size effect method requires only a few (typically about six) tests for each of three or four structure sizes to obtain a tight upper bound on the failure probability distribution. Finally, comparisons of the model predictions and actual histograms are presented. [ABSTRACT FROM AUTHOR]

Outage probability analysis of diversity reception over lognormal fading channels is challenging because the cumulative distribution function (CDF) of a sum of lognormal random variables (RVs) does not yield a closed-form expression. Furthermore, traditional asymptotic analysis techniques fail for lognormal fading channels. The well-applied approximation techniques, such as Fenton, Wilkinson, and Schwartz and Yeh approximations, become unreliable when the signal-to-noise ratio (SNR) is high. In this paper, we derive closed-form bounds of outage probability for dual-branch equal-gain combining system over non-identically distributed lognormal fading channels. The bounds are shown tight from low-to-high SNR regimes, which provides an efficient way to evaluate the EGC system without resorting to expensive Monte Carlo simulation or numerical integration. Furthermore, the new analytical expressions can be used to test at what SNR the existing approximation techniques become inaccurate, and can also be used as criteria for system design. [ABSTRACT FROM AUTHOR]

• EBFSR generates output ensemble of size M = M I R from input ensemble of size M I < M. • Randomization factor, R , is the number of ensemble members per hydrologic model run. • Tradeoff between R and statistical efficiency of output ensemble must be understood. • Tradeoff depends on model informativeness and uncertainty magnitude. • Two algorithms are formulated to aid user in specifying M and R. A typical deficiency of ensemble forecasts is the lack of calibration; e.g., ensemble members are under-dispersed and, consequently, probabilities estimated from them cannot be taken at their face values. Self-calibration is one of the unique theoretic properties of the Bayesian forecasting system, BFS. Its ensemble version, EBFS, inherits this property, provided the ensemble size is large enough. That requirement motivated the version with randomization, EBFSR, presented in part I. Its unique advantage is the operational feasibility of generating a hydrologic ensemble forecast of large size, M (having hundreds or even thousands of members), from a meteorologic input ensemble forecast of small size, M I (having only tens or hundreds of members): M I < M , with M = M I R , where R stands for randomization factor (R > 1 and integer). This R -fold enlargement of the ensemble size is achieved through a Monte Carlo generator (sans extra runs of the hydrologic model) and, therefore, requires little extra effort or computing time. But what are the statistical implications? The objective of this part II is to identify experimentally the sampling properties of the EBFSR so that its advantage can be properly harnessed in operational forecasting. The core matter to understand and to quantify is the tradeoff between the computing efficiency and the statistical efficiency of the EBFSR. This is so because the user must specify (i) the largest acceptable sampling error [measured in terms of the average expected maximum absolute difference, E ¯ (M A D) , between the true but unknown predictive distribution function of the forecasted variate and the empirical distribution function estimated from a sample of size m , a fraction of the ensemble size M , and (ii) the tradeoff between M I and R. Critical to deciding this tradeoff are two relationships: E ¯ M A D = ξ (m) and M E = τ (M P , R) , where M P is the size of the input sub-ensemble with positive precipitation amounts, and M E is the effective sample size of the output sub-ensemble of size M P R. In terms of output/input ensemble sizes, the computing efficiency is M / M I = R > 1 , and the statistical efficiency is M E / M P R < 1 because M P < M E < M P R. Based on results of large numerical experiments, general parametric forms of ξ and τ are identified, and the dependence of the parameter values is established on factors such as the forecast type (probabilistic river stage forecast, probabilistic stage transition forecast, probabilistic flood forecast), the forecast lead time, the magnitude of the precipitation input uncertainty, and the informativeness of the deterministic hydrologic model employed by the EBFSR. Finally, two algorithms are formulated to aid the user in specifying the values of M and R , such that E ¯ (M A D) does not exceed the largest acceptable value and that operational constraints are satisfied (e.g., constraints regarding the input ensemble size M I or the feasible number of runs of the hydrologic model). [ABSTRACT FROM AUTHOR]

Most of the well-known fading distributions, if not all of them, could be encompassed by the Fox's $H$ -function fading. Consequently, we investigate the physical layer security over Fox's $H$ -function fading wiretap channels, in the presence of non-colluding and colluding eavesdroppers. In particular, for the non-colluding scenario, closed-form expressions are derived for the secrecy outage probability (SOP), the probability of non-zero secrecy capacity, and the average secrecy capacity. These expressions are given in terms of either univariate or bivariate Fox's $H$ -function. In order to show the effectiveness of our derivations, three metrics are respectively listed over the following frequently used fading channels, including Rayleigh, Weibull, Nakagami- $m$ , $\alpha -\mu$ , Fisher-Snedecor (F-S) $\mathcal {F}$ , and extended generalized- $\mathcal {K}$. Our tractable results are not only straightforward and general, but also feasible and applicable, especially the SOP, which is usually limited to the lower bound in the literature due to the difficulty of deriving closed-from analytical expressions. For the colluding scenario, a super eavesdropper equipped with maximal ratio combining (MRC) or selection-combining (SC) schemes is characterized. The lower bound of SOP and exact PNZ are thereafter derived with closed-form expressions in terms of the multivariate Fox's $H$ -function. In order to validate the accuracy of our analytical results, Monte Carlo simulations are subsequently conducted for the aforementioned fading channels. One can observe that for the former non-colluding scenario, we have perfect agreement between the exact analytical and simulation results, and highly accurate approximations between the exact and asymptotic analytical results. On the contrary, the SOP and PNZ of colluding eavesdropper is greatly degraded with the increase of the number of eavesdroppers. Also, the so-called super eavesdropper with MRC is much powerful to wiretap the main channel than the one with SC. [ABSTRACT FROM AUTHOR]

Abstract Monte Carlo molecular simulations in grand canonical ensemble were performed to study the H 2 S, CO 2 and CH 4 adsorption and separation on the MIL-47 and the functionalized MIL-47-X (X = -OH and -OCH 3). The preferred situation for the adsorption of gas molecules on the studied MOFs was also investigated. The calculated adsorption isotherms of CO 2 on the MIL-47 agreed well with the experimental uptakes. Furthermore, the simulated adsorption selectivities of three MOFs for the CO 2 /CH 4 , H 2 S/CH 4 and H 2 S/CO 2 mixtures were in the order H 2 S/CH 4 > H 2 S/CO 2 > CO 2 /CH 4. These MOFs had higher selectivity for H 2 S over CH 4 indicating that they can be good adsorbents for the H 2 S separation. The probability distribution of the adsorbed molecules showed that the guest molecules were more localized near the metal centers and functional groups. Graphical abstract Unlabelled Image Highlights • The functionalized MIL-47 have higher adsorption than pristine frameworks at low pressure. • The adsorption selectivities of MIL-47-X (X = −OH and –OCH 3) are higher than MIL-47. • It is found that gas molecules preferentially adsorbed in the vicinity of metal centers of MIL-47. [ABSTRACT FROM AUTHOR]

To account for the model bias in reliability analysis, various methods have been developed to validate simulation models using precise experimental data. However, it still lacks a strategy to actively seek critical information from both sources for effective uncertainty reduction. This paper presents an active resource allocation approach (ARA) to improve the accuracy of reliability approximations while reducing the computational, and more importantly, experimental costs. In ARA, the Gaussian process (GP) modeling technique is employed to fuse both simulation and experimental data for capturing the model bias, and further predicting actual system responses. To manage the uncertainty due to the lack of data, a two-phase updating strategy is developed to improve the fidelity of GP models by actively collecting the most valuable simulation and experimental data. With the high-fidelity predictive models, sampling-based methods such as Monte Carlo simulation are used to calculate the reliability accurately while the overall costs of conducting simulations and experiments can be significantly reduced. The effectiveness of the proposed approach is demonstrated through four case studies. [ABSTRACT FROM AUTHOR]

Timing analysis is a key step in the digital design process. By modeling device delay variations statistical static timing analysis (SSTA) reduces pessimism compared to traditional static timing analysis (STA). However, it ignores the circuit’s logic which causes some timing paths to never, or only rarely, be sensitized. We introduce a general timing analysis approach and tool to calculate the probability that individual timing paths are sensitized, enabling the calculation of bounding delay distributions over all input combinations. We show how this analysis is related to the well-known #SAT problem and present approaches to improve scalability, achieving, on average, results 75% to 37% less pessimistic than STA while running 569 to 16 times faster than Monte-Carlo timing simulation. [ABSTRACT FROM AUTHOR]

A new method is developed for integer ambiguity resolution in carrier-phase differential global navigation satellite system positioning. The method is novel in that it is simultaneously data-driven, generalized to include partial ambiguity resolution, and amenable to a full analytical characterization of the prior probabilities of correctly and incorrectly resolved ambiguities. The technique is termed generalized integer aperture bootstrapping (GIAB). A full development of the GIAB is provided herein, including sizing its integer aperture to usually produce a higher prior probability of full ambiguity resolution than comparable existing methods. In Monte-Carlo simulations, the GIAB is shown to provide nearly optimal ambiguity resolution success rates of full ambiguity resolution for relevant integrity requirements under strong models while enabling partial ambiguity resolution. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the secrecy outage probability and computation overhead of a multi-user multi-eavesdropper wireless system which is composed of one cluster head (CH) transmitting to multiple users, while multiple eavesdroppers attempt to tap the transmission from CH to multiple users in the presence of co-channel interference (CCI). To achieve high security and low computation overhead, we present two multi-user scheduling schemes, namely, the switch-and-examine combining with post-selection user scheduling (SECpsUS) scheme and the selection combining-based user scheduling (SCbUS) scheme. For the purpose of comparison, the conventional round-robin scheduling (RRS) is also considered as a benchmark scheme. Furthermore, we derive closed-form secrecy outage probability expressions and analyze computation overheads for the conventional RRS, SCbUS, and SECpsUS schemes. Numerical results and Monte Carlo simulations are provided to verify the correctness of our theoretical secrecy outage analysis and to demonstrate the effect of CCI on the system performance. It is shown that the SECpsUS and SCbUS schemes outperform the conventional RRS scheme in terms of their secrecy outage probabilities, where SCbUS achieves the best secrecy outage performance but at cost of the highest computation overhead, while the conventional RRS scheme has no computation overhead with the worst secrecy performance. Moreover, a flexible tradeoff between the secrecy outage performance and computation overhead can be achieved by the proposed SECpsUS scheme by adjusting a so-called switching threshold. In addition, as the number of users increases, the secrecy outage performance of the SECpsUS and SCbUS is improved significantly, whereas no secrecy benefit is achieved by the conventional RRS scheme. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for reliability evaluation of composite power systems by combining Monte Carlo simulation and multi label k-nearest neighbor (MLKNN) algorithm. MLKNN is a classification technique in which target vector of each instance is assigned into multiple classes. In this paper, MLKNN is used to classify states (failure or success at system or bus level) of a complete power system without requiring optimal power flow (OPF) analysis, except in the training phase. As a result, the computational burden to perform OPF is reduced dramatically. For illustration, the proposed method is applied to the IEEE 30 BUS Test System and IEEE Reliability Test System. The obtained results from various case studies demonstrate that MLKNN based reliability evaluation provides promising results in both classification accuracy and computation time in evaluating the composite power system reliability. [ABSTRACT FROM AUTHOR]

Visible light communications (VLC) has been studied thoroughly in recent years as an alternative or complementary technology to radio frequency communications. The reliability of VLC channels highly depends on the availability and alignment of line of sight links. In this paper, we study the effect of random receiver orientation for mobile users over VLC downlink channels, which affects the existence and quality of line of sight links. Based on the statistics of the receiver location and relative orientation with respect to the transmitter LED, we develop an analytical framework to characterize the statistical distribution of VLC downlink channels, which is then utilized to obtain the outage probability and the bit error rate. Our analysis is generalized for arbitrary distributions of relative orientation and location for a single transmitter, and extended to multiple transmitter case for some certain scenarios. Extensive Monte Carlo simulations show a perfect match between the analytical and the simulation data in terms of both the statistical channel distribution and the resulting bit error rate. Our results also characterize the channel attenuation due to random receiver orientation and location for various scenarios of interest. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is currently considered as a promising technology for the next-generation wireless networks. In this paper, the error rate performance of NOMA systems is investigated over Nakagami- $m$ fading channels, while considering imperfect successive interference cancelation. In particular, this paper focuses on the pairwise error probability (PEP) analysis, where exact PEP expressions are derived to characterize the performance of all users under different fading conditions. The obtained PEP expressions are then used to derive an exact union bound on the bit error rate (BER). Through the derived PEP expressions, the asymptotic PEP analysis is presented to investigate the maximum achievable diversity gain of NOMA users. Moreover, using the derived BER bound, the power allocation problem for all users in NOMA systems is considered under average power and users BER constraints, which allows realizing the full potential of NOMA. Monte Carlo simulation and numerical results are presented to corroborate the derived analytical expressions and give valuable insights into the error rate performance of each user and the achievable diversity gain. [ABSTRACT FROM AUTHOR]

A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors. [ABSTRACT FROM AUTHOR]

Summary: This contribution shows a simple implementation of Monte Carlo simulation method when presenting Bayes' rule. The implementation is carried out in the environment of Microsoft Excel spreadsheets by means of a generator of random numbers. The empiric results gained by simulation serve to confirm the correctness of the chosen procedures in calculations using Bayes' formula. [ABSTRACT FROM AUTHOR]

This paper considers the problem of approximating the failure frequency of large-scale composite $\boldsymbol{k}$ -terminal reliability systems. In such systems, the nodes ($\boldsymbol{k}$ of which are terminals) are connected through components, which are subject to random failure and repair processes. At any time, a system failure occurs if the surviving system fails to connect all the $\boldsymbol{k}$ terminals together. We assume that each component's up times and down times follow statistically independent stationary random processes, and these processes are statistically independent across the components. In this setting, the exact computation of failure frequency is known to be computationally intractable (NP-hard). In this paper, we present an algorithm to approximate the failure frequency for any given multiplicative error factor that runs in polynomial time in the number of (minimal) cutsets. Moreover, for the special case of all-terminal reliability systems, i.e., where all the nodes are terminals, we propose an algorithm for approximating the failure frequency within an arbitrary multiplicative error that runs in polynomial time in the number of nodes (which can be much smaller than the number of cutsets). Our simulation results confirm that the proposed method is much faster and more accurate than the standard Monte Carlo simulation technique for approximating the failure frequency. [ABSTRACT FROM AUTHOR]

Probabilistic production simulation is an important tool used in conventional power systems to calculate generated energy and evaluate reliability. With the continuous expansion of the renewable energy plants, new characteristics such as intermittency and volatility of power are widely discussed, and it is necessary to study the application of probabilistic production simulation for renewable energy power generation. This study proposes a probabilistic production simulation method based on sequence operation theory (SOT) to simulate the operation of a wind/photovoltaic/energy storage power system. Both the uncertainty of renewable resources and the outage of wind turbines are considered in this study. Considering the complementary property of the renewable energy, pattern clustering is used to analyse the meteorological conditions and to assist in the probabilistic production simulation. Moreover, the output model of the energy storage device is developed using the Monte Carlo method and controlled using a smoothing strategy of the energy storage device. Ultimately, the simulation example shows the feasibility and the higher efficiency of the algorithm compared with Monte Carlo method and a production simulation method based on equivalent energy function. [ABSTRACT FROM AUTHOR]

Due to errors in the phase and amplitude of element excitations in an antenna array, the array radiation pattern gets distorted. In order to derive statistical results on this distortion, specifically on determining the probability of exceeding a certain sidelobe level (SLL), it is often assumed that the magnitude of the array amplitude pattern follows a Rician distribution. It is shown that the Rician distribution implies two assumptions, and therefore, a more general Beckmann distribution is proposed to describe the distribution of the magnitude of the array amplitude pattern. Using Monte Carlo simulations, it is shown that the use of a Beckmann distribution outperforms the Rician distribution. Also, the seemingly counterintuitive result that the maximum probability of exceeding a certain SLL, in general, does not have to be at the angle where the highest sidelobe in the error-free case is located, is obtained. Due to this result, the importance of using an angular probability plot is emphasized. Furthermore, a physical explanation for the observed seemingly counterintuitive behavior is given. [ABSTRACT FROM PUBLISHER]

Torsional vibration of a drillstring, bit stick-slip in particular, is investigated with a simplified single degree of freedom (DOF) model in this paper. The friction in the bit-rock interaction is modeled as random, and the motions in stick and slip stages are treated separately. In the stick stage, it is treated as in static equilibrium. A single point or a range of points, at which the stick stage is completed and the slip stage is started, are determined depending on deterministic or random static friction coefficient assumed respectively. In the slip stage, the response at a time instant becomes a spreading area centered around the means due to the randomness of the kinetic friction. By assuming the random kinetic friction as White noise, the probabilistic distribution of the responses with displacement and velocity is calculated with the numerical path integration (PI) technique. For verification purpose, Monte Carlo (MC) simulation is also conducted. Also due to the randomness in friction, the time instants leaving the slip stage, or entering the stick stage, become random. Comparison between PI and MC shows that results from the two methods are in good agreement. In addition, parametric studies on damping, rotary speed, weight on bit, drillstring length and different combination of pipe and collar are conducted for both deterministic and random cases. [ABSTRACT FROM AUTHOR]

The inherent spatial variability of natural soil deposits should be considered to obtain realistic probabilistically-based estimates of the time for soil consolidation. Although a variety of commercial software packages for solving consolidation analyses within numerical framework exist, they cannot incorporate spatial variability in a robust manner. Therefore, the consideration of the spatial variability is difficult and time consuming. This study proposes two efficient approaches for probabilistic analysis of consolidation considering the spatial variability of coefficient of consolidation ( c ), as well as a technique for calculating a representative c for combined vertical and radial consolidation. The proposed approaches, which include a first-order reliability (FORM) and stochastic response surface method (SRSM), were applied to examples of 1-D vertical consolidation and combined vertical and radial consolidation problems, and the applicability of the models and spatial variability are identified. As a result, the random field and numerical consolidation analysis were decoupled, and the proposed approaches were able to perform probabilistic consolidation analysis considering spatial variability using existing deterministic finite element codes without any modification. The probability of under-consolidation can be predicted by only 6 and 11 consolidation analysis using the FORM and SRSM approaches, respectively, and the accuracy of these two approaches was shown to be identical to the results of more computationally-expensive, conventional Monte Carlo simulation (MCS) results. It was found that the probabilistic consolidation analyses are sensitive to the spatial variability of c , and the variability of average degree of consolidation increases as the scale of fluctuation increases, which can be effectively identified and treated using either of the proposed approaches. [ABSTRACT FROM AUTHOR]

In this study, a clustering scheme based on the particle swarm optimisation (PSO) algorithm is used for probabilistic load flow calculation in the presence of wind generations. In this method, input random variables are first clustered in several groups according to their similarity and then a representative is assigned to each group by the PSO algorithm; finally, the deterministic load flow is performed. Using this technique, computational time is meaningfully decreased, while an acceptable level of accuracy is achieved. The IEEE 57‐bus and IEEE 118‐bus test systems were selected for the case study to demonstrate the performance of the proposed method. The results were compared with those of the Monte Carlo as well as K‐means clustering methods from the accuracy and computation time points of view. Simulation results show that the introduced method significantly reduced the computational burden while keeping a high level of accuracy. [ABSTRACT FROM AUTHOR]