Monte Carlo Simulation Method Research Articles

Reliability Analysis of High-Pressure Tunnel System Under Multiple Failure Modes Based on Improved Sparrow Search Algorithm–Kriging–Monte Carlo Simulation Method

It is often difficult for a structural safety design method based on deterministic analysis to fully and reasonably reflect the randomness of mechanical parameters, while the traditional reliability analysis method has a large calculation cost and low accuracy. In this paper, based on the seepage–stress coupling numerical model, the random variables affecting the reliability of the collaborative bearing of surrounding rock and lining structures are successfully identified. Then, the improved sparrow search algorithm (ISSA) is used to optimize the hyper-parameters of the Kriging surrogate model, in order to improve the computational efficiency and accuracy of the reliability analysis model. Finally, the ISSA-Kriging-MCS model is used to quantitatively evaluate the reliability of the surrounding rock-reinforced concrete lining structure under multiple failure modes, and the sensitivity of each random variable is discussed in depth. The results show that the high-pressure tunnel structure has high safety and reliability. The reliability indexes of each failure mode decrease with the increase in the coefficient of variation (COV) of random variables. In addition, the same random variable also exhibits varying degrees of influence in different failure modes.

Achieving successful gas plant construction: A risk modelling framework with quantitative risk-based approach to schedule management

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

Multimodel-based quantitative source apportionment and risk assessment of soil heavy metals: A reliable method to achieve regional pollution traceability and management

To strengthen the control of pollution sources and promote soil pollution management of agricultural land, this study constructed a comprehensive source apportionment framework, which significantly improved the reliability of potential source analysis compared with the traditional single model. The spatial distribution pattern of agricultural soil heavy metals (SHMs) content in Lintong, a typical river valley city in China, was determined and the degree of contamination was evaluated. A scientific source apportionment methodological framework was constructed through correlation analysis methods together with multiple source apportionment receptor models. Finally, the Monte Carlo simulation method was used to derive the results of the human health risk assessment (HHRA). The results revealed the following: (1) Agricultural soils were moderately and mildly polluted, accounting for 28.8 % and 71.2 % of the total number of sampling points, respectively. (2) The overall correlation of heavy metals (HMs) was strong according to the coupling analysis of the SHMs, in which a strong correlation (0.8–1) was reached among Cu, Ni, Pb, Cr and Zn, indicating that these HMs were most likely homologous or composite. (3) Multimodel analysis of the SHMs sources revealed that the first and second principal components were agricultural (41.36 %) and industrial (19.69 %) sources, respectively, and the remaining principal components were road traffic, natural factors, and atmospheric deposition or surface runoff, respectively. (4) The average comprehensive noncarcinogenic health risk indices for adults and children were 4.2259E-02 and 1.4194E-01, respectively, which were within the slight risk range, indicating that the risk caused by SHMs to the human body can be almost negligible. This study adopted a mixed method to reveal the risk of SHMs pollution and its sources, which provides some reference and technical support for traceability analysis, zoning control, and health risk studies of regional pollutants and is helpful for formulating scientific management measures and targeting control policies.

Machine learning-based axial compressive capacity estimation of cold-formed steel build-up sections

To consider the highly nonlinear and complex buckling behaviour of various sections of cold-formed steel (CFS) built-up columns, experimental and finite element (FE) methods are commonly used for calculating their axial compressive capacity, although these methods are time-consuming and costly. This paper proposes machine learning (ML) methods to overcome the issues of traditional methods for predicting the maximum axial load capacity (MALC) of CFS built-up columns. A total of 3839 samples from more than 33 different types of sections were collected from 43 published papers, including 817 experimental data and 3,022 FE simulation data. A total of 15 characteristic parameters, such as the second moment of area, the radius of gyration, and the polar second moment of area, are considered when nine different ML models are trained. The robustness of these models was compared via the Monte Carlo simulation method, and the predicted results were compared with the calculated results from the current codes. The results show that the extreme gradient boosting (XGB) model has higher accuracy and smaller prediction errors in estimating the MALC. The proportion of data with a relative percentage error less than 10% in the prediction results of the AISI-DSM codes is 45.31%, whereas the XGB model achieves a proportion of 87.57%, and the results calculated from current codes tend to be more conservative, with a large deviation, which needs to be further considered.

Investigation on the methane emissions from permeation of urban gas polyethylene pipes under the background of hydrogen-mixed natural gas transportation

Methane, a more potent greenhouse gas than carbon dioxide, aggravates the greenhouse effect of ecological environment through its emissions. When transporting hydrogen-mixed natural gas through urban polyethylene pipes, methane will be emitted due to the permeation characteristic of polyethylene pipe material. Therefore, it is crucial to pay attention to the methane emissions from permeation of polyethylene pipes for protecting the environment and climate. In this paper, the solubility and diffusion coefficients of methane within hydrogen-mixed natural gas in polyethylene pipe materials are calculated through the Monte Carlo method and molecular dynamics simulation to determine the permeation coefficient of methane. Simultaneously, the methane emissions and equivalent carbon dioxide emissions of the hydrogen-mixed natural gas in polyethylene pipes are investigated. Results indicate the methane solubility coefficient decreases with increasing temperature and hydrogen mixing ratio. The methane diffusion and permeation coefficients increase with the rising temperature and pressure, and decreasing hydrogen mixing ratio. The pressure exerts minor influence while the temperature and hydrogen mixing ratio have more significant impact. That is, the lower the temperature and the higher the hydrogen mixing ratio, the less methane emissions caused by permeation. Therefore, the effective reduction of methane emissions can be achieved by adding the insulation layer to mitigate the impact of high temperature and increasing the hydrogen mixing ratio. For example, for the polyethylene pipe with the operating pressure of 4 bar and the size specification of SDR11, when the temperature drops from 300 K to 260 K, the methane emissions of hydrogen-mixed natural gas with hydrogen mixing ratios of 0 vol%, 20 vol%, 50 vol% and 80 vol% will be drastically reduced by 73.3%, 74.4%, 77.2% and 78.5% respectively, and when 20 vol%∼80 vol% hydrogen are mixed in the natural gas at 260 K, 300 K and 310 K, the methane emissions will be effectively reduced by 26.7%∼85.6%, 23.3%∼82.1% and 23.9%∼82.4% respectively. The findings of this study can serve as guidance for reducing the methane emissions from permeation of polyethylene pipes transporting hydrogen-mixed natural gas.

Impact of metrological correlation on the total combined risk in pharmaceutical equivalence evaluations

Pharmaceutical equivalence evaluation requires a multiparametric conformity assessment for both generic and reference medicines. This paper investigates the impact of metrological correlations on the total combined risk in pharmaceutical equivalence evaluations. The study focused on the equivalence between ranitidine hydrochloride tablets, assessed by determining the average weight, the assay of the active pharmaceutical ingredient, and the uniformity of dosage units. The risks of false conformity decisions were evaluated using Monte Carlo method simulations across four scenarios, each reflecting different correlation conditions. The results of the study focus on evaluating pharmaceutical equivalence between ranitidine hydrochloride tablets from two manufacturers. The tablets were tested for three parameters: average weight, active pharmaceutical ingredient (API) assay, and uniformity of dosage units. The measured values were within the regulatory specifications for both medicines A and B. Four scenarios of metrological correlation were assessed: #1 – actual correlation from shared analytical steps, #2 – correlation between parameters within the same medicine, #3 – correlation between generic and reference medicines, and #4 – uncorrelated parameters. The study revealed that correlations significantly affect total and combined risk values. The correlations between different parameters of the same medicine affect the total risk values, while the correlations between generic and reference medicines for a given parameter influence the combined particular risk values. Correlations between parameters of the same medicine affect total risk values, while correlations between generic and reference medicines impact combined particular risk values. Both types of correlations significantly influence combined total risk values, making metrological correlations crucial in pharmaceutical equivalence evaluations. Proper consideration of these correlations ensures the quality, efficacy, and safety of generic and reference medicines.

Research on Safety Separation Distance in Paired Approach Based on Monte Carlo Simulation

Faced with the increasing number of flights, efficient and orderly operations have become a critical issue for busy airports. The closely spaced parallel runway paired approach technology has been proven to be an effective solution for increasing runway capacity and operational efficiency. This research focuses on determining the safety separation between paired aircraft, particularly the collision safety limit. This study refines the kinematic modeling process by constructing a three-dimensional kinematic model and advancing the starting point of paired approach, making the simulation process more aligned with reality. Subsequently, the Monte Carlo simulation method is applied, integrating the kinematic model and considering collision risks to simulate the entire paired approach process. Multiple scenarios are created, and simulation experiments are conducted to determine the safety separation between the two paired aircraft. Finally, by adjusting parameter settings and taking the example of the two closely spaced parallel runways at Chongqing Jiangbei International Airport, the correlation between various influencing factors and the safety separation is analyzed. Different scenarios are simulated 100,000 times each, and the results show that the speed difference between the two paired aircraft has the most significant impact on the safety separation, followed by the paired approach mode and runway centerline spacing. The wake turbulence category matching has a minimal effect on the safety separation. The results provide a theoretical basis for airports with closely spaced parallel runways to further reduce the separation between paired aircraft, thereby enhancing airport capacity and runway operational efficiency.

Monte Carlo Simulation Via Visual Basic Application

Objective: The objective of this study is to implement the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. Theoretical Framework: Implementing the results of the changes we desire on a real-life system through trial and error can lead to costly and risky outcomes. Simulation allows us to develop a computer model of an existing system, enabling us to understand the system's behavior. Method: Monte Carlo Simulation, a widely used simulation technique, utilizes random sampling to estimate possible outcomes of a process or experiment. Monte Carlo Simulation utilizes random sampling to estimate the outcomes of an experiment. Results and Discussion: The purpose of this study is to implement the Monte Carlo Simulation method using MS Excel Visual Basic Application. When the user interacts with the Excel interface by clicking the "Calculate" button, the system generates random numbers based on predefined probability distributions. Subsequently, statistical measures such as the mean, variance, standard deviation, median, mode, range, and extreme values (maximum and minimum) are calculated using VBA. Research Implications: Thanks to the developed application, Monte Carlo Simulation applications can be calculated easily. Originality/Value: This study contributes to the literature by implementing the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. These statistics provide insights into the underlying randomness and variability of the system, which aids in decision-making processes.

Portfolio Optimization Using Machine Learning Method and Monte Carlo Simulation

Investment portfolio optimization is a crucial aspect of quantitative finance, aiming to maximize returns and minimize risks. This study focuses on optimizing investment allocation among five selected stocks (BAIC Blue Valley, BYD, Chang’ an Automobile, Kweichow Moutai, Sunwoda) over 100 days using Monte Carlo simulation and machine learning methods. This study uses the sliding window approach with the linear regression model to predict future returns and calculate the variance-covariance matrix to determine the optimal portfolio weights, leading to two key strategies: maximizing the Sharpe ratio and minimizing the risk. The results reveal that the Max Sharpe Ratio portfolio achieved a cumulative return of 0.0909, significantly outperforming the CSI 300 index’s return of -0.0348. Additionally, the Min Risk portfolio exceeded the market index with a cumulative return of 0.0062. These findings demonstrate that both the Max Sharpe Ratio and Min Risk strategies are effective in achieving a balance between risk and return and surpassing the market, offering valuable insights for investors seeking optimized portfolio allocations.

Medium- and Long-Term Power System Planning Method Based on Source-Load Uncertainty Modeling

In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings.

Human health risk assessment of potentially toxic and essential elements in medicinal plants consumed in Zabol, Iran, using the Monte Carlo simulation method

Medicinal plants (MPs) have long been used for their therapeutic properties in traditional forms of medicine. However, the presence of potentially toxic elements (PTEs) in MPs raises concerns about their safety, efficacy, and potential adverse effects on human health. The current study aimed to determine the level of potentially toxic and essential elements (PTEEs) in commonly consumed MPs in Zabol, Iran, along with their health risk assessments. To conduct the present study, 10 types of MPs widely used in Zabol, Iran, were selected, and 15 samples of each type (150 samples in total) were taken. Each sample was analyzed for the presence of various PTEEs using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) for Lead (Pb), cadmium (Cd), chrome (Cr), nickel (Ni), copper (Cu), zinc (Zn), iron (Fe), aluminum (Al) and manganese (Mn); and Atomic Absorption Spectrometry (AAS) for arsenic (As) and mercury (Hg). Finally, to better comprehend the scope of exposure and its possible effects, the Monte Carlo simulation method is successfully applied to assess human health risks related to PTEs in MPs. Statistical analysis revealed statistically significant (P < 0.001) variations in PTE averages among MP types. Furthermore, all samples’ overall PTE mean concentration (range: 0.18 to 215.5 µg/kg) was below the World Health Organization’s (WHO) regulatory standards. Probabilistic health risks, including non-carcinogenic-target hazard quotient (THQ) for each element, total target hazard quotient (TTHQ) for all elements, and carcinogenic-incremental lifetime cancer risk (ILCR) for each element, and total carcinogenic risk (TCR) for all elements, were significantly lower than the acceptable limit for children and adults. Accordingly, it can be said that consuming MPs sold in Zabol is safe for children and adults regarding carcinogenic (ILCR/TCR = 10− 4) and non-carcinogenic (THQ/TTHQ = 1) effects. In light of the findings presented here, and to our understanding, the primary factor contributing to lower levels of PTEs in MPs in Zabol City markets is the cultivation of plants in nonindustrialized areas, separate from urban and industrial zones. This practice keeps them from environmental contaminations, including soil quality and water sources. It is recommended that it is essential to regulate the sources that enhance the transfer of PTEs and other harmful pollutants from surroundings to the soil and, consequently, MPs. It is also suggested that, like chemical drugs, MPs should undergo rigorous testing by quality control agencies before being made available to the market.

European vulnerable options pricing under sub-mixed fractional jump-diffusion model with stochastic interest rate

With the development of financial markets, the trading of financial derivatives has become more flexible. To meet the demands of investors, over-the-counter (OTC) trading of options has also become increasingly frequent, raising concerns about accurately pricing vulnerable options. This study investigates the pricing problem of European vulnerable options under the sub-mixed fractional jump-diffusion (SMFJ) model. Firstly, the underlying asset price model and the counterparty company value model are established under the SMFJ model with a sub-mixed fractional Vasicek stochastic interest rate. Then, analytical solutions for the European vulnerable options are obtained using the quasi-martingale measure transformation and the risk-neutral pricing method. Additionally, the accuracy of the pricing formula is verified through the Monte Carlo simulation method. Finally, the rationality and validity of the established model are confirmed by numerical simulations, and the influence of key parameters on the pricing models is analyzed.

Reliability analysis for running safety of vehicle on slab track via an improved second-order fourth-moment approach

The reliability assessment for running safety of vehicle on slab track under temperature action and seismic excitation is challenging due to the extensive computational efforts. This study proposes an improved second-order fourth-moment (ISOFM) approach for the reliability analysis for running safety of vehicle on slab track under temperature action and seismic excitation. The dimensionless limit state function (LSF) for running safety of vehicle on slab track is formulated considering four failure modes. The proposed ISOFM approach is verified by the Monte Carlo simulation (MCS) method. The proposed ISOFM approach greatly reduces the number of computations of nonlinear LSF. The merit of the proposed ISOFM approach lies in significantly enhancing the computation efficiency and precision of reliability considering nonlinear LSF with multi-failure modes. Additionally, the influences of temperature action and seismic excitation on the reliability for running safety of vehicle on slab track are discussed. The reliability for vehicle running safety decreases more significantly with the increment in overall temperature compared to the positive temperature gradient (PTG). The reliability for running safety of vehicle on slab track decreases nonlinearly with the increase of the earthquake intensity.

Assessing health risks of polycyclic aromatic hydrocarbons (PAHs) in cooked fish using monte carlo simulation: a global review and meta-analysis.

Cooking food at high temperatures can lead to the formation of harmful chemical compounds called polycyclic aromatic hydrocarbons (PAHs). The purpose of this study was to conduct a systematic review and meta-analysis to evaluate the concentrations of 16 PAHs in cooked fish using roasting, barbecuing, or grilling techniques. The Monte Carlo simulation method was employed to accurately assess and quantify the uncertainties associated with risk estimation. This study compiled data on PAH levels in cooked fish using gas or charcoal from 57 original published articles in the PubMed, Science Direct, Scopus, Google Scholar, and Web of Science databases between January 1, 2010 to December 30, 2023. The investigation showed that 55.1% of cooked fish was made by grilling, 35.1% by barbecuing, and 9.8% by roasting. Based on the 95th percentile Hazard Quotient (HQ) from fish consumption, the ranking of 8 PAHs was as follows: Benzo[a]pyrene (BaP = 14.10) > Pyrene (Pyr = 0.29) > Fluorene (Flu = 0.23) > Naphthalene (Nap = 0.22) > Fluoranthene (Flrt = 0.12) > Acenaphthene (Ace = 0.11) > Acenaphthylene (Acy = 0.04) > Anthracene (Anth = 0.02). However, the non-carcinogenic risk ratio for other PAH compounds in fish consumption, excluding BaP, was found to be less than one (HQ < 1). The 95th percentile lifetime excess cancer risk (LTCR) values for 8 PAH compounds (BaP (4.35E- 9) > Anth (6.10E- 11) > Flrt (9.35E- 12) > Pyr (7.04E- 12) > Ace (6.56E- 12) > Flu (4.97E- 12) > Nap (4.39E- 12) > Acy (2.57E- 12)) from fish consumption were negligible and can be disregarded (LTCR < 10- 6). Based on the analysis of the findings, it can be concluded that the consumption of cooked fish using various methods worldwide does not present a carcinogenic risk linked to PAHs.

A new adaptive EWMA chart for two‐parameter exponential distribution based on type‐II censored data

AbstractIn this article, we propose a new adaptive exponentially weighted moving average (AEWMA) chart for monitoring the joint shifts in the location and scale parameters of the two‐parameter exponential distribution, namely the AEWMA‐II chart. A key feature of the proposed chart is its straightforward post‐signal diagnostics. The Monte Carlo simulation method is used to compute the steady‐state run‐length profiles of the proposed chart. The run‐length properties include the average run‐length and standard deviation of the run‐length. Through a comprehensive run‐length comparison, it is found that the AEWMA‐II chart performs better than the existing AEWMA chart. Moreover, with post‐signal diagnostics, the proposed chart also surpasses the existing chart. Finally, a real dataset is applied to illustrate the excellent performance and practical application of the proposed chart.

Stability and failure probability analysis of super-large irregularly shaped deep excavation in coastal area considering spatial variability in soil properties

ABSTRACT The objective of this paper is to investigate the effects of spatial variability in coastal silty soil properties on the stability and failure probability of deep excavation. A super-large irregularly shaped deep excavation in the coastal area in Wenzhou, China, is considered. A numerical model is established based on the random field theory, finite difference method and Monte-Carlo simulation method. Focusing on the friction angle and cohesion, the effects of horizontal and vertical correlation distances and variation coefficients on excavation stability are systematically studied. Meanwhile, the reliability evaluation approach is also applied to systematically examine the impact of the correlation distance on the surface settlement and horizontal displacement of pile. The results show that the greater correlation distance leads to a larger dispersion degree of the ground settlement curve and the displacement curve of the diaphragm wall around. The influence of the horizontal correlation distance on land settlement is more significant. The horizontal and vertical displacements of the pile top are less affected by the correlation distance. Compared with the deterministic analysis, the random analysis results considering the soil spatial variability produce larger displacement.

Reliability analysis and calibration for flexural design provisions of GFRP-RC beams

This study aims to conduct reliability assessment and calibration of the design provisions for flexural capacity in existing design codes for glass fiber reinforced polymer bar reinforced concrete (GFRP-RC) beams. Firstly, model uncertainties for three design codes including GB 50608-2020, ACI 440.1R-15 and CSA S806-12 were evaluated using the statistical analysis methods. Subsequently, the reliability analysis of flexural capacity in three design codes was investigated based on Monte Carlo Simulation (MCS) method, considering stochastic factors such as material properties, geometric parameters, model uncertainties and load effect combinations. Moreover, the influences of tensile strength of GFRP bar, the ratio of reinforcement ratio to balanced reinforcement ratio (ρf/ρfb) and the live-to-dead load effect ratio (θ) on reliability index were explored. Meanwhile, the calibration of GFRP material partial factor (γf) for GB code was performed with the target reliability index of 3.7, based on the least squares analysis. Finally, the flexural capacity provisions of GFRP-RC beams in the GB code was revised for improving the reliability index under a relatively low live-to-dead load effect ratio θ by re-calibrating parameter γf for GFRP bar tensile failure and introducing revision coefficient ξ for concrete compressive failure, achieving target reliability index over 3.7.

Efficient reliability analysis of generalized [formula omitted]-out-of-[formula omitted] phased-mission systems

A k-out-of-n phased-mission system (PMS) is a PMS where the system structure is k-out-of-n: G in each phase. This paper investigates k-out-of-n PMSs with phase-K-out-of-N requirement, where the entire mission is successful if at least K out of the N phases achieve success. Such system is referred to as a generalized k-out-of-n PMS (k/n-GPMS). The k/n-GPMSs are prevalent in applications such as satellites, unmanned aerial vehicles (UAVs), wireless sensor networks and so on. In this paper, a novel method based on multi-valued decision diagram (MDD) is proposed to analyze the reliability of k/n-GPMSs, where the number of available components n, the required number of components k, and the components failure behaviors in different phases may vary. Distinguishing from the traditional phase-by-phase MDD generation method, the proposed method considers the behavior of all phases simultaneously and generates only one MDD model in a top-down manner. To illustrate the application of the proposed method, the reliability and the sensitivity of a four UAVs system which conducts supplies delivery mission is analyzed. The complexity analysis is performed. The correctness and efficiency are verified and demonstrated by several case studies. The proposed method is also compared with Monte Carlo simulation method.

A video speed measurement method for UAV targeting ground targets based on kalman filtering

Abstract Measuring the velocity information of moving targets, as an application scenario for drone target velocity measurement, is an important focus in the process of drone intelligence reconnaissance. To improve the speed and accuracy of unmanned aerial vehicles (UAVs) in locating and measuring moving targets, a reliable target localization and velocity model based on UAV reconnaissance video frames and Kalman filtering is proposed. This algorithm model can complete the localization and velocity measurement tasks of moving targets under complex conditions without the need to obtain ranging information from drones. To verify the algorithm, the Monte Carlo simulation method was used for simulation experiments. The experimental results show that the error of this velocity measurement algorithm can meet the accuracy requirements of unmanned aerial vehicles for ground-moving targets.