Improved Monte Carlo Method Research Articles

Local parallel free generation and dynamic affine transformation method for soil three-dimensional pore structure information model

The soil three-dimensional (3D) pore structure information model has become a new hotspot in the research of cross-scale disposal of information such as ecology, engineering, and geology. It can solve problems such as the intelligent evaluation of ecological water–soil–air–plant feedback, intelligent risk control of foundation compression deformation, and multiscale intelligent disaster prediction and prevention. However, the current soil 3D pore structure model primarily draws on the global one-time static generation in simulation software, leading to low reliability, low stability of multiple generations, poor variability, difficulty in real-time transformation, high consumption of computing resources, and long generation time, making it challenging to meet the requirements of the rapid processing of information models. Therefore, this study adopts the 3D Drawing Protocol (WebGL) technology and implements the scaling, rotation, and translation transformation of 3D pore structures based on the principle of dynamic affine transformation. The random generation and distribution of pores are realized using the improved Monte Carlo method, and the loading response efficiency of the system is optimized. The dynamic interactive operation of arbitrary increasing and decreasing of pore model is realized using the ray and plane intersection detection principle. Finally, this information system established a visual correlation between the content and reaction time of water-retaining agents and soil porosity and pore distribution through tests on the impact of water-retaining agents on soil pore changes. The information system achieved the real-time tracking of soil pore changes under the action of water-retaining agents. This research solves the problems of static and low generation efficiency of the soil mesopore structure in traditional models and realizes the real-time characterization and visualization of dynamic changes and the distribution of the soil pore structure under the influence of external conditions, which can provide an intuitive visualization platform for mesogeological structure research.

Estimating the environmental impacts of backup generation after power outages: A case study of China

Backup generators (BG) are indispensable to address the potential electricity security risks caused by extreme weather, cyber attack and infrastructure failure. Diesel generators, the major BG component, are more polluting than grid electricity, which are in the blind spot of environmental regulation and statistics. To contribute to better environmental policies for the electricity sector, this study estimates the environmental impacts of backup generation in China. A BG capacity evaluation model is firstly developed based on the safety design rules of different buildings. Then, an environmental cost assessment model is constructed to quantify the impacts of pollutant emissions from BG after power outages. At last, this paper uses an improved Monte Carlo method to re-examine the estimated results considering multiple uncertain factors and analyzes their contributions to the total cost variance. Our major findings are: (1) The estimated total BG capacity is 169.46 GW in 2020, representing 7.70% of the total generation capacity in China. (2) The environmental cost of BG generation is 6.60 billion yuan in 2020. (3) Sichuan and Hubei suffer the highest environmental impacts at the provincial level.

Kinematics analysis, motion planning and control of the continuum manipulator in minimally invasive surgery

Due to its unique dexterity, flexibility, and safety, continuum manipulator is more suitable for the detection, diagnosis, and maintenance in complex, narrow, and unstructured constrained environment. This paper presented a new cable-driven flexible endoscope robot for diagnosis and treatment, and its kinematics model, motion planning and control performance were systematically studied. First of all, the kinematics model of the proposed continuum manipulator was established by D-H method, and the workspace was analyzed based on the improved Monte Carlo method. In order to make the multi-segment continuum robot safety and quickly intervene into the target area, a motion planning method was proposed by using the ridge method under the constraints of the navigation path. In other words, the minimum distance between the beginning point, middle point and distal point of the robot and the theoretical trajectory points was taken as the optimization function, the configuration parameters of each bending segment were solved by inverse recursion to control the robot move along the pre-given navigation path. Then, the motion planning simulations of the plane path and spatial path were carried out to verify the proposed motion planning method. Finally, the experimental prototype of the proposed endoscopic robot was set up to verify the feasibility of the proposed continuum structure and the correctness of the established model, which can be used as a feedback control and navigation reference.

Improved Monte Carlo method for radiative heat transfer in semitransparent media with BRDF surface

This paper proposes an improved Monte Carlo method (IMCM) for the radiative heat transfer solution in semitransparent media with BRDF surface for the first time. This method not only keeps the strong points of high precision and excellent applicability to the calculation of radiative heat transfer in complex shape scattering media with BRDF surface, but also overcomes the shortcomings of time-consuming for huge amounts of energy beams tracing in the Monte Carlo method. In the IMCM we developed, the energy beams of reflection or scattering can be worked out by proportional iteration accumulation. The scattering and reflection process only need to be sampled twice rather than lots of scattering and reflection energy beams tracing. This improvement not only retains the high accuracy of the MCM, but also greatly improves the calculation efficiency. A number of radiative heat transfer problems in semitransparent media enclosure with BRDF surface are studied. The effects of absorption coefficient, wall emissivity and scattering characteristics on radiative heat transfer are analyzed. Results indicate that the IMCM is a very efficient method with high precision for solving radiative heat transfer.

Dynamic Reliability Evaluation and Life Prediction of Transmission System of Multi-Performance Degraded Wind Turbine

Wind power is a kind of important green energy. Thus, wind turbines have been widely utilized around the world. Wind turbines are composed of many important components. Among these components, the failure rate of the transmission system is relatively high in wind turbines. It is because the components are subjected to aerodynamic loads for a long time. In addition, its inertial load will result in fatigue fracture, wear and other problems. In this situation, wind turbines have to be repaired at a higher cost. Moreover, the traditional reliability methods are difficult to deal with the above challenges when performing the reliability analysis of the transmission system of wind turbines. To solve this problem, a stress-strength interference model based on performance degradation is introduced. Based on considering the strength degradation of each component, the improved Monte Carlo method simulation based on the Back Propagation neural network is used to obtain the curve of system reliability over time. Finally, the Miner linear cumulative damage theory and the Carten-Dolan cumulative damage theory method are used to calculate the cumulative damage and fatigue life of the gear transmission system.

Improved Monte Carlo Method for Total Radiant Exchange Areas in Cylindrical Enclosure

An improved Monte Carlo method (IMCM) is developed to solve radiative heat transfer in participating media in this work. Monte Carlo method (MCM) as a high-precision method has good applicability for the calculation of radiative heat transfer in a complex-shape medium. However, it is highly time-consuming for a large number of energy beams tracing, which limits its application in practical engineering. To overcome this problem of the MCM, there are two improvements proposed: 1) the location of the zone emission points and the number of emission energy beams are subdivided; and 2) the energy of reflection and scattering part is solved by proportional iteration accumulation. In the IMCM, the reflection and scattering processing only needs to be calculated once, which significantly reduces the computing time of a large number of energy beams tracing. A series of radiative heat transfer problems in a two-dimensional cylindrical enclosure with different absorption coefficients, wall emissivity, and scattering albedo are studied, and results show that the IMCM is a high-precision and high-efficiency method for radiative heat transfer.

An Improved Monte Carlo Method Based on Neural Network and Fuzziness Analysis: A Case Study of the Nanpo Dump of the Chengmenshan Copper Mine

The landslide of dump is a man-made geological disaster which will bring great harm to the surrounding people and environment, and probabilistic reliability analysis is commonly used to analyze the probability of slope landslide or whether protective measures should be taken. Monte Carlo simulation is the most commonly used method, but there are some problems, such as low efficiency, statistical ambiguity of small samples, and the fuzzy transition interval of the stability criterion. This paper proposes an improved Monte Carlo method that uses an improved bootstrap method to process small samples of geotechnical data, employs ELM (extreme learning machine) based on PSO (particle swarm optimization) to fit the limit equilibrium method function, and constructs the safety factor membership function of the dump site considering the fuzzy transition interval. This method was applied to an example slope of the dump site in Chengmenshan, Jiangxi. Comparing the analysis result with the result of the traditional MCS (Monte Carlo Search) method, it was found that after adding the safety factor membership function, the result was closer to the actual situation of the dump site, and the probability of failure and reliability index values were closer to those of the dangerous state; after the original function was replaced by the PSO-ELM model, the efficiency of the MCS method was greatly improved while the results maintained high consistency with the original results; the MCS method combined with the bootstrap method not only simulated the fuzzy uncertainty of the original sample statistics and distribution type but also expressed the reliability index and probability of failure as a two-sided confidence interval with a certain confidence level. The above conclusion proves the effectiveness and superiority of this method compared with the original MCS method.

Static workspace analysis of a multi-robot collaborative towing system with floating base

The static workspace of the multi-robot collaborative towing system with floating base was analyzed. The spacial configuration of the system was built, and the system was divided into three types according to the actuation. The kinematic and dynamic models of the floating base and the towing system were established, respectively, by using the Newton-Euler equation. Then the static workspace of the system was analyzed, and the specific steps to solve the workspace with the improved Monte Carlo method were obtained. Finally, the static workspaces of three types of systems were solved though actual simulation calculation, the simulation results show that the improved Monte Carlo method can get a workspace with higher precision compare to the traditional Monte Carlo method. Results of this study lay a basis for the subsequent trajectory plan, stability analysis and configuration optimization of the system.

Study in Network Stability based on MC

Monte Carlo is a convenient method for statistics sampling theory of mathematical and physical. It is widely applied to the field of staff performance in geological, electric, medical, optical, water conservancy and so on. In order to effectively assess the stability and reliability of the network, this paper put forward an improved Monte Carlo evaluation method on the basis of the traditional Monte Carlo method. First of all, the paper elaborated the principle of improved Monte Carlo method and the specific method of how to calculate the stability of the network. The improved Monte Carlo method employed the recursive variance reduction method to get a smaller sample in the original sample, to ensure the estimate is unbiased. Secondly, the paper applied the ideology of permutation and combination to the network nodes and links in the model, and combined with the upper and lower boundary of network performance, to calculate the connected stability. Finally, the paper carried out experiments between the improved and traditional Monte Carlo method in Matlab simulation to contrast and analyze. The results show that the improved Monte Carlo method to evaluate the stability of the network has higher accuracy and smaller variance. So the experiment proved that it has the high feasibility and validity.

An improved Monte Carlo method for radiative heat transfer in participating media

An improved Monte Carlo method (IMCM) is developed in this paper to solve the total radiant exchange areas (TEAs), which can greatly improve calculation accuracy and save CPU time. This approach, features two main improvements on the basis of the Monte Carlo method (MCM). First, the location of the surface emission points and the number of emission energy beams are subdivided, thereby improving the calculation accuracy. Specifically, it is assumed that the energy beams emitted by one micro-element are emitted from M points on average. Second, the energy beams emitted by the surface and gas micro-elements are only arranged once, and the sampling of surface reflection is eliminated, thereby improving the calculation efficiency. Specifically, the ratio of the energy beams emitted by one micro-element directly to other micro-elements can be calculated. The reflection and scattering energy of one micro-element to other micro-elements are obtained by iterative accumulation. This approach can greatly save CPU time compared with sampling the reflected and scattered energy beams. The application of IMCM to a two-dimensional (2D) square cavity is considered in this study. The effects of surface emissivity and boundary temperature on radiative heat transfer are analyzed. IMCM was used to solve the TEAs, and the calculated results of IMCM, MCM and the pathlength method (PLMCM) were compared based on the results of the reduced integration scheme (RIS). The all results show that the IMCM is more accurate than the MCM and PLMCM and is more efficient than the MCM and PLMCM for calculating the TEAs.

Improved Monte Carlo method to Study Road Reliability

This paper firstly introduces the development of Monte-Carlo method in connectivity reliability and its advantages and disadvantages in the study of road connectivity reliability. Secondly, introduce the traditional Monte Carlo method to calculate the reliability of the connection, and improve the accuracy and speed of the traditional Monte Carlo method, that is, replace the pseudo-random number with Sobol sequence, which overcomes the The shortcomings make the result more accurate and increase the calculation speed from the side. Finally, by comparing the results of the Monte Carlo method and Monte Carlo method for calculating the reliability of road connectivity, the advantages of the improved Monte Carlo method in this respect are obtained. It shows that this method has certain correctness and superiority, and has a positive reference value for the evaluation of the reliability of the road connectivity. The research on the reliability of road connectivity, this paper improves the accuracy and speed of the traditional Monte Carlo method, that is, replacing the pseudo-random numbers with Sobol sequences, which overcomes the shortcomings caused by pseudo-random sequence sampling and makes the results It is more accurate and speeds up calculations from the side. Finally, by comparing the results of improved Monte Carlo method and Monte Carlo method for the seismic reliability calculation of roads, the advantages of improved Monte Carlo method in this respect are obtained. It shows that the method has certain correctness and superiority, and has a positive reference value for road reliability assessment.

不同湿度环境下可见光波段激光偏振特性研究

Haze weather interferes with the visible light imaging effect, and the polarization characteristic of visible light can effectively improve the detection efficiency. Haze environment is affected by aerosol particle humidity, which is an important physical parameter of haze environment. In order to obtain the polarization characteristics of visible light in haze environment, the influence of humidity in haze environment on polarization characteristics was analyzed. Based on the single particle scattering characteristics of non-polarized light aerosol, the polarization transmission model was established by improved Monte Carlo method, research on transmission characteristics of polarized light in visible bands under different humidity and water mist was conducted, the influence of humidity change in water mist environment on polarization characteristics of polarized light in different visible bands was analyzed, and a near-real water mist environment was built. The polarization model was verified by laboratory experiments, the change of polarization degree and polarization state of linear polarized light on 450, 532 and 671 nm was compared and analyzed under different humidity conditions, the confidence of simulation model was more than 60%. The results show that the polarization degree of polarized light decreases with the increase of humidity of water-fog environment. With the increase of the wavelength, the polarization tends to be flat, while the exit polarization degree increases with the increase of wavelength. The humidity values of the descending point of polarization degree are 50%, 70% and 90% when the laser wavelengths are 450, 532, 671 nm, respectively. For water mist, which is easily affected by humidity, polarized light with longer wave length should be selected as far as possible for transmission detection in visible band. Because humidity has a greater influence on shorter wavelengths than longer wavelengths, circularly polarized light with longer wavelengths has the best polarization retention characteristics in environments with higher humidity. In the environment with high humidity, polarized light imaging with long wavelength should be selected as far as possible to achieve better imaging effect.

STUDY OF THE STATIC CHARACTERISTICS OF THE WORKSPACE AR600E ROBOT

The workspace is one of the most important parameters for evaluating robot flexibility and is important for optimizing robotic configuration, motion planning and control. Firstly, a kinematic model of the manipulator based on its basic structure was put forward. The systems of connection coordinates are established and the direct kinematic solution derived using DH methods. On its basis, the working space of the manipulator analyzed by the Monte Carlo method, based on random probability and software simulation MATLAB for the structural parameters of the robot. A cloud of workspace points has been compiled. Considering the problem of insufficient accuracy of the traditional Monte Carlo method in calculating the working space of the robot, an improved Monte Carlo method using the Beta distribution proposed.

Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures.

Health disparities are commonplace and of broad interest to policy makers, but are also challenging to measure and communicate. The Health Disparity Calculator software (HD*Calc, v1.2.4) offers Monte Carlo simulation (MCS)-based confidence interval (CI) estimation of eleven disparity measures. The MCS approach provides accurate CI estimation, except when data are scarce (e.g., rare cancers). To address sparse data challenges to CI estimation, we propose two solutions: 1) employing the gamma distribution in the MCS and 2) utilizing a zero-inflated Poisson estimate for Poisson sampling in simulation experiments. We evaluate each solution through simulation studies using female breast, female brain, lung, and cervical cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. We compare the coverage probabilities (CPs) of eleven health disparity measures based on simulated datasets. The truncated normal distribution implemented in the MCS with the standard Poisson samples (the default setting of HD*Calc) leads to less-than-optimal coverage probabilities (<95%). When both the gamma distribution and the estimated mean from the zero-inflated Poisson are used for the MCS, the coverage probabilities are close to the nominal level of 95%. Simulation studies also demonstrate that collapsing age categories for better CI estimation is not a pragmatic solution.

A Revised Monte Carlo Method for Target Location with UAV

Target location using UAV equipped with vision system has played an important role in many applications but there remain challenges. One of the principal difficulties is to position a target with a high accuracy, particularly in some specific conditions. There are many factors impacting location accuracies, such as turret setup process, sensors intrinsic properties, movement noise and GPS data precision. The most common and notable factors are the movement noise and sensors noise, which are tricky to be eliminated or compensated. Solutions to dealing with noise are mainly from methods such as recursive least square method, least square and Kalman filtering methods. But these routine methods will meet their bottlenecks when locating some plane based targets, a common scenario in target location applications. In this case, the usual methods are subject to target pointing bias of line of sight owing to the specific geometric condition. To eliminate this kind of location bias, an improved Monte Carlo method is proposed in this paper which first estimates the bias of pointing deviation for each measurement with statistical methods and then subtracts the estimated biases in a variance optimization process. Relevant experiments are conducted showing an obvious advantage of the proposed method over the other methods.

Improved Monte Carlo methods for computational modelling of thermal radiation applied to porous cellular materials

Highly porous cellular media such as plastic, ceramic and metal foams present specific characteristics that make them interesting for a number of applications related to thermal engineering. In many of these applications, thermal radiation heat transfer can have a significant, ranging from a 20–30% effect for plastic foam insulation to becoming the predominant mode of heat transfer at higher temperatures. To model radiation heat transfer in these media, accurate models of radiation and microscopic structure of the foam are required. Over time, the state of the art has evolved from basic approaches using radiative conductivity to multi-scale models based on equivalent homogeneous media and the Radiative Transfer Equation, with more recent techniques such as MPA and GRTE allowing to model multiple phases, non-Beerian behavior, and history effects. Still, comparisons of homogenized models with reference results are sparse in literature, and results mixed. Furthermore, what more advanced models afford in accuracy they lose in simplicity, leading to cumbersome calculations.In this paper, we combine in a novel way hybrid direct-inverse identification of radiative properties and accounting of history effects, to present modified and improved version of the standard Homogenous Phase Approach (HPA+) and Multi Phase Approach (MPA+), with the intent of increasing accuracy while keeping maximum simplicity. We then compare existing and new models with a reference model based on Monte Carlo Ray Casting. In the comparison, we consider a varied set of porous morphologies and boundary conditions. The improved models consistently outperform existing homogenized models.

Study on Reliability Evaluation Method Based on Improved Monte Carlo Method

The advancement in science and technology comes with continuously expanding power system scale, increasingly complex system operation condition and higher requirements for accuracy and speed of power system reliability evaluation, but actual calculation methods cannot meet the needs. Therefore, there is need to improve the reliability of conventional power distribution network so that requirements of calculation speed and calculation accuracy can be met. In this paper, reliability of the power distribution network will be evaluated using improved Monte Carlo method with uniform sampling. The average value is obtained through calculation of state of multiple sub-intervals and test functions, which effectively improves calculation accuracy, and further increases the utilization of random numbers. By improving the uniform sampling method, the Monte Carlo simulation variance is reduced, and evaluation and calculation efficiency is improved. At the same time, unqualified power grid is selected for analysis. Based on the simulation results, qualified power distribution networks are compared to point out where the requirements are not met. Also, comparative analysis is made on the effect of power distribution network grid structure etc. on the user’s power supply. Finally, suggestions for improving power distribution network reliability are given from equipment reliability, grid structure.

An improved Monte Carlo method based on Gaussian growth to calculate the workspace of robots

This paper presents a new Monte Carlo method to calculate the workspace of robot manipulators, which we called the Gaussian Growth method. In contrast to classical brute-force Monte Carlo methods, which rely on increasing the number of randomly generated points in the whole workspace to attain higher accuracy, the Gaussian Growth method focuses on populating and improving the precision of poorly defined regions of the workspace. For this purpose, the proposed method first generates an inaccurate seed workspace using a classical Monte Carlo method, and then it uses the Gaussian distribution to densify and grow this seed workspace until the boundaries of the workspace are attained. The proposed method is compared with previous Monte Carlo methods using a 10-degrees-of-freedom robot as a case study, and it is demonstrated that the Gaussian Growth method can generate more accurate workspaces than previous methods requiring the same or less computation time.

Combining Wang–Landau sampling algorithm and heuristics for solving the unequal-area dynamic facility layout problem

The dynamic facility layout problem (DFLP) is the problem of placing facilities in a certain plant floor for multiple stages so that facilities do not overlap and the sum of the material handling and rearrangement costs are minimized. We describe a model, where the facilities have unequal-areas and the layout for each stage is produced on the continuous plant floor. The most difficulty of solving this problem consists in the lack of a powerful optimization method. Wang–Landau (WL) sampling algorithm is an improved Monte Carlo method, and has been successfully applied to solve many optimization problems. In this paper, we combine the WL sampling algorithm and some heuristic strategies to solve the unequal-area DFLP. In the WL sampling algorithm, a vacant point strategy is applied to update layout at one stage. To prevent overlapping of facilities and reduce the empty space among facilities, a pushing strategy and a pressuring strategy are applied. We have tested the proposed algorithm on four groups of cases and the computational results show that the proposed algorithm is effective in solving the unequal-area DFLP.

Multi-objective layout optimization of a satellite module using the Wang-Landau sampling method with local search

The layout design of satellite modules is considered to be NP-hard. It is not only a complex coupled system design problem but also a special multi-objective optimization problem. The greatest challenge in solving this problem is that the function to be optimized is characterized by a multitude of local minima separated by high-energy barriers. The Wang-Landau (WL) sampling method, which is an improved Monte Carlo method, has been successfully applied to solve many optimization problems. In this paper we use the WL sampling method to optimize the layout of a satellite module. To accelerate the search for a global optimal layout, local search (LS) based on the gradient method is executed once the Monte-Carlo sweep produces a new layout. By combining the WL sampling algorithm, the LS method, and heuristic layout update strategies, a hybrid method called WL-LS is proposed to obtain a final layout scheme. Furthermore, to improve significantly the efficiency of the algorithm, we propose an accurate and fast computational method for the overlapping depth between two objects (such as two rectangular objects, two circular objects, or a rectangular object and a circular object) embedding each other. The rectangular objects are placed orthogonally. We test two instances using first 51 and then 53 objects. For both instances, the proposed WL-LS algorithm outperforms methods in the literature. Numerical results show that the WL-LS algorithm is an effective method for layout optimization of satellite modules.