Improved Markov Chain Research Articles

Permeability prediction of soft clay based on digital models reconstructed by an improved Markov chain Monte Carlo method

Permeability prediction of soft clay based on digital models reconstructed by an improved Markov chain Monte Carlo method

Data-driven Cultural and Creative Industry Development Trend Forecast

Abstract Under the deep integration of emerging technologies and industries, big data + cultural and creative industry is not only a simple industrial superposition but also the cooperation and complementarity of natural sciences, humanities, and social sciences. In this paper, the development of intelligent cultural and creative industries is predicted by the gray model, then the residuals are corrected, and finally, the residuals are corrected again by the Markov chain to improve the prediction accuracy. The modified GM(1,1) model and the improved Markov chain are used to predict and analyze the development trend of the data-driven cultural and creative industry, and the actual total output is not much different from the predicted output, which indicates that the method constructed in this paper can reasonably predict the results. According to the prediction method designed in this paper, the development of intelligent cultural and creative industries from 2024 to 2033 is predicted, and from 2029 to 2033, the maximum total output is predicted to be 28.02×107 yuan, 32.77×107 yuan, 38.33×107 yuan, 44.83×107 yuan, and 52.43×107 yuan, respectively, and China’s intelligent cultural and creative industry will enter a stage of rapid development.

Analysis of Spatial Correlation and Influencing Factors of Building a Carbon Emission Reduction Potential Network Based on the Coordination of Equity and Efficiency

Collaborative promotion of carbon emission reduction has become one of the most significant strategies for China to realize the dual-carbon goal. The purpose of this study is to utilize “relational data” to investigate overall and regional building carbon emission reduction networks based on the coordination of equity and efficiency. Specifically, the difference in importance between equity and efficiency principles is measured by an improved Markov chain. The spatial correlation network is constructed under the principle of coordinating equity and efficiency, and the network is analyzed using the modified gravity model and social network analysis. The results indicate that (1) the long-term “low-efficiency” problem of building carbon emissions is more serious than the long-term “low-equity” problem, and (2) the efficiency principle should be given greater weight in calculating carbon emission reduction potential. (3) The strength of network spatial association is increasing, and the spillover effect is significant, but the network form remains unstable. (4) The network is significantly impacted by geographic proximity, environmental regulations, energy consumption intensity, and the development level of the construction industry. The main achievement will assist developing countries in promoting sustainable development and collaborative carbon emission reduction in the construction sector.

Short-term load and price forecasting using artificial neural network with enhanced Markov chain for ISO New England

Nowadays, forecasting methods have gained significant attention, particularly with the design and development of energy systems. In fact, accurate load and price forecasting is crucial for effective planning, controlling, and operation of power systems, especially with renewable energy sources (RES). This paper implemented an improved Markov Chain Artificial Neural network (ANN-MC) for load forecasting. The proposed design involved a two-step implementation process, considering various statistical factors such as daily and weekly load, date/time of the year, environmental factors (e.g., dry bulb temperature and dew point), and user behaviour on weekdays and weekends. The test cases were conducted using historical data from ISO New England spanning the years 2004 to 2020. Moreover, the validation of the proposed model has been confirmed through comparing the results with those of Gaussian Process Regression (GPR), Regression Decision Tree (RDT), deep learning Bi-Long Short Memory (bi-LSTM), MLP, and conventional ANN. This article discusses the use of various performance indices such as MAPE, MPE, skewness, kurtosis, and risk indices for evaluating model performance. The performance of a developed model is compared with a conventional ANN model, and its performance is studied for both yearly and seasonal variations. In addition to existing indices, the article proposes two risk indices. The first is based on evaluating the standard deviation of load increment for each time, while the second is based on MC-ANN, the error between forecasted and actual loads. The risk assessment is compared between different cases such as actual load, load forecasting with ANN, and enhanced ANN-MC. Finally, the result confirms that the enhanced ANN-MC provides a higher yearly MPE value compared to other methods. In addition, it has a higher computational time than the conventional ANN-MC model, which is approximately 180.7s and 221.8s, respectively.

Modified replica exchange-based MCMC algorithm for estimation of structural reliability based on particle splitting method

We consider the problem of evaluation of the reliability integral when the limit state functions could have complicating features such as (a) discontinuity/rapid changes in values of GU in the space spanned by U, (b) existence of multiple, possibly unimportant, regions of failure with rapid changes in performance function near one or more of the important regions, and (c) multiple regions of failure with substantial contributions to failure probability. We tackle this problem by using the particle splitting framework and introduce an improved Markov chain Monte Carlo sampler based on replica exchange strategy. This is shown to enhance the capacity of samples to detect and explore important regions of failure. The application of the bootstrap technique to deduce the sampling variance of the estimator of the probability of failure is developed. Also given are a few examples of problems in structural mechanics where limit state functions with the aforementioned difficulties arise. The performance of the proposed method in dealing with these difficulties is compared with those of existing methods.

Constructing a Gene Regulatory Network Based on a Nonhomogeneous Dynamic Bayesian Network

Since the regulatory relationship between genes is usually non-stationary, the homogeneity assumption cannot be satisfied when modeling with dynamic Bayesian networks (DBNs). For this reason, the homogeneity assumption in dynamic Bayesian networks should be relaxed. Various methods of combining multiple changepoint processes and DBNs have been proposed to relax the homogeneity assumption. When using a non-homogeneous dynamic Bayesian network to model a gene regulatory network, it is inevitable to infer the changepoints of the gene data. Based on this analysis, this paper first proposes a data-based birth move (ED-birth move). The ED-birth move makes full use of the potential information of data to infer the changepoints. The greater the Euclidean distance of the mean of the data in the two components, the more likely this data point will be selected as a new changepoint by the ED-birth move. In brief, the selection of the changepoint is proportional to the Euclidean distance of the mean on both sides of the data. Furthermore, an improved Markov chain Monte Carlo (MCMC) method is proposed, and the improved MCMC introduces the Pearson correlation coefficient (PCCs) to sample the parent node-set. The larger the absolute value of the Pearson correlation coefficient between two data points, the easier it is to be sampled. Compared with other classical models on Saccharomyces cerevisiae data, synthetic data, RAF pathway data, and Arabidopsis data, the PCCs-ED-DBN proposed in this paper improves the accuracy of gene network reconstruction and further improves the convergence and stability of the modeling process.

Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System

There are various types of autonomous unmanned systems, covering different spaces of sea, land, and air, and they are comprehensively going deep into multiple fields of national security and social life. Due to the development of technology, the scale of unmanned systems is getting larger and larger, the number of components in the system is increasing, and the operating environment of the system is also becoming more and more complex. Therefore, the probability of failure of the components of the system will also be significantly increased. In order to eliminate the impact of the fault in time, the fault diagnosis method is significant. Considering the differences of components in autonomous unmanned systems, if a specific fault diagnosis algorithm is designed for each type of component, it will bring difficulties to the coordinated control of the system. Therefore, this paper analyzes the data characteristics of unmanned autonomous system devices (such as sensors) and finds that these data have time series. Therefore, the data of different devices can be converted into time series, and a general fault diagnosis algorithm suitable for most devices can be studied. The fault diagnosis algorithm is based on the clustering algorithm. In order to improve the clustering effect, the time series of different devices are represented by Gaussian mixture clustering to reduce the computational complexity of the clustering calculation. Then, a time series similarity measurement method based on the improved Markov chain is proposed. This method can better distinguish normal samples from abnormal samples so as to classify and identify faults effectively.

A mixed simulation methodology for long-term wind power with 4D fluctuation features clustering method and asymmetric fluctuations

Long-term wind power time series (WPTS) simulation plays an important role in long-term grid planning. The study focuses on building a mixed simulation methodology for the long-term WPTS based on asymmetric fluctuations. First, we combine fixed threshold wavelet denoising with wavelet transform to get less noise and more detailed information. Second, a 4D fluctuation features clustering method (FFCM) is proposed to optimize the clustering results. In addition, based on multiple asymmetric function fitting models (AFFMs), an improved Markov Chain Monte Carlo (MCMC) method is proposed to prevent wind power ramp events. Numerical experimental results show that the average simulation error of simulated WPTS based on AFFMs is 3.02% lower than that of the symmetric function fitting model (SFFM). Therefore, the mixed simulation methodology can decrease the simulation error and improve the model accuracy in simulating long-term WPTS and provides a mathematical model for policy guidance on efficient use of wind resources.

Economic Benefit Analysis of Medical Tourism Industry Based on Markov Model

To study the impact of the medical Tourism Industry on regional economic performance, a Markov prediction method was proposed. An improved Markov chain combination forecasting method was established by analyzing the economy of healthcare tourism industry through Markov chain forecasting method and various processing methods for economic results of different years. The research results show that healthcare tourism industry service is a new and highly potential tourism product service. It can generate significant economic and social benefits. The value and market size of healthcare tourism industry is analyzed and studied by using Markov model to explore the complementary roles of Medical and Tourism, which helps to predict the development of market size and benefits. The model results are also analyzed and calculated. The benefits and scale of the development of the healthcare tourism industry are evaluated by combining the actual data situation and development conditions in each year.

Predicting transient particle transport in periodic ventilation using Markov chain model with pre-stored transition probabilities

Periodic ventilation has been proposed for effectively removal of air contaminants in indoor environments. To facilitate the design of periodic ventilation, it is essential to predict the transient particle transport. Compared with traditional models, the Markov chain model is faster as it does not require iteration in each time step. However, for unsteady airflow, since the transient transition probabilities need to be updated in each time step, the potential of the Markov chain model in terms of computing speed has been significantly limited. This study developed an improved Markov chain model with pre-stored transition probabilities to reduce the computing cost for predicting transient particle transport under periodic ventilation. The proposed model was validated with experimental data from chamber tests. The validated model was then used to investigate the particle removal performance of periodic ventilation under different conditions. The results show that the periodic ventilation removed particles more effectively than steady ventilation, especially under a high ventilation rate and when the particle source was located in the recirculation zone. Furthermore, the supply-air period and mode had little impact on particle removal. Finally, the computing costs for the two Markov chain models were compared. For the case with six supply-air periods investigated in this study, the pre-storage Markov chain model was 4.8 times faster than the existing Markov chain model. Furthermore, the computing cost ratio of the pre-storage model to the existing model decreased with the product of period and source number.

An improved Markov chain model with modified turbulence diffusion for predicting indoor particle transport

Correctly predicting indoor particle transport is crucial for the design of air distribution to reduce the transmission of airborne infectious diseases. In recent years, a model for predicting particle transport based on the Markov chain technique has emerged, with the advantage of fast computing speed. However, the turbulence diffusion modeling approach in this Markov chain model is semi-empirical without justification. The present study aims to develop an improved Markov chain model with modified turbulence diffusion to increase its accuracy in predicting indoor particle transport. The proposed Markov chain model with modified turbulence diffusion was first validated with experimental data on transient particle transport under steady airflow from the literature. In addition, laboratory experiments on transient particle transport under unsteady periodic ventilation, which are rarely found in the literature, were conducted to further validate the improved Markov chain model. The results show that the improved Markov chain model can correctly predict transient particle transport under both steady and unsteady airflow in comparison with the experimental data. In addition, the improved Markov chain model was more accurate than the existing model in predicting transient particle transport, especially under unsteady airflow with periodic ventilation. By improving the turbulence diffusion modeling, the normalized root-mean-square deviation values of the Markov chain model decreased from 0.115/0.084 to 0.108/0.064 for the two cases in this study.

Algorithm of Classroom Teaching Quality Evaluation Based on Markov Chain

The Markov chain model teaching evaluation method is a quantitative analysis method based on probability theory and stochastic process theory, which establishes a stochastic mathematical model to analyse the quantitative relationship in the change and development process of real activities. Applying it to achieve a more comprehensive, reasonable, and effective evaluation of the classroom teaching quality of college teachers is of positive significance for promoting the continuous improvement of the teaching level of teachers and the teaching quality of schools. Therefore, after an in-depth study of Markov chain algorithm theory, this research proposes an improved Markov chain hybrid teaching quality evaluation model and designs comparative experiments and applies it to the hybrid teaching quality evaluation system of universities, designs a corresponding hybrid teaching quality evaluation model, and finally verifies its effectiveness through experiments. The mathematical model of mixed classroom teaching quality evaluation given in this research focuses on the development and change of the teaching process. For the teaching process that is closely related to the causality of teaching quality, the model established in this paper is more objective and reasonable for evaluating the quality of teaching.

Bayesian Frequency-Dependent AVO Inversion Using an Improved Markov Chain Monte Carlo Method for Quantitative Gas Saturation Prediction in a Thin Layer

One of the main objectives in the hydrocarbon reservoir characterization is determining rock and fluid properties that rely extensively on inference from seismic observations. In this letter, we present a novel Bayesian prestack inversion method using frequency-dependent amplitude versus offset (AVO) analysis with the goal to directly estimate gas saturation and porosity of a target thin reservoir zone. The proposed methodology is based on an improved Markov chain Monte Carlo (MCMC) sampling algorithm, which is computationally very coefficient due to its satisfactory acceptance probability and the convergence speed of Markov chains. Using a nonlinear rock physics model (RPM), properly calibrated for the investigating area, and a seismic forward operator based on the frequency-domain propagator matrix approach in the Bayesian inversion framework, we then evaluate the full posterior probability distribution of petrophysical parameters conditioned to seismic data and available prior information, using the MCMC algorithm in which we iteratively sample within the petrophysical property space. The proposed inversion approach is validated through applications to a synthetic reservoir model and the real seismic data from gas-bearing reservoirs with strong velocity dispersion.

Quantifying the effect of cooled initial conditions on cosmic string network evolution

Quantitative studies of the evolution and cosmological consequences of networks of cosmic strings (or other topological defects) require a combination of numerical simulations and analytic modeling with the velocity-dependent one-scale (VOS) model. In previous work, we demonstrated that a GPU-accelerated code for local Abelian-Higgs string networks enables a statistical separation of key dynamical processes affecting the evolution of the string networks and thus a precise calibration of the VOS model. Here we further exploit this code in a detailed study of two important aspects connecting the simulations with the VOS model. First, we study the sensitivity of the model calibration to the presence (or absence) of thermal oscillations due to high gradients in the initial conditions. This is relevant since in some Abelian-Higgs simulations described in the literature a period of artificial (unphysical) dissipation---usually known as cooling---is introduced with the goal of suppressing these oscillations and accelerating the convergence to scaling. We show that a small amount of cooling has no statistically significant impact on the VOS model calibration, while a longer dissipation period does have a noticeable effect. Second, in doing this analysis we also introduce an improved Markov Chain Monte Carlo based pipeline for calibrating the VOS model, Comparison to our previous bootstrap based pipeline shows that the latter accurately determined the best-fit values of the VOS model parameter, but underestimated the uncertainties in some of the parameters. Overall, our analysis shows that the calibration pipeline is robust and can be applied to future much larger field theory simulations.

Prediction of subsidence deformation of subway project by improved markov chain and optimized wavelet neural network model

In view of the fact that the factors affecting the subsidence deformation of subway engineering are complex and the subsidence monitoring data are random, this paper constructs an optimized wavelet neural network model with markov chain improvement to improve the accuracy of the predicted data. Firstly, the wavelet neural network is optimized by adding momentum and adjusting learning rate adaptively. Finally, based on markov chain theory, the residual between the predicted value and the actual monitoring value of the optimized wavelet neural network is improved. Taking the settlement data of a settlement monitoring point of xi'an metro line 14 as the research object, the prediction accuracy of the model was compared and analyzed by C++ program. The results of the program show that the additional momentum and the adaptive learning rate are effective for the optimization of the wavelet neural network model, and the prediction accuracy of the optimized wavelet neural network model improved by the markov chain is higher than that of the single model, which is suitable for the prediction of the subway engineering settlement.

An Improved MCMC-Based Particle Filter for GPS-Aided SINS In-Motion Initial Alignment

In many practical applications, the initial alignment of the strapdown inertial navigation system (SINS) must be performed under large misalignment angles. However, the error model of SINS in this situation is nonlinear, making it hard to obtain an accurate initial alignment result. Aiming at solving this problem via nonlinear filtering, an improved Markov chain Monte Carlo (MCMC)-based particle filter (PF) algorithm is designed in this article, which uses a square-root cubature Kalman filter (SCKF), PF, and MCMC algorithm synthetically. The algorithm is dedicated to extract particles from the posterior distribution of the state by the improved MCMC algorithm and avoid the particle degeneration and the particle impoverishment in a PF. This can effectively improve the performance of particle filtering for nonlinear systems. Furthermore, to solve the problem caused by the fixed number of iterations in the typical MCMC algorithm, a novel adaptive approach is introduced according to the maximum mean discrepancy. The simulation and experimental results of GPS-aided SINS in-motion initial alignment show that the proposed algorithm can provide good filtering accuracy and have good real-time performance.

Improving the ARL profile and the accuracy of its calculation for Poisson EWMA charts

AbstractThe Poisson exponentially weighted moving average (PEWMA) chart was proposed in 1998 by Borror, Champ, and Rigdon to monitor the mean of counts of nonconformities. This chart regrettably fails to have an in‐control average run length (ARL) larger than any out‐of‐control ARL, ie, the PEWMA chart is ARL‐biased. Moreover, due to the discrete character of its control statistic the PEWMA chart and the resulting subtleties of its ARL calculation, it is difficult to set the control limits in such way that the in‐control ARL takes a desired value, say . In this paper, we establish an improved Markov chain technique to approximate the ARL of EWMA for count output; propose two ARL‐unbiased counterparts of the PEWMA chart; and use the R statistical software to provide illustrations of these charts with a decidedly superior ARL profile and an in‐control ARL equal to . We also compare the ARL performance of the proposed charts with the one of a few competing control charts for the mean of independent and identically distributed (i.i.d.) Poisson counts.

Amount of Escape Estimation Based on Bayesian and MCMC Approaches for RNA Interference

The amount of short interfering RNA (siRNA) escaping from the endosome has a significant impact on the efficiency of RNAi. In general, the initial injected amount of siRNAs during the experiment is known, and also the amount of siRNAs after the experiment can be revealed by the level of mRNA measured. However, it is impossible to measure the amount of siRNAs that escape from the endosome and really take part in the chemical reaction of RNAi by detecting the biological organism and its tissues. Inspired by the bottleneck effect in the virus, we introduce the Bayesian approach to infer the amount of escape based on a single type and multiple types of siRNA, respectively. With the consideration of the large calculation quantity of the accurate posterior distribution and the unavailable analytic expression of the likelihood function, our article proposes to take samples by the improved Markov chain Monte Carlo (MCMC) method. The article takes the silencing gene of the synthesis of chitin and the interfering multiple target oncogene as numerical examples to show that our improved MCMC method has higher operation efficiency compared to the Bayesian approach. Our research models siRNA endosome escape using statistical methods for the first time. It perhaps provides a theoretical basis to decrease the cost of a biotic experiment for the future and the standardized statistical approaches for the amount of escape estimation.

Combined probabilistic forecasting method for photovoltaic power using an improved Markov chain

A novel combined probabilistic forecasting method based on an improved Markov chain for photovoltaics (PVs) to enhance the accuracy of probabilistic PV power forecasting is presented. First, a Markov chain (MC) forecasting structure combining precise factors is proposed that considers more influence factors beyond the statistical information of historical data as compared with conventional MCs. Rough set theory is then used to refine the major factors to quantify the influence of those factors. Furthermore, a k-nearest neighbours algorithm is used to select similar samples for building an accurate forecasting model. Based on similar samples, the changing PV trends are more obvious than when using whole historical samples, thus further improving forecasting accuracy. Finally, the effectiveness and superiority of the proposed method are verified by comparing results from simulations with the results from competing methods for two cases using datasets from DESERT KNOWLEDGE AUSTRALIA Solar Centre and GEFCom2014. The simulation results show that the proposed method can provide probabilistic forecasting results with better performance, also, the proposed method can be adapted to various forecasting scenarios.

Markov chain‐based wind power time series modelling method considering the influence of the state duration on the state transition probability

Due to the inherent uncertainties of wind power, its large-scale integration strongly impacts the planning and operation of power systems. To investigate these impacts, a stochastic model is required to more accurately capture the wind power's characteristics. This study proposes an improved Markov chain (MC)-based time series (TS) modelling method for the stochastic generation of synthetic wind power TS. First, a self-adaptive state division strategy is proposed to objectively classify historical data into several typical states. This strategy combines a state optimisation clustering model with a random-variable-modelling-oriented filter parameter optimisation method. Then, a three-dimensional state transition probability matrix (STPM) is proposed and constructed to generate synthetic wind power state TS. In contrast to the previous STPMs, the proposed STPM can capture the changing pattern of the transition probability against the state duration. Finally, the fluctuation quantity and noise are separately and sequentially added to the generated state TS, as an improvement over previous fluctuation characteristic addition methods, to obtain the final synthetic wind power TS. The results show that the proposed method outperforms previous MC-based TS modelling methods in reproducing historical characteristics, such as the transition and fluctuation characteristics, and does not increase the STPM construction algorithm's time complexity.