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Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Searching of lost radioactive sources rapidly and accurately is the core technology for radioactive source locating by aerial measurement. In order to solve the problem of radioactive source location in shielded condition, an adaptive radioactive source location algorithm based on Markov chain Monte Carlo (MCMC) method was proposed. Firstly, the initial values of radioactive source parameters were randomly given, and the physical model of the lost radioactive source parameter estimation was established by parameterizing the shielding attenuation coefficient. Then, based on the Metropolis-Hastings sampling method, the parameters of the radioactive source were sampled to generate Markov chain, and the appropriate sample set was selected to calculate the posterior distribution of the parameters of the radioactive source. Finally, the convergence state of the Markov chain was judged in real time, and the length of the sample set was adaptive, which can greatly shorten the time while ensuring the localization accuracy. To verify the feasibility and effectiveness of this method, based on numerical simulation and field experiment, the radiation field without shielding and the marble shielding radiation field with unknown attenuation coefficient were designed respectively. The original MCMC method, the MCMC method of attenuation coefficient parameterized and the MCMC method of adaptive sample set with convergence judgment were used to locate the radioactive sources respectively. The simulation and experimental results show that the proposed algorithm can locate radioactive sources in the environment with unknown attenuation coefficient shielding. Within the 60 m×60 m simulation search range, the localization accuracy is better than 1 m. Within the 5.4 m×2.4 m field experimental search range, the localization accuracy is better than 0.1 m, and the accuracy of localization is at the same level of magnitude as that in the environment without shielding. Due to the presence of shielding, the number of unknown parameters is increased, and the running time of the program is increased compared with that without shielding. However, by judging the convergence state in real time, it can greatly shorten the time while ensuring the accuracy of localization. Under experimental conditions, there is little difference in accuracy before and after realtime judgment of convergence state, but the time of the latter is about 10% of the former. In addition, the search environment for real radioactive sources is more complex, and in the actual operation process, the influence of irrelevant factors on measurement should be minimized as much as possible to improve positioning accuracy.

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Biased calibration can often be found in the model parameter calibration using the traditional Gaussian likelihood function (GLF). Particularly, there are measurement errors in the observation data and complex algorithmic structures in the model. The GLF is also limited to capturing the heteroscedastic characteristics of model residuals, leading to the inaccurate calibration in Markov chain Monte Carlo (MCMC). In this study, two modified likelihood functions were introduced: the Gaussian likelihood function with coefficient of variation transformation (GLF-CV), and the Gaussian likelihood function with Box-Cox transformation (GLF-BC). These modified likelihood functions were then incorporated the heteroscedasticity induced by both the observation data and the model structure. A more accurate representation was obtained for the complex characteristics of the model residuals. The performance of the modified likelihood functions was also evaluated to conduct the parameter calibration and uncertainty analysis using the uncertainty ratio (UR). The field experimental data was collected from three ecological sites: Gao Yaoxue Sticky Rice (early-maturing) from 2004 to 2009, Xinghua Wuyu Jing 3 (mid-maturing) from 2001 to 2004, and Liu'an Shanyou 63 (late-maturing) from 1991 to 2004. The RiceGrow and Oryza2000 phenology models were selected to compare the effects of GLF-CV, GLF-BC, and GLF on the calibration. Model parameter calibration was also conducted using the Ensemble Sampling for Affine-invariant MCMC (EMCEE). The research findings were summarized as follows: 1) The UR ranges were 2.66-4.54, and 2.30-4.47 days, respectively, for the RiceGrow and Oryza2000 phenology models under all three likelihood functions. There were some effects of all three likelihood functions on the parameter calibration, leading to reasonable UR values within the specified ranges. 2) The GLF-BC of the RiceGrow model was achieved in the smallest UR values for the Gao Yaoxue Sticky Rice, Xinghua Wuyu Jing 3, and Liu'an Shanyou 63 varieties. Specifically, the predicted UR values with the GLF-BC were 0.09, 0.07, and 0.80 days smaller than those with the GLF, while 1.21, 0.20, and 0.07 days smaller than those with the GLF-CV. The superior adaptability of GLF-BC was then achieved in the RiceGrow phenology model, indicating the better-improved calibration. 3) The likelihood function with the smallest UR varied greatly among the different rice varieties in the Oryza2000 phenology model. The GLF obtained the smallest UR for the Gao Yaoxue Sticky Rice variety, while the GLF-BC and GLF-CV were the smallest UR for the Xinghua Wuyu Jing 3 and Liu'an Shanyou 63 rice varieties, with values of 2.30, 4.17, and 3.50 days, respectively. Consequently, the likelihood function depended mainly on the primary source of heteroscedasticity in the model residuals. The optimal model was achieved in the GLF-CV, when the main source was the observation data, whereas, the GLF-BC was preferred, when the main source was the model structure. The GLF was selected as the suitable likelihood function with the minimal heteroscedasticity of model residuals. In conclusion, the heteroscedastic characteristics of model residuals were captured from the measurement errors and complex algorithmic structures. Two modified likelihood functions (GLF-CV and GLF-BC) can also provide more accurate descriptions of heteroscedasticity induced by both observation data and model structure. The comparative analysis of parameter calibration and model uncertainty with the UR demonstrated that the modified likelihood functions can be expected to effectively improve the calibration. The likelihood function also depended on the main source of heteroscedasticity, with the preferable GLF-CV under the primary source of observation data, and the preferred GLF-BC under the primary source of the model structure. The GLF was suitable for the minimal heteroscedasticity of model residuals. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Video synopsis is an effective technique for surveillance video browsing and storage. Existing video synopsis generation methods are prone to cause rearrangement objects collision and chronological disorder under the limitation of compression ratio. To solve this problem, a video synopsis generation model incorporating object speed change mechanism is proposed in this study. In the energy function of object rearrangement, object speed variable is added in addition to object start position variable, so that object start position and speed can be changed simultaneously to avoid collision and chronological disorder problems. Then the Markov chain Monte Carlo random sampling algorithm is used to solve the optimal value of the energy function, and the optimal solution of the target rearrangement scheme is obtained. Experiments show that when the compression rate is the same, compared with other methods, the summary video generated by the proposed model has fewer problems with target collision and timing disorder. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

After the COVID-19 epidemic has stabilized in China, the domestic epidemic situation may rebound slightly due to imported people and goods carrying the virus. The COVID-19 urban space agent model is constructed to deal with the impact of the city's containment measures on the operation of the city after the epidemic rebounds. Combined with multi-agent simulation and small-world networks in complex network models with GIS, the key parameters of the model are calibrated by the Markov chain Monte Carlo method (MCMC). The COVID-19 urban space agent model is used to simulate the secondary epidemic in a city and analyze the degree of influence of the spatial spread and quantitative growth of the epidemic by adopting different spatial scales (community siege, district and county siege). The results show that the best time for siege is 3 to 5 days after the emergence of infected people, and the effect of community siege on reducing the spatial spread range of epidemic situation and reducing the increase of the number of infected persons is basically similar under the two modes of single point outbreak and multi-point distribution, so it is suitable for dealing with single point outbreak mode. District and county siege should be more effective. Combined with the results of the analysis, suggestions are made for the formulation of containment measures after the epidemic rebounds. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the lack of observation frequency of very long baseline interferometry (VLBI) in China, universal time (UT1) lacks continuity, a combination approach is proposed by combining the UT1 sequence produced by the domestic broadband VLBI system (VLBI2010) and the variation of length of day ΔLOD sequence produced by the international global navigation satellite system (GNSS) monitoring and assessment system (iGMAS). The Vondrak algorithm is used to combine the UT1 sequence of VLBI2010 and the ΔLOD sequence of iGMAS. The Markov chain Monte Carlo (MCMC) simulation is used to investigate the relationship between the combined accuracy of UT1 sequences and the input measurement accuracy and time interval, and the optimization of the weight ratio between UT1 and ΔLOD. Finally, the combined UT1 sequence is compared with the IERS 14 C04 UT1 sequence. It is shown that the combination algorithm can effectively improve the accuracy of the output UT1 series: the accuracies of 2018 and 2020 UT1 series are 32 µs and 51 µs respectively. Compared with the original series, the accuracy of the combined result is improved by more than 30%. [ABSTRACT FROM AUTHOR]

The development of photovoltaic power generation projects is essential for China to achieve its targets of achieving peak carbon emissions before 2030 and carbon neutrality before 2060, and to better complete the task of low-carbon energy transformation. China should accelerate the development of photovoltaic power generation projects and make good use of abundant solar energy resources. This article studied the impact of social networks on residents' willingness to install photovoltaic power generation projects. Based on the questionnaire in Changping District in Beijing in 2019, this article used a spatial probit model and Bayesian estimation based on Markov chain Monte Carlo to study the impact of network effects on household distributed photovoltaic generation behavior. The results showed that: 1 The presence of residents who had installed photovoltaic power generation equipment would significantly reduce the participation probability of residents who had not installed photovoltaic power generation equipment. This indicated that most residents were not satisfied with photovoltaic power generation projects after installing the equipment, the reason of which was explored in this article. 2 Residents' understanding of subsidy policy would significantly improve their probability of participating in photovoltaic power generation. 3 The free riding psychology of residents would significantly reduce the probability of their participation in photovoltaic power generation. To this end, this article offers three policy suggestions: 4 Manufacturers should increase publicity, shorten the distance between the projects and the public, change the residents' misunderstanding of photovoltaic projects, and use network effect to promote the development of family-distributed photovoltaic projects. 5 Government departments should implement the existing subsidy policies, correct the adverse effects of the subsidy policies in the minds of residents, and especially improve the identity of installed families for photovoltaic power generation projects, in order to encourage residents to install the equipment. 6 Government departments should strengthen grassroots education in environmental protection, improve people's awareness and participation in environmental protection, and correct the negative influence of residents' free riding psychology. This article studies the impact of network effects on household distributed photovoltaic power generation behavior based on micro-level household survey data. Our study might contribute to the improvement of the 'Whole County Promotion' policy of household distributed photovoltaic projects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The selection of state number depends highly on the subjective experience in the traditional Markov Chain Monte Carlo (MCMC). However, an inappropriate value of state number can lead to a great reduction in the accuracy of load simulation, even an increase in the running time during the simulation of agricultural equipment loads. This study aims to clarify the effect of state number on the simulation when the MCMC was applied to agricultural equipment load. Specifically, the mean error, standard deviation error, and deviation of rain flow matrix between the simulated and original load decreased rapidly to stabilize, as the state number increased. Moreover, the indicators were not generalizable, if there was no significance between them. An optimization of state number was also proposed using pseudo damage consistency. As such, the damage consistency between the simulated and original load gradually improved and smoothed out, as the state number increased, whereas, the rate of increase in the operation time continued to increase. The optimal state number was calculated to satisfy the damage consistency and minimum operation time, where a threshold value was set for the pseudo damage factor. Furthermore, the field tests were carried out for both tractor ploughing and soil preparation. The specific parameters were measured to validate, including the front axle vibration, front axle stress, and driveshaft torque load. The vibration loads were also utilized to apply for the tractor front drive axle during ploughing operations. It was found that the MCMC using optimal state number can be expected torealize the load simulation with pseudo damage differences within 1%. Furthermore, there were more significant differences between the load segments in the adjustment stage, where the optimal state numbers for each load segment were more dispersed than that in the operation stage. A cyclic simulation was also developed for the loads of key components, according to the operational characteristics of a tractor. Subsequently, the MCMC cycle simulations were also performed on the front axle vibration loads for ploughing. The results show that the simulated load retained the alternating switching between the operating and adjustment stages under tractor ploughing. The same procedure was used to simulate the stress load on the front axle under ploughing, where the torque was separately loaded on the driveshaft under soil preparation. The statistical characteristic indicators were selected, including the mean, standard deviation, and the maximum load cycle amplitude for each load segment. The deviation range of each statistical eigen value was also obtained, compared with the original. The eigen values simulation for each load segment was in a higher agreement with the original eigen values. The generality was further validated when applied to the load simulation of agricultural equipment with the objective of load spectrum preparation. Consequently, the MCMC using optimal state number was better matched to the target requirements of load spectrum preparation, compared with the conventional. The finding can also effectively reduce the computational cost for the higher accuracy during load simulation of agricultural machinery. [ABSTRACT FROM AUTHOR]

Because the performance functions of many engineering problems are more complicated and the calculation cost is high in the reliability analysis of mechanical structures, the agent model is often used to fit the implicit performance function to reduce the calculation cost. In order to use less sample information and obtain higher reliability calculation accuracy, the Kriging agent model is combined with the learning function, and an active learning reliability analysis and calculation method is proposed. This method finds the sample points with the best learning effect to update the Kriging model, which improves the fitting accuracy of the model. Markov chain Monte Carlo (MCMC) method is used to evaluate the reliability of the structure, which speeds up the convergence speed of sample points and saves sample space. Analyzing the results of 4 calculation examples shows that compared with the other methods, this method can obtain higher precision calculation results with fewer sample points, and reduce the calculation cost. [ABSTRACT FROM AUTHOR]

In order to solve the problem of aero-engines' reliability evaluation of multi-degraded systems under competitive failure, a reliability evaluation method of related competitive failure between multi-degraded failure and sudden failure is proposed. The non-linear Wiener process and Gamma process with random effects are used to describe the degenerate failure process of the system, the appropriate Copula function is chosen to model the failure correlation of the multivariate degenerate system, and parameter of multivariate degradation-related failure model is estimated by Markov chain Monte Carlo method. The Weibull distribution is introduced to describe the sudden failure time, the proportional hazard model is used to construct the sudden failure rate function about the performance degradation, and the two-step maximum likelihood estimation method is used to identify the model parameters. Combined with the performance degradation data of aero-engines, the reliability evaluation of the multivariate degradation system under competitive failure is realized, and the validity and accuracy of the model are verified through the analysis results. [ABSTRACT FROM AUTHOR]

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bulletin of Botanical Research is the property of Bulletin of Botanical Research Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Degradation dependency and individual difference have a direct influence on the reliability of bivariate degradation system. To solve this problem, based on the degradation process model, the corresponding system reliability and residual life prediction models are developed. Firstly, considering the individual differences of the degradation process and dependency simultaneously, a bivariate dependent degradation model of the system is developed using the Gamma process and the Copula function with random parameters, and to improve the applicability of the model, the assumption of non-conjugate prior distribution is adopted for random parameters. On this basis, the influence of stochastic parameters on system reliability is analyzed, and a residual life prediction method based on the Bayesian theory is proposed. The unknown parameters of the model are estimated by Markov chain Monte Carlo (MCMC) method. The case analysis shows the necessity of considering individual differences in reliability estimation of such systems, and also verifies the accuracy of the residual life prediction method. [ABSTRACT FROM AUTHOR]

To evaluate the instability probability of deep submergence ring stiffened pressure cylindrical shell structures with small failure probability， an innovative adaptive analysis method based on Gaussian process classification (GPC) and importance sampling (IS) was proposed. By introducing the Markov Chain Monte Carlo (MCMC) and the Euclidean distance， a new adaptive strategy for design of experiments (DoE)， considering the prediction uncertainty and the sampling uniformity， was developed to establish the Gaussian process classifier more efficiently. Furthermore， the quasi-optimal importance sampling density function was constructed by adopting the kernel density estimation (KDE). Based on the stability of failure probability estimation， a more accurate stopping criterion was also proposed. A piecewise function was utilized to verify the accuracy and efficiency of the proposed analysis method. The instability probability of a deep submergence ring stiffened pressure cylindrical shell structure obtained by the proposed method is about 8.242×10-5. [ABSTRACT FROM AUTHOR]

One kind of Q-precentile life prediction method for gear pumps with small sample sizes was proposed based on bivariate Wiener process. The degradation processes of volume efficiency and total efficiency for gear pumps were modeled by Wiener process with random effects, and the fluctuation of gear pump performance characteristics with time and individual differences were characterized. The appropriate Copula function was selected to describe the correlation between the two degradation performance characteristics based on Akaike Information criterion (AIC). A bivariate degradation model was established and the unknown parameters of the model were estimated by Markov chain Monte Carlo (MCMC) method. The Bootstrap sampling method was used to re-sample the parameters representing individual differences by considering the facts that the performance degradation test of gear pumps was usually a small sample size. Finally, through gear pump performance degradation tests, the Q-precentile life prediction was realized, and the validity and accuracy of the proposed method were verified. The testing results show that there is a nonlinear correlation between the two characteristics in the degradation processes. Q-precentile life prediction considering correlation is more conservative than single feature prediction, and is more affected by the performance characteristics that first reach the failure threshold. [ABSTRACT FROM AUTHOR]

Fukuda and Johnson (2010) proposed an inversion method associated with the Bayesian theory (hereinafter termed the F-J method) for linear-non-linear geophysical/geodetic problems. Since then this method has been widely applied to estimating globally optimal parameters and their precisions for linearnon- linear geophysical/geodetic models. Yet for the application of the F-J method the choice of Markov chain Monte Carlo (MCMC) sampling parameters somewhat affects the invered results. Additionally, searching the most appropriate sampling parameters is time-consuming. On the other hand, an iterative least squares method can also be applied to the inversion for linear-non-linear models if their non-linear parameters are linearized beforehand. However, this method depends on optimum initial values. To make full use of the advantages of the both methods, here we propose a hybrid method termed as the iterative least squares with initial values constrained by using the F-J method. The proposed method refers to the calculation of linear-nonlinear parameters using the F-J method just one time and to refining those parame⁃ ters using the iterative least squares method a few times. Thus, it reduces the computation time relevant to the F-J method on the one hand and gains a global solution after a few times of employing the iterative least squares method on the other hand. To verify the efficacy of the hybrid method, we make comparisons using synthetic and real data sets. We employ the F-J method, iterative least squares method with random initial values and iterative least squares method with initial values provided by the F-J method, respectively. Our results show that： (1) the choice of sampling parameters indeed affects the results by using the F-J method; (2) based on the iterative least squares method with random initial values, inverted results generally diverge and sometimes converge to wrong results for some synthetic tests, and (3) using the iterative least squares method with initial values provided by the F-J method produces converged results without the dependence on MCMC sampling parameters and initial values, as expected thanks to absorbing the merits of the global optimality of the F-J method and efficiency of the iterative least squares method. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Vibration Engineering is the property of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The high erroneous results of the stereo matching occur at the following three cases where there are depth discontinuity region， the slanted surface or the non-fronto-parallel surface. A stereo matching algorithm was proposed based on the improved Patchmatch and slice sampling particle belief propagation. An edge-preserving similarity function of the Patchmatch was defined. Then， a model of the depth estimation for the non-fronto-parallel surface was introduced. The nearest neighbor search was replaced with the particle belief propagation， and the target distribution was approximated with a finite set of particles. At the same time， the sampled particles from the belief distribution was typically done by using slice sampling Markov chain Monte Carlo method to solve the particle update problem. The experiments on the Middlebury indicate that the mismatching at the depth discontinuity region can be reduced， and the match accuracy for the slanted surface and the non-fronto-parallel surface can be improved. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to implement the mobile robot simultaneous localization and mapping in unknown environments, this paper presented an improved particle filter algorithm. To decrease the large number of particles that needed in normal particle filter, it integrated the observed information of laser range finder into importance sampling. When severe particle impoverishment occured after resampling, the algorithm took Markov Chain Monte Carlo moving process to reduce particle impoverishment effect. It tested the method on a Pioneer3-DX robot with robot operating system. Actual experiment results show that the method can generate high-precision grid map in real time. [ABSTRACT FROM AUTHOR]

A novel differential evolution adaptive metropolis algorithm (DREAM) is presented, which combines the advantages of differential evolution algorithm and Markov chain Monte Carlo (MCMC) sampler. DREAM solves an important problem in MCMC, namely that of choosing an appropriate scale and orientation for the jumping distribution. Meanwhile, it can make a good trade-off between population diversity and convergence for differential evolution algorithm. Moreover, multi-objective DREAM is proposed based on the modified fitness assignment and external archive strategy, which is applied in parameter optimization of CMD-3PAR hydrologic model in the Min River Basin. The results show that DREAM is capable to infer the posterior distribution of model parameters, and multi-objective differential evolution adaptive metropolis (MODREAM) is capable to generate a lot of non-dominated solutions with wide and uniform distribution for decision-makers. [ABSTRACT FROM AUTHOR]

In order to cope with the performance bottleneck and the time complexity of particle filters algorithm for the blind separation of two MPSK mixture signals obtained by a single channel receiver, a novel algorithm based on Markov Chain Monte Carlo (MCMC) method is established which is based on the over-sampling signal model. By using over-sampling of the received signal, more information of waveform is utilized, so noise suppression is more effective. In the new algorithm the Gibbs sampling method is used to estimate the posterior probabilities of the symbols modulated in MPSK, so that the optimized Bayesian estimation can be obtained approximately. Furthermore, the unknown parameters in the signal model are iteratively estimated by the least Squares (LS) method. Theoretical analysis and the simulation results show the good performance and low complexity of the new algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a classification method of polarimetric SAR data by combining the Pauli polarization decomposition and multi-scale markov random field. We first created the features of polarimetric SAR data by doing Pauli polarization decomposition. Then, these features were decomposed by three-layer Haar wavelet decomposition. Finally, iterative classifications were carried out in a layer and between layers to produce classification result of the polarimetric data. The experimental results by using NASA / JPL data show that the proposed method have better performance both on keeping the contour of the features and describing the details of the features. [ABSTRACT FROM AUTHOR]

There are more flexibilities mixtures of multi-distribution models than a single-distribution model for complicated random variables. Gaussian mixture models, alpha-stable mixture models and Gamma mixture models are main hybrid probability distribution ones. Gaussian mixture distributions are fitter for some symmetric distribution random variables and alpha-stable mixture distributions for not only symmetric but also skewed, but more parameters and intricate estimating algorithms are its shortcoming. Pixel values of SAR images are non-negative and skewed distributions, so fit for using Gamma mixture distribution models. Simulation analysis and data tests show that Gamma mixture distribution models have a higher operation efficiency for pixel statistical distribution of SAR image pixel distributions. [ABSTRACT FROM AUTHOR]

This paper proposes a new panel quantile regression model with change points at unknown positions, which parameters are estimated by Bayesian inference and MCMC sampling. By an empirical study to explore structural changes in the high-frequency dependence from trading volume and market trend to IPO's initial return in China's growth enterprise market, it is found that: structural change points are near the market opening for higher quantiles of returns, and close to the median trading time for lower quantiles; the IPO's trading volume gives a greater impact on IPO's return before change points than after change points on listing date, while the volume of Shenzhen composite index contributes larger impact on IPO's return after change points than before change points; the separation of return and volume of IPOs is detected both at lower quantiles before the change points and at any quantile after the change points; the volume of Shenzhen composite index is positively correlated to IPO's return through all quantiles before or after change points, moreover, the correlation coefficients after change points are stronger than ones before change points, and the same is true at extreme quantiles than at median quantiles. [ABSTRACT FROM AUTHOR]

This paper presents an economically designed VSI control chart for short-run production. According to the different parameter/parameters that changes/change over time, VP (variable parameter) control charts can be classified into several different types, including VSI control chart. While bringing cost savings, VP control chart adds up to the complexity of the operation. VSI chart has been reckoned one of the most effective and efficient sort of control chart considering both cost savings and operational complexity. The crux of the economic design of control chart is the optimization of cost, but the impact of the occurrence time of the disturbance on cost has not been taken into account in the traditional economic design of control charts. In this paper, process state is re-divided and the influence of the time of the disturbance occurrence on process cost is taken into consideration. Employing the Markov chain method and the Bayesian theory, we construct a dynamic control chart of which the sampling interval changes with the update of the process information. Simulation results show that compared to prior VSI control chart, control chart designed in this paper is more cost-advantageous and thus of more practical value. [ABSTRACT FROM AUTHOR]

In manufacturing system, machine state constrains the production capacity and increases the manufacturing uncertainty. Customer differentiation, which provides different services to different customers, arises to cope with this uncertainty. This paper considered the optimal production and inventory allocation control of a multi-state production-inventory system with Poisson arrival and lost sales. The demand that can't be satisfied immediately is lost and incurs a lost sale cost, which varies from class to class. The production rate depends on the machine state. By formulating the system as a Markov decision process, we obtained that the structural properties of the optimal control policies under the expected total discounted cost criterion are similar with that under the average cost criterion. The optimal production rate policy is the threshold policy with the threshold level non-increasing in the machine state, that is, if it is optimal to produce, produce with highest rate. The optimal inventory allocation policy is the rationing policy with the rationing level non-increasing in the machine state. The demand with higher lost sale cost has lower rationing level. [ABSTRACT FROM AUTHOR]

Because the general Markov Chain Monte Carlo (MCMC) method is easily to get trapped in some fixed state especially in the case of high SNR condition or iteration process that leads to performance degradation, in this paper, an improved MCMC algorithm is presented. Determining the initial sample value based on MMSE principle, the Markov chain is convergent rapidly. Simulation results show that the mentioned algorithm can improve the BER performance and reduce computational complexity. [ABSTRACT FROM AUTHOR]

Covariance of proposal distribution and the acceptance rate of SCEM-UA algorithm are modified based on the principle of adaptive Metropolis. The modified SCEM-UA algorithm can effectively ensure the diversity of the population and capability of the global search capability as well as computational efficiency, avoiding the premature convergence. The efficiency and effectiveness of modified SCEM-UA algorithm for sampling the posterior distribution of model parameters is discussed based on the ease study of the Min River Basin. The results show that modified SCEM-UA algorithm is consistent, effective and efficient in inferring the parameter posterior distribution, and is much better than the original algorithm in the aspects of computation efficiency and accuracy. [ABSTRACT FROM AUTHOR]

The problems of stability and stabilization are investigated for a kind of discrete-time singular Markov jump systems with partly unknown transition probabilities. Including two special cases of completely known and completely unknown transition probabilities, the proposed systems with partly unknown transition probabilities are more practical. A sufficient condition for stochastic stability of open-loop discrete-time singular Markov jump systems is derived from the results of discrete-time Markov jump systems by employing linear matrix inequality technique. Moreover, a criterion for feedback stabilization of closed-loop systems is proposed in terms of a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the validity of the proposed results. [ABSTRACT FROM AUTHOR]

Objective: To compare results of different methods: in organizing HIV viral load (VL) data with missing values mechanism. Methods We used software SPSS 17.0 to simulate complete and missing data with different missing value mechanism from HIV viral loading data collected from MSM in 16 cities in China in 2013. Maximum Likelihood Methods Using the Expectation and Maximization Algorithm (EM), regressive method, mean imputation, delete method, and Markov Chain Monte Carlo (MCMC) were used to supplement missing data respectively. The results: of different methods were compared according to distribution characteristics, accuracy and precision. Results HIV VL data could not be transferred into a normal distribution. All the methods showed good results in iterating data which is Missing Completely at Random Mechanism (MCAR). For the other types of missing data, regressive and MCMC methods were used to keep the main characteristic of the original data. The means of iterating database with different methods were all close to the original one. The EM, regressive method, mean imputation, and delete method under-estimate VL while MCMC overestimates it. Conclusion: MCMC can be used as the main imputation method for HIV virus loading missing data. The iterated data can be used as a reference for mean HIV VL estimation among the investigated population.