On the identification of ship coupled heave–pitch motions using neural networks

This paper outlines a procedure for the derivation of the differential equations describing the free response of a heaving and pitching ship from its stationary response to random waves. The coupled heave–pitch motion of a ship in random seas is modelled as a multi-dimensional Markov process. The partial differential equation describing the transition probability density function, known as the Fokker-Planck equation, for this process is derived. The Fokker-Planck equation is used to derive the random decrement equations for the coupled heave–pitch motion. The parameters in these equations are then identified using a neural network approach. The method is validated using numerical simulations and experimental results. The experimental data was obtained using an icebreaker ship model heaving and pitching in random waves. It is shown that the method produces good results when the system is lightly damped. An extension for using this method to identify couple heave–pitch motion in realistic seas is suggested.     

基于局部采样MCMC方法的时移探地雷达反演

马尔科夫链蒙特卡洛方法(MCMC)是一种启发式的全局寻优算法,可以用来解决概率反演的问题.基于MCMC方法的反演不依赖于准确的初始模型,可以引入任意复杂的先验信息,通过对先验概率密度函数的采样来获得大量的后验概率分布样本,在寻找最优解的过程中可以跳出局部最优得到全局最优解.MCMC方法由于计算量巨大,应用难度较高,在地...     

一种基于高斯马尔可夫随机场的异常目标探测方法

利用高斯马尔可夫随机场模型描述像元的邻域相关性信息,并将这种邻域信息引入到局域异常探测器中,提出了一种顾及邻域信息的高光谱遥感影像局域异常目标探测算法。实验证明,该方法克服了传统异常探测方法仅仅利用光谱信息的不足,比经典的RX算法的探测效果更好,并且可以更有效地探测出大于一个像元的异常目标。     

Application of Markovian models for non-ergodic and non-stationary earthquake times series for the identification of seismic patterns and future projections*

The current earthquake forecast algorithms are not free of shortcomings due to inherent limitations. Especially, the requirement of stationarity in the evaluation of earthquake time series as a prerequisite, significantly limits the use of forecast algorithms to areas where stationary data is not available. Another shortcoming of forecast algorithms is the ergodicity assumption, which states that certain characteristics of seismicity are spatially invariant. In this study, a new earthquake forecast approach is introduced for the locations where stationary data are not available. For this purpose, the spatial activity rate density for each spatial unit is evaluated as a parameter of a Markov chain. The temporal pattern is identified by setting the states at certain spatial activity rate densities. By using the transition patterns between the states, 1- and 5-year forecasts were computed. The method is suggested as an alternative and complementary to the existing methods by proposing a solution to the issues of ergodicity and stationarity assumptions at the same time.     

基于Markov链的金川铜镍矿床超基性岩体侵位过程模拟及找矿启示

金川铜镍矿床是世界第三大铜镍硫化物岩浆矿床,现有研究表明其成矿模式为岩浆通道成矿,但是对于金川超基性岩体的侵位过程存在较大争议。为探索岩体的侵位过程,将岩浆侵位描述为马尔可夫(Markov)过程,提出一种基于Markov链的岩体侵位模拟算法,实现对金川超基性岩体侵位过程的模拟。以金川Ⅱ矿区为例,探讨了侵位过程与矿化的关联及岩浆通道骨架,为在矿床深部寻找第二成矿空间提供方向和线索。     

基于模糊分类的UBGM(1,1)-Markov模型在露天矿边坡沉降预测中的应用

针对UBGM(1,1)-Markov模型中存在2个邻近值可能被归属到不同状态,导致预测值产生偏差的问题,结合模糊分类理论,构建基于模糊分类的无偏灰色-马尔科夫模型(unbiased gray-Markov model based on fuzzy classification, FC-UBGM(1,1)-Markov)。首先对UBGM(1,1)模型进行残差修正,然后将修正后拟合值的相对残差序列作为Markov链进行区间划分,再结合模糊分类的隶属度函数,计算相对残差的模糊向量,根据隶属度确定其所属的状态。实际算例表明,该模型比传统UBGM(1,1)-Markov模型的预测效果更好。     

Analysis of the 2012–2013 Torreperogil-Sabiote seismic swarm

This study analyses the temporal clustering, spatial clustering, and statistics of the 2012–2013 Torreperogil-Sabiote (southern Spain) seismic swarm. During the swarm, more than 2200 events were located, mostly at depths of 2–5 km, with magnitude event up to m_bLg 3.9 (M_w 3.7). On the basis of daily activity rate, three main temporal phases are identified and analysed. The analysis combines different seismological relationships to improve our understanding of the physical processes related to the swarm's occurrence. Each temporal phase is characterized by its cumulative seismic moment. Using several different approaches, we estimate a catalog completeness magnitude of m_c≅ 1.5. The maximum likelihood b-value estimates for each swarm phase are 1.11 ± 0.09, 1.04 ± 0.04, and 0.90 ± 0.04, respectively. To test the hypothesis that a b-value decrease is a precursor to a large event, we study temporal variations in b-value using overlapping moving windows. A relationship can be inferred between change in b-value and the regime style of the rupture. b-values are indicators of the stress regime, and influence the size of ruptures. The fractal dimension D₂ is used to perform spatial analysis. Cumulative gamma and beta functions are used to analyse the behaviour of inter-event distances during the earthquake sequence.     

Linear interpolation and joint model fitting of experimental transiograms for Markov chain simulation of categorical spatial variables

In Markov chain random field (MCRF) simulation of categorical spatial variables with multiple classes, joint modeling of a large number of experimental auto and cross-transiograms is needed. This can be tedious when mathematical models are used to fit the complex features of experimental transiograms. Linear interpolation can be used to perform the joint modeling quickly regardless of the number and the complexity of experimental transiograms. In this paper, we demonstrated the mathematical validity of linear interpolation as a joint transiogram-modeling method, explored its applicability and limitations, and tested its effect on simulated results by case studies with comparison to the joint model-fitting method. Simulations of a five-class variable showed little difference in patterns for interpolated and fitted transiogram models when samples were sufficient and experimental transiograms were in regular shapes; however, they neither showed large difference between these two kinds of transiogram models when samples were relatively sparse, which might indicate that MCRFs were not much sensitive to the difference in the detail of the two kinds of transiogram models as long as their change trends were identical. If available, expert knowledge might play an important role in transiogram modeling when experimental transiograms could not reflect the real spatial variation of the categorical variable under study. An extra finding was that class enclosure feature (i.e., a class always appears within another class) was captured by the asymmetrical property of transiograms and further generated in simulated patterns, whereas this might not be achieved in conventional geostatistics. We conclude that (i) when samples are sufficient and experimental transiograms are reliable, linear interpolation is satisfactory and more efficient than model fitting; (ii) when samples are relatively sparse, choosing a suitable lag tolerance is necessary to obtain reliable experimental transiograms for linear interpolation; (iii) when samples are very sparse (or few) and experimental transiograms are erratic, coarse model fitting based on expert knowledge is recommended as a better choice whereas both linear interpolation and precise model fitting do not make sense anymore.     

Predicting human mobility with activity changes

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R²) derived from the data with and without implementing the activity change detection. The results show that the R² is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.     

基于权马尔可夫链模型的庐江县降水量预测

由于降水过程的随机性与不确定性,使得降水量预测存在一定的难度。对安徽省庐江县1952~2009年逐年降水资料进行了分析,采用样本-标准差分级法将这58年的逐年降水量序列分为枯水年,偏枯水年,平水年,偏丰水年,丰水年5个状态,采用权马尔可夫链模型预测了2010年的降水量,预测结果与实测结果相吻合。