Asynchronous control for two‐dimensional hidden Markovian jump systems with partly known mode observation conditional probabilities

This study focuses on the asynchronous control problem for two‐dimensional discrete‐time hidden Markovian jump systems where the mode observation conditional probability matrix is partly known. Considering the original system modes are invisible, the observed modes emitted from an observer serve as an alternative for stability analysis and controller design where a mode observation conditional probability matrix is constructed to characterize the emission between system modes and observed modes. Specially, only partly known information of the mode observation conditional probability matrix is accessible. With the introduction of the free‐connection weighting matrices, the asymptotic mean square stability criterion is firstly derived based on Lyapunov method. This introduction provides a further degree of relaxation and less conservatism is therefore achieved. Secondly, we present synthesis conditions for asynchronous state feedback controller design given in terms of a set of interconnected linear matrix inequalities. Moreover, cluster concept based on the partitions of observed modes is adopted which helps to decrease the number of controllers and simplify the design complexity. A numerical example, regarding the cases with and without clustering of the observed modes, is presented to illustrate the effectiveness of the proposed method.     

薄煤层中垂直裂缝地震响应特征模拟分析

通过定义一个简单的薄煤层模型,在不断增加煤层中垂直裂缝数量的条件下,利用基于分裂节点的有限元数值方法模拟了含有不同条数裂隙煤层的地震反射波场;通过与不含裂缝、各向同性薄煤层反射的多波波场对比分析发现:裂缝的存在会对煤层的反射产生影响,但裂缝密度低时影响微弱,裂缝密度增加到一定程度后会出现类似调谐作用的反射能量干涉加强;裂缝密度进一步升高,对煤层反射的影响主要反映在高频成分的增加,对振幅影响较弱,说明煤层的反射强度主要受薄层的干涉作用影响较大;从理论地震波场分析的角度证明了薄煤层含裂缝的不可忽视性及反演的可能性。     

基于熵加权K-means全局信息聚类的高光谱图像分类

目的 高光谱图像波段数目巨大，导致在解译及分类过程中出现“维数灾难”的现象。针对该问题，在K-means聚类算法基础上，考虑各个波段对不同聚类的重要程度，同时顾及类间信息，提出一种基于熵加权K-means全局信息聚类的高光谱图像分类算法。方法 首先，引入波段权重，用来刻画各个波段对不同聚类的重要程度，并定义熵信息测度表达该权重。其次，为避免局部最优聚类，引入类间距离测度实现全局最优聚类。最后，将上述两类测度引入K-means聚类目标函数，通过最小化目标函数得到最优分类结果。结果 为了验证提出的高光谱图像分类方法的有效性，对Salinas高光谱图像和Pavia University高光谱图像标准图中的地物类别根据其光谱反射率差异程度进行合并，将合并后的标准图作为新的标准分类图。分别采用本文算法和传统K-means算法对Salinas高光谱图像和Pavia University高光谱图像进行实验，并定性、定量地评价和分析了实验结果。对于图像中合并后的地物类别，光谱反射率差异程度大，从视觉上看，本文算法较传统K-means算法有更好的分类结果；从分类精度看，本文算法的总精度分别为92.20%和82.96%， K-means算法的总精度分别为83.39%和67.06%，较K-means算法增长8.81%和15.9%。结论 提出一种基于熵加权K-means全局信息聚类的高光谱图像分类算法，实验结果表明，本文算法对高光谱图像中具有不同光谱反射率差异程度的各类地物目标均能取得很好的分类结果。     

基于加权法的砾类土含水率检测

砾类土在工程建设中有着广泛的应用,其含水率是一项非常重要的指标,但现有测试方法耗时较长。以砂砾石料的砾类土为试验对象,基于加权法思想,将饱和面干含水率替代法和虚拟比重法进行结合,开展砾类土含水率快速检测方法的研究,并对现有的饱和面干含水率测定操作进行一定的改进和优化。采用多种数学方法(绝对误差、均方偏差平方和,相关曲线的相关系数和F显著性检验分析法)对加权法和烘干法的结果进行对比分析。通过试验统计数据分析,证实加权法与烘干法的测定结果基本一致。该分析结果为现场土料含水率快速测定提供了一种新的思路和手段。     

基于星座图对射频指纹识别方法的研究

针对星座图的射频(RF)指纹识别方案中,低信噪比环境下识别准确率低的问题,提出一种基于欧式距离与幅度距离的二维识别算法来进行RF指纹识别。该方案通过对星座图进行优化处理,可从优化后的星座图中提取识别性能更好的RF指纹,再通过二维识别算法来提高识别准确率。仿真结果表明:与仅用欧式距离作为判断依据的方法相比,所提出的二维识别算法的识别准确率最高可提升8%,在设备容量为50组的情况下识别准确率为77.8%,并且从优化后的星座图中所提取的RF指纹具有更好的唯一性和稳定性。     

常村煤矿聚能预裂爆破弱化顶板技术研究

为缩短常村煤矿S2采区S206综放面的初次来压步距,以邻近的S208综放面为工程背景,根据聚能爆破机理分析,研究出一种聚能装药结构,利用聚能爆破的特性来提高顶板预裂效果。再通过ANSYS/LS-DYNA数值模拟,从应力云图、裂隙半径及压力-时间历程分析直接装药和聚能装药两种方式爆破的预裂效果,得出聚能装药爆破具有聚能效应,预裂效果更佳,更能弱化顶板,保障初采期安全。最终得出S208综放面初次来压步距为15.2m,与S206综放面相比,缩短了2.4m,提高了初采采出率,具有良好的经济价值和应用价值。     

石蜡与石蜡/膨胀石墨熔化性能的实验研究

为改善相变储能过程中石蜡(PA)的熔化性能，向PA中添加少量膨胀石墨(EG)制备了4种配比的石蜡/膨胀石墨复合相变材料(PA-EG)。通过热物性分析筛选出合适配比的PA-EG，并对其和PA在水平管壳式相变储能单元中的熔化过程进行了实验研究。根据相变材料的温度场变化以及加权法计算得到的熔化分数变化，对比分析了添加EG前后PA的熔化性能，并探究了加热温度对相变材料熔化性能的影响。结果表明，PA-EG3的热导率比PA高了7倍，且两者的相变温度和潜热相差不大。PA-EG3熔化过程中的自然对流效应弱于PA，但是较高的热导率能够显著改善相变储能单元中下部的熔化，使得其整体熔化速度快于PA。当加热温度为80℃时，PA-EG3的熔化过程比PA缩短了78.16%。此外，降低加热温度会使PA和PA-EG3的完全熔化时间都显著增加，但相同条件下PA-EG3的增加幅度更小。     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.