Econometric analysis to differentiate effects of various ship safety inspections

This article provides a refined technique to measure and interpret variables associated with the quality of an inspection—be it port state control or vetting inspections towards the probability of a very serious, serious or less serious casualty. It concentrates on filtering out the effect of variables such as detention, the port state control regime that inspected the vessel, time in-between inspections, deficiencies found during an inspection and the effect of vetting inspections. The consensus amongst policy makers in the shipping industry is that data cannot be combined to target vessels. While this article does demonstrate that the decrease in the probability of casualty is stronger for the South American Region, the Indian Ocean Region and Australia versus North Europe, North America or the Caribbean, it also demonstrates that the data can be combined to target vessels for inspections. Since the time in-between inspections and detention is mostly not significant towards decreasing the probability of casualty, these results reflect the lack of coordination amongst port state control regimes and industry inspections. Due to this lack of coordination and trust, a ship might be inspected in several regimes during a relative short time period where the benefit of an inspection can be easily saturated. Our recommendation on direct policy implication is to promote the harmonization of inspection databases across port state control regimes, preferably with the coordination of the development of the Global Integrated Ship Information System (GISIS) of the International Maritime Organization (IMO), to review the policy of a release of a vessel from detention and to increase cooperation amongst regimes with respect to the follow up of the rectification of deficiencies.     

几种非线性滤波算法的性能比较与分析

在非线性状态估计中,传统的扩展卡尔曼滤波通过线性化来实现高斯近似,由于截断误差的存在很难保证估计精度;而基本粒子滤波容易出现粒子退化,导致滤波发散。针对粒子滤波的两个基本假设：蒙特卡罗假设和重要采样假设,采用蒙特卡罗随机链的方法来提高粒子的多样性,并利用无味卡尔曼滤波来产生更高精度的替代分布,发展了无味粒子滤波。通过仿真实验证明,相比较扩展卡尔曼滤波和基本粒子滤波,改进后的无味粒子滤波算法性能更优越,对含有非线性非高斯的状态估计问题有更好的滤波效果。     

多尺度带乘性噪声系统的最优状态平滑算法

针对多尺度带乘性噪声系统,在多尺度最优滤波融合的基础上,进行状态最优固定域平滑算法的研究.通过推广得到的平滑算法需要大量的局部传感器参数,而分布式多尺度滤波融合后不能保留这些信息.针对这一弊端对算法进行改进,推导出仅使用融合后的一步预测及滤波值的平滑算法.该算法在线性最小方差意义下是最优的.计算机仿真验证了算法的可行性.     

带乘性噪声广义系统状态最优估计算法

针对带乘性噪声广义系统,提出1种在线性最小方差意义下的状态最优估计算法.首先,采用标准分解将系统变换为2个子系统;其次,通过估计子系统的状态,获得原系统的状态最优滤波.同时,算法还给出了动态噪声与量测噪声的估计.仿真结果表明了该算法的有效性.     

基于Gabor变换的嵌入式气表自动识别系统

介绍一种基于嵌入式操作系统WinCE开发的天然气表字符自动识别系统。系统运用区域增长法和最大类间方差法对图像进行字符区域定位及字符分割,提出了将Gabor特征用于气表字符识别并讨论了Gabor滤波器参数优化设置方法。实验表明系统具有良好的识别效果及实时性。     

复杂工程系统优化设计初探--以某泥石流防治工程系统的优化设计为例

以某泥石流防治工程系统的优化设计为例 ,建立了优化模型 ,分析确定了模型参数 ,并利用基于随机模拟的遗传算法对模型进行了求解。对其他泥石流防治工程系统的优化设计具有重要指导意义 ,对复杂工程系统的优化设计也具有普遍的参考价值     

捷联式海洋重力数据的小波阈值滤波

基于捷联式海洋重力测量数据,对比不同小波、阈值及施加方式对滤波效果的影响。结果表明,db6、db7、db8、db9、db10、sym6、sym7、sym8、sym9、sym10、coif3、coif4和coif5小波较适用于海洋重力测量,小波分解层次可取至8层或9层,采用史坦无偏风险阈值滤波效果较好;小波分解至第8、9层时,阈值滤波结果与截止频率为0.005 Hz和0.003 3 Hz的Butterworth低通滤波器的滤波结果吻合较好,但小波阈值滤波结果更加平滑,两者差值的RMSE在0.25 mGal以内,且小波阈值滤波更容易分解出噪声成分,可以更有效地消除重力畸变。     

Locality Attributes and Networks Serving to Reveal Amazonian Urbanization beyond the Cities

Beyond the Amazonian cities, distinct spatial units arranged in networks compose an extended urban space, usually invisible to government agencies and public polities. This article empirically reveals this space by studying the localities’ characteristics and connections. In southwestern Pará, Brazil, 236 communities were sampled using field expeditions. Variables related to the locality organization, history, health and education services, urban infrastructure, and land use were explored in a fuzzy cluster analysis. Local production and consumption flows were considered in the locality network analysis. The fuzzy analysis allowed identifying a hierarchy of localities for which the geographical location influences the spatial distribution patterns of five groups. On the other hand, consumption and production networks identified different locality's articulations, with persistent dependence on cities. These results characterize extensive urbanization at the local level and highlights the participation of the local populations configuring the Amazonian territory.     

Temporal and Spatial Dynamics Analysis of Grassland Ecosystem Pressure in Kazakhstan

Affected by climate change and policy factors, Kazakhstan is the country with the most severe ecological degradation and grassland conflicts in Central Asia. Therefore, studying the state of grassland carrying resources in Kazakhstan is particularly important for understanding the responses of grassland ecosystems to climate change and human activities. Based on Kazakhstan's remote sensing data and animal husbandry statistics, this study analyzes the patterns of changes in grassland ecosystems in Kazakhstan based on the supply and consumption of these ecosystems. The results show that: 1) From 2003 to 2017, the number of livestock raised in Kazakhstan showed a trend of sustained and steady growth. Due to freezing damage, the scale of livestock farming decreased in 2011, but a spatial difference in the livestock farming structure was not obvious. 2) The fluctuation of grassland supply in Kazakhstan has increased, while the consumption due to animal husbandry has also continued to increase, resulting in an increasing pressure on the grassland carrying capacity. 3) Between 2003 and 2017, the overall grassland carrying status of Kazakhstan have been abundant, but the grassland carrying pressure index has shown a steadily increasing trend, the grassland carrying pressure is growing, and it is mainly determined by grassland productivity. The greater pressure in lower Kyzylorda state, the southern Kazakhstan state of the cultivated land and the northern Kazakhstan state has gradually expanded to include the agro-pastoral zone and the semi-desert zone.     

Robust adaptive unscented Kalman filter for bearings-only tracking in three dimensional case

This paper proposes an improved version of Unscented Kalman Filter (UKF), namely Robust Adaptive UKF (RAUKF), with a special focus on Bearings-Only Target Tracking for three-dimensional case (3DBOT). The automatic tuning of the noise covariance matrices and the robust estimation of the target states form a critical point for the performance of the Kalman-type filtering algorithms, especially in the variable environmental conditions exposed in underwater. The key idea of the proposed filter is to combine robust aspects of UKF and adaption of the process and measurement noise covariance matrices with low computational complexity. The main contribution of this paper is to adjust these matrices by means of the steepest descent algorithm, and the H_∞ technique is embedded to achieve superior performance in terms of accuracy and robustness against initial conditions and model uncertainties. Different experiments are performed to evaluate the performance of the proposed algorithm in the 3DBOT problem with a single moving observer. Simulations demonstrate that the proposed filter produce more accurate results with satisfactory computational burden in comparison with other methods.