Improving the prediction of extreme FPSO hawser tension,using another highly correlated hawser tension with a longer time record

The floating production storage and offloading unit (FPSO) is an offshore vessel that produces and stores crude oil prior to tanker transport. Robust prediction of extreme hawser tensions during the FPSO offloading operation is an important safety concern. Excessive hawser tension may occur during certain sea conditions, posing an operational risk. In this paper, the finite element method (FEM) software ANSYS AQWA has been employed to analyze vessel response due to hydrodynamic wave loads, acting on a specific FPSO vessel under actual sea conditions.In some practical situations, it would be useful to improve the accuracy of some statistical predictions based on a certain stochastic random process, given another synchronous highly correlated stochastic process that has been measured for a longer time, than the process of interest. In this paper, the issue of improving extreme value prediction has been addressed. In other words, an efficient transfer of information is necessary between two synchronous, highly correlated stochastic processes. Two such highly correlated FPSO hawser tension processes were simulated in order to test the efficiency of the proposed technique.     

Analysis of the extreme wave elevation due to second-order diffraction around a vertical cylinder

Statistical analysis of nonlinear random waves is important in coastal and ocean engineering. One approach for modeling nonlinear waves is second-order random wave theory, which involves sum- and difference-frequency interactions between wave components. The probability distribution of the non-Gaussian surface elevation can be solved using a technique developed by Kac and Siegert [21]. The wave field can be significantly modified by wave diffraction due to a structure, and the nonlinear diffracted wave elevation can be of interest in certain applications, such as the airgap prediction for an offshore structure. This paper investigates the wave statistics due to second-order diffraction, motivated by the scarcity of prior research. The crossing rate approach is used to evaluate the extreme wave elevation over a specified duration. The application is a bottom-supported cylindrical structure, for which semi-analytical solutions for the second-order transfer functions are available. A new efficient statistical method is developed to allow the distribution of the diffracted wave elevation to be obtained exactly, accounting for the statistical dependency between the linear, sum-frequency and difference-frequency components. Moreover, refinements are proposed to improve the efficiency for computing the free surface integral. The case study yields insights into the problem. In particular, the second-order nonlinearity is found to significantly amplify the extreme wave elevation, especially in the upstream region; conversely, the extreme elevation at an oblique location downstream is attenuated due to sheltering effects. The statistical dependency between the linear and sum-frequency components is also shown to be important for the extreme wave statistics.     

A stochastic method for the prediction of icebreaker bow extreme stresses

It is well established that the ship-ice interaction process is quite complex and associated ice loads on the icebreaker hull is a stochastic process. Obviously, novel accurate statistical methods and models should be developed and applied to estimate extreme bow stresses.This paper studies icebreaker bow stresses based on measured distribution of ice thickness in the Arctic Ocean on the way to and from the North Pole. Since the vessel route was carefully selected searching for easier ice conditions, the Arctic Ocean crossing was not a straight linear but a meandering path. Thus, the specific ship route data was biased with respect to general ice statistics in the region, but true with respect to the route specific ice data encountered by a ship navigating in that region. Therefore the route specific ice thickness data is directly needed for ship design and navigation analysis. It is assumed that captains are competent and knowledgeable, and therefore will select a route that provides the most favourable ice conditions.This paper contributes to study of the newest Chinese self-designed polar icebreaker, serving the purpose of enhancing icebreaker operational reliability. Finite Element Method software package ANSYS/LS-DYNA has been employed to simulate bow stress pattern for a particular icebreaker operating in the Arctic Ocean. Extreme bow stresses were estimated using Naess-Gaidai method. The latter is a first application of Naess-Gaidai method to a distribution with lower bound. Thus this paper aims at introducing an efficient method of estimating route-specific icebreaker extreme bow stresses.     

海洋环境因子对澳洲鲐亲体补充量关系的影响——基于贝叶斯模型平均法的研究

澳洲鲐（Scomber australasicus）是西北太平洋重要的中上层经济鱼类,生命周期相对较短,资源量受补充量影响明显,了解澳洲鲐太平洋群系补充量状况对掌握其资源量及确保其可持续利用具有重要的意义。本文利用产卵场1（30°~32°N,130°~132°E）海表面温度（sea surface temperature,SST1）、产卵场2（34°~35°N,138°~141°E）海表面温度（SST2）、索饵场（35°~45°N,140°~160°E）海表面温度（SST3）、潮位差（tidal range,TR）、太平洋年代际涛动（Pacific decadal oscillation,PDO）和亲体量（spawning stock biomass,SSB）6个影响因子任意组合与补充量构建多个模型,运用贝叶斯模型平均法（Bayesian model averaging,BMA）分析各个环境因子对资源补充量的解释能力,并预测其补充量的变化。结果表明,SSB对补充量具有最长期且稳定的解释能力,其次是SST3,PDO、TR、SST2、SST1也对补充量模型具有一定的解释能力。SST3是环境因子中影响最大的因子,可能是由于补充群体在索饵场内生活时间较长,索饵场温度对仔鱼或鱼卵的生长存活有较大的影响。研究认为,基于BMA的组合预报综合考虑了各个模型的优势,优于单一模型,可用于澳洲鲐资源补充量的预测。     

一种海洋混合层深度的智能识别方法研究

文章提出了一种识别混合层深度的人工智能方法。该方法在温度(密度)与压强(或深度)间建立线性模型, 并且将其系数和方差做成一组表征廓线特征的统计量。初始时为模型设定一个主观的先验分布, 在一个自海表向下移动的窗口内通过贝叶斯链式法则和最小描述长度原理学习新数据, 得到系数均值的最大后验概率估计。用F-检验识别系数发生突变的位置, 以此确定混合层的存在性及其深度。通过2017年2月太平洋海域的地转海洋学实时观测阵(Array for Real-time Geostrophic Oceanography, ARGO)数据进行测试, 并且以质量因子(Quality Index, QI)值作为判断识别混合层深度结果准确性的依据, 发现该方法相比于梯度法、阈值法、混合法、相对变化法、最大角度法和最优线性插值法在识别结果上具备更大的QI值。表明该方法能够准确识别混合层深度。     

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.     

近12年北京暖季对流天气的气候特征

对北京地区1994～2005年暖季(5～9月)雷暴、冰雹、暴雨和大风等各种对流天气进行了气候统计和分析。统计结果表明:北京地区暖季发生对流的概率很高,按日数统计的气候概率达47.77%,有雷暴相伴的强对流天气大风、暴雨和冰雹气候概率分别为27.29%、10.84%和6.29%。另外,北京地区对流天气一般可连续出现3 d,强对流天气也可连续出现2 d。北京地区对流季节长达4个月,其中6、7、8月为主要的对流月,这三个月中雷暴发生的气候概率均超过50%。暴雨多发季节为7月中旬到8月上旬。冰雹集中于6月中、下旬。在对流天气的地理分布上,北京西北部、东北部山区及西南部山区多对流天气,中心区和东南部平原地区对流天气较少。暴雨呈西南-东北方向带状分布,东北部山区、中部和东南部平原地区多发生暴雨,而西北部和西南部山区很少发生暴雨。山区冰雹明显多于平原。西北部和东北部山区大风偏多,西南部霞云岭大风最少。暴雨有明显的夜发性,即夜间次数多,降水量更大。冰雹集中发生在午后到傍晚,占冰雹总站次的76.72%。夜间发生冰雹的概率非常小,上午到中午也不多。     

香坊老工业区搬迁改造监测分析

利用地理信息技术及遥感监测方法,监测香坊老工业区搬迁改造试点进展情况,主要针对搬迁改造过程中地表覆盖、用地性质、空气质量变化情况,开展综合统计分析,客观评价香坊老工业搬迁改造实施成效。     

中国沿海小黄鱼种群动力学及其空间异质性研究

小黄鱼是我国近海四种最重要的经济鱼类之一,在过去的几十年中小黄鱼种群及其两个地理亚种群经历了巨大的变化。小黄鱼的种群动力学研究,对于维持这一重要渔业的可持续管理是至关重要的。目前仅有的两个小黄鱼种群动力学模型只涵盖了较短的时间,且没有关注其空间差异。本文研究了1968年至2015年间黄渤海和东海小黄鱼的种群动力学模型,包含和不包含空间分层结构的两种贝叶斯模型被用于分析其种群动力学的大尺度空间异质性。本文分析了不同的假设,来研究小黄鱼种群动力学潜在的变化趋势。研究结果表明小黄鱼种群动力学特征具有明显的时间和空间变化。种群的增长速度从20世纪八十年代开始增加,而可捕系数从1981年到2015年增加了两倍多。与黄渤海亚种群相比,东海的小黄鱼亚种群生长速度更快,遭受的捕捞压力也更大。基于最大可持续产量MSY的参考点表明,无论是整个小黄鱼种群还是两个亚种群,近年来都有非常高的过度捕捞风险。因此我国小黄鱼的渔业管理急需更加保守的管理策略,同时考虑其地域差异。本文所用的方法可以应用于其他种类的资源评估和渔业管理,尤其是具有空间异质性和数据有限的种类。     

Photographic photometry for stellar statistics with Tautenburg Schmidt telescope

Photometric properties of deep Tautenburg Schmidt plates in the UBV system over the whole field and over a wide range of stellar magnitudes are discussed. Different reduction models based on the calibration curve alone and with additional terms containing colour and plate coordinates are considered. With the filter-emulsion combinations used the Johnson UBV system can be reproduced quite well by the Tautenburg telescope. The accuracy of photometric data can be improved if geometrical terms are taken into account. For deep plates with a significant effect of background nebulosity a supplementary term is to be included in the reduction model. The polynomial coefficients vary in dependence on spectral bands and individual properties of each plate.