基于深度卷积嵌入式聚类（DCEC）的海洋环境特征提取对渔情预报模型的改进研究

In order to improve the forecasting ability of the fishery forecast model for the longline bigeye tuna (Thunnus obesus), we proposed a marine environment feature extraction method based on deep convolutional embedded clustering (DCEC), combined with generalized additive model (GAM) for forecasting the longline bigeye tuna fishing grounds in the Southwest Indian Ocean. We used the MODIS-Aqua and MODIS-Terra sea surface temperature (SST) three-level inversion image data (in days) from January to December in 2018 at 0.041 6°×0.041 6° to construct a DCEC model, determined the optimal number of clusters based on the Davies-Bouldi index (DBI), and extracted the category feature value (F_M) of each month’s sea surface temperature (SST); we used monthly 1°×1° bigeye tuna longline fishery data from January to December in 2018 generated from the Indian Ocean Tuna Commission (IOTC), and calculated the catch per unit effort (CPUE); we matched the monthly category feature value F_M and the monthly average value of Chl a concentration with the CPUE data to construct an improved GAM; we matched the monthly average SST, the monthly average Chl a concentration and CPUE data to build a basic GAM; we used the joint hypothesis test (F test) to verify the influence of model explanatory variables; we used akaike information criterion (AIC), mean square error (MSE), and draw the frequency distribution diagrams and box diagrams of measured and predicted values, etc., to analysis the improvement effect of the improved GAM compared to the basic GAM. The results showed that: (1) the category feature value (F_M) extracted based on the DCEC model could better reflect the temporal and spatial dynamic characteristics of SST in the Southwest Indian Ocean, and was related with the climatic conditions, monsoon conditions, and hydrological characteristics in the Southwest Indian Ocean; (2) the factor interpretation of F_M was higher than that of the monthly average SST in GAM, which means F_M had more significant impact on the CPUE of bigeye tuna. The high catch rate was concentrated in the areas where the F_M category was 2, 10, 24 with intersections between the warm and cold currents; (3) the AIC of the improved GAM was reduced by 9.17% than that of the basic GAM and MSE of the improved GAM was reduced by 26.7% than that of the basic GAM; the frequency distribution of the CPUE logarithmic value predicted by the improved GAM was closer to the normal distribution, and the high frequency distribution interval was closer to that of the measured value; the scatter plot showed that the CPUE predicted by the improved GAM had a significant correlation with the measured CPUE, with r equaled to 0.60. This study proves the effectiveness of the DCEC model in extracting marine environmental features, and can provide a reference for the further study on the bigeye tuna fishery forecast.     

基于GAM和GLM模型的西北太平洋秋刀鱼CPUE标准化比较研究

秋刀鱼（Cololabis saira）是中国在西北太平洋海域的重要的捕捞对象之一,单位捕捞努力量渔获量（CPUE） 标准化是开展其资源评估研究的重要内容,许多统计模型被运用到CPUE标准化研究中。本文根据2003-2017年中国大陆在西北太平洋海域的秋刀鱼生产统计资料,结合卫星遥感获得的海洋环境数据如：海表面温度、海表面高度以及海温梯度等,基于广义线性模型（general linear model,GLM） 和广义加性模型（generalized additive model,GAM） 对中国大陆西北太平洋秋刀鱼渔业进行CPUE 标准化,并对两种模型的结果进行了对比分析研究。通过贝叶斯信息准则选择最佳GLM和GAM模型,使用解释偏差和5-fold交差验证来对比两个模型结果。GLM模型的最佳模型对CPUE偏差的解释率为21.57%,GAM的最佳模型对CPUE偏差的解释率为38.95%。通过5-fold交差验证分析发现,GAM模型标准化结果较优于GLM模型,因此,认为GAM模型更适合于西北太平洋秋刀鱼渔业CPUE标准化。     

Spatial considerations when monitoring reef fishes

Spatial dependence can obscure relationships between response and explanatory variables because of structuring within the residuals reducing variance and biasing coefficient estimates. Here, we highlight the influence of the spatial component, in the presence of spatial dependence, on abundance trends. This is illustrated using abundance data for a Critically Endangered reef fish, dageraad Chrysoblephus cristiceps, which were obtained from a long-term monitoring programme in the Tsitsikamma National Park marine protected area, South Africa. Correlograms illustrate distinct spatial structuring in the abundance data, and spatial variables were determined as more important than temporal variables when ranked according to predictive power using a random forest analysis. A generalised additive model (GAM) that did not account for spatial dependencies was compared to a generalised additive mixed model (GAMM) that incorporated a spatial residual correlation structure. Results derived from the spatially explicit GAMM differed considerably from the GAM lacking a spatial component, with the latter deemed to produce over-precise and partially biased abundance trends. The study emphasises the importance of space in accurately modelling abundance estimates, particularly temporal trends, and provides an introduction to the minimal statistical requirements necessary to address the violations associated with spatial autocorrelation.     

基于多源信息融合的浅海超低频声源目标探测关键技术及实现

以多传感器信息融合理论为指导,结合现代信息处理技术与数据驱动建模及科学计算技术,研究浅海超低频声源目标激发共存地震波的复合声场中超低频声波传播特性和数据驱动建模的水下目标深度识别等关键技术,并论证了技术实现方案及路径。结果表明:以复合矢量水听器、地震波监测仪等多传感器信息融合理论为指导研究前海超低频声源目标可以克服传统声场建模存在的问题,有助于浅海超低频声源目标探测及改善海洋水下声学监测手段。在提高声呐探测设备的测量准确度、精度方面具有重要的理论意义,对周边海域为浅海的我国海防具有实战价值。     

Projecting distributions of Argentine shortfin squid(Illex argentinus) in the Southwest Atlantic using a complex integrated model

We developed an approach that integrates generalized additive model(GAM) and neural network model(NNM)for projecting the distribution of Argentine shortfin squid(Illex argentinus). The data for this paper was based on commercial fishery data and relevant remote sensing environmental data including sea surface temperature(SST), sea surface height(SSH) and chlorophyll a(Chl a) from January to June during 2003 to 2011. The GAM was used to identify the significant oceanographic variables and establish their relationships with the fishery catch per unit effort(CPUE). The NNM with the GAM identified significant variables as input vectors was used for predicting spatial distribution of CPUE. The GAM was found to explain 53.8% variances for CPUE. The spatial variables(longitude and latitude) and environmental variables(SST, SSH and Chl a) were significant. The CPUE had nonlinear relationship with SST and SSH but a linear relationship with Chl a. The NNM was found to be effective and robust in the projection with low mean square errors(MSE) and average relative variances(ARV).The integrated approach can predict the spatial distribution and explain the migration pattern of Illex argentinus in the Southwest Atlantic Ocean.     

Modelling ocean waves in ice-covered seas

Over the past decade there has been a rapid growth of interest in wave propagation through ice covers. This paper summarizes the author’s observation of the modeling efforts on this topic. Models can be theory-based, data-driven, or a combination of the two. A pure data-driven model relies on a large amount of observations and is only becoming available recently. Theory-based models on the other hand have a long history. They are always a simplified version of the reality. As our knowledge grows, theories become more complicated. A theory for waves-in-ice that captures all possible processes does not exist. However, when integrated with observation through calibration, these combined theory + data-based models may be used with some confidence. In this paper, different models, their basic concepts, their calibration and validation are discussed. The present theory-based models do not have the correct spectral attenuation trend as observed from field or laboratory experiments. Hence, through calibration they may fit different parts of the wave spectra but not all. Pure data-driven models can reproduce the correct trend, but its dependability outside the situation where the data are collected is uncertain. In addition to offering tools to forecast waves-in-ice, these model building and validating efforts point to missing mechanisms that should be carefully studied. Despite the many challenges towards building a satisfactory general waves-in-ice model, significant progress has been made for models that work reasonably well in the marginal ice zone. We anticipate much more data will become available in the coming years to help us improve the existing models.     

北太平洋柔鱼不同群体耳石日增量对海洋环境的响应研究

为了研究北太平洋柔鱼(Ommastrephes bartramii)索饵场不同群体耳石日增量与环境之间的关系,采用梯度森林法和广义加性模型对2010-2016年在北太平洋采集的柔鱼进行了耳石日增量与海洋环境间的关系的分析.结果表明,柔鱼生命周期大约为1 a,秋生群体柔鱼个体的日龄范围为165～345 d,冬春生群体柔鱼...     

Coastal risk adaptation: the potential role of accessible geospatial Big Data

Increasing numbers of people are living in and using coastal areas. Combined with the presence of pervasive coastal threats, such as flooding and erosion, this is having widespread impacts on coastal populations, infrastructure and ecosystems. For the right adaptive strategies to be adopted, and planning decisions to be made, rigorous evaluation of the available options is required. This evaluation hinges on the availability and use of suitable datasets. For knowledge to be derived from coastal datasets, such data needs to be combined and analysed in an effective manner. This paper reviews a wide range of literature relating to data-driven approaches to coastal risk evaluation, revealing how limitations have been imposed on many of these methods, due to restrictions in computing power and access to data. The rapidly emerging field of ‘Big Data’ can help overcome many of these hurdles. ‘Big Data’ involves powerful computer infrastructures, enabling storage, processing and real-time analysis of large volumes and varieties of data, in a fast and reliable manner. Through consideration of examples of how ‘Big Data’ technologies are being applied to fields related to coastal risk, it becomes apparent that geospatial Big Data solutions hold clear potential to improve the process of risk based decision making on the coast. ‘Big Data’ does not provide a stand-alone solution to the issues and gaps outlined in this paper, yet these technological methods hold the potential to optimise data-driven approaches, enabling robust risk profiles to be generated for coastal regions.     

Modeling of the nonlinear drift oscillations of moored vessels subject to non-Gaussian random sea-wave excitation

A quadratic system model based on Volterra series representation is utilized to model the nonlinear response of moored vessels subjected to random seas. The key idea is to represent the relationship between the incident sea wave (input) and corresponding sway response of the moored vessel (output) with a parallel combination of linear and quadratic transfer functions, and to estimate them by processing actual input and output data. Compared to previous approaches, we take the important step of removing the restriction that the random input must possess Gaussian statistics. The feasibility and validity of the approach is demonstrated by analyzing experimental data taken in model basin tests. We also describe some of the deleterious consequences of assuming Gaussian sea-wave excitation when in fact the excitation is non-Gaussian.     

基于复合模型的西南大西洋阿根廷滑柔鱼（Illex argentinus）时空分布研究

本文利用2003-2011年西南大西洋阿根廷滑柔鱼渔业数据和海洋环境数据,包括海表温度（sea surface temperature, SST）,海面高度（sea surface height, SSH）和叶绿素浓度（chlorophyll a, Chl a）,开发基于广义加性模型（GAM）和神经网络模型（NNM）的复合模型研究滑柔鱼资源时空分布。GAM用于选择关键影响因子,并分析与单位捕捞努力量渔获量（catch per unit effort, CPUE）的关系,NNM用于建立关键影响因子与CPUE之间的预报模型。结果表明：GAM选择的影响因子的偏差解释率为53.8%,空间变量（经度和纬度）,环境变量（SST、SSH、Chl a）均匀CPUE之间存在显著相关性。CPUE与SST和SSH之间为非线性关系,与Chl a之间为线性关系。NNM模型的MSE和ARV较低,其精度高且稳定。此复合模型也能够解释解释西南大西洋阿根廷滑柔鱼时空变化趋势和迁徙模式。     

Multiple criteria optimization applied to high speed catamaran preliminary design

The demand for high-speed craft (mainly catamarans) used as passenger vessel has increased significantly in the recent years. Looking towards the future and trying to respond to the increasing requirement, high-speed crafts international market is passing through deep changes. Different types of high-speed crafts are being used for passenger transport. However, catamarans and monohulls have been the main choice not only for passenger vessel but also as ferryboat.Generally speaking, the efficient hydrodynamic hull shapes, engine improvements, and lighter hull structures using aluminum and composite materials make possible the increase in cruising speed.The high demand for catamarans are due to its proven performance in calm waters, large deck area compared to monohull crafts and higher speed efficiency using less power. Although the advantages aforementioned, the performance of catamaran vessels in wave conditions still needs to be improved.The high-speed crafts (HSC) market is demanding different HSC designs and a wide range of dimensions focusing on lower resistance and power for higher speed. Therefore, the hull resistance optimization is a key element for a high-speed hull success.In addition to that, trade-off high-speed catamaran (HSCat) design has been improved to achieve main characteristics and hull geometry. This paper presents a contribution to HSCat preliminary design phase. The HSCat preliminary design problem is raised and one solution is attained by multiple criteria optimization technique.The mathematical model was developed considering: hull arrangement (area and volume), lightweight material application (aluminum hull), hull resistance evaluation (using a slender body theory), as well as wave interference effect between hulls, calculated with 3D theory application. Goal programming optimization system was applied to solve the HSCat preliminary design.Finally this paper includes an illustrative example showing the mathematical model and the optimization solution. An HSCat passenger inland transport in Amazon area preliminary design was used as case study. The problem is presented, the main constrains analyzed and the optimum solution shown. Trade off graphs was also included to highlight the mathematical model convergence process.     

Large-scale offshore sand extraction: What could be the results of interaction between model and decision process?

The Netherlands is considering large-scale offshore sand extraction to meet the increasing demand for building sand, as the current supply of sand from land is insufficient. To develop a well-considered management policy to address this problem, knowledge about future morphological changes offshore caused by such an extraction is necessary. Such knowledge is not yet available. To support decisions about large-scale sand extraction, we developed a morphological model, which indicates possible effects of such extraction. However, because no field data is available, we cannot meet the requirement of decision makers to validate this model. Therefore, its results are controversial and difficult to use in decision-making. In this study, firstly we evaluate whether validation of the model would help the decision-making process about large-scale sand extraction? Secondly, we explore how we can use the invalidated model results in decision-making. And finally, we explore how to improve both the model and the use of the model without validation.Our opinion is that validation of the model will not solve the problem that decision makers deal with, and that although invalidated, decision makers can use the model results by using them as early warning signals. Interviews with the key players, to define the willingness to use the model results for decision-making, lead to useful recommendations to improve the model. These interviews were the first step of constructive technology assessment (CTA), which focuses on broadening the design and implementation process to stimulate the integration of societal criteria in the development of the model. Besides, these interviews appeared to have a positive influence on the willingness of the key players to use the model for decision-making. In general, we conclude that CTA, modulating the interaction between model and decision process, is a useful method for model makers that can help to make their models useful tools for decision-making.     

基于GAM和GWR模型分析环境因子对鱼类分布的影响

多鳞鱚（Sillago sihama）是山东近海重要的渔业种类之一。本研究根据2016年秋季（10月）在山东近海开展渔业资源底拖网调查取得的数据,分析该海域多鳞鱚的空间分布特征,并运用广义可加模型（GAM）和地理加权回归（GWR）模型探究影响其分布的因素及其与环境因子的非线性和空间非平稳性关系。GAM拟合结果显示,影响秋季多鳞鱚分布的环境因子主要有水深、底层水温和底层盐度,水深的偏差解释率最大,为23.50%。GWR模型拟合结果显示,多鳞鱚分布与水深和底层水温之间存在空间非平稳性关系。水深与多鳞鱚相对资源量呈负相关关系,底层水温与多鳞鱚相对资源量呈正相关关系。赤池信息准则和决定系数（R2）指标对比结果显示,GWR模型的表现优于GAM,在渔业生态数据分析中表现出较好的发展潜力。本研究为今后开展渔业生物空间分布提供了一种新的方法。     

新型双船起重拆除平台试验研究

拆除大型海上结构物是一项非常复杂且充满挑战的工程。现提出一种新型海上平台拆除方案,该方案利用三条半潜船来代替具有重型起重装置的单一船舶,通过两艘相同的半潜船将平台上层建筑托起,随后运至第三艘半潜船上完成平台拆除。在风浪流作用下,半潜船和平台的所处方位和运动姿态一直处于动态变化中,这对船舶调节压载的稳定性以及双船运动的同步性提出了要求。为了确保该方案的高效性和安全性,设计了一套完整的模型试验装置,包括船舶模型、平台拆卸辅助设备、六自由度运动采集与分析系统、控制系统、无线通讯系统以及测量系统。开展了相应的水池试验,试验结果验证了双船起重拆除平台方案中双船协同运输这一关键环节的可行性和安全性。     

鲐鱼灯光围网渔船水上集鱼灯水中照度分布及优化配置的理论计算

采用叠加法照度计算模式,根据一组鲐鱼灯光围网渔船中3艘不同渔船的水上集鱼灯布置参数,使用自行开发的水上集鱼灯水下光场计算系统,对各船的水中照度分布进行了计算,获得船舷右侧中部水深方向200 m范围内不同深度的照度,并使用Surfer 8.0软件绘制等值曲线图,结果表明:主灯船在总功率为180 kW的情况下,10 lx的等照度曲线水平方向最远在56 m左右,水深方向不超过20 m;离船水平距离40 m、垂直距离40 m处的照度约为0.01 lx。副灯船在总功率为120 kW的情况下,10 lx的等照度曲线水平方向最远在46 m左右,水深方向不超过18 m;离船水平距离33 m、垂直距离38 m处的照度约为0.01 lx。网船在总功率为40 kW的情况下,10 lx的等照度曲线水平方向最远在45 m左右,水深方向不超过15 m;离船水平距离30 m、垂直距离35 m处的照度为0.01 lx左右。从集鱼灯的配置情况来看,主灯船、副灯船与网船目前的配置均较好,但主灯船在灯距增大到0.26 m、灯高增大到5 m时可增加1.6%的有效水体体积,副灯船在灯距减少到0.28 m、灯高增大到5 m时可增加2.1%的有效水体体积,网船在灯距增大到0.64 m、灯高增大到9 m时可增加1.7%的有效水体体积。计算结果还表明,大幅度的增加光诱渔船的集鱼灯功率并不能很有效的提高该船的光诱范围。     

基于优化模型的莱州湾三疣梭子蟹(Portunus trituberculatus)栖息地适宜性研究

三疣梭子蟹(Portunus trituberculatus)是莱州湾最重要的经济蟹类,其资源丰度受栖息环境影响显著,为了解不同的环境因子对其栖息地分布的影响,根据2010~2020年夏季底拖网调查数据,研究莱州湾三疣梭子蟹栖息地适宜性及其影响因子。利用广义可加模型(generalized additive models,GAM)选取变量因子,通过提升回归树模型(boosting regression tree,BRT)对因子进行权重分析,构建了4种栖息地适宜性指数(habitat suitability index,HSI)模型,并通过实测值和预测值的Pearson检验,对模型比较和验证。结果表明,GAM和BRT优化的HSI模型好于其他3种模型(未优化模型,GAM优化HSI模型,BRT优化HSI模型),以生物量表征资源丰度的模型好于尾数表征资源丰度的模型,算术平均法构建的HSI模型的整体预测准确率和相关系数高于几何平均法,对莱州湾三疣梭子蟹栖息地有较强的预测能力。底层水温、底层盐度和水深对三疣梭子蟹栖息地影响较大,夏季栖息地适宜性较高的海域(HSI>0.7)主要分布在莱州湾南部、东南部和东北部海域,中部和西南部海域分布较少。研究结果为莱州湾三疣梭子蟹增殖放流工作提供了科学依据。     

一种优化模糊度搜索方法的研究

对于高精度测量和导航,GPS载波相位整周模糊度的快速求解仍然是一个难点,尤其对于单频接收机。提出一种快速求解整周模糊度的方法,其基本思想采用分步求解,首先应用最小二乘模糊去耦调节法(LAMBDA)搜索出来的模糊度作为初始值,然后应用卫星分组方法降低搜索维数,并应用极大似然准则,构造搜索函数,最后应用最优化原理,搜索出最优的模糊度参数,并从三个方面对其进行检验,即RATIO检验,OVT检验,多项式拟合残差检验。为验证该算法,我们用单频GPS接收机进行了实验,利用本文方法在11 S以内正确确定了模糊度,其基线长误差小于3MM,表明该方法不但可以改进模糊度的搜索速度,而且可以进一步提高其可靠性和成功率。该方法可广泛应用于定向及姿态测量。     

Exploring the spatial distribution patterns of South African Cape hakes using generalised additive models

We developed delta generalised additive models (GAMs) to predict the spatial distribution of different size classes of South African hakes, Merluccius capensis and M. paradoxus, using demersal trawl survey data and geographical (latitude and longitude) and environmental features (depth, temperature, bottom dissolved oxygen and sediment type). Our approach consists of fitting, for each hake size class, two independent models, a binomial GAM and a quasi-Poisson GAM, whose predictions are then combined using the delta method. Delta GAMs were validated using an iterative cross-validation procedure, and their predictions were then employed to produce distribution maps for the southern Benguela. Delta GAM predictions confirmed existing knowledge about the spatial distribution patterns of South African hakes, and brought new insights into the factors influencing the presence/absence and abundance of these species. Our GAM approach can be used to produce distribution maps for spatially explicit ecosystem models of the southern Benguela in a rigorous and objective way. Ecosystem models are critical features of the ecosystem approach to fisheries, and distribution maps constructed using our GAM approach will enable a reliable allocation of species biomasses in spatially explicit ecosystem models, which will increase trust in the spatial overlaps and, therefore, the trophic interactions predicted by these models.     

Changes in the spatial structure of Grand Bank yellowtail flounder: testing MacCall's basin hypothesis

MacCall's basin model postulates that the geographic range of marine fish will co-vary with population density as a function of habitat selection. Therefore the geographic range of a stock will increase with increasing abundance, while the opposite is true of declining stocks. In this paper we investigated range contraction, and expansion, in the distribution of yellowtail flounder on the Grand Banks in relation to sediment type, temperature and depth. Yellowtail flounder were mainly distributed on gravely sand, sand-shell hash, rock-sandy sediments an to a lesser extent on rocky bottoms. As well, yellowtail flounder are highly associated with shallow, warmer waters more frequently than expected based on its occurrence in the environment. Employing a generalised additive model (GAM), we modelled the spatial distribution of yellowtail flounder in association with the environmental variables. The GAM provided a reasonable fit to the spatial distribution of yellowtail (58% overall). During periods of lower abundance, the fit of the spatial model increased, demonstrating the importance of depth and temperature in influencing the distribution of this species. We concluded that the observed range contraction of yellowtail flounder at low population levels represents selection for preferred habitats, whereas during periods of stock increase, the range of yellowtail flounder expands into less favourable habitats in support of MacCall's basin hypothesis.     

Alternative approach for dynamic-positioning thrust allocation using linear pseudo-inverse model

Application of ship Dynamic-Positioning systems strongly depends on physical ability of actuators (Thrusters) in providing commanded loads. This will consequently introduce some constraints and limitations to Thrust Allocation problem in design and control of such systems. However, there is a special case in which a simple explicit solution could be found by fixing orientation of azimuth thruster relative to vessel regarded as linear model. In this paper, three new alternative approaches based on linear model are introduced. Case study is a time domain simulation of Station-Keeping (instant Point-Tracking) operation for a supply vessel called Northern Clipper in North Sea with Beaufort number 6. As is evident by results, these approaches improve maneuvering performance and power consumption efficiency compared to the conventional linear model. Results show improved robustness of yaw control in Point-Tracking operation and decreased overall consumption of power compared to linear model. Furthermore, it is apparent from the results that these approaches are particularly efficient in tunnel thrusters compared to linear counterpart.