基于模糊神经网络(FNN)的赤潮预警预测研究

为研究各种理化因子与赤潮藻类浓度间的非线性对应规律和有效预测赤潮藻类浓度,构建了基于BP 算法的一个四层模糊神经网络模型。将模糊神经网络(FNN)技术引入赤潮预测研究,并与普通 BP 网络、RBF 网络的结果作比较,结果表明,该模型能够较好地反演出各种理化因子与夜光藻密度的非线性对应变化规律,有更好的预测功能。     

GPS与数字水准仪相结合进行三维控制网的布设

通过三维控制网的实际布设,对GPS平面控制网的精度和数字水准仪布设的高程控制网的精度进行了分析探讨,并对二者如何更加有效的结合提出了几点有益的见解。     

双基站双采样RTK测量在布设一级导线中的应用

通过对RTK测量工作原理的分析,得出单基站单采样RTK测量方法在实际应用中的缺点,从而提出了双基站双采样的测量方法,并在天津临港工业区测量项目中进行了实际应用。通过该项目测量成果,分析了采用双基站双采样的RTK测量方法,可有效消除基准站坐标误差对采样点平面坐标的系统影响,消弱对流层随机变化对RTK高程测量的影响,从而使RTK平面坐标测量满足城市一级导线测量的需要,使RTK高程测量接近或达到四等水准测量的精度。     

网络测试中新方法的探讨

吞吐量是网络测试非常重要的测试指标。文中探讨吞吐量测试的1个新方法,该方法仅用2个端口便可实现对被测设备大量端口的吞吐量测试。此法不仅可以用于2层网络流量的测试,还可以用于3层IP网的测试,具有很好的现实意义。在第3层测试中需要通过VPN来实现IP包转发,测试可以实现跨接口卡的流量测试;应用证明,该方法不仅能测试吞吐量,而且能更好地实现多项综合测试,得到更真实可靠的数据。同时,该方法可以大量节约测试成本,具备多种优点,是1种值得在高校推广的测试方法。     

水下滑翔器主体外形优化设计

对水下滑翔器主体外形的优化设计问题进行研究.首先,在考虑内部机械结构限制的条件下,建立了主体外形优化数学模型,并采用CFX计算了若干主体形状的绕流阻力.在此基础上建立了映射主体外形的尺寸参数与绕流阻力的BP神经网络.然后将建立的神经网络作为优化设计问题的目标函数,采用坐标轮换法对滑翔器主体外形进行了优化设计,确定了尺寸参数的最优解.     

Network analysis to inform invasive species spread among lakes

Often facilitated by human-mediated pathways,aquatic invasive species are a threat to the health and biodiversity of global ecosystems.We present a novel approach incorporating survey data of watercraft movement in a social network analysis to reconstruct potential pathways of aquatic invasive species spread between lakes.As an example,we use the green alga Nitellopsis obtusa,also known as starry stonewort,an aquatic invasive species affecting the Great Lakes region in the United States and Canada.The movement of algal fragments via human-mediated pathways(i.e.,watercraft)has been hypothesized as the primary driver of starry stonewort invasion.We used survey data collected at boat ramps during the 2013 and 2014 openwater seasons to describe the flow of watercraft from Lake Koronis,where N.obtusa was first detected in Minnesota,to other lakes in the state.Our results suggest that the risk of N.obtusa expansion is not highly constrained by geographic proximity and management efforts should consider highly connected lakes.Estimating human movement via network analysis may help to explain past and future routes of aquatic invasive species infestation between lakes and can improve evidence-based prevention and control efforts.     

Network analysis to inform invasive species spread among lakes

Often facilitated by human-mediated pathways,aquatic invasive species are a threat to the health and biodiversity of global ecosystems.We present a novel approach incorporating survey data of watercraft movement in a social network analysis to reconstruct potential pathways of aquatic invasive species spread between lakes.As an example,we use the green alga Nitellopsis obtusa,also known as starry stonewort,an aquatic invasive species affecting the Great Lakes region in the United States and Canada.The movement of algal fragments via human-mediated pathways(i.e.,watercraft)has been hypothesized as the primary driver of starry stonewort invasion.We used survey data collected at boat ramps during the 2013 and 2014 openwater seasons to describe the flow of watercraft from Lake Koronis,where N.obtusa was first detected in Minnesota,to other lakes in the state.Our results suggest that the risk of N.obtusa expansion is not highly constrained by geographic proximity and management efforts should consider highly connected lakes.Estimating human movement via network analysis may help to explain past and future routes of aquatic invasive species infestation between lakes and can improve evidence-based prevention and control efforts.     

Prediction of the dynamic response of a slender marine structure under an irregular ocean wave using the NARX-based quadratic Volterra series

The time series of the dynamic response of a slender marine structure was predicted in approximate sense using a truncated quadratic Volterra series. The wave-structure interaction system was identified using the NARX (Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through supervised training using prepared datasets. The dataset used for network training was obtained by nonlinear finite element analysis of the slender marine structure under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of the relative velocity between the water particle and structure in the Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of the response of the structure was predicted using the quadratic Volterra series. To check the applicability of the method, the response of a slender marine structure under a realistic ocean wave environment with a given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. The predicted time series of the response of structure with quadratic Volterra series successfully captured the slowly varying response with reasonably good accuracy. This method can be used to predict the response of the slender offshore structure exposed to a Morison type load without relying on the computationally expensive time domain analysis, especially for screening purposes.     

Floc size variability under strong turbulence: Observations and artificial neural network modeling

The flocculation of cohesive sediment in the presence of waves is investigated using high-resolution field observations and a newly-developed flocculation model based on artificial neural networks. Vertical profiles of suspended sediment concentration and turbulent intensity are estimated using measurements of current profile and acoustic backscatter. The vertical distribution of floc size is estimated using an artificial neural network (ANN) that is trained and validated using floc size measurements at one vertical level. Data analysis suggests a linear correlation between suspended sediment concentration and turbulence intensity. Observations and numerical simulations show that floc size is inversely related to sediment concentration, turbulence intensity and water temperature. The numerical results indicate that floc growth is supported by low concentration and low turbulence. In the vertical direction, mean size of flocs decreases toward the bottom, suggesting floc breakage due to increasing turbulence intensity toward the bed. A significant decrease in turbulent shear could occur within the bottom few-cm, related to increased damping of turbulence by sediment induced density stratification. The results of the numerical simulations presented here are consistent with the concept of a cohesive sediment particle undergoing aggregation-fragmentation processes, and suggest that the ANN can be a precise tool to study flocculation processes.     

知识不确定条件下的海外保障基地自然风险情景模拟评估

针对海外保障基地面临的自然风险,在风险辨识基础上,构建了风险评价指标体系和海外保障基地自然风险的贝叶斯网络评估模型,并进行了情景模拟下的量化实验评估。针对目前贝叶斯网络评估过程中的群决策和决策偏好问题,引入了犹豫层次分析思想来优化计算指标风险隶属度,改进了评估过程中的客观性和适用性。