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。     

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。     

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.     

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。     

沿海GNSS观测网中BERNESE软件处理方案比较

采用BERNESE软件"OBS-MAX"和"SHORTEST"两种独立基线生成方式和三种网平差基准定义方法制定出6种GNSS数据后处理方案,分别对中国沿海GNSS业务化观测网2009年与2011年的两批数据处理,并从外符合和内符合精度两个方面进行分析比较。结果表明:采用"OBS-MAX"独立基线生成方式得出的坐标精度更优,尤其是在原始观测数据质量不高的情况下;在网平差基准定义上,采用固定解得出的坐标内符合精度优于最小约束解,但其外符合精度较差。     

Matlab在矿区GPS网间接平差中的应用

针对矿区GPS网间接平差,首先,详细地分析了GPS网间接平差原理,按一定格式将GPS网中的观测数据编制成观测文件,其次,按观测文件的格式编制算法将数据读入并存储,然后用间接平差原理处理得到平差结果,最后绘制网形和误差椭圆并生成平差报告,为矿区控制网的建立和矿区生产管理提供了便利。实验验证了本文程序的实用性。     

Application of probabilistic neural network to design breakwater armor blocks

This study presents a probabilistic neural network (PNN) technique for predicting the stability number of armor blocks of breakwaters. The PNN is prepared using the experimental data of Van der Meer. The predicted stability numbers of the PNN are compared with those of previous studies, i.e. by an empirical formula and a previous neural network model. The agreement index between the measured and predicted stability numbers by PNN are better than those by the previous studies. The PNN offers a way to interpret the network's structure in the form of a probability density function and it is easy to implement. Therefore, it can be an effective tool for designers of rubble mound breakwaters.     

基于神经网络的南太平洋长鳍金枪鱼渔场预报

南太平洋长鳍金枪鱼是我国远洋渔业的重点捕捞对象;对南太平洋长鳍金枪鱼进行准确的渔场预报;可以提高捕捞效率;提高渔业的生产能力。本研究根据1993-2010年南太平洋长鳍金枪鱼的延绳钓生产数据以及海洋卫星遥感数据（海水表面温度;SST;海面高度;SSH）和ENSO（El Niño-Southern Oscillation）指标;采用DPS（data processing system）数据处理系统中的BP人工神经网络模型;以渔获产量（单位时间的渔获尾数）和单位捕捞努力量渔获量（CPUE;Catch per unit of effort）分别作为中心渔场的表征因子;并作为BP模型的输出因子;以月、经度、纬度、SST、SSH和ENSO指标等作为输入因子;分别构建4-3-1;5-4-1;5-3-1;6-5-1;6-4-1;6-3-1等BP模型结构;比较渔场预报模型优劣。研究结果表明;以CPUE作为输出因子的BP人工神经网络结构总体上较优;其中以6-4-1模型结构为最优;相对误差只有0.006 41。研究认为;以CPUE为输出因子的6-4-1结构的人工神经网络模型;能够准确预报南太平洋长鳍金枪鱼的渔场位置。