Spatial Distribution and Seasonal Variation of Hypoxic Zone in the Eastern Equatorial Indian Ocean

The spatial distribution and seasonal variations of the hypoxic zone in the eastern equatorial Indian Ocean were investigated using survey data collected from four cruises from 2013 to 2018.Results showed that hypoxic zone occurred all year round in the eastern equatorial Indian Ocean,and it spread southward in the shape of a double tongue at two depths with one at subsurface centered at a depth of 150 m and the other in intermediate water centered at a depth of 800 m.The southward expansion and maximum thickness of the hypoxic zone were greatest in the spring inter-monsoon and least in the summer monsoon.The hypoxic zone originated from the southward expansion of the hypoxic water in the Bay of Bengal and its spatial distribution was driven by southward output flux of mid-deep(100–1000 m)hypoxic water from the Bay of Bengal.The hypoxia southward expansion was blocked near the equator in the subsurface layer,because of mixing with multiple zonal circulations(e.g.,Wyrtki Jets and the equatorial undercurrent),which meant that the hypoxic zone extended over a smaller area than in the intermediate water.These new findings will contribute to an improved understanding of the hypoxic zone and will contribute to circulation research,particularly about intermediate circulation in the eastern equatorial Indian Ocean.

Community structure and spatial-temporal variation of netzphytoplankton in the Bering Sea in summer

Marine biodiversity is changing in response to altered physical environment, subsequent ecological changes as well as anthropogenic disturbances. In this study, phytoplankton samples in situ collected in the Bering Sea in July of 1999 and 2010 were analyzed to obtain phytoplankton community structure and spatial-temporal variation between the beginning and end of this decade, and the correlation of phytoplankton community dynamics and environmental factors was investigated. A total of 5 divisions, 58 genera and 153 species of phytoplankton belonging to 3 ecological groups were identified. The vast majority of phytoplankton consisted of diatoms accounting for 66.7% of the total species and 95.2% of the total abundance. Considering differentiation in spatial extent and phytoplankton sample types, there were subtle changes in species composition, large altering in abundance and significant variation in spatial distribution between two surveys. The abundance peak area was located at the Bering Strait while sub peak was found at the Bering Sea Basin. The boreal-temperate diatom was the dominant flora, which was subsequently replaced by eurythermal and frigid-water diatom. Phytoplankton community in the Bering Sea was not a simplex uniform community but composed of deep-ocean assemblage and neritic assemblage. The deep-ocean assemblage was located in the northwestern Pacific Ocean and Bering Sea Basin, dominated by boreal-temperate species（Neodenticula seminae, Thalassiothrix longissima, Amphiprora hyperborean, Chaetoceros atlanticus, Thalassiosira trifulta, etc.） and eurychoric species（Thalassionema nitzschioides, Ch. compressus, Rhizosolenia styliformis, etc.）, and characterized by low abundance, even interspecies abundance allocations, diverse dominant species and high species diversity. The neritic assemblage was distributed on the continental shelf and slope of Bering Sea and was mainly composed of frigid-water species（Th.nordenski？ldii, Ch. furcellatus, Ch. socialis, Bacteriosira fragilis, etc.） and eurythermal and euryhaline species（L.danicus, Ch. curvisetus, Coscinodiscus curvatulus, etc.）, and it was characterized by high abundance, uneven interspecies allocations, prominent dominant species and low species diversity. Spatial-temporal variation of species composition and abundance of phytoplankton in the Bering Sea was directly controlled by surface circulation,nutrient supply and ice edge.

Numerical simulation of rock pore-throat structure effects on NMR T_2 distribution

We built a three-dimensional irregular network model which can adequately describe reservoir rock pore-throat structures. We carried out numerical simulations to study the NMR T2 distribution of water-saturated rocks. The results indicate that there is a good correlation between T2 distribution and the pore radius frequency histogram. The total T2 distribution can be partitioned into pore body and pore throat parts. The effect of parameters including throat radius, pore-throat ratio, and coordination number of the micro- pore structure on the T2 distribution can be evaluated individually. The result indicates that： 1 ） with the increase of the pore throat radius, the T2 distribution moves toward longer relaxation times and its peak intensity increases; 2） with the increase of the pore-throat ratio, the T2 distribution moves towards longer T2 with the peak intensity increasing and the overlap between pore body T2 and pore throat T2 decreasing; 3） With the increase of connectivity, the short T2 component increases and peak signal intensity decreases slightly.

GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems

Owing to the expansion of the grid interconnection scale,the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important.These characteristics can provide effective support in coordinated security control.However,traditional model-based frequencyprediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models.Therefore,this study presents a rolling frequency-prediction model based on a graph convolutional network(GCN)and a long short-term memory(LSTM)spatiotemporal network and named as STGCN-LSTM.In the proposed method,the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input.An improved GCN embedded with topology information is used to extract the spatial features,while the LSTM network is used to extract the temporal features.The spatiotemporal-network-regression model is further trained,and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information.The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response.The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.

A Novel Simulation Framework for Predicting the Formation Parameters Variation in Unconsolidated Sandstone Reservoir

After long-term waterflooding in unconsolidated sandstone reservoir, the high-permeability channels are easy to evolve, which leads to a significant reduction in water flooding efficiency and a poor oilfield development effect. The current researches on the formation parameters variation are mainly based on the experiment analysis or field statistics, while lacking quantitative research of combining microcosmic and macroscopic mechanism. A network model was built after taking the detachment and entrapment mechanisms of particles in unconsolidated sandstone reservoir into consideration. Then a coupled mathematical model for the formation parameters variation was established based on the network modeling and the model of fluids flowing in porous media. The model was solved by a finite-difference method and the Gauss-Seidel iterative technique. A novel field-scale reservoir numerical simulator was written in Fortran 90 and it can be used to predict 1) the evolvement of high-permeability channels caused by particles release and migration in the long-term water flooding process, and 2) well production performances and remaining oil distribution. In addition, a series of oil field examples with inverted nine-spot pattern was made on the new numerical simulator. The results show that the high-permeability channels are more likely to develop along the main streamlines between the injection and production wells, and the formation parameters variation has an obvious influence on the remaining oil distribution.

Design and implementation of dual-mode configurable memory architecture for CNN accelerator

With the rapid development of deep learning algorithms,the computational complexity and functional diversity are increasing rapidly.However,the gap between high computational density and insufficient memory bandwidth under the traditional von Neumann architecture is getting worse.Analyzing the algorithmic characteristics of convolutional neural network(CNN),it is found that the access characteristics of convolution(CONV)and fully connected(FC)operations are very different.Based on this feature,a dual-mode reronfigurable distributed memory architecture for CNN accelerator is designed.It can be configured in Bank mode or first input first output(FIFO)mode to accommodate the access needs of different operations.At the same time,a programmable memory control unit is designed,which can effectively control the dual-mode configurable distributed memory architecture by using customized special accessing instructions and reduce the data accessing delay.The proposed architecture is verified and tested by parallel implementation of some CNN algorithms.The experimental results show that the peak bandwidth can reach 13.44 GB·s^(-1)at an operating frequency of 120 MHz.This work can achieve 1.40,1.12,2.80 and 4.70 times the peak bandwidth compared with the existing work.

基于PROFIBUS的分散控制系统的设计与研究

通过分析我国火电厂分散控制系统(DCS)的不足,根据火电厂的现状和PROFIBUS的技术特点,设计了一种基于PROFIBUS的分散控制系统。探讨了在现有分散控制系统基础上融入PROFIBUS的总体实现方案,给出了提高系统实时性和可靠性的原则以及PROFIBUS在分散控制系统中的应用范围。系统采用混合型两级网络结构,详细讨论了PROFIBUS之间及其与DCS之间的网络通信,分析了系统的实时性、可靠性和经济性指标。对解决我国火电厂分散控制系统的不足和实现全面信息化有重要的借鉴意义。

PHYTOPLANKTON BIOMASS (chl α) IN THE TAIWAN STRAIT (1997 - 1999)

The concentration, distribution, size-fraction structure and diurnal variation of phyto-plankton biomass ( chl α) in the Taiwan Strait were investigated during four cruises conducted in the summer (August) of 1997, 1998, 1999 and winter (February-March) of 1998, respectively. The results showed that phytoplankton biomass in the Taiwan Strait was largely influenced by water masses and up-welling, high biomass mainly occurred at the frontal zones. Nano-and pico-phytoplankton dominated the phytoplankton biomass and primary productivity in the Taiwan Strait, they contributed 60% - 80% to biomass and 80% to primary productivity. But size-fractionated phytoplankton biomass was quite different in the northern Taiwan Strait (NTS) and southern Taiwan Strait (STS), and varied significantly annually. Diurnal variation of chl α concentration in the water column and water layers indicated that phytoplankton biomass at most stations had one-day variation cycle, with some difference, which coincide with the tidal rhythm. The diurnal variation of the size-fractionated structure of phytoplankton biomass was strongly influenced by the hydrodynamics and grazing pressure of zooplankton. This study also showed that unusual phenomena observed in phytoplankton biomass during the investigating periods might be the biological response to ENSO events.

Genome assembly of KA105,a new resource for maize molecular breeding and genomic research

Superior inbred lines are central to maize breeding as sources of natural variation.Although many elite lines have been sequenced,less sequencing attention has been paid to newly developed lines.We constructed a genome assembly of the elite inbred line KA105,which has recently been developed by an arti-ficial breeding population named Shaan A and has shown desirable characteristics for breeding.Its pedigree showed genetic divergence from B73 and other lines in its pedigree.Comparison with the B73 reference genome revealed extensive structural variation,58 presence/absence variation(PAV)genes,and 1023 expanded gene families,some of which may be associated with disease resistance.A network-based integrative analysis of stress-induced transcriptomes identified 13 KA105-specific PAV genes,of which eight were induced by at least one kind of stress,participating in gene modules responding to stress such as drought and southern leaf blight disease.More than 200,000 gene pairs were differentially correlated between KA105 and B73 during kernel development.The KA105 reference genome and transcriptome atlas are a resource for further germplasm improvement and surveys of maize genomic variation and gene function.

Spatial and temporal variations of zooplankton composition and quantity distribution in the upper waters around Nansha Islands

This study aims to examine spatial and temporal variations of zooplankton species composition,density and biomass distribution and community structure, based on the data obtained from three separate cruises carried out in November 1997, April and July 1999. Results show that 244 species of zooplankton and 8 groups of planktonic larvae were identified, which were dominated by copepods, followed by amphipods, ostracods and medusae. The total species were 201 and 198 for the cruises of November 1997 and July 1999, respectively, and no obvious seasonal variation of species richness was observed. The distribution of zooplankton species richness decreased from pelagic to coastal waters. Average richness of species in each station was higher in the cruises of November 1997(62) and April 1999(61) than in the cruise in July 1999 (56), which was mainly a result from the pelagic or coastal water mass movement made by the monsoon. Zooplankton in the upper waters (0―100 m) around Nansha Islands belonged to the typical tropic pelagic fauna, most of them were pelagic warm-water species, followed by coastal warm-water species and euryhaline warm-water species. The number of dominant species ranged from 5 to 7 in each cruise. No obvious seasonal succession of dominant species was observed. Sagitta enflata, Cypridina nami, Cosmocalanus darwinii, Pleuromamma gracilis and Echinopluteus larva were the main dominant species. The average of zooplankton biomass and density in three cruises were 31, 32, 28 mg·m 3 and 31, 39, 35 ind·m 3, respectively. Copepods were the most abundant, followed by chaetognaths. Zooplankton high biomass distributed mainly in the northwestern waters around Nansha Islands, and generally occurred in the areas of oceanic front and upwelling. The main reason for zooplankton quantity without obvious seasonal variation was the relative steady temperature dynamics in the waters around Nansha Islands.

FPGA Implementation of Deep Leaning Model for Video Analytics

In recent years,deep neural networks have become a fascinating and influential research subject,and they play a critical role in video processing and analytics.Since,video analytics are predominantly hardware centric,exploration of implementing the deep neural networks in the hardware needs its brighter light of research.However,the computational complexity and resource constraints of deep neural networks are increasing exponentially by time.Convolutional neural networks are one of the most popular deep learning architecture especially for image classification and video analytics.But these algorithms need an efficient implement strategy for incorporating more real time computations in terms of handling the videos in the hardware.Field programmable Gate arrays(FPGA)is thought to be more advantageous in implementing the convolutional neural networks when compared to Graphics Processing Unit(GPU)in terms of energy efficient and low computational complexity.But still,an intelligent architecture is required for implementing the CNN in FPGA for processing the videos.This paper introduces a modern high-performance,energy-efficient Bat Pruned Ensembled Convolutional networks(BPEC-CNN)for processing the video in the hardware.The system integrates the Bat Evolutionary Pruned layers for CNN and implements the new shared Distributed Filtering Structures(DFS)for handing the filter layers in CNN with pipelined data-path in FPGA.In addition,the proposed system adopts the hardware-software co-design methodology for an energy efficiency and less computational complexity.The extensive experimentations are carried out using CASIA video datasets with ARTIX-7 FPGA boards(number)and various algorithms centric parameters such as accuracy,sensitivity,specificity and architecture centric parameters such as the power,area and throughput are analyzed.These results are then compared with the existing pruned CNN architectures such as CNN-Prunner in which the proposed architecture has been shown 25%better performance than the existing architectures.

基于VMD模糊熵与GG聚类的直流配电网故障检测方法

针对直流配电网存在的故障信号难以提取、不易对各类故障进行诊断等问题,提出一种基于变分模态分解(VMD)模糊熵与Gath-Geva(GG)聚类的故障检测方法。首先,提取出暂态电流,采用VMD算法将故障暂态电流分解成若干个固有模态分量(IMF)。然后,分别计算分解得到的若干个IMF的模糊熵,将其作为特征向量。最后,采用GG聚类算法对故障特征的特征向量进行聚类识别。GG聚类的主要算法为将聚类样本划分为c类,设出隶属度矩阵,通过设定迭代来计算聚类中心与最大似然估计距离,更新隶属度矩阵,当隶属度矩阵满足条件矩阵时终止迭代,从而实现对单极故障、极间故障以及区外交流侧接地故障的聚类识别。仿真结果表明,所提保护方案可靠性强、准确率高,在不同故障类型、故障位置和过渡电阻等工况下均能可靠检测直流线路故障并准确识别故障类型,且具备一定的抗干扰能力。

智能配电网技术在配电网规划中的应用

全面分析了智能配电网技术在配电网规划中的应用及其对电力系统现代化建设的推动作用。首先指出随着全球能源结构的转型和电力需求的增长,传统配电网面临前所未有的挑战,包括新能源接入的不确定性和负荷波动等,智能配电网技术通过整合先进的信息通信技术和综合管理策略为配电网的智能化管理和高效运行提供了新的思路。详细讨论了智能配电网技术的特点和优势,如提高电网效率及支持低碳发展,并通过技术分析展示了智能配电网对配电网规划的影响,包括规划方法的创新和规划策略的优化。

城市中压配电网雪花格式网架结构初探

为适应新型电力系统建设需求,提出一种适合国情的雪花格式网架结构。首先,总结国内外典型城市中压配电网结构的特征与优缺点;然后,结合天津已有网架结构,提出雪花格式网架结构及其构建方法;其次,分析测算雪花网结构的负荷转移能力、可靠性与效率效益水平;最后,从结构形式、适合场景等方面给出雪花网结构与花瓣结构等的综合对比情况。结果表明,雪花格式网架结构具有一定综合比较优势,可为中压配电网远景目标网架规划提供指导。

10 kV配网耐张型复合绝缘横担研究综述

复合绝缘横担具有轻质高强、绝缘可靠性高且防雷效果优异等特点,正逐步在10 kV配电线路中得到推广应用。围绕配网耐张型复合绝缘横担结构设计、耐久性研究及性能测试三个问题,首先综述10 kV配网耐张型复合绝缘横担的发展现状,指出研究必要性,进而针对其设计依据、现有设计型式、以疲劳振动老化为代表的耐久性研究进展及关键性能测试与评估展开论述,同时分析目前存在的问题及有待解决的困难,最后结合应用情况进行总结与展望,以期为配网耐张型复合绝缘横担的结构优化、性能提升以及后续大规模应用提供参考。

基于分布式光纤的蜂窝夹层结构脱粘损伤监测

航空航天蜂窝夹层结构服役环境恶劣,需要发展相应的结构健康监测技术以保证结构的安全运行。脱粘损伤因发生在内部胶层而难以监测,该文采用埋入胶层的分布式光纤传感器对蜂窝夹层结构脱粘损伤进行监测。在预埋损伤监测试验中采用高强光纤和边界浅槽的方式提高传感器存活效率,得到脱粘损伤应变特征。采用参数化建模方式对不同损伤位置进行大量有限元模拟,加入白噪声后得到含有脱粘损伤特征的13500组数据。将模拟数据代入卷积神经网络进行训练,训练后的网络对试验损伤数据的识别准确率达到98.84%。该方法能够识别20 mm^(2)脱粘损伤,同时定位平均误差小于4 mm。

考虑不确定性的配电网-多综合能源系统分布式协同规划

综合能源系统的多点并网加剧了配电网规划的“维数灾害”,提出兼顾多主体利益均衡性和分析计算高效性的配电网-多综合能源系统的分布式协同规划方法。以区间数刻画系统内的源-荷不确定性,构建计及多利益主体的配电网-多综合能源系统分布式协同规划框架;考虑多源联供的低碳清洁性、综合能源系统对配电网的支撑性和多能互补性,建立考虑奖惩碳交易机制的配电网-多综合能源系统双层规划模型;采用基于最大变差分析的目标级联分析法求解含有区间不确定性和多主体耦合性的规划模型。仿真结果表明,所提方法有效降低了配电网耦合综合能源系统的复杂度,提升了系统内各利益主体的均衡性。

中国海洋研究型人才流动网络及动态变化——基于知网数据分析

人才是区域发展的核心要素,人才的开发和培育是衡量一个区域创新能力的重要维度,对人才的吸引力更是一个区域发展活力的最好体现。为此,文章以知网涉海类核心期刊作者在2000—2020年发表的文献为基础数据,对海洋研究型人才的动态变化进行分析。结果表明:1)20年间,海洋研究型人才流动呈现先增后减的变化态势,网络表现出典型的“金字塔结构”特征,网络被少数枢纽型节点城市控制。2)海洋研究型人才流动的空间分布格局相对稳定,形成以青岛为核心,大连、北京、上海、广州为顶点的菱形结构,海洋研究型人才迁移分为城市内的闭环迁移和异地迁移2种模式,闭环迁移占比逐年增加,市内迁移现象凸显。3)海洋研究型人才流动表现出明显的空间非均衡性,迁入人次的空间分布逐渐均衡化,迁出人次则趋向集中分布。华东、华南地区是海洋研究型人才净流入活跃区,东北、华中地区以及北京等地是海洋研究型人才净迁出活跃区,且这些区域的高强度净流入现象和净流出问题在逐渐缓解。4)海洋研究型人才流动网络具有明显的“核心-边缘”结构,“马太效应”显著,青岛一直处于网络的核心,半边缘城市与核心城市保持较强的人才往来,彼此之间也存在较强的关系流,边缘城市的海洋研究型人才主要流向核心城市、半边缘城市。

基于模糊结构元的新零售末端配送网络优化研究

新零售模式下,高时效性的物流末端配送对提高顾客满意度具有重要作用,而对末端配送中心位置和数量的确定,是优化末端配送网络的关键。首先,综合考虑影响配送中心位置与数量的模糊因素,构建以配送时间最少、配送中心覆盖范围最大以及系统成本最低为目标的模糊多目标规划模型;其次,采用结构元加权序准则法求解模型,生成三角模糊数简化求解过程;最后,以算例检验方法的有效性。结果表明,所提出的模型能够将模糊因素融入模糊决策过程中,且结构元加权序准则法有助于决策者灵活调整方案和决策,从而对新零售末端配送网络布局进行优化。

基于FPGA的两阶段配电网拓扑实时辨识算法

对配电网拓扑进行准确的实时辨识是电力系统安全稳定运行的基础,但随着新能源的接入以及配电网规模不断增大,配电网拓扑结构的动态变化愈加频繁且难以辨识。然而,现有配电网拓扑辨识算法所使用的历史数据需要人工对其进行拓扑标注,且拓扑辨识时间长,难以实现配电网拓扑实时辨识。因此,文中提出了一种基于现场可编程逻辑门阵列(FPAG)的两阶段配电网拓扑结构实时辨识算法。该算法不需要预先给出配电网拓扑类别的数量,即可对已有历史数据进行相应的拓扑标注及分类,并且基于FPGA实现了对配电网拓扑的实时辨别。该算法分为2个阶段:第1阶段采用变分贝叶斯高斯混合模型,对已有历史数据进行相应的拓扑标注及分类;第2阶段采用麻雀搜索算法,使得支持向量机快速收敛得到最优参数,以实现对配电网拓扑结构的精准辨识。基于该算法,利用FPGA并行架构以及高速高密度特性建立了实时拓扑结构辨识平台。最后,通过算例分析验证了所提辨识方法的有效性和优越性。