A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

In the past few years,deep learning has developed rapidly,and many researchers try to combine their subjects with deep learning.The algorithm based on Recurrent Neural Network(RNN)has been successfully applied in the fields of weather forecasting,stock forecasting,action recognition,etc.because of its excellent performance in processing Spatio-temporal sequence data.Among them,algorithms based on LSTM and GRU have developed most rapidly because of their good design.This paper reviews the RNN-based Spatio-temporal sequence prediction algorithm,introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction,and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.At the same time,it also compares the advantages and disadvantages,and innovations of each algorithm.The purpose of this article is to give readers a clear understanding of solutions to such problems.Finally,it prospects the future development of RNN in the Spatio-temporal sequence prediction algorithm.

Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.

Classification and Facies Sequence Model of Subaqueous Debris Flows

Objective Debris flows are cohesive sediment gravity flows which occur in both subaerial and subaqueous settings. Compared to subaerial debris flows which have been well studied as a geological hazard, subaqueous debris flows showing complicated sediment composition and sedimentary processes were poorly understood. The main objective of this work is to establish a classification scheme and facies sequence models of subaqueous debris flows for well understanding their sedimentary processes and depositional characteristics.

Evolution and controlling factors of the gravity flow deposits in the Miocene sequence stratigraphic framework,the Lower Congo-Congo Fan Basin,West Africa

To understand the evolution of the Miocene gravity flow deposits in the Lower Congo-Congo Fan Basin,this paper documents the Miocene sequence stratigraphic framework,the depositional characteristics and the controlling factors of the gravity flow system.Based on the establishment of high-resolution sequence stratigraphic framework,lithofacies characteristics and sedimentary units of the gravity flow deposits in the region are identified by using seismic,well logging and core data comprehensively,and the sedimentary evolution process is revealed and the controlling factors are discussed.The Miocene can be divided into four 3 rd-order sequences(SQ1-SQ4).The gravity flow deposits mainly include siliciclastic rock and pelite.The main sedimentary units include slumping deposits,mass transport deposits(MTD),channel fills,levee-overbank deposits,and frontal lobes.In the Early Miocene(SQ1),mainly gull-wing,weakly restricted to unrestricted depositional channel-overbank complexes and lobes were formed.In the early Middle Miocene(SQ2),W-shaped and weakly restricted erosional-depositional channels(multi-phase superposition)were subsequently developed.In the late Middle Miocene(SQ3),primarily U-shaped and restricted erosional channels were developed.In the Late Miocene(SQ4),largely V-shaped and deeply erosional isolated channels were formed in the study area.Climate cooling and continuous fall of the sea level made the study area change from toe of slope-submarine plain to lower continental slope,middle continental slope and finally to upper continental slope,which in turn affected the strength of the gravity flow.The three times of tectonic uplifting and climate cooling in the West African coast provided abundant sediment supply for the development of gravity flow deposits.Multistage activities of salt structures played important roles in redirecting,restricting,blocking and destroying the gravity flow deposits.Clarifying the characteristics,evolution and controlling factors of the Miocene gravity flow deposits in the Lower Congo-Congo Fan Basin can provide reference for deep-water petroleum exploration in this basin.

Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

Division and Correlation of Lacustrine Gravity Flow Reservoirs Based on High-Resolution Sequence Stratigraphic Analysis—Taking Oil Formation I of Lower Es3 in Wuhaozhuang Oilfield as an Example

The phase change of lacustrine gravity flow deposition is fast and complex. In its reservoir division and correlation, the isochronous problem is very important. Taking the oil formation I of Es3 in Wuhaozhuang oilfield as an example, through the analysis of stratigraphic drilling and logging data in the study area, according to the genetic types of different levels of base level cycle interfaces and the characteristics of high-resolution sequence stratigraphy, this paper subdivides the lacustrine gravity flow oil layer of lower Es3 in Wuhaozhuang Oilfield, divides it into four short-term base level cycle sequences, and establishes the high-resolution isochronous stratigraphic framework of this interval. It is found that the mid-term, short-term and ultra short-term base level cycles correspond to the oil formation, sand layer group and single layer in the oil layer correlation unit of the oilfield respectively. Based on this, the oil layer correlation unit of the interval is divided, and the sublayer correlation model is established according to the identification characteristics of the short-term base level cycle.

Sequencing method for dual-shuttle flow-rack automated storage and retrieval systems

The dual-retrieval （DR） operation sequencing problem in the flow-rack automated storage and retrieval system （AS/RS） is modeled as an assignment problem since it is equivalent to pairing outgoing unit-loads for each DR operation. A recursion symmetry Hungarian method （RSHM）, modified from the Hungarian method, is proposed for generating a DR operation sequence with minimal total travel time, in which symmetry marking is introduced to ensure a feasible solution and recursion is adopted to break the endless loop caused by the symmetry marking. Simulation experiments are conducted to evaluate the cost effectiveness and the performance of the proposed method. Experimental results illustrate that compared to the single-shuttle machine, the dual-shuttle machine can reduce more than 40% of the total travel time of retrieval operations, and the RSHM saves about 5% to 10% of the total travel time of retrieval operations compared to the greedy-based heuristic.

Flow-rSSO及PCR-SBT方法检测KIR基因有无的对比研究

目的研究流式磁珠序列特异性寡核苷酸探针(Flow-rSSO)杂交及测序分型(PCR-SBT)方法鉴定KIR基因有无的一致性。方法对131例汉族人群的DNA标本采用Flow-rSSO方法鉴定全部16种KIR基因的有无,并采用本实验室建立的PCR-SBT方法对14种功能性KIR基因进行高分辨水平基因检测;分析Flow-rSSO及PCR-SBT两种方法鉴定14种功能性KIR基因有无的一致性。对初检结果不一致的标本,采用同一厂家、不同批号的Flow-rSSO试剂盒进行复检,并采用序列特异性引物-PCR(PCR-SSP)方法进行检测。结果131例标本中有129例完全一致,2例不一致,占1.5%(2/131)。不一致的两例标本,1例标本3DL1基因、另1例标本2DS3及2DS5基因,其Flow-rSSO初检结果均为阴性,而PCR-SBT结果均为阳性。更换新的批号Flow-rSSO试剂盒复检,两例标本的结果均为阳性;采用PCR-SSP方法检测的结果亦为阳性。结论经Flow-rSSO试剂盒检测KIR基因有无,出现2例标本初检结果错误,提示检测KIR基因有无时,试剂的质控工作至关重要。

Migrating Birds Optimization for Flow Shop Sequencing Problem

FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.

Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events

Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.

中国断陷和坳陷湖盆高可容纳空间层序细粒重力流沉积及其页岩油意义

重力流包括粗粒和细粒重力流,是深湖环境最重要的沉积类型之一,其中,细粒重力流能成为非常规页岩油的“甜点段”。研究该沉积在陆相湖盆层序格架中的分布规律,对页岩油“甜点段”的预测具有重要意义。作者通过对中国主要湖盆重力流发育与层序格架的系统对比研究发现,重力流沉积体系主要发育在高可容纳空间沉积层序中。断陷湖盆的低位域(LST)湖底扇近端发育粗粒重力流沉积,远端发育薄层细粒重力流沉积,其分布受沟谷和同沉积构造坡折的控制。水进体系域(TST)发育细粒水道—堤—朵叶体系。高位体系域(HST)发育非限定水道湖底扇,细粒重力流沉积分布在扇的远端。坳陷湖盆的水进体系域(TST)主要发育受洪泛影响的大型细粒水道—堤—朵叶体系,而在湖盆挠曲坡折的较陡斜坡发育细粒湖底扇体系。高位域(HST)及低位域(LST)既可形成细粒湖底扇体系,也可发育细粒的水道—堤—朵叶体系。分布在湖底扇远端和水道—堤—朵叶体系的细粒重力流沉积能形成页岩油的“甜点段”。泥流沉积形成页理型“甜点段”,细粒碎屑流、细粒浊流、细粒异重流和细粒过渡流沉积形成纹层型、层理型“甜点段”,细粒浓缩密度流沉积只形成层理型“甜点段”。细粒重力流沉积是页岩油勘探开发的有利“甜点段”。

三角洲型被动陆缘盆地陆坡地貌演化研究——以尼日尔三角洲盆地某深水研究区为例

【目的】陆坡因其巨大的沉积物总量和丰富的油气资源而成为勘探的热点领域。明确陆坡地貌演化特征能够助力于深水沉积学研究和油气勘探。研究旨在针对三角洲型被动陆缘盆地陆坡具有强物源供给条件的特征,探索一种基于沉积构型原理的古陆坡地貌演化研究新方法。【方法】以尼日尔三角洲盆地某陆坡研究区为例,综合应用地震、测井和岩心资料,根据13个层序的沉积构型时—空演化规律反演古陆坡地貌演化特征。【结果】研究区中新世至今的地貌演化过程经历了两个阶段。阶段1中,地貌演化受大型三角洲进积驱动,研究区由深海平原演化为陆坡,地貌始终为非限制型。阶段2中,在重力滑动作用下先后经历了由逆冲断层、泥底辟、填平补齐主导的演化过程,地貌由限制型、半限制型演变为现今的非限制型。逆冲断层期,泥底辟强度有限,地貌形态受控于断层活动,研究区处于重力滑动体系的远缘逆冲构造区。泥底辟期,底辟强度大幅增强,具备了独立改造地貌格局的能力,研究区由逆冲构造区向泥底辟构造区转化。【结论】重力滑动体系由沉积作用驱动,表现出与沉积相类似的空间组合与演化特征。平面上相邻的伸展、泥底辟、逆冲构造区存在成因关联,与具有排序特征的沉积相类似。因此,研究区两期构造活动的叠合关系指示了陆坡向海推进的过程。综上,典型深水研究区沉积构型的时—空演化特征能够为陆坡古地貌演化过程的恢复提供重要依据。

多属性神经网络反演在重力流储层预测中的应用——以歧口凹陷歧南斜坡沙一段为例

歧口凹陷歧南斜坡沙一段属于典型的重力流水道沉积,受湖盆深陷扩张、沉积坡度陡等因素影响,各层砂体普遍发育,但纵向相互叠置、横向变化大,地震属性分析对纵向相互叠置砂体的区分能力较差、精度不高。基于特征曲线重构的多属性神经网络反演技术先通过叠后地震资料拓频处理,提高薄层砂岩识别能力,再利用基于高阶统计量的时频分析技术构建五级层序格架,细化地质研究对象,最后采用基于GR曲线重构的多属性神经网络反演进行储层预测。该技术使得砂体叠置区分辨能力更强,边界刻画更清晰,提高了反演结果的精度和准确性,实践证实储层预测符合率可达到80%以上,为纵横向变化大的砂体分布预测提供了一种新的技术思路。

增强鲁棒性的点融合系统进场排序与调度

现有进场航班排序与调度模型较少考虑不确定因素的影响,本文基于点融合系统(PMS,point merge system)连续下降进近时间符合高斯分布的特点,建立增强鲁棒性的两阶段进场排序与调度模型。第一阶段,描述随机变量带来的不确定性,基于机会约束规划理论设计带有额外缓冲区的静态排序与调度模型,以飞行时间和次序变更量为指标确定最佳额外缓冲区;第二阶段,补偿不可预知原因导致飞行时间超出缓冲区的情况,采用基于滑动时间窗的启发式算法对第一阶段的方案进行动态调整。通过Monte-Carlo模拟不确定性场景下的连续下降进近时间,仿真结果表明,在高、中、低3种交通密度下,所提出的两阶段模型相较于其他模型在鲁棒性和公平性的平衡方面表现出了良好性能。

基于任务优先权的军民航飞行活动协同排序模型

随着空域资源需求的不断增大,军民航间飞行矛盾日益突显。为解决此问题,本文以国务院、中央军事委员会空中交通管制委员会提出的“军民航空管联合运行”为背景,引入军民航共享空域的概念,重点研究了在此类空域中军民航飞行活动协同排序(CMFCS,civil-military aviation flight activity collaborative sequencing)问题。首先,基于军民航各自飞行任务特点与差异,对军民航飞行任务的种类进行划分,并使用层次分析法确定各类飞行任务的优先权原则;其次,以军民航飞行活动总延误时间成本最小为目标,建立CMFCS模型;最后,使用遗传算法对模型进行求解,确定军民航飞行活动批准进入共享空域的时间序列。研究结果表明,与经典的先到先服务(FCFS,first come first service)策略相比,协同排序策略得到的总延误时间成本降低了72.17%,优化效果显著且更符合实际,能够实现军民航共同使用国家空域资源,保障飞行活动安全、有序、高效地运行。

面向加密流量的社交软件用户行为识别

随着智能终端和社交网络越来越融入人们的日常生活,针对社交软件的用户行为识别在网络管理、网络环境监管和市场调研等方面发挥越来越重要的作用.社交软件普遍使用端到端加密协议进行加密数据传输,现有方法通常提取加密数据的统计特征进行行为识别.但这些方法识别的性能不稳定且需要的数据量多,这些缺点影响了方法的实用性.提出了一种面向加密流量的社交软件用户行为识别方法.首先,从加密流量中识别出稳定的控制流数据,并提取控制服务数据分组负载长度序列.然后设计了2种神经网络模型,用于自动从控制流负载长度序列中提取特征,细粒度地识别用户行为.最后,以WhatsApp为例进行了实验,2种神经网络模型对WhatsApp用户行为的识别精准率、召回率和F1-score均超过96%.与类似工作的实验比较证明了该方法识别性能的稳定性,此外,该方法能够通过很少的控制流数据分组达到较高的识别精准率,对实时行为识别的研究具有重要的现实意义.

LOCA事故下安全注射系统可靠性的GO-FLOW法分析

反应堆安全注射系统是包含复杂操作时序的动态系统,本文研究了应用GO-FLOW方法对其进行可靠性分析,导出了能将GO-FLOW用于含两种失效模式的可修部件状态概率计算的可靠性参数等效模型,并验证了模型的正确性。给出了实际算例,结果表明,GO-FLOW方法是对含时序问题的动态系统进行可靠性分析的有效工具,本文导出的可靠性参数等效模型提高了GO-FLOW对多状态问题的分析能力。

求解 Flow Shop 排序问题的模拟进化法

为ＦｌｏｗＳｈｏｐ排序问题的求解构造了一类模拟进化寻优方法——基于启发式规则的有序遗传算法，并对不同规模的实例进行了计算机模拟。

一类Flow Shop排序问题的混合遗传算法

】描述了在平行顺序移动方式下ＦｌｏｗＳｈｏｐ排序问题的数学模型，构造了求解该问题的混合遗传算法．用计算机模拟计算的结果表明。