河南地矿CORS省域地面沉降监测与机理分析

为了进一步研究省域尺度范围地面沉降,分析河南地矿CORS省域地面沉降的监测方法与其形成机理:针对河南省地面沉降分布特征,采用河南省地质信息连续采集运行系统(HNGICS)的62个基准站数据,时间序列为2012—2019年,获得国际地球参考框架下无净旋转条件的河南省三维速度场;然后分解三维速度场获得河南垂直速度场,发现河南省地面沉降呈现“西稳东沉”的现象,开封附近出现沉降漏斗;最后将河南省地质图、河南省耕地面积占比、河南省年均降雨量与沉降等值线对比分析。结果表明:不同地质条件下,河南省地面沉降分布于豫东地区的第四系冲积层,而相同地质条件下,耕地占比和区域年均降雨量是地表差异性沉降的主要原因,如豫东地区耕地约占总面积的70%,地下水用于农业灌溉是豫东平原出现沉降漏斗的主要原因。

北斗三号带状CORS系统基准站布设距离研究

针对传统的CORS系统在高速公路等带状区域场景下存在的定位性能不足问题,尝试通过构建单站RTK和多站VRS结合的带状CORS系统从而提升定位性能,并实地采集多组不同基线长度的北斗三频观测数据,测试在满足平面2 cm、高程3 cm的最大定位误差前提下,该种CORS服务模式下基站的合理布设距离。实验结果表明,在开阔、轻微遮挡和中度遮挡环境下,满足该定位精度的基线长度分别可以达到30 km、19.7 km、15 km,而对于更长的基线,其定位精度及模糊度固定成功率均明显下降,不能保证定位的可靠性。

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

基于北斗CORS的铁路快速静态测量方法研究

复杂环境下由于卫星信号质量差等原因,常规地区的测量技术标准不具有适用性。为研究复杂环境下快速静态测量的技术方案,利用某铁路带状稀疏卫星导航定位基准站(Continuously Operating Reference Stations,CORS)综合服务系统,开展一系列快速静态测量实验,通过箱形图等方式,分析不同星座、不同观测时长下的测量精度。与铁路控制网的成果比较后发现,观测时长为5~25min且解算成功的前提下,基于CORS的GPS+BDS、单GPS与单BDS的快速静态测量成果计算的外符合精度差异较小,平面和高程中误差整体优于16 mm和45 mm,均可满足像控点测量精度要求;存在解算失败的情况,解算失败的像控点与其距CORS站的距离无显著联系,随着观测时长的增加,解算失败的时段数明显减少。此外,在相同观测时长下,基于CORS的GPS+BDS与单BDS模式测量精度较高,单GPS相对较差。对于复杂环境,北斗CORS系统在铁路快速静态测量方面替代GPS是可行的,建议快速静态测量时长在25 min以上为宜。

铁路带状CORS的VRS内插模型研究

针对铁路带状CORS系统存在的网外收敛速度慢、定位精度低的问题,本文对3种VRS的大气延迟内插模型,即最小二乘配置模型、距离相关内插模型和线性内插模型进行了误差仿真分析。真结果显示,在带状网型中,LSC模型的抗差性能最佳,其次为DIM模型,LIM模型则表现最差。然后基于某高速铁路CORS网络的GNSS观测数据,3种模型进行了实证测试,结果与仿真相吻合。具体为,LSC/DIM/LIM模型的定位精度在水平方向约为3.0、5.3、14.1 cm,高程为3.5、6.8、43.1 cm。LSC模型最适用于带状网络VRS定位。此外,本研究可为铁路CORS系统的精化提供参考。

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

厦门市台风事件CORS站网数据水汽反演及分析

针对厦门市3次台风事件,利用厦门市连续运行参考站(CORS)的GNSS观测数据,使用PANDA软件进行解算,反演出大气可降水量(PWV),将获取的ERA5再分析数据与反演出的PWV数据进行对比分析,在此基础上分析厦门市受台风影响期间GNSS-PWV变化特征与实际降雨量的关系.结果表明:PWV与ERA5数据具有较好的一致性;GNSS反演PWV具有较高的精度;GNSS-PWV能够较好地反映台风期间水汽的时空动态变化过程,其与降水量之间呈现出明显的相关性.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

地形测量中GPS-RTK测量CORS模式结合电台模式的运用研究

分析了CORS模式与电台模式的优缺点,并结合两者联合运用的测绘实例,评价两者联合运用的价值。研究结果表明,CORS模式与电台模式结合使用能够提升地形车辆的效率与准确率,针对复杂地形展开有效的地形测量。

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Factorized Smith Method for A Class of High-Ranked Large-ScaleТ-Stein Equations

We introduce a factorized Smith method(FSM)for solving large-scale highranked J-Stein equations within the banded-plus-low-rank structure framework.To effectively reduce both computational complexity and storage requirements,we develop techniques including deflation and shift,partial truncation and compression,as well as redesign the residual computation and termination condition.Numerical examples demonstrate that the FSM outperforms the Smith method implemented with a hierarchical HODLR structured toolkit in terms of CPU time.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

CORS系统在内河航道测量中的有效运用分析

随着我国卫星定位技术的发展,CORS系统在测绘工程实际工作中得到了广泛的应用。相比于传统的RTK测量作业方式而言,CORS系统具备测量范围广、测量效率高、误差可控等优势。如今,CORS系统已经成为城市建设、市政工程及内河航道工程等测量的核心技术和重要手段。该系统的问世得益于现代信息技术以及卫星定位系统的高质量发展。本文从CORS系统的特点出发,结合本人参与的新万福河复航工程中利用CORS系统测量的工作实际,探索该系统在内河航道工程测量中的有效运用方法。

Numerical and theoretical study of large-scale failure of strata overlying sublevel caving mines with steeply dipping discontinuities

The deformation and fracture evolution mechanisms of the strata overlying mines mined using sublevel caving were studied via numerical simulations.Moreover,an expression for the normal force acting on the side face of a steeply dipping superimposed cantilever beam in the surrounding rock was deduced based on limit equilibrium theory.The results show the following:(1)surface displacement above metal mines with steeply dipping discontinuities shows significant step characteristics,and(2)the behavior of the strata as they fail exhibits superimposition characteristics.Generally,failure first occurs in certain superimposed strata slightly far from the goaf.Subsequently,with the constant downward excavation of the orebody,the superimposed strata become damaged both upwards away from and downwards toward the goaf.This process continues until the deep part of the steeply dipping superimposed strata forms a large-scale deep fracture plane that connects with the goaf.The deep fracture plane generally makes an angle of 12°-20°with the normal to the steeply dipping discontinuities.The effect of the constant outward transfer of strata movement due to the constant outward failure of the superimposed strata in the metal mines with steeply dipping discontinuities causes the scope of the strata movement in these mines to be larger than expected.The strata in the metal mines with steeply dipping discontinuities mainly show flexural toppling failure.However,the steeply dipping structural strata near the goaf mainly exhibit shear slipping failure,in which case the mechanical model used to describe them can be simplified by treating them as steeply dipping superimposed cantilever beams.By taking the steeply dipping superimposed cantilever beam that first experiences failure as the key stratum,the failure scope of the strata(and criteria for the stability of metal mines with steeply dipping discontinuities mined using sublevel caving)can be obtained via iterative computations from the key stratum,moving downward toward and upwards away from the goaf.

CORS基站在净水管网工程中应用

随着CORS基站技术的发展,它的技术应用越来越广,随着施工节奏越来越快,就需要高效、快速、准确的测量方法。CORS基站是一种快速、高效为主要特点的测量模式。利用多基站网络RTK技术建立的连续运行(卫星定位服务)参考站(Continuously OperatingReferenceStations,英文缩写为CORS,中文为千寻),已成为卫星导航系统应用的发展热点之一。在市政工程中施工放样优势明显,已成为市政测量的主流模式。