Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

Depression filling is a critical step in distributed hydrological modeling using digital elevation models (DEMs). The traditional Priority‐Flood (PF) approach is widely used due to its relatively high efficiency when dealing with a small‐sized DEM. However, it seems inadequate and inefficient when dealing with large high‐resolution DEMs. In this work, we examined the relationship between the PF algorithm calculation process and the topographical characteristics of depressions, and found significant redundant calculations in the local micro‐relief areas in the conventional PF algorithm. As such calculations require more time when dealing with large DEMs, we thus propose a new variant of the PF algorithm, wherein redundant points and calculations are recognized and eliminated based on the local micro‐relief water‐flow characteristics of the depression‐filling process. In addition, depressions and flatlands were optimally processed by a quick queue to improve the efficiency of the process. The proposed method was applied and validated in eight case areas using the Shuttle Radar Topography Mission digital elevation model (SRTM‐DEM) with 1 arc‐second resolution. These selected areas have different data sizes. A comparative analysis among the proposed method, the Wang and Liu‐based PF, the improved Barnes‐based PF, the improved Zhou‐based PF, and the Planchon and Darboux (P&D) algorithms was conducted to evaluate the accuracy and efficiency of the proposed algorithm. The results showed that the proposed algorithm is 43.2% (maximum) faster than Wang and Liu's variant of the PF method, with an average of 31.8%. In addition, the proposed algorithm achieved similar performance to the improved Zhou‐based PF algorithm, though our algorithm has the advantage of being simpler. The optimal strategies using the proposed algorithm can be employed in various landforms with high efficiency. The proposed method can also achieve good depression filling, even with large amounts of DEM data.     

Robust abnormality detection methods for spatial search of radioactive materials

Radiological dirty bombs and improvised nuclear devices pose a significant threat to both public health and national security. Growing networks of radiation sensors have been deployed by a number of offices within the U.S. and international agencies. Detecting such threats while minimizing false alarm rates presents a considerable challenge to homeland security and public health. This research aims to achieve a higher probability of detection with a lower probability of false alarms. It focuses on the local spatial instability of radiation levels in order to detect radioactive materials based on robust outlier detection methods. Our approach includes a three‐step abnormality detection method consisting of one‐dimensional robust outlier detection for all gamma‐ray counts, a density‐based clustering analysis, and a two‐dimensional robust outlier detection method using a bagplot, based on spatial associations. The effectiveness of the method proposed is demonstrated through a case study, wherein radioactive materials are detected in urban environments, and its performance is compared with alternative methods employing a k‐sigma approach, local Getis–Ord () statistic, and the goodness of fit of the Poisson distribution.     

Mining spatial associations between daily activities and health using EMA–GPS data

The geographical explicit ecological momentary assessment (GEMA) data collection platform provides extremely rich geospatial datasets and is very promising to gain behavior insights linking mobility, activities, and health. However, the task of analyzing these large datasets effectively is not straightforward, because they often involve a large multivariable dimension and rich qualitative data formats. Responding to the call for innovative analytic approaches in GIScience, this article advocates the use of spatial association rule mining (SARM) to extract frequent associations among daily activities, daily mobility, and health, including both physical health (e.g. pain) and mental health (e.g. happiness). This inductive mining approach works robustly with large datasets and is suitable for both qualitative and quantitative studies. A novel visualization technique to analyze the mined rules is also developed and presented.     

三维虚拟仿真的学生校园活动监控系统

针对目前校园安保监控措施脆弱的实际问题,以及积极推动我国智慧城市建设的实际需求,以中南大学新校区为实验区,基于CityEngine软件的快速建模方法与WebGIS三维模型渲染技术,使用Java语言开发了基于三维虚拟仿真的学生校园活动监控系统。该系统不仅可以进行校园的三维场景动画漫游,还提供了基于学生实时手机定位的监控功能。     

2～18 GHz超宽带低噪声放大器芯片研制

低噪声放大器在射电天文望远镜接收机中是一个重要的前端组件,其性能对接收机的灵敏度和噪声有至关重要的影响。采用OMMIC公司70 nm GaAs mHEMT工艺研究和设计了一款工作频率为2~18 GHz的超宽带单片微波集成低噪声放大器芯片,芯片面积为2 mm×1 mm。放大器电路采用三级级联放大、双电源供电拓扑结构,常温在片测试结果显示,全频带增益大于28 dB,噪声温度平均值为93 K,直流功耗150 mW,无条件稳定。该放大器芯片覆盖了射电天文S,C,X,Ku 4个传统观测波段,适用于厘米波段超宽带接收前端和毫米波段超宽带中频放大模块。     

More than two hundred and fifty thousand spectroscopic binary or variable star candidates discovered by LAMOST

About 786.4 thousand stars were observed by LAMOST twice or more during the first stage of its spectroscopic survey. The radial velocity differences for about 256 thousand targets are larger than10 km s^-1 and they are possible spectroscopic binary or variable candidates(SBVCs). It is shown that most SBVCs are slightly metal poorer than the Sun. There are two peaks in the temperature distribution of SBVCs around 5760 K and 4870 K, while there are three peaks in the distribution of the gravitational acceleration at 2.461, 4.171 and 4.621 cm s^-2. The locations of SBVCs on the [Fe/H]-T, [Fe/H]-log g, log g-T and H-R diagrams are investigated. It is found that the detected SBVCs could be classified into four groups. The first group has higher log g～4.621 and lower T ～ 4870 K which are mainly cool red dwarf binaries. The second group of SBVCs has logg around 4.171 cm s^-2 that includes binaries and pulsating stars such as δSet and γ Dor variables. The gravitational accelerations of the third group of SBVCs are higher and some of them are below the zero-age main sequence. They may be contact binaries in which the primary components are losing energy to the secondaries in the common envelopes and are at a special stellar evolutionary stage.The last group is composed of giants or supergiants with log g around 2.461 cm s^-2 that may be evolved pulsating stars. One target(C134624.29+333921.2) is confirmed as an eclipsing binary with a period of 0.65 days. A preliminary analysis suggests that it is a detached binary with a mass ratio of 0.46. The primary fills its critical Roche lobe by about 89%, indicating that mass transfer will occur between the two components.     

The galaxy luminosity function in the LAMOST Complete Spectroscopic Survey of Pointing Area at the Southern Galactic Cap

We present optical luminosity functions(LFs) of galaxies in the~(0.1) g,~(0.1) r,~(0.1) i bands, calculated using data in~40 deg~2 sky area of the LAMOST Complete Spectroscopic Survey of Pointing Area(LaCoSSPAr) in the Southern Galactic Cap. Redshifts for galaxies brighter than r = 18.1 were obtained mainly with LAMOST. In each band, LFs derived using both parametric and non-parametric maximum likelihood methods agree well with each other. In the0.1 r band, our fitting parameters of the Schechter function are φ_*=(1.65 ± 0.36) × 10~(-2) h~3 Mpc~(-3), M_*=-20.69 ± 0.06 mag and α =-1.12 ± 0.08,which agree with previous studies. Separate LFs are also derived for emission line galaxies and absorption line galaxies. The LFs of absorption line galaxies show a dip at~(0.1) r ~18.5 and can be fitted well by a double-Gaussian function, suggesting a bimodality in passive galaxies.     

无人机倾斜摄影三维模型在城市雨洪风险评估中的应用

研究城市雨洪风险问题,对提高城市洪涝灾害监测、预报的准确性,以及促进城市防洪决策制定具有重要的意义。鉴于高精度的城市三维模型可以提供丰富地理信息,便于准确分析淹没情况,本文针对当前城市洪涝模型对地形数据的高敏感性,且雨洪风险评估研究的准确性受限于地形数据精度的问题,提出利用无人机倾斜摄影测量技术重建高精度实景三维模型,并结合GIS的空间分析功能,以淹没深度为关键指标进行研究区的雨洪风险评估。通过提取不同重现期下研究区的淹没深度信息,进行可视化渲染实现三维淹没分析,可以直观地看到研究区的淹没情况,作为暴雨内涝风险管理依据,同时对城市规划布局有一定的参考价值。     

一种顾及精细层级的室内拓扑模型

针对室内精细化管理的需求,提出了一种顾及精细层级的室内拓扑模型。该模型首先将室内空间构件分为“主体对象-连通对象-虚拟对象”三种类型;接着以主体对象为基准,在垂直方向上又将室内空间划分为“楼栋-楼层-房间-设备”4个级别,并抽象出虚拟对象实现对对象的二次划分;然后针对同级别对象和跨级别对象建立了拓扑组织规则;最后结合三维引擎Cesium.js对模型进行了管理可视化验证。结果表明,该数据模型能够有效地满足精细场景的数据管理需求。