应用多传感器目标跟踪融合技术实现滑坡多点监测数据综合信息的提取

目前的滑坡预报模型只能利用一个关键监测点的监测数据，而不能同时利用多个监测点的监测信息，即只能处理一维时间序列问题.为了解决这个技术难题，本文将滑坡视为一个机动目标，将对滑坡的监测视为对机动目标的跟踪，提出了利用多传感器目标跟踪融合技术来处理滑坡多个点的监测数据的方法，分析了其可行性，并用实例说明了本文提出的方法在实际应用中是有效的.     

风暴分类识别技术在人工防雹中的应用

利用新一代多普勒天气雷达资料,在风暴跟踪识别算法的基础上,发展了风暴分类技术,以提高人工防雹作业指挥的效率。首先以SCIT算法为基础,结合风暴的结构特征,综合利用雷达、探空资料,自动提取风暴结构特征指数;其次采用基于决策树模型的风暴自动分类技术,将风暴按强度分为雷雨云、单体风暴、多单体风暴和强风暴;最后根据风暴强度、高度和位置等属性,对有可能产生冰雹的单体,结合GIS,自动对下游方向或附近作业点进行预警或输出作业参数。通过对2006—2014年期间重庆、辽宁大连和河南三门峡三地发生的较为典型的31次冰雹天气过程、182站次冰雹样本的检验来看:该方法通过对风暴按强度、垂直结构等综合属性进行分类,能有效提高冰雹识别的命中率、降低空报率,其中强风暴的命中率能达到100%,空报率仅为11.4%。能有效提高人工防雹作业的自动化程度,对防雹作业的科学决策有着重要参考作用。     

多源卫星重力场模型精度与频谱分析

针对新一代卫星重力探测技术对地球重力场的频谱贡献问题,该文提出了一种基于GPS/水准数据获取多源卫星重力场模型频谱变化特征的方法。采用GPS/水准外符合检验,有效分析评估了多源卫星重力场模型在中国东、西部地区的精度水平。研究结果表明,以CHAMP、GRACE和GOCE卫星为代表的高-低卫星跟踪卫星、低-低卫星跟踪卫星和卫星重力梯度技术,对地球重力场的频谱贡献分别集中在600km以上的长波和中长波、300km以上的中波、200~350km之间的中短波部分。     

固相吸附毒素跟踪技术(SPATT)在浙江南麂海域的应用

浙江南麂岛海域是我国近岸贝类的重点养殖区,近年来一直遭受脂溶性贝类毒素(lipophilic shellfish toxins,LSTs)的污染,威胁着人类健康。为了建立有效的食品安全预警方法,本研究利用固相吸附毒素跟踪技术(solid phase adsorption toxin tracking,SPATT)在该海域进行了为期一年的野外监测和LSTs毒素分析。结果表明:7种LSTs毒素在SPATT(DIAION?HP20)中被检出,分别是大田软海绵酸(okadaic acid,OA)、鳍藻毒素-1(dinophysistoxin-1,DTX-1)、虾夷扇贝毒素(yessotoxin,YTX)及其衍生物homo YTX、扇贝毒素-2(pectenotoxin-2,PTX-2)及其衍生物7-epi-PTX2sa和环亚胺毒素(gymnodimine,GYM);有8种毒素在厚壳贻贝(Mytilus galloprovincialis)中被检出,分别是OA、DTX1、homo YTX、PTX2、7-epi-PTX2sa、GYM、原多甲藻酸贝类毒素-3(azaspiracid-3,AZA-3)和虾夷扇贝毒素衍生物45-OH-homo YTX。整体而言,SPATT(HP20)中吸附的毒素种类与贝肉中监测出的毒素种类大体上一致,且两者间OA、DTX1和PTX2的浓度在时间上具有较好的相关性,可见SPATT(HP20)对这些LSTs毒素具有较高的灵敏度,有望作为水体中LSTs毒素的预警监测材料。南麂岛海域LSTs分布特征明显,夏季最高,其次是春秋季,冬季最低,其中2014年夏季贝肉中OA毒素含量高达77.19ng/g贝肉,超出国家限量标准(45ng/g贝肉)比例达11.76%,为浙江南麂海域贻贝的食用安全带来隐患。     

Opportunistic natural experiments using digital telemetry: a transit disruption case study

ABSTRACT

In the past decade society has entered a technological period characterized by handheld computing that supports input and processing from numerous sensors. Today’s mobile phones offer the ability to integrate input from sensors monitoring various external and internal sources (e.g., accelerometer, magnetometer, microphone, GPS, wireless Internet, and Bluetooth). Furthermore, these raw inputs can be integrated and processed in ways that can offer novel representations of human behaviour. As a result, new opportunities to examine and better understand human spatial behaviour are available; one such application is the constant monitoring of a group of people over an extended period of time. Such a research setting lends itself to natural experiments that emerge as a result of regular and on-going observations. We report here on the observation of a natural experiment that took place in the context of a month-long monitoring study of 28 participants using mobile phone-based ubiquitous sensor monitoring. The implications for public health and transportation planning are discussed.     

Optimal routing for waste collection: a case study in Singapore

ABSTRACT

To collect and dispose growing amounts of municipal solid waste (MSW) changed to be a hot topic along with the rapid urbanization in past decades. Cities are more and more dependent on the incineration instead of landfilling due to the cost-efficiency and environmental concerns. Considering the limited number of incineration plants and complicated situation of transportation in both spatial and temporal dimensions in different cities, the optimal routing for waste collection turns to be meaningful research topic. In this research, the ant colony optimization (ACO)-based multi-objective routing model coupled with min-max model and Dijkstra’s algorithm is proposed to address the question of which route to take from these waste-generating points to the target incineration plant(s) considering travel time, accident probability (black spots), and population exposure, so as to support the routing decision-making. The model is successfully implemented in Singapore and the effectiveness of the model has also been justified. Besides, few limitations of this research have also been discussed, some of which would also be the future directions of our research, especially the design and integration of a web-based routing decision-making support system.     

The terrestrial fauna of the Late Triassic Pant-y-ffynnon Quarry fissures,South Wales,UK and a new species of Clevosaurus (Lepidosauria: Rhynchocephalia)

Pant-y-ffynnon Quarry in South Wales yielded a rich cache of fossils in the early 1950s, including articulated specimens of new species (the small sauropodomorph dinosaur Pantydraco caducus and the crocodylomorph Terrestrisuchus gracilis), but no substantial study of the wider fauna of the Pant-y-ffynnon fissure systems has been published. Here, our overview of existing specimens, a few described but mostly undescribed, as well as freshly processed material, provides a comprehensive picture of the Pant-y-ffynnon palaeo-island of the Late Triassic. This was an island with a relatively impoverished fauna dominated by small clevosaurs (rhynchocephalians), including a new species, Clevosaurus cambrica, described here from a partially articulated specimen and isolated bones. The new species has a dental morphology that is intermediate between the Late Triassic Clevosaurus hudsoni, from Cromhall Quarry to the east, and the younger C. convallis from Pant Quarry to the west, suggesting adaptive radiation of clevosaurs in the palaeo-archipelago. The larger reptiles on the palaeo-island do not exceed 1.5?m in length, including a small carnivorous crocodylomorph, Terrestrisuchus, and a possible example of insular dwarfism in the basal dinosaur Pantydraco.     

Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization

An origin-destination (OD) flow can be defined as the movement of objects between two locations. These movements must be determined for a range of purposes, and strong interactions can be visually represented via clustering of OD flows. Identification of such clusters may be useful in urban planning, traffic planning and logistics management research. However, few methods can identify arbitrarily shaped flow clusters. Here, we present a spatial scan statistical approach based on ant colony optimization (ACO) for detecting arbitrarily shaped clusters of OD flows (AntScan_flow). In this study, an OD flow cluster is defined as a regional pair with significant log likelihood ratio (LLR), and the ACO is employed to detect the clusters with maximum LLRs in the search space. Simulation experiments based on AntScan_flow and SaTScan_flow show that AntScan_flow yields better performance based on accuracy but requires a large computational demand. Finally, a case study of the morning commuting flows of Beijing residents was conducted. The AntScan_flow results show that the regions associated with moderate- and long-distance commuting OD flow clusters are highly consistent with subway lines and highways in the city. Additionally, the regions of short-distance commuting OD flow clusters are more likely to exhibit ‘residential-area to work-area’ patterns.     

Robust adaptive unscented Kalman filter for bearings-only tracking in three dimensional case

This paper proposes an improved version of Unscented Kalman Filter (UKF), namely Robust Adaptive UKF (RAUKF), with a special focus on Bearings-Only Target Tracking for three-dimensional case (3DBOT). The automatic tuning of the noise covariance matrices and the robust estimation of the target states form a critical point for the performance of the Kalman-type filtering algorithms, especially in the variable environmental conditions exposed in underwater. The key idea of the proposed filter is to combine robust aspects of UKF and adaption of the process and measurement noise covariance matrices with low computational complexity. The main contribution of this paper is to adjust these matrices by means of the steepest descent algorithm, and the H_∞ technique is embedded to achieve superior performance in terms of accuracy and robustness against initial conditions and model uncertainties. Different experiments are performed to evaluate the performance of the proposed algorithm in the 3DBOT problem with a single moving observer. Simulations demonstrate that the proposed filter produce more accurate results with satisfactory computational burden in comparison with other methods.