Long-term trends in the abundance and breeding performance in Adélie penguins:the Argentine Ecosystem Monitoring Program

In this work,we report long-term trends in the abundance and breeding performance of Adélie penguins(Pygoscelis adeliae)nesting in three Antarctic colonies(i.e.,at Martin Point,South Orkneys Islands;Stranger Point/Cabo Funes,South Shetland Islands;and Esperanza/Hope Bay in the Antarctic Peninsula)from 1995/96 to 2022/23.Using yearly count data of breeding groups selected,we observed a decline in the number of breeding pairs and chicks in crèche at all colonies studied.However,the magnitude of change was higher at Stranger Point than that in the remaining colonies.Moreover,the index of breeding success,which was calculated as the ratio of chicks in crèche to breeding pairs,exhibited no apparent trend throughout the study period.However,it displayed greater variability at Martin Point compared to the other two colonies under investigation.Although the number of chicks in crèche of Adélie penguins showed a declining pattern,the average breeding performance was similar to that reported in gentoo penguin colonies,specifically,those undergoing a population increase(even in sympatric colonies facing similar local conditions).Consequently,it is plausible to assume a reduction of the over-winter survival as a likely cause of the declining trend observed,at least in the Stranger Point and Esperanza colonies.However,we cannot rule out local effects during the breeding season affecting the Adélie population of Martin Point.

A Secure Key Agreement Scheme for Unmanned Aerial Vehicles-Based Crowd Monitoring System

Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial applications.For example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real time.However,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management center.In addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication network.Therefore,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD environment.However,the AKA scheme involves many vulnerabilities in terms of security and privacy.In this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling entities.Our scheme supports key security features,including anti-forgery attacks,and confirms user privacy.The security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle model.Our simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.

Technical Specification for Application of Unmanned Aerial Vehicle( UAV) Monitoring of Rocky Desertification in Karst Area

Taking the opportunity of the third monitoring of rocky desertification in the karst area of China,Zigui County of Hubei Province applied Unmanned Aerial Vehicle( UAV) for the first time for monitoring. Through repeated trials and studies,it established technical requirements including the UAV monitoring technology for the rocky desertification,the feature point photographing,UAV video judgment of rocky desertification degree,UAV video correction misclassification subcompartment,and UAV video observation of rocky desertification control. It completed the third rocky desertification monitoring task of karst area in Zigui County.

Investigation on long-term progressive deformation of engineering slope based on comprehensive monitoring

A landslide always results from a progressive process of slope deformation. In recent years, an increasing number of slope instabilities have occurred with regard to human engineering activities such as hydropower or traffic construction in mountainous area, which cause even greater casualties and economic loss compared with the natural hazards. The development of such earth surface process may hold long period with mechanisms still not fully understood. Using monitoring technology is an effective and intuitive approach to assist analyzing the slope deformation process and their driving factors. This study presents an engineering slope excavated during the construction of Changheba Hydropower Station, which is located in the upper reaches of Dadu River, Sichuan Province, southwest China. The engineering slope experienced and featured a five-year continuous deformation which caused continuous high risks to the engineering activities. We conducted in-depth analysis for such a long-term deformation process based on ground and subsurface monitoring data, collected successive data with a series of monitoring equipment such as automated total station, borehole inclinometers and other auxiliary apparatus, and identified the deformation process based on the comprehensive analysis of monitoring data as well as field investigation. After analyzing the effects of engineering activities and natural factors on the continuous deformation, we found that the overburden strata provided deformable mass while the excavation-produced steep terrain initiated the slope deformation in limit equilibrium state over a long period of time;afterwards, the intense rainwater accelerated slope deformation in the rainy season.

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi- Spectral Image

Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space （RK_HS） for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor （used to control the coarse-to-fine level of detection） is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

A landslide monitoring method using data from unmanned aerial vehicle and terrestrial laser scanning with insufficient and inaccurate ground control points

Non-contact remote sensing techniques,such as terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)photogrammetry,have been globally applied for landslide monitoring in high and steep mountainous areas.These techniques acquire terrain data and enable ground deformation monitoring.However,practical application of these technologies still faces many difficulties due to complex terrain,limited access and dense vegetation.For instance,monitoring high and steep slopes can obstruct the TLS sightline,and the accuracy of the UAV model may be compromised by absence of ground control points(GCPs).This paper proposes a TLS-and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics(RTK)-based control points(RCPs),low-precision TLS-based control points(TCPs)and assumed control points(ACPs)to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions.The effects of GCP accuracy,GCP quantity and automatic tie point(ATP)quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed.The results show that,the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs.The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County,China,and was validated against data from multiple sources.

Multitemporal UAV-based photogrammetry for landslide detection and monitoring in a large area:a case study in the Heifangtai terrace in the Loess Plateau of China

With high spatial resolution,on-demand-flying ability,and the capacity for obtaining threedimensional measurements,unmanned aerial vehicle(UAV)photogrammetry is widely used for detailed investigations of single landslides,but its effectiveness for landslide detection and monitoring in a large area needs to be investigated.The Heifangtai terrace in the Loess Plateau of China is a loess terrace that is extremely susceptible to irrigation-induced loess landslides.This paper used UAV-based photogrammetry for a series of highresolution images spanning over 30 months for landslide detection and monitoring of the terrace with an area of 32 km^2.Dense and evenly distributed ground control points were established and measured to ensure the high accuracy of the photogrammetry results.The structure-from-motion(Sf M)technique was used to convert overlapping images into orthographic images,3D point clouds,digital surface models(DSMs)and mesh models.Using multitemporal differential mesh models,landslide vertical movements and potential landslides were detected and monitored.The results indicate that a combination of UAV-based orthophotos and differential mesh models can be used for flexible and accurate detection and monitoring of potential loess landslides in a large area.

GPS monitoring and analysis of ground movement and deformation induced by transition from open-pit to underground mining

To trace the potential hazards of open-pit slope in Longshou mine,global positioning system（GPS） is applied to monitoring ground movement and deformation induced by transition from open-pit to underground mining.Through long-term monitoring from 2003 to 2008,huge amounts of data were acquired.Monitoring results show that large-scale ground movement and deformation have occurred in mining area,and the movement area is ellipse-shaped.The displacement boundary of settlement trough is 2.0 km long along the exploratory line,and 1.5 km long along the strike of ore body.GPS monitoring results basically agree with the practical deformation state of open-pit slope.It is indicated that the long-term GPS monitoring is an effective way to understand the mechanism of ground movement and deformation in mine area. 更多

Design and Implementation of Long-Term Single-Lead ECG Monitor

Some heart diseases need long-term monitoring to diagnose. In this paper, we present a wearable single lead ECG monitoring device with low power consumption based on MSP430 and single-lead ECG front-end AD8232, which could acquire and store patient’s ECG data for 7 days continuously. This device is available for long-term wearing with a small volume. Also, it could detect user’s motion status with an acceleration sensor and supports Bluetooth 4.0 protocol. So it could be expanded to be a dynamic heart rate monitor and/or sleep quality monitor combined with smart phone. The device has huge potential of application for health care of human daily life.

Railway Transport Infrastructure Monitoring by UAVs and Satellites

Improving the rail transport security requires development and implementation of neoteric monitoring and control facilities in conditions of increasing speed and intensity of the train movement and high level of terrorist threat. Use of Earth remote sensing (ERS), permitting to obtain information from large areas with a sufficiently high resolution, can provide significant assistance in solving the mentioned problems. This paper discusses the possibility of using various means of remote sensing such as satellites and unmanned aerial vehicles (UAV), also known as drones, for receiving information in different ranges of the electromagnetic spectrum. The paper states that joint using of both these means gives new possibilities in improving railroad security.

Field testing innovative differential geospatial and photogrammetric monitoring technologiesin mountainous terrain near Ashcroft,British Columbia,Canada

This paper presents a novel approach to continuously monitor very slow-moving translational landslides in mountainous terrain using conventional and experimental differential global navigation satellite system(d-GNSS)technologies.A key research question addressed is whether displacement trends captured by a radio-frequency“mobile”d-GNSS network compare with the spatial and temporal patterns in activity indicated by satellite interferometric synthetic aperture radar(InSAR)and unmanned aerial vehicle(UAV)photogrammetry.Field testing undertaken at Ripley Landslide,near Ashcroft in south-central British Columbia,Canada,demonstrates the applicability of new geospatial technologies to monitoring ground control points(GCPs)and railway infrastructure on a landslide with small and slow annual displacements(<10 cm/yr).Each technique records increased landslide activity and ground displacement in late winter and early spring.During this interval,river and groundwater levels are at their lowest levels,while ground saturation rapidly increases in response to the thawing of surficial earth materials,and the infiltration of snowmelt and runoff occurs by way of deep-penetrating tension cracks at the head scarp and across the main slide body.Research over the last decade provides vital information for government agencies,national railway companies,and other stakeholders to understand geohazard risk,predict landslide movement,improve the safety,security,and resilience of Canada’s transportation infrastructure;and reduce risks to the economy,environment,natural resources,and public safety.

A Study of Temperature and Aging Effects on Eigenfrequencies of Concrete Bridges for Health Monitoring

This paper discusses the influence of environmental factors and of normal material aging on the eigenfrequencies of concrete bridges based on monitoring data registered during 4 years of a specific bridge. It is a new composite steel-concrete bridge built in 2006 in Luxembourg. The measurements are analyzed and compared to literature data. The final objective is the use of real monitored eigenfrequencies for structural health monitoring and damage detection based on identification of stiffness losses in practical applications. Therefore, it is very important to identify and compensate for outdoor influences namely temperature, excitation force level and normal aging effects, like creep and shrinkage of concrete and their impact on material properties. The present paper aims at describing these effects in order to separate them from damage effects. It is shown that temperature change rates and temperature gradients within the bridge have an influence on the eigenfrequencies. Hence the key idea for assessment from the full database is to select only measurements with small temperature differences and slow temperature change rates.

Evaluation and Forecasting of Elapsed Fatigue Life of Ship Structures by Analyzing Data from Full Scale Ship Structural Monitoring

This paper describes the activities carried out by CETENA in collaboration with the Italian Navy to assess the behavior of new FREMM frigates by means of an automatic hull monitoring system and to predict the expected fatigue life of ship structure by analyzing recorded data through a specifically developed post-processing tool.

城市智慧排水管控中多元监测体系构建研究

城市河流与排水管网污染溯源的时效性差与监测体系空间覆盖不平衡等仍是城市水环境管理的突出问题。随着无人机、物联网、水质在线监测、高精度定位等技术的发展,实现快速污染溯源的多元监测体系正逐步实现。该研究通过对中山市濠头涌区域的排水管网关键节点与河流关键断面安装分布式检测仪表,并应用无人监测船对城市河流进行地形测量、水质监测,完成了监测体系的构建和多元监测数据的获取,实现了分布式仪表与无人监测船的优势互补。此外,基于电导率与氨氮相关性系数大于0.92的结果及氨氮作为主要污染物的现状,验证了电导率作为主要水质实时监测指标的合理性。

智慧系统在液体散货码头装卸工艺中的应用

结合智慧港口应用热点和当前行业发展现状,以某液体散货码头为例,针对码头装卸工艺中智慧系统的应用,从智能装卸臂、在线腐蚀监测、在线自动取样、管道无人巡检等系统进行多角度的分析,提出可在当前液体散货码头装卸工艺中落实的智慧系统模块的应用方案。该方案可减少现场工人的工作量,通过数据赋能提升码头运营、管理等作业效率,提高港口管理的信息化、精准化,并可为类似工程提供参考。

基于鸿蒙系统的无人方程式赛车远程监控与数据管理系统开发

为了提高无人方程式赛车运行的安全性,设计了一种基于鸿蒙系统的无人方程式赛车远程监控与数据管理系统。该系统主要包括数据采集软件、远程监控平台、云数据库。数据采集软件使用鸿蒙系统开发,通过使用控制器局域网(controller area network,CAN)总线传输、蓝牙传输、WebSocket协议、HTTP协议,实现对CAN数据的解析、显示、转发。监控系统使用Spring Boot框架开发,实现对无人方程式赛车的远程监控。云数据库使用MySQL数据库开发,实现无人方程式赛车数据的快速存储。测试结果表明,该套系统可以实现对无人方程式赛车的远程监控,实现十万级以上数据量低延迟储存,数据丢失率平均为0.0424%。目前,该套系统已经应用到无人方程式赛车的调试中。

基于航空摄影测量的填埋堆体动态重构精度及影响因素

填埋堆体表面形变监测是填埋场库容管理和堆体失稳等风险分析的核心,其时空高分辨率监测研究近年来引起广泛关注。基于航空摄影的地表测量技术具有采集速度快、时空分辨率高等优点,但在填埋场特殊环境下,面对高频填埋活动、显著的堆体变化以及防雨膜覆盖等干扰时,要同时满足高时空分辨率和耗时短的动态监测要求,无人机的最佳飞行参数设定亟待研究。为此,该研究模拟中等规模危险废物填埋场的规模和日填埋量,利用专业级无人机获取模拟区域图像,空三加密处理生成三维点云数据,利用Arcmap叠加分析多期监测数据,从点位坐标、重构尺度、重构体积、体积差分精度4个角度分析重构误差。结果表明:三维重构的坐标精度可以达到米级;重构尺度误差为2~3 cm,重构体积误差为0.16~0.17 m^(3),差分体积误差为0.16~0.17 m^(3);进一步研究发现,高度为25~55 m时,高度越高精度越低,相机倾角为[-53°,-60°]时,精度最高,旁向重叠率大于80%时,误差骤减且基本稳定。考虑到填埋场的填埋作业间隔、无人机续航能力等对飞行时间的约束,为获得最佳精度,建议无人机飞行高度为37 m、相机倾斜角度为[-53°,-60°]、旁向重叠率为80%。

基于分层监测域人工势场法的无人驾驶车辆路径规划

针对传统人工势场法在路径规划中存在易陷入局部极小值点、障碍物附近目标点不可达以及固定步长下易发生碰撞的问题,提出一种基于分层监测域的自适应人工势场法(APFLMD)。设计了分层监测域模型,通过建立安全避障范围,实现无人驾驶车辆速度的自适应变化,以提高车辆的避障能力。为避免无人驾驶车辆陷入局部极小值点区域,利用路径点的聚集情况实现局部极小值点的检测。设计了等势圈切线点,并结合二次贝塞尔曲线对路径进行优化,且目标点位于斥力等势圈内部时,提出一种虚拟随机引导策略,以帮助车辆逃离局部极小值点。最后,在斥力场函数中添加距离因子以解决障碍物附近目标点不可达问题。仿真结果表明,在复杂环境下APFLMD算法相较于对比算法,可分别降低49.23%的车辆行驶时间、19.4%的路径长度和车辆能耗以及82.12%的路径平滑度。