Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.

A Diffusion-Based Distributed Collaborative Energy Detection Algorithm for Spectrum Sensing in Cognitive Radio

Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.

A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods.

中国地面自动气象站小时降水质量控制方法

高时空分辨率自动气象站降水观测作为重要数据来源,已被广泛应用于强对流天气监测、模式评估、预报复盘等研究工作。仪器故障、特殊天气条件下观测设备的局限性等因素是自动气象站降水数据不确定性的主要来源,这些问题在无人值守气象站尤为突出。该研究基于2021—2023年中国自动气象站实时观测降水量数据、高时空分辨率雷达数据和高灵敏性降水类天气现象数据,发展适应于中国自动气象站小时降水数据的多源数据协同质量控制方法(multi-source data collaborative quality control,MDC)。通过综合定量指标与典型个例分析,对MDC的应用效果进行全面评估。结果显示:MDC判识正确率为99.92%,错误数据命中率较现行业务提升39.3%。基于多源降水观测数据时空一致性,MDC显著提升了晴空降水、融雪性降水和虚假零值降水等异常数据的甄别能力,有效弥补了传统方法的不足。

基于边界约束粒子滤波的多UUV纯方位协同目标跟踪

面向海上跨域协同中多无人水下航行器(UUV)协同探测水面目标需求,针对现有纯方位目标跟踪算法所面临的滤波器初始化困难和水声数据传输丢包问题,提出了一种基于边界约束粒子滤波的多UUV协同纯方位目标跟踪算法。首先提出了主从式协同探测模型,利用跟随者向领航者上报状态估计结果进行数据融合。其次,基于UUV传感器和目标的先验信息设计了初始阶段可靠粒子生成方法和更新阶段的指标函数粒子权重优化方法。最后提出了基于灰色预测的分布式融合算法,得到目标预测结果。仿真实验将所提算法和其他常见算法进行对比,在通信丢包以及噪声干扰情况下验证了算法的有效性和可行性。

一种多用户协同的BOM数据一致性校核与检测算法设计

针对航空航天制造领域中多用户协同工作场景下,由人为错误操作或数据库共享异常导致的数据一致性较差问题,文中基于改进函数依赖和着色PETRI网构建了BOM数据一致性校核与检验模型。该模型根据多用户BOM数据的结构特点,界定了零部件的层次结构与关系定义。同时对传统函数依赖算法加以改进,并控制数据迁移规模及并行度以改善函数依赖发现效率,从而进行数据信息的不一致校核。通过解构HDFS系统数据写入流程,利用着色PETRI网进行数据共享流程建模,进而实现数据共享一致性校核算法的构建。经仿真验证,所述方案的两种耗时指标与对照组算法相比分别降低了26.61%和38.23%,且状态空间及强连通构建图中状态节点和变迁的数量一致,不存在回路,由此证明了该方案的可行性。

航空重(磁)多参量梯度探测与反演技术研究进展

航空重磁梯度、张量梯度测量能有效地减弱环境噪声的干扰,更高分辨率地描述地质体的分布并突出浅部地质体,随着测量装备的成熟,已经在油气与矿产资源勘查中得到应用。本文系统地总结了航空重(磁)多参量梯度探测技术的研究进展,为后续相关研究提供借鉴。基于航空重磁多参量梯度技术的研究背景,总结了国内外装备的进展。针对航空重磁多参量梯度数据的特点,介绍了其数据处理流程;针对航空重磁多参量梯度的分辨率特征,总结了现今重磁多参量梯度的高分辨率反演以及联合反演方法;基于航空重磁多参量梯度探测技术在矿产和油气资源勘查中的应用特征,分析了该项技术在深部资源勘查等方面的应用前景,明确了其在识别地下构造、寻找矿产资源、探测地下小型目标体等方面的应用进展。

National Cancer Institute’s early detection research network:a model organization for biomarker research

For many cancers a primary cause of poor survival is that they are detected at a late stage when therapies are less effective.Although screening methods exist to detect some types of cancer at an early stage,there are currently no effective methods to screen for most types of cancer.Biomarkers have the potential to improve detection of early-stage cancers,risk stratification,and prediction of which pre-cancerous lesions are likely to progress and to make screening tests less invasive.Although thousands of research articles on biomarkers for early detection are published every year,few of these biomarkers have been validated and shown to be clinically useful.This reflects both the inherent difficulty in detecting early-stage cancers and a disconnect between the process of discovering biomarkers and their use in the clinic.To overcome this limitation the US National Cancer Institute created the Early Detection Research Network.It is a highly collaborative program that brings together biomarker discoverers,assay developers,and clinicians.It provides an infrastructure that is essential for developing and validating biomarkers and imaging methods for early cancer detection and has successfully completed several multicenter validation studies.

基于数据挖掘的分布式协同入侵检测

将数据挖掘技术用于协同入侵检测 ,提出基于数据挖掘的协同入侵规则生成算法 ,采用基于数据挖掘的分布式协同入侵检测技术 ,可以有效的检测协同入侵 。

基于视频内容检测的协同过滤视频推荐系统

针对视频推荐系统的稀疏性问题、冷启动问题与灰羊用户问题,提出一种基于视频内容检测的协同过滤视频推荐系统。首先,为视频包含的人体动作进行建模并提取时空兴趣点,使用梯度描述符计算兴趣点的主要移动方向与尺度;然后,为每个视频序列构建一个梯度向量的矩阵,使用聚类算法从梯度矩阵中选择初始化向量与代表向量;最终,通过对受欢迎视频作者与用户的评分分析,获得灰羊用户的分组。仿真实验的结果表明,该视频推荐系统较好地解决了视频推荐系统的稀疏性问题、冷启动问题与灰羊用户问题,获得了较高的推荐准确率。

大数据环境下威胁的协作式检测综述

恶意的不法分子采用直接或间接的方法攻击个人、机构、国家,从而使其遭受不同程度的威胁。此类信息的形式多种多样,数据量巨大,而且需要被高速地处理。因此,首先对5种典型的协作式检测模型Esper,Hadoop,Agilis,Storm和Spark进行分析、比较,阐述不同模型所适用的网络环境;然后对网络环境中常用的攻击手段DDoS,MITM,APT进行分析,说明检测这些攻击适合采用的模型;最后给出威胁的协作式检测架构模型部署方案,该方案包括发送和接收处理两个组件,并指出可根据实际需要进行不同模型的架构部署;特别地,给出了对等网络、分等级的安全域网络、分层结构网络中架构模型的部署方案。

基于网络数据的经管学科研究趋势测度

随着越来越多的交流、合作、共享、搜寻和收集信息发生在多样化的社会网络中,海量的网络数据正成为科学研究的数据源。为了解以网络数据作为数据源的经管领域科学研究的动态景观,本文基于从SSCI及SCI-E数据库提取的相关文献数据,应用科学计量的方法及指标、社会网络分析方法和突现检测算法,开展该领域的测度研究。研究发现该领域的科学产出增长显著;美国在该领域占据绝对领导地位,中国论文世界份额上升最为明显,具有较高的科学影响力及合作影响力;该领域跨国家/地区的科学合作网络呈现出相对稳定的扩张;该领域的研究热点主题有行为、信任、挖掘、内容、情绪、态度、预测等,前沿主题有内容、大数据、社会商务、情绪分析、行为等。

基于多路图像融合的目标跟踪系统设计

针对传统视频监控在区域面积较大的场所具有目标跟踪范围有限、小目标特征不全、数据存储浪费、信息检索复杂等问题,提出基于多路图像融合的目标跟踪系统的设计方案及相关算法。根据现有智能视频分析技术,提出基于双重机制的目标检测算法以提高目标检测效率和准确度,设计单摄像头目标跟踪与特征放大并行方案解决目标跟踪不流畅及目标特征不全的问题,提出多摄像头协同跟踪与视频图像融合算法解决大范围连续跟踪问题。实验测试表明,系统能较好地在摄像头之间连续跟踪目标,并将各摄像头获取的目标关键数据进行有效融合,数据存储量小,能满足无人值守时监测需要,具有一定的实用性和可推广性。

高速铁路下感知路由器群的协同拥塞控制

在高速移动环境下,由于较少考虑单路由器与相邻路由器的连接速率,容易导致拥塞检测准确性差以及数据流整体传输误差率较高的问题。为解决多节点网络拥塞问题,结合网络大数据动态性、快速性和时效性强的特性,提出一种基于路由器群的感知网络控制算法。通过采集参数和数据感知获得更为精准的信息作为判断拥塞情况的依据,并将相互连接的多路由器分层划分,邻近两层的路由器设置成一个虚拟节点。针对虚拟节点内部拥塞发生的3种拥塞情况,由控制器对虚拟节点中的边缘路由器参数进行控制和调整。实验仿真结果验证了该算法的可靠性及其稳定性。

认知无线电中协作频谱感知技术研究

由于无线通信用户的增多和采用的固定频谱分配模式,使频谱资源越来越紧缺,认知无线电的出现极大地提高了频谱的利用率。该文对基于能量检测的单节点检测和协作检测技术进行了分析和讨论,重点分析了协作检测中的数据融合算法,并对几种融合算法和能量检测算法进行了性能的仿真分析。通过分析及仿真结果可以看出,协作检测与单节点检测相比,有效地提高了检测概率。

入侵报警模式挖掘分析算法研究

为有效地缩减报警的数量,提取报警中的有用信息,提出了一个基于CLOSET算法的入侵报警模式挖掘分析算法,在分布式入侵检测系统中,帮助响应部件对入侵检测部件的报警消息进行挖掘分析,挖掘出报警中的频繁闭模式,以此为依据进行响应.为了发现潜在的入侵行为,扩展了IDMEF格式,提出了怀疑度概念.为了不忽略出现不频繁但怀疑度高的报警,对该算法进行了改进,增加了最小怀疑度参数.实验结果表明,两个算法都可以有效地缩减报警的数量,而改进的算法能够更好地提取报警中的有用信息.

多用户协作频谱感知的性能分析

实际的无线信号传输会受到阴影效应、噪声等因素的影响,单个认知用户的频谱感知性能会下降。因此,采用硬合并协作方案,分析了多用户协作感知在高斯、瑞利和莱斯信道下的检测性能。同时,对控制信道由于衰落和噪声影响而产生误码情况下的系统检测性能进行探讨。结果表明,多用户协作检测可以提高系统的检测概率,但虚警概率会随着参与协作的用户数的增多而上升,当控制信道不理想时,协作检测会导致虚警概率下限的出现,造成频谱利用率低下。

基于LabVIEW的协作机器人安全检测系统

为对人机碰撞安全性进行量化测量与表征,有效解决人机碰撞对人体造成伤害的问题,研发了一种协作机器人安全检测系统。系统以CompactRIO为控制器,通过力传感器和激光位移传感器进行数据采集,实现了人机碰撞中的力、功率和速度等信息的精确检测,并用LabVIEW实现了数据处理和显示。通过砝码在固定高度下自由落体进行了平台碰撞模拟试验,实验结果表明检测系统绝对测试精度小于0.5 N,相对测量精度在2.5%之内。

云环境下异常波动状态协作检测方法研究

传统异常检测技术受到冗余数据影响,检测准确率较低,存在一定片面性,由此,本文提出云环境下异常波动状态协作检测方法。分析云计算大规模、虚拟化、高可靠性等特征,据此建立具有部署简单、方便维护、延时较小等优势的云平台集中式监控结构;其次,将准确率、灵敏度与误报率确定为异常波动状态检测指标,利用小数定标方法对数据进行标准化处理,统一数据属性,提取融合后数据的异常特征参数,为数据融合奠定基础;在信息熵分析与非参数CUSUM算法协作基础上判断异常状态,确定异常波动状态,完成检测。仿真结果表明,所提方法检测准确度高,虚警率低,在理论创新的同时具有较强实用价值。