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.

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Collaborative prediction for bus arrival time based on CPS

To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.

Context-Aware Collaborative Filtering Framework for Rating Prediction Based on Novel Similarity Estimation

Recommender systems are rapidly transforming the digital world into intelligent information hubs.The valuable context information associated with the users’prior transactions has played a vital role in determining the user preferences for items or rating prediction.It has been a hot research topic in collaborative filtering-based recommender systems for the last two decades.This paper presents a novel Context Based Rating Prediction(CBRP)model with a unique similarity scoring estimation method.The proposed algorithm computes a context score for each candidate user to construct a similarity pool for the given subject user-item pair and intuitively choose the highly influential users to forecast the item ratings.The context scoring strategy has an inherent capability to incorporate multiple conditional factors to filter down the most relevant recommendations.Compared with traditional similarity estimation methods,CBRP makes it possible for the full use of neighboring collaborators’choice on various conditions.We conduct experiments on three publicly available datasets to evaluate our proposed method with random user-item pairs and got considerable improvement in prediction accuracy over the standard evaluation measures.Also,we evaluate prediction accuracy for every user-item pair in the system and the results show that our proposed framework has outperformed existing methods.

The Dynamic Prediction Model of Number of Participants in Software Crowd Sourcing Collaboration Development Project

Many online platforms providing crowd with opportunities to participate in software development projects have been existed for a while. Meanwhile, many enterprises are using crowd source to collaboratively develop their software via these platforms in recent years. However, some software development projects in these platforms hardly attract users to join. Therefore, these project owners need a way to effectively predict the number of participants in their projects and accordingly well plan their software and project specifications, such as the program language and the size of the documentation, in order to attract more individuals to participant in the projects. Compared with the past prediction models, our proposed model can dynamically add the factors, such as number of participants in the initial stage of the project, within the project life cycle and make the adjustment to the prediction model. The proposed model was also verified by using cross validation method. The results show that: 1) The models with the factor “the number of user participation” is more accurate than the model without it. 2) The factors of crowd dimension are more influential on the prediction accuracy than those of software project and owner dimensions. It is suggested that the project owners not only just consider those factors of the software project dimension in the initial stage of the project life cycle but also those factors of crowd and interaction dimensions in the late stage to attract more participants in their projects.

A Probabilistic Rating Prediction and Explanation Inference Model for Recommender Systems

Collaborative Filtering(CF) is a leading approach to build recommender systems which has gained considerable development and popularity. A predominant approach to CF is rating prediction recommender algorithm, aiming to predict a user's rating for those items which were not rated yet by the user. However, with the increasing number of items and users, thedata is sparse.It is difficult to detectlatent closely relation among the items or users for predicting the user behaviors. In this paper,we enhance the rating prediction approach leading to substantial improvement of prediction accuracy by categorizing according to the genres of movies. Then the probabilities that users are interested in the genres are computed to integrate the prediction of each genre cluster. A novel probabilistic approach based on the sentiment analysis of the user reviews is also proposed to give intuitional explanations of why an item is recommended.To test the novel recommendation approach, a new corpus of user reviews on movies obtained from the Internet Movies Database(IMDB) has been generated. Experimental results show that the proposed framework is effective and achieves a better prediction performance.

QoE Assessment of Group Synchronization Control Scheme with Prediction in Work Using Haptic Media

This paper proposes a group synchronization control scheme with prediction in work using haptic media. The scheme adjusts the output timing among multiple terminals and keeps the interactivity high. It outputs position information by predicting the future position later than the position included in the last-received information by a fixed amount of time. It also advances the output time of position information at each local terminal by the same amount of time. We deal with two different types of work using haptic media so as to demonstrate the effectiveness of the scheme. We assess the output quality of haptic media for the two types of work subjectively and objectively by Quality of Experience (QoE) assessment. We further clarify the relationship between subjective and objective assessment results.

A SON solution for cell outage detection using a cooperative prediction approach

In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed.By the improved collaborative filtering prediction algorithm,the location correlation of users in the wireless network is considered.By incorporating the cooperative grey model prediction algorithm,the time correlation of users motion trajectory is also introduced.Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach.The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods.The detection rate of the proposed approach outperforms the traditional method by around 14%,especially when there are sparse users in the network,and it is able to detect the outage cell with no active users with the help of neighbor cells.

Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment

Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)environments.A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems.In the real world,the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage.In addition to pure manual rebalancing,in several works,attempts were made to predict the demand for bikes.In this paper,we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion.We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering.We carefully analyzed and discovered the group of features that impact the demand of bikes,from historical bike-sharing records and 5G IoT environment data.We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand.We performed sufficient experiments on two real-world datasets.The results confirm that compared to some existing methods,our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.

考虑风电出力不确定性的多源联合系统双层优化调度

针对含风-火-储的多源联合系统,风电出力具有不确定性的特点,风机在特定时间段内的预测功率与实际功率之间存在误差,当风机实际出力无法满足调度计划中安排的功率时会导致系统经济效益大幅下降。为此,文中提出了考虑风电预测误差和需求侧响应的双层优化策略,上层模型以风电、火电和可平移负荷总运行成本最少为目标,采用改进粒子群算法(Improved Particle Swarm Algorithm, IPSO)制定火电和可平移负荷的最优调度策略,然后通过Gibbs法对风机最大出力预测误差的概率密度函数进行抽样获取一定量的样本,得到各样本上层电源的功率缺额;下层模型以储能和可中断负荷总运行成本最少为目标,采用线性规划方法对冲上层电源功率缺额,进而制定下层模型电源调度策略。在大量抽样样本背景下,通过对比各样本总成本函数值的期望和方差验证了所提双层优化策略的经济性和有效性。

船舶远程驾驶人机主从博弈控制方法研究

针对船舶远程驾驶场景下的人机目标非一致性问题,本文设计了系统人主机辅的运行模式,提出一种主从博弈框架下的共享控制方法,将船舶协同避碰转向任务中的人机交互作用描述为完全信息条件下的非合作博弈关系,通过构造驾驶员和共驾控制器的状态空间推导主从博弈的Stackelberg微分对策,使用Fan-GFlicksberg不动点定理证明纳什均衡解的存在性与唯一性。根据驾驶风格和操纵技能预分配驾驶权重,基于模型预测控制方法设计轨迹跟踪器,采取反馈校正方式在有限时域内滚动优化,综合考虑船舶安全航行边界、碰撞风险和人机冲突程度在线进行调节。选取船舶横向偏移误差和驾驶员操作负荷等评价指标,在内河操纵环境下验证了方法的有效性。仿真结果表明:本文所提出的主从博弈控制方法能够为具备不同驾驶风格和操纵技能的作业人员提供个性化辅助,当驾驶员和共驾控制器发生意图冲突时,根据航行风险对预分配权重进行调节,在确保航行安全的前提下使船舶尽可能满足驾驶员的操纵意图。

基于协同降噪与IGWO-SVR的高填方路基沉降预测

高填方路基沉降影响山岭重丘区重载铁路运营安全。为克服实测沉降数据掺杂随机噪声、现有预测模型适用性差的不足,提出基于协同降噪算法与IGWO-SVR模型的沉降预测方法。运用互补集合经验模态分解法(CEEMD)与小波包变换法(WPT)对含噪沉降数据进行协同降噪处理;提出基于佳点集初始化均布、非线性收敛控制与自身历史最优记忆位置更新的改进灰狼优化(IGWO)算法,并结合支持向量回归模型(SVR),构建IGWO-SVR沉降预测模型。进一步地,利用大准铁路工点及现有文献研究成果,验证IGWO-SVR模型的优越性。结果表明:协同降噪法可有效消除原数据中噪声项的干扰波动;在小样本数据集上,IGWO-SVR模型较传统沉降预测模型与现有文献所述预测模型,具有更高的预测精度与稳定性。研究成果为重载铁路高填方路基沉降预测提供了新途径。

占空比传输机制下基于协同预测的时变不确定系统递推滤波

以工业互联网为背景,研究占空比传输机制下一类时变不确定系统的滤波问题,结合协同预测方法设计了新颖的递推滤波算法,解决了占空比传输机制下滤波性能降低的问题。首先给出描述占空比传输机制的数学模型,然后提出结合协同预测方法的递推滤波方案,设计基于占空比机制的递推滤波算法,推导了滤波误差协方差矩阵的一个上界,随后分析这个上界的有界性,实现了在稀疏数据情形下提高滤波性能的目的。仿真结果验证了所提算法的高效性和有效性。

基于协同滤波轨迹预测的机动目标RTPN拦截制导律

针对当前空中威胁目标拦截的实际需求,结合拦截器本身的机动能力,基于全覆盖协同策略,提出一种协同探测的现实真比例导引律(RTPN)制导拦截方法。所提方法解决了传统RTPN方法未考虑拦截器饱和过载限制及对任意机动目标捕获区域的确定问题。此外,针对拦截过程中对目标运动轨迹测量误差及协同探测数据丢包所引起的数据融合精度和鲁棒性问题,提出一种分布式协同滤波算法;针对数据传输和拦截器本身动力学响应延迟等问题,提出一种航迹预测算法。仿真结果验证所提方法能够有效解决饱和过载下的捕获区域确定及动力学延迟问题,及协同探测数据融合中数据丢包所引起的鲁棒性和精度问题。

基于图卷积网络的发明人跨领域合作伙伴识别方法

[研究目的]科学技术与社会的发展促进了不同领域理论、方法和技术的交叉融合,跨领域合作愈发成为合作创新的主流形式,如何帮助发明人定位并准确识别跨领域合作伙伴成为亟待解决的问题。[研究方法]提出一种基于图卷积网络的发明人跨领域合作伙伴识别方法,从多维特征视角下基于发明人专利信息中的合作关系特征、摘要文本特征、领域信息特征使用图卷积网络识别和预测发明人潜在合作伙伴,构建同领域指数和跨领域指数准确识别发明人跨领域合作伙伴。[研究结论]通过对比实验,证明了借助图卷积网络对合作关系特征、摘要文本特征、领域信息特征三维特征联用在进行伙伴识别时能够有效提升模型准确性。借助识别跨领域合作伙伴,有助于促进不同领域之间的交叉合作和知识转移,创造出更具创新性和前瞻性的成果。

融合多主体需求频率特征的复杂产品全生命周期价值链协同设计

高周转率和短保质期的复杂产品具有较高订单频率的特征。传统的价值链设计大多基于复杂产品订单数据的时序和销量对销售周期的影响,忽略了其订单频率中蕴含的细节信息,因而难以准确捕捉多主体间快速变化的供需关系。为了解决这一问题,提出了一种融合多主体需求频率特征的复杂产品全生命周期价值链协同设计方法。首先,采用门控卷积的频率序列提取方法识别多主体需求;其次,将基于频率分段的Transformer时序预测模型融合于订单频率信息,根据改进的时序-频率多头自注意力(seq-fremulti-head attention)结构建立全生命周期价值链,不同分段的时序和频率特征对应不同的注意力头,以实现多段时序和频率特征的融合;最后,将新型价值链协同设计方法应用于某复杂产品多主体需求预测问题,进行实验验证。研究表明,所提出的融合需求频率特征的价值链协同设计方法预测准确度较高,具有很好的应用前景。

基于人体运动预测的协作机器人避障方法研究

针对人机协作系统中的误识别及碰撞检测问题,对人体关节信息提取和建模方法进行研究。设计了一种基于关节间距离的粗大误差剔除策略;在分析了手部运动轨迹规律后,提出一种基于历史轨迹的关节位置跟踪预测方法,通过差分法求导速度的方式,对下一时间节点位置进行预测,在相机采集数据出现异常时,进行校正补充。基于方向包围盒(OBB)思想,建立了一种人机碰撞检测模型,找到了一种机械臂包络体与障碍物实际最小距离的计算方法,避免了系统的错误判断和多余避碰动作的产生;对所述方法进行了碰撞检测仿真验证。结果表明:所提出的方法稳定可靠,可以有效避免人机协作中碰撞的发生。

基于云边协同的新能源功率预测系统研究

为满足当前多时间尺度新能源功率预测的数据分析计算要求,文中提出了基于云边协同的新能源功率预测系统设计方案。构建了基于云边协同预测系统的总体架构,云端对功率预测任务和服务器资源进行总体管控,边缘靠近任务需求侧分担存储、计算任务,并与云端形成层级关系;采用Docker容器技术实现云边系统资源的高效利用,通过资源配置算法优化系统多服务器的延时和功耗平衡;针对大数据文件的云边交互提出通信优化传输机制以节省延时和带宽。最后通过实例验证了所提系统可满足多时间尺度大规模新能源功率预测任务的需求。

基于多模态模糊特征融合的脑龄协同预测算法

深度神经网络可通过训练从大脑图像中预测年龄,作为识别衰老相关疾病的生物标志物.传统的脑龄预测方法往往依赖于单一模态的图像数据,而多模态数据可提供更全面的信息,提高预测精度.然而,现有的多模态融合方法往往不能充分利用不同模态之间的相关性和互补性.为了克服上述问题,文中提出基于多模态模糊特征融合的脑龄协同预测算法(CMFF),设计模糊融合模块和多模态协同卷积模块,可有效利用多模态信息之间的相关信息和互补信息.首先,利用卷积神经网络从多模态脑图中提取特征张量,径向拼接后整合到一个全局特征张量中.然后,利用模糊融合模块学习被模糊化的特征,再将特征应用到多模态协同卷积模块,通过特定的卷积层增强模态间的互补信息.最后,基于性别信息和经过模糊协同处理的特征执行年龄预测回归任务,得到准确的预测年龄.在SRPBS多重障碍MRI数据集上的实验表明,CMFF性能较优.

基于供需两侧协同优化的电动汽车V2G充放电负荷时空分布预测研究

为准确预测电动汽车的V2G充放电负荷,以调节电网负荷峰谷差,保证供电稳定性,提出了一种基于供需两侧协同优化的电动汽车V2G充放电负荷时空分布预测方法。构建供需两侧协同优化目标模型,利用鲸鱼优化算法迭代求解,得出最优充放电负荷曲线,据此明确最优充放电时段。采集不同空间区域最优充放电时段内的充放电负荷影响指标,并以此为输入,构建基于多元线性回归的预测模型,实现电动汽车V2G充放电负荷时空分布预测。试验结果表明,采用所提出的方法得到的负荷预测模型具有较大的决定系数,表明该方法的预测结果更接近实际负荷,具有较高的预测准确性。