Hierarchical Bayesian Calibration and On-line Updating Method for Influence Coefficient of Automatic Dynamic Balancing Machine

Measurement error of unbalance＇s vibration response plays a crucial role in calibration and on-line updating of influence coefficient（IC）. Focusing on the two problems that the moment estimator of data used in calibration process cannot fulfill the accuracy requirement under small sample and the disturbance of measurement error cannot be effectively suppressed in updating process, an IC calibration and on-line updating method based on hierarchical Bayesian method for automatic dynamic balancing machine was proposed. During calibration process, for the repeatedly-measured data obtained from experiments with different trial weights, according to the fact that measurement error of each sensor had the same statistical characteristics, the joint posterior distribution model for the true values of the vibration response under all trial weights and measurement error was established. During the updating process, information obtained from calibration was regarded as prior information, which was utilized to update the posterior distribution of IC combined with the real-time reference information to implement online updating. Moreover, Gibbs sampling method of Markov Chain Monte Carlo（MCMC） was adopted to obtain the maximum posterior estimation of parameters to be estimated. On the independent developed dynamic balancing testbed, prediction was carried out for multiple groups of data through the proposed method and the traditional method respectively, the result indicated that estimator of influence coefficient obtained through the proposed method had higher accuracy; the proposed updating method more effectively guaranteed the measurement accuracy during the whole producing process, and meantime more reasonably compromised between the sensitivity of IC change and suppression of randomness of vibration response.

Study on group air to ground attack-defends hierarchical dynamic decision-making

As to oppositional, multi-objective and hierarchical characteristic of air formation to ground attackdefends campaign, and using dynamic space state model of military campaign, this article establishes a principal and subordinate hierarchical interactive decision-making way, the Nash-Stackelberg-Nash model, to solve the problems in military operation, and find out the associated best strategy in hierarchical dynamic decision-making. The simulating result indicate that when applying the model to air formation to ground attack-defends decision-making system, it can solve the problems of two hierarchies, dynamic oppositional decision-making favorably, and reach preferable effect in battle. It proves that the model can provide an effective way for analyzing a battle,

A High-level Architecture for Intrusion Detection on Heterogeneous Wireless Sensor Networks: Hierarchical, Scalable and Dynamic Reconfigurable

Networks protection against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure;i.e. it is designed and applicable, in one, two or three levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Sensor-based Intrusion Detection System (SIDS) on sensor nodes, Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical, distributed and clustering structure along with clusters' overlapping. Finally, this paper has been designed a questionnaire to verify the proposed idea;then it analyzed and evaluated the acquired results from the questionnaires.

A first-order dynamical model of hierarchical triple stars and its application

For most hierarchical triple stars, the classical double two-body model of zeroth-order cannot describe the motions of the components under the current observational accuracy. In this paper, Marchal＇s first-order analytical solution is implemented and a more efficient simplified version is applied to real triple stars. The results show that, for most triple stars, the proposed first-order model is preferable to the zerothorder model both in fitting observational data and in predicting component positions.

基于区域注册机制移动IP系统Dynamics的实现、分析与改进

对HUTMobile IP系统Dynamics的区域注册机制进行了详细分析,讨论了层次型FA模型中的区域注册机制。详细阐述了区域注册机制的实现过程和区域注册机制的具体设计方法,最后从时延等方面进行分析,给出了进一步的改进措施。分析表明,区域注册机制可以大大降低注册时延、减少注册数据包的数量、改善移动IP的性能,将是未来移动IP实现的主流。

Method of Time Series Similarity Measurement Based on Dynamic Time Warping

With the rapid development of mobile communication all over the world,the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities.Mobile phone communication data can be regarded as a type of time series and dynamic time warping(DTW)and derivative dynamic time warping(DDTW)are usually used to analyze the similarity of these data.However,many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series.In this paper,a novel hybrid method based on the combination of dynamic time warping and derivative dynamic time warping is proposed.The new method considers not only the distance between time series,but also the shape characteristics of time series.We demonstrated that our method can outperform DTW and DDTW through extensive experiments with respect to cophenetic correlation.

Population dynamics modelling with spatial heterogeneity for yellow croaker(Larimichthys polyactis)along the coast of China

As one of the top four commercially important species in China,yellow croaker(Larimichthys polyactis)with two geographic subpopulations,has undergone profound changes during the last several decades.It is widely comprehended that understanding its population dynamics is critically important for sustainable management of this valuable fishery in China.The only two existing population dynamics models assessed the population of yellow croaker using short time-series data,without considering geographical variations.In this study,Bayesian models with and without hierarchical subpopulation structure were developed to explore the spatial heterogeneity of the population dynamics of yellow croaker from 1968 to 2015.Alternative hypotheses were constructed to test potential temporal patterns in yellow croaker’s population dynamics.Substantial variations in population dynamics characteristics among space and time were found through this study.The population growth rate was revealed to increase since the late 1980s,and the catchability increased more than twice from 1981 to 2015.The East China Sea’s subpopulation witnesses faster growth,but suffers from higher fishing pressure than that in the Bohai Sea and Yellow Sea.The global population and two subpopulations all have high risks of overfishing and being overfished according to the MSY-based reference points in recent years.More conservative management strategies with subpopulation considerations are imperative for the fishery management of yellow croaker in China.The methodology developed in this study could also be applied to the stock assessment and fishery management of other species,especially for those species with large spatial heterogeneity data.

Flame Recognition in Video Images with Color and Dynamic Features of Flames

Recently,video based flame detection has become an important approach for early detection of fire under complex circumstances.However,the detection accuracy of most existing methods remains unsatisfactory.In this paper,we develop a new algorithm that can significantly improve the accuracy of flame detection in video images.The algorithm segments a video image and obtains areas that may contain flames by combining a two-step clustering based approach with the RGB color model.A few new dynamic and hierarchical features associated with the suspected regions,including the flicker frequency of flames,are then extracted and analyzed.The algorithm determines whether a suspected region contains flames or not by processing the color and dynamic features of the area altogether with a classifier,which can be a BP neural network,a k nearest neighbor classifier or a support vector machine.Testing results show that this algorithm is robust and efficient,and is able to significantly reduce the probability of false alarms.

New multicast scheme based on Dynamic mobility prediction in mobile IPv6 environment

A new mobile multicast scheme called mobility prediction based mobile multicast(MPBMM) was proposed. In MPBMM, when a mobile node (MN) roams among subnets during a multicast session, MN predicts the next subnet, to which MN will attach, by the information of its position and mobility speed, consequently speeds up the handoff procedure. Simulation results show that the proposed scheme can minimize the loss of multicast packets, reduce the delay of subnet handoff, decrease the frequency of multicast tree reconfiguration, and optimize the delivery path of multicast packets. When MN moves among subnets at different speeds (from 5 to 25 ms), the maximum loss ratio of multicast packets is less than0.2%, the maximum inter-arrival time of multicast packets is 117 ms, so the proposed scheme can meet the QoS requirements of real-time services. In addition, MPBMM can support the mobility of multicast source.

An application of Bayesian multilevel model to evaluate variations in stochastic and dynamic transition of traffic conditions

This study seeks to investigate the variations associated with lane lateral locations and days of the week in the stochastic and dynamic transition of traffic regimes(DTTR).In the proposed analysis,hierarchical regression models fitted using Bayesian frameworks were used to calibrate the transition probabilities that describe the DTTR.Datasets of two sites on a freeway facility located in Jacksonville,Florida,were selected for the analysis.The traffic speed thresholds to define traffic regimes were estimated using the Gaussian mixture model(GMM).The GMM revealed that two and three regimes were adequate mixture components for estimating the traffic speed distributions for Site 1 and 2 datasets,respectively.The results of hierarchical regression models show that there is considerable evidence that there are heterogeneity characteristics in the DTTR associated with lateral lane locations.In particular,the hierarchical regressions reveal that the breakdown process is more affected by the variations compared to other evaluated transition processes with the estimated intra-class correlation(ICC)of about 73%.The transition from congestion on-set/dissolution(COD)to the congested regime is estimated with the highest ICC of 49.4%in the three-regime model,and the lowest ICC of 1%was observed on the transition from the congested to COD regime.On the other hand,different days of the week are not found to contribute to the variations(the highest ICC was 1.44%)on the DTTR.These findings can be used in developing effective congestion countermeasures,particularly in the application of intelligent transportation systems,such as dynamic lane-management strategies.

Combined Control Allocation and Sliding Mode Control in the Dynamic Control of a Vehicle with Eight In-Wheel Motors

An eight wheel independently driving steering（8 WIDBS）electric vehicle is studied in this paper.The vehicle is equipped with eight in-wheel motors and a steer-by-wire system.A hierarchically coordinated vehicle dynamic control（HCVDC）system,including a high-level vehicle motion controller,a control allocation,an inverse tire model and a lower-level slip/slip angle controller,is proposed for the over-actuated vehicle system.The high-level sliding mode vehicle motion controller is designed to produce desired total forces and yaw moment,distributed to longitudinal and lateral forces of each tire by an advanced control allocation method.And the slip controller is designed to use a sliding mode control method to follow the desired slip ratios by manipulating the corresponding in-wheel motor torques.Evaluation of the overall system is accomplished by sine maneuver simulation.Simulation results confirm that the proposed control system can coordinate among the redundant and constrained actuators to achieve the vehicle dynamic control task and improve the vehicle stability.

港口大规模冷箱负荷群用电的一致性分层优化调度方法

为解决港口大量冷藏集装箱负荷群优化调度面临的优化效果与计算效率难题,该文提出冷箱集群分层迭代调度架构及多智体制冷效率一致性优化策略。建立考虑热动态过程的冷箱负荷用电模型,并根据用电特性将冷箱聚类为集群,降低冷箱控制维度与信息交互量级。建立冷箱动态电价与集群用电功率迭代优化的预调度模型,提出冷箱制冷效率主从一致性的功率动态分配算法,冷箱个体根据电价、温度、制冷限值主动响应预调度策略,实现大规模冷箱自趋优运行和负荷功率有序转移。以日照港为算例,所提方法可将用电成本降低12.5%,计算效率提升4倍,优化结果与全局优化的偏差仅为0.5%,实现了大规模冷箱群高效优化。

大数据能力对科技型企业绩效的影响机制研究--环境不确定性调节效应

本文基于资源基础理论、动态能力理论等,利用河南省科技型企业216个样本数据,对大数据能力、环境动态性、环境复杂性和企业绩效的关系进行实证研究,研究结果显示:第一,企业大数据能力对企业绩效有显著的正向影响。第二,企业资源获取能力、企业洞察预测能力对企业绩效有正向影响,资源获取能力对企业绩效的影响最大;企业整合分析能力对企业绩效的影响没有显著的影响。第三,环境动态性和环境的复杂性对大数据能力与企业绩效的调节效应是显著。最后指出了本研究对管理实践的启示,并从政府和企业层面提出了对策建议。

基于分层有向图与动态时空相关性的小区域光伏超短期预测方法

小区域光伏发电功率准确预测,已成为高渗透多台区系统精准调控运行的技术瓶颈。目前光伏发电功率的预测方法缺乏对台区小区域内光伏集群效应的考虑,忽略了输入变量的内在因果关系以及动态相关性。针对于上述问题,提出一种基于分层有向图以及动态图卷积循环网络(dynamic graph convolutional recurrent network,DGCRN)的小区域光伏预测方法。首先,考虑出力数据与数值天气预报(numerical weather prediction,NWP)单向关系,生成具有因果联系的分层有向图。其次,依据节点属性在每个时间步生成动态图,与预先定义的静态图有机结合,以捕捉节点之间的动态时空相关性。最后,将具有动态时空相关性的图结构用于模型训练。以某个小区域台区29个光伏节点加以预测建模,实验结果表明,DGCRN模型具备捕获多参量间的因果规律和提取光伏功率的短期动态特征的能力,其在小区域多节点的光伏发电功率预测性能优越。

一种分层注意力机制与用户动态偏好融合的序列推荐算法

针对现有的序列推荐算法通常仅采用单一项目信息来捕获项目的潜在特征,以及循环神经网络存在时间依赖性随序列中位置单调变化的问题,提出一种分层注意力机制与用户动态偏好融合的序列推荐算法.首先,针对单一项目信息不足以学习项目准确表示的问题,提出一种分层注意力机制用于学习高质量的项目表示.其次,针对循环神经网络的时间依赖性严重损害了用户近期偏好建模的问题,引入文本卷积神经网络来提取循环隐藏状态之间的短期序列模式,并根据用户意图将用户长期偏好与近期偏好进行动态融合.此外,针对传统自注意力机制无法对序列中元素的相对位置信息进行建模的问题,对现有的自注意力机制进行了改进,充分捕获序列中元素的相对位置信息.并在公开数据集MovieLens-1M与Amazon-Book上与现有优秀算法作比较,实验结果证明了所提算法的有效性.

基于HTN的巡视器动态规划方法

为提升深空探测任务中巡视器动态规划处理效率,从航天工程实践经验出发,结合分层任务网络(hierarchical task network,HTN)理论研究成果,提出巡视器领域知识建模方法以及动态规划流程。针对经典动作规划系统不能处理时态约束关系的问题,在建模中引入受限状态事件的表示方法。针对动态规划中的时态约束关系,拓展Allen区间代数的定义,提出事件、全局时间点和虚拟事件的时序逻辑判断方法。针对动态规划中存在的状态空间一致性问题,提出虚拟指令继承原有状态空间的方法。研究结果完善了HTN在航天领域的知识建模方法,解决了经典动作规划缺少时间信息约束的缺陷。所提出的虚拟事件、虚拟指令解决动态规划状态判定方法,可有效提高深空探测任务中巡视器遥操作的工作效能和地面中心进行事件处理的灵活性。

基于k-sums分段聚类的动态组合学习光伏短期功率预测

目前单一模型预测精度存在难以随着功率波动保持最优的问题,为提高并网系统运行的稳定性和电网的节能调度,文中提出了一种基于k-sums分层聚类的动态学习组合光伏短期功率预测方法。利用k-sums算法经过分段聚类,将天气类型分为晴天A 1、多云A 2、阴雨天B。通过TCN(Temporal Convolutional Network)提取数据的时序特征,并结合GRU(Gate Recurrent Unit)建立融合提取时序特征模块的改进GRU结构,以达到对时序特征敏感的效果。将改进GRU结构与SVM(Support Vector Machine)动态组合,使用Elastic Net算法输出最佳权重值叠加得到最终预测值。文中采用江苏某地区的光伏发电功率数据及对应的气象数据对所提方法进行验证,结果表明动态组合学习模型的MAE(Mean Absolute Error)为1.888,RMSE(Root Mean Squared Error)为2.403。

基于双重注意力变换模型的分布式屋顶光伏变电站级日前功率预测

分布式屋顶光伏地理位置分散,受地理环境遮挡和多种气象因素影响,导致光伏出力特性存在差异,给变电站级分布式屋顶光伏日前功率预测造成挑战。针对上述问题,提出了一种基于双重注意力变换模型的分布式屋顶光伏变电站级日前功率预测方法。首先,基于动态时间规整算法计算分布式光伏用户出力特性间的相似度,并基于凝聚层次聚类法将其划分成若干类;然后,利用自主注意力网络学习各时间步间的时序关联特性,通道卷积注意力机制学习多特征变量间的相关性,构建日前功率预测模型;最后,将每一类日前预测结果相加,实现变电站级日前功率预测。算例结果表明所提方法在多种天气状况下,较Transformer、长短期记忆神经网络和时序卷积网络,预测精度显著提升。