Multiple time scales of fluvial processes—theory and applications

Fluvial processes comprise water flow,sediment transport and bed evolution,which normally feature distinct time scales.The time scales of sediment transport and bed deformation relative to the flow essentially measure how fast sediment transport adapts to capacity region in line with local flow scenario and the bed deforms in comparison with the flow,which literally dictates if a capacity based and/or decoupled model is justified.This paper synthesizes the recently developed multiscale theory for sediment-laden flows over erodible bed,with bed load and suspended load transport,respectively.It is unravelled that bed load transport can adapt to capacity sufficiently rapidly even under highly unsteady flows and thus a capacity model is mostly applicable,whereas a non-capacity model is critical for suspended sediment because of the lower rate of adaptation to capacity.Physically coupled modelling is critical for fluvial processes characterized by rapid bed variation.Applications are outlined on very active bed load sediment transported by flash floods and landslide dam break floods.

A real time processing system of seismic waves using personal computers-Function and characteristics

Seismological Bureau of Sichuan Province, Chengdu 610041, China2) Center for Analysis and Prediction, State Seismological Bureau, Beijing 100036, China3) Observation Center for Prediction of Earthquakes and Volcanic Eruptions, Faculty of Sciences, Tohoku University, Sendai 98077, Japan

Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.

Dynamic modeling and analysis of the closed-circuit grinding-classification process

Mathematical models of the grinding process are the basis of analysis, simulation and control. Most existent models in- cluding theoretical models and identification models are, however, inconvenient for direct analysis. In addition, many models pay much attention to the local details in the closed-circuit grinding process while overlooking the systematic behavior of the process as a whole. From the systematic perspective, the dynamic behavior of the whole closed-circuit grinding-classification process is consid- ered and a first-order transfer function model describing the dynamic relation between the raw material and the product is established. The model proves that the time constant of the closed-circuit process is lager than that of the open-circuit process and reveals how physical parameters affect the process dynamic behavior. These are very helpful to understand, design and control the closed-circuit grinding-classification process.

Real-Time Discrete Adaptive Control of Robot Arm Based on Digital Signal Processing

A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.

Identification Scheme for Fractional Hammerstein Models With the Delayed Haar Wavelet

The parameter identification of a nonlinear Hammerstein-type process is likely to be complex and challenging due to the existence of significant nonlinearity at the input side. In this paper, a new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix(HWOM). To determine all the parameters in the Hammerstein model, a special input excitation is utilized to separate the identification problem of the linear subsystem from the complete nonlinear process. During the first test period, a simple step response data is utilized to estimate the linear subsystem dynamics. Then, the overall system response to sinusoidal input is used to estimate nonlinearity in the process. A single-pole fractional order transfer function with time delay is used to model the linear subsystem. In order to reduce the mathematical complexity resulting from the fractional derivatives of signals, a HWOM based algebraic approach is developed. The proposed method is proven to be simple and robust in the presence of measurement noises. The numerical study illustrates the efficiency of the proposed modeling technique through four different nonlinear processes and results are compared with existing methods.

Effect of varying spatial orientations on build time requirements for FDM process: A case study

In this research, effect of varying spatial orientations on the build time requirements for fused deposition modelling process is studied. Constructive solid geometry cylindrical primitive is taken as work piece and modeling is accomplished for it. Response surface methodology is used to design the experiments and obtain statistical models for build time requirements corresponding to different orientations of the given primitive in modeller build volume. Contour width, air gap, slice height, raster width, raster angle and angle of orientation are treated as process parameters. Percentage contribution of individual process parameter is found to change for build time corresponding to different spatial orientations. Also, the average of build time requirement changes with spatial orientation. This paper attempts to clearly discuss and describe the observations with an aim to develop a clear understanding of effect of spatial variations on the build time for Fused Deposition Modelling process. This work is an integral part of process layout optimization and these results can effectively aid designers specially while tackling nesting issues.

Generic reconstruction technology based on RST for multivariate time series of complex process industries

In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.

ANALYTICAL SOLUTION OF FILLING AND EXHAUSTING PROCESS IN PNEUMATIC SYSTEM

The filling and exhausting processes in a pneumatic system are involved with many factors, and numerical solutions of many partial differential equations are always adapted in the study of those processes, which have been proved to be troublesome and less intuitive. Analytical solutions based on loss-less tube model and average friction tube model are found respectively by using fluid net theory, and they fit the experimental results well. The research work shows that： Fluid net theory can be used to solve the analytical solution of filling and exhausting processes of pneumatic system, and the result of loss-less tube model is close to that of average friction model, so loss-less tube model is recommended since it is simpler, and the difference between filling time and exhausting time is determined by initial and final pressures, the volume of container and the section area of tube, and has nothing to do with the length of the tube.

Modeling study on the flow patterns of gas–liquid flow for fast decarburization during the RH process

A water model and a high-speed video camera were utilized in the 300-t RH equipment to study the effect of steel flow patterns in a vacuum chamber on fast decarburization and a superior flow-pattern map was obtained during the practical RH process. There are three flow patterns with different bubbling characteristics and steel surface states in the vacuum chamber： boiling pattern（BP）, transition pattern（TP）, and wave pattern（WP）. The effect of the liquid-steel level and the residence time of the steel in the chamber on flow patterns and decarburization reaction were investigated, respectively. The liquid-steel level significantly affected the flow-pattern transition from BP to WP, and the residence time and reaction area were crucial to evaluate the whole decarburization process rather than the circulation flow rate and mixing time. A superior flow-pattern map during the practical RH process showed that the steel flow pattern changed from BP to TP quickly, and then remained as TP until the end of decarburization.

Hierarchical Colored Timed Petri Nets for Maintenance Process Modeling of Civil Aircraft

Civil aircraft maintenance process simulation model is an effective method for analyzing the maintainability of a civil aircraft. First, we present the Hierarchical Colored Timed Petri Nets for maintenance process modeling of civil aircraft. Then, we expound a general method of civil aircraft maintenance activities, determine the maintenance level for decomposition, and propose the methods of describing logic of relations between the maintenance activities based on Petri Net. Finally, a time Colored Petri multi-level network modeling and simulation procedures and steps are given with the maintenance example of the landing gear burst tire of a certain type of aircraft. The feasibility of the method is proved by the example.

Computation Method for the Real-Time Control Problems in Differential-Algebraic Systems

In this paper, a real-time computation method for the control problems in differential-algebraic systems is presented. The errors of the method are estimated, and the relation between the sampling stepsize and the controlled errors is analyzed. The stability analysis is done for a model problem, and the stability region is ploted which gives the range of the sampling stepsizes with which the stability of control process is guaranteed.

Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes

This paper intends to develop suitable methods to provide likely scenarios in order to support decision making for slow dynamic processes such as the underlying of agribusiness. A new method to analyze the short- and long-term time series forecast and to model the behavior of the underlying process using nonlinear artificial neural networks (ANN) is presented. The algorithm can effectively forecast the time-series data by stochastic analysis (Monte Carlo) of its future behavior using fractional Gaussian noise (fGn). The algorithm was used to forecast country risk time series for several countries, both for short term that is 30 days ahead and long term 350 days ahead scenarios.

A Statistical Model for Long-Term Forecasts of Strong Sand Dust Storms

Historical evidence indicates that dust storms of considerable ferocity often wreak havoc, posing a genuine threat to the climatic and societal equilibrium of a place. A systematic study, with emphasis on the modeling and forecasting aspects, thus, becomes imperative, so that efficient measures can be promptly undertaken to cushion the effect of such an unforeseen calamity. The present work intends to discover a suitable ARIMA model using dust storm data from northern China from March 1954 to April 2002, provided by Zhou and Zhang (2003), thereby extending the idea of empirical recurrence rate (ERR) developed by Ho (2008), to model the temporal trend of such sand dust storms. In particular we show that the ERR time series is endowed with the following characteristics: 1) it is a potent surrogate for a point process, 2) it is capable of taking advantage of the well developed and powerful time series modeling tools and 3) it can generate reliable forecasts, with which we can retrieve the corresponding mean number of strong sand dust storms. A simulation study is conducted prior to the actual fitting, to justify the applicability of the proposed technique.

Demand Model for Investment-oriented Life Insurance under uncertain lifetime

In this paper, we set up continuous time model with Poisson Process to analyze demand of investment-oriented life insurance. Individual life time is assumed random, and he is received fixed income, investment-oriented life insurance is an important financial asset under this model. Dynamic programming is applied to analyze this problem. The optimal explicit solutions are obtained in the case of CRRA utilities, and draw its demand curve with numerical simulation.

面向探地雷达常见地下目标的GDS-YOLOv8n检测方法

针对当前探地雷达(GPR)图像检测中存在准确率低、误检和漏检等问题,该文提出了一种GPR常见地下目标检测模型GDS-YOLOv8n。该模型首先使用DRRB特征提取模块替换YOLOv8n模型中的部分C2f模块,旨在增强模型对多尺度特征的提取能力。其次使用SPD-Conv下采样模块替换像素为320×320及以下特征图所对应的Conv模块,有效克服分辨率受限以及存在小目标的GPR图像在下采样过程中的信息损失问题;同时利用辅助训练模块,在不增加检测阶段模型复杂度的前提下提升GPR图像的检测性能。最后,引入Inner-SIoU损失函数,在添加新约束条件的基础上,通过比例因子生成适合于当前GPR图像的辅助边界框,以提高预测框的准确性。实验结果表明,GDS-YOLOv8n模型对金属管、PVC管和电缆线等6类常见地下目标在实测GPR图像数据集上的P,R和mAP50分别为97.1%, 96.2%和96.9%,较YOLOv8n模型分别提高了4.0%, 6.1%和4.1%,尤其对PVC管和电缆线目标的检测效果提升更明显。与YOLOv5n, YOLOv7-tiny和SSD等模型相比,其mAP50分别提高了7.20%,5.70%和14.48%。此外,将GDS-YOLOv8n模型部署到NVIDIA Jetson Orin NX嵌入式设备上,并使用TensorRT进行优化。经FP16量化后,模型的检测速度由22.0 FPS提高到40.6 FPS,能够满足移动场景下GPR地下目标实时探测任务的需求。

基于YOLO模型的车流量实时采集系统研究

对于一座现代化城市来说,合理的交通规划是一个城市高效运行的关键,作为交通规划的关键信息的城市车流量信息,原本需要人工进行识别、获取、验证的提取方式,随着计算机视觉技术的蓬勃发展弊端尽显,终将退出历史的舞台。为了提高城市车流量信息的准确性和及时性,利用现有的计算机技术设计一种基于YOLO模型的车流量实时采集系统。该系统基于YOLO视觉检测模型,采用DeepSORT算法对检测到的目标车辆进行跟踪识别、判断车辆的运行状态、实现当前路段的车流量统计、对已记录车流量信息进行可视化展示以及数据输出等。该系统可以有效地代替传统消耗人力的死板工作,实现自动化数据收集以及道路交通情况的快速监测。该系统操作简单,交互性强,为城市的交通管理和交通规划提供准确实时的信息数据。

动态网络局部社区演化过程溯源方法

随着网络规模的增大,全局社区演化分析越来越困难。考虑在某些场景中人们只关心某一局部社区的形成过程,即局部社区演化过程溯源,提出一种动态网络局部社区演化过程溯源方法,包括社区形成时刻识别、候选前驱社区集合检测和社区演化关系分析这3个环节。通过构造真伪社区分类器识别前驱社区形成时刻,在前驱社区形成时刻的网络快照上进行社区检测,获得包含待溯源社区中任意顶点的候选前驱社区;利用演化事件模型找出真正的前驱社区并识别对应演化事件。在真实网络数据集上的实验结果表明,所提方法优于基于均匀时间片划分的溯源方法,得到的待溯源社区形成路径图能够与真实路径图精准匹配,即社区形成时刻、前驱社区和演化事件均识别准确。

考虑设备劣化的加工工时预测方法

针对加工设备在不同寿命阶段造成的机加工工时波动问题,提出了考虑设备劣化的加工工时预测方法。针对单工况,构建了基于BiGCU-MHResAtt-Weibull模型的机加工工时预测模型,采用双向门控卷积单元(BiGCU)提取局部特征,利用多头残差自注意力网络,获取不同特征间的影响关系,全连层输出优化后的残余寿命值并通过Weibull概率分布函数实现加工工时倍率映射;针对多工况,结合单工况模型,设计了大数据集和特征迁移模型,并通过聚类和曲线拟合生成加工工时预测谱系。最后,采用美国国家航天局提供的涡扇发动机数据集完成了模型训练和预测,验证了所提方法的有效性。

一种预测流程剩余时间的可解释特征分层方法

流程剩余时间预测对于业务异常的预防和干预有着重要的价值和意义.现有的剩余时间预测方法通过深度学习技术达到了更高的准确率,然而大多数深度模型结构复杂难以解释预测结果,即不可解释问题.此外,剩余时间预测除了活动这一关键属性还会根据领域知识选择若干其他属性作为预测模型的输入特征,缺少通用的特征选择方法,对于预测的准确率和模型的可解释性存在一定的影响.针对上述问题,提出基于可解释特征分层模型(explainable feature-based hierarchical model,EFH model)的流程剩余时间预测框架.具体而言,首先提出特征自选择策略,通过基于优先级的后向特征删除和基于特征重要性值的前向特征选择,得到对预测任务具有积极影响的属性作为模型输入.然后提出可解释特征分层模型架构,通过逐层加入不同特征得到每层的预测结果,解释特征值与预测结果的内在联系.采用LightGBM(light gradient boosting machine)和LSTM(long short-term memory)算法实例化所提方法,框架是通用的,不限于选用算法.最后在8个真实事件日志上与最新方法进行比较.实验结果表明所提方法能够选取出有效特征,提高预测的准确率,并解释预测结果.