Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Modeling Freight Truck Trips in Birmingham Using Tour-Based Approach

This research study explores the use of an innovative freight tour-based approach to model truck trips as an alternative to the conventional trip-based approach. The tour-based approach is more realistic as it captures the intermediate stops of each truck and reflects the implications of those stops on vehicle miles traveled (VMT). The paper describes the truck tour-based model concept, and presents the framework of a truck tour-based travel demand forecasting approach. As a case study, Global Positioning System (GPS) truck data are used to determine origin, destination, and truck stops for trucks moving within the Birmingham, Alabama region. Such information is then utilized to model truck movements within the study region as individual truck tours. The tour-based model is ran, and the resulting performance measures are contrasted to those obtained from the conventional trip-based planning model used by the Regional Planning Commission of Greater Birmingham (RPCGB). This case study demonstrates the feasibility of using a tour-based freight demand forecasting model as an alternative to the conventional 4-step process currently used to estimate truck trips in the Birmingham region. The results and lessons learned from the Birmingham case study are expected to improve truck movement modeling practices in the region and advance the accuracy of truck travel demand forecasting models at other locations in the future.

Data Fusion and Sensors Model

In this paper, we take the model of Laser range finder based on synchronized scanner as example, show how to use data fusion method in the process of sensor model designing to get more robust output. Also we provide our idea on the relation of sensor model, data fusion and system structure, and in the paper, there is a solution that transform the parameter space to get linear model for Kalman filter.

Model Design for AMT Data Inversion

By analyzing the characters of the mainstream commercial magnetotelluric inversion softwares in dealing with audio magnetotelluric data, a dynamic model-making method for inversion has been developed based on the observed AMT data. This method focusing on model domain can adjust mesh’s scale and model’s dimension depending on the field data just with a few parameters. By this, it is convenient to study the geo-electrical anomalies variations of different scale or dimensional models. Applying such model-making technique into the known hardrock geological setting, it is easy to obtain a new geo-electrical model which agrees with the resistivity curves of core samples better than before. It is demonstrated that this can increase the recognition of the resistivity contrast and deserves studying further.

原料性质大幅变化情况下催化重整Delta-Base数据的生成方法

针对催化重整原料性质变化幅度较大的情况,提出采用变步长计算结合分子水平机理模型生成装置Delta-Base数据的方法。该方法通过计算原料性质扰动时产品质量分数变化量绝对值随原料性质变化量绝对值的变化率;并将该变化率的值域分成若干等份,根据每次计算得到变化率值域的范围不同,选择下一步计算时的变量扰动步长;继而在计算过程中取每次变量扰动的中点值产品质量分数进行验证,从而在提高计算效率的同时保证了计算结果的准确性。结果表明:利用该方法计算得到的各产品质量分数相对误差较小,基本不超过2.5%;对计算数据拟合可得到不同产品质量分数数据拟合方程,通过拟合方程可生成芳烃潜含量变化三阶段Delta-Base数据;比较原料芳烃潜含量(w)为42%时的产品预测结果,发现Delta-Base数据预测值和拟合方程计算值的平均偏差均为0.16%,说明该方法具有很好的准确性,利用分子水平机理模型生成的Delta-Base数据更能反映真实的分子反应历程。

Multimode Process Monitoring Based on the Density-Based Support Vector Data Description

Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.

用FoxBASE+管理高校田径运动会成绩

高校田径运动会成绩数据较多 ,需要进行大量的计算 ,管理起来很不方便。用FoxBASE +关系型数据库管理系统对田径运动会成绩进行管理 ,大大加快了运动会成绩输出 。

基于逻辑的数据模型Datalog的递归应用

重点探讨了基于逻辑的数据模型 Datalog在以关系数据库为基础的递归查询上的应用 ,给出了求解 Datalog递归规则的两种通用算法 。

Data Matrix二维条码在奥运会门票系统中的应用研究

运用Data Matrix二维条码的特点重新设计奥运体育场馆电子门票系统,并构建了一个完整的奥运电子门票Data Matrix二维条码自动识别系统。对Data Matrix二维条码系统进行详细地规划,并通过实际条码生成的例子,证明其可行性和可实践性。本文突破了Data Matrix二维条码本身的适用领域的限制,有一定新意和实践价值。

Modelling of an hydraulic excavator using simplifiedrefined instrumental variable(SRIV)algorithm

Instead of establishing mathematical hydraulic system models from physical laws usually done with the problems of complex modelling processes, low reliability and practicality caused by large uncertainties, a novel modelling method for a highly nonlinear system of a hydraulic excavator is presented. Based on the data collected in the excavator＇s arms driving experiments, a data-based excavator dynamic model using Simplified Refined Instrumental Variable （SRIV） identification and estimation algorithms is established. The validity of the proposed data-based model is indirectly demonstrated by the performance of computer simulation and the.real machine motion control exoeriments.

The Trends and Tasks of Modeling and Simulation for Reduction of Heat Treatment Distortion

The development of simulation model and benchmark work have expanded in the past decade and many models and soft ware are introduced. The leading software "Hearts’ developed by Prof Inoue[1] and several other have proved the effectiveness as the pre-production simulation work at many part of heat treatment processes[2-10]. Although, numerous other models and simulation studies dealt with many fundamental factors are reported at many conferences except very few models have not completed three dimensional computation methods, or lack of validation work to evaluate their tools exactly. In this paper, several distortion case studies will be introduced and the needs of fundamental study of distortion and internationally collaborative program on model evaluation and construction of materials database are proposed.

XBASE数据库系统数据字典的设计与实现

简述了为ＸＢＡＳＥ数据库应用系统开发数据字典的必要性及数据字典的功能；重点讨论了数据字典中数据内容及组织方式；

Modeling Reading and Replying Activities in a BBS Social Network

This paper is devoted to analyze and model user reading and replying activities in a bulletin board system （BBS） social network. By analyzing the data set from a famous Chinese BBS social network, we show how some user activities distribute, and reveal several important features that might characterize user dynamics. We propose a method to model user activities in the BBS social network. The model could reproduce power-law and non-power-law distributions of user activities at the same time. Our results show that user reading and replying activities could be simulated through simple agent-based models. Specifically, manners of how the BBS server interacts with Internet users in the Web 2.0 application, how users organize their reading lists, and how user behavioral trait distributes are the important factors in the formation of activity patterns.

基于Panel Data模型的出口贸易对大气环境影响分析

基于Panel Data模型就出口贸易对大气环境影响进行定量分析,将出口贸易的环境效应分解为规模效应、结构效应和技术效应,通过单位根检验、协整检验及回归分析研究四大工业基地所在的8个地区2003—2018年出口贸易影响因素对大气环境的作用程度。结果显示,结构效应加剧了四大工业基地的环境污染程度,而规模效应和技术效应改善我国的污染水平,因此出口贸易有利于四大工业基地的大气环境保护。

Data-Driven Microstructure and Microhardness Design in Additive Manufacturing Using a Self-Organizing Map

To design microstructure and microhardness in the additive manufacturing(AM)of nickel(Ni)-based superalloys,the present work develops a novel data-driven approach that combines physics-based models,experimental measurements,and a data-mining method.The simulation is based on a computational thermal-fluid dynamics(CtFD)model,which can obtain thermal behavior,solidification parameters such as cooling rate,and the dilution of solidified clad.Based on the computed thermal information,dendrite arm spacing and microhardness are estimated using well-tested mechanistic models.Experimental microstructure and microhardness are determined and compared with the simulated values for validation.To visualize process-structure-properties(PSPs)linkages,the simulation and experimental datasets are input to a data-mining model-a self-organizing map(SOM).The design windows of the process parameters under multiple objectives can be obtained from the visualized maps.The proposed approaches can be utilized in AM and other data-intensive processes.Data-driven linkages between process,structure,and properties have the potential to benefit online process monitoring control in order to derive an ideal microstructure and mechanical properties.

复杂系统测试性设计与故障诊断策略研究进展

测试性设计是提高系统可靠性、安全性、维修性、保障性的重要前沿技术,决定了系统故障检测率和隔离率,直接影响系统的维护(测试)成本。系统测试性设计包含结构化设计、模型化设计、数据驱动设计等多种设计策略。其中,数据驱动设计于近年逐渐兴起并成为重要发展方向之一,该类方法通过对系统测试与故障之间的关系进行建模,依据测试结果进行故障推理,形成故障诊断方案。首先,简要回顾了系统测试性设计的发展历程;其次,重点介绍了测试性设计的研究进展,分析总结了结构化、模型化、数据驱动3类测试方案;然后,介绍了测试性诊断策略构建,根据测试方案中的建模方法确定诊断策略的构建技术,并总结归纳了每类技术的研究特点和适用性;最后,探讨了当前复杂系统测试性设计面临的挑战性问题和可能的未来研究方向。

油气储层勘探建模技术新进展及未来展望

油气储层建模利用地质统计学等方法,综合测井、地质、地震等多学科信息,是油气田开发研究的利器,油藏地质模型可以将油藏各种地质特征在三维空间的变化及分布定量表征出来,是油气藏的类型、几何形态、规模、油藏内部结构、储层参数及流体分布的高度概括,储层地质模型是油藏地质模型的核心,可以对储层的沉积特征、非均质性、物性及流体等特征进行综合表征。但在勘探阶段,面对大尺度沉积体系和稀疏井网条件下的储层展布规律表征的建模难点为:①地质知识的量化表达问题,包括地质专家的经验认识如何数字化表征。②稀疏井网条件下无法直接用钻井资料对地质体的发育规模、展布方向和结构特征准确定量描述及构建地质模式,大尺度空间中复杂沉积体系无法用简单数学函数表征。③传统地质统计学等方法在勘探模型构建中如何实现地震、测井、地质、油藏等多维度数据的融合问题。因此,基于确定性建模和传统地质统计学等随机建模的储层建模理论和技术遇到极大挑战。笔者在系统剖析传统储层建模技术流程和方法的基础上,通过构建涵盖地质、测井、地震、分析化验等信息的多学科地学大数据知识库,开展多维数据凝聚层次聚类的沉积相模式库表征和基于生成式网络的智能建模,提出了多学科协同的油气储层勘探建模技术对策及技术体系,实现了构造、沉积及储层之间匹配关系的定量表征。该技术体系在东营凹陷北部陡坡带、洼陷带勘探部署中开展系统应用,构建融合古地貌、古物源、搬运通道、测井及地震属性等多信息的岩相、物性及油气运聚的地质模型,基于模型新范式指导部署井位,支撑了陆相断陷盆地复杂砂砾岩体、页岩油等勘探实践。笔者通过深度剖析东营凹陷北部陡坡带勘探建模实践难点及精度问题,进一步探讨了未来油气储层勘探建模技术发展趋势和应用前景。

Study on 3D Geological Model of Highway Tunnels Modeling Method

Geology is the base for highways and tunnels construction. With the fast development of national highway construction, highway tunnel construction project are more and more complex. The completeness and accuracy are essential for the planning, design and construction of projects, while the ground information is quite poor in systematic, reliable and timely aspects. Therefore, the development of underground road tunnels, and the implementation of informationized spatial information management is urgent for highway construction. 3D geological tunnel model is intuitive, high efficient and convenience which greatly facilitates the maintenance and security of highway tunnels construction and it will be the trend for the future highway tunnel development.

基于CBDM的建筑天然光环境研究进展

天然光作为绿色能源,对建筑可持续发展有着重要意义。正确评估建筑自然采光是积极利用天然光的关键因素,而目前的天然采光研究主要以静态计算评估为基础,未考虑天然光的高度动态性以及地域不同对室内采光的影响。因此,基于气候的采光模型(Climate-Based Daylight Modeling,CBDM)对建筑天然采光的适用性应用提出了新的要求。本文回顾了近十年来国内外对CBDM的研究成果,从“量化研究”、“技术应用”、“度量标准”三个方面进行了梳理,总结了基于CBDM的建筑天然光环境研究进展,提出了天然采光的未来发展趋势和需要解决的研究问题。

黄河流域资源型城市碳达峰情景模拟研究

自然灾害与极端天气频发,各城市有效推进碳减排已刻不容缓,尤其是黄河流域的资源型城市,地区经济发展主要依赖高耗能、高排放的资源产业,使得减排降碳任务更为艰巨.本文基于黄河流域资源型城市碳排放数据,首先采用贝叶斯模型平均法进行因子选择,从影响碳排放的多种影响因素中提取重要信息,找出后验概率值大于50%时的几个变量作为影响碳排放的重要因素.其次,基于黄河流域资源型城市面板数据,结合情景分析法,在基准情景、政策导向型情景和科技驱动型情境下,构建个体时点双固定效应面板数据模型预测不同情境下各地区的碳排放量.结果表明:煤炭消费量、城镇化率、水泥产量、人均GDP、天然气消费量和第二产业在GDP中的比重是影响黄河流域资源型城市碳排放量的主要因素;通过比较政策导向方案和技术驱动方案,可以发现政策导向方案是一个渐进的过程,而技术驱动的碳减排效果更加快速和明显,基准情形下黄河流域资源型地区难以实现2030碳达峰目标.