计算机模拟系统的建模与实现

本文在分析计算机系统结构本质特性的基础之上,运用面向对象的可视化建模方法,讨论了利用UML构建系统模型的基本思路和计算机模拟系统方案的实现。     

基于交互规则建模的信息系统实时状态监测技术研究

基于信息系统协同关联交互规则,利用XML(可扩展标记语言)技术,对信息系统中分系统、设备的相关属性、交互信息等进行分层建模描述,建立系统的实时状态监测模型;通过多线程并行处理技术,实时采集数据并匹配系统状态监测模型,实现对信息系统的交互状态、时序逻辑、工作状态、数据内容的合规性、完整性、正确性等方面的实时监测,并给出异常告警和提示;同时,结合系统的故障模式、影响及危害性分析,对当前系统故障的影响给出初步的分析,分析结果提供指挥员用于辅助决策参考.     

计算机辅助系统建模工具的研究与实现

以ＩＤＥＦ０、ＩＤＥＦＩＸ方法为基础,研究了基于知识的计算机辅助系统建模工具,ＫＢ＿ＣＡＳＭＴ的总体结构框架的设计思想,将系统建模过程划分为功能提取,信息提取,属性提取,实体提取和实体联系定义,给出了ＫＢ＿ＣＡＳＭＴ的功能结构划分和多级用户管理机制的设计,并以“属性提取”为例,描述了ＫＢ＿ＣＡＳＭＴ的关键环节实现技术。     

基于交通流量自适应控制的路口子系统

在经济快速发展、道路建设不足等多重压力下,城市交通负担越来越重.在现有的资源条件下,如何提高道路资源的利用效率和道路的通行效率是缓解交通问题的重要途径.本文以设计一种基于交通流量自适应控制的路口子系统为目标,首先分析、研究了交通管控系统的相关算法和相关信息系统的现状,在综合现有系统的基础上,设计了一个交通管控系统的体系结构,系统包括路口子系统、区域中心和信息与控制中心等3部分.通过路口子系统实现交通流数据的实时采集和路口本地的智能控制;设计并实现了路口子系统的检测方案,路口子系统的数据采集和本地的智能控制是云数据存储,以及实时的交通信息平台发布的关键,同时对于实现多个路口的协调管控具有重要的意义.     

计算机仿真建模方法（三）

本讲介绍线性系统数字仿真的预处理方法和小步合成方法，对于传递函数的数字仿真，还给出双线性变换方法。     

织机计算机实时监测系统

织机计算机实时监测系统ＴｈｅＲｅａｌ－ＴｉｍｅＣｏｍｐｕｔｅｒＭｏｎｉｔｏｒｉｎｇＳｙｓｔｅｍｏｆＷａｖｅＭａｃｈｉｎｅ●梅丽凤王艳秋张艳冬ＭｅｉＬｉｆｅｎｇＷａｎｇＹｉａｎｑｉｕＺｈａｎｇＹｉａｎｄｏｎｇ１前言织布是纺织生产中最后一道成品工序，是纺...     

基于CPN的单路口交通信号控制建模

针对城市道路交叉口的交通流特性,用CPN对单路口交通信号多相位实时控制进行建模。单路口交通信号控制系统是一种实时分布式复杂系统,具备同步,并发,冲突,互斥,资源共享的离散事件系统特征。针对离散事件系统,CPN网是个强大而实用的建模工具。     

面向对象的计算机仿真建模

本文概述了计算机仿真的特点及基本过程，根据面向对象技术的特征，提出了一种面向对象的计算机仿真建模的框架，并给出实例     

计算机仿真建模方法（五）

本讲给出仿真模型中间断特性的处理方法，特别给出了实时数字仿真建模的平均法。     

区域联网交通流量预测系统的设计与实现

在我国,交通流量监测系统主要存在着监测性能受限,实时性准确率低以及采集数据较分散等问题,这在很大程度上制约着我国交通工程的发展。为改善这一现状,本文以ZigBee无线通信技术为主要手段,由CC2430处理器、MODEL2420三轴加速度传感器组成设计一种区域基于物联网技术的交通流量监测系统,仿真实验进一步验证了区域联网交通流量预测系统通过实现区域内车辆类型的高效率、高性能,从而达到实时分类标识的目标,从而大大推动了区域联网交通流通预测系统的设计。     

Computer vision techniques for construction safety and health monitoring

For construction safety and health, continuous monitoring of unsafe conditions and action is essential in order to eliminate potential hazards in a timely manner. As a robust and automated means of field observation, computer vision techniques have been applied for the extraction of safety related information from site images and videos, and regarded as effective solutions complementary to current time-consuming and unreliable manual observational practices. Although some research efforts have been directed toward computer vision-based safety and health monitoring, its application in real practice remains premature due to a number of technical issues and research challenges in terms of reliability, accuracy, and applicability. This paper thus reviews previous attempts in construction applications from both technical and practical perspectives in order to understand the current status of computer vision techniques, which in turn suggests the direction of future research in the field of computer vision-based safety and health monitoring. Specifically, this paper categorizes previous studies into three groups—object detection, object tracking, and action recognition—based on types of information required to evaluate unsafe conditions and acts. The results demonstrate that major research challenges include comprehensive scene understanding, varying tracking accuracy by camera position, and action recognition of multiple equipment and workers. In addition, we identified several practical issues including a lack of task-specific and quantifiable metrics to evaluate the extracted information in safety context, technical obstacles due to dynamic conditions at construction sites and privacy issues. These challenges indicate a need for further research in these areas. Accordingly, this paper provides researchers insights into advancing knowledge and techniques for computer vision-based safety and health monitoring, and offers fresh opportunities and considerations to practitioners in understanding and adopting the techniques.     

Computer vision techniques for traffic flow computation

This paper describes an application of computer vision techniques to road surveillance. It reports on a project undertaken in collaboration with the Research and Innovation group at the Ordnance Survey. The project aims to produce a system that detects and tracks vehicles in real traffic scenes to generate meaningful parameters for use in traffic management. The system has now been implemented using two different approaches: a feature-based approach that detects and groups corner features in a scene into potential vehicle objects, and an appearance-based approach that trains a cascade of classifiers to learn the appearances of vehicles as an arrangement of a set of pre-defined simple Haar features. Potential vehicles detected are then tracked through an image sequence, using the Kalman filter motion tracker. Experimental results of the algorithms are presented in this paper.     

Computer vision-based approach for smart traffic condition assessment at the railroad grade crossing

Slow-moving or stopped trains at highway-railroad grade crossings, especially in the populated metropolitan areas, not only cause significant traffic delays to commuters, but also prevent first responders from timely responding to emergencies. In this study, the researchers introduce an automated video analysis, detection and tracking system to evaluate the traffic conditions, analyze blocked vehicle behaviors at grade crossings, and predict the decongestion time under a simplified scenario. A novel YOLOv3-SPP+ model has been developed to improve the detection performance with dividing the image from finer to coarser levels and enhance local features. The SORT module has been integrated to the model for a simple yet efficient manner to track vehicles at the railroad grade crossing. Two field datasets at the Columbia, SC, with train blockage video records have been tested. The model training performance has been evaluated by mAP @0.5, F1 score, and total loss. Based on the training results, our model outperforms other YOLO series models. The field tracking performance has been assessed by the ratio between prediction and ground truth. The mean value of accuracy of our test cases is 92.37%, indicating a reliable tracking performance. In addition, the present results indicate the traffic during and after the crossing blockage does follow a pattern, and there is a general trend of the behavior of the vehicles waiting or taking an alternative route. A good linear correlation between the decongestion time and the number of blocked vehicles has been observed at the monitored grade crossing at the City of Columbia, SC.     

道路交通控制系统计算机仿真的研究

阐述了道路交通控制系统的系统结构、控制方式和基本功能，以及如何利用计算开发一个交通信号控制系统的仿真模型，并对设计过程中的关键点进行了分析。     

Framework based on stochastic L-Systems for modeling IP traffic with multifractal behavior

This paper presents and compares a set of traffic models, and associated parameter fitting procedures, based on so-called stochastic L-Systems, which were introduced by biologist A. Lindenmayer as a method to model plant growth. Starting from an initial symbol, an L-System generates iteratively sequences of symbols, belonging to an alphabet, through successive application of production rules. In a traffic modeling context, the symbols are interpreted as packet arrival rates or mean packet sizes, and each iteration is associated to a finest time scale of the traffic. These models are able to capture the multiscaling and multifractal behavior sometimes observed in Internet traffic. We describe and compare four traffic models, one characterizing the packet arrival process, and the other three characterizing both the packet arrival and the packet size processes. The models are tested with several measured traffic traces: the well-known pOct Bellcore, a trace of aggregate WAN traffic and two traces of specific applications (Kazaa and Operation Flashing Point). We assess the multifractality of these traces using Linear Multiscale Diagrams. The traffic models are evaluated by comparing, for the measured traffic and for traffic generated according to the inferred models, the probability mass function, the autocovariance function and the queuing behavior. Our results show that the L-System based traffic models that characterize both the packet arrival and packet size processes can achieve very good fitting performance in terms of first- and second-order statistics and queuing behavior.     

Dynamic route planning with real-time traffic predictions

Situation aware route planning gathers increasing interest as cities become crowded and jammed. We present a system for individual trip planning that incorporates future traffic hazards in routing. Future traffic conditions are computed by a Spatio-Temporal Random Field based on a stream of sensor readings. In addition, our approach estimates traffic flow in areas with low sensor coverage using a Gaussian Process Regression. The conditioning of spatial regression on intermediate predictions of a discrete probabilistic graphical model allows us to incorporate historical data, streamed online data and a rich dependency structure at the same time. We demonstrate the system with a real-world use-case from Dublin city, Ireland.     

多协议标签交换流量工程隧道实时监测方法——MTRM

针对现有工作无法实时动态监测多协议标签交换(MPLS)流量工程(TE)隧道状态变化的问题,提出了一种MPLS TE隧道实时监测方法--MTRM。在网络中植入被动采集探针以采集OSPF-TE信令,以此为基础构建网络模型,使用隧道路径实时监测算法进行实时的隧道路径计算,最终实现动态监测。仿真实验在15个节点的MPLS网络中进行。结果表明,MTRM能够在5s之内监测到隧道变化,准确率超过90%。这种MPLS TE隧道实时监测方法,大大降低了MPLS网络管理和流量工程实施的难度,具有广阔的应用前景。     

Compact modeling of spatial continuum robotic arms towards real-time control

This paper presents a compact modeling approach of continuum robotic arms, with limited computations for faster numerical calculations in real-time implementations. It is assumed that the arm includes a backbone made of elastic rods, and the Cosserat rod theory is simplified for numerically faster modeling. The time-taking boundary-value problem (BVP) is detailed, and some numerical methods are introduced to solve the developed equations of modeling. Next, based on physical intuition, a fast and stable method is proposed to solve the BVP, as a moment-based approach. Also, a method to cancel some numerical integration errors with the least required computations and numerical efforts is presented. Next, a Jacobian-based control of multi-segment continuum robotic arms is developed. Finally, this procedure is experimentally verified to show the precision of proposed modeling approach and also to reveal the importance of faster solutions for real-time control.     

Analysis of the mental workload of city traffic control operators while monitoring traffic density: A field study

IntroductionIt is important to evaluate when and why the mental workload of operators increases during system operation. The city traffic control center (TCC) is a complex work system, and it is important to describe MW as a condition related to this. The purpose of this study is to evaluate the mental workload of operators while monitoring traffic loads in the city TCC.MethodsElectroencephalography and electrooculography data were collected from 16 operators while performing their daily work, in four conditions: resting state, low traffic density, high traffic density, and recovery. The Simplified-Subjective Workload Assessment Technique (S-SWAT) was used to evaluate the subjective workload of operators.ResultsThe findings indicate that operators experience a larger mental workload during high traffic density than during low traffic density (p < 0.001). TCC stressors led to significant changes in EEG bands, such as theta, alpha, and eye activity. Significant differences were observed for subjective ratings of MW (p < 0.001).ConclusionAlthough the working situations of TCC operators are repeated daily, their mental fatigue and stress level gradually increase, leading to deterioration in their mental health. It may be necessary periodically to monitor their mental health and to consider their organizational behavior during traffic density monitoring.Relevance to industrycomplex work systems have increased the requirement for many operators to conduct mental tasks in real work conditions such as city traffic density monitoring. When evaluating such workplaces, it is important to identify situations requiring increased mental workload that might impose additional stress on operators, decreasing their performance. Based on the results, the traffic control center director would be aware of the MW condition of the operators.     

高速公路交通状态的联合估计方法

提出一种有效的高速公路交通密度预测估计方法。通过分析交通模型，建立了系统的离散化状态和误差模型表达式，并采用推广Kalman滤波方法进行估计．为进一步增强数值稳定性以及提高计算效率，根据系统矩阵分块的特点，采用分块的正交化U-D分解算法实现时间更新，同时采用序列U-D分解方法进行测量更新．仿真计算和实际应用表明，该方法可以给出工程实用结果。