Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion

Peak ground acceleration is a very important factor that must be considered in construction site for examining the potential damage resulting from earthquake. The actual records by seismometer at stations related to the site may be taken as a basis, but a reliable estimating method may be useful for providing more detailed information of the strong motion characteristics. Therefore, the purpose of this study was by using back-propagation neural networks to develop a model for estimating peak ground acceleration at two main line sections of Kaohsiung Mass Rapid Transit in Taiwan. Additionally, the microtremor measurements with Nakamura transformation technique were taken to further validate the estimations. Three neural networks models with different inputs including epicentral distance, focal depth and magnitude of the earthquake records were trained and the output results were compared with available nonlinear regression analysis. The comparisons exhibited that the present neural networks model did have a better performance than that of the other methods, as the calculation results were more reasonable and closer to the actual seismic records. Besides, the distributions of estimating peak ground acceleration from both of computations and measurements might provide valuable information from theoretical and practical standpoints.     

Throughput models of CSMA network with stations uniformly distributed along the bus

The distribution of station locations in a CSMA network can have significant effect onnetwork throughput. In this paper, we develop an analytic model for unslotted, non-persistentCSMA bus network where the stations are uniformly distributed along the bus. We derive aclosod form expression for throughput approximation, and discuss the fairness of CSMAprotocol using the bus topology. Analytic and simulation results are presented and compared.     

轨道交通车站乘客集散系统Anylogic仿真优化

为了研究地铁轨道交通车站站台乘客行为的特殊性对地铁轨道交通车站设施设备布局的影响,本文运用基于社会力模型算法的Anylogic动态仿真软件构建了面向复杂交织乘客行人流的地铁轨道交通站台场景模型。以某市地铁轨道交通车站为例对该模型进行了校核与验证,针对地铁车站内的设施设备布局的缺陷提出了优化改进措施。实证研究表明:优化后的地铁轨道交通站台场景模型,能够科学合理地阐述客流组织与地铁轨道交通之间的动态关系,同时对其他地铁轨道交通车站的规划设计与运营具有重要的借鉴与指导意义。     

探究轨道交通网络信息系统网络规划简

结合城市轨道交通线网规划目标、原则及内容,剖析了城市轨道交通网络信息系统网络规划的需求分析、要点及安全设计原则。为优化整体资源配置,构建节约、高效、先进的轨道交通网络,轨道交通公安部门合理配置人力、提高效率、节省开支提供理论支持。     

Neural network estimation of air temperatures from AVHRR data

Multilayer feed-forward (MLF) neural networks were employed to estimate air temperatures in Southern Québec (Canada) using Advanced Very High Resolution Radiometer (AVHRR) images. The input variables for the networks were the five bands of the AVHRR image, surface altitude, solar zenith angle, and Julian day. The estimation was carried out using a dataset collected during the growing season from June to September 2000. Levenberg--Marquardt back-propagation (LM-BP) was used to train the networks. The early stopping method was applied to improve the LM-BP and to generalize the networks. Bands 4 and 5, which are used for retrieval of surface temperature, were the most critical components for the estimation. The contribution of Julian day to the precision of estimated air temperature was much superior to that of altitude and solar zenith angle for the dataset of inter-seasonal air temperatures. The network using all five bands, Julian day, altitude, and solar zenith angle provided the best results, with 22 nodes in the hidden layer. In the time series of estimated and station air temperatures, the difference between the temperatures was generally maintained within 2°C on various canopies, even during steep variations in August and September.     

高速网络流采集系统的设计与实现

针对基于软件的网络流采集系统不能高效处理高速网络流量,以及为了提高采集效率需要同时对多种网络流进行采集的问题,提出一种基于软硬件结合的高速网络流采集框架,探讨在NetFPGA-10G平台实现高速网络流采集系统,称之为HSNTCS。该系统在硬件上通过精确串匹配引擎或正则表达式匹配引擎过滤、分类出所需的多种网络流后,将其传至内核驱动层对应的数据缓冲区,然后直接拷贝至用户空间并存储至对应的数据库。经实验测试,在精确串匹配情况下,用硬件方式实现的高速网络流采集系统的用户数据报协议(UDP)、传输控制协议(TCP)吞吐率都达到1.2Gb/s,约是用软件方式实现的3倍;在正则表达式匹配情况下,用硬件方式实现的高速网络流采集系统的UDP、TCP吞吐率都达到640Mb/s,约是用软件方式实现的3倍。结果表明,相对于软件实现方式,硬件实现具有更高的采集性能。     

A queuing network simulation optimization method for coordination control of passenger flow in urban rail transit stations

Neural Computing and Applications - The imbalances between supply and demand in an urban rail transit system have received extensive attention. Addressing these imbalances by controlling the...     

交互式遗传算法基于NN的个体适应度分阶段估计

针对交互式遗传算法中人的疲劳问题,提出一种基于神经网络(NN)的个体适应度分阶段估计方法,给出了神经网络估计进化个体适应度与人的评价之问的转换策略以及神经网络学习效果的评价指标,并分析了算法的复杂性．实例结果验证了该方法的有效性。     

城市轨道交通综合监控系统网络结构分析

针对现在城市轨道交通的建设问题,介绍了一种基于系统骨干网,通过专业接口装置可以在SCADA系统软件平台上实现多专业、多系统的数据采集、信息集成和信息共享的一种地铁网络监控分层式计算机集成系统,即城市轨道交通综合监控系统(ISCS)。它为城市轨道交通科学和高效的运营组织和管理提供了先进的技术手段。通过详细分析ISCS的系统网络方案和组成部分,为今后轨道交通综合监控系统的建设提供了借鉴。     

高速网络入侵检测系统中基于Hash函数的分流算法

如何使入侵检测系统能适用于高速网络环境,成为当今入侵检测领域急需解决的技术难题.结合机群系统,提出一种基于散列函数的分流算法,将高流量数据流通过该分流算法分为多个数据流,交由机群系统中各节点上的IDS分析引擎处理.实验结果表明,该算法保证同一连接的所有数据报文由同一IDS分析引擎处理,在高速网络环境下保持高检测率,并有效地解决负载平衡问题.     

一种高速网络流识别处理系统的设计与实现

随着骨干网络传输速率不断提高,对高速网络信号分析处理系统的需求十分迫切。骨干网络高速率、大带宽的特点给整个网络空间的管理带来了许多困难。采用五元组定义的网络流作为研究对象,通过理论分析,设计和实现了高速网络流识别处理系统硬件平台,实现网络信号在流层面的分析识别,并根据分析结果执行不同的处理策略,从而为网络流的分类处理提供了依据。     

城市轨道交通车地无线通信系统性能评估平台的研究与实现

为了评估城市轨道交通车地无线通信系统的性能,通过分析城市轨道交通无线环境特点,利用半实物仿真,模拟无线传输信道,结合干扰信号源模拟不同的干扰环境,设计实现了一个城市轨道交通车地无线通信系统性能评估平台。通过该平台,可对车地无线通信系统处于不同干扰场景或者不同切换频率的场景下进行性能评估。同时,该系统能够直接应用于基于CBTC的城市轨道交通线路,评估其车地无线通信系统的性能。     

Neural network modeling of torque estimation and d–q transformation for induction machine

This paper presents a neural network approach in modeling of torque estimation and Parks d–q transformation for an open-loop induction machine. The nonlinear approximation capability of neural networks makes it possible to map the Parks d–q transformation and torque estimation in an induction motor, which would otherwise require extensive complex calculations. The neural network simulation results will be compared to those of directly DSP calculated transformation and estimation. The results show improved performance with the neural network approach. We conclude that machine systems transformations and estimations can take advantage of the neural network technology for improved performance and cost reduction in the long run.     

Neural network method: delay and system of delay differential equations

In the present article, delay and system of delay differential equations are treated using feed-forward artificial neural networks. We have solved multiple problems using neural network architectures with different depths. The neural networks are trained using the extreme learning machine algorithm for the satisfaction of delay differential equations and associated initial/boundary conditions. Further, numerical rates of convergence of the proposed algorithm are reported based on variation of error in the obtained solution for different number of training points. Emphasis is on analysing whether deeper network architectures trained with extreme learning machine algorithm can perform better than shallow network architectures for approximating the solutions of delay differential equations.

     

Neural network control system of cooperative robot based on genetic algorithms

Neural Computing and Applications - Attitude detection of cooperative robots can help robots recognize environment, understand tasks, and improve the safety, accuracy and efficiency of robot work....     

Two-phase neural network based estimation of degree of insecurity of power system

Increased loading and contingencies often lead to situations where the optimal power flow solution no longer remains within the secure region. In such situations there is a need of determining control actions to be taken quickly, as otherwise the system may become unstable. Hence it is important to quantify the degree of insecurity of the power system both in planning as well as at operational stages. The distance in parameter space between an insecure operating point and the closest point on feasible (secure) hyper-surface has been used as a measure of degree of insecurity. A method based on two-phase optimization neural network has been presented to compute the degree of insecurity and the voltages and angles at all the buses of the system corresponding to the closest secure point. Inclusion of security limits on power system variables assures a solution representing a secure system. When compared with conventional non-linear optimization techniques, the proposed neural network is superior, as it can be easily implemented using digital hardware and is highly suitable for real time implementation in energy management system.The proposed method has been tested on IEEE 30-bus test system and a practical 75-bus Indian system. The results achieved are compared with results from a conventional method. Insecurity arising due to increase in load and contingencies has been considered in this work.     

面向结构健康监测的高速数据采集系统的研究

高速实时地采集传感器信号以及激励信号的准确输出对结构健康监测至关重要。在VisualC++6.0环境下,研究了结构健康监测中高速数据采集与激励信号输出。结合多功能数据采集卡PCI 1710HG的应用,利用多线程、双缓存技术实现了数据的高速采集、存储与显示,并使用高精度定时器实现了激励信号的准确输出,从而实现了在一台装有多功能数据采集卡的PC机上,配以相应的外围设备,就能方便地进行结构健康在线实时监测。     

基于UKF 和神经网络的一类非线性系统状态估计

在模型未知的情况下,估计过程的重要变量尤为重要.鉴于此,采用不敏卡尔曼滤波(UKF)与神经网络相结合的方法,解决一类未知模型非线性系统的状态估计问题.采用动态神经网络对非线性系统进行建模,利用UKF对状态和权值进行同时更新,从而达到神经网络逼近真实模型,估计值跟随真实值的目的.通过两个仿真实例表明了所提出的方法具有良好的估计效果,并且状态在输出中的比重越大,其估计精度越高.     

A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series models are selected by Granger–Newbold test. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with genetic algorithm (GA) and artificial neural network (ANN). This is the first study that uses a hybrid ANFIS computer simulation for improvement of electricity consumption estimation.     

Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China

High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the travel experience of HSR passengers, this study aims to determine passenger demands (PDs) and to evaluate passenger satisfaction by using a combination of online review analysis and large-scale group decision-making (LSGDM). By using web crawler technology, online reviews related to HSR were harvested from a microblogging platform to extract PD data and information. The six PDs that reflect the most frequent concerns of passengers were identified by analyzing the online reviews. The level of satisfaction of passengers with respect to these PDs was analyzed based on the online responses from 100 HSR passengers and by adopting the interval-valued two-tuple linguistic representation model. The final degrees of satisfaction and rankings of the PDs were then determined by using the LSGDM approach with the k-means clustering method and a consensus-reaching process. This research thus constructs an index system of HSR passenger satisfaction evaluation based on online-review analysis and evaluates the process by using LSGDM approaches. The conclusions provide insights into the improvements desired by HSR passengers for in-cabin services and to improve passenger satisfaction.