Dynamic response mechanism and precursor characteristics of gneiss rockburst under different initial burial depths

To investigate the influence mechanism of geostress on rockburst characteristics,three groups of gneiss rockburst experiments were conducted under different initial geostress conditions.A high-speed photography system and acoustic emission(AE)monitoring system were used to monitor the entire rockburst process in real time.The experimental results show that when the initial burial depth increases from 928 m to 1320 m,the proportion of large fracture scale in rockburst increases by 154.54%,and the AE energy increases by 565.63%,reflecting that the degree and severity of rockburst increase with the increase of burial depth.And then,two mechanisms are proposed to explain this effect,including(i)the increase of initial geostress improves the energy storage capacity of gneiss,and then,the excess energy which can be converted into kinetic energy of debris ejection increases,consequently,a more pronounced violent ejection phenomenon is observed at rockburst;(ii)the increase of initial geostress causes more sufficient plate cracks of gneiss after unloading ofσh,which provides a basis for more severe ejection of rockburst.What’s more,a precursor with clear physical meaning for rockburst is proposed under the framework of dynamic response process of crack evolution.Finally,potential value in long term rockburst warning of the precursor obtained in this study is shown via the comparison of conventional precursor.

Dynamic characteristics of the planetary gear train excited by time-varying meshing stiffness in the wind turbine

Wind power has attracted increasing attention as a renewable and clean energy. Gear fault frequently occurs under extreme environment and complex loads. The time-varying meshing stiffness is one of the main excitations. This study proposes a 5 degree-of-freedom torsional vibration model for the planetary gear system. The influence of some parameters(e.g., contact ratio and phase difference) is discussed under different conditions of a single teeth pair and double pairs of teeth. The impact load caused by the teeth face fault, ramped load induced by the complex wind conditions, and the harmonic excitation are investigated. The analysis of the time-varying meshing stiffness and the dynamic meshing force shows that the dynamic design under different loads can be made to avoid resonance, can provide the basis for the gear fault location of a wind turbine, and distinguish the fault characteristics from the vibration signals.

RESEARCH ON FRACTAL CHARACTERISTICS OF URBAN TRAFFIC NETWORK STRUCTURE BASED ON GIS

Traffic network is an importance asp ect of researching controllable parameters of an urban spatial morpholo-gy.Based on GIS,traffic network str ucture complexity can be understood by using fractal geometry in which th e length-radius dimension describes change of network density,and ramification-radius dimension describes complexity and accessibility of urban network.It i s propitious to analyze urban traffic network and to understand dynamic c hange process of traffic network using expanding f ractal-dimension quantification.Meanwhile the length-radius dimension and ramifica-tion-radius dimension could be rega rd as reference factor of quantitative describing urban traffic network.

Efficient computation for dynamic responses of systems with time-varying characteristics

Based on Neumman series and epsilon-algorithm, an efficient computation for dynamic responses of systems with arbitrary time-varying characteristics is investigated. Avoiding the calculation for the inverses of the equivalent stiffness matrices in each time step, the computation effort of the proposed method is reduced compared with the full analysis of Newmark method. The validity and applications of the proposed method are illustrated by a 4-DOF spring-mass system with periodical time-varying stiffness properties and a truss structure with arbitrary time-varying lumped mass. It shows that good approximate results can be obtained by the proposed method compared with the responses obtained by the full analysis of Newmark method.

Real-time road traffic states estimation based on kernel-KNN matching of road traffic spatial characteristics

The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.

Network Traffic Generation Based on Statistical Packet-Level Characteristics

Network traffic is very important for testing network equipment, network services, and security products. A new method of generating traffic based on statistical packet-level characteristics is proposed. In every time unit, the generator determines the sent packets number, the type and size of every sent packet according to the statistical characteristics of the original traffic. Then every packet, in which the protocol headers of transport layer, network layer and ethernet layer are encapsulated, is sent via the responding network interface card in the time unit. The results in the experiment show that the correlation coefficients between the bandwidth, the packet number, packet size distribution, the fragment number of the generated network traffic and those of the original traffic are all more than 0.96. The generated traffic and original traffic are very highly related and similar.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

智能网联环境下混合交通流仿真设计与特性分析

以智能网联环境下混合交通流的特性为研究对象,在SUMO仿真软件中设置高速公路双向六车道的交通环境,利用SUMO仿真软件和Python软件对模拟智能网联汽车的CACC(cooperative adaptive cruise control)模型和模拟人工驾驶汽车的IDM(intelligent driver model)模型进行仿真实验,并对CACC汽车不同渗透率下的数据进行对比分析,从而对智能网联汽车与人工驾驶汽车形成的混合交通流模型进行研究。模拟仿真结果表明:在不同渗透率的智能网联汽车和人工驾驶汽车的混合交通流中,随着CACC汽车渗透率的不断增加,道路的流率不断提高,平均速度也不断提高;当CACC汽车渗透率达到100%时,道路的总时间延误可以减少约862 s;随着CACC汽车渗透率的不断增加,道路上车辆的换道频率也随之下降,减少了道路上车辆之间的速度差。智能网联汽车的参与可以提高高速公路的通行能力和车道的利用率,提高车辆的平均速度并减少行程的时间延误。

Effect of vehicle weight on natural frequencies of bridges measured from traffic-induced vibration

Recently,ambient vibration test(AVT)is widely used tu estimate dynamic characteristics of large civil struc- tures.Dynamic characteristics ean be affected by various envirnnmental factors such as humidity,intensity of wind,and temperature.Besides these environmental conditions,tire mass of vehicles may change the measured valnes when traffic-in- duced vibration is used as a source of AVT tor bridges.The effect of vehicle mass on dynamic characteristics is investigated through traffic-induced vibration tests on three bridges;(1)three-span suspension bridge(128m+404m+128m),(2) five-span continuous steel box girder bridge(59m+3@ 95m+59m),(3)simply supported plate girder bridge(46m). Acceleration histories of each measurement location under normal traffic are recorded for 30 minutes at field.These recor- ded histories are divided into individual vibrations and are combined into two groups aceording to the level of vibration;one by heavy vehicles such as trucks and buses and the other by light vehicles such as passenger cars.Separate processing of the two groups of signals shows that,for the middle and long-span bridges,the difference can be hardly detected,but,for the short span bridges whose mass is relatively small,the measured natural frequencies can change up to 5.4%.

A novel internet traffic identification approach using wavelet packet decomposition and neural network

Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.

Online Internet Traffic Identification Algorithm Based on Multistage Classifier

Internet traffic classification plays an important role in network management. Many approaches have been proposed to clas-sify different categories of Internet traffic. However, these approaches have specific us-age contexts that restrict their ability when they are applied in the current network envi-ronment. For example, the port based ap-proach cannot identify network applications with dynamic ports; the deep packet inspec-tion approach is invalid for encrypted network applications; and the statistical based approach is time-onsuming. In this paper, a novel tech-nique is proposed to classify different catego-ries of network applications. The port based, deep packet inspection based and statistical based approaches are integrated as a multi-stage classifier. The experimental results demonstrate that this approach has high rec-ognition rate which is up to 98% and good performance of real-time for traffic identifica-tion.

Modeling and Characterizing Internet Backbone Traffic

With enormous growth of the number of Internet users and appearance of new applications, characterization of Internet traffic has attracted more and more attention and has become one of the major challenging issues in telecommunication network over the past few years. In this paper, we study the network traffic pattern of the aggregate traffic and of specific application traffic, especially the popular applications such as P2P, VoIP that contribute most network traffic. Our study verified that majority Internet backbone traffic is contributed by a small portion of users and a power function can be used to approximate the contribution of each user to the overall traffic. We show that P2P applications are the dominant traffic contributor in current Internet Backbone of China. In addition, we selectively present the traffic pattern of different applications in detail.

Accurate P2P traffic identification based on data transfer behavior

Peer-to-Peer (P2P) technology is one of the most popular techniques nowadays, and accurate identification of P2P traffic is important for many network activities. The classification of network traffic by using port-based or payload-based analysis is becoming increasingly difficult when many applications use dynamic port numbers, masquerading techniques, and encryption to avoid detection. A novel method for P2P traffic identification is proposed in this work, and the methodology relies only on the statistics of end-point, which is a pair of destination IP address and destination port. Features of end-point behaviors are extracted and with which the Support Vector Machine classification model is built. The experimental results demonstrate that this method can classify network applications by using TCP or UDP protocol effectively. A large set of experiments has been carried over to assess the performance of this approach, and the results prove that the proposed approach has good performance both at accuracy and robustness.

Road Traffic Accidents (RTAs) Trends on Kathmandu-Bhaktapur Road after Addition of Lanes

Road traffic accidents are the outcome of the factors associated with the traffic system namely road users, road environment and vehicles. Despite good road surface, the Kathmandu-Bhaktapur road has high accident records. The traffic police record shows that 1530 accidents have occurred from July 2009 to June 2012. The area of study of this research is the Kathmandu-Bhaktapur road which starts at Tinkune Kathmandu and ends at Suryabinayak with a length of 9.1 kilometer, a section of Araniko highway heading towards China. The road is the first ever six lane road constructed in Nepal. The objectives of this research work are to identify locations with high accident numbers, to investigate possible causes of accidents, and to propose countermeasures for traffic safety along the Kathmandu-Bhaktapur road.

DESCRIPTION AND WENO NUMERICAL APPROXIMATION TO NONLINEAR WAVES OF A MULTI-CLASS TRAFFIC FLOW LWR MODEL

A strict proof of the hyperbolicity of the multi-class LWR ( Lighthill-Whitham-Richards) traffic flow model, as well as the descriptions on those nonlinear waves characterized in the traffic flow problems were given. They were mainly about the monotonicity of densities across shocks and in rarefactions. As the system had no characteristic decomposition explicitly, a high resolution and higher order accuracy WENO( weighted essentially non-oscillatory) scheme was introduced to the numerical simulation, which coincides with the analytical description.

Perspective of Adaptive CN System for Forecasting Congestion of Road Traffic Flow

Basing upon the Weber-Fechner Law with respect to the stimulus (distance-headway) to the vehicle driver and the driver’s sensation (speed), the characteristic speed Vβ is defined, which is the critical vehicles flow speed just before going to congestion in road traffic flow. From the information of real time measurement of traffic flow speed (V) and time-headway (T) at the specific positions along the road, the value of Vβ is calculated and used for forecasting the flow. Discussed is how to use each Vβ to forecast the congestion. The CN system devoted to the management of road traffic flow is proposed. The idea may contribute not only to easing the traffic flow but also to optimizing it to get high efficient traffic flow.

Flow Direction Level Traffic Flow Prediction Based on a GCN-LSTM Combined Model

Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning.Due to the complexity of road traffic flow data,traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy.In this study,a combined flow direction level traffic flow prediction graph convolutional network(GCN)and long short-term memory(LSTM)model based on spatiotemporal characteristics is proposed.First,a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks.Additionally,due to the capability to handle long-term dependencies,the longterm memory is used to predict the time series of traffic flow and extract the time features.The proposed model is evaluated using real-world data,which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China.The results show that the developed combined GCNLSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model,and the combined ARIMA-LSTM model in traffic flow has a strong spatiotemporal correlation.

空间异质下地铁建成环境与站点覆盖客流吸引度关系研究

机器学习模型广泛应用于探究建成环境与客流的交互关系。然而,机器学习考虑的是全局关系,无法捕捉空间变化,为解决这一问题,本文从密度、多样性、设计、目的地可达性、可获得性和网络连通性等方面构建了11种建成环境指标,提出一种轻量级梯度提升机(LightGBM)与地理加权回归(GWR)的集成综合分析模型(SLightGBM),以探究建成环境对站点覆盖客流吸引度的空间异质性和非线性影响,并将该模型与LightGBM,普通最小二乘法(OLS)和GWR进行比较,以揭示SLightGBM模型在回归效果上的优势。针对西安市的研究结果表明:SLightGBM模型的R2、MAE与RMSE分别达到了0.68、8379.16和11797.19,显著优于对比模型;建成环境因素存在空间异质性,工作人口密度和公交站点密度在中心区域最为重要,而餐饮密度在南部区域更显著;工作人口密度和餐饮密度与客流吸引度呈正相关,而最小换乘次数与客流吸引度呈负相关关系,且具有明显的协同作用。研究表明,影响因子空间差异性和阈值分析结果对于指导城市建设和改善公共交通系统具有重要的启示意义。