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.

Distributed Optimal Control for Traffic Networks with Fog Computing

This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

Performance and robustness analysis of a fuzzy-immune flow controller in ATM networks with time-varying multiple time-delays

For the Asynchronous Transfer Mode （ATM） networks with time-varying multiple time-delays, a more realistic model for the available bit rate （ABR） traffic class with explicit rate feedback is introduced. A fuzzy-immune controller is designed, which can adjust the rates of ABR on-line, overcome the bad effect caused by the saturation nonlinearity and satisfy the weighted fairness. Also, the sufficient condition that guarantees the stability of the closed-loop system with a fuzzy-immune controller is presented in theory for the first time. The algorithm exhibits good performance, and most importantly, has a solid theoretical foundation and can be implemented in practice easily. Simulation results show that the control system is rapid, adaptive, robust, and meanwhile, the quality of service （QoS） is guaranteed.

Automated Routing Control System of Local Area Network Segment

Automated Routing Control System supersedes the prior approach to LAN redundancy which provides two or more LANs and each has a network (LAN) controller coupled to data communication devices. Devices require some software to switch between the network (LAN) controllers to counter some network segment failures. This approach is proven to be very costly due to demands for “off-the-shelf” data communication devices with built-in LAN controller drivers [1]. Automated Routing Control System, is the ultimate solution to the growing demands for inexpensive (less costly) and less difficult approach which requires less modification rather than upgrade of network devices yet it minimizes data and information exchange losses and interruptions caused by connection failures within the network and lessens the task of managing a complex network having many segments rather than subnets under a centralized monitoring and management. Automated Routing Control System rather than mechanism is a realistic approach applied to meet the ever growing demands for reliability, high efficiency and availability within data and information exchange networks.

PERFORMANCE ANALYSIS OF LEAKY BUCKET ALGORITHM WITH BURSTY TRAFFIC INPUT IN ATM NETWORKS

A bursty traffic model is introduced in this paper to describe the statistical characteristics of packet video. The performance of leady bucket algorithm with bursty traffic input is analyzed. The influences of various parameters on QOS (Quality of Service) are investigated. The analysis shows that although the loss probability decreases through expanding the buffer capacity, the delay and delay jitter increase, whose effect on QOS will not be negligible.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Class of Service Support Layer for Wireless Mesh Networks

This paper presents an add-on Class of Service (CoS) layer for wireless mesh networks. The proposed protocol is applicable for contention-based MACs and is therefore compatible with most of the Wireless Local Area Network (WLAN) and Wireless Sensor Network (WSN) protocols. The protocol has a locally centralized control for managing data flows, which either reserve a fixed bandwidth or are weighted by fair scheduling. The protocol reduces transmission collisions, thus improving the overall throughput. IEEE 802.11 adhoc WLAN has been taken as a platform for simulations and prototyping for evaluating the protocol performance. Network Simulator Version 2 (NS2) simulations show that the CoS protocol efficiently differentiates bandwidth, supports bandwidth reservations, and reaches less than 10 ms transfer delay on IEEE 802.11b WLAN. Testing with a full prototype implementation verified the performance of the protocol.

面向高性能计算的互连网络拥塞控制分析与评估

随着高性能计算技术的发展,高性能计算系统的网络节点数量不断增长,高性能计算应用对网络性能的要求越来越高,高性能互连网络的拥塞控制面临很大的压力与挑战。针对高性能计算互连网络的特点,研究高效、低开销的拥塞控制方法,是确保高性能互连网络性能和稳定性的关键。针对高性能计算系统中互连通信的核心问题,对主流的拥塞控制方法进行分析和实验比较;基于高性能计算系统的结构特点和通信特性,设计用于大规模模拟仿真的数据流模型和流文件生成工具,并提出一种拥塞控制综合评价指标;使用所提出的数据流模型,在较大规模网络中对不同拥塞控制方法进行模拟,并基于所提出的评价指标对几种拥塞控制方法的性能进行分析和评估。提出的分析和评估技术可以对高性能互连网络的拥塞控制方法进行更客观和准确的分析与评估。

Wireless Hybrid QoS Architecture with an Enhancement of Fair Intelligent Congestion Control

More subtle and explicit QoS control mechanisms are required at the radio access level, even though the simple and scalable Differentiated Services (DiffServ) QoS control model is acceptable for the core of the network. At the radio access level, available resources are severely limited and the degree of traffic aggregation is not significant, thus rendering the DiffServ principles less effective. In this paper we present a suitable hybrid QoS architecture framework to address the problem. At the wireless access end, the local QoS mechanism is designed in the context of IEEE 802.11 WLAN with 802.11e QoS extensions;so streams of those session-based applications are admitted, established according to the traffic profile they require, and guaranteed. As the core in the Admission Control of the hybrid QoS architecture, the Fair Intelligent Congestion Control (FICC) algorithm is applied to provide fairness among traffic aggregates and control congestion at the bottleneck interface between the wireless link and the network core via mechanisms of packet scheduling, buffer management, feedback and adjustments. It manages effectively the overloading scenario by preventing traffic violation from uncontrolled traffic, and providing guarantee to the priority traffic in terms of guaranteed bandwidth allocation and specified delay.

考虑负载和时延的船舶无线网络节点流量控制

针对船舶无线网络稳定性较差,易出现负载失衡、信息传输时延高的问题,研究考虑负载和时延的船舶无线网络节点流量控制方法。此方法利用有向图优化船舶无线网络结构与节点分层结构;计算船舶无线网络节点负载占用率与可用率,提取负载均衡的节点通信路径;使用基于RTT的拥塞流量分配算法,以缩短延时为目标,考虑负载和时延的船舶无线网络节点流量控制。实验结果显示:此方法使用后,船舶紧急业务、关键业务、普通业务在无线网络中分组投递率提升,解决了负载失衡问题;紧急业务端到端时延低于100 ms,关键业务与普通业务端到端时延低于1 000 ms,流量控制效果较好。

基于异构多智能体自注意力网络的路网信号协调顺序优化方法

针对路网交通信号控制的复杂性,本文提出基于异构多智能体自注意力网络的路网信号协调顺序优化方法,提升路网范围内多交叉口信号控制策略性能。首先,模型考虑多交叉口交通流的空间相关性,采用基于自注意力机制的价值编码器学习交通观测表征,实现路网级通信;其次,面向多智能体策略更新的非稳态环境,模型在前序智能体的联合动作基础上,基于多智能体优势分解的策略解码器,顺序决策最优反应动作;最后,设计基于有效行驶车辆的动作掩码机制,在时效完备区间自适应调节决策频率,并提出考虑等待公平性的时空压力奖励函数,进一步提高策略性能与实用性。在杭州路网数据集上验证模型有效性,结果表明:所提模型在2个数据集和5个性能指标上均优于基准模型;相比最优基准模型,所提模型平均行程时间降低10.89%,平均排队长度降低18.84%,平均等待时间降低22.21%。此外,所提模型的泛化能力更强,且显著减少车辆等待时间过长的情形。

基于双流自适应图卷积网络的管制员睡岗行为识别

为识别空中交通管制员的睡岗行为,减少管制差错,保障航空器飞行安全,提出了一种基于双流自适应图卷积网络的管制员睡岗行为识别方法。该方法设计双流网络分别处理管制员骨架的一阶信息和二阶信息,实现对骨架数据的充分提取;通过自适应学习的骨骼拓扑连接矩阵,挖掘管制员不同关节之间的功能连接关系;同时在卷积层引入时空通道注意力机制,增强管制员睡岗行为识别模型在时间、空间、通道3个方向提取重要信息的能力。仿真结果表明,该方法能有效识别管制员3种睡岗行为,相较于传统的时空图卷积网络,识别准确率提高了3.08百分点,达到95.03%,可以提高民航运行安全管理水平。

Analysis of Completion of Call with on-off Data Source in Wireless Networks

In wireless network, call completion probability accounts for users' satisfaction since the admitted ongoing call may be interrupted during hand-off process or even stay in the same cell when dynamically allocating resource to calls because of the loss of resource. We focus on the relationship between call's completion probability and these interruptions and develop an analytical relationship model for homogeneous cellular networks based on probability analysis. Then assuming call's data source is modeled by on-off traffic model, a two dimensional Markov process is established to compute these blocking and dropping probabilities for call's completion probability. The impacts of different new call arrival rate, call's traffic characteristic, user's mobility, call's holding time and call's admission threshold on call's completion are evaluated and compared through numerical examples. These results show that call's completion reaches its maximum value if making no difference between hand-off call and new call in the case of light traffic load. But some resource should be reserved for the hand-off call in high traffic scenario. The analytical model provides a basis for helping to set the call admission threshold. Key words on-off traffic source - call completion probability - call admission control - wireless networks CLC number TN 919. 72 Foundation item: Supported by the National Natural Science Foundation of China (60172077)Biography: XUAN Xiao-ying (1972-), female, Ph. D candidate, research direction: radio resource management and scheduling in wireless networks.

基于高精度地图的智能网联技术师资培训探究——以《交通管理与控制》为例

在智能化、网联化的大环境下,随着相关政策落地和人才市场的需求,导致高校教师理论和实践技能遇到新的挑战,因此交通智能技术领域的教育教学改革也将成为必然的趋势。据此,文章首先简要介绍了基于高精度地图的智能网联技术的发展现状,随后围绕该新型技术对人才的需求探讨了该专业教学领域师资现状以及存在的问题,最后结合交通工程专业核心课程《交通管理与控制》对教师队伍的建设和培训给予了建议和意见,为更好地构建以高精度地图为基础的智能网联技术专业师资队伍提供借鉴。

应急情境下管制员情境意识可靠性研究

为提升管制员的应急处置能力,从管制员的个人能力、管制任务特点和管制设备3个方面构建管制员情境意识(SA)可靠性模型;利用塔台管制模拟软件和SA全面评价技术(SAGAT)测量管制员在应急情境下的SA水平;基于贝叶斯网络(BN)对SA可靠性进行定量分析,进而预测管制员的SA水平,并基于贝叶斯推理分析影响管制员SA可靠性的关键因素。研究结果表明:应急情境下,管制员SA可靠性和SA水平显著正相关,可通过管制员的SA可靠性预测SA水平;通过BN推理分析发现,对管制员的SA可靠性影响程度和敏感程度较高的因素为调配飞机可用时间、管制设备的精准度和管制员的记忆力;对管制员SA可靠性影响最大的致因链为调配飞机可用时间→任务特点→SA可靠性。

信息中心网络传输控制技术综述

信息中心网络(ICN)作为一种面向内容分发的新型网络架构,改变了内容检索和路由的方式。它直接对内容进行命名和寻址,并为网络节点配备了自决策计算、缓存和响应请求等能力,因而丰富了网络节点的功能。ICN的这些新特性和功能为网络传输控制技术研究提供了新的解决思路。同时,ICN的传输复杂度更高,且对传输控制有新的需求,从而对传输控制技术研究带来了新的挑战。本文对ICN的传输控制技术进行综述,在简要介绍传统TCP/IP网络的传输控制技术的基础上,从传输路径控制、传输速率控制和网内缓存控制等方面对ICN传输控制技术及其研究进展进行了梳理和分析,并对ICN传输控制技术的未来研究方向进行了展望。

无线局域网应用多重USG防火墙的安全策略研究

无线网络是一种方便灵活的网络接入方式,同时也存在一系列的安全威胁。通过对无线网络中可能产生的攻击和入侵方式进行详细的研究,确定基于华为USG5500防火墙的多重综合防御措施,包括身份验证、访问控制、数据加密、监测防御等多个方面,重点放在无线网络的身份验证和访问控制上。基于eNSP平台部署仿真拓扑,规划IP地址和防火墙区域,在无线网络中采用WPA2-PSK加密技术和VLAN技术,在多重防火墙中设置访问控制策略和流量过滤规则。仿真结果表明,在正确设置策略规则的情况下,多重USG5500防火墙能够及时监控和识别网络中的异常行为,有效检测和阻止网络攻击,提升无线网络的安全性和可靠性。

基于价值函数分解和通信学习机制的异构多智能体强化学习方法

许多现实世界的系统可以被建模为多智能体系统,多智能体强化学习为开发这些系统提供了一种有效的方法,其中基于集中训练与分散执行范式的价值函数分解方法得到了广泛的研究.然而现有的价值分解方法一般缺乏通信机制,在处理需要通信学习的多智能体任务时表现不佳.同时,目前大多数通信机制都是针对同构多智能体环境设计的,没有考虑异构多智能体场景.在异构场景中,由于智能体动作空间或观测空间的异构性,智能体之间的信息共享并不直接.如果不能对智能体的异构性进行有效地建模处理,通信机制将变得无效,甚至会影响多智能体的协作性能.为了应对这些挑战,本文提出一个融合价值函数分解和通信学习机制的异构多智能体强化学习框架.具体地:(1)与采用同构图卷积网络的方法不同,该框架利用异构图卷积网络融合智能体的异构特征信息得到有效的嵌入;(2)利用通信学习模块获得的嵌入信息和局部观测历史计算每个智能体的动作价值,以选择和协调智能体的动作;(3)通过设计的互信息损失函数和价值函数分解模块的损失函数联合训练,能够有效地训练整个方法.本文首先在两个异构多智能体平台上进行实验,实验结果表明该方法能学到比基线方法更有效的策略,在两个平台上相比基线方法分别提高了 13%的平均奖励值和24%的平均胜率.此外,在交通信号控制场景中验证了该方法在现实系统中的可行性.

手机欠费用户自助缴费方案设计与应用

为避免手机欠费停机给用户带来不便,提升用户体验,减少用户离网风险,同时降低实体营业厅的运营压力和成本,湖南电信针对如何构建手机欠费用户自助缴费的绿色通道(简称手机绿通)进行了研究,设计了一套创新方案,在无须增设新设备的前提下,解决了4G网络下绿通流程中多个核心问题,并通过不断的迭代优化证明该业务系统取得了显著的成效,实现了服务资源的合理配置及客户价值与服务成本的匹配,为企业树立了良好的口碑,大大提升了企业的竞争力,被集团采纳在全国推广。