一种新的互连网络拥挤控制

本文详细讨论了互连网络拥挤控制;指出了一般网络内拥挤控制方法不能成功地应用于互连网络的原因。首次提出了一种新的互连网络的拥挤控制方法,它视互连网络为一黑匣,完全利用互连设备网间连接器(Gateway)的功能进行拥挤控制。计算和模拟分析验证了该方法的突出特性,并与R。Jain(1986)提出的拥挤控制法进行了比较。     

考虑静态电压安全的输电阻塞管理

在电力系统中,输电网络是电力市场的基础和载体,过负荷或违反静态电压安全指标是输电阻塞的主要表现.阻塞将降低电力市场的公平有序竞争,削弱输电系统稳定性,降低电能质量,增加能量成本.因此,研究由于违反静态电压安全标准引起的系统阻塞问题,对于保证电力市场的正常运行和提高电力系统的效率具有重要的现实意义.考虑有载调压变压器以及可投切电容器组在保证系统安全性方面的重要作用,本文把这两个控制方法引入阻塞管理的优化策略中,与发电机组再调度一起,共同优化以处理由于系统静态电压安全引起的阻塞问题.     

拥塞控制卫星PRMA-AC协议及性能分析

卫星通信中PRMA协议在负荷较重时由于终端竞争加剧会引起信道拥塞,而较长的传播时延更进一步加剧了拥塞引起的丢包。该文提出一种利用话音终端在通话的不同阶段对信道资源的不同需求对终端进行区分的方案PRMA-AC,据此引入一种接入控制机制,以减少信道竞争,提高系统服务质量。文中给出了系统模型,对协议性能进行了理论分析,获得了新协议下的接入阻塞率、丢包率等性能指标,最后通过仿真与几种卫星通信中常用的PRMA协议进行了对比,证明了协议的性能。     

异构网络中丢包识别研究综述

异构网是将具有不同媒体、不同性能的子网统一构建成的单个逻辑网络,在这种网络中随着信道的变化丢包事件会更频繁地发生,同时丢包的原因比单一网络环境更加复杂,为有线环境设计的传统TCP不能满足应用的要求,因此改进TCP协议成为异构网中研究的热点。本文在研究各种TCP协议的基础上,深入分析了其中丢包识别这一关键问题,概括了丢包识别的一般性策略,然后时典型的区分丢包原因的方法进行了归纳和比较,最后提出了通过丢包控制提高异构网性能的研究方向,为各种环境下TCP协议的改进提供了较好的研究基础。     

主动队列管理RED算法改进与实验仿真研究

分析了RED及其改进算法的原理和局限性,提出了一种非线性高阶RED拥塞控制机制。该算法利用一个高阶分组丢弃函数,在下限阈值附近以较低的概率标记丢弃分组,在上限阈值附近迅速提高分组的标记丢弃概率。并对网络仿真器NS2进行扩展,通过系列仿真实验验证了改进算法有效提高了网络性能。     

塔里木盆地下古生界碳酸盐岩油气盖层及其控藏作用

塔里木盆地下古生界发育巨厚的碳酸盐岩沉积，垂向上具有良好的生储盖配置条件，在长期的地质历史演化过程中，经历了多期成藏和多期改造，油气成藏条件十分复杂，下古生界碳酸盐岩盖层研究对于优选勘探层系和钻探目标至关重要。采用典型油气藏盖层分析、地震资料解释及实验分析等手段，总结归纳出两类区域盖层与两类局部盖层：两类区域盖层分别为上奥陶统暗色泥岩（“黑被子”）与中寒武统蒸发岩（“白被子”）；两类局部盖层分别为碳酸盐岩潜山上覆泥岩（“新被子”）与内幕致密碳酸盐岩，并通过地震解释与井控的方法厘定了“黑被子”“白被子”“新被子”的分布。通过对已发现油气田（藏）盖层与成藏的关系、盖层之下油气藏类型的统计分析，认为区域盖层与下伏致密碳酸盐岩构成复式盖层控制主力成藏层位；局部盖层之下的油气藏类型主要受储层渗流能力控制，即局部盖层与均质储层组合表现为构造控藏特点，局部盖层与非均质储层组合表现为大面积准层状分布特点。建议将下古生界碳酸盐岩新区勘探重点方向调整为满西低凸起奥陶系与寒武系盐下白云岩；致密高阻碳酸盐岩的封盖能力存在风险，建议在开发阶段进行精细评价。     

Intelligent traffic light systems using edge flow predictions

In this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations.     

Public transport congestion detection using incremental learning

In the past decade, intelligent transportation systems have emerged as an efficient way of improving transportation services, while machine learning has been the key driver that created scopes for numerous innovations and improvements. Still, most machine learning approaches integrate paradigms that fell short of providing cost-effective and scalable solutions. This work employs long short-term memory to detect congestion by capturing the long-term temporal dependency for short-term public bus travel speed prediction to detect congestion. In contrast to existing methods, we implement our solution as incremental learning that is superior to traditional batch learning, enabling efficient and sustainable congestion detection. We examine the real-world efficacy of our prototype implementation in Pécs, the fifth largest city of Hungary, and observed that the incrementally updated model can detect congestion of up to 82.37%. Additionally, we find our solution to evolve sufficiently over time, implying diverse real-world practicability. The findings emerging from this work can serve as a basis for future improvements to develop better public transportation congestion detection.     

Efficient Routing Protocol with Localization Based Priority & Congestion Control for UWSN

The nodes in the sensor network have a wide range of uses, particularly on under-sea links that are skilled for detecting, handling as well as management. The underwater wireless sensor networks support collecting pollution data, mine survey, oceanographic information collection, aided navigation, strategic surveillance, and collection of ocean samples using detectors that are submerged in water. Localization, congestion routing, and prioritizing the traffic is the major issue in an underwater sensor network. Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource. Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper. We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio, dispatching of packets, power, and distance among the nodes. This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic. The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet, delay of latency, and power utilization than the existing algorithms.