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.

Mathematical Modeling and Control Algorithm Development for Bidirectional Power Flow in CCS-CNT System

As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.

A Genetic Based Fuzzy Q-Learning Flow Controller for High-Speed Networks

For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. Simulation results show that the proposed controller can learn to take the best action to regulate source flow with the features of high throughput and low packet loss ratio, and can avoid the occurrence of congestion effectively.

Intelligent Flow Control Technique of ABR Service in ATM Networks Based on Fuzzy Neural Networks

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Binary ABR flow control over ATM networks with uncertainty using discrete-time variable structure controller

A binary available bit rate （ABR） scheme based on discrete-time variable structure control （DVSC） theory is proposed to solve the problem of asynchronous transfer mode （ATM） networks congestion in this paper. A discrete-time system model with uncertainty is introduced to depict the time-varying ATM networks. Based on the system model, an asymptotically stable sliding surface is designed by linear matrix inequality （LMI）. In addition, a novel discrete-time reaching law that can obviously reduce chatter is also put forward. The proposed discrete-time variable structure controller can effectively constrain the oscillation of allowed cell rate （ACR） and the queue length in a router. Moreover, the controller is self-adaptive against the uncertainty in the system. Simulations are done in different scenarios. The results demonstrate that the controller has better stability and robustness than the traditional binary flow controller, so it is good for adequately exerting the simplicity of binary flow control mechanisms.

A dynamic network Qo S control mechanism based on traffic prediction

A dynamic network Qo S control mechanism was proposed based on traffic prediction. It first predicts network traffic flow and then dynamically distributes network resources, which makes full use of network flow self-similarity and chaos. So it can meet changing network needs very well. The simulation results show that the dynamic Qo S control mechanism based on prediction has better network performance than that based on measurement.

Dynamic Characteristics Analysis on MHTGR Plant’s Secondary Side Fluid Flow Network

Multipe NSSS (Nuclear Steam Supply System) modules use the common feeding-water system to drive the common turbine power generation set. The SSFFN (secondary side fluid flow network) of MHTGR plant has features i.e. strong-coupling and nonlinearity. A wide range of power switching operation will cause unsteady flow, which may destroy the working elements and will be a threat for normal operation. To overcome those problems, a differential-algebraic model and PI controllers are designed for the SSFFN. In MATLAB\SIMULINK environment, a simulation platform is established and used to make a simulation of SSFFN of a MHTGR plant with two NSSS modules, which uses feedwater valves to control the mass flow rate in each module instead of feedwater pump. Results reflect good robustness of controllers.

Classification of Oil-Gas-Water Three-Phase Flow in a Pipeline Based on BP Neural Network Analysis

The flow pattern in a pipeline is a very important topic in petroleum exploitation. This paper is to classify the flow pattern of oil-gas-water flow in a pipeline by using BP neural network. The effects of different parameter combinations are investigated to find the most important ones. It is shown that BP neural network can be used in the analysis of the flow pattern of three-phase flow in pipelines. In most cases, the mean square error is large for the horizontal pipes. The optimized neuron number of the middle layer changes with conditions. So, we must changes the neuron number of the middle layer in simulation for any conditions to seek the best results. These conclusions can be taken as references for further study of the flow pattern of oil-gas-water in a pipeline.

COMBINED ADMISSION CONTROL AND SCHEDULING IN MULTI-SERVICE NETWORKS

Providing the required metrics for different service respectively is a basic characteristic in multi-service networks. The different service can be accessed and forwarded differently to provide the different transmission performance. The state information between admission control and scheduling can be exchanged each other by the defined correlation coefficient to adjust the flow distribution in progress. The priority queue length measured by scheduler implicitly can describe the priority flows load. And the fair rate can describe the non-priority flows load. Different admission decision will be made according to the state of scheduler to assure the time-delay upper threshold for the priority flows under heavy load and the fairness for elastic flows in light load, respectively. The stability condition was conduced and proved. Simulation results show the policy can ensure both the delay for the priority flows and the minimal throughput for non-priority flows.

A NEURAL NETWORK-BASED MODEL FOR PREDICTION OF HOT-ROLLED AUSTENITE GRAIN SIZE AND FLOW STRESS IN MICROALLOY STEEL

For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of BP algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted.

Gyration:基于RTT测量的报文偏转拥塞控制算法

高效的拥塞控制一直是数据中心网络领域的一个重要挑战。往返时延RTT的精确测量是基于RTT的反应型拥塞控制算法的关键。基于Swift拥塞控制算法,提出一种基于RTT测量的报文偏转拥塞控制算法——Gyration,将偏转报文延迟添加到RTT计算过程,在偏转拥塞控制算法中增加偏转延迟的测量,使RTT计算更准确,更加精确地评估网络拥塞情况。实验结果表明,相比Swift算法,在重负载的Cache Follower、Data Mining、Web Search和Web Server流量模式下,Gyration将流完成时间FCT缩短了20%,80%,13%和60%,吞吐量提高了38%,6%,15%和2%,实现了对数据中心网络更加及时、精确的拥塞控制,有效缓解了数据中心网络拥塞问题。

电力信息物理系统中信息系统物理化的建模及分析方法

该文基于信息系统物理化的设想提出电力信息物理系统(cyber-physical power system,CPPS)中的信息流建模和计算分析方法。采用连续时间函数来刻画信息流的特征,并定义信息网络运行参数为流量累积函数、信息流速和时延。首先,基于遍历法搜索出信息流路径,建立信息流速矩阵的范式;然后利用改进的网络演算(network calculus,NC)特性赋值流速矩阵的元素;进一步采用流量累积函数表征信源数据发送规律,从而显式求解时延上界。最后将提出的信息流建模方法应用于智能变电站自动化系统的时延计算,通过与OPNET的仿真结果相比较,验证所提出模型的有效性,而且该方法可以提供定量分析指标以优化变电站组网方案设计中的信息流分布。

基于深度学习的LSTM-GRU复合模型矿井涌水量预测方法研究

为了解决矿井涌水预测问题,引入深度学习理论,将长短期记忆网络(LSTM)和门控循环单元(GRU)进行结合,选取矿井涌水量为研究对象,建立一种LSTM-GRU的矿井涌水预测模型。以陕西某矿的矿井涌水量为样本数据,采用7∶3的比例将数据集划分为训练集和测试集,选择模型训练效果较好的梯度下降算法确定网络模型参数和正则化参数,为了证明LSTM-GRU模型的预测精度,同时将结果分别与传统的ARIMA模型和LSTM模型预测矿井涌水所得到的预测结果进行对比。结果表明:LSTM-GRU复合模型的平均绝对百分比误差(RMSE)为70.51,均方根误差(MAE)为53.4,平均绝对误差(MAPE)为2.80%,可决系数(R^(2))为0.86,具有较高的预测精度和可靠性,预测效果优于传统的ARIMA模型和LSTM模型。

局部土地利用类型变化对城市水文过程的影响研究

为探究城市下垫面局部土地利用类型变化对城市水文过程的影响,采用高效高精度城市雨洪模型(GAST)耦合管网模型(SWMM),模拟不同降雨重现期下动态改变土地利用类型(单块无下渗土地C_(1)、多块组合无下渗土地C_(2)、单块裸土地C_(3)、单块草地C_(4)和单块林地C_(5))对城市地表积水、管网排水的影响。结果表明:改变局部原始土地利用类型(B1)在小降雨重现期下降低了城市积水量,而在大降雨重现期下改变土地下渗面积和区域位置无法进一步减少积水,此时则需考虑非工程措施的极端暴雨应对模式。不同动态规划区域下渗强度导致管网排口流量增幅不同(C_(2)>C_(1)>C_(3)>C_(4)>C_(5)),隔渗能力越强对管网排口流量增幅影响越大,而管网径流控制率则呈相反趋势。结合对积水量的影响可知,增加下垫面下渗能力并结合排水系统可进一步降低城市内涝风险和管网排水负荷,且管网径流控制率同样可以作为评估动态规划可行性的一个重要指标。

OpenFlow网络冗余控制报文消除机制研究

OpenFlow网络数据平面将未匹配流表的数据包发送给控制器,其中的无连接突发流量将产生冗余控制报文,对网络性能造成不良影响,而目前的OpenFlow协议并未对此进行处理.研究了在控制平面和数据平面分别消除冗余控制报文的方法 ERCMC(eliminating redundant control messages on the control plane)和ERCMD(eliminating redundant control messages on the data plane),分别在NOX和Open vSwitch上进行实现,并进行性能评价.实验结果表明,ERCMC方法能够消除冗余控制报文,但增加了额外的处理开销;ERCMD方法在减少冗余控制报文数量的情况下能够减小控制器和OpenFlow交换机负载.

考虑通信工况的主动配电网多层级协作电压控制方法

随着主动配电网的发展,通信工况对电压控制的影响更加显著。主站与光伏逆变器的通信中断会影响逆变器的无功出力调整,进而导致配电网电压越限。文中提出一种考虑通信工况的主动配电网多层级协作电压控制方法,其对配电网信息侧进行建模以描述控制过程的各轮信息交互,再通过协作集中层级与就地层级,实现了在不同通信工况下对各逆变器无功出力的合理调整。集中层级建立了考虑通信工况的逆变器下垂出力约束、配电网潮流约束以及考虑网络损耗和电压偏差的多目标函数,并对其构建的线性优化问题进行求解。就地层级根据逆变器通信状态来选用经集中层级决策的或提前预设的下垂参数。通过基于IEEE 118节点系统的仿真分析,验证了所提方法能在逆变器通信中断时段内保证配电网电压不越限,并能实现网络损耗与电压分布的全局优化。

基于电压补偿的双端直流配电网电压就地协调控制

双端直流配电网是一种双端电源供电的网络结构,可为直流负荷提供更加稳定和可靠的电源电压。然而,负荷的功率波动和不平衡使线路电压跌落增大,可能导致直流负荷的电压不平衡度和电压偏差指标越限,影响直流负荷的正常运行。文中基于电压源型换流器(VSC)外环电压控制,考虑中线电压补偿,提出基于电压补偿等效模型的直流配电网电压偏差及不平衡度联合抑制策略,实现直流配电网电压就地协调控制。首先,建立双端直流配电网潮流模型,将VSC电压下垂控制引入潮流模型,分析不同控制策略下的电压偏差和不平衡度的特性。其次,在此基础上,以电压最低点为分点获得双端电源供电回路压降的简化等效模型,并利用最小二乘法实现等效阻抗参数辨识,构建双端直流配电网的电压补偿等效模型。以参数辨识结果作为电压外环控制输入,提出考虑中线电压补偿的双端直流配电网电压就地协调控制策略。最后,在MATLAB/Simulink中搭建仿真模型,验证了双端直流配电网潮流模型的正确性和控制策略的有效性。

新型单心式独立型移相变压器及其调节特性研究

为提升高比例新能源配电网的潮流控制能力和精度,提出了一种新型单心式独立型移相变压器(SCIPST),对调压绕组匝数配置进行了优化,通过独立控制2个调压绕组,SCIPST能够独立调节线路电压的相位和幅值。与传统移相变压器相比,增加了独立调节线路电压幅值的功能和可调移相角的数量,提高了调节精度。通过独立调节线路电压相位和幅值,能够在一定范围内实现独立调节线路有功、无功。介绍了SCIPST的拓扑结构,详细分析了工作原理并给出了控制策略。建立了SCIPST的仿真模型,对SCIPST调节电网潮流和电压的稳态和暂态性能进行仿真验证。研制出小容量SCIPST实验模型,搭建了潮流调节特性测试平台,开展了调节潮流模拟实验。仿真和实验结果证明了所提出的SCIPST的可行性和有效性。

面向加密流量的社交软件用户行为识别

随着智能终端和社交网络越来越融入人们的日常生活,针对社交软件的用户行为识别在网络管理、网络环境监管和市场调研等方面发挥越来越重要的作用.社交软件普遍使用端到端加密协议进行加密数据传输,现有方法通常提取加密数据的统计特征进行行为识别.但这些方法识别的性能不稳定且需要的数据量多,这些缺点影响了方法的实用性.提出了一种面向加密流量的社交软件用户行为识别方法.首先,从加密流量中识别出稳定的控制流数据,并提取控制服务数据分组负载长度序列.然后设计了2种神经网络模型,用于自动从控制流负载长度序列中提取特征,细粒度地识别用户行为.最后,以WhatsApp为例进行了实验,2种神经网络模型对WhatsApp用户行为的识别精准率、召回率和F1-score均超过96%.与类似工作的实验比较证明了该方法识别性能的稳定性,此外,该方法能够通过很少的控制流数据分组达到较高的识别精准率,对实时行为识别的研究具有重要的现实意义.

基于OpenFlow的SDN网络安全分析与研究

基于Open Flow的SDN技术将网络的数据平面和控制平面相分离,通过部署中央控制器来实现对网络的管控,为未来网络的发展提供了一种新的解决思路。然而,这种新型网络管控方法与传统网络在分布式控制平面上通过封闭网络设备进行管控的方法有着根本区别,因而在实现集中化管理的同时将引入新的管理和安全问题。文章首先介绍了其三层架构的自身缺陷和可能存在的安全问题,并从SDN架构的基础设施层、南向接口、控制层、北向接口和应用层等几个方面分别进行分析,总结出SDN不同层次存在安全问题的原因;随后,文章从认证机制、控制层的备份和恢复、网络异常识别和防御机制、应用隔离和权限管理等四个方面总结了当前的相关研究进展和研究思路,并提出了可行的解决方案;最后,对全文进行总结和展望。