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 ...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.展开更多
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 ...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.展开更多
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-s...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.展开更多
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. s...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.展开更多
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 p...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.展开更多
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 with...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.展开更多
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 transmiss...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.展开更多
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...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.展开更多
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 o...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.展开更多
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(elimin...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.展开更多
为了有效缓解空中航路网络中大规模航空器集群排放引发的环境问题,研究提出一种基于聚合网络交通流的空中交通绿色优化方法。以航空器集群聚合形成的网络交通流为研究对象,构建空中航路网络交通流动力学模型,用以描述管制干预下网络交...为了有效缓解空中航路网络中大规模航空器集群排放引发的环境问题,研究提出一种基于聚合网络交通流的空中交通绿色优化方法。以航空器集群聚合形成的网络交通流为研究对象,构建空中航路网络交通流动力学模型,用以描述管制干预下网络交通流的动态演化行为。在此基础上,以最小化航空排放所致的全球绝对温变潜力(Absolute Global Temperature Potential, AGTP)为目标,构建空中航路网络交通流绿色优化模型。该模型的维度与大规模航空器集群数量无关,而取决于有限的空域单元数量,显著降低了建模维度。此外,为了进一步削减计算复杂度,通过引入辅助变量约束的方法,将原本的非线性规划问题转化为混合整数线性规划(Mixed-Integer Linear Programming, MILP)问题,并借助Cplex进行求解。最后,以华东区域5个高空管制扇区构成的航路网络为实例,对所提方法开展验证。实例分析结果显示:网络交通流动力学模型能够较好地描述空中交通的动态演化行为,平均推演误差为4.55%;网络交通流绿色管控模型可有效降低航空排放的环境影响,在25 a、50 a、100 a的时间影响尺度下,AGTP分别降低38.73%、47.62%、47.65%。展开更多
合环转供电是提高供电可靠性的关键手段,但直接合环可能产生较大冲击电流影响电网运行安全性。采用旋转潮流控制器(rotary power flow controller,RPFC)的方式能够实现台区的零感知合环,但电压调节轨迹不当将会带来RPFC接入点的电压越...合环转供电是提高供电可靠性的关键手段,但直接合环可能产生较大冲击电流影响电网运行安全性。采用旋转潮流控制器(rotary power flow controller,RPFC)的方式能够实现台区的零感知合环,但电压调节轨迹不当将会带来RPFC接入点的电压越限问题。对此,提出一种基于两阶段最优路径的RPFC配电网柔性合环方法。首先,对输出电压运行轨迹进行合理规划,将其分为两阶段:第1阶段要求合环点两端电压相位一致并约束电压幅值相等,实现端电压的平稳过渡;第2阶段在相位一致的基础上,控制RPFC输出电压至目标值,使合环点两端电压幅值一致。然后,采用分相量夹角计算和旋转角协调控制,选择就近的旋转角设定值作为目标值,从而实现合环点两端电压无差调节;最后,仿真和实验对比分析所提方法将电压越限至少降低至30%,展示RPFC在实现配电网柔性合环中的重要应用价值。展开更多
文摘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.
基金the National Natural Science Foundation of China (No.60274009)Specialized Research Fund for the DoctoralProgram of Higher Education (No.20020145007)
文摘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.
基金Funded by the National Natural Science Foundation of China(No.41301084)the Scientific Research Project of Hunan Province Education Department,China(No.13C713)the Natural Science Foundation of Hunan Province,China(No.13JJ6075)
文摘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.
文摘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.
文摘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.
文摘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.
基金Supported by the National Natural Science Foundation of China (No. 60872002, 61003237)the Open Research Foundation of National Mobile Communications Research Lab, Southeast University, China (W200912)the Natural Science Foundation of Nantong Universty (No. 08Z025)
文摘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.
文摘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.
文摘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.
文摘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.
文摘为了有效缓解空中航路网络中大规模航空器集群排放引发的环境问题,研究提出一种基于聚合网络交通流的空中交通绿色优化方法。以航空器集群聚合形成的网络交通流为研究对象,构建空中航路网络交通流动力学模型,用以描述管制干预下网络交通流的动态演化行为。在此基础上,以最小化航空排放所致的全球绝对温变潜力(Absolute Global Temperature Potential, AGTP)为目标,构建空中航路网络交通流绿色优化模型。该模型的维度与大规模航空器集群数量无关,而取决于有限的空域单元数量,显著降低了建模维度。此外,为了进一步削减计算复杂度,通过引入辅助变量约束的方法,将原本的非线性规划问题转化为混合整数线性规划(Mixed-Integer Linear Programming, MILP)问题,并借助Cplex进行求解。最后,以华东区域5个高空管制扇区构成的航路网络为实例,对所提方法开展验证。实例分析结果显示:网络交通流动力学模型能够较好地描述空中交通的动态演化行为,平均推演误差为4.55%;网络交通流绿色管控模型可有效降低航空排放的环境影响,在25 a、50 a、100 a的时间影响尺度下,AGTP分别降低38.73%、47.62%、47.65%。
文摘合环转供电是提高供电可靠性的关键手段,但直接合环可能产生较大冲击电流影响电网运行安全性。采用旋转潮流控制器(rotary power flow controller,RPFC)的方式能够实现台区的零感知合环,但电压调节轨迹不当将会带来RPFC接入点的电压越限问题。对此,提出一种基于两阶段最优路径的RPFC配电网柔性合环方法。首先,对输出电压运行轨迹进行合理规划,将其分为两阶段:第1阶段要求合环点两端电压相位一致并约束电压幅值相等,实现端电压的平稳过渡;第2阶段在相位一致的基础上,控制RPFC输出电压至目标值,使合环点两端电压幅值一致。然后,采用分相量夹角计算和旋转角协调控制,选择就近的旋转角设定值作为目标值,从而实现合环点两端电压无差调节;最后,仿真和实验对比分析所提方法将电压越限至少降低至30%,展示RPFC在实现配电网柔性合环中的重要应用价值。
