1,316 research outputs found
Adaptive performance optimization for large-scale traffic control systems
In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators
Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review
To
deliver electricity to customers safely and economically, power companies
encounter numerous economic and technical challenges in their operations. Power
flow analysis, planning, and control of power systems stand out among these
issues. Over the last several years, one of the most developing study topics in
this vital and demanding discipline has been electricity short-term load
forecasting (STLF). Power system dispatching, emergency analysis, power flow
analysis, planning, and maintenance all require it. This study emphasizes new
research on long short-term memory (LSTM) algorithms related to particle swarm
optimization (PSO) inside this area of short-term load forecasting. The paper
presents an in-depth overview of hybrid networks that combine LSTM and PSO and
have been effectively used for STLF. In the future, the integration of LSTM and
PSO in the development of comprehensive prediction methods and techniques for
multi-heterogeneous models is expected to offer significant opportunities. With
an increased dataset, the utilization of advanced multi-models for
comprehensive power load prediction is anticipated to achieve higher accuracy
MODELING AND SIMULATION OF PM MOTOR TESTING ENVIRONMENT TOWARDS EV APPLICATION CONSIDERING ROAD CONDITIONS
The electric vehicle (EV) performance testing is an indispensable aspect of the design study and marketing of electric vehicle. The development of a suitable electric motor testing environment for EVs is very significant. On the one hand, it provides a relatively realistic testing environment for the study of the key technologies of electric vehicles, and it also plays an essential role in finding a reasonable and reliable optimization scheme. On the other hand, it provides a reference to the evaluation criteria for the products on the market. This thesis is based on such requirements to model and simulate the PM motor testing environment towards EV applications considering road conditions. Firstly, the requirements of the electric motor drive as a propulsion system for EV applications are investigated by comparing to that of the traditional engine as a propulsion system. Then, as the studying objective of this work, the mathematical model of PMSM is discussed according to three different coordinate systems, and the control strategy for EV application is developed. In order to test the PM motor in the context of an EV, a specific target vehicle model is needed as the virtual load of the tested motor with the dyno system to emulate the real operating environment of the vehicle. A slippery road is one of the severe driving conditions for EVs and should be considered during the traction motor testing process. Fuzzy logic based wheel slip control is adopted in this thesis to evaluate the PM motor performance under slippery road conditions. Through the proposed testing environment, the PM motor can be tested in virtual vehicle driving conditions, which is significant for improving the PM motor design and control
A methodology for passenger-centred rail network optimisation
Optimising the allocation of limited resources, be they existing assets or
investment, is an ongoing challenge for rail network managers. Recently,
methodologies have been developed for optimising the timetable from the
passenger perspective. However, there is a gap for a decision support tool
which optimises rail networks for maximum passenger satisfaction, captures
the experience of individual passengers and can be adapted to different
networks and challenges. Towards building such a tool, this thesis develops a
novel methodology referred to as the Sheffield University Passenger Rail
Experience Maximiser (SUPREME) framework. First, a network assessment
metric is developed which captures the multi-stage nature of individual
passenger journeys as well as the effect of crowding upon passenger
satisfaction. Second, an agent-based simulation is developed to capture
individual passenger journeys in enough detail for the network assessment
metric to be calculated. Third, for the optimisation algorithm within SUPREME,
the Bayesian Optimisation method is selected following an experimental
investigation which indicates that it is well suited for ‘expensive-to-compute’
objective functions, such as the one found in SUPREME. Finally, in case studies
that include optimising the value engineering strategy of the proposed UK High
Speed Two network when saving £5 billion initial investment costs, the
SUPREME framework is found to improve network performance by the order
of 10%. This thesis shows that the SUPREME framework can find ‘good’
resource allocations for a ‘reasonable’ computational cost, and is sufficiently
adaptable for application to many rail network challenges. This indicates that a
decision support tool developed on the SUPREME framework could be widely
applied by network managers to improve passenger experience and increase
ticket revenue. Novel contributions made by this thesis are: the SUPREME
methodology, an international comparison between the Journey Time Metric
and Disutility Metric, and the application of the Bayesian Optimisation method
for maximising the performance of a rail network
Mecanismos para controlo e gestão de redes 5G: redes de operador
In 5G networks, time-series data will be omnipresent for the monitoring of network
metrics. With the increase in the number of Internet of Things (IoT) devices
in the next years, it is expected that the number of real-time time-series
data streams increases at a fast pace. To be able to monitor those streams,
test and correlate different algorithms and metrics simultaneously and in a
seamless way, time-series forecasting is becoming essential for the pro-active
successful management of the network.
The objective of this dissertation is to design, implement and test a prediction
system in a communication network, that allows integrating various networks,
such as a vehicular network and a 4G operator network, to improve the network
reliability and Quality-of-Service (QoS). To do that, the dissertation has
three main goals: (1) the analysis of different network datasets and implementation
of different approaches to forecast network metrics, to test different
techniques; (2) the design and implementation of a real-time distributed
time-series forecasting architecture, to enable the network operator to make
predictions about the network metrics; and lastly, (3) to use the forecasting
models made previously and apply them to improve the network performance
using resource management policies.
The tests done with two different datasets, addressing the use cases of congestion
management and resource splitting in a network with a limited number
of resources, show that the network performance can be improved with proactive
management made by a real-time system able to predict the network
metrics and act on the network accordingly.
It is also done a study about what network metrics can cause reduced accessibility
in 4G networks, for the network operator to act more efficiently and
pro-actively to avoid such eventsEm redes 5G, séries temporais serão omnipresentes para a monitorização
de métricas de rede. Com o aumento do número de dispositivos da Internet
das Coisas (IoT) nos próximos anos, é esperado que o número de fluxos de
séries temporais em tempo real cresça a um ritmo elevado. Para monitorizar
esses fluxos, testar e correlacionar diferentes algoritmos e métricas simultaneamente
e de maneira integrada, a previsão de séries temporais está a
tornar-se essencial para a gestão preventiva bem sucedida da rede.
O objetivo desta dissertação é desenhar, implementar e testar um sistema
de previsão numa rede de comunicações, que permite integrar várias redes
diferentes, como por exemplo uma rede veicular e uma rede 4G de operador,
para melhorar a fiabilidade e a qualidade de serviço (QoS). Para isso,
a dissertação tem três objetivos principais: (1) a análise de diferentes datasets
de rede e subsequente implementação de diferentes abordagens para
previsão de métricas de rede, para testar diferentes técnicas; (2) o desenho
e implementação de uma arquitetura distribuída de previsão de séries temporais
em tempo real, para permitir ao operador de rede efetuar previsões
sobre as métricas de rede; e finalmente, (3) o uso de modelos de previsão
criados anteriormente e sua aplicação para melhorar o desempenho da rede
utilizando políticas de gestão de recursos.
Os testes efetuados com dois datasets diferentes, endereçando os casos de
uso de gestão de congestionamento e divisão de recursos numa rede com
recursos limitados, mostram que o desempenho da rede pode ser melhorado
com gestão preventiva da rede efetuada por um sistema em tempo real capaz
de prever métricas de rede e atuar em conformidade na rede.
Também é efetuado um estudo sobre que métricas de rede podem causar
reduzida acessibilidade em redes 4G, para o operador de rede atuar mais
eficazmente e proativamente para evitar tais acontecimentos.Mestrado em Engenharia de Computadores e Telemátic
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