One of the major causes of road traffic congestion in urban transportation networks during the morning rush hour can be attributed to the fact that schools start at the same time. In a modern city center, a large port...
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One of the major causes of road traffic congestion in urban transportation networks during the morning rush hour can be attributed to the fact that schools start at the same time. In a modern city center, a large portion of commuters need to drop off their children at school before going to work. When the schools start at the same time, commuters pursuing school-related trips enter the network simultaneously, creating a high demand that the network cannot directly accommodate, resulting in performance degradation. To remedy this shortcoming, we propose a novel approach that regulates the start time of schools, anticipating the emergence of congestion during the morning commute. We consider regional traffic dynamics to capture the movement of vehicles in the urban network through the Macroscopic Fundamental Diagram. The related problem is formulated as a bi-objective mixed integer nonlinear program whose target is to jointly minimize i) the total time spent by all vehicles inside the network and ii) the associated overall delay observed between the initial and the shifted start time of each school located in the urban network. Dealing with such optimization problems is challenging due to their combinatorial and non-convex nature. To this end, we develop an Exhaustive Search Algorithm, which pinpoints the school start time pair that minimizes the total time spent objective metric. We demonstrate that by properly selecting the school start time, we can shift the peak demand to less congested periods and, as a result, alleviate congestion. Copyright (c) 2024 The Authors.
A fast sequencing genetic algorithm, NSGA-ii is applied to the performance optimization of axial flow electric fans on multi eletric aircraft to improve the effectiveness of environmental controlsystems (ECS) and red...
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Photovoltaic-thermal (PV-T) systems are expected to fulfil an increasingly vital role in future energy production. The current research endeavors to showcase machine learning modeling and control of a water-based PV-T...
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ISBN:
(纸本)9798350315431
Photovoltaic-thermal (PV-T) systems are expected to fulfil an increasingly vital role in future energy production. The current research endeavors to showcase machine learning modeling and control of a water-based PV-T collector. In this work, the PV-T collector is modeled using a decision tree algorithm and artificial neural network (ANN). The predicted outputs are compared with the actual outputs to validate the models. The ANN-based model performed better and proved its efficacy in training and testing. Further, various control strategies are implemented and their performance is compared. All the techniques presented are illustrated through simulation results.
Data centers can provide high aggregate bandwidth for large-scale network services, but traditional TCP protocols do not meet the requirements of data center environments. The emergence of software-defined networking ...
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ISBN:
(纸本)9798350350227;9798350350210
Data centers can provide high aggregate bandwidth for large-scale network services, but traditional TCP protocols do not meet the requirements of data center environments. The emergence of software-defined networking (SDN) provides a global view of the data center network and enables centralized traffic control, which can effectively solve the data center congestion challenge. Taking advantage of the SDN architecture, we propose an adaptive congestion control method called E-DCTCP. It combines Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) to model congestion awareness and ECN marking. It selects appropriate ECN markers based on queue lengths and their growth slopes and adjusts the Congestion Window (CWND) by calculating the RTT, which can alleviate false congestion caused by sudden flows. Evaluations performed with Mininet simulations show that E-DCTCP offers advantages in terms of throughput, flow completion time (FCT), latency, and prevention of packet loss. These benefits help reduce data center congestion and improve data transfer performance.
The effect of the chaotic vibration of train wheels on the adhesion state is investigated, and an optimisation mechanism for locomotive adhesion control is established to improve the safety and stability of locomotive...
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The current e-health systems suffer from multiple difficulties due to their centralized system structure for managing Electronic Medical Records (EMR), this approach exposed security and privacy issues, leading to ser...
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This paper addresses the high-precision control issues in CNC machine tool servo systems by proposing a feedforward compensation algorithm based on Radial Basis Function Neural network (RBFNN) composite learning contr...
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ISBN:
(纸本)9798350373707;9798350373691
This paper addresses the high-precision control issues in CNC machine tool servo systems by proposing a feedforward compensation algorithm based on Radial Basis Function Neural network (RBFNN) composite learning control. Unlike previous studies that updated neural networks solely based on tracking errors, this research prioritizes the accuracy of neural network learning. The paper employs the Selective Memory Recursive Least Squares (SMRLS) method to construct system information prediction errors, which, combined with tracking errors, update the neural network. This enables the neural network to learn the model of the CNC machine tool servo system more accurately, thereby achieving more precise feedforward compensation. Consequently, this method achieves exceptional tracking controlperformance. The stability of the closed-loop system and the boundedness of the errors are proven using the Lyapunov method. Experimental results on a three-axis CNC machine tool demonstrate that the proposed control algorithm effectively estimates system nonlinearity, thus enhancing tracking control precision.
Communication optimization algorithms in the complex and changing environments are an important research topic in the information field. Optimization of the multi-path transmission control protocol model in 5G network...
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LoRa, as a representative Low-Power Wide-Area network (LPWAN) technology, holds tremendous potential for various city and industrial applications. However, as there are few real large-scale deployments, it is unclear ...
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ISBN:
(纸本)9798400704147
LoRa, as a representative Low-Power Wide-Area network (LPWAN) technology, holds tremendous potential for various city and industrial applications. However, as there are few real large-scale deployments, it is unclear whether and how well LoRa can eventually meet its prospects. In this paper, we demystify the real performance of LoRa by deploying and measuring a citywide LoRa network, named CityWAN, which consists of 100 gateways and 19,821 LoRa end nodes, covering an area of 130 km2 for 12 applications. Our measurement focuses on the following perspectives: (i) performance of applications running on the citywide LoRa network;(ii) Infrastructure efficiency and deployment optimization;(iii) Physical layer signal features and link performance;(iv) Energy profiling and cost estimation for LoRa applications. The results reveal that LoRa performance in urban settings is bottlenecked by the prevalent blind spots, and there is a gap between the gateway efficiency and network coverage for the infrastructure deployment. Besides, we find that LoRa links at the physical layer are susceptible to environmental variations, and LoRa and other LPWANs show diverse costs for different scenarios. Our measurement provides insights for large-scale LoRa network deployment and also for future academic research to fully unleash the potential of LoRa.
The most common techniques employed for the control of doubly-fed induction generator (DFIG) wind turbine systems are restricted to either the well-known field-orientation control (FOC) or the direct-power control (DP...
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ISBN:
(纸本)9798350315431
The most common techniques employed for the control of doubly-fed induction generator (DFIG) wind turbine systems are restricted to either the well-known field-orientation control (FOC) or the direct-power control (DPC), with each one of them, however, suffering in one way or another from distinctive drawbacks. Instead of these standard methods, in this paper, a novel and nonlinear model-based control approach is adopted, which is developed in view of the entire system structure and characteristics. The key novelties introduced by the proposed design are due to an innovative technique, defined as 3s-FOC, which is formulated to enable the implementation of a simple cascade-mode PI-based control scheme that i) achieves stator field orientation without the need for estimating the actual flux, ii) guarantees system stability while simultaneously provide a relaxation on the transient response, iii) improves the closed-loop system dynamic behavior by employing extra damping terms in the inner-loop current regulators. The stability and state convergence properties of the complete system is firmly ensured as it is verified by a rigorous analysis based on advanced Lyapunov-based methods and input-to-state stability (ISS) techniques. Finally, a thorough simulation is conducted, which firmly verifies the theoretical results and the superior controlled system dynamic performance.
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