In order to solve the problem of how to effectively optimize the topological structure in complex systems, improve the accuracy and reliability of decision-making, and reduce the false alarm rate and false alarm rate,...
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In the complex space environment, targeting the characteristics of high integration and complexity in the new generation of spacecraft systems, and driven by the design needs of long lifespan and high reliability of s...
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Wireless communications have become essential to modern life. They allow user to stay connected to their applications while supporting their mobility requirements. Wi-Fi equipped devices are getting increasingly wides...
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ISBN:
(纸本)9798350361261;9798350361278
Wireless communications have become essential to modern life. They allow user to stay connected to their applications while supporting their mobility requirements. Wi-Fi equipped devices are getting increasingly widespread, and high-bandwidth applications like video streaming are becoming more critical in many areas. Thus, many performance challenges and issues have emerged in the past decades. Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) is a Wi-Fi multiple access control method used to manage access to the channel and to mitigate collisions. However, it suffers from performance degradation when the density of Wi-Fi nodes or the offered load increase. The random behavior of CSMA/CA is one of the reasons behind the performance degradation. In this paper, we propose a Deep Reinforcement Learning (DRL) mechanism, intelligent CSMA/CA (ICSMA/CA), to dynamically adapt the backoff duration of CSMA/CA algorithm in dense Wi-Fi environments. We train and evaluate our DRL model using the Network Simulator (NS-3) and Tensorflow. Results show an improvement in the capacity utilization of the channel and a reduction in the channel access delay
Flexible systems are difficult to control because they deflect in response to any applied force and they tend to oscillate around the desired path or set point. Human operators driving such systems are challenged by t...
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ISBN:
(纸本)9781665476331
Flexible systems are difficult to control because they deflect in response to any applied force and they tend to oscillate around the desired path or set point. Human operators driving such systems are challenged by the deflection and vibration that makes the system difficult to move and accurately position. Such systems can be augmented with an intelligentcontrol scheme that aids the human operator. Numerous types of controllers can be used for such applications;however, it is challenging to balance the control authority of the human operator and the augmenting controller. Input shaping is a control technique that reduces unwanted flexible system responses by modifying the human-operator command in real-time. This paper investigates the use of input shaping as an augmenting controller to aid in the accurate positioning of highly-oscillatory systems. Results from operator testing verify some of the key advantages of this controller.
With the continuous development of Internet of Things (IoT) technology, edge processing platforms for power system characteristics have become an important technology. At present, many professional information systems...
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Model predictive control has emerged as a prominent technique in control engineering due to its ability to handle constraints on both control signals and system states. This capability makes model predictive control a...
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ISBN:
(纸本)9798350364309;9798350364293
Model predictive control has emerged as a prominent technique in control engineering due to its ability to handle constraints on both control signals and system states. This capability makes model predictive control a powerful tool, particularly for complex systems with operational limitations. However, a major challenge associated with model predictive control is the "curse of dimensionality" arising from the constrained optimization problem solved at each time step. This problem becomes computationally expensive as the system dimension increases. This study proposes an accelerated model predictive control algorithm that addresses the curse of dimensionality. We achieve this by solving an equivalent suboptimal model predictive control problem within a reduceddimensional subspace. The subspace is efficiently calculated using singular value decomposition of the Hessian matrix associated with the quadratic cost function. An adaptation law dynamically determines the subspace size, balancing accuracy and computational efficiency of the model predictive controlcontroller.
This paper focuses on the development of optimal control strategies under disturbance conditions for a variable-speed wind energy system (VSWES) in the presence of disturbance. The fractional-order PID controller (FOP...
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Clustering is a type of unsupervised learning, which can be broadly classified into static and dynamic methods. Whole time-series clustering, one of the dynamic methods, is performed on multiple time-series data and c...
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The continuous trend of raising traffic volumes in urban areas causes waiting times and exhaust emissions. As one promising response to these challenges, increasingly intelligent and adaptive traffic management system...
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ISBN:
(纸本)9789897586521
The continuous trend of raising traffic volumes in urban areas causes waiting times and exhaust emissions. As one promising response to these challenges, increasingly intelligent and adaptive traffic management systems are being developed. For instance, self-organised approaches such as the Organic Traffic control offer advantages in terms of scalability and robustness compared to traditional systems. This can be increased by taking locally detected incidents into account. To improve the accuracy of automatically detected incidents and to allow for integration in the traffic control strategies, this paper proposes algorithms for the validation of potential incidents. This is done by incorporating respective insights of varying levels from neighbouring intersections and consequently determining a neighbour-supported view of local incident information.
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