Software-Defined networking (SDN) improves network management and flexibility by separating control and data plane functions. However, the centralized architecture of SDN can increase cybersecurity risks, such as an i...
详细信息
Smart power grid enables smart city to be operated on efficient level for sustainable urban planning, economic growth and become an innovation hub. Various types of energy user or provider plants can contribute to pro...
详细信息
Neural networks show significant promise in addressing recognition tasks conducted by brain-computer interfaces (BCIs) within the steady-state visual evoked potential (SSVEP) paradigm, but they still encounter challen...
详细信息
ISBN:
(纸本)9798350366907;9789887581581
Neural networks show significant promise in addressing recognition tasks conducted by brain-computer interfaces (BCIs) within the steady-state visual evoked potential (SSVEP) paradigm, but they still encounter challenges in tasks involving a larger number of targets, particularly in end-to-end manners. Therefore, this paper proposes an end-to-end time-frequency domain joint network, namely TF-SSVEPNet, for recognizing multi-channel SSVEP signals with different data lengths in the dataset with larger targets. TF-SSVEPNet consists of three modules: time-domain extraction, time-frequency conversion, and frequency-domain extraction. Experiments on the benchmark open-source dataset have verified that TF-SSVEPNet exhibits state-of-the-art performance in both user-dependent and user-independent training tasks in shorter gaze times. With a gaze time of no more than one second, TF-SSVEPNet achieves accuracies of 86.60% and 74.30% in two types of tasks, and information transmission rates reach 170.14 bpm and 132.43 bpm, respectively. This network structure improves the accuracy and efficiency of SSVEP recognition, which is beneficial for the practical promotion of BCI systems.
Virtual tube is a two-dimensional or three-dimensional strip or tubular area similar to RSFC (Relative Safe Flight Corridor), which can provide smooth, feasible, and safe space for UAV swarm in environments with dense...
详细信息
ISBN:
(纸本)9798350377712;9798350377705
Virtual tube is a two-dimensional or three-dimensional strip or tubular area similar to RSFC (Relative Safe Flight Corridor), which can provide smooth, feasible, and safe space for UAV swarm in environments with dense obstacles. In order to address the problem that current virtual tube planning methods are mainly based on complex heuristic algorithm with consuming time complexity, we modify the model architecture by introducing generative adversarial network (GAN), and propose a Tube-GAN model. Tube-GAN takes the key point prompt image and obstacle environment image as inputs, and outputs the image of the virtual tube, which transforms the optimization problem into an image generation problem, leveraging the performance of computational efficiency for the construction of virtual tube. The experimental results demonstrate that the proposed Tube-GAN model can quickly generate virtual tube in random environments (less than 25ms), providing a new direction for the construction of virtual tube in real-time.
Fast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing ...
详细信息
Fast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing one or more rotors. We present a motor fault detection and isolation (FDI) method for multi-rotor UAVs based on an external wrench estimator and a recurrent neural network composed of long short-term memory nodes. The proposed approach considers the partial or total motor fault as an external disturbance acting on the UAV. Hence, the devised external wrench estimator trains the network to promptly understand whether the estimated wrench comes from a motor fault (also identifying the motor) or from unmodelled dynamics or external effects (i.e., wind, contacts, etc.). Training and testing have been performed in a simulation environment endowed with a physic engine, considering different UAV models operating under unknown external disturbances and unexpected motor faults. To further assess this approach's effectiveness, we compare our method's performance with a classical model-based technique. The collected results demonstrate the effectiveness of the proposed FDI approach.
An improved fuzzy neural network model predictive control (MPC) method based on dynamic partial least squares (DPLS) framework is proposed for the control of highly coupled nonlinear systems. Firstly, the PLS framewor...
详细信息
ISBN:
(纸本)9798350307627
An improved fuzzy neural network model predictive control (MPC) method based on dynamic partial least squares (DPLS) framework is proposed for the control of highly coupled nonlinear systems. Firstly, the PLS framework of dynamic neural network is established by adding neural network into the traditional DPLS framework. Secondly, the internal model of latent variable space is established by using the Dung Beetle optimized fuzzy neural network algorithm, and the parameters of the model are optimized by the improved adaptive LM algorithm to obtain the accurate prediction model. Furthermore, the adaptive learning rate gradient descent algorithm is used to optimize the control quantity of the latent variable loop. Finally, the PH neutralization titration process was used for the experiment. The results show that the fuzzy neural network model predictive control based on dynamic PLS framework has good controlperformance.
The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To...
详细信息
ISBN:
(纸本)9798331540920;9783907144107
The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional Long-Short Term Memory network with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstrate our findings.
High performancenetworking is at the core of every industrial system, as more and more companies look inwards towards the digital transformation of their entire manufacturing environment. The driving factor for all t...
详细信息
ISBN:
(纸本)9798350340570
High performancenetworking is at the core of every industrial system, as more and more companies look inwards towards the digital transformation of their entire manufacturing environment. The driving factor for all these changes is Industry 4.0, there are many options for communication systems for the latest generations of hardware with one of the most promising technologies being private 5G networks. A smart automation use case will be implemented using private 5G as the communication backbone, the performance of this communication type will be compared to other communication types that were historically used within smart automation and manufacturing settings. performance metrics will be compared to show typical performance that may be expected when deploying a private 5G communication network. The paper will also explore the roadmap for future 5G performance increases through updates and network optimization.
This study investigates the trade-off between Quality of Transmission (QoT) performance and spectrum efficiency through low-margin operation of multicarriers. Margin reduction is enabled by implementing tight filterin...
详细信息
ISBN:
(纸本)9798350377330;9798350377323
This study investigates the trade-off between Quality of Transmission (QoT) performance and spectrum efficiency through low-margin operation of multicarriers. Margin reduction is enabled by implementing tight filtering and slight subcarriers overlap. Experiments conducted on a Software Defined networking (SDN)-controlled Elastic Optical network (EON) testbed demonstrate spectrum-efficient multicarriers composed of three subcarriers, achieving savings of up to 22% in spectrum occupation for 80 km optical connections.
With the development of smart grids, distribution network dispatch systems are facing increasingly complex challenges in multi-source data processing and security management. This article proposes a secure control met...
详细信息
暂无评论