During super low frequency (SLF) magnetic induction communication, the sensitive antenna carried by autonomous underwater vehicles (AUVs) is highly susceptible to vibration, resulting in a large amount of noise in com...
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ISBN:
(数字)9798331540319
ISBN:
(纸本)9798331540326
During super low frequency (SLF) magnetic induction communication, the sensitive antenna carried by autonomous underwater vehicles (AUVs) is highly susceptible to vibration, resulting in a large amount of noise in communication signals, which seriously affects communication performance. The type of motion-induced noise typically exhibits randomness and nonlinearity. Based on the strong correlation between the motion-induced noise and the antenna's vibration signal, we propose a multi-channel normalized least mean square (MCNLMS) algorithm. The algorithm utilizes the six degrees of freedom vibration acceleration information from both ends of the magnetic antenna as the reference signal input for the canceller, providing more comprehensive information related to motion-induced noise than the single-channel adaptive noise cancellation algorithm. Experimental results indicate that the MCNLMS algorithm demonstrates effective suppression of the motion-induced noise, achieving an average reduction of approximately 7 dB within the 160–170 Hz communication band. Additionally, it exhibits improved stability and faster convergence.
Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavi...
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This paper proposes an inverse compensation feed-forward control strategy for a vertical pneumatic artificial muscle (PAM) system. Firstly, we conduct a preliminary experiment on the vertical PAM system, and on this b...
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An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for...
An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO’s electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems.
Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain informatio...
Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work.
Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. Firs...
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This paper proposes a depth control strategy for an unmanned underwater vehicle (UUV) using an adaptive event-triggered integral sliding mode control method under unknown marine environment disturbance. Firstly, the U...
This paper proposes a depth control strategy for an unmanned underwater vehicle (UUV) using an adaptive event-triggered integral sliding mode control method under unknown marine environment disturbance. Firstly, the UUV longitudinal plane motion model is established by using the T-S fuzzy modeling method to reduce modeling errors. Secondly, an adaptive event-triggered integral sliding mode control method is designed to improve the control performance under an unknown disturbance upper bound. The method reduces mechanical losses and saves network resources by dynamically adjusting the event triggering threshold. The sufficient conditions for the existence and stability of sliding motion are established by linear matrix inequality. Finally, a simulation example is given to verify the effectiveness of the developed strategy.
This paper addresses the regulation and trajectory-tracking problems for two classes of weakly coupled electromechanical systems. To this end, we formulate an energy-based model for these systems within the port-Hamil...
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Real-time environmental monitoring using a multi-agent system (MAS) has long been a focal point of cooperative control. It is still a challenging task to provide cost-effective services for potential emergencies in su...
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In this paper, the problem of guarding a circular area is proposed and solved using a Stackelberg differential game theoretic approach. The objective of the attacker is to breach the perimeter of the defended area, wh...
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In this paper, the problem of guarding a circular area is proposed and solved using a Stackelberg differential game theoretic approach. The objective of the attacker is to breach the perimeter of the defended area, while the defenders endeavor to thwart such attempts. The dynamics of the attack-defense game are modeled according to the distance and position relations among defenders, attackers, and the center of defense area. The optimal Stackelberg equilibrium control strategies for both defenders and attackers are designed to guarantee the defense mission's success. Then, the effectiveness of the proposed method is validated through numerical simulation.
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