This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method...
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This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.
A way behind the past where teaching and learning were only by video, images, and 2D animations. Now we have live sessions where a classroom is bounded on just a laptop screen. What happened if that live session and 3...
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A way behind the past where teaching and learning were only by video, images, and 2D animations. Now we have live sessions where a classroom is bounded on just a laptop screen. What happened if that live session and 3D imaging are presented in the form of real sculpture by using Augmented Reality? In this paper, we are introducing a low-cost 3D display that presents both recorded and live sessions. Also, this display will be controlled with our fingertips. This application covers sectors of education, medical imaging, and advertisements with good effects and cheap cost.
Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, t...
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ISBN:
(数字)9798350380323
ISBN:
(纸本)9798350380330
Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, the inherent limitations of EEG acquisition equipment during real driving scenarios have contributed to the constrained robustness of existing models. Moreover, most of recent methods failed to extract robust multi-domain features, leading to a suboptimal performance. To address these challenges, we propose a novel channel-augmented multi-domain graph convolutional network (CA-MDGCNet). Specifically, the initial EEG signals are enriched by incorporating supplementary virtual EEG channels, distinguished as learnable parameters within the network architecture. Then, differential entropy features are extracted from the augmented EEG signals. Following this, a multi-domain graph convolutional network is designed to encode high-level EEG features by means of convolutions in diverse paths, which is beneficial to integrating the characteristics extracted from multiple domains. Finally, the classification block derives the detection outcome from the refined feature maps. To substantiate the potency of the proposed method, the validation was conducted on the publicly accessible SEED-VIG. The proposed CA-MDGCNet not only demonstrates more promising performance compared to state-of-the-art approaches but also underscores the potential viability of our method for the realm of fatigue driving detection.
Imitation learning method transfers human behavior to the robots or machines. This method aims to allow robots or machines to learn by observing tasks performed by human operators and imitating these tasks, rather tha...
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ISBN:
(数字)9798331518158
ISBN:
(纸本)9798331518165
Imitation learning method transfers human behavior to the robots or machines. This method aims to allow robots or machines to learn by observing tasks performed by human operators and imitating these tasks, rather than direct programming. ACT as an imitation learning method shows the high capability for automating dexterous manipulation tasks. From the viewpoint of industrial application, pose of the target object will be varied. However, even if only for the initial object pose variation, imitation learning method like ACT usually needs a lot of demonstration data that covers pose variation to train the policy that can generalize for. Collecting large demonstration dataset takes many efforts. This study created an object pick-and-place controller to eliminate pose variation as a preprocess step with YOLOv8, which is a recent object detection technique. The preprocess step automatically moves the object to a specific position and eliminates the pose variation. We show that our system effectiveness on the randomly placed bag opening task that requires both generalization for object pose variation and dexterous bimanual manipulation. The bag opening task was conducted with ACT and preprocess applied ACT methods, and the results were evaluated to examine the effect of the preprocess method to generalization process.
In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution ...
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ISBN:
(数字)9798350380323
ISBN:
(纸本)9798350380330
In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.
Cloud manufacturing is a service-oriented networked manufacturing model that aims to provide manufacturing resources as services in an on-demand manner. Scheduling is one of the key techniques for cloud manufacturing ...
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Visual odometry (VO) system is challenged by complex illumination environments. Image quality and its consistency in the time domain directly determine feature detection and tracking performance, which further affect ...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Visual odometry (VO) system is challenged by complex illumination environments. Image quality and its consistency in the time domain directly determine feature detection and tracking performance, which further affect the robustness and accuracy of the entire system. In this paper, an image acquisition scheme with image bracketing patterns is proposed. Images with different exposure levels are continuously captured to sufficiently explore the scene under varying illumination. An attribute control method is designed to adjust image exposures within the brackets online. Gaussian process regression fits the relationship between image quality metric and exposure via image synthesis technique. The optimal exposures for the next bracket are obtained directly without attempts to ensure a quick response. Experiments show our acquisition system’s effectiveness and performance improvement for VO tasks in complex illumination scenes.
Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making ***,MASs have a wide range of applications in smart grids,smart manufactu...
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Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making ***,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation *** of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation ***,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of *** paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber ***,the paper concludes with some potential future research topics on the security issues of MASs.
Railway wheels are one of the most critical components of the railway infrastructure, functioning as the load carrier. They are exposed to various forms of damage, caused due to intense sliding friction and inadequate...
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ISBN:
(数字)9798331534271
ISBN:
(纸本)9798331534288
Railway wheels are one of the most critical components of the railway infrastructure, functioning as the load carrier. They are exposed to various forms of damage, caused due to intense sliding friction and inadequate inspection procedures. This research introduces a real-time wheel fault detection system using YOLOv8, leveraging computer vision and AI-based object detection. The main contribution of this research is the utilization of a custom data acquisition setup for capturing wheel images and detecting faults with high accuracy and speed. Our results demonstrate an overall F1 score of 0.80, with precision and recall curves indicating strong wheel detection and moderate defect detection capabilities. This approach aims the reduction of inspection costs and the improvement of railway safety through automation and digitization.
Realizing optimal control performance for continuum robots (CRs) poses huge challenges on traditional model-based optimal control approaches due to their high degrees of freedom, complex nonlinear dynamics and soft co...
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