In the realm of collaborative software development, version control systems (VCS) like Git play an indispensable role, enabling concurrent development and facilitating seamless integration of disparate code contributi...
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Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of ***,few methods exist for generating full-body animations,and the po...
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Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of ***,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient *** Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body *** parameterization method can be used to drive full-body animations of virtual characters and improve their *** the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial *** Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio ***,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss *** By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape ***,different 3D virtual character animations could be created through *** The experimental results showed that the proposed method could generate significant gestures and facial animations.
Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i...
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Object detection and recognition in unmanned aerial vehicle-based images is critical for various applications but is often challenged by complex backgrounds, diverse object scales, densely clustered small objects, and...
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Object detection and recognition in unmanned aerial vehicle-based images is critical for various applications but is often challenged by complex backgrounds, diverse object scales, densely clustered small objects, and uneven object distributions. This paper introduces a novel deep learning-based artificial intelligence framework that integrates the Multiscale Self-Attention Guidance and Feature Fusion Network with the You Only Look Once model, tailored explicitly for artificial intelligence-driven unmanned aerial vehicle-based infrared thermal image analysis. The proposed methodology offers four key advancements in the You Only Look Once architecture to enhance object detection performance. First, the Multi-Head Self-Attention Transformer module combines global and local information, enabling precise object localization while mitigating the influence of complex backgrounds. Second, the Multiscale parallel Sampling Feature Fusion module optimizes the fusion of multiscale features. Third, fine-grained shallow feature maps are integrated into the fusion process to detect densely packed small objects accurately. Lastly, the Inverse-Residual Feature Enhancement module, positioned before the detection head, enhances feature extraction for small objects. Experimental evaluations on the High Altitude Infrared Thermal Unmanned Aerial Vehicle dataset demonstrate significant improvements, achieving a Mean Average Precision of 95.1%, Recall of 92.0%, and F1-Score of 91.0%. The framework's robustness is further validated on the Wildland-fire Infrared Thermal Unmanned Aerial System dataset, achieving a Mean Average Precision of 82.1%, Recall of 88.0%, and F1-Score of 82.0%. Comparative analyses with state-of-the-art methods confirm its superiority and offer a scalable artificial intelligence-driven solution for unmanned aerial vehicle applications, advancing object detection capabilities in critical scenarios.
Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recogni...
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In multi-institutional patient data sharing scenarios, maintaining fine-grained access control while safeguarding privacy and adapting to real-world environments is crucial. Traditional attribute-based encryption (ABE...
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Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms...
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Understanding human's emotion is a vital task in the computer science and machine learning. People could easily distinguish the genuine and posed anger while it could be difficult for computers to judge the authen...
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The neural network memorization problem is to study the expressive power of neural networks to interpolate a finite dataset. Although memorization is widely believed to have a close relationship with the strong genera...
Smart control techniques have been implemented to address fluctuating power levels within isolated crogrids,mi-mitigating the risk of unstable frequencies and the potential degradation of power supply ***,a challenge ...
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Smart control techniques have been implemented to address fluctuating power levels within isolated crogrids,mi-mitigating the risk of unstable frequencies and the potential degradation of power supply ***,a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication *** study introduces a flexible real-time approach for the frequency control problem using an artificial neural network(ANN)constrained to stabilized *** solution integrates stabilizing PID controllers,computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning(RL)-based selected ***,we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion,specifically addressing communication ***,we refine the controller parameters online through an automated process that identifies optimal coefficient combinations,leveraging a constrained ANN to manage frequency deviations within a restricted parameter *** approach is further enhanced by employing the RL technique,which trains the tuning system using an interpolated stability boundary curve to ensure both stability and *** one-of-a-kind combination of ANN,RL,and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC *** experiments show that our solution outperforms traditional methods due to its reduced parameter search *** particular,the proposed method reduces transient and steady-state frequency deviations more than semi-and unconstrained *** improved metrics and stability analysis show that the method improves system performance and stability under changing conditions.
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