Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data

Wind farms, typically in high-latitude regions, face a high risk of blade icing. Traditional centralized training methods raise serious privacy concerns. To enhance data privacy in detecting wind turbine blade icing, traditional federated learning (FL) is employed. However, data heterogeneity, resulting from collections across wind farms in varying environmental conditions, impacts the model's optimization capabilities. Moreover, imbalances in wind turbine data lead to models that tend to favor recognizing majority classes, thus neglecting critical icing anomalies. To tackle these challenges, we propose a federated prototype learning model for class-imbalanced data in heterogeneous environments to detect wind turbine blade icing. We also propose a contrastive supervised loss function to address the class imbalance problem. Experiments on real data from 20 turbines across two wind farms show our method outperforms five FL models and five class imbalance methods, with an average improvement of 19.64\% in \( mF_{\beta} \) and 5.73\% in \( m \)BA compared to the second-best method, BiFL.

SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures

Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in balancing segmentation quality with low computational resource usage. To overcome these limitations, we propose a lightweight Structure-Aware Vision Mamba Network (SCSegamba), capable of generating high-quality pixel-level segmentation maps by leveraging both the morphological information and texture cues of crack pixels with minimal computational cost. Specifically, we developed a Structure-Aware Visual State Space module (SAVSS), which incorporates a lightweight Gated Bottleneck Convolution (GBC) and a Structure-Aware Scanning Strategy (SASS). The key insight of GBC lies in its effectiveness in modeling the morphological information of cracks, while the SASS enhances the perception of crack topology and texture by strengthening the continuity of semantic information between crack pixels. Experiments on crack benchmark datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods, achieving the highest performance with only 2.8M parameters. On the multi-scenario dataset, our method reached 0.8390 in F1 score and 0.8479 in mIoU. The code is available at https://github.com/Karl1109/SCSegamba.

Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation

First-order Policy Gradient (FoPG) algorithms such as Backpropagation through Time and Analytical Policy Gradients leverage local simulation physics to accelerate policy search, significantly improving sample efficiency in robot control compared to standard model-free reinforcement learning. However, FoPG algorithms can exhibit poor learning dynamics in contact-rich tasks like locomotion. Previous approaches address this issue by alleviating contact dynamics via algorithmic or simulation innovations. In contrast, we propose guiding the policy search by learning a residual over a simple baseline policy. For quadruped locomotion, we find that the role of residual policy learning in FoPG-based training (FoPG RPL) is primarily to improve asymptotic rewards, compared to improving sample efficiency for model-free RL. Additionally, we provide insights on applying FoPG's to pixel-based local navigation, training a point-mass robot to convergence within seconds. Finally, we showcase the versatility of FoPG RPL by using it to train locomotion and perceptive navigation end-to-end on a quadruped in minutes.

Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation

Detecting cracks with pixel-level precision for key structures is a significant challenge, as existing methods struggle to effectively integrate local textures and pixel dependencies of cracks. Furthermore, these methods often possess numerous parameters and substantial computational requirements, complicating deployment on edge devices. In this paper, we propose a staircase cascaded fusion crack segmentation network (CrackSCF) that generates high-quality crack segmentation maps using minimal computational resources. We constructed a staircase cascaded fusion module that effectively captures local patterns of cracks and long-range dependencies of pixels, and it can suppress background noise well. To reduce the computational resources required by the model, we introduced a lightweight convolution block, which replaces all convolution operations in the network, significantly reducing the required computation and parameters without affecting the network's performance. To evaluate our method, we created a challenging benchmark dataset called TUT and conducted experiments on this dataset and five other public datasets. The experimental results indicate that our method offers significant advantages over existing methods, especially in handling background noise interference and detailed crack segmentation. The F1 and mIoU scores on the TUT dataset are 0.8382 and 0.8473, respectively, achieving state-of-the-art (SOTA) performance while requiring the least computational resources. The code and dataset is available at https://github.com/Karl1109/CrackSCF.

An End-to-End Model for Time Series Classification In the Presence of Missing Values

Time series classification with missing data is a prevalent issue in time series analysis, as temporal data often contain missing values in practical applications. The traditional two-stage approach, which handles imputation and classification separately, can result in sub-optimal performance as label information is not utilized in the imputation process. On the other hand, a one-stage approach can learn features under missing information, but feature representation is limited as imputed errors are propagated in the classification process. To overcome these challenges, this study proposes an end-to-end neural network that unifies data imputation and representation learning within a single framework, allowing the imputation process to take advantage of label information. Differing from previous methods, our approach places less emphasis on the accuracy of imputation data and instead prioritizes classification performance. A specifically designed multi-scale feature learning module is implemented to extract useful information from the noise-imputation data. The proposed model is evaluated on 68 univariate time series datasets from the UCR archive, as well as a multivariate time series dataset with various missing data ratios and 4 real-world datasets with missing information. The results indicate that the proposed model outperforms state-of-the-art approaches for incomplete time series classification, particularly in scenarios with high levels of missing data.

Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers

Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neural networks have demonstrated empirical and qualitative robustness against real-world uncertainties, including sensor noise and external perturbations. However, formally investigating the vulnerabilities of these locomotion controllers remains a challenge. This difficulty arises from the requirement to pinpoint vulnerabilities across a long-tailed distribution within a high-dimensional, temporally sequential space. As a first step towards quantitative verification, we propose a computational method that leverages sequential adversarial attacks to identify weaknesses in learned locomotion controllers. Our research demonstrates that, even state-of-the-art robust controllers can fail significantly under well-designed, low-magnitude adversarial sequence. Through experiments in simulation and on the real robot, we validate our approach's effectiveness, and we illustrate how the results it generates can be used to robustify the original policy and offer valuable insights into the safety of these black-box policies.

Towards Generative Abstract Reasoning: Completing Raven's Progressive Matrix via Rule Abstraction and Selection

Endowing machines with abstract reasoning ability has been a long-term research topic in artificial intelligence. Raven's Progressive Matrix (RPM) is widely used to probe abstract visual reasoning in machine intelligence, where models need to understand the underlying rules and select the missing bottom-right images out of candidate sets to complete image matrices. The participators can display powerful reasoning ability by inferring the underlying attribute-changing rules and imagining the missing images at arbitrary positions. However, existing solvers can hardly manifest such an ability in realistic RPM problems. In this paper, we propose a conditional generative model to solve answer generation problems through Rule AbstractIon and SElection (RAISE) in the latent space. RAISE encodes image attributes as latent concepts and decomposes underlying rules into atomic rules by means of concepts, which are abstracted as global learnable parameters. When generating the answer, RAISE selects proper atomic rules out of the global knowledge set for each concept and composes them into the integrated rule of an RPM. In most configurations, RAISE outperforms the compared generative solvers in tasks of generating bottom-right and arbitrary-position answers. We test RAISE in the odd-one-out task and two held-out configurations to demonstrate how learning decoupled latent concepts and atomic rules helps find the image breaking the underlying rules and handle RPMs with unseen combinations of rules and attributes.

Revolutionizing Personalized Voice Synthesis: The Journey towards Emotional and Individual Authenticity with DIVSE (Dynamic Individual Voice Synthesis Engine)

This comprehensive paper delves into the forefront of personalized voice synthesis within artificial intelligence (AI), spotlighting the Dynamic Individual Voice Synthesis Engine (DIVSE). DIVSE represents a groundbreaking leap in text-to-voice (TTS) technology, uniquely focusing on adapting and personalizing voice outputs to match individual vocal characteristics. The research underlines the gap in current AI-generated voices, which, while technically advanced, fall short in replicating the unique individuality and expressiveness intrinsic to human speech. It outlines the challenges and advancements in personalized voice synthesis, emphasizing the importance of emotional expressiveness, accent and dialect variability, and capturing individual voice traits. The architecture of DIVSE is meticulously detailed, showcasing its three core components: Voice Characteristic Learning Module (VCLM), Emotional Tone and Accent Adaptation Module (ETAAM), and Dynamic Speech Synthesis Engine (DSSE). The innovative approach of DIVSE lies in its adaptive learning capability, which evolves over time to tailor voice outputs to specific user traits. The paper presents a rigorous experimental setup, utilizing accepted datasets and personalization metrics like Mean Opinion Score (MOS) and Emotional Alignment Score, to validate DIVSE's superiority over mainstream models. The results depict a clear advancement in achieving higher personalization and emotional resonance in AI-generated voices.

RefineNet: Enhancing Text-to-Image Conversion with High-Resolution and Detail Accuracy through Hierarchical Transformers and Progressive Refinement

In this research, we introduce RefineNet, a novel architecture designed to address resolution limitations in text-to-image conversion systems. We explore the challenges of generating high-resolution images from textual descriptions, focusing on the trade-offs between detail accuracy and computational efficiency. RefineNet leverages a hierarchical Transformer combined with progressive and conditional refinement techniques, outperforming existing models in producing detailed and high-quality images. Through extensive experiments on diverse datasets, we demonstrate RefineNet's superiority in clarity and resolution, particularly in complex image categories like animals, plants, and human faces. Our work not only advances the field of image-to-text conversion but also opens new avenues for high-fidelity image generation in various applications.

HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation

Transferring human motion skills to humanoid robots remains a significant challenge. In this study, we introduce a Wasserstein adversarial imitation learning system, allowing humanoid robots to replicate natural whole-body locomotion patterns and execute seamless transitions by mimicking human motions. First, we present a unified primitive-skeleton motion retargeting to mitigate morphological differences between arbitrary human demonstrators and humanoid robots. An adversarial critic component is integrated with Reinforcement Learning (RL) to guide the control policy to produce behaviors aligned with the data distribution of mixed reference motions. Additionally, we employ a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1 distance with a novel soft boundary constraint to stabilize the training process and prevent model collapse. Our system is evaluated on a full-sized humanoid JAXON in the simulator. The resulting control policy demonstrates a wide range of locomotion patterns, including standing, push-recovery, squat walking, human-like straight-leg walking, and dynamic running. Notably, even in the absence of transition motions in the demonstration dataset, robots showcase an emerging ability to transit naturally between distinct locomotion patterns as desired speed changes.