Automated visual inspection is critical for quality control in industrial manufacturing, yet training robust defect detection models is often hindered by the scarcity of defective samples. To address this challenge, we introduce a diffusion-based data augmentation framework capable of generating diverse and realistic synthetic defect images. Our method personalizes a text-to-image Latent Diffusion Model using DreamBooth, introducing a spatially weighted loss that forces the model to prioritize learning specific defect characteristics without attempting to reconstruct the non-defective background. To synthesize new data, we utilize Differential Diffusion to generate the learned defects onto defect-free images, eliminating boundary artifacts. We systematically evaluate the efficacy of our generated data on downstream supervised defect detection and localization tasks in two practical scenarios: as a complete substitute for real defect data and as a complement to it. Extensive experiments on the MVTec-AD dataset and a real-world industrial dataset demonstrate that our synthetic defects significantly boost downstream task performance. Furthermore, ablation studies confirm that these performance gains are robust and architecture-agnostic across various detectors.

Deep Reinforcement Learning (DRL) methods have shown remarkable success in many applications, yet their high energy consumption limits their practicability. Recent studies incorporated energy-efficient Spiking Neural Networks (SNNs) to build Spiking DRL methods and lower energy consumption by setting a shorter simulation duration for SNNs to compute fewer gradients. However, these existing Spiking DRL methods fail to sample sufficient high-quality samples within a fixed-size replay buffer and perform poorly when the simulation duration is small, introducing the challenging tradeoff between energy consumption and model performance. Motivated by such observations, we develop a generic resilient experience replay method that can be seamlessly integrated into existing spiking DRL methods to effectively address the above tradeoff. Specifically, we allow the replay buffer to dynamically expand as the number of training samples increases, thereby accommodating more potentially valuable candidate samples for policy training. Meanwhile, we introduce an adaptive approach to manage the buffer size by determining when to shrink the replay buffer and removing redundant samples automatically. This strategy prevents the buffer from expanding unnecessarily, thereby mitigating the potential negative impact on model performance. Extensive experimental results demonstrate that our approach significantly enhances the performance of five state-of-the-art (SOTA) spiking DRL methods across various simulation durations in sixteen tasks, in terms of return, without compromising their energy efficiency.

Lifelong deep reinforcement learning (DRL) methods enable continuous adaptation to new tasks and retention of old knowledge. However, these methods often necessitate large model sizes, leading to substantial computational and storage resource requirements during training and inference. Unfortunately, existing research has not yet provided a lightweight solution to address this issue. This work aims to develop a generic method that can be seamlessly integrated into existing lifelong DRL methods to facilitate their achievement of lightweight models while also yielding higher returns. While sparse training (ST) methods have been extensively used in the DRL community to achieve lightweight models, they exacerbate the issue of catastrophic forgetting and compromise generalization when applied in lifelong DRL. To improve generalization, we develop a gradient optimization method that leverages sharpness-aware minimization (SAM) to smooth the gradient surface of the model without introducing excessive computational complexity. In addition, to alleviate catastrophic forgetting and promote model convergence, we introduce a priority-based approach that samples effective past experiences from the replay buffer. Extensive experiments demonstrate that our approach achieves 90% sparsity in five representative lifelong DRL methods while achieving higher episode return and average return (up to 34% improvement) across all episodes compared to the dense models.

In-mold electronics can reduce deformation by integrating multiple components and encapsulating them with plastic injection molding. However, complex components and electronic functional films can cause heat concentration problems. We used an in-mold radiating structure to heat-dissipating from electronic devices, and thermal simulation analysis was used to model the structure and process, and the results showed that local contact with heat-dissipating ink can effectively reduce heat energy by 25%. SMD LED components with plastic injection molding can increase thermal energy by 36%. Proper heat dissipation structures and sealing with plastic injection molding can effectively discharge heat energy, but if the heat-dissipating structure in contact with the heat source does not match, the temperature can reach 154.2°C, causing the LED to malfunction.