Zhenhong Li

Wei Wang, Xin Zhang, Jiaqi Yi et al.

This study aimed to improve the surface defect detection accuracy of ZrO2 ceramic bearing balls. Combined with the noise damage of the image samples, a surface defect detection method for ZrO2 ceramic bearing balls based on cartoon-texture decomposition model was proposed. Building a ZrO2 ceramic bearing ball surface defect detection system. The ZrO2 ceramic bearing ball surface defect image was decomposed by using the Gaussian curvature model and the decomposed image layer was filtered by using Winner filter and wavelet value domain filter. Then they were fused into a clear and undamaged ZrO2 ceramic bearing ball surface defect image and detected. The experimental results show that the image denoising method of ZrO2 ceramic bearing ball surface defect based on cartoon-texture decomposition model can denoise while retaining the image details. The PSNR of image is 34.1 dB, the SSIM is 0.9476, the detection accuracy is 95.8%, and the detection speed of a single defect image is 191ms / img. This method can effectively improve the efficiency and accuracy of ZrO2 ceramic bearing ball surface defect detection.

Hongyu Zhao, Zezhi Tang, Zhenhong Li et al.

The optimisation of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialisation. Nowadays, the utilisation of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of convolutional neural networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The you only look once algorithmic framework is employed with traditional rectangular bounding boxes for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

Yuhui Sun, Xiyao Wang, Zixi Li et al.

Large language models (LLMs) demonstrate strong generalization across a wide range of language tasks, but often generate outputs that misalign with human preferences. Reinforcement Learning from Human Feedback (RLHF) addresses this by optimizing models toward human preferences using a learned reward function and reinforcement learning, yielding improved alignment but suffering from high computational cost and instability. Direct Preference Optimization (DPO) simplifies the process by treating alignment as a classification task over binary preference pairs, reducing training overhead while achieving competitive performance. However, it assumes fixed, single-dimensional preferences and only supports pairwise supervision. To address these limitations, we propose Multi-Preference Lambda-weighted Listwise DPO, which allows the model to learn from more detailed human feedback and flexibly balance multiple goals such as helpfulness, honesty, and fluency. Our method models full-ranked preference distributions rather than binary comparisons, enabling more informative learning signals. The lambda vector controls the relative importance of different alignment goals, allowing the model to generalize across diverse human objectives. During inference, lambda can be adjusted without retraining, providing controllable alignment behavior for downstream use. We also introduce a learned scheduler that dynamically samples performant lambda configurations to improve robustness. Notably, our method requires only 20GB of GPU memory for training, making it suitable for compute-constrained settings such as academic labs, educational tools, or on-device assistants. Experiments on 1B-2B scale models show that our method consistently outperforms standard DPO on alignment benchmarks while enabling efficient, controllable, and fine-grained adaptation suitable for real-world deployment.