In recent years, permanent magnet synchronous motors (PMSM) have gained widespread adoption in high-precision servo systems, such as robotics, aerospace, and astronomy due to their remarkable performance characteristics. However, their inherent nonlinearities and susceptibility to disturbances posed challenges in achieving optimal control.
Researchers from the Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, have developed a novel low-light image enhancement method, the Zero-Reference Camera Response Network (ZRCRN). Published in the renowned journal Sensors, This study addresses the limitations of existing low-light image enhancement (LLIE) techniques, which often suffer from complex network structures or require multiple iterations, hampering their efficiency.
Researchers from the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, have developed a novel autofocus method that harnesses the power of deep learning to dynamically select regions of interest (ROI) in grayscale images.
Researchers from the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, have developed a groundbreaking method published in the journal LWT - Food Science and Technology has revolutionized the way we identify lactic acid bacteria (LAB) colonies. This innovative approach overcomes the challenges of fluorescence background interference and slow detection speeds, enabling the swift and accurate identification of bacterial colonies.
A groundbreaking study by researchers from the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, has successfully developed an automated method to determine the malignancy grade of glioma pathological sections. Published in the renowned journal Sensors, the research presents a novel hyperspectral imaging system and a feature extraction model, SMLMER-ResNet, that significantly advances the precision and efficiency of glioma grading.
Scientists from the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, have developed a groundbreaking robot learning method that endows robots with human-like arm skills. Published in the journal Sensors, the study introduces a hybrid primitive framework that significantly enhances robots' motion flexibility, adaptability, and skill acquisition.
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