Based on the vector diffraction theory, the effect of complex phase filters on intensity distribution of a radially polarized multi Gaussian beam in the focal region of high NA lens is theoretically investigated. It i...Based on the vector diffraction theory, the effect of complex phase filters on intensity distribution of a radially polarized multi Gaussian beam in the focal region of high NA lens is theoretically investigated. It is observed that a properly designed multi belt complex phase filter can generate subwavelength novel focal patterns including splitting of focal spots and generation of multiple focal spot segments such as eight, six and four focal spots along the optical axis are obtained. We expect that such an investigation is useful for optical manipulation and material processing, multiple high refractive index particle trapping technologies.展开更多
This paper presents a holistic multimodal approach for real-time anomaly detection and classification in largescale photovoltaic plants.The approach encompasses segmentation,geolocation,and classification of individua...This paper presents a holistic multimodal approach for real-time anomaly detection and classification in largescale photovoltaic plants.The approach encompasses segmentation,geolocation,and classification of individual photovoltaic modules.A fine-tuned Yolov7 model was trained for the individual module’s segmentation of both modalities;RGB and IR images.The localization of individual solar panels relies on photogrammetric measurements to facilitate maintenance operations.The localization process also links extracted images of the same panel using their geographical coordinates and preprocesses them for the multimodal model input.The study also focuses on optimizing pre-trained models using Bayesian search to improve and fine-tune them with our dataset.The dataset was collected from different systems and technologies within our research platform.It has been curated into 1841 images and classified into five anomaly classes.Grad-CAM,an explainable AI tool,is utilized to compare the use of multimodality to a single modality.Finally,for real-time optimization,the ONNX format was used to optimize the model further for deployment in real-time.The improved ConvNext-Tiny model performed well in both modalities,with 99%precision,recall,and F1-score for binary classification and 85%for multi-class classification.In terms of latency,the segmentation models have an inference time of 14 ms and 12 ms for RGB and IR images and 24 ms for detection and classification.The proposed holistic approach includes a built-in feedback loop to ensure the model’s robustness against domain shifts in the production environment.展开更多
To address the limitations of traditional numerical simulation methods in determining the optimal structure parameters of thermoelectric module,such as complex modeling procedures,low computational efficiency,and poor...To address the limitations of traditional numerical simulation methods in determining the optimal structure parameters of thermoelectric module,such as complex modeling procedures,low computational efficiency,and poor adaptability to multi-objective design,this paper introduces an efficient structural optimization approach of segmented annular thermoelectric module that combines the uniformly equivalent element integral method and multi-parameter and multi-objective optimization algorithm under both constant temperature and heat flux boundary conditions.The optimization results show that the optimal resistance ratio is independent of the boundary conditions,and the optimal thermoelectric leg ratios remain approximately 1.2 across all studied cases in this study.Notably,the optimal segment ratios are highly sensitive to the temperatures at the two ends of the optimized segmented annular thermoelectric module under all conditions and can be directly calculated using the proposed fitting formulas.In addition,an optimal total thermoelectric leg angle exists for the segmented annular thermoelectric module to achieve the maximum temperature difference within the operating temperature range of the thermoelectric materials.The output power and efficiency of the optimized segmented annular thermoelectric module can be predicted using the parameter-based fitting formulas,with relative errors below 3%when compared to the direct optimization results.The proposed method in this paper offers significant advantages in terms of modeling simplicity,computational efficiency,and highly compatible with machine learning frameworks,thereby enabling artificial intelligence-assisted design and optimization pipelines for segmented annular thermoelectric modules.展开更多
文摘Based on the vector diffraction theory, the effect of complex phase filters on intensity distribution of a radially polarized multi Gaussian beam in the focal region of high NA lens is theoretically investigated. It is observed that a properly designed multi belt complex phase filter can generate subwavelength novel focal patterns including splitting of focal spots and generation of multiple focal spot segments such as eight, six and four focal spots along the optical axis are obtained. We expect that such an investigation is useful for optical manipulation and material processing, multiple high refractive index particle trapping technologies.
基金Digitalized PV plant project financed by OCP group[Reference:Digital and smart photovoltaic power plant].
文摘This paper presents a holistic multimodal approach for real-time anomaly detection and classification in largescale photovoltaic plants.The approach encompasses segmentation,geolocation,and classification of individual photovoltaic modules.A fine-tuned Yolov7 model was trained for the individual module’s segmentation of both modalities;RGB and IR images.The localization of individual solar panels relies on photogrammetric measurements to facilitate maintenance operations.The localization process also links extracted images of the same panel using their geographical coordinates and preprocesses them for the multimodal model input.The study also focuses on optimizing pre-trained models using Bayesian search to improve and fine-tune them with our dataset.The dataset was collected from different systems and technologies within our research platform.It has been curated into 1841 images and classified into five anomaly classes.Grad-CAM,an explainable AI tool,is utilized to compare the use of multimodality to a single modality.Finally,for real-time optimization,the ONNX format was used to optimize the model further for deployment in real-time.The improved ConvNext-Tiny model performed well in both modalities,with 99%precision,recall,and F1-score for binary classification and 85%for multi-class classification.In terms of latency,the segmentation models have an inference time of 14 ms and 12 ms for RGB and IR images and 24 ms for detection and classification.The proposed holistic approach includes a built-in feedback loop to ensure the model’s robustness against domain shifts in the production environment.
基金support by Postdoctoral Fellowship Program of CPSF(GZC20232004)this research was funded by the National Key R&D Program of China(2023YFB4604700).
文摘To address the limitations of traditional numerical simulation methods in determining the optimal structure parameters of thermoelectric module,such as complex modeling procedures,low computational efficiency,and poor adaptability to multi-objective design,this paper introduces an efficient structural optimization approach of segmented annular thermoelectric module that combines the uniformly equivalent element integral method and multi-parameter and multi-objective optimization algorithm under both constant temperature and heat flux boundary conditions.The optimization results show that the optimal resistance ratio is independent of the boundary conditions,and the optimal thermoelectric leg ratios remain approximately 1.2 across all studied cases in this study.Notably,the optimal segment ratios are highly sensitive to the temperatures at the two ends of the optimized segmented annular thermoelectric module under all conditions and can be directly calculated using the proposed fitting formulas.In addition,an optimal total thermoelectric leg angle exists for the segmented annular thermoelectric module to achieve the maximum temperature difference within the operating temperature range of the thermoelectric materials.The output power and efficiency of the optimized segmented annular thermoelectric module can be predicted using the parameter-based fitting formulas,with relative errors below 3%when compared to the direct optimization results.The proposed method in this paper offers significant advantages in terms of modeling simplicity,computational efficiency,and highly compatible with machine learning frameworks,thereby enabling artificial intelligence-assisted design and optimization pipelines for segmented annular thermoelectric modules.
