A key step is to extract valid information region in the fusion of multi-voltage X-ray image sequence for complicated components. To improve the self-adaption of extraction, a method is presented in this paper. In thi...A key step is to extract valid information region in the fusion of multi-voltage X-ray image sequence for complicated components. To improve the self-adaption of extraction, a method is presented in this paper. In this paper, the valid informa-tion region is selected by the grey level interval, which is computed by the optimization of image quality evaluation model. The model is based on the histogram equalization and the grey level interval. Then, every valid region of images at different voltages is extracted and they are fused according their grey level transformation function. The fusion image contains completed struc-ture information of the component. The fusion experiment of a cylinder head shows the effectiveness of the presented method.展开更多
A multi-voltage probe array system is designed to measure the coupling resistance of an ion cyclotron resonance frequency antenna. In the process of the antenna coupling resistance data extraction, the minimization al...A multi-voltage probe array system is designed to measure the coupling resistance of an ion cyclotron resonance frequency antenna. In the process of the antenna coupling resistance data extraction, the minimization algorithm, the original Levenberg–Marquardt algorithm, is replaced by the Broyden–Fletcher–Goldfarb–Shanno algorithm to achieve more stable and accurate results. Moreover, a simple model of the multi-voltage probe array was applied to simulate the performance of the Kalman filter, and to optimize the distance and position of the probes and probe number to mitigate the influence of the system noise on the rebuilt results. During the EAST experiment in 2019, a four-voltage probe array was applied to measure the coupling resistance of line 6 during high confined mode discharge. The measurement results by the multivoltage probe array system and the voltage/current probe pair show a good agreement.展开更多
Modern smart grids face significant challenges in short-term load forecasting due to increasing complexity across transmission,distribution,and consumer levels.While recent studies have explored large language models ...Modern smart grids face significant challenges in short-term load forecasting due to increasing complexity across transmission,distribution,and consumer levels.While recent studies have explored large language models for load forecasting,existing methods are limited by computational overhead,voltage-level specificity,and inadequate cross-domain generalization.This paper introduces Multi-Voltage Load Forecasting Large Model(MVLFLM),a unified Transformer-based framework that addresses multi-voltage STLF through efficient parameter-fine-tuning of a Llama 2-7B foundation model.Unlike previous LLM-based forecasting methods that focus on single voltage levels or require extensive retraining,MVLFLM employs selective layer freezing to preserve pre-trained knowledge while adapting only essential parameters for load pattern recognition.prehensive Com-evaluation across four real-world datasets spanning high(transmission),medium(distribution),and low(consumer)voltage levels demonstrates MVLFLM’s superior performance,achieving higher performance than benchmarks.Most significantly,MVLFLM exhibits exceptional zero-shot generalization with only 9.07%average performance degradation when applied to unseen grid entities,substantially outperforming existing methods.These results establish MVLFLM as the unified,computationally efficient solution for multi-voltage load forecasting that maintains forecasting accuracy while enabling seamless deployment across heterogeneous smart grid infrastructures.展开更多
FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of nanostructures.This approach involves collecting in-p...FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of nanostructures.This approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image stacks.However,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current plane.This artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm insufficient.Inthis study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of nanostructures.By treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.展开更多
基金National Natural Science Foundation of China(No.61227003,No.61301259,No.61471325and No.61571404)Natural Science Foundation of Shanxi Province(No.2015021099)
文摘A key step is to extract valid information region in the fusion of multi-voltage X-ray image sequence for complicated components. To improve the self-adaption of extraction, a method is presented in this paper. In this paper, the valid informa-tion region is selected by the grey level interval, which is computed by the optimization of image quality evaluation model. The model is based on the histogram equalization and the grey level interval. Then, every valid region of images at different voltages is extracted and they are fused according their grey level transformation function. The fusion image contains completed struc-ture information of the component. The fusion experiment of a cylinder head shows the effectiveness of the presented method.
基金China Fusion Engineering Experimental Reactor General Integration and Engineering Design (No. 2017YFE0300503)National Natural Science Foundation of China (No. 11775258)the Comprehensive Research Facility for Fusion Technology Program of China (No. 2018-000052-73-01-001228)。
文摘A multi-voltage probe array system is designed to measure the coupling resistance of an ion cyclotron resonance frequency antenna. In the process of the antenna coupling resistance data extraction, the minimization algorithm, the original Levenberg–Marquardt algorithm, is replaced by the Broyden–Fletcher–Goldfarb–Shanno algorithm to achieve more stable and accurate results. Moreover, a simple model of the multi-voltage probe array was applied to simulate the performance of the Kalman filter, and to optimize the distance and position of the probes and probe number to mitigate the influence of the system noise on the rebuilt results. During the EAST experiment in 2019, a four-voltage probe array was applied to measure the coupling resistance of line 6 during high confined mode discharge. The measurement results by the multivoltage probe array system and the voltage/current probe pair show a good agreement.
基金supported in part by the National Natural Sci-ence Foundation of China(Key Program 71931003,72061147004,72171206,72192805,and 42105145)in part by the Shenzhen Institute of Artificial Intelligence and Robotics for Society.
文摘Modern smart grids face significant challenges in short-term load forecasting due to increasing complexity across transmission,distribution,and consumer levels.While recent studies have explored large language models for load forecasting,existing methods are limited by computational overhead,voltage-level specificity,and inadequate cross-domain generalization.This paper introduces Multi-Voltage Load Forecasting Large Model(MVLFLM),a unified Transformer-based framework that addresses multi-voltage STLF through efficient parameter-fine-tuning of a Llama 2-7B foundation model.Unlike previous LLM-based forecasting methods that focus on single voltage levels or require extensive retraining,MVLFLM employs selective layer freezing to preserve pre-trained knowledge while adapting only essential parameters for load pattern recognition.prehensive Com-evaluation across four real-world datasets spanning high(transmission),medium(distribution),and low(consumer)voltage levels demonstrates MVLFLM’s superior performance,achieving higher performance than benchmarks.Most significantly,MVLFLM exhibits exceptional zero-shot generalization with only 9.07%average performance degradation when applied to unseen grid entities,substantially outperforming existing methods.These results establish MVLFLM as the unified,computationally efficient solution for multi-voltage load forecasting that maintains forecasting accuracy while enabling seamless deployment across heterogeneous smart grid infrastructures.
基金funded by the Deutsche Forschungsgemein-schaft(DFG,German Research Foundation)-SFB 986-Project number 192346071.
文摘FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of nanostructures.This approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image stacks.However,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current plane.This artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm insufficient.Inthis study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of nanostructures.By treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.
