During the decommissioning of nuclear and particle accelerator facilities, a considerable amount of large-scale radioactive waste may be generated. Accurately defining the activation level of the waste is crucial for ...
详细信息
During the decommissioning of nuclear and particle accelerator facilities, a considerable amount of large-scale radioactive waste may be generated. Accurately defining the activation level of the waste is crucial for proper disposal. However, directly measuring the internal radioactivity distribution poses challenges. This study introduced a novel technology employing machine learning to assess the internal radioactivity distribution based on external measurements. Random radioactivity distribution within a structure were established, and the photon spectrum measured by detectors from outside the structure was simulated using the FLUKA Monte-Carlo code. Through training with spectrum data corresponding to various radioactivity distributions, an evaluation model for radioactivity using simulated data was developed by above Monte-Carlo simulation. Convolutional Neural Network and transformer methods were utilized to establish the evaluation model. The machine learning construction involves 5425 simulation datasets, and 603 datasets, which were used to obtain the evaluated results. Preprocessing was applied to the datasets, but the evaluation model using raw spectrum data showed the best evaluation results. The estimation of the intensity and shape of the radioactivity distribution inside the structure was achieved with a relative error of 10%. Additionally, the evaluation based on the constructed model takes only a few seconds to complete the process.
Nowadays, images are being used more extensively for communication purposes. A single image can convey a variety of stories, depending on the perspective and thoughts of everyone who views it. To facilitate comprehens...
详细信息
Nowadays, images are being used more extensively for communication purposes. A single image can convey a variety of stories, depending on the perspective and thoughts of everyone who views it. To facilitate comprehension, inclusion image captions is highly beneficial, especially for individuals with visual impairments who can read Braille or rely on audio descriptions. The purpose of this research is to create an automatic captioning system that is easy to understand and quick to generate. This system can be applied to other related systems. In this research, the transformerlearning process is applied to image captioning instead of the convolutional neural networks (CNN) and recurrent neural networks (RNN) process which has limitations in processing long-sequence data and managing data complexity. The transformerlearning process can handle these limitations well and more efficiently. Additionally, the image captioning system was trained on a dataset of 5,000 images from Instagram that were tagged with the hashtag "Phuket" (#Phuket). The researchers also wrote the captions themselves to use as a dataset for testing the image captioning system. The experiments showed that the transformerlearning process can generate natural captions that are close to human language. The generated captions will also be evaluated using the Bilingual Evaluation Understudy (BLEU) score and Metric for Evaluation of Translation with Explicit Ordering (METEOR) score, a metric for measuring the similarity between machine-translated text and human-written text. This will allow us to compare the resemblance between the researcher-written captions and the transformer-generated captions.
暂无评论