• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2498-2515
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• 2024

In this study, we propose a fully parallelized adaptive particle refinement (APR) algorithm for smoothed particle hydrodynamics (SPH) to construct a stable and efficient multi-resolution computing system for nuclear safety analysis. The APR technique, widely employed by SPH research groups to adjust local particle resolutions, currently operates on a serialized algorithm. However, this serialized approach diminishes the computational efficiency of the system, negating the advantages of acceleration achieved through high-performance computing devices. To address this drawback, we propose a fully parallelized APR algorithm designed to enhance both efficiency and computational accuracy, facilitated by a new adaptive smoothing length model. For model validation, we simulated both hydrostatic and hydrodynamic benchmark cases in 2D and 3D environments. The results demonstrate improved computational efficiency compared to the conventional SPH method and APR with a serialized algorithm, and the model's accuracy was confirmed, revealing favorable outcomes near the resolution interface. Through the analysis of jet breakup, we verified the performance and accuracy of the model, emphasizing its applicability in practical nuclear safety analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1062-1065
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• 2002

We have evaluated the ability of a time-delayed artificial neural network (TDANN) to predict muscle forces using only eletromyographic(EMG) signals. To achieve this goal, tendon forces and EMG signals were measured simultaneously in the gastrocnemius muscle of a dog while walking on a motor-driven treadmill. Direct measurements of tendon forces were performed using an implantable force transducer and EMG signals were recorded using surface electrodes. Under dynamic conditions, the relationship between muscle force and EMG signal is nonlinear and time-dependent. Thus, we adopted EMG amplitude estimation with adaptive smoothing window length. This approach improved the prediction ability of muscle force in the TDANN training. The experimental results indicated that dynamic tendon forces from EMG signals could be predicted using the TDANN, in vivo.

• International Journal of Contents
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• v.11 no.2
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• pp.15-19
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• 2015

In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms.