• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3833-3840
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• 2022

A method for calibrating the energy scale and detection efficiency of stilbene scintillators is presented herein. This method can be used to quantitatively analyze the Compton continuum of gamma-ray spectra obtained using such scintillators. First, channel-energy calibration was conducted by fitting a semi-empirical equation for the Compton continuum to the acquired energy spectrum and a new method to evaluate the intrinsic detection efficiency, called intrinsic Compton efficiency, of stilbene scintillators was proposed. The validity of this method was verified by changing experimental conditions such as the number of sources being measured simultaneously and the detector-source distance. According to the energy calibration, the standard error for the estimated Compton edge position was ±1.56 keV. The comparison of the intrinsic Compton efficiencies calculated from the single- and two-source spectra showed that the mean absolute difference and the mean absolute percentage difference are 0.031 %p and 0.557%, respectively, demonstrating reasonable accuracy of this method. The feasibility of the method was confirmed for an energy range of 0.5-1.5 MeV, showing that stilbene scintillators can be used to quantitatively analyze gamma rays in mixed-radiation fields.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2910-2917
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• 2020

Digital light processing three-dimensional (3D) printing technique is a powerful tool to rapidly manufacture plastic scintillators of almost any shape or geometric features. In our previous study, the main properties of light output and transmission were analyzed. However, a more detailed study of the other properties is required to develop 3D printed plastic scintillators with expectable and reproducible properties. The 3D printed plastic scintillator displayed an average decay time constants of 15.6 ns, intrinsic energy resolution of 13.2%, and intrinsic detection efficiency of 6.81% for 477 keV Compton electrons from the ¹³⁷Cs γ-ray source. The 3D printed plastic scintillator showed a similar decay time and intrinsic detection efficiency as that of a commercial plastic scintillator BC408. Furthermore, the presented estimates for the properties showed good agreement with the analyzed data.

• Progress in Superconductivity
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• v.14 no.2
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• pp.96-98
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• 2012

Bias current dependence of a superconducting strip photon detector is studied in the wavelength range of 405 to 1310 nm. The detector is made of an $MgB_2$ meander pattern with the line width of 135 nm and thickness of 10 nm. At 1310 nm, the detection efficiency exponentially decreases as the bias current decreases. While at 405 nm, the detection efficiency almost saturates in the high bias current region. These features suggest that the intrinsic detection efficiency of the $MgB_2$ detector is high at 405 nm.

• Journal of Electrochemical Science and Technology
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• v.10 no.2
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• pp.206-213
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• 2019

The development of simple methods for As detection has received great attention because As is a toxic chemical element causing environmental and health-related issues. In this work, the effect of nanostructures of Au electrodes on their electroanalytical performance during As detection was investigated. Different Au nanostructures with various surface morphologies such as nanoplate Au, nanospike Au, and dendritic Au structures were prepared, and their electrochemical behaviors toward square-wave anodic stripping voltammetric As detection were examined. The difference in intrinsic efficiency for As detection between nanostructured and flat Au electrodes was explained based on the crystallographic orientations of Au surfaces, as examined by the underpotential deposition of Pb. The most efficient As detection performance was obtained with nanoplate Au electrodes, and the effects of the pre-deposition time and interference on As detection of the nanoplate Au electrodes were also investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2576-2589
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• 2017

Co-saliency detection is a task of detecting same or similar objects in multi-scene, and has been an important preprocessing step for multi-scene image processing. However existing methods lack efficiency to match similar areas from different images. In addition, they are confined to single image detection without a unified framework to calculate co-saliency. In this paper, we propose a novel model called Superpixel Matching-Cellular Automata (SMCA). We use Hausdorff distance adjacent superpixel sets instead of single superpixel since the feature matching accuracy of single superpixel is poor. We further introduce Cellular Automata to exploit the intrinsic relevance of similar regions through interactions with neighbors in multi-scene. Extensive evaluations show that the SMCA model achieves leading performance compared to state-of-the-art methods on both efficiency and accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.30-33
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• 2023

The recent scholarly focus has been directed towards the expeditious and accurate detection of salient objects, a task that poses considerable challenges for resource-limited edge devices due to the high computational demands of existing models. To mitigate this issue, some contemporary research has favored inference speed at the expense of accuracy. In an effort to reconcile the intrinsic trade-off between accuracy and computational efficiency, we present novel model for salient object detection. Our model incorporate group-wise attentive module within the decoder of the encoder-decoder framework, with the aim of minimizing computational overhead while preserving detection accuracy. Additionally, the proposed architectural design employs attention mechanisms to generate boundary information and semantic features pertinent to the salient objects. Through various experimentation across five distinct datasets, we have empirically substantiated that our proposed models achieve performance metrics comparable to those of computationally intensive state-of-the-art models, yet with a marked reduction in computational complexity.

• Computers and Concrete
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• v.33 no.5
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• pp.535-544
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• 2024

In the rapidly advancing landscape of computer vision (CV) technology, there is a burgeoning interest in its integration with the construction industry. Camera calibration is the process of deriving intrinsic and extrinsic parameters that affect when the coordinates of the 3D real world are projected onto the 2D plane, where the intrinsic parameters are internal factors of the camera, and extrinsic parameters are external factors such as the position and rotation of the camera. Camera pose estimation or extrinsic calibration, which estimates extrinsic parameters, is essential information for CV application at construction since it can be used for indoor navigation of construction robots and field monitoring by restoring depth information. Traditionally, camera pose estimation methods for cameras relied on target objects such as markers or patterns. However, these methods, which are marker- or pattern-based, are often time-consuming due to the requirement of installing a target object for estimation. As a solution to this challenge, this study introduces a novel framework that facilitates camera pose estimation using standardized materials found commonly in construction sites, such as concrete forms. The proposed framework obtains 3D real-world coordinates by referring to construction materials with certain specifications, extracts the 2D coordinates of the corresponding image plane through keypoint detection, and derives the camera's coordinate through the perspective-n-point (PnP) method which derives the extrinsic parameters by matching 3D and 2D coordinate pairs. This framework presents a substantial advancement as it streamlines the extrinsic calibration process, thereby potentially enhancing the efficiency of CV technology application and data collection at construction sites. This approach holds promise for expediting and optimizing various construction-related tasks by automating and simplifying the calibration procedure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3338-3355
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• 2018

Images acquired by Large Aperture Static Imaging Spectrometer (LASIS) exhibit obvious interference stripes, which are vertical and stationary due to the special imaging principle of interference hyperspectral image (IHI) data. As the special characteristics above will seriously affect the intrinsic structure and sparsity of IHI, decomposition of IHI has drawn considerable attentions of many scientists and lots of efforts have been made. Although some decomposition methods for interference hyperspectral data have been proposed to solve the above problem of interference stripes, too many times of iteration are necessary to get an optimal solution, which will severely affect the efficiency of application. A novel algorithm for decomposition of interference hyperspectral images based on split Bregman iteration is proposed in this paper, compared with other decomposition methods, numerical experiments have proved that the proposed method will be much more efficient and can reduce the times of iteration significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4117-4135
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• 2020

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.3
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• pp.218-224
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• 2011

A modal method is presented for the investigation of the effects of measurement errors in damage detection for dynamic structural systems. The structural modifications to the baseline system result in the response changes of the perturbed structure, which are measured to determine a unique system in the inverse problem of damage detection. If the numerical modal data are exact, mathematical programming techniques can be applied to obtain the accurate structural changes. In practice, however, the associated measurement errors are unavoidable, to some extent, and cause significant deviations from the correct perturbed system because of the intrinsic instability of eigenvalue problem. Hence, a self-equilibrating inverse system is allowed to drift in the close neighborhood of the measured data. A numerical example shows that iterative procedures can be used to search for the damaged structural elements. A small set of selected degrees of freedom is employed for practical applicability and computational efficiency.