• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.869-877
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• 2020

A 24-month Advanced Power Reactor 1400 (APR1400) core with a very-low-boron (VLB) concentration has been investigated for an inherently safe and high-performance PWR in this work. To develop a high-performance APR1400 which is able to do the passive frequency control operation, VLB feature is essential. In this paper, the centrally-shielded burnable absorber (CSBA) is utilized for an efficient VLB operation in the 24-month cycle APR1400 core. This innovative design of the VLB APR1400 core includes the optimization of burnable absorber and loading pattern as well as axial cutback for a 24-month cycle operation. In addition to CSBA, an Er-doped guide thimble is also introduced for partial management of the excess reactivity and local peaking factor. To improve the neutron economy of the core, two alternative radial reflectors are adopted in this study, which are SS-304 and ZrO₂. The core reactivity and power distributions for a 2-batch equilibrium cycle are analyzed and compared for each reflector design. Numerical results show that a VLB core can be successfully designed with 24-month cycle and the cycle length is improved significantly with the alternative reflectors. The neutronic analyses are performed using the Monte Carlo Serpent code and 3-D diffusion code COREDAX-2 with the ENDF/B-VII.1.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.488-495
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• 2021

We propose a speech recognition system based on conformer. Conformer is known to be convolution-augmented transformer, which combines transfer model for capturing global information with Convolution Neural Network (CNN) for exploiting local feature effectively. The baseline system is developed to be a transfer-based speech recognition using Long Short-Term Memory (LSTM)-based language model. The proposed system is a system which uses conformer instead of transformer with transformer-based language model. When Electronics and Telecommunications Research Institute (ETRI) speech corpus in AI-Hub is used for our evaluation, the proposed system yields 5.7 % of Character Error Rate (CER) while the baseline system results in 11.8 % of CER. Even though speech corpus is extended into other domain of AI-hub such as NHNdiguest speech corpus, the proposed system makes a robust performance for two domains. Throughout those experiments, we can prove a validation of the proposed system.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.682-689
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• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• Journal of IKEEE
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• v.23 no.3
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• pp.910-916
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• 2019

Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.285-304
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• 2022

This study investigated Korean diaspora literature with the bilingual feature, which was published in a local language other than Korean, focusing on literature classification status in domestic library's materials. As a result of the investigation, domestic universities and public libraries that owned the diaspora literature materials were classifying the original work by the languages or focusing on the author's work. Nowadays, the Literature classification codes of KDC have on the language of the original work but no code on the author. Nevertheless, domestic libraries were classifying diaspora literature works by the author, not by the language of the original work, so that the same author's works were gathered in one place. This study proposes an option to classify "Korean national literature" that covers Korean diaspora literature and Korean literature into 810 of KDC to resolve the confusion in the classification of Korean diaspora literature. However, this option is a trial proposal for libraries with special needs for Korean diaspora literature classification, and further investigation and research will be necessary to apply this option.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2991-3007
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• 2022

Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It's able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don't always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Journal of Korean Neurosurgical Society
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• v.65 no.2
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• pp.186-195
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• 2022

Objective : To explore the histological feature of the cervical disc degeneration in patients with degenerative ossification (DO) and its potential mechanisms. Methods : A total of 96 surgical segments, from cervical disc degenerative disease patients with surgical treatment, were divided into ossification group (group O, n=46) and non-ossification group (group NO, n=50) based on preoperative radiological exams. Samples of disc tissues and osteophytes were harvested during the decompression operation. The hematoxylin-eosin staining, Masson trichrome staining and Safranin O-fast green staining were used to compare the histological differences between the two groups. And the distribution and content of transforming growth factor (TGF)-β1, p-Smad2 and p-Smad3 between the two groups were compared by a semi-quantitative immunohistochemistry (IHC) method. Results : For all the disc tissues, the content of disc cells and collagen fibers decreased gradually from the outer annulus fibrosus (OAF) to the central nucleus pulposus (NP). Compared with group NO, the number of disc cells in group O increased significantly. But for proteoglycan in the inner annulus fibrosus (IAF) and NP, the content in group O decreased significantly. IHC analysis showed that TGF-β1, p-Smad2, and p-Smad3 were detected in all tissues. For group O, the content of TGF-β1 in the OAF and NP was significantly higher than that in group NO. For p-Smad2 in IAF and p-Smad3 in OAF, the content in group O were significantly higher than group NO. Conclusion : Histologically, cervical disc degeneration in patients with DO is more severe than that without DO. Local higher content of TGF-β1, p-Smad2, and p-Smad3 are involved in the disc degeneration with DO. Further studies with multi-approach analyses are needed to better understand the role of TGF-β/Smads signaling pathway in the disc degeneration with DO.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.