• Nuclear Engineering and Technology
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• v.49 no.1
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• pp.277-284
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• 2017

This paper presents the establishment and characterization of a neutron calibration field using a bare $^{252}Cf$ source of low neutron source strength in Vietnam. The characterization of the field in terms of neutron flux spectra and neutron ambient dose equivalent rates were performed by Monte Carlo simulations using the MCNP5 code. The anisotropy effect of the source was also investigated. The neutron ambient dose equivalent rates at three reference distances of 75, 125, and 150 cm from the source were calculated and compared with the measurements using the Aloka TPS-451C neutron survey meters. The discrepancy between the calculated and measured values is found to be about 10%. To separate the scattered and the direct components from the total neutron flux spectra, an in-house shadow cone of 10% borated polyethylene was used. The shielding efficiency of the shadow cone was estimated using the MCNP5 code. The results confirmed that the shielding efficiency of the shadow cone is acceptable.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.407-414
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• 2020

SVM (Support vector machine) analysis was performed after applying a deep learning technique based on an Inception-based model (GoogLeNet). The accuracy of automatic image classification was analyzed using an SVM with multiple virtual grid sizes. Six classes were selected from a standard land cover map. Cars were added as a separate item to increase the classification accuracy of roads. The virtual grid size was 2-5 m for natural areas, 5-10 m for traffic areas, and 10-15 m for building areas, based on the size of items and the resolution of input images. The results demonstrate that automatic classification accuracy can be increased by adopting an integrated approach that utilizes weighted virtual grid sizes for different classes.

• Journal of Platform Technology
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• v.6 no.4
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• pp.34-40
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• 2018

Currently, artificial neural networks perform well for a single task, but NN have the problem of forgetting previous learning by learning other kinds of tasks. This is called catastrophic forgetting. To use of artificial neural networks in general purpose this should be solved. There are many efforts to overcome catastrophic forgetting. However, even though there was a lot of effort, it did not completely overcome the catastrophic forgetting. In this paper, we propose sequential iterative learning using core concepts used in elastic weight consolidation (EWC). The experiment was performed to reproduce catastrophic forgetting phenomenon using EMNIST data set which extended MNIST, which is widely used for artificial neural network learning, and overcome it through sequential iterative learning.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.875-883
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• 2021

In view of integration and development, compliance with regulations on information disclosure has important implications for users. Corporate social responsibility disclosure (CSRD) is an increasing concern of the community and society. CSRD always poses many challenges for the profitability of banks. The article uses the ordinary least square method to examine this relationship and employs timeseries data of five years from 18 Vietnamese listed banks from 2015 to 2019. The analysis is informed by Jensen and Meckling's Agency theory, Freeman's Stakeholder theory, and Dowling and Pfeffer's Legitimacy theory. The study results show that, with the CSRD dependent variable, return on assets (ROA) and net interest margin (NIM) have an opposite influence, but return on equity (ROE) has no effect on CSRD, while on the profitability dependent variable, CSRD has a different influence from ROA, ROE, and NIM. To enhance the relationship between CSRD and profitability, Vietnamese listed banks need to comply with CSRD as well as demonstrate responsibility to the community and society. Managers need to have clear development policies and strategies to ensure both profitability and responsibility regarding social and community activities. The State Securities Commission of Vietnam should enforce strict sanctions, conduct inspection, and complete evaluation criteria for Vietnamese listed banks.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.135-142
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• 2021

This study aims to examine the impact of audit committee characteristics on income management of companies listed on the Stock Exchange of Vietnam. Research data was collected from all 745 listed companies on Vietnam's stock market over four years, from 2015 to 2018. After excluding companies that did not qualify, there were 216 companies with 864 observations. With the help of dedicated software Stata 15, the impact of audit committee characteristics (through independent variables and control variables such as Audit Committee Independence, Auditing Committee size, Auditing Committee Expertise, Auditing Committee Meeting Frequency, Company Size, Financial Leverage, and Operating Cash Flow) to earning management through a multivariate regression model was determined. Research results from Vietnamese listed companies during this period show that the size and expertise of the audit committee are inversely related to the discretionary accruals representing earning management. At the same time, the research results also identify a positive relationship between firm size and earning management, and the inverse relationship between financial leverage, net cash flow from operating operations and earning management. However, the multivariate regression results do not find clear evidence of a relationship between audit committee independence and the audit committee meeting frequency to earning management.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2836-2846
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• 2020

During severe nuclear power plant (NPP) accidents, a H₂/CO mixture can be generated in the reactor pressure vessel by core degradation and in the containment as well by molten corium-concrete interaction. In spite of its importance, a state-of-the-art methodology predicting H₂/CO combustion risk relies predominantly on empirical correlations. It is therefore necessary to develop a proper methodology for flammability evaluation of H₂/CO mixtures at ex-vessel phases characterized by three factors: CO concentration, high temperature, and diluents. The developed methodology adopted Le Chatelier's law and a calculated non-adiabatic flame temperature model. The methodology allows the consideration of the individual effect of the heat transfer characteristics of hydrogen and carbon monoxide on low flammability limit prediction. The accuracy of the developed model was verified using experimental data relevant to ex-vessel phase conditions. With the developed model, the prediction accuracy was improved substantially such that the maximum relative prediction error was approximately 25% while the existing methodology showed a 76% error. The developed methodology is expected to be applicable for flammability evaluation in chemical as well as NPP industries.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.43-51
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• 2022

This paper examines the main factors affecting the bank performance under the Basel II implementation in Vietnam, a transitional economy in Asia. We pay special attention to the implementation duration of the Basel II convention and how it affects profitability. Thereby, we can evaluate the effectiveness of Basel II in the whole system according to the roadmap to 2020. We employ the panel data regression method to analyze a sample of 300 bank-year observations from 25 commercial banks during the 2008-2019 period. Our empirical findings show that the size of the bank, net interest margin, state ownership, and Basel II convention have positive effects on bank profitability. However, our empirical findings indicate that bank age and branch number negatively reduce bank performance. Finally, our results indicate that commercial banks earn extra profit from delaying the implementation of Basel II. However, commercial banks will encounter higher credit, and operational risks arising from delaying the implementation of Basel II standards. Therefore, our study contributes to the insights into the bank's management to enhance profitability, especially after implementing Basel II in a transitional economy. Finally, our study also provides policy implications for bank managers and banking supervisory to maintain the sustainable development of the banking system.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.611-614
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• 2023

갈비뼈 병변 진단 과정은 방사선 전문의가 CT 스캐너를 통해 생성된 2 차원 CT 이미지들을 해석하며 진행된다. 병변의 위치를 파악하고 정확한 진단을 내리기 위해 수백장의 2차원 CT 이미지들이 세밀하게 검토되며 갈비뼈를 분류한다. 본 연구는 이런 노동 집약적 작업의 문제점을 개선시키기 위해 Biaxial Rib Segmentation(BARS)을 제안한다. BARS 는 흉부 CT 볼륨의 관상면과 수평면으로 구성된 2 차원 이미지들을 U-Net 모델에 학습한다. 모델이 산출한 세그멘테이션 마스크들의 조합은 서로 다른 평면의 공간 정보를 보완하며 3 차원 갈비뼈 볼륨을 재건한다. BARS 의 성능은 DSC, Recall, Precision 지표를 사용해 평가하며, DSC 90.29%, Recall 89.74%, Precision 90.72%를 보인다. 향후에는 이를 기반으로 순차적 갈비뼈 레이블링 연구를 진행할 계획이다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.273-281
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• 2013

Based on the results of continuous flux measurements at the Gimje paddy flux site in the southwestern coast of Korea, carbon dioxide and energy exchanges between customarily cultivated rice-barley double cropping paddy field and the atmosphere during the 2012 rice growing season (from $9^{th}$ Jun. 2012 through $20^{th}$ Oct. 2012) were analyzed. Carbon dioxide and energy (H, LE) fluxes were estimated by the eddy covariance method. Environmental parameters (net radiation, precipitation, etc.) and plant biomass (LAI, plant height, etc.) were measured along with fluxes. After the quality control and gap-filling, the observed fluxes were analyzed. The results have been showed that net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (Re) during the rice cropping period were -277.1, 710.3, and 433.2 g C $m^{-2}$, respectively.