• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.394-399
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• 2005

In this paper, a structural damage identification method (SDIM) is developed to identify the line crack-like directional damages generated within a cylindrical shell. First, the equations of motion fur a damaged cylindrical shell are derived. Based on a theory of continuum damage mechanics, a small material volume containing a directional damage is represented by the effective orthotropic elastic stiffness, which is dependent of the size and the orientation of the damage with respect to the global coordinates. The present SDIM is then derived from the frequency response function (FRF) directly solved from the dynamic equations of the damaged cylindrical shell. In contrast with most existing SDIMs which require the modal parameters measured in both intact and damaged states, the present SDIM requires only the FRF-data measured in damaged state. By virtue of utilizing FRF-data, one may choose as many sets of excitation frequency and FRF measurement point as needed to acquire a sufficient number of equations fer damage identification analysis. The numerically simulated damage identification tests are conducted to study the feasibility of the present SDIM.

• International Journal of Reliability and Applications
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• v.4 no.1
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• pp.1-12
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• 2003

Many software reliability growth models (SRGM's) based on a nonhomogeneous Poisson process (NHPP) have been proposed by many researchers. Most of the SRGM's which have been proposed up to the present treat the event of software fault-detection in the testing and operational phases as a counting process. However, if the size of the software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore, in such a situation, we can model the software fault-detection process as a stochastic process with a continuous state space. In this paper, we propose a new software reliability growth model describing the fault-detection process by applying a mathematical technique of stochastic differential equations of an Ito type. We also compare our model with the existing SRGM's in terms of goodness-of-fit for actual data sets.

• Smart Structures and Systems
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• v.15 no.1
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• pp.15-40
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• 2015

This paper proposes a structural damage identification approach based on the power spectral density transmissibility (PSDT), which is developed to formulate the relationship between two sets of auto-spectral density functions of output responses. The accuracy of response reconstruction with PSDT is investigated and the damage identification in structures is conducted with measured acceleration responses from the damaged state. Numerical studies on a seven-storey plane frame structure are conducted to investigate the performance of the proposed damage identification approach. The initial finite element model of the structure and measured acceleration measurements from the damaged structure are used for the identification with a dynamic response sensitivity-based model updating method. The simulated damages can be identified accurately without and with a 5% noise effect included in the simulated responses. Experimental studies on a steel plane frame structure in the laboratory are performed to further verify the accuracy of response reconstruction with PSDT and validate the proposed damage identification approach. The locations of the introduced damage are detected accurately and the stiffness reductions in the damaged elements are identified close to the true values. The identification results demonstrated the accuracy of response reconstruction as well as the correctness and efficiency of the proposed damage identification approach.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.785-793
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• 2021

The purpose of the research is to assess the factors affecting the liquidity of the commercial banks that are conducting mergers and acquisitions activities in Vietnam during the 2008-2018 period. This study employs samples based on 2-component data sets with cross-section and time-series data collected from the annual report of the State Bank and the audited acquisitions financial statements of nine commercial banks engaged in mergers and acquisitions activities. To carry out the research objectives, the authors conducted quantitative analysis through the Pooled OLS, REM, FEM and GMM models. The results shown that: (i) bank liquidity is positively affected by liquidity lagged, the return on equity (ROE) and economic growth; negatively affected by bank size, non-performing loan, short-run loan to deposit ratio; (ii) there is not enough evidence to conclude about the relationship between net profit margin, equity-to-assets ratio and inflation rate to bank liquidity; (iii) notably, we found evidence that, after the mergers and acquisitions, the liquidity of Vietnamese commercial banks decreased. The findings of this study suggest that bank managers take a more comprehensive view of the results of mergers and acquisitions and implications for banks to improve liquidity in the post-merger and acquisitions conditions.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.296-311
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• 2021

Topological Data Analysis (TDA), a relatively new field of data analysis, has proved very useful in a variety of applications. The main persistence tool from TDA is persistent homology in which data structure is examined at many scales. Representations of persistent homology include persistence barcodes and persistence diagrams, both of which are not straightforward to reconcile with traditional machine learning algorithms as they are sets of intervals or multisets. The problem of faithfully representing barcodes and persistent diagrams has been pursued along two main avenues: kernel methods and vectorizations. One vectorization is the Betti sequence, or Betti curve, derived from the persistence barcode. While the Betti sequence has been used in classification problems in various applications, to our knowledge, the stability of the sequence has never before been discussed. In this paper we show that the Betti sequence is unstable under the 1-Wasserstein metric with regards to small perturbations in the barcode from which it is calculated. In addition, we propose a novel stabilized version of the Betti sequence based on the Gaussian smoothing seen in the Stable Persistence Bag of Words for persistent homology. We then introduce the normalized cumulative Betti sequence and provide numerical examples that support the main statement of the paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5800-5818
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• 2018

Interference may occur when several co-located wireless body area networks (WBANs) share the same channel simultaneously, which is compressed by resource scheduling generally. In this paper, a QoS-aware Adaptive Coloring (QAC) scheduling algorithm is proposed, which contains two components: interference sets determination and time slots assignment. The highlight of QAC is to determine the interference graph based on the relay scheme and adapted to the network QoS by multi-coloring approach. However, the frequent resource assignment brings in extra energy consumption and packet loss. Thus we come up with a launch condition for the QAC scheduling algorithm, that is if the interference duration is longer than a threshold predetermined, time slots rescheduling is activated. Furthermore, based on the relative distance and moving speed between WBANs, a prediction model for interference duration is proposed. The simulation results show that compared with the state-of-the-art approaches, the QAC scheduling algorithm has better performance in terms of network capacity, average delay and resource utility.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1146-1156
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• 2021

Single-unit probabilistic safety assessment (SUPSA) has complex Boolean logic equations for accident sequences. Multi-unit probabilistic safety assessment (MUPSA) model is developed by revising and combining SUPSA models in order to reflect plant state combinations (PSCs). These PSCs represent combinations of core damage and non-core damage states of nuclear power plants (NPPs). Since all these Boolean logic equations have complemented gates (not gates), it is not easy to generate exact Boolean solutions. Delete-term approximation method (DTAM) has been widely applied for generating approximate minimal cut sets (MCSs) from the complex Boolean logic equations with complemented gates. By applying DTAM, approximate conditional core damage probability (CCDP) has been calculated in SUPSA and MUPSA. It was found that CCDP calculated by DTAM was overestimated when complemented gates have non-rare events. Especially, the CCDP overestimation drastically increases if seismic SUPSA or MUPSA has complemented gates with many non-rare events. The objective of this study is to suggest a new quantification method named probability subtraction method (PSM) that replaces DTAM. The PSM calculates accurate CCDP even when SUPSA or MUPSA has complemented gates with many non-rare events. In this paper, the PSM is explained, and the accuracy of the PSM is validated by its applications to a few MUPSAs.

• Smart Media Journal
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• v.8 no.2
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• pp.46-57
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• 2019

In recent research, deep learning-based methods have achieved state-of-the-art performance in various computer vision tasks. However, these methods are commonly supervised, and require huge amounts of annotated data to train. Acquisition of data demands an additional costly effort, particularly for the tasks where it becomes challenging to obtain large amounts of data considering the time constraints and the requirement of professional human diligence. In this paper, we present a data level synthetic sampling solution to learn from small and imbalanced data sets using Generative Adversarial Networks (GANs). The reason for using GANs are the challenges posed in various fields to manage with the small datasets and fluctuating amounts of samples per class. As a result, we present an approach that can improve learning with respect to data distributions, reducing the partiality introduced by class imbalance and hence shifting the classification decision boundary towards more accurate results. Our novel method is demonstrated on a small dataset of 2789 tomato plant disease images, highly corrupted with class imbalance in 9 disease categories. Moreover, we evaluate our results in terms of different metrics and compare the quality of these results for distinct classes.

• Journal of the Korean Chemical Society
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• v.65 no.1
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• pp.9-14
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• 2021

In this study, the DFT calculation was performed using the B3LYP/6-31G(d,p) basis sets for the reaction process in which phenanthrene decomposes due to the chain reaction of two OH radicals on phenanthrene in the gaseous state of 298 K at 1 atm. As a result of the calculation, even when two OH radicals act on phenanthrene in a chain, the reaction for producing phenanthren-9-ol is predicted to be more advantageous than the reaction for producing phenanthren-1-ol. On the other hand, it was predicted that the OH addition process at room temperature would be advantageous for the priority of the OH addition and H abstraction process.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.66-73
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• 2021

This report documents the results of an at-power internal events Level 1 Probabilistic Safety Assessment (PSA) for a Korea research reactor (KRR). The aim of the study is to determine the accident sequences, construct an internal level 1 PSA model, and estimate the core damage frequency (CDF). The accident quantification is performed using the AIMS-PSA software version 1.2c along with a fault tree reliability evaluation expert (FTREX) quantification engine. The KRR PSA model is quantified using a cut-off value of 1.0E-15/yr to eliminate the non-effective minimal cut sets (MCSs). The final result indicates a point estimate of 4.55E-06/yr for the overall CDF attributable to internal initiating events in the core damage state for the KRR. Loss of Electric Power (LOEP) is the predominant contributor to the total CDF via a single initiating event (3.68E-6/yr), providing 80.9% of the CDF. The second largest contributor is the beam tube loss of coolant accident (LOCA), which accounts for 9.9% (4.49E-07/yr) of the CDF.