• Nuclear Engineering and Technology
• /
• v.51 no.3
• /
• pp.702-708
• /
• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.2
• /
• pp.289-295
• /
• 2021

This study investigates the role of corporate governance in the dividend decision of 198 non-financial companies listed on the Colombo Stock Exchange of Sri Lanka, over the period from 2009 to 2016. Four corporate governance indicators are used in this study; managerial ownership, the board size, board independence, and CEO duality. Furthermore, this study considers three control variables such as profitability, firm size, and corporate tax. This study employed the Generalized Method of Moments (GMM) model to estimate the regression models on panel data study. The major contribution of this study is exploring the insight into the effect of corporate governance factors on dividend decisions. The results of the study revealed that managerial ownership showed a significant positive impact on the dividend payout ratio. Board size showed a significant positive influence on the dividend payout ratio. Board independence negatively but significantly influenced the dividend payout ratio. CEO duality showed an insignificant negative impact on the dividend payout ratio. In the framework of these CG indicators, Sri Lankan listed firms are recommended to have dispersed ownerships, large Board size and maintain a balance of power and authority by separating the individual who is assuming the position of the CEO from the Chairperson of the Board and maintain at least two independent directors.

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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.3043-3067
• /
• 2022

With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.45 no.4
• /
• pp.227-231
• /
• 2000

Small seed size is one of the major traits of soybean cultivars for sprouts with regard to high sprout yield. This study was conducted to identify quantitative trait loci (QTL) for seed size and weight in a set of F 6 seeds of 89 lines derived from a cross between 'Pureunkong', a soybean cultivar developed for sprouts and 'Jinpumkong 2', a soybean cultivar with no beany taste in seed due to the lack of lipoxygenases. The genetic map of 25 linkage groups with a total of 98 markers including RFLP, RAPD, SSR and classical markers was constructed from this F/sbu 5/-derived population and was used for QTL analysis. 'Pureunkong' was significantly smaller (P＜0.01) than 'Jinpumkong 2' in seed size and seed weight. Genetic variation was detected and transgressive segregation was common in the population for these traits. Seven DNA markers including opT14-1600 in LG A2, opF02-400 in LG B2, Satt100, opC09-700, opG04-730 and opQll-650 in LG C2, and opY07-1100 ＆ 1000 in LG(unknown) were significantly associated and accounted for 4.7 to 10.9% and 5.1 to 10.1 % of the phenotypic variation in seed size and seed weight, respectively. 'Pureunkong' alleles increased seed size and seed weight at the all four significant marker loci on the LG C2. These marker loci in LG C2 were closely linked and were presumed to be a single QTL. Overall, at least three independent QTLs from 3 linkage groups (A2, B2, and C2) were putatively involved in the control of seed size and seed weight.

• Journal of Ecology and Environment
• /
• v.41 no.8
• /
• pp.223-234
• /
• 2017

Background: Spatial structure of plants in a population reflects complex interactions of ecological and evolutionary processes. For dioecious plants, differences in reproduction cost between sexes and sizes might affect their spatial distribution. Abiotic heterogeneity may also affect adaptation activities, and result in a unique spatial structure of the population. Thus, we examined sex- and size-related spatial distributions of old-growth forest of dioecious tree Torreya nucifera in extremely heterogeneous Gotjawal terrain of Jeju Island, South Korea. Methods: We generated a database of location, sex, and size (DBH) of T. nucifera trees for each quadrat ($160{\times}300m$) in each of the three sites previously defined (quadrat A, B, C in Site I, II, and III, respectively). T. nucifera trees were categorized into eight groups based on sex (males vs. females), size (small vs. large trees), and sex by size (small vs. large males, and small vs. large females) for spatial point pattern analysis. Univariate and bivariate spatial analyses were conducted. Results: Univariate spatial analysis showed that spatial patterns of T. nucifera trees differed among the three quadrats. In quadrat A, individual trees showed random distribution at all scales regardless of sex and size groups. When assessing univariate patterns for sex by size groups in quadrat B, small males and small females were distributed randomly at all scales whereas large males and large females were clumped. All groups in quadrat C were clustered at short distances but the pattern changed as distance was increased. Bivariate spatial analyses testing the association between sex and size groups showed that spatial segregation occurred only in quadrat C. Males and females were spatially independent at all scales. However, after controlling for size, males and females were spatially separated. Conclusions: Diverse spatial patterns of T. nucifera trees across the three sites within the Torreya Forest imply that adaptive explanations are not sufficient for understanding spatial structure in this old-growth forest. If so, the role of Gotjawal terrain in terms of creating extremely diverse microhabitats and subsequently stochastic processes of survival and mortality of trees, both of which ultimately determine spatial patterns, needs to be further examined.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.5
• /
• pp.617-623
• /
• 2008

This paper presents a human motion recognition method using both centroid shift and local basis images. The centroid shift based on 1st moment balance technique is applied to get the robust motion images against position or size changes, the extraction of local basis images based on independent component analysis(ICA) is also applied to find a set of statistically independent motion features, which is included in each motions. Especially, ICA of fixed-point(FP) algorithm based on Newton method is used for being quick to extract a local basis images of motions. The proposed method has been applied to the problem for recognizing the 160(1 person * 10 animals * 16 motions) sign language motion images of 240*215 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate) than the method using local eigen images and the method using local basis images without centroid shift respectively.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.9
• /
• pp.3703-3708
• /
• 2015

Background: The neutrophil-to-lymphocyte ratio (NLR) is a strong predictor of mortality in patients with colorectal, lung, gastric cancer, pancreatic and metastatic renal cell carcinoma. We here evaluated whether preoperative NLR is an independent prognostic factor for non-metastatic renal cell carcinoma (RCC). Materials and Methods: Data from 327 patients who underwent curative or palliative nephrectomy were evaluated retrospectively. In preoperative blood routine examination, neutrophils and lymphocytes were obtained. The predictive value of NLR for non-metastatic RCC was analyzed. Results: The NLR of 327 patients was $2.72{\pm}2.25$. NLR <1.7 and NLR ${\geq}1.7$ were classified as low and high NLR groups, respectively. Chi-square test showed that the preoperative NLR was significantly correlated with the tumor size (P=0.025), but not with the histological subtype (P=0.095)and the pT stage (P=0.283). Overall survival (OS) and disease-free survival (DFS) were assessed using the Kaplan-Meier method. Effects of NLR on OS (P=0.007) and DFS (P=0.011) were significant. To evaluate the independent prognostic significance of NLR, multivariate COX regression models were applied and identified increased NLR as an independent prognostic factor for OS (P=0.015), and DFS (P=0.019). Conclusions: Regarding patient survival, an increased NLR represented an independent risk factor, which might reflect a higher risk for severe cardiovascular and other comorbidities. An elevated blood NLR may be a biomarker of poor OS and DFS in patients with non-metastatic RCC.

• The KIPS Transactions:PartB
• /
• v.9B no.5
• /
• pp.643-652
• /
• 2002

This paper proposes an efficient hybrid fixed-point (FP) algorithm for improving performances of the independent component analysis (ICA) based on neural networks. The proposed algorithm is the FP algorithm based on secant method and momentum for ICA. Secant method is applied to improve the separation performance by simplifying the computation process for estimating the root of objective function, which is to minimize the mutual informations of the independent components. The momentum is applied for high-speed convergence by restraining the oscillation if the process of converging to the optimal solution. It can simultaneously achieve a superior properties of the secant method and the momentum. The proposed algorithm has been applied to the composite fingerprints and the images generated by random mixing matrix in the 8 fingerprints of $256\times{256}$-pixel and the 10 images of $512\times{512}$-pixel, respectively. The simulation results show that the proposed algorithm has better performances of the separation speed and rate than those using the FP algorithm based on Newton and secant method. Especially, the secant FP algorithm can be solved the separating performances depending on initial points settings and the nonrealistic learning time for separating the large size images by using the Newton FP algorithm.

• Journal of Digital Convergence
• /
• v.17 no.5
• /
• pp.217-223
• /
• 2019

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.