• Journal of Animal Science and Technology
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• v.47 no.1
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• pp.133-140
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• 2005

The aim of this study was to divide vocalizations of sows into general(GVs) and estrus-related vocalizations( EVs) and to find out their phonetic characteristics. Ten sows(Landrace) were recorded using digital video recorders twice daily(06: 00 - 08 : 00h and 17: 00 - 19 : 00h) during the anestrus and estrus periods. The GVs and EVs were divided based on the shapes of spectrum and spectrogram. The GVs and EVs were identified as 5 and 3 types, respectively. Pitch, formant I, formant 2, and formant 3 between GVs and EVs were not significantly different(P> 0.05), whereas intensity(P < 0.001), duration(P < 0.05), and formant 4(P < 0.01) were significantly different. Three parameter groups(Group I : Formant vector alone, Group II: Formant veetor+ parameters from time signal, Group III: Formant vector+parameters from time signal-parameters eliminated by stepwise discriminant analysis backward) were compared by discriminant function analysis. The classification system adopted in the Group II represented the higher discrimination rate than those in other groups(Group I : 76.1 0/0, Group II : 88.1 0/0, Group Ill: 87.3 %). These results suggest that EVs are present and intensity, formant 2, and formant 4 are available parameters for discrimination of EVs in sows.

• IE interfaces
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• v.24 no.1
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• pp.8-14
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• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2129-2147
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• 2019

This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.832-843
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• 2019

Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.91-101
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• 2021

The paper examines the impact of the COVID-19 pandemic on the stock market prices. The vector autoregression model (VAR) has been used in this analysis to survey 341 stocks on the Ho Chi Minh City Stock Exchange (HOSE) for the period from January 23, 2020 to December 31, 2020. The empirical results obtained from the analysis of 11 economic sectors suggest that there is a statistically significant impact relationship between COVID-19 and the healthcare and utility industries. Additional findings show a statistically significant negative impact of COVID-19 on the utility share price at lag 1. Analysis of impulse response function (IRF) and forecast error variance decomposition (FEVD) show an inverse reaction of utility stock prices to the impact of COVID-19 and a gradual disappearing shock after two steps. Major findings show that there is a clear negative effect of the COVID-19 pandemic on share prices, and the daily increase in the number of confirmed cases, indicate that, in future disease outbreaks, early containment measures and positive responses are necessary conditions for governments and nations to protect stock markets from excessive depreciation. Utility stocks are among the most severely impacted shares on financial exchanges during a pandemic due to the high risk of immediate or irreversible closure of manufacturing lines and poor demand for basic amenities.

• Structural Engineering and Mechanics
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• v.77 no.2
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• pp.231-243
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• 2021

In this paper, a dynamic response mapping model of the wheel-rail system is established by using the support vector regression (SVR) method, and the hierarchical safety thresholds of the subgrade void are proposed based on the reliability theory. Firstly, the vehicle-track coupling dynamic model considering the subgrade void is constructed. Secondly, the subgrade void area, the subgrade compaction index K30 and the fastener stiffness are selected as random variables, and the mapping model between these three random parameters and the dynamic response of the wheel-rail system is built by using the orthogonal test and the SVR. The sensitivity analysis is carried out by the range analysis method. Finally, the hierarchical safety thresholds for the subgrade void are proposed. The results show that the subgrade void has the most significant influence on the carbody vertical acceleration, the rail vertical displacement, the vertical displacement and the slab tensile stress. From the range analysis, the subgrade void area has the largest effect on the dynamic response of the wheel-rail system, followed by the fastener stiffness and the subgrade compaction index K30. The recommended safety thresholds for the subgrade void of level I, II and III are 4.01㎡, 6.81㎡ and 9.79㎡, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.1-9
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• 2021

This paper proposes to automate the process of predicting crypto-operations from the power signal generated in RSA decoding process by frequency analysis and K-means algorithm. RSA decoding process is divided into square and multiply operation, and if we can predict the type of operations over time, we will know the RSA key value. After converting the power signal generated in the process of decoding into two-dimensional frequency signal, this paper used K-means algorithm to classify the frequency vector according to the type of operation. these classified frequency vector were used to predict the types of operations.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.27-39
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• 2022

The exchange rate is an important macroeconomic variable that influences internal and external balances. Nepal follows a dual exchange rate such that the Nepali rupee (NPR) is pegged with the Indian rupee (INR) but floats with the United States dollar (USD) and all other currencies. There have been very few studies on the exchange rate of Nepal, of which the majority focus on the bivariate relationship between exchange rate and another variable. However, this paper analyses the multivariate relationship between the USD-NPR exchange rate and major macroeconomic variables. Determinants of Nepal's exchange rate have been derived with multiple regression using the ordinary least square (OLS) approach. Since the explanatory variables could not significantly capture the movement of the dependent variable, a long-run relationship between Nepal and India's exchange rate has been analyzed using Engle-Granger cointegration to establish a relationship as suggested by a graphical representation. This explains that Nepal's exchange rate long run is determined by India's exchange rate than its own fundamentals. In addition, the macro-linkages of Nepal's macroeconomic variables have been analyzed using Standard Vector Autoregressive models followed by impulse response analysis which is useful for policy decisions. Some policy implications indicating the sustainability of Nepal's pegged regime have been drawn based on the empirical analysis.

• Journal of Astronomy and Space Sciences
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• v.40 no.3
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• pp.123-129
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• 2023

This paper presents an analysis of the trans-lunar trajectory insertion performance of the Korea Pathfinder Lunar Orbiter (KPLO), the first lunar exploration spacecraft of the Republic of Korea. The successful launch conducted on August 4, 2022 (UTC), utilized the SpaceX Falcon 9 rocket from Cape Canaveral Space Force Station. The trans-lunar trajectory insertion performance plays a crucial role in ensuring the overall mission success by directly influencing the spacecraft's onboard fuel consumption. Following separation from the launch vehicle (LV), a comprehensive analysis of the trajectory insertion performance was performed by the KPLO flight dynamics (FD) team. Both orbit parameter message (OPM) and orbit determination (OD) solutions were employed using deep space network (DSN) tracking measurements. As a result, the KPLO was accurately inserted into the ballistic lunar transfer (BLT) trajectory, satisfying all separation requirements at the target interface point (TIP), including launch injection energy per unit mass (C3), right ascension of the injection orbit apoapsis vector (RAV), and declination of the injection orbit apoapsis vector (DAV). The precise BLT trajectory insertion facilitated the smoother operation of the KPLO's remainder mission phase and enabled the utilization of reserved fuel, consequently significantly enhancing the possibilities of an extended mission.

• BMB Reports
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• v.40 no.5
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• pp.656-661
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• 2007

In this report, we describe an optimized method for generation of ephA8 BAC transgenic mice expressing the lacZ reporter gene under ephA8 regulatory sequences. First, we constructed a targeting vector that carries a 1.2 kb ephA8 DNA upstream of its first exon, a lacZ expression cassette, a kanamycin cassette, and a 0.7 kb ephA8 DNA downstream of its first exon. Second, the targeting vector was electroporated into cells containing the ephA8 BAC and pKOBEGA, in which recombinases induce a homologous recombination between the ephA8 BAC DNA and the targeting vector. Third, the FLP plasmid expressing the Flipase was electroporated into these bacteria to eliminate a kanamycin cassette from the recombinant BAC DNA. The appropriate structures of the modified ephA8 BAC DNA were confirmed by Southern analysis. Finally, BAC transgenic mouse embryos were generated by pronuclear injection of the recombinant BAC DNA. Whole mount X-gal staining revealed that the lacZ reporter expression is restricted to the anterior region of the developing midbrain in each transgenic embryo. These results indicate that the ephA8 BAC DNA contains most, if not all, regulatory sequences to direct temporal and spatial expression of the lacZ gene in vivo.