• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.321-331
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• 2015

The K-means clustering algorithm is a popular and widely used method for clustering. For covariance matrices, we consider a geodesic clustering algorithm based on the K-means clustering framework in consideration of symmetric positive definite matrices as a Riemannian (non-Euclidean) manifold. This paper considers a geodesic clustering algorithm for data consisting of symmetric positive definite (SPD) matrices, utilizing the Riemannian geometric structure for SPD matrices and the idea of a K-means clustering algorithm. A K-means clustering algorithm is divided into two main steps for which we need a dissimilarity measure between two matrix data points and a way of computing centroids for observations in clusters. In order to use the Riemannian structure, we adopt the geodesic distance and the intrinsic mean for symmetric positive definite matrices. We demonstrate our proposed method through simulations as well as application to real financial data.

• Korean Journal of Veterinary Service
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• v.46 no.3
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• pp.203-210
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• 2023

Breeding ducks are susceptible to fungal infections due to being bred in confined spaces for long periods. The objective of this study was to show the real state of the clinical fungal contamination of 22 duck breeding farms in Chungcheongbuk-do, South Korea. Out of the 430 carcasses obtained from the 22 duck breeding farms, 80 were diagnosed with invasive pulmonary aspergillosis (IPA). Aspergillus spp. were detected as the causative agents, including 26 cases of A. fumigatus, 35 cases of A. flavus, and 19 cases of A. terreus. The clinical lesions in the breeding ducks had circumscribed cream-and-yellow-colored plaques and/or white-to-greenish mycelium. Septate hyphae with parallel walls and dichotomous branching were observed in the histopathological lesions. AGMAg ELISA was performed to determine the overall positive rate of Aspergillus spp. in duck breeding farms. These results showed a positive rate of 58.97% for Aspergillus spp. Additionally, the positive rate increased with the age of the host.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• Asian Journal of Business Environment
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• v.8 no.4
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• pp.37-44
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• 2018

Purpose - The artificial intelligence industry plays an increasingly significant role in stimulating the development of United States of America's economy. On account of this background, this paper attempts to explore the impact of artificial intelligence industry on United States of America's macroeconomy. Research design, data, and methodology - This paper mainly focuses on the impact of artificial intelligence industry on GDP, employment, real income, import, export and foreign direct investment. Furthermore, the Phillips-Perron test and Canonical cointegrating regression will be employed to examine the impact of artificial intelligence industry on United States of America's macroeconomy with a sample form 2010-Q1 to 2017-Q4. Results - Via the empirical analysis, the results reveal that the artificial intelligence industry has a positive effect on United States of America's GDP, employment, real income, export and foreign direct investment. Conversely, the artificial intelligence industry has a negative effect on United States of America's import. Conclusions - In summary, the impact of artificial intelligence industry on United States of America's macroeconomy is positive and significant in statistics. Therefore, the government of United States of America should put more input into artificial intelligence industry.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.11
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• pp.4907-4911
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• 2016

Glioblastoma multiforme (GBM) is the most aggressive of the gliomas, a collection of tumors arising from glia in the central nervous system. Possible associations between the human cytomegalovirus (HCMV) and the JC virus with GBM are now attracting interest. Our present aim was to investigate the prevalence of the two viruses in Iranian patients from Kerman's cities in the south of Iran. In addition, the expression rates of pp65, large T antigen and p53 proteins were assessed and their relation with GBM evaluated using reverse transcription real time PCR (rReal Time PCR). A total of 199 patients with GBM cancer were enrolled, with $mean{\pm}SD$ ages of $50.0{\pm}19.5$ and $50.7{\pm}19.6$ years for males and females, respectively. The P53 rate was dramatically low suggesting an aetiological role,. Large T antigen expression was found in JC positive samples, while the PP65 antigen was observed in patients positive for CMV and JC. HCMV products and JC virus with oncogenic potential may induce the development of various tumors including glioblastomas. The JC virus produces an early gene product, T-antigen, which has the ability to associate with and functionally inactivate well-studied tumor suppressor proteins including p53 and pRB.

• Bulletin of the Korean Mathematical Society
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• v.45 no.4
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• pp.717-728
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• 2008

Let H be a real Hilbert space and S = {T(s) : $0\;{\leq}\;s\;<\;{\infty}$} be a nonexpansive semigroup on H such that $F(S)\;{\neq}\;{\emptyset}$ For a contraction f with coefficient 0 < $\alpha$ < 1, a strongly positive bounded linear operator A with coefficient $\bar{\gamma}$ > 0. Let 0 < $\gamma$ < $\frac{\bar{\gamma}}{\alpha}$. It is proved that the sequences {$x_t$} and {$x_n$} generated by the iterative method $$x_t\;=\;t{\gamma}f(x_t)\;+\;(I\;-\;tA){\frac{1}{{\lambda}_t}}\;{\int_0}^{{\lambda}_t}\;T(s){x_t}ds,$$ and $$x_{n+1}\;=\;{\alpha}_n{\gamma}f(x_n)\;+\;(I\;-\;{\alpha}_nA)\frac{1}{t_n}\;{\int_0}^{t_n}\;T(s){x_n}ds,$$ where {t}, {${\alpha}_n$} $\subset$ (0, 1) and {${\lambda}_t$}, {$t_n$} are positive real divergent sequences, converges strongly to a common fixed point $\tilde{x}\;{\in}\;F(S)$ which solves the variational inequality $\langle({\gamma}f\;-\;A)\tilde{x},\;x\;-\;\tilde{x}{\rangle}\;{\leq}\;0$ for $x\;{\in}\;F(S)$.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.35-42
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• 2020

This study aims to investigate the relationship between the variables of Current Ratio (CR), Return-on-Equity (ROE), Return-on-Assets (ROA), Debt-to-Equity Ratio (DER), and Firm Size (FS) on Dividend Policy (DP) in real estate and property companies listed on the Indonesia Stock Exchange in the period 2016-2019, looking at nine real estate companies in Indonesia. The research methodology uses an explanatory analysis approach and linear regression. Based on the eligibility and homogeneity of the data, the number of sample companies selected was nine companies. The company's financial statement data derived from primary data obtained on the Indonesia Stock Exchange, such as current ratio (CR), return-on-equity (ROE), return-on-assets (ROA), debt-to-equity ratio (DER) and firm size and dividend policy variables. The data analysis procedure is first to transform financial data from the original ratio data into interval data and, then, transform it to ordinal data. Furthermore, the validity and reliability process are ignored because the data is primary. Finally, regression testing is part of the hypothesis testing stage. The results of this study showed that the CR, ROE, and firm size had no positive and significant effect on dividend policy. In contrast, DER and ROA have a positive and significant impact on dividend policy.

• Culinary science and hospitality research
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• v.4
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• pp.317-345
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• 1998

According to this study, Korean food department rather than Western food department and business department rather than food & beverage department is more separated with the greater difference. It is surveyed that the main reason to separate from food service is dissatisfaction to wage followed by six reasons. In the light of management the separation of employees gives management a positive aspect, but the separation of necessary employees bring about a big problem. That the separation rate of food service industry which greatly depends on personal service is high causes many bottleneck to development of food service industry. If the saparation rate of employees is high, it is difficult to maintain consistency of service quality. It is impossible to think that immature service new employee gives customer strong impression. Subsequently the spending of time and cost to train new employees and management reproaching supervisors makes morale of employees dropped. That is why the very this influences selling. To improve the vicious cycle of that serve absence in food service Operators of food service industry should be escaped from their fixed ideas. It is time to need management consciousness which wants to have real content rather than external growth. It is revealed that more than 80% of investment of most of food service industry goes to facility investment. A part of the investment cost should be invested to foster professional manpower. And if employees can have owner consciousness by continuous support and positive investment in training which can give real intention to their lives and vocational pride, it is believed that the future of food service industry in Korea is promising.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.156-165
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• 2003

This paper describes the design of a robust adaptive fuzzy observer for uncertain nonlinear dynamical system. We propose a new method in which no strictly positive real (SPR) condition is needed. No a priori knowledge of an upper bound on the lumped uncertainty is required. The Lyapunov synthesis approach is used to guarantee a semi-global uniform ultimate boundedness property of the state observation error, as well as of all other signals in the closed-loop system. The theoretical results are illustrated through a simulation example of a mass-spring-damper system.

• Journal of IKEEE
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• v.24 no.4
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.