• Journal of the Korean Mathematical Society
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• v.46 no.3
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• pp.551-560
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• 2009

A theorem of Robert Blumenthal is used here in order to obtain a sufficient condition for a function between two Finsler manifolds to be a fibre bundle map. Our study is connected with two possible constructions: 1) a Finslerian generalization of usually Kaluza-Klein theories which use Riemannian metrics, the well-known particular case of Finsler metrics, 2) a Finslerian version of reduction process from geometric mechanics. Due to a condition in the Blumenthal's result the completeness of Euler-Lagrange vector fields of Finslerian type is discussed in detail and two situations yielding completeness are given: one concerning the energy and a second related to Finslerian fundamental function. The connection of our last framework, namely a regular Lagrangian having the energy as a proper (in topological sense) function, with the celebrated $Poincar{\acute{e}}$ Recurrence Theorem is pointed out.

• Journal of Environmental Health Sciences
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• v.37 no.1
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• pp.73-79
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• 2011

The growing interest in nanotechnology has resulted in increasing concern and a number of published environmental and workplace measurements for assessing occupational exposure to engineered nanomaterials. However, the amount of previous exposure data remains limited. Furthermore the data available was collected with extensive variation in terms of exposure measurement strategy, which limits the ability to pool the data in the future. In response, this paper reviewed several pertinent issues related to exposure measurement strategy to suggest a harmonized measurement strategy which would make exposure data more useful in the future, e.g. correlation between exposure metrics, relationship between activity and exposure, task-based or shift-based assessment, background concentration, limitation of personal exposure monitoring and other determinants of exposure/modeling. An improved sampling strategy for nanomaterial exposure assessment should be considered in order to maximize the use of the data from various real time monitoring instruments.

• Journal of the Korean Mathematical Society
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• v.57 no.3
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• pp.707-719
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• 2020

In this article we study almost contact manifolds admitting weakly Einstein metrics. We first prove that if a (2n + 1)-dimensional Sasakian manifold admits a weakly Einstein metric, then its scalar curvature s satisfies -6 ⩽ s ⩽ 6 for n = 1 and -2n(2n + 1) ${\frac{4n^2-4n+3}{4n^2-4n-1}}$ ⩽ s ⩽ 2n(2n + 1) for n ⩾ 2. Secondly, for a (2n + 1)-dimensional weakly Einstein contact metric (κ, μ)-manifold with κ < 1, we prove that it is flat or is locally isomorphic to the Lie group SU(2), SL(2), or E(1, 1) for n = 1 and that for n ⩾ 2 there are no weakly Einstein metrics on contact metric (κ, μ)-manifolds with 0 < κ < 1. For κ < 0, we get a classification of weakly Einstein contact metric (κ, μ)-manifolds. Finally, it is proved that a weakly Einstein almost cosymplectic (κ, μ)-manifold with κ < 0 is locally isomorphic to a solvable non-nilpotent Lie group.

• Journal of the Korea Society of Computer and Information
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• v.21 no.8
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• pp.65-75
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• 2016

In this paper, we present a novel approach to help MSR researchers obtain necessary data with a tool, termed General Purpose Extractor for Source code (GPES). GPES has a single function extracts high-quality data, e.g., the version history, abstract syntax tree (AST), changed code diff, and software quality metrics. Moreover, features such as an AST of other languages or new software metrics can be extended easily given that GPES has a flexible data model and a component-based design. We conducted several case studies to evaluate the usefulness and effectiveness of our tool. Case studies show that researchers can reduce the overall cost of data analysis by transforming the data into the required formats.

• ETRI Journal
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• v.41 no.5
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2649-2669
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• 2020

In this paper, we analyze the secrecy performance of single-input multiple-output (SIMO) relay systems over κ-μ shadowed fading channels. Based on considering relay model employing decode-and-forward (DF) protocol, two security evaluation metrics, namely, secure outage probability (SOP) and probability of strictly positive secrecy capacity (SPSC) are studied, for which closed-form analytical expressions are derived. In addition, Monte Carlo results prove the validity of the theoretical derivation. The simulation results confirm that the factors that enhance the security include large ratio of (μ_D, μ_E), (m_D, m_E), (L_D, L_E) and small ratio of (k_D, k_E) under the high signal-to-noise ratio regime.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• Journal of Distribution Science
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• v.15 no.9
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• pp.85-93
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• 2017

Purpose - The volume and valence of online word-of-mouth(eWOM) have become an important part of the retailer's market success for a wide range of products. This study aims to investigate how the growth of eWOM has generated the product's final financial outcomes in the introductory period influences. Research design, data, and methodology - This study uses weekly box office performance for 117 movies released in the South Korea from July 2015 to June 2016 using Korean Film Council(KOFIC) database. 292,371 posted online review messages were collected from NAVER movie review bulletin board. Using regression analysis, we test whether eWOM incurred during the opening week is valuable to explain the last of box office performance. Three major eWOM metrics were considered after controlling for the major distributional factors. Results - Results support that major eWOM variables play a significant role in box-office outcome prediction. Especially, the growth rate of the positive eWOM volume has a significant effect on the growth potential in sales. Conclusions - The findings highlight that the speed of eWOM growth has an informational value to understand the market reaction to a new product beyond valence and volume. Movie distributors need to take positive online eWOM growth into account to make optimal screen allocation decisions after release.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Journal of Digital Convergence
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• v.3 no.2
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• pp.143-157
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• 2005

Many papers point out that the e-Learning is one of the most important industries, and the effect on other industries can be more powerful than any other business. Therefore, we think about social, cultural, industrial and technological effect of the e-Learning in order to enlarge industry scale as well as educational performances. In many cases of developed countries, various kinds of study have been performed for the e-Learning quality assurance because quality of the e-learning should operate on effective and efficient learning and continuous market development of education industries. The e-Learning quality assurance has import function not only for learning contents reusability like a SCORM and metadata but also for learning system, solution and service operation, so activities for the quality assurance should consider of cultural and tactical approach when it is applied in the e-learning business. In this paper, we present the concept, domain and purpose of the e-Learning quality assurance. Furthermore, this paper proposes the process and methodology in order to make the quality assurance standard model which is consist of 6 phase such as Environment Research, Needs Analysis, Framework, Metrics, Development and Implementation, Evaluation and Feedback through the analysis and comparison of pre-studied worldwide quality control, management and assurance documents.