• The KSFM Journal of Fluid Machinery
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• v.2 no.2 s.3
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• pp.5-12
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• 1999

This study presents the measured unsteady flctuation of impeller discharge flow for a centrifugal compressor in an unstable operating region. The characteristics of the blade-to-blade flow at rotating stall onset were investigated by measuring unsteady velocity fluctuations at several different diffuser axial distances using a hot wire anemometer. The flow characteristics in terms of the radial and tangential velocity components and the flow angle distribution at the impeller exit were analyzed using phase-locked ensemble averaging techniques. As a result, increase or decrease of the radial velocity component during the rotating stall is dominated by that of the suction side. The radial velocity distributions show the opposite trends in the regions where the radial velocity during rotating stall onset increases and decreases.

• 유체기계공업학회:학술대회논문집
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• 1998.12a
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• pp.129-134
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• 1998

This study presents the measured unsteady fluctuation of impeller discharge flow for a centrifugal compressor in unstable operating region. The characteristics of the blade-to-blade flow at rotating stall onset were investigated by measuring unsteady velocity fluctuations at several different diffuser axial distances using a hot wire anemometer. The flow characteristics in terms of the radial and tangential velocity components and the flow angle distribution at the impeller exit were analyzed using phase-locked ensemble averaging techniques. As a result, increase or decrease of the radial velocity component during the rotating stall is dominated by that on the suction side. The radial velocity distributions show the opposite trends in the regions where the radial velocity during rotating stall onset increases and decreases.

• Korea-Australia Rheology Journal
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• v.16 no.1
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• pp.1-15
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• 2004

The computation of viscoelastic flow using neural networks and stochastic simulation (CVFNNSS) is developed from the point of view of Eulerian CONNFFESSIT (calculation of non-Newtonian flows: finite elements and stochastic simulation techniques). The present method is based on the combination of radial basis function networks (RBFNs) and Brownian configuration fields (BCFs) where the stress is computed from an ensemble of continuous configuration fields instead of convecting discrete particles, and the velocity field is determined by solving the conservation equations for mass and momentum with a finite point method based on RBFNs. The method does not require any kind of element-type discretisation of the analysis domain. The method is verified and its capability is demonstrated with the start-up planar Couette flow, the Poiseuille flow and the lid driven cavity flow of Hookean and FENE model materials.

• Rapid Communication in Photoscience
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• v.2 no.1
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• pp.9-17
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• 2013

Visible light-sensitive $TiO_2$ and ZnO nanostructure materials have attracted great attention as the promising material for solar energy conversion systems such as photocatalysts for water splitting and environmental purification as well as nano-biosensors. Success of their applications relies on how to control their surface state behaviors related to the exciton dynamics and optoelectronic properties. In this paper, we briefly review some recent works on single nanoparticle photoluminescence (PL) technique and its application to observation of their surface state behaviors which are raveled by the conventional ensemble-averaged spectroscopic techniques. This review provides an opportunity to understand the temporal and spatial heterogeneities within an individual nanostructure, allowing for the potential use of single-nanoparticle approaches in studies of their photoenergy conversion and nano-scale optical biosensing.

• BMB Reports
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• v.46 no.2
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• pp.65-72
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• 2013

In situ detection of RNAs is becoming increasingly important for analysis of gene expression within and between intact cells in tissues. International genomics efforts are now cataloging patterns of RNA transcription that play roles in cell function, differentiation, and disease formation, and they are demon-strating the importance of coding and noncoding RNA transcripts in these processes. However, these techniques typically provide ensemble averages of transcription across many cells. In situ hybridization-based analysis methods complement these studies by providing information about how expression levels change between cells within normal and diseased tissues, and they provide information about the localization of transcripts within cells, which is important in understanding mechanisms of gene regulation. Multi-color, single-molecule fluorescence in situ hybridization (smFISH) is particularly useful since it enables analysis of several different transcripts simultaneously. Combining smFISH with immunofluorescent protein detection provides additional information about the association between transcription level, cellular localization, and protein expression in individual cells.

• Molecules and Cells
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• v.41 no.9
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• pp.809-817
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• 2018

Discovery of the naturally evolved fluorescent proteins and their genetically engineered biosensors have enormously contributed to current bio-imaging techniques. These reporters to trace dynamic changes of intracellular protein activities have continuously transformed according to the various demands in biological studies. Along with that, light-inducible optogenetic technologies have offered scientists to perturb, control and analyze the function of intracellular machineries in spatiotemporal manner. In this review, we present an overview of the molecular strategies that have been exploited for producing genetically encoded protein reporters and various optogenetic modules. Finally, in particular, we discuss the current efforts for combined use of these reporters and optogenetic modules as a powerful tactic for the control and imaging of signaling events in cells and tissues.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.166-174
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• 2021

A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.

• Journal of the Korean Society of Visualization
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• v.17 no.2
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• pp.39-47
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• 2019

Impingement cooling utilizing synthetic jets is emerging as a popular cooling technique because of its high local cooling efficiency. The interaction between the vortex structure of the synthetic jet and the surface is crucial in understanding the mechanism of this technique. In this study, the impinging vortex structure and its advection are investigated by experiments with jet-to-surface spacing $2{\leq}H/D{\leq}7$, and synthetic jet Reynolds number $5120{\leq}Re{\leq}9050$. Using phase-locked particle image velocimetry, ensemble averaged (phase averaged) flow fields are obtained, and vortex identification and quantification techniques are applied. The shape, trajectory, and intensity change of the vortex are assessed. A sharp decline in the vortex intensity and the occurrence of a counter-rotating vortex at the impingement point are observed.

• East Asian Economic Review
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• v.25 no.4
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• pp.403-424
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• 2021

AdaBoost tweaks the sample weight for each training set used in the iterative process, however, it is demonstrated that it provides more correlated errors as the boosting iteration proceeds if models' accuracy is high enough. Therefore, in this study, we propose a novel way to improve the performance of the existing AdaBoost algorithm by employing heterogeneous models and a stochastic twist. By employing the heterogeneous ensemble, it ensures different models that have a different initial assumption about the data are used to improve on diversity. Also, by using a stochastic algorithm with a decaying convergence rate, the model is designed to balance out the trade-off between model prediction performance and model convergence. The result showed that the stochastic algorithm with decaying convergence rate's did have a improving effect and outperformed other existing boosting techniques.