• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.223-233
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• 2024

This study investigates the impact of personalization recommendation systems on customer loyalty in e-commerce, focusing on the role of information presentation, system interaction, and social community functions. It examines how these elements influence flow state, word of mouth (WOM), and repurchase intention (RPI). Using structural equation modeling (SEM) and data collected from 500 respondents in SPSS and AMOS, the study finds that all three personalization aspects significantly enhance flow state, which, in turn, positively affects WOM and RPI. System interaction directly boosts both WOM and RPI, while information presentation and social community functions influence only one of these loyalty measures. Flow state mediates the relationship between personalization factors and loyalty outcomes. These findings suggest e-commerce platforms should enhance system interaction and embed social community features to foster customer loyalty.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.165-171
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• 2006

This paper proposes a data fusion algorithm of nonlinear multi sensor dynamic systems of synchronous sampling based on filtering step by step. Firstly, the object state variable at the next time index can be predicted by the previous global information with the systems, then the predicted estimation can be updated in turn by use of the extended Kalman filter when all of the observations aiming at the target state variable arrive. Finally a fusion estimation of the object state variable is obtained based on the system global information. Synchronously, we formulate the new algorithm and compare its performances with those of the traditional nonlinear centralized and distributed data fusion algorithms by the indexes that include the computational complexity, data communicational burden, time delay and estimation accuracy, etc.. These compared results indicate that the performance from the new algorithm is superior to the performances from the two traditional nonlinear data fusion algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.832-835
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• 2003

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1870-1873
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• 2004

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.171-176
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• 2016

In this paper, we consider a regulation problem of nonlinear systems under two triangular conditions where there possibly exist unbounded parameters in the systems. We propose a state feedback controller with dynamic gains in order to deal with unbounded parameters based on the condition of the time-varying rate of the growing parameter. The analysis of our control scheme is carried out by Lyapunov stability method. Our control method is verified by simulation results.

• Journal of Microbiology and Biotechnology
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• v.31 no.7
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• pp.1028-1034
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• 2021

The effect of medium composition on enzyme and β-glucan production by Aspergillus oryzae KCCM 12698 was investigated. Brown rice, rice bran, nitrogen, and ascorbic acid are key components of the synthetic medium used in liquid-state fermentation. To determine the optimal concentrations of these components for enzyme and β-glucan production, we conducted one factor at a time experiments, which showed that the optimal concentrations were 30 g/l brown rice, 30 g/l rice bran, 10 g/l soytone, and 3 g/l ascorbic acid. Pretreatment of brown rice for 60 min prior to inoculation enhanced fungal biomass, while increasing the production of enzymes and β-glucan using solid-state fermentation. Maximum fungal biomass of 0.76 mg/g, amylase (26,551.03 U/g), protease (1,340.50 U/g), and β-glucan at 9.34% (w/w) were obtained during fermentation. Therefore, solid-state fermentation of brown rice is a process that could enhance yield and overall production of enzymes and β-glucan for use in various applications.

• Journal of Ecology and Environment
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• v.47 no.4
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• pp.219-227
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• 2023

The US National Science Foundation's National Ecological Observatory Network (NEON) is a continental-scale program intended to provide open data, samples, and infrastructure to understand changing ecosystems for a period of 30 years. NEON collects co-located measurements of drivers of environmental change and biological responses, using standardized methods at 81 field sites to systematically sample variability and trends to enable inferences at regional to continental scales. Alongside key atmospheric and environmental variables, NEON measures the biodiversity of many taxa, including microbes, plants, and animals, and collects samples from these organisms for long-term archiving and research use. Here we review the composition and use of NEON resources to date as a whole and specific to biodiversity as an exemplar of the potential of national research infrastructure to contribute to globally relevant outcomes. Since NEON initiated full operations in 2019, NEON has produced, on average, 1.4 M records and over 32 TB of data per year across more than 180 data products, with 85 products that include taxonomic or other organismal information relevant to biodiversity science. NEON has also collected and curated more than 503,000 samples and specimens spanning all taxonomic domains of life, with up to 100,000 more to be added annually. Various metrics of use, including web portal visitation, data download and sample use requests, and scientific publications, reveal substantial interest from the global community in NEON. More than 47,000 unique IP addresses from around the world visit NEON's web portals each month, requesting on average 1.8 TB of data, and over 200 researchers have engaged in sample use requests from the NEON Biorepository. Through its many global partnerships, particularly with the Global Biodiversity Information Facility, NEON resources have been used in more than 900 scientific publications to date, with many using biodiversity data and samples. These outcomes demonstrate that the data and samples provided by NEON, situated in a broader network of national research infrastructures, are critical to scientists, conservation practitioners, and policy makers. They enable effective approaches to meeting global targets, such as those captured in the Kunming-Montreal Global Biodiversity Framework.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.409-438
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• 2000

In this paper we develop a new procedure to control stepsize for Runge- Kutta methods applied to both ordinary differential equations and semi-explicit index 1 differential-algebraic equation In contrast to the standard approach, the error control mechanism presented here is based on monitoring and controlling both the local and global errors of Runge- Kutta formulas. As a result, Runge-Kutta methods with the local-global stepsize control solve differential of differential-algebraic equations with any prescribe accuracy (up to round-off errors)

• The International Journal of Costume Culture
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• v.5 no.1
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• pp.34-57
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• 2002

It appears our global society and economy are here to stay, despite current events. Mass communications including The Internet, increased world travel, and more and more consumers developing the ability to afford consumer goods, are all helping to bring world cultures closer together. Indeed, the world is rapidly changing and the fashion consumer, is especially affected. Despite the increased commonalities of peoples of the world, culture is a concept crucial to the understanding of consumer behavior. Marketers must understand the values, ethics, and customs of societies around the world to be successful in the global marketplace.