• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.16 no.1
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• pp.9-18
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• 2010

For screening test of the non-volatile compounds which migrate from food packaging materials into foodstuffs, the traditional high performance liquid chromatography (HPLC) systems suffer from the lack of universal detector with high sensitivity and universality and high efficiency HPLC separation column which provides complete separation of complex mixtures into all individual substances. In this work, the use possibility of online two-dimensional liquid chromatography (2D-LC) system coupled with a charged aerosol detector (CAD), a universal detector, was reviewed. 2D-LC system permits to improve peak capacity and resolving power for complex mixtures. Charged aerosol detector (CAD) offers a new feasibility for detection of any non-volatile compounds with high sensitivity and constant response factor in a calibration range. The combination of size exclusion chromatography (SEC) and normal phase HPLC (NP-HPLC) is most frequently used for the separation of the natural and synthetic polymers which are mainly used as raw materials for the manufacture of food packaging materials. However, there is no commercial software available for data acquisition and handling and therefore the quantification in 2D-LC analysis is still rare.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.275-280
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• 2009

While database(DB) and information retrieval(IR) have been developed independently, there have been emerging requirements that both data management and efficient text retrieval should be supported simultaneously in an information system such as health care, customer support, XML data management, and digital libraries. The great divide between DB and IR has caused different manners in index maintenance for newly arriving documents. While DB has extended its SQL layer to cope with text fields due to lack of intact mechanism to build IR-like index, IR usually treats a block of new documents as a logical unit of index maintenance since it has no concept of integrity constraint. However, In the DB-IR integrations, a transaction on adding or updating a document should include maintenance of the posting lists accompanied by the document. Although DB-IR integration has been budded in the research filed, the issue will remain difficult and rewarding areas for a while. One of the primary reasons is lack of efficient online transactional index maintenance. In this paper, performance of a few strategies for per-document basis transactional index maintenance - direct index update, pulsing auxiliary index and posting segmentation index - will be evaluated. The result shows that the pulsing auxiliary strategy and posting segmentation indexing scheme, can be a challenging candidates for text field indexing in DB-IR integration.

• Journal of Power Electronics
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• v.14 no.5
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• pp.980-988
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• 2014

This paper proposes an online gain tuning algorithm for a robust sliding mode speed controller of surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed controller is constructed by a fuzzy neural network control (FNNC) term and a sliding mode control (SMC) term. Based on a fuzzy neural network, the first term is designed to approximate the nonlinear factors while the second term is used to stabilize the system dynamics by employing an online tuning rule. Therefore, unlike conventional speed controllers, the proposed control scheme does not require any knowledge of the system parameters. As a result, it is very robust to system parameter variations. The stability evaluation of the proposed control system is fully described based on the Lyapunov theory and related lemmas. For comparison purposes, a conventional sliding mode control (SMC) scheme is also tested under the same conditions as the proposed control method. It can be seen from the experimental results that the proposed SMC scheme exhibits better control performance (i.e., faster and more robust dynamic behavior, and a smaller steady-state error) than the conventional SMC method.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.186-196
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• 2018

In this paper, we propose an on-line tracking method using convolutional neural network (CNN) for tracking object. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. While conventional methods have been used to learn models by training samples offline, we demonstrate that a small group of samples are sufficient for online object tracking. In addition, we define a loss function containing color information, and prevent the model from being trained by wrong training samples. Experiments validate that tracking performance is equivalent to four comparative methods or outperforms them.

• Asia Marketing Journal
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• v.22 no.3
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• pp.1-28
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• 2020

With the development of information and communication technology and spread of smart devices, online information exchange has become a daily routine. Accordingly, the management and utilization of online word of mouth (WOM) has become an important issue for companies. Numerous studies have examined the impact of online WOM on firm performance. This study analyzes the impact of negative word of mouth (NWOM) on firm value, considering the influence of corporate social responsibility (CSR) activity and research and development (R&D) investment. Using a hierarchical linear model, we find that 1) NWOM has a negative impact on firm value, 2) CSR activities do not significantly influence this impact, and 3) R&D investment reduces this negative impact. This study contributes by demonstrating the effect of NWOM on firm value, examining the influence of CSR activities and R&D investment on the impact of NWOM, and confirming that the hierarchical linear model can be applied effectively to panel data in empirical studies. As a practical implication, companies must prevent and manage NWOM, whose impact, when caused by an unavoidable incident, can be alleviated by proactively announcing that the company is striving for competitiveness, for instance, by investing in R&D.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.19-22
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• 2022

In this work, we studied consumers' attractiveness/usefulness perceptions (CAUP) of online commerce product photos when exposed to alternative dark/light user interface (UI) modes. We analyzed time-series EEG data from 31 individuals and performed neuroscience mining (NSM) to ascertain (a) how the CAUP of products differs among UI modes; and (b) which deep learning model provides the most accurate assessment of such neuroscience mining (NSM) business difficulties. The dark UI style increased the CAUP of the products displayed and was predicted with the greatest accuracy using a unique EEG power spectra separated wave brainwave 2D-ConvLSTM model. Then, using relative importance analysis, we used this model to determine the most relevant power spectra. Our findings are considered to contribute to the discovery of objective truths about online customers' reactions to various user interface modes used by various online marketplaces that cannot be uncovered through more traditional research approaches like as surveys.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.22-34
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• 2022

This study examines the forms and current status of online performance content production in the field of the performing arts through diversified video media platforms. For this, it studied the leading case of Cirque du Soleil Entertainment and analyzed the unique brand value innovation elements of Cirque du Soleil, the background and current status of the digital hub platform of "Cirque Connect", and its various sub-contents that have diversified original contents. Digital platform applications and sub-content production in the field of the performing arts require an understanding of the needs of the public, who are familiar with media content appreciation, and strategic planning that takes into consideration everything from the initial stages of performance planning to the creation of varied sub-contents. This will promote the improvement of sub-content quality and increase the product value of digital contents in the performing arts through distinctions made from other various forms of cultural and artistic contents. environments in which information from various perspectives related to performance works can easily be accessed through online platforms will enhance the popularity of the performing arts field and allow the performing arts industry to expand its base in rapidly changing cultural enjoyment methods. For the performing arts field to be competitive within cultural trends that are being diversified, the most important tasks to be completed are gaining brand value innovation that enhances the artistic and cultural value of performance works and based on this, producing various sub-contents.

• Information Systems Review
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• v.12 no.3
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• pp.141-158
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• 2010

In this research, we analyzed the performance of the e-village shopping mall as an online agricultural business platform. The results suggested some critical factors that might assist the e-village owners to increase their sales by implementing the e-village information systems. We hypothesized that IT education, IT usage, online community activity, and organizational knowledge sharing influenced the e-village sales. Moreover, we investigated the moderating effect of rural experience tourism on those independent variables (IT education, IT usage, online community activity, and organizational knowledge sharing). The results indicated that online community activity had a positive effect on the online business sales, while IT education, IT usage, and organizational knowledge sharing showed insignificant effects. Furthermore, the interaction effects between rural experience tourism and both IT education and the IT usage were positive and significant. Thus, we conclude that the rural experience tourism moderated the relationship between (1) IT education and e-village sales, and (2) IT usage and e-village sales, but not the relationship between (1) online community activity and e-village sales, and (2) organizational knowledge sharing and e-village sales.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.453-463
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• 2018

An analysis including ASE model accessing based on the intention of behavior performance of online university students is a new approach to improve academic persistence considering the characteristics of students with extensive personal variables, a uniqueness of learning environment. This study aimed to identify the relationship between ASE model including academic stress and academic persistence, and the effect of these factors on academic persistence of online university students. Data were collected from 181 sophomores in K open university from March to June, 2018. Frequency analysis, ${\chi}^2-test$, t-test, F-test, Pearson's correlation analysis, and multiple regression analysis used for data analysis. For factors affecting academic persistence, academic stress (${\beta}=-.16$, p=.016), online learning attitude (${\beta}=.44$, p<.001), and social support among social influential factors (${\beta}=.16$, p=.045) were statistically significant and the prediction model of academic persistence showed 29% explanation power (F=15.76, p<.001). To enhance academic persistence of online university students, it is needed to develop programs to reduce academic stress, improve attitude toward online learning, and improve social support.