• Clean Technology
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• v.24 no.1
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• pp.41-49
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• 2018

Y zeolites with different extra-framework cations, such as $Na^+$, $N^+$, $Ca^{2+}$, and $Cu^{2+}$, with different charge and ionic radius have been investigated to greatly enhance a working capacity (W) of $CO_2$ adsorption at $25^{\circ}C$ and a $CO_2/CO$ selectivity factor (S). A sample of NaY with a very small amount of 0.012% $Ca^{2+}$ was fully reversible for seven times repeated $CO_2$ adsorption/desorption cycles, thereby forming no surface carbonates unlikely earlier reports. Although at pressures above 4 bar, 2.00% CaY, 1.60% CuY and 1.87% LiY all showed a $CO_2$ adsorption very similar to that measured for NaY, they gave a significant decrease in the adsorption at lower pressures, depending on the metal ion. At 0.5 ~ 2.5 bar, the extent of $CO_2$ adsorption was in the order NaY > 1.60% CuY > 2.00% CaY > 1.87% LiY. All the $Na^+-based$ metals-exchanged zeolites have a FAU (faujasite) framework and a Si/Al value near 2.6; thus, there is no discernible difference in the framework topology, framework chemical compositions, effective aperture size, and channel structure between the zeolite samples. Therefore, the distinctive behavior in the adsorption of $CO_2$ with a character as a weak Lewis acid is associated with the site basicity of the zeolites, and the interaction potentials of the cations. Different trend was shown for a CO adsorption due to weaker quadrupole interactions. Adsorption of $CO_2$ and CO on samples of CaY with 0.012 to 5.23% Ca disclosed a significant dependence on the Ca loading. The $CO_2$ adsorption increased when the cation exists up to ca. 0.05%, while it decreased at higher Ca amounts. However, values for both W and S could greatly increase as the bare zeolite is enriched by $Ca^{2+}$ ions. The 5.23% CaY had $W=2.37mmol\;g^{-1}$ and S = 4.37, and the former value was comparable to a benchmark reported in the literature.

• Broadcasting and Media Magazine
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• v.11 no.4
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• pp.104-117
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• 2006

In this Paper, we analyze the application of Interactive Data Broadcasting in DMB(Digital Multimedia Broadcasting) in the accordance with convergence of service and technology. With the acceleration of digital convergence in the Ubiquitous period substantial development of digital media technology and convergence of broadcasting and telecommunication industry are being witnessed. Consequently these results gave rise to newly combined-products such as DMB(Digital Multimedia Broadcasting), WCDMA(Wide-band code division multiple access), Wibro(Wireless Broadband Internet), IP-TV (Internet protocol TV) and HSDPA(High speed downlink packet access). The preparatory stage for the implementation of Interactive Data Broadcasting Service will be reached by the end of December, 2006. DMB is the first result of a successful convergence service between Broadcasting and Telecommunication in new media era. Multimedia technology and services are the core elements of DMB. The Data Broadcasting will not only offer various services of interactive information such News, Weather, Broadcasting Program etc, but also be linked with characteristic function of mobile phone such as calling and SMS(Short Message Service) via Return Channel.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.177-191
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• 2021

Due to the development of mobile devices and streaming technology, many changes in consumption patterns have appeared. In addition, social impact is becoming an era of non-face-to-face consumption due to the panthermic environment of COVID-19. Accordingly, in line with the non-face-to-face consumption trend, we focused on the importance of live commerce, which is emerging as a new distribution channel, and tried to investigate the causal relationship that the characteristics of live commerce and show hosts have on purchase intention. The respondents of this study were 235 general adults of live commerce users. Interaction, economics, entertainment as the characteristics of live commerce and attractiveness, professionality, and awareness as the characteristics of show hosts were set as independent variables. Purchase intention was set as the dependent variable, and content flow was set as the mediating variable. As a result of the study, it was found that the characteristics of live commerce such as Interaction, economics, entertainment, and the characteristics of show hosts such as attractiveness, professionality, and awareness all had a positive (+) significant effect on purchase intention. The impact was shown in the following order: entertainment of live commerce, awareness, attractiveness, professionality of show hosts, economics, interaction of live commerce. In addition, the results of the mediating effect of content flow on purchase intention are as follows. Content flow was found to play a mediating role between interaction, entertainment, attractiveness, professionality, awareness and purchase intention. On the other hand, economics was analyzed to have no mediating effect. The implications of this study are as follows. Companies and show hosts that sell products in live commerce should sell products that can inspire consumers rather than simply sell products. In addition, it is considered that content that provides entertainment and attractions gives pleasure to consumers. If not only a well-recognized show host, but also people with high recognition in various fields such as influencers and creators, become show hosts, consumers' content flow and purchase intentions will increase. And vendors must offer interesting content development and reasonable prices. Show hosts need to focus on active communication with consumers.