• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.103-111
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• 2019

Due tothe popularity of personal web-casting 'Food Web Casting' program, the present study aims to examine its effects on real life. The study conducted a survey with Korean college students. Results from a survey of 256 participants showed that first, the most salient reason for watching 'Food Web Casting' was gratification for killing time and gratifications for food entertainment, information, vicarious, and para social interaction are followed. Second, vicarious gratification, killing time gratification, and vicarious gourmandism gratification had a positive effect on Food Wed Casting programs viewing time, while it is not found for effects of gratifications for information and pro-social interaction. Third, it is found that Food Wed Casting programs viewing had effects on viewing happiness and negative perception of fat people. However, it did not have an effect on frequency of food delivery service. Based on these findings,implication, limitations, and topics for future research are discussed.

• Journal of Aerospace System Engineering
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• v.12 no.6
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• pp.1-8
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• 2018

ACF, which is used for the transparent electrode film is manufactured by the thermocompression method with conductive particles. However, the method has disadvantages since there are many wasted materials and the process is complex. To overcome the demerits of the conventional method, EHD printing technology with conductive particles ink is proposed. The line thickness of patterning is influenced by the characteristics of the inks and the printing conditions. Therefore, it is salient to find the most conducive conditions for the micro patterning. In this paper, the ink with conductive particles was manufactured, and the patterning results were obtained by varying the nozzle thickness and the flow rate. The electrical conductivity according to the ejection of the particles ink is obtained.

• The Korean Journal of Food And Nutrition
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• v.31 no.6
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• pp.897-910
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• 2018

The purpose of this study was to examine sodium intakes from soup, stew and noodle in school lunch using sodium content database separately developed for the solid part, liquid part of soup and stew (liquid based dishes) in middle school students. Two hundred fifty two middle school students in Seoul were asked about awareness towards reducing sodium intake and soup/stew intake provided in school lunch in September 2015. Only 68% of students were aware of the 'Day without soup/stew' event and why those events were held. Girls tend to consume more all solid and liquid parts of soups than boys in Miso soup (50.0% vs 36.2%), Bean sprout soup (56.6% vs 44.8%), Seaweed soup (61.8% vs 45.7%), Beef Radish soup (61.8% vs 59.5%), and Korean pasta soup with Kimchi (58.1% vs 46.6%). Average sodium intake from soup/stew/noodle dishes in school lunch was $379.6{\pm}183.9mg$ if behaviors of eating solid or liquid parts were considered and $556.8{\pm}190.6mg$ if behaviors of eating solid or liquid parts were not considered. Based on the results, the difference of sodium intake depends on the consuming behaviors of liquid parts of soup and stew dishes. It is necessary to establish and use a sodium database for each solid part and liquid part separately in soup, stew and noodle dishes to assess more accurate sodium intake. Education on the reduction of sodium intake through proper behaviors is salient in the achievement of a healthy diet.

• Smart Structures and Systems
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• v.23 no.6
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• pp.565-577
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• 2019

One of the most effective countermeasures for mitigating cable vibration is to install mechanical dampers near the anchorage of the cable. Most of the dampers used in the field are so-called passive dampers where their parameters cannot be changed once designed. The parameters of passive dampers are usually determined based on the optimal damper force obtained from the universal design curve for linear dampers, which will provide a maximum additional damping for the cable. As the optimal damper force is chosen based on a predetermined principal vibration mode, passive dampers will be most effective if cable undergoes single-mode vibration where the vibration mode is the same as the principal mode used in the design. However, in the actual engineering practice, multi-mode vibrations are often observed for cables. Therefore, it is desirable to have dampers that can suppress different modes of cable vibrations simultaneously. In this paper, MR dampers are proposed for controlling multi-mode cable vibrations, because of its ability to change parameters and its adaptability of active control without inquiring large power resources. Although the highly nonlinear feature of the MR material leads to a relatively complex representation of its mathematical model, effective control strategies can still be derived for suppressing multi-mode cable vibrations based on nonlinear modelling, as proposed in this paper. Firstly, the nonlinear Bouc-wen model is employed to accurately portray the salient characteristics of the MR damper. Then, the desired optimal damper force is determined from the universal design curve of friction dampers. Finally, the input voltage (current) of MR damper corresponding to the desired optimal damper force is calculated from the nonlinear Bouc-wen model of the damper using a piecewise linear interpolation scheme. Numerical simulations are carried out to validate the effectiveness of the proposed control algorithm for mitigating multi-mode cable vibrations induced by different external excitations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4290-4309
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• 2020

In recent years, advancements in machine learning capabilities have allowed it to see widespread adoption for tasks such as object detection, image classification, and anomaly detection. However, despite their promise, a limitation lies in the fact that a network's performance quality is based on the data which it receives. A well-trained network will still have poor performance if the subsequent data supplied to it contains artifacts, out of focus regions, or other visual distortions. Under normal circumstances, images of the same scene captured from differing points of focus, angles, or modalities must be separately analysed by the network, despite possibly containing overlapping information such as in the case of images of the same scene captured from different angles, or irrelevant information such as images captured from infrared sensors which can capture thermal information well but not topographical details. This factor can potentially add significantly to the computational time and resources required to utilize the network without providing any additional benefit. In this study, we plan to explore using image fusion techniques to assemble multiple images of the same scene into a single image that retains the most salient key features of the individual source images while discarding overlapping or irrelevant data that does not provide any benefit to the network. Utilizing this image fusion step before inputting a dataset into the network, the number of images would be significantly reduced with the potential to improve the classification performance accuracy by enhancing images while discarding irrelevant and overlapping regions.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.7-14
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• 2018

Salient information extraction from multi-view videos is a very challenging area because of inter-view, intra-view correlations, and computational complexity. There are several techniques developed for keyframes extraction from multi-view videos with very high computational complexities. In this paper, we present a keyframes extraction approach from multi-view videos using entropy and complexity information present inside frame. In first step, we extract representative shots of the whole video from each view based on structural similarity index measurement (SSIM) difference value between frames. In second step, entropy and complexity scores for all frames of shots in different views are computed. Finally, the frames with highest entropy and complexity scores are considered as keyframes. The proposed system is subjectively evaluated on available office benchmark dataset and the results are convenient in terms of accuracy and time complexity.

• Journal of Forest and Environmental Science
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• v.37 no.1
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• pp.1-13
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• 2021

The realities of Climate change and its untold implications on the livelihood of man are no longer new worldwide. In attempts to subdue the negative implications of Climate change scenario globally, several measures have being suggested and being put in place. One of such measures is urban reforestation especially in the developing nations where forest resources have extremely and uncontrollably exploited. Most of cities in developing nations are almost devoid of regularly maintained trees for whatever purpose. Thus, the enormous roles which urban tree performs are lacked in most cities. In order to subdue excessive heat in cities arising from exposure of urban land areas urban reforestation exercise needs to be embarked upon. The investigation was carried out through desk studies and review of relevant publications to examine what it entails to have a sustainable reforestation programme in cities. The study revealed that several factors need to be taken into consideration if sustainable urban reforestation will be achieved, especially in developing countries. These factors include urban soil nutrients status investigation, appropriate tree type study, public perception about the tree types, relevant legal instrument to achieve successful reforestation exercise in cities among others were found to be salient to this exercise. Urban reforestation has enormous potentials to subdue Climate change consequences, including urban renewal if adequate provision is made for its sustainability, especially in developing countries. To ensure this is realized it is recommended that relevant ministry/agency could be put in charge for the maintaining, cutting and replanting of urban tree and all that are involved in urban tree sustainability.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.53-62
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• 2022

Disposition Effect (DE) is one of the many investment biases, wherein the investors sell the profitable stocks rather quickly and they tend to hold on the loss making stocks. Various factors related to the DE are the character of investors applying risk management which is also influenced by the social media, Salient Shock (COVID-19), and in the specific case of Indonesia, the phenomenon of rumor stocks wherein the price can rise as much as up to 8500%. The study aims to provide empirical evidence regarding the DE with specific explanatory factors, namely investor behavior and rumors. Data was obtained through a questionnaire sent to 248 Indonesian Stock Exchange Investors (IDX) during the period October-November 2021 by using Ordinary Least Square (OLS) method. The results show: Generation Z, women, and investors with a low education has a greater DE, risk-takers tend to have lower DE, and professionals have negative DE. Implementation of risk management will reduce DE. Social Media and the COVID-19 situation positively affect DE. Especially on stock rumors, there is evidence that investors who own rumor stocks will have a low DE. The results indicate the need for: (i) risk management, especially for Z Generation, women and low education Investors, (ii) to provide positive information so that information on social media can be responded to positively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.518-524
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• 2022

In this paper, we propose a method for combining convolutional neural networks and attention mechanism to improve the recognition performance of emotional stress from Electroencephalogram (EGG) signals. In the proposed method, EEG signals are decomposed into five frequency domains, and spatial information of EEG features is obtained by applying a convolutional neural network layer to each frequency domain. As a next step, salient frequency information is learned in each frequency band using a gate transformer-based attention mechanism, and complementary frequency information is further learned through inter-frequency mapping to reflect it in the final attention representation. Through an EEG stress recognition experiment involving a DEAP dataset and six subjects, we show that the proposed method is effective in improving EEG-based stress recognition performance compared to the existing methods.