• Knowledge Management Research
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• v.23 no.4
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• pp.275-294
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• 2022

In recent years, with the continuous integrative development of e-commerce and social media, social commerce, as a trust-centered social transaction mode, has become an important performance form of e-commerce. The good experience of online community and abundant user-generated content (UGC) attract more and more users and businesses to participate in the community contribution. In this context, the cost of accessing information is continuously decreasing, which not only makes the purchase process more concise and efficient, but also greatly increases the possibility of consumers' impulsive consumption. However, there are very few empirical studies on the internal influencing mechanism of consumers' impulsive consumption based on the characteristics of UGC for social commerce. In view of this, based on S-O-R model, this study constructs a model of consumers' impulsive consumption in the context of social commerce from the characteristics of UGC, with perceived risk as the mediating variable and tie strength as the moderating variable. The results show that content authenticity, content usefulness, and content valence of UGC have significant negative impacts on consumers' risk perception in the process of purchase decision-making, and consumers' perceived risk has a significant negative impact on consumers' impulsive consumption. Meanwhile, the tie strength between UGC producer and UGC receiver plays a moderating role between content usefulness and perceived risk, as well as between perceived risk and impulsive consumption. Finally, combined with the above findings, this study provides effective suggestions for relevant participants in social commerce in terms of business management.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.133-145
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• 2022

Compared to the continuously increasing dog population and industry size in Korea, systematic analysis of related data and research on breed classification methods are very insufficient. In this paper, an automatic breed classification method is proposed using deep learning technology for 14 major dog breeds domestically raised. To do this, dog images are collected for deep learning training and a dataset is built, and a breed classification algorithm is created by performing transfer learning based on VGG-16 and Resnet-34 as backbone networks. In order to check the transfer learning effect of the two models on dog images, we compared the use of pre-trained weights and the experiment of updating the weights. When fine tuning was performed based on VGG-16 backbone network, in the final model, the accuracy of Top 1 was about 89% and that of Top 3 was about 94%, respectively. The domestic dog breed classification method and data construction proposed in this paper have the potential to be used for various application purposes, such as classification of abandoned and lost dog breeds in animal protection centers or utilization in pet-feed industry.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.31-43
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• 2022

Traditional video compression has developed so far based on hybrid compression methods through motion prediction, residual coding, and quantization. With the rapid development of technology through artificial neural networks in recent years, research on image compression and video compression based on artificial neural networks is also progressing rapidly, showing competitiveness compared to the performance of traditional video compression codecs. In this paper, a new method capable of improving the performance of such an artificial neural network-based video compression model is presented. Basically, we take the rate-distortion optimization method using the auto-encoder and entropy model adopted by the existing learned video compression model and shifts some components of the latent information that are difficult for entropy model to estimate when transmitting compressed latent representation to the decoder side from the encoder side, and finally compensates the distortion of lost information. In this way, the existing neural network based video compression framework, MFVC (Motion Free Video Compression) is improved and the BDBR (Bjøntegaard Delta-Rate) calculated based on H.264 is nearly twice the amount of bits (-27%) of MFVC (-14%). The proposed method has the advantage of being widely applicable to neural network based image or video compression technologies, not only to MFVC, but also to models using latent information and entropy model.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.197-216
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• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.91-103
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• 2022

Determination of sound source characteristics such as: sound volume, direction and distance to the source is one of the important techniques for unmanned systems like autonomous vehicles, robot systems and AI speakers. There are multiple methods of determining the direction and distance to the sound source, e.g., using a radar, a rider, an ultrasonic wave and a RF signal with a sound. These methods require the transmission of signals and cannot accurately identify sound sources generated in the obstructed region due to obstacles. In this paper, we have implemented and evaluated a method of detecting and identifying the sound in the audible frequency band by a method of recognizing the volume, direction, and distance to the sound source that is generated in the periphery including the invisible region. A cross-shaped based sound source recognition algorithm, which is mainly used for identifying a sound source, can measure the volume and locate the direction of the sound source, but the method has a problem with "blind spots". In addition, a serious limitation for this type of algorithm is lack of capability to determine the distance to the sound source. In order to overcome the limitations of this existing method, we propose a QRAS-based algorithm that uses rectangular-shaped technology. This method can determine the volume, direction, and distance to the sound source, which is an improvement over the cross-shaped based algorithm. The QRAS-based algorithm for the OSSD uses 6 AITDs derived from four microphones which are deployed in a rectangular-shaped configuration. The QRAS-based algorithm can solve existing problems of the cross-shaped based algorithms like blind spots, and it can determine the distance to the sound source. Experiments have demonstrated that the proposed QRAS-based algorithm for OSSD can reliably determine sound volume along with direction and distance to the sound source, which avoiding blind spots.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.1-28
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• 2022

In recent years, many Internet giants have begun providing their own content services, which attract online users by offering personalized services based on artificial intelligence technologies. This study investigates the role of two firms' content services within the firms' online service network. We examine the role of Naver Webtoon, which can be characterized as a professional-generated content, within Naver's service portfolio, and that of Google YouTube, which can be characterized as a user-generated content, within Google's service portfolio. Using survey data on viewers' use of the two services, we analyze a valued directed service network, where a node denotes an online service and a relationship between two nodes denotes a sequential use of two services. We found that both Webtoon and YouTube show higher out-degree centrality than in-degree centrality, which implies these content services are more likely to be starting services rather than arriving services within the firms' interactive network. The gap between the out-degree and in-degree centrality of YouTube is much smaller than that of Webtoon. The high centrality of YouTube, a user-generated content service, within the Google service network shows that YouTube's initial role of providing specific-content videos (e.g., entertainment) has expanded into a general search service for users.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.6
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• pp.229-240
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• 2016

Academic resilience of individual competencies that have overcome fatal or chronic adversity seen as a excessive threat in academic scene is the psychological ability to overcome stress. This study is a survey research carried try to identify the factors that affect the academic resilience of nursing students. Interpersonal communication competence and happiness to target the nursing students of the total 554 people to investigate the effect on the academic resilience, were analyzed using the SPSS version 21.0. The main results are as follows. (1) academic resilience could vary depending on the grade, living arrangements, Interpersonal relationship, personality and academic performance. (2) academic resilience had a positive correlation with interpersonal communication competence(r=.46, p<.001) and happiness(r=.35, p<.001). (3) factors that had significant impact on academic resilience of nursing students were grade(p=.002), living arrangements(p=.001), academic performance(p<.001), interpersonal communication competence(p<.001) and happiness(p<.001) and explanatory power was 36.9%. Through this study, interpersonal communication competence and happiness were identified as having the positive effect on academic resilience. As a follow-up, in order to improve academic resilience, a study to develop programs that would promote interpersonal communication competence and happiness are suggested.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.4
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• pp.169-179
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• 2016

This study was to investigate special education teachers' teaching in reading and writing to university students with intellectual disabilities. For this study, we surveyed 71special education teachers who work in Gwangju, Daegu and Busan. As a result, in order to identify students' reading and writing abilities, they were more likely to use their own tests which they made for themselves than standardized tests. When teaching students, they used their own teaching experiences and advices from colleagues and senior teachers regarding problem-solving methods and reliable information while the knowledge they learned at school showed low frequency in use. Despite using mainly whole-word approach when instructing reading and writing, it appeared that teachers' teaching experiences and diversity of textbooks also have an influence. Regarding needs of education participation for teaching students, there were high needs and interests in teaching methods of writing, textbooks and teaching materials by the characteristics of disability, reading and writing development, reading and writing disabilities. In case of difficulties and needs in teaching students, there was a high demand of development of a wide variety of teaching materials and tools and, preparation for sufficient textbooks and test tools, while difficulties in teaching appeared in lack of knowledge about reading and writing, lack of screening/evaluating tools, and evaluating and teaching oriented to each disability characteristic.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.447-455
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• 2017

At the modern industry, the composite material has been widely used. Particularly, the material of carbon fiber reinforced plastic hardened with resin on the basis of fiber is excellent. As the specific strength and rigidity are also superior, it receives attention as the light material. Among these materials, the carbon fiber reinforced plastic using carbon fiber has the superior mechanical property different from another fiber. So, it is utilized in vehicle and airplane at which high strength and light weight are needed at the same time. In this paper, the tensile property due to the fiber design is investigated through the analysis study with CT specimen composed of carbon plastic reinforced plastic. At the stress analysis of CFRP composite material with hole, the fracture trend at the tensile environment is examined. Also, it is shown that the lowest stress value happens and the deformation energy of the pre-crack becomes lowest at the analysis model composed of the stacking angle of 60° through the result due to the stacking angle. On the basis of this study result, it is thought to apply the foundation data to anticipate the fracture configuration at the structure applied with the practical experiment.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.5
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• pp.471-479
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• 2015

This study was conducted to make use of Korea Meteorological Administration(KMA)'s Automatic Weather Station(AWS) data to operate smart green greenhouse. A Web-based KMA AWS data receiving system using JAVA and APM_SETUP 8 on windows 7 platform was developed. The system was composed of server and client. The server program was developed by a Java application to receive weather data from the KMA every 30 minutes and to send the weather data to smart greenhouse. The client program was developed by a Java applets to receive the KMA AWS data from the server every 30 minutes through communicating with the server so that smart greenhouse could recognize the KMA AWS data as the ambient weather information. This system was evaluated by comparing with local weather data measured by Inc. Ezfarm. In case of ambient air temperature, it showed some difference between virtual data and measured data. But, the average absolute deviation of the difference has a little difference as less than 2.24℃. Therefore, the virtual weather data of the developed system was considered available as the ambient weather information of the smart greenhouse.