• International journal of advanced smart convergence
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• 제12권1호
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• pp.41-52
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• 2023

The purpose of this study is to review the proposed solutions to the Blockchain trilemma put forward by various research scholars and to draw conclusions by comparing the findings of each study. We found that the models so far developed either compromise scalability, decentralization, or security. The first model compromises decentralization. By partially centralizing the network, transaction processing speed can be improved, but security strength is weakened. Examples of this include Algorand and EOS. Because Algorand randomly selects the node that decides the consensus, the security of Algorand is better than EOS, wherein a designated selector decides. The second model recognizes that scalability causes a delay in speed when transactions are included in a block, reducing the system's efficiency. Compromising scalability makes it possible to increase decentralization. Representative examples include Bitcoin and Ethereum. Bitcoin is more vital than Ethereum in terms of security, but in terms of scalability, Ethereum is superior to Bitcoin. In the third model, information is stored and managed through various procedures at the expense of security. The application case is to weaken security by applying a layer 1 or 2 solution that stores and reroutes information. The expected effect of this study is to provide a new perspective on the trilemma debate and to stimulate interest in continued research into the problem.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.25-33
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• 2023

The purpose of this study is to evaluate the impact of intermediate features on FER performance. To achieve this objective, intermediate features were extracted from the input images at specific layers (FM1~FM4) of the pre-trained network (Resnet-18). These extracted intermediate features and original images were used as inputs to the vision transformer (ViT), and the FER performance was compared. As a result, when using a single image as input, using intermediate features extracted from FM2 yielded the best performance (training accuracy: 94.35%, testing accuracy: 75.51%). When using the original image as input, the training accuracy was 91.32% and the testing accuracy was 74.68%. However, when combining the original image with intermediate features as input, the best FER performance was achieved by combining the original image with FM2, FM3, and FM4 (training accuracy: 97.88%, testing accuracy: 79.21%). These results imply that incorporating intermediate features alongside the original image can lead to superior performance. The findings can be referenced and utilized when designing the preprocessing stages of a deep learning model in FER. By considering the effectiveness of using intermediate features, practitioners can make informed decisions to enhance the performance of FER systems.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.407-417
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• 2023

The purpose of this study is to identify the level of safety perception, ethical awareness, and safety activities of nursing students for patients, and to identify the correlation and impact between them. The research design is a descriptive survey study, and the subject of the study were 197 nursing college students in G City. Safety perception, ethical awareness, and safety activity tools were used for, and the data collection period was from October 17 to 28 in 2022. T-test, one-way ANOVA, Pearson's correlation coefficient, Regression analysis were used to analyze data. The result of the study indicated that the average level of safety perception of nursing students was 3.72 points, the average ethical awareness of patients, professional work, and cooperators perceived by nursing students was 3.04 points, and the safety activities of nursing students were 4.20 points. In the case of safety awareness and ethics awareness, r=.327, a significant positive correlation, in the case of safety awareness and safety activities, r=.399, significant positive correlation, ethics awareness and safety activities as r=.296. And so on these results showed that high safety perception increases safety activities, and high ethical awareness increases safety activities. Therefore, we need practical and step-by-step convergence education to equip nursing students with patient safety nursing capabilities. To this end, a safer environment will be created if the social support network for the systematic application of safety education is well formed.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.119-129
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• 2024

We look at Web 3.0 business model canvas (BMC) of metaverse gaming platform, The Sandbox (TS). As results, the decentralized, blockchain-based platform, TS benefits its creators and players by providing true ownership, tradability of decentralized assets, and interoperability. First, in terms of the governance and ownership, The SAND functions a governance token allowing holders to participate in decision and SAND owners can vote themselves or delegate voting rights to other players of their choice. Second, in terms of decentralized assets and activities, TS offers three products as assets like Vox Edit as a 3D tool for voxel ASSETS, Marketplace as NFT market, and Game Maker as a visual scripting toolbox. The ASSETS made in Vox Edit, sold on the Marketplace, can be also utilized with Game Maker. Third, in terms of the network technology, in-game items are no longer be confined to a narrow ecosystem. The ASSETS on the InterPlanetary File System (IPFS) are not changed without the owner's permission. LAND and SAND are supported on Polygon, so that users interact with their tokens in a single place. Last, in terms of the token economics, users can acquire in-game assets, upload these assets to the marketplace, use for paying transaction fees, and use these as governance token for supporting the foundation.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.166-171
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• 2024

Recently, there has been an increasing trend in the role of social media in tourism marketing. We analyze changes in tourism marketing trends using tourism marketing keywords through social media networks. The aim is to understand marketing trends based on the analyzed data and effectively create, maintain, and manage customers, as well as efficiently supply tourism products. Data was collected using web data from platforms such as Naver, Google, and Daum through TexTom. The data collection period was set for one year, from December 1, 2022, to December 1, 2023. The collected data, after undergoing refinement, was analyzed as keyword networks based on frequency analysis results. Network visualization and CONCOR analysis were conducted using the Ucinet program. The top words in frequency were 'tourists,' 'promotion,' 'travel,' and 'research.' Clusters were categorized into four: tourism field, tourism products, marketing, and motivation for visits. Through this, it was confirmed that tourism marketing is being conducted in various tourism sectors such as MICE, medical tourism, and conventions. Utilizing digital marketing via online platforms, tourism products are promoted to tourists, and unique tourism products are developed to increase city branding and tourism demand through integrated tourism content. We identify trends in tourism marketing, providing tourists with a positive image and contributing to the activation of local tourism.

• International journal of advanced smart convergence
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• 제13권1호
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• pp.37-47
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• 2024

This study presents the results of designing a system that determines the location of a person in an indoor environment based on a single IMU sensor attached to the tip of a person's shoe in an area where GPS signals are inaccessible. By adjusting for human footfall, it is possible to accurately determine human location and trajectory by correcting errors originating from the Inertial Measurement Unit (IMU) combined with advanced machine learning algorithms. Although there are various techniques to identify stepping, our study successfully recognized stepping with 98.7% accuracy using an artificial intelligence model known as Long Short-Term Memory (LSTM). Drawing upon the enhancements in our methodology, this article demonstrates a novel technique for generating a 200-meter trajectory, achieving a level of precision marked by a 2.1% error margin. Indoor pedestrian navigation systems, relying on inertial measurement units attached to the feet, have shown encouraging outcomes.

• International journal of advanced smart convergence
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• 제13권1호
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• pp.212-220
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• 2024

When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.

• 아태비즈니스연구
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• 제14권4호
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• pp.115-128
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• 2023

Purpose - Due to the spread of COVID-19, non-face-to-face transactions are widely growing. In particular, the development of streaming and network technology has rapidly grown the one-person broadcasting market, an influencer market using YouTube and Instagram. However, prior research on the influencer in Mogolian market is very scarce. Therefore, this study aims to identify the factors influencing consumer purchasing behavior in Mongolian market. Design/methodology/approach - We tested a research model of our study through the analysis using survey data of experienced users in Mongolian influencer market. In this study, reliability test and factor analysis, multiple regression were conducted using SPSS. Findings - We found that the characteristics of an influencer and contents in Mogolian market are positively related with brand reliability and contents authenticity, respectively. In addition, the brand reliability and contents authenticity are positively associated with the customer's purchase intention. Research implications or Originality - Since it is the first study of the influencer market in Mongolia, it is expected that it will serve as a guide study for the follow-up studies in the future and serve as a reference for the strategic direction of related companies.

• 방송공학회논문지
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• 제11권3호
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• pp.336-348
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• 2006

최근 멀티미디어 부호화 기술 및 전송기술의 급격한 발전과 DMB(Digital Multimedia Broadcasting)와 같은 이동형 TV의 등장으로 인해 작은 LCD 패널을 통해 시청하는 사용자가 증가하고 있다. 본 논문에서는 위와 같은 이동형 단말에서의 축구 동영상 시청 시 쾌적한 시청 경험을 제공하기 위한 지능형 디스플레이 기술을 소개하고 이러한 기술의 필요성을 조사, 분석하였다. 111명의 실험자를 대상으로 실시된 이 연구에서는 쾌적한 축구 동영상 시청을 보장하기 위한 최소화면 크기를 조사하였으며, 또한 화면 내에서 관심 영역만을 추출하여 디스플레이 하는 경우의 시청자 만족도의 변화를 알아보았다. 실험 결과는 영상의 해상도가 $320{\times}240$ 이하로 감소되는 경우, 관심 영역만을 확대하여 시청하는 것이 시청자의 시청 만족도를 향상시켜 주는 것으로 나타났으며, 따라서 필요시 관심 영역만을 디스플레이 해줄 수 있는 지능형 디스플레이 기술이 필요함을 확인하였다.

• 인터넷정보학회논문지
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• 제12권4호
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• pp.35-43
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• 2011

최근 들어 무선인지 애드 혹 네트워크(CRAHN)는 임시적으로 활용되지 않은 라이선스 스펙트럼을 기회주의적 방법으로 사용하여 통신하는 차세대 애드 혹 네트워크로 각광받고 있다. 여타 무선인지 시스템에서와 같이, 정확한 스펙트럼 센싱은 무선인지 애드 혹 네트워크 구현을 위한 필수 요소 기술이다. 중앙 제어국 중심의 협력 스펙트럼 센싱 기법은 센싱 성능을 향상 시킬 수 있으나, 무선인지 애드 혹 네트워크와 같은 비인프라망에서는 중앙 제어국이 없어 협력 센싱 구현이 어렵다. 본 논문에서는 무선인지 애드 혹 네트워크를 위한 새로운 순차적 협력 스펙트럼 센싱 기법을 제안한다. 제안된 기법에서는 Dempster Shafer 이론 기반으로 국부 스펙트럼 센싱 데이터의 신뢰도가 가장 높은 인지 노드가 임시 중앙국이 되어 주변 센서 노드로부터 스펙트럼 센싱값을 수집하여 스펙트럼 사용 유무에 대한 최종 결정을 만들고 그 최종 결정을 이웃 노드들에게 전송한다. 또한 본 논문에서는 분산 방법으로 개별 인지 노드들이 자신의 센싱 데이터를 효율적으로 교환하기 위한, 센싱데이터 신뢰도 순서 기반의 전송 메커니즘을 제안하였다. 시뮬레이션을 통해 제안된 방식은 기존 중앙 제어국 중심의 협력 스펙트럼 센싱 기법과 유사한 스펙트럼 센싱 성능을 제공하는 동시에, 최종 스펙트럼 결정까지 소요되는 센싱 데이터 수집시간 및 교환되는 센싱 데이터 량을 현저히 줄일 수 있음을 보였다.