• International journal of advanced smart convergence
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• v.13 no.2
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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.

• Journal of Ocean Engineering and Technology
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• v.33 no.5
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• pp.427-434
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• 2019

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.20-21
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• 2014

Recently, activation of related construction projects due to the large traditional gas resource development of frozen soil region of Russia are expected. It is necessary to provide a plan that can be utilized and collectively managed the occurrence data in the engineering & construction stage for continued contracts of the pipe construction. Therefore, this research is aimed to provide a management plan of occurrence data for efficient management in engineering & construction stage of pipeline business in frozen soil region. The data of the engineering & construction(related pipe construction projects) can be accumulated each version and multiply managed. Furthermore, I will be expected to be the foundation of the systematic management of the classifying based on metadata and the optimizing operations using big data method.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.31-36
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• 2018

The tourism industry has a great influence on national economy activation. The development of IT technology has enabled the collection and analysis of personal profile information, location information and activity information based on the characteristics, behavior, purchase propensity and interest of tourists. In order to realize this, the implementation of convergence smart tourism information service platform is completed by developing business model, IoT & Big Data integration management system, big data algorithm development and analysis platform in three stages. The underlying technology of the platform and algorithm needs a process of adopting open source, expanding the service element on the basis of it, and then complementing the problem through the test-bed demonstration test that connects the area. Using this platform, it is possible to develop a smart tourism environment that can provide customized services for each tourist by analyzing various information in an integrated manner. Also, it will be possible to improve the life of tourist destination residents and contribute to regional revitalization and job creation through the creation of smart tourism ecosystem focused on the region.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.241-246
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• 2021

Although there have been many studies on laws and systems for the proliferation of autonomous vehicles, studies on the activation of autonomous vehicles from a strategic perspective are insufficient. This study examines the issues and problem solving methods of autonomous vehicles. Based on this, plans to activate autonomous vehicles from a strategic point of view are proposed. In order to solve the issues and problems of autonomous vehicles, it is necessary to clearly establish legal and institutional standards based on the reinforcement of the safety of autonomous vehicles. In the event of a traffic accident, who is responsible for the accident and responsibility for compensation should be prioritized. Diffusion strategies are established according to the level of autonomous driving for the activation of autonomous vehicles in strategic perspective. In addition, governmental support policies should be used as triggers for initial activation, and marketing mix strategies should be implemented based on segmentation, targeting, and positioning strategies.

• Journal of Information Technology Services
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• v.22 no.3
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• pp.29-47
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• 2023

With the recent advancement of information and communication technologies such as artificial intelligence, big data, cloud computing, and 5G, data is being produced and digitized in unprecedented amounts. As a result, data has emerged as a critical resource for the future economy, and overseas countries have been revising laws for data protection and utilization. In Korea, the 'Data 3 Act' was revised in 2020 to introduce institutional measures that classify personal information, pseudonymized information, and anonymous information for research, statistics, and preservation of public records. Among them, it is expected to increase the added value of data by combining pseudonymized personal information, and to this end, "the Expert Data Combination Agency" and "the Expert Data Agency" (hereinafter referred to as the Expert Data Processing Agency) system were introduced. In comparison to these domestic systems, we would like to analyze similar overseas systems, and it was recently confirmed that the Vermont government in the United States enacted the first "Data Broker Act" in the United States as a measure to protect personal information held by data brokers. In this study, we aim to compare and analyze the roles and functions of the "Expert Data Processing Agency" and "Data Broker," and to identify differences in designated standards, security measures, etc., in order to present ways to contribute to the activation of the data economy and enhance information protection.

• Journal of the Korean Institute of Rural Architecture
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• v.24 no.3
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• pp.47-57
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• 2022

This study attempted to grasp the status and perception of Uljin Geumgangsong by grasping mass media issues and user perception using big data, and to present basic data when constructing monitoring using user perception by examining the establishment relationship of agenda setting from a time-series perspective. The results of collecting and analyzing text data that can identify mass media and visitor awareness are as follows. First, both mass media and visitor keywords were related to the importance of the value and meaning of Uljin Geumgangsong. Second, in the case of the connection network, Geumgang Pine Agriculture was centered, but in the case of difference in perception between mass media and visitors, such results were derived due to the object of interest. Third, in the case of the connection relationship structure, the connection strength was strong because there were many overlapping contents of mass media. Fourth, as a result of the centrality analysis, both mass media and visitor-aware keywords were positively recognized as spaces created and maintained through institutional support, and objective perception could be grasped by finding hidden keywords. Fifth, as a result of time series analysis, it was possible to grasp the flow through the issue keywords that appeared by period, and unlike the past, it was recognized as a place for tourism and travel. Finally, as a result of examining whether the agenda setting is consistent, there is a mass media influence, so it is thought that more diverse and more information and publicity are needed by utilizing it.

• Journal of Fashion Business
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• v.27 no.3
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• pp.132-149
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• 2023

This study aims to analyze Instagram hashtags based on big data to investigate changes in consumer trends and perceptions of vegan fashion, and to derive strategies for revitalizing vegan fashion brands based on derived results. Among social media, Instagram was selected as a collection channel, and Instagram hashtags for 'Vegan Fashion' were collected from July 1, 2021 to December 31, 2021. After conducting semantic network analysis with the Ucinet 6 program based on the collected data, the CONCOR analysis on vegan fashion showed the following four clusters: 'Veganism practiced with fashion', 'Bag type of vegan fashion brand', 'Sharing vegan fashion', and 'Diversification of eco-friendly products'. Analysis results showed that the Instagram hashtag for vegan fashion confirmed the MZ generation's increased interest in vegan fashion and their thoughts to recommend and share frequently used items or brand products to people around them. CONCOR analysis of vegan fashion brands showed the following four groups: 'Differentiating the material of vegan bags', 'Eco-friendly products of vegan fashion brands', 'Interest in vegan shoes', and 'Donation campaign of vegan fashion brands'. CONCOR analysis on Meaningout showed the following four clusters: 'MZ Generation's Meaningout Start-up', 'Recommendation Platform for Skin Products', 'Value Consumption Trend for Eco-friendly Clothing', and 'Interest in Eco-friendly Packaging'. The results of this study on vegan fashion, a practical eco-friendly movement that can require changes in social responsibility and perception as issues that directly affect animals, the environment, and humans, are expected to provide basic data to help domestic vegan fashion brands develop marketing strategies.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.29-37
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.