• International journal of advanced smart convergence
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• v.10 no.4
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• pp.134-140
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• 2021

Big data has recently been used in various industries such as tourism, medical care, distribution, and marketing. And it is evolving to the stage of collecting real-time information or analyzing correlations and predicting the future. In the tourism industry, big data can be used to identify the size and shape of the tourism market, and by building and utilizing a large-capacity database, it is possible to establish an efficient marketing strategy and provide customized tourism services for tourists. This paper has begun with anticipation of the effects that would occur when big data is actively used in the tourism field. Because the method of use must have applicability and practicality, the spatial scope will be limited to Seosan, Chungcheongnam-do, and research will be conducted. In this paper, to improve the quality of tourism courses by collecting and analyzing the number of mention data and sentiment index data on social media, which reflect the tourist's interest, preference and satisfaction. Therefore, it is used as basic data necessary for the development of new local tourism courses in the future. In addition, the development of tourism courses will be able to promote tourism growth and also revitalizing the local economy.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.102-108
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• 2019

Big data was associated with three key concepts, volume, variety, and velocity. Securities and investment services produce and store a large data of text/numbers. They have also the most data per company on the average in the US. Gartner found that the demand for big data in finance was 25%, which was the highest. Therefore securities and investment companies produce the largest data such as text/numbers, and have the highest demand. And insurance companies and credit card companies are using big data more actively than banking companies in Korea. Researches on the use of big data in securities and investment companies have been found to be insignificant. We surveyed 22 major securities and investment companies in Korea for activating big data. We can see they actively use AI for investment recommend. As for big data of securities and investment companies, we studied open API. Of the major 22 securities and investment companies, only six securities and investment companies are offering open APIs. The user OS is 100% Windows, and the language used is mainly VB, C#, MFC, and Excel provided by Windows. There is a difficulty in real-time analysis and decision making since developers cannot receive data directly using Hadoop, the big data platform. Development manuals are mainly provided on the Web, and only three companies provide as files. The development documentation for the file format is more convenient than web type. In order to activate big data in the securities and investment fields, we found that they should support Linux, and Java, Python, easy-to-view development manuals, videos such as YouTube.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.173-178
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• 2020

As the fourth industrial revolution, in which people, objects, and information are connected as one, various fields such as smart energy, smart cities, artificial intelligence, the Internet of Things, unmanned cars, and robot industries are becoming the mainstream, drawing attention to big data. Among them, Smart Grid is a technology that maximizes energy efficiency by converging information and communication technologies into the power grid to establish a smart grid that can know electricity usage, supply volume, and power line conditions. Smart meters are equient that monitors and communicates power usage. We start with the goal of building a virtual smart grid and constructing a virtual environment in which real-time data is generated to accommodate large volumes of data that are small in capacity but regularly generated. A major role is given in creating a software/hardware architecture deployment environment suitable for the system for test operations. It is necessary to identify the advantages and disadvantages of the software according to the characteristics of the collected data and select sub-projects suitable for the purpose. The collected data was collected/loaded/processed/analyzed by the Hadoop ecosystem-based big data platform, and used to predict power demand through deep learning.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.55-63
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• 2022

Although research on the leader's or speaker's voice has been continuously conducted, existing research has a single point of view. Sound analysis of voice characteristics has been studied from engineering perspectives, and leadership trait theory has been studied from a business perspective. Convergence studies on leader voice and member cognition are being attempted today. Convergence research on voice has a positive effect on refinement of voice analysis, diversification of voice use, and establishment of voice utilization strategy. This study explains the current flow of research on convergence between speaker's voice and listener's perception, and suggests a direction for the future development of voice fusion research. Furthermore, in connection with AI in the 4th industrial age, new attempts for voice research are sought. First, advances in AI focus on strategically generating the voices needed for individual situations. Second, the voice corrected in real time will support the leader and speaker to utilize the desired voice type. Third, voices through AI based on big data will affect the cognition, attitude and behavior of individual listeners who members, customers, and students in more diverse situations. The purpose and significance of this study is to suggest the way to research the leader's voice recognized by members, and to suggest a method that can be applied in various situations.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.153-158
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• 2023

Most agriculture plantings are based on traditional farming and demand a lot of human work processes. In order to improve the efficiency as well as the productivity of their farms, modern agricultural technology was proven to be better than traditional practices. Internet of Things (IoT) is usually related in modern agriculture which provides the farmer with a real-time monitoring condition of their farm from anywhere and anytime. Therefore, the application of IoT with a sensor to measure and monitors the humidity and the temperature in the mushroom farm that can overcome this problem. This paper proposes an IoT based monitoring system forindoor Ganoderma lucidum cultivation at a minimal cost in terms of hardware resources and practicality. The results show that the data of temperature and humidity are changing depending on the weather and the preliminary experimental results demonstrated that all parameters of the system were optimized and successful to achieve the objective. In addition, the analysis results show that the quality of Ganoderma lucidum produced on the research method conforms to regulations in Vietnam.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.120-128
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• 2022

While online social interactions have been more prevalent with the increased popularity of Metaverse platforms, little has been studied the effects of facial expressions in virtual reality (VR), which is known to play a key role in social contexts. To understand the importance of presenting facial expressions of a virtual avatar under different contexts, we conducted a user study with 24 participants where they were asked to have a conversation and play a charades game with an avatar with and without facial expressions. The results show that participants tend to gaze at the face region for the majority of the time when having a conversation or trying to guess emotion-related keywords when playing charades regardless of the presence of facial expressions. Yet, we confirmed that participants prefer to see facial expressions in virtual reality as well as in real-world scenarios as it helps them to better understand the contexts and to have more immersive and focused experiences.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.182-189
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• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.378-385
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• 2023

The importance of wearing respiratory protective equipment has been highlighted even more during the COVID-19 pandemic. Even if the suitability of respiratory protection has been confirmed through testing in a laboratory environment, there remains the potential for leakage points in the respirators due to improper application by the wearer, damage to the equipment, or sudden movements in real working conditions. In this paper, we propose a method to detect the occurrence of leak holes by measuring the pressure changes inside the mask according to the wearer's breathing activity by attaching an IoT sensor to a full-face respirator. We designed 9 experimental scenarios by adjusting the degree of leak holes of the respirator and the breathing cycle time, and acquired respiratory data for the wearer of the respirator accordingly. Additionally, we analyzed the respiratory data to identify the duration and pressure change range for each breath, utilizing this data to train a neural network model for detecting leak holes in the respirator. The experimental results applying the developed neural network model showed a sensitivity of 100%, specificity of 94.29%, and accuracy of 97.53%. We conclude that the effective detection of leak holes can be achieved by incorporating affordable, small-sized IoT sensors into respiratory protective equipment.

• International journal of advanced smart convergence
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• v.13 no.1
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.432-443
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• 2017

The primary goal of the MPEG-H 3D Audio standard is to provide immersive audio environments for high-resolution broadcasting services such as UHDTV. This standard incorporates a wide range of technologies such as encoding/decoding technology for multi-channel/object/scene-based signal, rendering technology for providing 3D audio in various playback environments, and post-processing technology. The reference software decoder of this standard is a structure combining several modules and can operate in various modes. Each module is composed of independent executable files and executed sequentially, real time decoding is impossible. In this paper, we make DLL library of the core decoder, format converter, object renderer, and binaural renderer of the standard and integrate them to enable frame-based decoding. In addition, by measuring the computation complexity of each mode of the MPEG-H 3D-Audio decoder, this paper also provides a reference for selecting the appropriate decoding mode for various hardware platforms. As a result of the computational complexity measurement, the low complexity profiles included in Korean broadcasting standard has a computation complexity of 2.8 times to 12.4 times that of the QMF synthesis operation in case of rendering as a channel signals, and it has a computation complexity of 4.1 times to 15.3 times of the QMF synthesis operation in case of rendering as a binaural signals.