• International journal of advanced smart convergence
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• v.12 no.4
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• pp.104-110
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• 2023

In the user-centered design paradigm, information systems are created entirely tailored to the users who will use them. When the functions of a complex system meet a simple user interface, users can use the system conveniently. While web personalization services are emerging as a major trend in portal services, portal companies are competing for a second service, such as introducing 'integrated communication platforms'. Until now, the role of the portal has been content and search, but this time, the goal is to create and provide the personalized services that users want through a single platform. Personalization service is a login-based cloud computing service. It has the characteristic of being able to enjoy the same experience at any time in any space with internet access. Personalized web services like this have the advantage of attracting highly loyal users, making them a new service trend that portal companies are paying attention to. Researchers spend a lot of time collecting research-related information by accessing multiple information sources. There is a need to automatically build interest information profiles for each researcher based on personal presentation materials (papers, research projects, patents). There is a need to provide an advanced customized information service that regularly provides the latest information matched with various information sources. Continuous modification and supplementation of each researcher's information profile of interest is the most important factor in increasing suitability when searching for information. As researchers' interest in unstructured information such as technology markets and research trends is gradually increasing from standardized academic information such as patents, it is necessary to expand information sources such as cutting-edge technology markets and research trends. Through this, it is possible to shorten the time required to search and obtain the latest information for research purposes. The interest information profile for each researcher that has already been established can be used in the future to determine the degree of relationship between researchers and to build a database. If this customized information service continues to be provided, it will be useful for research activities.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.93-100
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• 2024

With the advent of the Fourth Industrial Revolution, cutting-edge technologies such as artificial intelligence, big data, the Internet of Things, and cloud computing are driving innovation across industries. These technologies are generating massive amounts of data that many companies are leveraging. However, there is a notable reluctance among users to share sensitive information due to the privacy risks associated with collecting personal data. This is particularly evident in the healthcare sector, where the collection of sensitive information such as patients' medical conditions poses significant challenges, with privacy concerns hindering data collection and analysis. This research presents a novel technique for collecting and analyzing medical data that not only preserves privacy, but also effectively extracts statistical information. This method goes beyond basic data collection by incorporating a strategy to efficiently mine statistical data while maintaining privacy. Performance evaluations using real-world data have shown that the propose technique outperforms existing methods in extracting meaningful statistical insights.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.245-256
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• 2021

This study was conducted to identify the applicability of a LiDAR sensor to forest resources inventories by comparing data on a tree's position, height, and DBH obtained by the sensor with those by existing forest inventory methods, for the tree species of Criptomeria japonica in Jeolmul forest in Jeju, South Korea. To this end, a backpack personal LiDAR (Greenvalley International, Model D50) was employed. To facilitate the process of the data collection, patterns of collecting the data by the sensor were divided into seven ones, considering the density of sample plots and the work efficiency. Then, the accuracy of estimating the variables of each tree was assessed. The amount of time spent on acquiring and processing the data by each method was compared to evaluate the efficiency. The findings showed that the rate of detecting standing trees by the LiDAR was 100%. Also, the high statistical accuracy was observed in both Pattern 5 (DBH: RMSE 1.07 cm, Bias -0.79 cm, Height: RMSE 0.95 m, Bias -3.2 m), and Pattern 7 (DBH: RMSE 1.18 cm, Bias -0.82 cm, Height: RMSE 1.13 m, Bias -2.62 m), compared to the results drawn in the typical inventory manner. Concerning the time issue, 115 to 135 minutes per 1ha were taken to process the data by utilizing the LiDAR, while 375 to 1,115 spent in the existing way, proving the higher efficiency of the device. It can thus be concluded that using a backpack personal LiDAR helps increase efficiency in conducting a forest resources inventory in an planted coniferous forest with understory vegetation, implying a need for further research in a variety of forests.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.9
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• pp.1029-1036
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• 2014

Several types of automatic 3D scanners are available for use in the 3D scanning industry, e.g., an automatic 3D scanner that uses a robot arm and one that uses an automatic rotary table. Specifically, these scanners are used to obtain a 3D shape using automatic assisting devices. Most of these scanners are required to perform numerous operations, such as merging, aligning, trimming, and filling holes. We are interested in developing an automatic 3D shape collection device using a spherical-coordinate-based guiding system. Then, the aim of the present study is to design an automatic guiding system that can automatically collect 3D shape data. We develop a 3D model of this system and measuring data which are collected by a personal computer. An optimal design of this system and the geometrical accuracy of the measured data are both evaluated using 3D modeling software. The developed system is then applied to an object having a highly complex shape and manifold sections. Our simulation results demonstrate that the developed system collects higher-quality 3D data than the conventional method.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.239-247
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• 2014

In modern society, the transportation sector is having difficulties with mobility, safety, energy and environment issues so that solutions should be prepared progressively. Recently developed countries are paying greater attention to car-sharing systems as alternatives for relieving problems caused by increasing number of vehicles. Car-sharing systems are believed to keep the number of personal vehicles down and traffic congestion due to the fact that this system induces efficiency of vehicle operation. In this study, authors focused on solving the imbalance problem of car-sharing systems dynamically, to specifically improve one-way car sharing service. Therefore vehicle relocation algorithm was designed for maximizing profit of car sharing operators. For the application of the vehicle relocation algorithm among rental offices, we selected Haeundae-gu, Busan as the area of study. Rental offices have been designated to the zone centroid and we calculated the shortest path between rental offices by using TransCAD. In the study, we adapted MATLAB for the application of vehicle relocation algorithm. As the results, the total vehicle relocation time was estimated as 59.9 minutes. In addition, vehicle relocation algorithm was verified successfully by using Decision Tree method.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.603-613
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• 2017

Many people have recently posted about personal interests on social media. The development of the Internet and computer technology has enabled the storage of digital forms of documents that has resulted in an explosion of the amount of textual data generated; subsequently there is an increased demand for technology to create valuable information from a large number of documents. A text mining technique is often used since text-based data is mostly composed of unstructured forms that are not suitable for the application of statistical analysis or data mining techniques. This study analyzed the Meteorological Yearbook data of the Korea Meteorological Administration (KMA) with a text mining technique. First, a term dictionary was constructed through preprocessing and a term-document matrix was generated. This term dictionary was then used to calculate the annual frequency of term, and observe the change in relative frequency for frequently appearing words. We also used regression analysis to identify terms with increasing and decreasing trends. We analyzed the trends in the Meteorological Yearbook of the KMA and analyzed trends of weather related news, weather status, and status of work trends that the KMA focused on. This study is to provide useful information that can help analyze and improve the meteorological services and reflect meteorological policy.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.1-12
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• 2021

With the recent 4th Industrial Revolution, the growth and value of big data are continuously increasing, and the government is also actively making efforts to open and utilize public data. However, the situation still does not reach the level of demand for public data use by citizens, At this point, it is necessary to identify research trends in the public data field and seek directions for development. In this study, in order to understand the research trends related to public data, the analysis was performed using topic modeling, which is mainly used in text mining techniques. To this end, we collected papers containing keywords of 'Public data' among domestic and foreign research papers (1,437 domestically, 9,607 overseas) and performed topic modeling based on the LDA algorithm, and compared domestic and foreign public data research trends. After analysis, policy implications were presented. Looking at the time series by topic, research in the fields of 'personal information protection', 'public data management', and 'urban environment' has increased in Korea. Overseas, it was confirmed that research in the fields of 'urban policy', 'cell biology', 'deep learning', and 'cloud·security' is active.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.3
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• pp.374-383
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• 2010

Microwave radar can penetrate cloud cover regardless of weather conditions and can be used day and night. Especially a ground-based polarimetric scatterometer has advantages of monitoring crop conditions continuously with full polarization and different frequencies. Kim et al. (2009) have measured backscattering coefficients of paddy rice using L-, C-, X-band scatterometer system with full polarization and various angles during the rice growth period and have revealed the necessity of near-continuous automatic measurement to eliminate the difficulties, inaccuracy and sparseness of data acquisitions arising from manual operation of the system. In this study, we constructed an X-band automatic scatterometer system, analyzed scattering characteristics of paddy rice from X-band scatterometer data and estimated rice growth parameter using backscattering coefficients in X-band. The system was installed inside a shelter in an experimental paddy field at the National Academy of Agricultural Science (NAAS) before rice transplanting. The scatterometer system consists of X-band antennas, HP8720D vector network analyzer, RF cables and personal computer that controls frequency, polarization and data storage. This system using automatically measures fully-polarimetric backscattering coefficients of rice crop every 10 minutes. The backscattering coefficients were calculated from the measured data at a fixed incidence angle of $45^{\circ}$ and with full polarization (HH, VV, HV, VH) by applying the radar equation and compared with rice growth data such as plant height, stem number, fresh dry weight and Leaf Area Index (LAI) that were collected at the same time of each rice growth parameter. We examined the temporal behaviour of the backscattering coefficients of the rice crop at X-band during rice growth period. The HH-, VV-polarization backscattering coefficients steadily increased toward panicle initiation stage, thereafter decreased and again increased in early-September. We analyzed the relationships between backscattering coefficients in X-band and plant parameters and predicted the rice growth parameters using backscattering coefficients. It was confirmed that X-band is sensitive to grain maturity at near harvesting season.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.