• Convergence Security Journal
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• v.19 no.1
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• pp.121-126
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• 2019

The big data utilization category in the sports field was mainly focused on the big data analysis to improve the competence of the athlete and the performance. Since then, 'big data technology' which collect and analyze more detailed and diverse data through the application of ICT technology such as IoT and AI has been applied. The use of big data of sports contents in future has value and possibility in the smart environment, but it is necessary to overcome the shortage and limitation of platform to manage and share sports contents. In order to solve such problems, it is important to change the perception of the companies or providers that provide sports contents and cultivate and secure professional personnel capable of providing sports contents. Also, it is necessary to implement policies to systematically manage and utilize big data poured from sports contents.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.975-980
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• 2021

As technology utilizing low-power short-range wireless communication is applied in conjunction with medical information as a development of the fourth industrial revolution, interest in medical information is increasing. As the age group of smart devices increases and becomes more common, related research such as linking mobile devices with various objects and equipment is continuously being conducted. In addition, interest in untact (non-face-to-face) is increasing due to the prevalence of Coronavirus Disease-19 (COVID-19), including various infectious diseases. As a result, it is a state that requires distance not only in social but also in life. In this study, using a low-power near-field wireless communication technology of beacons to create an IoT device by communicating with the web server of the medical information system was studied to make it easier to receive medical treatment during visits to medical institutions.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.69-79
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• 2020

TRecently, smart cities that solve various problems in cities based on IoT technology are in the spotlight. In particular, cases of BIM application for smooth management of construction and maintenance are increasing, and spatial information is converted into 3D data through convergence technology and used for safety diagnosis. The purpose of this study is to create and combine point clouds of a multi-story building by using a ground laser scanner and a handheld LiDAR SLAM among UAV and LiDAR equipment, supplementing the Occluded area and disadvantages of each technology, examine the effectiveness of indoor and outdoor reverse design by observing shape reproduction and accuracy. As a result of the review, it was confirmed that the coordinate accuracy of the data was improved by creating and combining the indoor and outdoor point clouds of the multi-story building using three technologies. In particular, by supplementing the shortcomings of each technology, the completeness of the shape reproduction of the building was improved, the Occluded area and boundary were clearly distinguished, and the effectiveness of reverse engineering was verified.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1483-1486
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• 2013

최근 무선통신의 급격한 발전과 스마트 기기의 확산으로 인해 Tving, pooq 등 다양한 모바일 방송 서비스가 급속도로 증가하고 있다. 또한 다양한 영상처리 기법 등이 등장함에 따라 4K, 8K급의 UHD 동영상들이 속속들이 등장하고 있다. 이로 인해 트랜스코딩을 통해 가공되는 동영상의 포맷 및 해상도 또한 매우 다양해질 것으로 전망된다. 현재까지의 트랜스코딩 연구사례는 사용자의 이동환경을 고려한 안정적 QoS 보장 또는 서버의 부하를 줄이기 위한 분산처리 기법 등의 연구 위주로 진행되어 온 것이 현실이다. 하지만 상기 조건(adaptive streaming 및 서버부하 처리)들을 충족시키긴 위해선 보다 효율적인 트랜스코딩 시스템의 제공이 선행 되어야 할 것이다. 이에 따라 본 논문에서는 사용자 관점에서 보다 빨리 스트리밍 서비스를 제공 받기 위하여 우선순위 큐 알고리즘을 적용한 시스템을 설계 및 구현하였다. 검증을 위하여 4가지 콘테이너(.MOV, .FLV, .MKV, .AVI)를 실험대상으로 하였고, 비교 대상 트랜스코딩 시스템은 상용 스트리밍 서비스인 YouTube를 활용하였다. 성능 측정결과, 총 트랜스코딩 완료시간은 YouTube에 비해 41.61%로 시간이 단축되었다. 또한 모바일 TV시청자가 55%를 차지한다는 점을 고려하여 컨트롤 서버에서는 최단시간 서비스 제공을 위하여 저해상도부터 추출하여 스트리밍 서버를 통해 송출하도록 구현하였다. 본 연구결과는 트랜스코딩 성능개선 뿐만 아니라 모바일 대상자를 위한 맞춤형 서비스를 보다 빨리 제공할 수 있을 것이며, 그 수요는 점차 증대될 것으로 예상된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.537-542
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• 2021

Smart TVs provide a variety of services and information compared to existing TVs based on the Internet. In order to provide more personalized services or information, it is necessary to analyze users' viewing patterns and provide customized services or information based on them. The proposed system receives the user's TV viewing pattern, analyzes it, and recommends a TV program or movie as customized information to the user. For this, the system was constructed with a preprocessor and a deep learning model. The preprocessor refines the name of the TV program watched by the user, the date the TV program was watched, and the watched time. Then, the multi-attribute LSTM model trains the refined data and performs prediction.The proposed system is a system that provides customized information to users, and is believed to be a leading technology in digital convergence that combines existing IoT technology and deep learning technology.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.43-57
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• 2021

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

• Journal of Internet of Things and Convergence
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• v.7 no.1
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• pp.55-62
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• 2021

The world is changing rapidly as the Internet spreads and various smart devices appear. High-performance PCs and high-speed communication networks are rapidly spreading in every home, and all kinds of the internet sites are emerging. In particular, the high education enthusiasm of Korean parents adds to this, and the ratio of the internet users among teenagers is exploding every day. In the case of adolescents, most of them use the Internet for online games, indicating that online games are the main cause of the internet addiction. This study was conducted using a questionnaire for male and female high school students using the Internet, and demographic and sociological characteristics were used only as basic data. In this study, as much as parents, students and teachers think, the results of the internet addiction type analysis according to academic achievement in humanities high school students are to be investigated to determine whether internet use has an effect on academic achievement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1046-1052
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• 2021

Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.126-132
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• 2022

At present, due to the rapid spread of smartphones and activation of IoT, malicious codes are disseminated using SNS, or intelligent intrusions such as intelligent APT and ransomware are in progress. The damage caused by the intelligent intrusion is also becoming more consequential, threatening, and emergent than the previous intrusion. Therefore, in this paper, we propose an intelligent intrusion situation-aware reasoning system to detect transgression behavior made by such intelligent malicious code. The proposed system was used to detect and respond to various intelligent intrusions at an early stage. The anticipated system is composed of an event monitor, event manager, situation manager, response manager, and database, and through close interaction between each component, it identifies the previously recognized intrusive behavior and learns about the new invasive activities. It was detected through the function to improve the performance of the inference device. In addition, it was found that the proposed system detects and responds to intelligent intrusions through the state of detecting ransomware, which is an intelligent intrusion type.