• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1882-1883
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• 2007

본 논문은 근거리무선통신을 포함한 위치서비스를 위한 무선통신응용분야에 적용될 수 있는 고신뢰 고정밀 무선위치인식기술 방법을 제안한다. 제안된 위치인식방법은 양방향통신을 통해 추정된 무선태그의 신호세기 및 시간차를 측정된 거리정보를 바탕으로 상대적 위치에 대한 확률적 계산을 수행하게 되며, 측정데이터의 부정확성으로 인한 위치측정의 오차를 줄일 수 있다. 또한 제안된 고신뢰 무선측위 코아 스택을 근거리무선네트워크 형성이 가능한 2.4 GHz ISM 협대역 밴드의 ZigBee 무선비콘 및 무선태그 디바이스들에 탑재하고 실증시험을 위하여 스마트 홈 실내 환경 아래에서 $1m\;{\times}\;1m$ 의 위치인지를 가능케 하는 위치인식시스템을 구현한다.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.251-253
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• 2008

RFID 태그에 통신 기능이 부가되고 점차 주위 환경을 감지하는 센싱 기능이 부가되면, 능동적으로 정보를 처리하는 지능형 초소형 스마트 센서 네트워크로 발전되어 현재의 고정된 개체 인식 코드 획득 수준에서 다기능 태그에 의한 상황인지 처리 수준으로 진화하여, 개체간 통신 기능을 갖춘 지능형 USN으로 발전할 것으로 전망 된다. ZigBee는 기기간 센서 네트워크를 구성, 단순 제어와 관리를 수행할 수 있는 WPAN의 최적의 기술로 평가 받고 있으며 저 전력, 저가 등의 장점 등으로 시장 성장성이 높은 기술이다. 유/무선 인프라를 확보하고 있는 우리는 ZigBee를 기존, 또는 현재 구축중인 유무선 네트워크와 연결해 다양한 유비쿼터스 서비스를 개발할 수 있는 최적의 테스트베드 환경을 갖추고 있으므로 u-Health의 기반에 꼭 필요한 기술이다.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.790-793
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• 2004

본 논문은 유비쿼터스 환경에서 RFID를 이용하여 접근할 수 있는 기본적인 비즈니스 모델인 입·출입 시스템에 대하여 연구하고 시뮬레이션 하였다. 본 논문의 입·출입 시스템은 학교 및 다양한 회사에서 적용될 수 있는 시스템으로서, 기존의 입·출입 시스템의 한계를 극복하고 보다 효율적이고 편리한 구조를 제안함으로서 다양한 모델에 적용할 수 있도록 하였다. 또한 위치인식 및 상황 인식을 기반으로 유비쿼터스 환경에서의 입·출입 시스템의 초석이 될 것으로 예상된다. 기본적으로 RFID 태그는 입·출입을 위해 학생이나 각 회사의 사원에게 부여되며, RFID 태그와 관련된 모든 데이터 관리는 IDS(Information Database Seuer)에서 관리하도록 한다. 제안된 시스템은 모든 입·출입 상황을 웹으로 확인할 수 있으며, 입·출입에 관련된 정보만을 제공하는 것이 아니라 상황에 따라 필요한 메시지를 사용자에게 포워딩 함으로서 융통성과 업무 효율을 증가시킬 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.841-844
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• 2015

이미지 인식과 내용분석은 이미지 검색과 멀티미디어 데이터 활용 분야에서 핵심기술이라 할 수 있다. 특히 최근 스마트폰, 디지털 카메라, 블랙박스 등에서 수집되는 영상 데이터 양이 급격히 증가하고 있다. 이에 따라 이미지를 인식하고 내용을 분석하여 활용할 수 있는 기술에 대한 요구가 점차 증대되고 있다. 본 논문에서는 이미지 내용정보를 이용하여 자몽으로 이미지로부터 태그정보를 추출하는 방법을 제안한다. 이 방법은 기계학습 기법인 CNN(Convolutional Neural Network)에 ImageNet의 이미지 데이터와 라벨을 학습시킨 후, 새로운 이미지로부터 라벨정보를 추출하는 것이다. 추출된 라벨을 태그로 간주하고 검색에 활용한다면 기존 검색시스템의 정확도를 향상시킬 수 있다는 것을 실험을 통하여 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.111-112
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• 2023

본 논문에서는 안드로이드 플랫폼 기반의 스마트폰에 내장된 다양한 센서를 이용하여 운동량을 측정하고 확인할 수 있는 애플리케이션을 설계하고 구현한다. 이 애플리케이션에서는 가속도 센서, 근접 센서, 기압 센서를 사용하여 줄넘기, 팔굽혀펴기, 계단 오르기 등의 운동량을 측정하고 확인할 수 있도록 구현한다. 그리고 NFC 태그를 이용하여 매일 운동을 꾸준히 할 수 있도록 도와주는 모니터링 기능을 구현한다.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.2
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• pp.103-123
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• 2018

The purpose of this study is to evaluate the user's perception and usability of the unmanned smart library and to derive the improvements. After reviewing the literature on smart technology and smart equipment, we surveyed users who use unmanned smart library installed in Sejong City and Pohang City, and questioned the use, operation and service satisfaction of unmanned smart library. Based on the results of the analysis, we suggested ways to improve the unmanned smart library and draw users to the library. Unattended smart library users varied in age and educational background, and they were most interested in borrowing books and were satisfied with their overall use.

• Journal of KIISE
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• v.43 no.1
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• pp.47-53
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• 2016

While the Internet develops rapidly, a huge amount of image data collected from smart phones, digital cameras and black boxes are being shared through social media sites. Generally, social images are handled by tagging them with information. Due to the ease of sharing multimedia and the explosive increase in the amount of tag information, it may be considered too much hassle by some users to put the tags on images. Image retrieval is likely to be less accurate when tags are absent or mislabeled. In this paper, we suggest a method of extracting tags from social images by using image content. In this method, CNN(Convolutional Neural Network) is trained using ImageNet images with labels in the training set, and it extracts labels from instagram images. We use the extracted labels for automatic image tagging. The experimental results show that the accuracy is higher than that of instagram retrievals.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.214-220
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• 2021

With the rapid spread of smartphones, it has become common to listen to music everywhere, just like background music in life, so it is necessary to create a music database that can make recommendations according to individual circumstances and conditions. This paper proposes a music recommendation model through social media. Since emotions, situations, time of day, weather, etc. are included in hashtags, it is possible to build a social media-based database that reflects the opinions of various people with collective intelligence. We use web crawling to collect and categorize different hashtags from posts with music title hashtags to use real listeners' opinions about music in a database. Data from social media is used to create a music database, and music is classified in a different way from collaborative filtering, which is mainly used by existing music platforms.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.293-304
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• 2011

The world of ubiquitous computing is one in which we will be surrounded by an ever-richer set of networked devices and services. Especially, mobile phone now becomes one of the key issues in ubiquitous computing environments. Mobile phones have been infecting our normal lives more thoroughly, and are the fastest technology in human history that has been adapted to people. In Korea, the number of mobile phones registered to the telecom company, is more than the population of the country. Last year, the numbers of mobile phone sold are many times more than the number of personal computer sold. The new advanced technology of mobile phone is now becoming the most concern on every field of technologies. The mix of wireless communication technology (wifi) and mobile phone (smart phone) has made a new world of ubiquitous computing and people can always access to the network anywhere, in high speed, and easily. In such a world, people cannot expect to have available to us specific applications that allow them to accomplish every conceivable combination of information that they might wish. They are willing to have information they want at easy way, and fast way, compared to the world we had before, where we had to have a desktop, cable connection, limited application, and limited speed to achieve what they want. Instead, now people can believe that many of their interactions will be through highly generic tools that allow end-user discovery, configuration, interconnection, and control of the devices around them. Serendipity is an application of the architecture that will help people to solve a concern of achieving their information. The word 'serendipity', introduced to scientific fields in eighteenth century, is the meaning of making new discoveries by accidents and sagacity. By combining to the field of ubiquitous computing and smart phone, it will change the way of achieving the information. Serendipity may enable professional practitioners to function more effectively in the unpredictable, dynamic environment that informs the reality of information seeking. This paper designs the Serendipity Service based on NFC (Near Field Communication) technology. When users of NFC smart phone get information and services by touching the NFC tags, serendipity service will be core services which will give an unexpected but valuable finding. This paper proposes the architecture, scenario and the interface of serendipity service using tag touch data, serendipity cases, serendipity rule base and user profile.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.