• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.15-22
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• 2006

This paper presents an efficient localization scheme for an indoor mobile robot using RFID tags on the floor. The mobile robot carries an RFID reader at the bottom, which reads the RFID tags on the floor to localize the mobile robot. Each RFID tar on the floor stores its own absolute position which is used to calculate the position and velocity of the mobile robot. Locating the RFID tags on the floor, which constructs an intelligent sensor space, may require several factors to be considered: economics feasibility and accuracy. In this paper, the optimal allocation scheme of the RFID tags on the floor to satisfy the accuracy constraint has been proposed and verified by the experiments. Based on the RFID reading, the mobile robot navigation has been successfully demonstrated to avoid obstacles and to reach the goal within a pre-specified time.

• Journal of the Korean Society for Library and Information Science
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• v.42 no.4
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• pp.463-480
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• 2008

This study was to review the possible characteristics and patterns that occur when comparing control language constructing guidelines, by analyzing the formal characteristics of folksonomy tags in university libraries. Based on subjected tags at university libraries for a period of 6 months the structure and form of folksonomy was examined. The object tags were analyzed based on the thesaurus development guidelines. The results for this research will provide baseline data for the use of folksonomy tag applications in digital libraries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.885-888
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• 2009

Radio Frequency IDentification (RFID) is a method of remotely storing and retrieving data using small and inexpensive devices called RFID tags. In this paper we propose a proxy agent framework that uses a personal device for privacy enforcement and increased protection against eavesdropping, impersonation and cloning attacks. Using the proxy model a user decides when and where information carried in a tag will be released. In particular, the user can put tags under her/his control, authenticated requests, release tags, transfer them to new owners, and so on. In this paper, we analyses a new type of simple a framework for enhancing RFID security by means of a proxy, a personal device that assumes control of a user's tags.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1271-1274
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• 2021

RF fingerprinting based on deep learning (DL) has gained interests as a means to improve the security of near field communication (NFC) by allowing identification of NFC tags based on unique physical characteristics. To achieve high accuracy in the identification of NFC tags, it is crucial to utilize a large number of training data, however it is hard to collect such dataset in practice. In this study, we have provided new methodology to generate RF waveform from NFC tags, i.e., data augmentation, based on a conditional generative adversarial network (CGAN). By using the RF waveform of NFC tags which is collected from the testbed with software defined radio (SDR), we have confirmed that the realistic RF waveform can be generated through our proposed scheme.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.1
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• pp.39-46
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• 2019

This paper proposes an efficient tag identification scheme for ISO/IEC18000-7 standard by dividing the tags into smaller groups. Tag grouping is based on the reader's transmission power. This can reduce the responding tags in the collection round. If the small number of tags exists, we can anticipate the collision probability will decrease. And it makes the identification speed high. A collection round initiated by the reader's collection command. It also proceeds with increasing the power of the reader until all tags are identified. The results showed that 25% of the performance improved.

• Proceedings of the Materials Research Society of Korea Conference
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• 2008.05a
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• pp.6-6
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1034-1039
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• 2005

SALM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important tasks in mobile robot research. Until now expensive sensors such as a laser sensor have been used for mobile robot localization. Currently, the proliferation of RFID technology is advancing rapidly, while RFID reader devices, antennas and tags are becoming increasingly smaller and cheaper. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying location of the mobile robot in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, the localization error results from the sensing area of the RFID reader, because the reader just knows whether the tag is in the sensing range of the sensor and, until now, there is no study to estimate the heading of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. The Markov localization method is used to reduce the location(X,Y) error and the Kalman Filter method is used to estimate the heading($\theta$) of mobile robot. The algorithms which are based on Markov localization require high computing power, so we suggest fast Markov localization algorithm. Finally we applied these algorithms our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors such as odometers and RFID tags for mobile robot localization in the smart floor

• Journal of the Korean Society for Library and Information Science
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• v.47 no.3
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• pp.343-361
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• 2013

We proposed and evaluated a tag semantic analysis method in which original tags are expanded and the semantic relations between original or expanded tags are used to extract key sentences from lecture speech transcripts. To do that, we first investigated how useful Flickr tag clusters and WordNet synonyms are for expanding tags and for detecting the semantic relations between tags. Then, to evaluate our proposed method, we compared it with a latent semantic analysis (LSA) method. As a result, we found that Flick tag clusters are more effective than WordNet synonyms and that the F measure mean (0.27) of the tag semantic analysis method is higher than that of LSA method (0.22).

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1797-1801
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• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.

• Journal of Information Science Theory and Practice
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• v.6 no.2
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• pp.46-61
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• 2018

The mobile social networking application Instagram is a well-known platform for sharing photos and videos. Since it is folksonomy-oriented, it provides the possibility for image indexing and knowledge representation through the assignment of hashtags to posted content. The purpose of this study is to analyze how Instagram users tag their pictures regarding different kinds of picture and hashtag categories. For such a content analysis, a distinction is made between Food, Pets, Selfies, Friends, Activity, Art, Fashion, Quotes (captioned photos), Landscape, and Architecture image categories as well as Content-relatedness (ofness, aboutness, and iconology), Emotiveness, Isness, Performativeness, Fakeness, "Insta"-Tags, and Sentences as hashtag categories. Altogether, 14,649 hashtags of 1,000 Instagram images were intellectually analyzed (100 pictures for each image category). Research questions are stated as follows: RQ1: Are there any differences in relative frequencies of hashtags in the picture categories? On average the number of hashtags per picture is 15. Lowest average values received the categories Selfie (average 10.9 tags per picture) and Friends (average 11.7 tags per picture); for highest, the categories Pet (average 18.6 tags), Fashion (average 17.6 tags), and Landscape (average 16.8 tags). RQ2: Given a picture category, what is the distribution of hashtag categories; and given a hashtag category, what is the distribution of picture categories? 60.20% of all hashtags were classified into the category Content-relatedness. Categories Emotiveness (about 4.38%) and Sentences (0.99%) were less often frequent. RQ3: Is there any association between image categories and hashtag categories? A statistically significant association between hashtag categories and image categories on Instagram exists, as a chi-square test of independence shows. This study enables a first broad overview on the tagging behavior of Instagram users and is not limited to a specific hashtag or picture motive, like previous studies.