• Journal of electromagnetic engineering and science
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• v.7 no.1
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• pp.47-52
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• 2007

We investigated the effects of substrate, metal-line, and surface material on the performance of radio frequency identification(RFID) tag antenna using a tag antenna with a meander line radiator and T-matching network. The results showed that readability of the tag antenna with a thin high-loss substrate could be increased so that it was similar to that of a low-loss substrate if the substrate was very thin. The readability of the tag antenna decreased significantly when the metal line was thinner than the skin depth. The readability of the tag also decreased drastically when the tag was attached to high-permittivity high-loss target objects.

• Fisheries and Aquatic Sciences
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• v.10 no.4
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• pp.226-229
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• 2007

Survival, tag retention and tag readability were compared among the control and three treatment groups of soft-shelled turtles, Pelodiscus sinensis Crother, 2000 (mean body $weight{\pm}SD$: $182.6{\pm}13.7\;g$), marked with visible implant fluorescent elastomer (VIFE) tags for 16 months. Mortality 4 to 16 months after tagging was attributed to collection and handling stress rather than to the tagging itself. Tags applied to the web surface between the fourth and fifth dactyl of the hindfoot appeared to have the highest retention rates, while adipose eyelid tagging had high tag readability but a high loss rate. We conclude that in soft-shelled turtles, the most suitable region for VIFE tagging is on the web surface between the fourth and fifth dactyls of the hindfoot.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.754-757
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• 2009

The current concrete maturity method implemented with temperature sensors requires an extensive wiring, which is not often acceptable on construction site due to harsh working environment. Radio Frequency Identification (RFID) technology appears to provide a solution for the wiring issue because of its ability of sending data wirelessly. An RFID tag integrated with a temperature sensor and placed within fresh concrete may be able to read temperatures of concrete and transmit them to an RFID reader wirelessly in real-time. However the previous research illustrated that the RFID signal gets dispersed in liquid medium. One may speculate then whether RFID signals travel through fresh concrete with high water content. Would the tag's burying depth within fresh concrete affect its readability? The paper presents the preliminary results of our on-going investigation on the readability of RFID tags in concrete against water content and burying depth of tags.

• Fisheries and Aquatic Sciences
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• v.16 no.1
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• pp.35-39
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• 2013

The aim of this study was to assess visible implant fluorescent elastomer (VIE) tagging in greenling Hexagrammos otakii. The experiental fish were anesthetized individually and marked with orange, yellow, red, and green elastomer at the following five body locations, respectively: the adipose eyelid, the surface of the dorsal fin base, the inside surface of the pectoral fin base, the inside surface of the pelvic fin base, and the surface of the anal fin base. Control fish were anesthetized but not marked. During the 20-month trial, fish growth and retention, underwater visibility, and readability of the tags were determined. After 20 months, body length of marked greenling ($43.2{\pm}3.5cm$, mean ${\pm}$ standard deviation [SD]) did not differ from that of the control ($41.4{\pm}3.7cm$). Additionally, the body weight of marked greenling ($527.4{\pm}39.8g$, mean ${\pm}$ SD) did not differ from that of the controls ($505.9{\pm}31.7g$). Greenling retained >90% of the tags at the surface of the dorsal fin base. The anal fin base showed a higher tag retention rate than the inside surfaces of the pectoral fin and the pelvic fin bases (P < 0.05). Red and orange tags were identified more easily underwater than green and yellow tags. Green and yellow tags emitted fluorescence in response to a narrower range of light wavelengths. Thus, the VIE mark was easy to apply to greenling (< 1 min per fish) and was readily visible when viewed under an ultraviolet lamp.

• Fisheries and Aquatic Sciences
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• v.20 no.9
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• pp.21.1-21.10
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• 2017

The aim of this study was to assess visible implant fluorescent elastomer (VIE) tagging and stress response in marine medaka, Oryzias dancena. The experimental fish were anesthetized individually and marked with red, yellow, or green elastomer at each of the following three body locations: (1) the abdomen, (2) the back, and (3) the caudal vasculature. During 12 months, the accumulated survival rates of fish in the experimental treatments were not different among red, yellow, and green elastomers. The experimental fish retained > 85% of the tags injected in the back, > 70% of the tags injected in the caudal vasculature, and > 60% of the tags injected in the abdomen (P < 0.05). An important observation was that the abdomen site was associated with poor tag retention. For all injected sites, the red and green tags were able to be detected more easily than the yellow tags when observed under both visible and UV lights. Tag readability was lower for the abdomen site than for the other sites (back and caudal vasculature). Thus, VIE tags were easy to apply to marine medaka (< 1 min per fish) and were readily visible when viewed under UV light.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.24-27
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• 2008

As shown in Wikipedia, tagging or cross-linking through major key-words improves the readability of documents. Recently, the Semantic Web rises the importance of social tagging as a key feature of the Web 2.0 and Tag Cloud has emerged as its crucial phenotype. In this paper we provides an efficient method of automated keyword tagging based on controlled term collection, where the computational complexity of O(mN) - if pattern matching algorithm is used - can be reduced to O(mlogN) - if Information Retrieval is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that IR-based tagging speeds up 5.6 times compared with fast pattern matching algorithm.

• Journal of Information Processing Systems
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• v.5 no.3
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• The Korean Journal of Archival Studies
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• no.67
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• pp.99-138
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• 2021

In the era of big data, analyzing not only structured data but also unstructured data is emerging as an important task. Official documents produced by government agencies are also subject to big data analysis as large text-based unstructured data. From the perspective of internal work efficiency, knowledge management, records management, etc, it is necessary to analyze big data of public documents to derive useful implications. However, since many of the public documents currently held by public institutions are not in open format, a pre-processing process of extracting text from a bitstream is required for big data analysis. In addition, since contextual metadata is not sufficiently stored in the document file, separate efforts to secure metadata are required for high-quality analysis. In conclusion, the current official documents have a low level of machine readability, so big data analysis becomes expensive.