• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.61-66
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• 2009

This Paper describes a new program called DEM Generator need to process DEM from LiDAR data or digital map data. It is difficult to generate raster DEM from LiDAR mass point data sets and digital maps too large to fit into memory. The DEM Generator was designed to process DEM and shaded relief image of GeoTiff format in order of streaming meshes; I/O minimize tag, delaunay triangle, natural neighborhood or TIN, temporary files and grid. It is expected that we can be improved the precision of DEM and solved the time consuming problem of DEM generating of a wider area.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.390-397
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• 2021

As digital twin technology is applied to various industrial fields, technologies to effectively process large amounts of data are required. In this paper, we discuss a customized service method for fast search and effective delivery of large-scale data for underground facility for public utilities management. The proposed schemes are divided into two ways: a fast search data generation method and a customized information service segmentation method to efficiently search and abbreviate vast amounts of data. In the high-speed search data generation, we discuss the configuration of the synchronization process for the time series analysis of the sensors collected in the underground facility and the additional information method according to the data reduction. In the user-customized service method, we define the types of users in normal and disaster situations, and discuss how to service them accordingly. Through this study, it is expected to be able to develop a systematic data generation and service model for the management of underground utilities that can effectively search and receive large-scale data in a disaster situation.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.71-95
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• 2016

In recent years, text mining has been used to extract meaningful insights from the large volume of unstructured text data sets of various domains. As one of the most representative text mining applications, topic modeling has been widely used to extract main topics in the form of a set of keywords extracted from a large collection of documents. In general, topic modeling is performed according to the weighted frequency of words in a document corpus. However, general topic modeling cannot discover the relation between documents if the documents share only a few terms, although the documents are in fact strongly related from a particular perspective. For instance, a document about "sexual offense" and another document about "silver industry for aged persons" might not be classified into the same topic because they may not share many key terms. However, these two documents can be strongly related from the R&D perspective because some technologies, such as "RF Tag," "CCTV," and "Heart Rate Sensor," are core components of both "sexual offense" and "silver industry." Thus, in this study, we attempted to discover the differences between the results of general topic modeling and R&D perspective topic modeling. Furthermore, we package social issues from the R&D perspective and present a prototype system, which provides a package of news articles for each R&D issue. Finally, we analyze the quality of R&D perspective topic modeling and provide the results of inter- and intra-topic analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.157-162
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• 2008

This paper presents a learning method of tagging ontology using large tagging data such as a folksonomy, which stands for classification structure informally created by the people. There is no common agreement about the semantics of a tagging, and most social web sites internally use different methods to represent tagging information, obstructing interoperability between sites and the automated processing by software agents. To solve this problem, we need a tagging ontology, defined by analyzing intrinsic attributes of a tagging. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tagging ontology is also suggested as an applying field.

• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.545-554
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• 2006

RFID could be applied in the various fields such as distribution beside, circulation, traffic and environment on information communication outside. So this can speak as point of ubiquitous computing's next generation technology. However, it is discussed problem of RFID security recently, so we must prepare thoroughly about RFID security for secure information. In this paper, we proposed a method which could protect private information and ensure RFID's identification effectively storing face feature information on RFID tag. Our method which is improved linear discriminant analysis has reduced dimension of feature information which has large size of data. Therefore, we can sore face feature information in small memory field of RFID tag. Our propose d algorithm has shown 92% recognition rate in experimental results and can be applied to entrance control management system, digital identification card and others.

• Current Optics and Photonics
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• v.8 no.2
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• pp.127-137
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• 2024

The distribution of tags is an important factor that affects the performance of radio-frequency identification (RFID). To study RFID performance, it is necessary to obtain RFID tags' coordinates. However, the positioning method of RFID technology has large errors, and is easily affected by the environment. Therefore, a new method using optical measurement is proposed to achieve RFID performance analysis. First, due to the possibility of blurring during image acquisition, the paper derives a new image prior to removing blurring. A nonlocal means-based method for image deconvolution is proposed. Experimental results show that the PSNR and SSIM indicators of our algorithm are better than those of a learning deep convolutional neural network and fast total variation. Second, an RFID dynamic testing system based on photoelectric sensing technology is designed. The reading distance of RFID and the three-dimensional coordinates of the tags are obtained. Finally, deep learning is used to model the RFID reading distance and tag distribution. The error is 3.02%, which is better than other algorithms such as a particle-swarm optimization back-propagation neural network, an extreme learning machine, and a deep neural network. The paper proposes the use of optical methods to measure and collect RFID data, and to analyze and predict RFID performance. This provides a new method for testing RFID performance.

• BMB Reports
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• v.39 no.2
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• pp.183-188
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• 2006

Using the Phred/Phrap/Polyphred/Consed pipeline established in the National Livestock Research Institute of Korea, we predicted candidate coding single nucleotide polymorphisms (cSNPs) from 7,600 expressed sequence tags (ESTs) derived from three cDNA libraries (liver, M. longissimus dorsi, and intermuscular fat) of Hanwoo (Korean native cattle) steers. From the 7,600 ESTs, 829 contigs comprising more than two EST reads were assembled using the Phrap assembler. Based on the contig analysis, 201 candidate cSNPs were identified in 129 contigs, in which transitions (69%) outnumbered transversions (31%). To verify whether the predicted cSNPs are real, 17 SNPs involved in lipid and energy metabolism were selected from the ESTs. Twelve of these were confirmed to be real while five were identified as artifacts, possibly due to expressed sequence tag sequence error. Further analysis of the 12 verified cSNPs was performed using the program BLASTX. Five were identified as nonsynonymous cSNPs, five were synonymous cSNPs, and two SNPs were located in 3'-UTRs. Our data indicated that a relatively high SNP prediction rate (71%) from a large EST database could produce abundant cSNPs rapidly, which can be used as valuable genetic markers in cattle.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.27-35
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• 2022

In the most IPS (Indoor Positioning System), it is available to specify the user's movement by sending a specific signal from a tag such as a beacon to multiple receivers. This method is very efficiently used in places where the number of people is limited. On the other hand, in large commercial facilities, it is nearly difficult to apply the existing IPS method because it is necessary to attach a tag to each customer. In this paper, we propose a system that uses an external sensor grid to identify people's movement without using tags. Each sensor node uses both an ultrasonic sensor and an infrared sensor to monitor people's movements and sends collected data to the main server through wireless transmission for easy system maintenance. The operation was verified using the FPGA board, and we designed a VLSI circuit in 180nm process.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.771-776
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• 2023

Scratchpad memory is a software-controlled on-chip memory designed and used to mitigate the disadvantages of existing cache memories. Existing cache memories have TAG-related hardware control logic, so users cannot directly control cache misses, and their sizes are large and energy consumption is relatively high. Scratchpad memory has advantages in terms of size and energy consumption because it eliminates such hardware overhead, but there is a burden on software to manage data. In this study, data management techniques of scratchpad memory were classified and examined, and ways to maximize the advantages were discussed.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.455-460
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• 2023

Text augmentation is a methodology that creates new augmented texts by transforming or generating original texts for the purpose of improving the performance of NLP models. However existing text augmentation techniques have limitations such as lack of expressive diversity semantic distortion and limited number of augmented texts. Recently text augmentation using large language models and few-shot learning can overcome these limitations but there is also a risk of noise generation due to incorrect generation. In this paper, we propose a text augmentation method called TAGS that generates multiple candidate texts and selects the appropriate text as the augmented text. TAGS generates various expressions using few-shot learning while effectively selecting suitable data even with a small amount of original text by using contrastive learning and similarity comparison. We applied this method to task-oriented chatbot data and achieved more than sixty times quantitative improvement. We also analyzed the generated texts to confirm that they produced semantically and expressively diverse texts compared to the original texts. Moreover, we trained and evaluated a classification model using the augmented texts and showed that it improved the performance by more than 0.1915, confirming that it helps to improve the actual model performance.