• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.299-304
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• 2015

This paper introduces development of the MATLAB GUI based software for generating GPS RINEX observation file. The purpose of this software is to generate GPS measurements of reference station or dynamic user, which are similar to the real GPS receiver data, accurately and efficiently. This software includes two data generation modes. One is Precision mode which generates GPS measurements as accurate as possible using post-processing data. The other is Real-time mode which generates GPS measurements using GPS error modeling technique. GPS error sources are calculated on the basis of each data generation mode, and L1/L2 pseudorange, L1/L2 carrier phase, and Doppler measurements are produced. These generated GPS measurements are recorded in the RINEX observation version 3.0 file. Using received GPS data at real reference station, we analyzed three items to verify software reliability; measurement bias, rate of change, and noise level. Consequently, RMS error of measurement bias is about 0.7 m, and this verification results demonstrate that our software can generate relatively exact GPS measurements.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.666-677
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• 2015

The importance of application traffic analysis for efficient network management has been emphasized continuously. Snort is a popular traffic analysis system which detects traffic matched to pre-defined signatures and perform various actions based on the rules. However, it is very difficult to get highly accurate signatures to meet various analysis purpose because it is very tedious and time-consuming work to search the entire traffic data manually or semi-automatically. In this paper, we propose a novel method to generate signatures in a fully automatic manner in the form of sort rule from raw packet data captured from network link or end-host. We use a sequence pattern algorithm to generate common substring satisfying the minimum support from traffic flow data. Also, we extract the location and header information of the signature which are the components of snort content rule. When we analyzed the proposed method to several application traffic data, the generated rule could detect more than 97 percentage of the traffic data.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.767-776
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• 2008

Microarray technology is extensively being used in experimental molecular biology field. Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification of many diseases. One of the two major problems in microarray data classification is that the number of genes exceeds the number of tissue samples. The other problem is that current methods generate classifiers that are accurate but difficult to interpret. Our paper addresses these two problems. We performed a direct integration of individual microarrays with same biological objectives by transforming an expression value into a rank value within a sample and generated rank-comparison decision rules with variable number of genes for cancer classification. Our classifier is an ensemble method which has k top scoring decision rules. Each rule contains a number of genes, a relationship among involved genes, and a class label. Current classifiers which are also ensemble methods consist of k top scoring decision rules. However these classifiers fix the number of genes in each rule as a pair or a triple. In this paper we generalized the number of genes involved in each rule. The number of genes in each rule is in the range of 2 to N respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Also our classifier is readily interpretable, accurate with small number of genes, and shed a possibility of the use in a clinical setting.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.309-328
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• 2022

This study empirically analyzed a Korean pre-trained language models (PLMs) designed for natural language generation. The performance of two PLMs - BART and GPT - at the task of abstractive text summarization was compared. To investigate how performance depends on the characteristics of the inference data, ten different document types, containing six types of informational content and creation content, were considered. It was found that BART (which can both generate and understand natural language) performed better than GPT (which can only generate). Upon more detailed examination of the effect of inference data characteristics, the performance of GPT was found to be proportional to the length of the input text. However, even for the longest documents (with optimal GPT performance), BART still out-performed GPT, suggesting that the greatest influence on downstream performance is not the size of the training data or PLMs parameters but the structural suitability of the PLMs for the applied downstream task. The performance of different PLMs was also compared through analyzing parts of speech (POS) shares. BART's performance was inversely related to the proportion of prefixes, adjectives, adverbs and verbs but positively related to that of nouns. This result emphasizes the importance of taking the inference data's characteristics into account when fine-tuning a PLMs for its intended downstream task.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.189-198
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• 2024

Research combining deep learning-based models and physical simulations is making important advances in the medical field. This extracts the necessary information from medical image data and enables fast and accurate prediction of deformation of the skeleton and soft tissue based on physical laws. This study proposes a system that integrates Neural Radiance Fields (NeRF), Position-Based Dynamics (PBD), and Parallel Resampling to generate 3D volume data, and deform and visualize them in real-time. NeRF uses 2D images and camera coordinates to produce high-resolution 3D volume data, while PBD enables real-time deformation and interaction through physics-based simulation. Parallel Resampling improves rendering efficiency by dividing the volume into tetrahedral meshes and utilizing GPU parallel processing. This system renders the deformed volume data using ray casting, leveraging GPU parallel processing for fast real-time visualization. Experimental results show that this system can generate and deform 3D data without expensive equipment, demonstrating potential applications in engineering, education, and medicine.

• Korean Journal of Cognitive Science
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• v.13 no.3
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• pp.33-46
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• 2002

Recently, unstructured random data such as website logs, texts and tables etc, have been flooding in the internet. Among these unstructured data there are potentially very useful data such as bulletin boards and e-mails that are used for customer services and the output from search engines. Various text mining tools have been introduced to deal with those data. But most of them lack accuracy compared to traditional data mining tools that deal with structured data. Hence, it has been sought to find a way to apply data mining techniques to these text data. In this paper, we propose a text mining system which can incooperate existing data mining methods. We use text mining as a preprocessing tool to generate formatted data to be used as input to the data mining system. The output of the data mining system is used as feedback data to the text mining to guide further categorization. This feedback cycle can enhance the performance of the text mining in terms of accuracy. We apply this method to categorize web sites containing adult contents as well as illegal contents. The result shows improvements in categorization performance for previously ambiguous data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.709-713
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• 2009

Recently, a lot of research that applies data acquired from devices such as cameras and RFIDs to context aware services is being performed in the field on Life-Log and the sensor network. A variety of analytical techniques has been proposed to recognize various information from the raw data because video and audio data include a larger volume of information than other sensor data. However, manually watching a huge amount of media data again has been necessary to create supervised data for the update of a class or the addition of a new class because these techniques generally use supervised learning. Therefore, the problem was that applications were able to use only recognition function based on fixed supervised data in most cases. Then, we proposed a method of acquiring supervised data from a video sharing site where users give comments on any video scene because those sites are remarkably popular and, therefore, many comments are generated. In the first step of this method, words with a high utility value are extracted by filtering the comment about the video. Second, the set of feature data in the time series is calculated by applying functions, which extract various feature data, to media data. Finally, our learning system calculates the correlation coefficient by using the above-mentioned two kinds of data, and the correlation coefficient is stored in the DB of the system. Various other applications contain a recognition function that is used to generate collective intelligence based on Web comments, by applying this correlation coefficient to new media data. In addition, flexible recognition that adjusts to a new object becomes possible by regularly acquiring and learning both media data and comments from a video sharing site while reducing work by manual operation. As a result, recognition of not only the name of the seen object but also indirect information, e.g. the impression or the action toward the object, was enabled.

• Journal of the Korean Dietetic Association
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• v.10 no.3
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• pp.284-292
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• 2004

The purpose of this study was to develop a manipulation system for nutrition counseling based on internet. This system offers convenient user interface and the synthetic counseling results with various utilities. This system consists of the general information of clients, the anthropometry data and the calculation of obesity and body index, the state of eating habits, calorie expenditure, clinical symptoms, the convenient method for analysis of nutrients, biochemical data and nutrition prescription. Having interoperability, these functions preserve the information of clients and manage the historical data. This system can insert, store, print out and generate the synthetic information of clients to provide a suitable and efficient nutrition counseling information. With accumulated client data, It does the nutrition education and counseling simultaneously. As it is developed based on internet, it provides friendly user interface. Also, Managing clients' information connected to database, it can provide a systematic and formal information. It is possible for the system to retrieve information and counsel in real time. It is expected that the nutrition counseling management system can improve the national health with animated nutrition counseling.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.449-454
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• 2004

Nowadays, flood hazard maps have been generated to minimize the loss of human lives due to flooding domestically. To generate the flood hazard maps, LiDAR data have mainly been used to provide topographic data. The LiDAR data requires, however, relatively higher cost and processing time. Therefore, the needs of validating possible use of topographic maps as an alternative source of LiDAR, which have been already existed from the NGIS project over the nation, has been raised. In this background, this study has generated a DEM over City of Kuri as a pilot study using conventional 1:1,000 and 1:5,000 topographic maps emphasizing the linkage of river profile with breakline processing algorithm to build the essential topographic data as accurate as possible. The results showed that the RMSE from topographic maps and LiDAR were 3.49 and 2.282 meter, respectively. Further study needs to be made to decide possible use of topographic maps instead of LiDAR including more easier updating of topographic maps to support flood map generation. In addition, 1:1,000 topographic mapping, which is limited to the urban areas so far, needs to be extended to the river areas.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.239-246
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• 2005

In this paper. we generate a virtual right image corresponding to the input left image by using given RGB texture data and 8 bit gray scale depth data. We first transform the depth data to disparity data and then produce the virtual right image with this disparity. We also proposed a stereo image synthesis algorithm which is adaptable to a viewer's position and an real-time processing algorithm with a fast LUT(look up table) method. Finally, we could synthesize a total of eleven stereo images with different view points for SD quality of a texture image with 8 bit depth information in a real time.