• Ocean and Polar Research
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• v.37 no.3
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• pp.179-188
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• 2015

This study was carried out to determine the optimal number of ARGO floats in the East Sea in order to maximize their applications. The dominant spatio-temporal scale, size of the domain, and the typical float lifetimes in the East Sea were taken into consideration. The mean spatial de-correlation scale of temperature on isobaric surfaces reaches about 60 km. The minimum necessary number of floats is about 82 on average in order to secure independent ARGO profiles with the de-correlation scale. Considering the float lifetimes, about 27 floats per year should be deployed to maintain the 82 ARGO float array every year. To obtain spatially uniform distribution of ARGO float data, mean residence time and dispersion rate (basin area/residence time) of ARGO floats were evaluated in each basin of the East Sea. A faster (slower) dispersion rate requires more (less) ARGO floats to maintain the spatially uniform number of floats. According to the analysis, it is likely that the optimal ratio of the number of floats for each basin is 1:2:4 corresponding to Ulleung Basin:Yamato Basin:Japan Basin. In order to maintain relatively uniform ARGO observing networks, it is necessary to establish a long-term plan for deployment strategy based on float pathways and the dispersion rate parameters estimated by using currently active ARGO float trajectory data as well as reanalysis data.

• Fisheries and Aquatic Sciences
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• v.2 no.2
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• pp.210-217
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• 1999

Chemical oxygen demand (COD), phytoplankton cell number and chlorophyll-a concentration (Chl-a), sediment mean grain size and ignition loss were studied to determine their temporal trends in the study area. Historical data of COD, cell number and Chl-a were gathered from the late 1960s or early 1980s to 1997, and trends in temporal domain were obtained from a simple regression. Sediments for grain size and ignition loss (as organic contents in sediments) were sampled from the Chokchon macrotidal flat bimonthly from September 1990 to November 1996, and were analyzed using the decomposition method of time series analysis. In general, the first three data showed increasing trends based on regression analysis. The trends of sediment grain size fluctuated in a neutral pathway while those of ignition loss yielded no increasing pattern. In contrast with the suggestions from Ahn and Choi (1998) who reported a coarsening variation in sediment grain size to be a cause of the directional and remarkable changes of macrofaunal communities in this area, we could not find such a corresponding variation pattern from our samples. In diagnosing eutrophication, a paradoxical phenomenon was encountered between the trends in water column (COD, cell number and Chl-a) and sediment (ignition loss) data. In this paper, we inferred the possible processes that produce the discrepancy. Some explanations and biological responses to eutrophication were predicted and discussed.

• Genomics & Informatics
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• v.19 no.3
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• pp.21.1-21.5
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• 2021

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present. However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don't generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2C
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• pp.206-212
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• 2004

In this paper, we propose a STF(Space-Time-Frequency) coded OFDM(Orthogonal Frequency Division Multiplexing) transmission scheme as an attractive solution for high bit rate data transmission in a multipath fading environment. STBC(Space-Time Block Coding) has been proposed as a simple diversity scheme using two transmit antennas. Also ST-OFDM(Space-Time Block Coded OFDM) and SF-OFDM(Space-Frequency Block Coded OFDM) transmission scheme, that the STBC is applied to the OFDM, has been proposed. In this paper, we propose STF-OFDM transmission scheme that to coded in time, space and frequency domain. The STF-OFDM transmission scheme that we propose in this paper is the way to improve a performance of conventional ST-OFDM, by using frequency diversity.

• The Journal of the Acoustical Society of Korea
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• v.24 no.3E
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• pp.87-89
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• 2005

The acoustical sources of intake and exhaust systems in fluid machines are often characterized by the source impedance and strength using linear frequency-domain modeling. In the case of the sources which are nonlinear and time-variant, however, the source parameters were sometimes incorrectly obtained. In this paper, the source model and direct measurement technique are modified in order to evaluate the effect due to nonlinear and periodically time-varying source character as well as the linear property of the reflectivity of in-duct fluid machine source. With a priori known kinematical information of the source, the types of nonlinear time-variant terms can be presumed by a simple physical model, in which there is practically no restriction on the form of the model. The concept of source impedance can be extendable by introducing the linear frequency response function for each nonlinear or time-variant input. Extending the conventional method and adapting the reverse MISO technique, it is possible to develop a direct method that can deal with the nonlinear time-variant source parameters. The proposed direct method has a novel feature that there is no restriction on the probability or spectral natures of the excited sound pressure data. The present method is verified by the simulated measurements for simplified fluid machines. It is thought that the proposed method would be useful in predicting the insertion loss or the radiated sound level from intake or exhaust systems.

• Macromolecular Research
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• v.12 no.1
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• pp.1-10
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• 2004

The structural changes occurring in the isothermal crystallization processes of polyethylene (PE), poly-oxymethylene (POM), and vinylidene fluoridetrifluoroethylene (VDFTrFE) copolymer have been reviewed on the basis of our recent experimental data collected by the time-resolved measurements of synchrotron-sourced wide-angle (WAXS) and small-angle X-ray scatterings (SAXS) and infrared spectra. The temperature jump from the melt to a crystallization temperature could be measured at a cooling rate of 600-1,000 $^{\circ}C$/min, during which we collected the WAXS, SAXS, and infrared spectral data successfully at time intervals of ca. 10 sec. In the case of PE, the infrared spectral data clarified the generation of chain segments of partially disordered trans conformations immediately after the jump. These segments then became transformed into more-regular all-trans-zigzag forms, followed by the formation of an orthorhombic crystal lattice. At this stage, the generation of a stacked lamella structure having an 800-${\AA}$-long period was detected in the SAXS data. This structure was found to transfer successively to a more densely packed lamella structure having a 400-${\AA}$-long period as a result of the secondary crystallization of the amorphous region in-between the original lamellae. As for POM, the formation process of a stacked lamella structure was essentially the same as that mentioned above for PE, as evidenced from the analysis of SAXS and WAXS data. The observation of morphology-sensitive infrared bands revealed the evolution of fully extended helical chains after the generation of lamella having folded chain structures. We speculate that these extended chains exist as taut tie chains passing continuously through the neighboring lamellae. In the isothermal crystallization of VDFTrFE copolymer from the melt, a paraelectric high-temperature phase was detected at first and then it transferred into the ferroelectric low-temperature phase at a later stage. By analyzing the reflection profile of the WAXS data, the structural ordering in the high-temperature phase and the ferroelectric phase transition to the low-temperature phase of the multi-domain structure were traced successfully.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3501-3518
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• 2020

The method of DNS data collection is one of the most important parts of DNS simulation. DNS data contains a lot of information. When it comes to analyzing the DNS security issues by simulation on the cyber range with customized features, we only need some of them, such as IP address, domain name information, etc. Therefore, the data we need are supposed to be light-weighted and easy to manipulate. Many researchers have designed different schemes to obtain their datasets, such as LDplayer and Thales system. However, existing solutions consume excessive computational resources, which are not necessary for DNS security simulation. In this paper, we propose a light-weighted active data collection method to prepare the datasets for DNS simulation on cyber range. We evaluate the performance of the method and prove that it can collect DNS data in a short time and store the collected data at a lower storage cost. In addition, we give two examples to illustrate how our method can be used in a variety of applications.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.31-43
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• 2007

Sensor data have the characteristics such as numerous and continuous data. Therefore, it is required to develop an index which could retrieve a specific sensor data efficiently from numerous sensed data. The index should have an efficient delete operation for the past data to support the data archiving. In this paper, we have proposed and implemented an index splitting technique to support the sensor data archiving. These splitted indexes compose of a virtual index (that is, index management component), which is shown as single tree from outside. Experimental results show that in the case of 100,000 insert operations the splitted index performs 8% better than the traditional TB-tree maximumly. And the splitted index outperforms TB-tree with retrieving queries when the region of query is small and the size of time domain is large.

• Journal of KIISE
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• v.42 no.1
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• pp.54-67
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• 2015

Nowadays, studies on the fusion of Semantic Web technologies are being carried out to promote the interoperability and value of sensor data in an IoT environment. To accomplish this, the semantic translation of sensor data is essential for convergence with service domain knowledge. The existing semantic translation technique, however, involves translating from static metadata into semantic data(RDF), and cannot properly process real-time and large-scale features in an IoT environment. Therefore, in this paper, we propose a technique for translating large-scale streaming sensor data generated in an IoT environment into semantic data, using real-time and parallel processing. In this technique, we define rules for semantic translation and store them in the semantic repository. The sensor data is translated in real-time with parallel processing using these pre-defined rules and an ontology-based semantic model. To improve the performance, we use the Apache Storm, a real-time big data analysis framework for parallel processing. The proposed technique was subjected to performance testing with the AWS observation data of the Meteorological Administration, which are large-scale streaming sensor data for demonstration purposes.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.83-85
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• 2000

A lot of microbial genome sequencing projects is being done in many genome centers around the world, since the first genome, Haemophilus influenzae, was sequenced in 1995. The deluge of microbial genome sequence data demands new and highly automatic data flow system in order for genome researchers to manage and analyze their own bulky sequence data from low-level to high-level. In such an aspect, we developed the automatic data management system for microbial genome projects, which consists mainly of local database, analysis programs, and user-friendly interface. We designed and implemented the local database for large-scale sequencing projects, which makes systematic and consistent data management and retrieval possible and is tightly coupled with analysis programs and web-based user interface, That is, parsing and storage of the results of analysis programs in local database is possible and user can retrieve the data in any level of data process by means of web-based graphical user interface. Contig assembly, homology search, and ORF prediction, which are essential in genome projects, make analysis programs in our system. All but Contig assembly program are open as public domain. These programs are connected with each other by means of a lot of utility programs. As a result, this system will maximize the efficiency in cost and time in genome research.