• Proceedings of the KSRS Conference
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• v.1
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• pp.150-152
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• 2006

The DISCOVER Project (${\underline{D}}istributed$ ${\underline{I}}nformation$ ${\underline{S}}ervices$ for ${\underline{C}}limate$ and ${\underline{O}}cean$ products and ${\underline{V}}isualizations$ for ${\underline{E}}arth$ ${\underline{R}}esearch$) is a NASA funded Earth Science REASoN project that strives to provide highly accurate, carefully calibrated, long-term climate data records and near-real-time ocean products suitable for the most demanding Earth research applications via easy-to-use display and data access tools. A key element of DISCOVER is the merging of data from the multiple sensors on multiple platforms into geophysical data sets consistent in both time and space. The project is a follow-on to the SSM/I Pathfinder and Passive Microwave ESIP projects which pioneered the simultaneous retrieval of sea surface temperature, surface wind speed, columnar water vapor, cloud liquid water content, and rain rate from SSM/I and TMI observations. The ocean products available through DISCOVER are derived from multi-sensor observations combined into daily products and a consistent multi-decadal climate time series. The DISCOVER team has a strong track record in identifying and removing unexpected sources of systematic error in radiometric measurements, including misspecification of SSM/I pointing geometry, the slightly emissive TMI antenna, and problems with the hot calibration source on AMSR-E. This in-depth experience with inter-calibration is absolutely essential for achieving our objective of merging multi-sensor observations into consistent data sets. Extreme care in satellite inter-calibration and commonality of geophysical algorithms is applied to all sensors. This presentation will introduce the DISCOVER products currently available from the web site, http://www.discover-earth.org and provide examples of the scientific application of both the diurnally corrected optimally interpolated global sea surface temperature product and the 4x-daily global microwave water vapor product.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1431-1437
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• 2012

The growth of many breast tumors is stimulated by IGF-1, which activates signal transduction pathways inducing cell proliferation. $ER{\alpha}$ is important in this process. The aim of the study was to investigate relationships in vitro among inhibitory effects of luteolin on the growth of MCF-7 cells, IGF-1 pathway and $ER{\alpha}$. Our results showed that luteolin could effectively block IGF-l-stimulated MCF-7 cell proliferation in a dose- and time-dependent manner and block cell cycle progression and induce apoptosis evidenced by the flow cytometric detection of sub-G1DNA content. Luteolin markedly decreased IGF-l-dependent IGF-IR and Akt phosphorylation without affecting Erk1/2 phosphorylation. Further experiments pointed out that $ER{\alpha}$ was directly involved in IGF-l induced cell growth inhibitory effects of luteolin, which significantly decreased $ER{\alpha}$ expression. Knockdown of $ER{\alpha}$ in MCF-7 cells by an $ER{\alpha}$-specific siRNA decreased the IGF-l induced cell growth inhibitory effects of luteolin. $ER{\alpha}$ is thus a possible target of luteolin. These findings indicate that the inhibitory effect of luteolin on the growth of MCF-7 cells is via inhibiting IGF-l mediated PI3K-Akt pathway dependent of $ER{\alpha}$ expression.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.313-334
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• 2022

This study examines the economic use value of Discover, an access service of research papers and a subsystem of the National Open Access Platform (AccessON). Based on previous literature on scholarly communications and in-depth interviews with 14 academic and industrial researchers, the service value of Discover was identified as the value of time savings. Administering a survey with 1,313 researchers, the following empirical data required for estimation were obtained: for an average Korean researcher, s/he publishes 2.84 papers per year and takes 30.13 minutes in searching and using one relevant research paper. Discover was found to save 5.64 minutes per paper, saving a total of 225 minutes and KRW 99,384 per researcher per year. Finally, a total economic value of Discover was estimated as KRW 82 billion for the entire 8-year business period. An additional value of KRW 11.3 billion was estimated with the legalization of mandatory deposit of OA research papers. The study demonstrates a sufficient economic value of the national OA platform project, and positive economic impact of the OA legalization, futher offering important domestic data for future research on economic value of scholarly communications.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.331-336
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• 2005

Post translational modifications (PTMs) discovery is an important problem in proteomic. In the past, people discover PTMs by Tandem Mass Spectrometer based on ‘bottom-up’ strategy. However, such strategy suffers from the problem of failing to discover all PTMs. Recently, due to the improvement in proteomic technology, Taylor et al. proposed a database software to discover PTMs with ‘topdown’ strategy by FTMS, which avoids the disadvantages of ‘bottom-up’ approach. However, their proposed algorithm runs in exponential time, requires a database of proteins, and needs prior knowledge about PTM sites. In this paper, a new algorithm is proposed which can work without a protein database and can identify modifications in polynomial time. Besides, no prior knowledge about PTM sites is needed.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.37-42
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• 2019

Skyline queries are used in a variety of fields to make optimal decisions. However, as the volume of data and the dimension of the data increase, the number of skyline points increases with the amount of time it takes to discover them. Mainly, because the number of skylines is essential in many real-life applications, various studies have been proposed. However, previous researches have used the k-parameter methods such as top-k and k-means to discover representative skyline points (RSPs) from entire skyline point set, resulting in high query response time and reduced representativeness due to k dependency. To solve this problem, we propose a new Connected Component Clustering based Representative Skyline Query (3CRS) that can discover RSP quickly even in high-dimensional data through connected component clustering. 3CRS performs fast discovery and clustering of skylines through hash indexes and connected components and selects RSPs from each cluster. This paper proves the superiority of the proposed method by comparing it with representative skyline queries using k-means and DBSCAN with the real-world dataset.

• Bulletin of the Korean Mathematical Society
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• v.60 no.4
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• pp.1101-1129
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• 2023

In this study we propose a model of optimal retirement, consumption and portfolio choice of an individual agent, which encompasses a large class of the models in the literature, and provide a methodology to solve the model. Different from the traditional approach, we consider the problems before and after retirement simultaneously and identify the difference in the dual value functions as the utility value of lifetime labor. The utility value has an option nature, namely, it is the maximized value of choosing the retirement time optimally and we discover it by solving a variational inequality. Then, we discover the dual value functions by using the utility value. We discover the value function and optimal policies by establishing a duality between the value function and the dual value function. The model and approach offer a significant advantage for computation of optimal policies for a large class of problems.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.97-109
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• 2016

The rapid deployment of millions of mobile sensors and smart devices has resulted in high demand for opportunistic encounter-based networking. For the cooperative video surveillance of dashboard cameras in nearby vehicles, a fast and energy-efficient asynchronous neighbor discovery protocol is indispensable because a dashboard camera is an energy-hungry device after the vehicle's engine has turned off. In the existing asynchronous neighbor discovery protocols, all nodes always try to discover all neighbors. However, a dashboard camera needs to discover nearby dashboard cameras when an event is detected. In this paper, we propose a fast and energy-efficient asynchronous neighbor discovery protocol, which enables nodes : 1) to have different roles in neighbor discovery, 2) to discover neighbors within a search range, and 3) to report promptly the exact discovery result. The proposed protocol has two modes: periodic wake-up mode and active discovery mode. A node begins with the periodic wake-up mode to be discovered by other nodes, switches to the active discovery mode on receiving a neighbor discovery request, and returns to the periodic wake-up mode when the active discovery mode finishes. In the periodic wake-up mode, a node wakes up at multiples of number ${\alpha}$, where ${\alpha}$ is determined by the node's remaining battery power. In the active discovery mode, a node wakes up for consecutive ${\gamma}$ slots. Then, the node operating in the active discovery mode can discover all neighbors waking up at multiples of ${\beta}$ for ${\beta}{\leq}{\gamma}$ within ${\gamma}$ time slots. Since the proposed protocol assigns one half of the duty cycle to each mode, it consumes equal to or less energy than the existing protocols. A performance comparison shows that the proposed protocol outperforms the existing protocols in terms of discovery latency and energy consumption, where the frequency of neighbor discovery requests by car accidents is not constantly high.

• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.109-118
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• 2012

This paper theoretically derives an algorithm to discover a new type of workflow-based knowledge from workflow models, which is termed workflow-based affiliation network knowledge. In general, workflow intelligence (or business process intelligence) technology consists of four types of techniques that discover, analyze, monitor and control, and predict a series of workflow-based knowledge from workflow models and their execution histories. So, this paper proposes a knowledge discovery algorithm which is able to discover workflow-based affiliation networks that represent the association and participation relationships between activities and performers defined in ICN-based workflow models. In order particularly to prove the correctness and feasibility of the proposed algorithm, this paper tries to apply the algorithm to a specific workflow model and to show that it is able to derive its corresponding workflow-based affiliation network knowledge.

• Journal of the Korean Society of Food Culture
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• v.18 no.4
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• pp.365-378
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• 2003

The purpose of this research was to have a proper understanding, to have a concern about native local foods and to discover unknown them in Wonju. The data was obtained by use of questionaries to residents(1977 persons) and cook(172 persons) in Wonju and statistical analysis was performed. Frequency and percentage were acquired using SPSS program. To turn out the significant difference between general conditions and each groups t-test and F-test were used. The results of this survey had briefly summarized as follows 1. The results were total 99 kinds of food revealed as a native local foods and 9 kinds of the special products of Wonju. 2. The main materials were potato, com, buckwheat, acorn, pheasant, mushroom, arrowroot, and wild edible greens. 3. The local foods in Wonju were formed with it's own natural material and cooked by original cooking method. 4. The highest perceptional score items were the hot Tang(Hwangtaehaejangguk, Chueotang, Oritang, Gamjatang, Minmulmaeuntang, Tojongdakbaeksuk, Hwanggibaeksuk, Eomnamusamgyetang), and the Bap(Bibimbap, Ssambap, Sanchae-jeongsik) and the noodles(Potato sujebi, Dongchimimakhukso, Olchaengiguksu, Jangsujebi, arrowroot noodle,Gamjaongsimi).

• Speech Sciences
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• v.9 no.3
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• pp.223-235
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• 2002

Unlike Seoul dialect, in the Busan dialect, /ㅅ/ and /ㅆ/ are phonemically non-distinctive and realization of tensing is non-productive, on the other hand, that of voicing is productive. In order to discover causes of such characteristics in Busan dialect, this paper firstly compared acoustic characteristics of Seoul dialect with those of Busan dialect on fricative /ㅅ/ and /ㅆ/. The result showed that Busan dialect has much shorter length of friction and aspiration intervals of word initial and word-medial position than Seoul dialect. I expect that these results are important keys to discover causes of the following characteristics of Busan-dialect - non-distinction, non-productivity of tensing, and productivity of voicing - on Fricative /ㅅ/ and /ㅆ/.