• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.632-639
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• 2011

Searching distributed resource efficiently is very important in distributed computing, cloud computing environment. Distributed resource searching may have system overheads and take much time in proportion to the searching number, because distributed resource searching has to circuit many peers for searching information. The open-source community project JXTA defines an open set of standard protocols for ad hoc, pervasive, peer-to-peer computing as a common platform for developing a wide variety of decentralized network applications. In this paper, we proposed peer search algorithm based on JXTA-Sim. original JXTA peer searching algorithm selected a loosely-consistent DHT. Our Lookup algorithm decreases message number of WALK_LOOKUP and reduce the network system overload, and we make a result of same performance both original algorithm and our proposed algorithm.

• Korean Journal of Community Nutrition
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• v.25 no.2
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• pp.150-158
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• 2020

Objectives: This study was conducted to estimate usual meat intake distribution, which may have been over/underestimated when estimations were made using only the third food codes of the Korea National Health and Nutrition Examination Survey (KNHANES). Methods: For this purpose, 24-hour recall data from the 2009 Korea National Health and Nutrition Examination Survey, which conducted a partial 2-day survey of food intake, were used. The Multiple Source Method (MSM) was used to estimate the distribution of the usual intake of red and processed meats. Results: The results of this study show that the mean intake of red meat was 45.07 g while that of processed meat was 4.33 g. These results are slightly higher than the consumption calculated using only tertiary food code, and the difference was statistically significant. Furthermore, characteristics of the estimated usual intake distribution were a smaller standard deviation, increased lower percentiles, and decreased upper percentiles compared to the 2-day mean intake distribution for both red and processed meats. The proportion of individuals not consuming red meat decreased substantially from approximately 37% to 0.7%. The proportion of consumption that exceeded 90 g, which is the upper limit of red meat intake recommended by the National Health Service (NHS), was only approximately 10% in the distribution of usual intake. Conclusions: As the consumption of processed foods is expected to continuously increase, caution is needed regarding the processes used to calculate food (group) intake to avoid over/underestimation. Moreover, use of KNHANES data to calculate the proportion of the population at risk of insufficiency or excess intake of certain nutrients or food (group), based on one day intake that does not address within-individual variation, may lead to biased estimates.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• The Journal of Korean Philosophical History
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• no.23
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• pp.67-97
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• 2008

In this paper, I found the leadership core-value of the Sunbai(선배) a man of refined taste. I studied twelve Sunbai considered as a man of refined taste in this research. I focused on the aspect showing common worth among their various abilities and cultural contributions, a point of view of classifying them into one form called Sunbai a man of refined taste. As the core value is the basis of supporting the individual identity, so the leadership core-value is the source and philosophical base of displaying the leadership. Our Sunbai combining character and knowledge show common structure in their personal growth and showing the leadership. It is that they embodied the stage of cultivating themselves(修身, Self), considering the relation with others(齊家, Relation), leading the organization(治國, Team), and making efforts for and having influence on the community(平天下, Community) throughout their own life. I examined how to display the leadership in the four fields on a case by case basis and then constructed the leadership model of the Sunbai a man of refined taste by synthesizing them. As a result, I considered their leadership core-value as the intercommunication and named it 'Leadership of Muaesabang(無碍四方)'. Also, I confirmed that it is still very valuable in these days of aiming at the open society and the harmony of the diverse civilization.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.219-240
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• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.

• Journal of Distribution Science
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• v.16 no.12
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• pp.47-56
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• 2018

Purpose - The potential use of cryptocurrencies in a retail environment proposes a rapid shift from the traditional financial system. Nakamoto(2008) defines Bitcoin as an open source alt-coin based on the blockchain technology. Luther(2016) insists that the new technology will be widely adopted for the digital payment processes. However, the use of Bitcoin is in the real world is still sparse. Despite the growing attention and purported benefits, it is doubtful whether the Bitcoin will be eagerly accepted by ordinary consumers in the mainstream market. To answer this question, this paper develops a causal model that has a dual path to explain the motivation to adopt Bitcoin. According to Glaser, Zimmermann, Haferkorn, Weber, and Siering(2014), Bitcoin is both an asset and a currency at the same time. In summary, the attitude towards Bitcoin may vary depending on whether the fin-tech product is viewed as an asset or as a currency. Based on the arguments, we propose that asset attitude and currency attitude will give influence to consumers' intention to adopt Bitcoin. Research design, data, and methodology - Quantitative data collection is conducted from a Bitcoin SIG(special interest group) working in an internet community. As a result, 192 respondents who know Bitcoin completed the survey. To analyze the causal relations in the research model, PLS-SEM(partial least squares structural equation modeling) method is used. Also, reliability and validity of measures are tested by performing Cronbach's alpha test, Fornell-Larcker test and confirmatory factor test. Results - Our test results show that every hypothesis is supported except the influence of perceived ease of use. In addition, we find that the relationships between constructs are different between the high innovative group and low innovative group. Conclusions - We provide evidence that asset attitude and currency attitude are key antecedents of Bitcoin adoption.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.411-418
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• 2003

After the general and multi-function PC age is gone, A new type of computing devices is developing. The need of consumer increases a new device with very necessary function instead of all-mighty functional PC. The operating system of this works is linux because of open source licensing and a strong developer community. The Post PC industry represents and environment that will foster dramatic growth and new development. The adoption of embedded linux in this market will not be driven by simple replacement of expensive proprietary operating systems. In this paper we describe the practical design and implementation project of Post-PC Platform. We illustrate "the development of digital consumer Post-PC platform technology," focus the fourth project, and describe the development works of this project.s project.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.1-7
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• 2013

Big data management is becoming increasingly important in both industry and academia of information science community. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used as information for qualification. This paper presents a big data management architecture specialized for log data. Specifically, it provides the aggregation of log messages sent from multiple clients and provides intelligent functionalities such as analyzing log data. The proposed architecture supports an asynchronous process in client-server architectures to prevent the potential bottleneck of accessing data. Accordingly, it does not affect the client performance although using remote data store. We implement the proposed architecture and show that it works well for processing big log data. All components are implemented based on open source software and the developed prototypes are now publicly available.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.581-591
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• 2020

In this study, two types of reactors were operated to examine the properties of methanol uptake under the high-rate denitrification process. In a sequencing batch reactor, the denitrifying activity was enriched up to 0.80 g-N/g-VSS-day for 72 days. Then, the enriched denitrifying sludge was transferred to a completely stirred tank reactor (CSTR). At the final phase on Day 46-50, the nitrogen removal efficiency was around 100% and the total nitrogen removal rate reached 0.097±0.003 kg-N/㎥-day. During the continuous process, the sludge settling index (SVI30) was stabilized as 118.3 mL/g with the biomass concentration of 1,607 mg/L. The continuous denitrifying process was accelerated by using a sequencing batch reactor (SBR) with a total nitrogen removal rate of 0.403±0.029 kg-N/㎥-day with a high biomass concentration of 8,433 mg-VSS/L. Because the reactor was open to ambient air with the dissolved oxygen range of 0.2-0.5 mg-O2/L, an increased organic carbon requirement of 5.58±0.70 COD/NO3--N was shown for the SBR in comparison to the value of 4.13±0.94 for the test of the same biomass in a completely anaerobic batch reactor. The molecular analysis based on the 16S rRNA gene showed that Methyloversatilis discipulorum and Hyphomicrobium zavarzinii were the responsible denitrifiers with the sole organic carbon source of methanol.