• KSBB Journal
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• v.21 no.1 s.96
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• pp.28-33
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• 2006

A sewage was treated using a horizontal flow zeolite-filled reed bed. The sewage from the student dormitory of Changwon National University was fed into the reed bed for 10 minutes every 6 hours at the hydraulic load of $314L/m^2$ day. The filtering height of the reed bed was 100 cm and the zeolite mixture was filled in the reed bed. The mixture consisted of the same volume of two types of zeolite : $0.5{\sim}1mm$ and $1{\sim}3mm$ in diameter. Annual average removal efficiency was SS $88.5%,\;COD_{cr},\;86.1%,\;COD_{Mn}\;81.0%,\;T-N\;48.6%,\;NH_4^+-N\;97.1%$ and T-P 42.8%. T-N of effluent was mostly $NO_3^--N$ and the concentration of $NO_2^--N$ in effluent was lower than 0.1 mg/L. All removal efficiencies did not show a remarkable seasonal change.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9A
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• pp.905-915
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• 2007

This paper proposes a filter-based algorithm to adaptively adjust the contention window in IEEE 802.11 DCF. The proposed mechanism is focused on the general and realistic environments that have various conditions regarding to noise, media types and network load. For this flexible adaptation, Filter-based DCF(FDCF) takes a more realistic policy such as median filter concept in the image processing technologies. We can handle these various environments by adjusting the contention window size according to the result of filtering based on history-buffer. We can ignore temporarily and randomly occurred transmission failures due to noise errors and collisions in noisy environments. In addition, by changing the reference number and history-buffer size, FDCF can be extended as a general solution including previous proposed mechanism. We have confirmed that the proposed mechanism can achieve the better performance than those of previous researches in aspects of the throughput and the delay in the realistic environments.

• The KIPS Transactions:PartC
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• v.8C no.6
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• pp.855-864
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• 2001

With the increasing importance and complexity of EC (Electronic Commerce) across the Internet, the need and expectation for intelligent software agents to support both consumers and suppliers through the whole process of EC are growing rapidly. To realize the intelligent EC. a multiagent based EC system. which includes foundational technologies such as the establishment of standard product ontology the definition of message and negotiation protocol and brockering, is required. In this paper we propose an intelligent EC System named ICOMA(Intelligent electronic CO mmerce system based on Multi-Agent) as an open infrastructure of multiagent-based EC. Concretely we have proposed. designed and implemented an architecture of multiagent-based EC system including 6-types of agents message protocol for inter-agent negotiation, personalized produst retrieval and filtering., We have confirmed the effectiveness of the system through experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.3
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• pp.452-460
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• 2008

ALE-complient RFID middleware system receives EPC code data from reader devices and converts the data into URN format data internally. After filtering and grouping, the system sends the resulting URN code to application and(or) users. Meanwhile, not only the types of EPC code are very diverse, but also another new kinds of EPC code can be emerged in the future. Therefore, a method to process all kinds of EPC code effectively is required by RFID middleware. In this paper, a method to process various kinds of EPC code acquired from RFID reader devices in ALE-complient RFID middleware is proposed. Especially, we propose an approach using ontology technology to process not only existing EPC code but also newly defined code in the future. That is, we build an ontology of conversion rules for each EPC data type to effectively convert EPC data into URL format data. In this case, we can easily extend RFID middleware to process a new EPC code data by adding a conversion rule ontology for the code.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.83-93
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• 2021

Agricultural reservoirs are important facilities for storing or managing water for the purpose of securing agricultural water, creating and expanding agricultural production bases, and using them to increase agricultural production. In particular, the Korea Rural Community Corporation (KRC) manages agricultural reservoirs scattered across the country, and officially recognizes and distributes hydrological data to increase their public utilization and aims to improve the value of water resources. Data on the water level of agricultural reservoirs are important. However, errors such as missing values and outliners limit utilization of the data in various fields of research and industry. Therefore, water quality data measures should be devised to increase reliability. this study categorized different error types and looked at automatic correction methods to enhance the reliability of the vast hydrological data. In addition, the water level data corrected from errors were compared to the reference hydrologic data through expert judgment in accordance with the quality control procedure, and the most appropriate measures were verified. As KRC manages more agricultural reservoirs than any other institution, the proposed method of efficient and automatic water level data correction in this study is expected to increase the availability and reliability of the hydrological data.

• Database Research
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• v.34 no.3
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• pp.99-123
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• 2018

Due to the ever expanding nature of the Web, separating more valuable information from the noisy data is getting more important. Although recommendation systems are widely used for addressing the information overloading issue, their performance does not seem meaningfully improved in currently suggested approaches. Hence, to investigate the issues, this study discusses different characteristics of popular, existing recommendation approaches, and proposes a new profiling technique that uses collaborative tags and test whether it successfully compensates the limitations of the existing approaches. In addition, the study also empirically evaluates rating/tagging patterns of users in various recommendation approaches, which include the proposed approach, to learn whether those patterns can be used as effective cues for improving the recommendations accuracy. Through the sensitivity analyses, this study also suggests the potential associated with a single recommendation system that applies multiple approaches for different users or items depending upon the types and contexts of recommendations.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.106-112
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• 2019

The Korea Copyright Commission has pursued copyright technology R&D projects to prevent illegal copying of comics and Webtoons. We developed a feature-based scanned comic filtering technology in order to apply technical measures to specific types of online service providers. We also developed technologies in order to monitor and identify illegally distributed comics on webhard sites and to monitor and identify illegally distributed webtoons. Even though all comic books posted on webhard sites are illegal, it is no trouble to download and access popular comics by accessing websites in foreign countries. Even under these circumstances, the comic and webtoon copyright protection technologies developed over the past six years have been used at all. In this paper, we examine what the problems are and find solutions to propose a copyright protection method for webtoons.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1234-1239
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• 2019

This paper proposes a method for measuring fine dust concentration through digital processing of images captured by only existing CCTVs without additional equipment. This image processing algorithm consists of noise reduction, edge sharpening, ROI setting, edge strength calculation, and correction through HSV conversion. This algorithm is implemented using the C ++ OpenCV library. The algorithm was applied to CCTV images captured over a month. The edge strength values calculated for the ROI region are found to be closely related to the fine dust concentration data. To infer the correlation between the two types fo data, a trend line in the form of a power equation is established using MATLAB. The number of data points deviating from the trend line accounts for around 12.5%. Therefore, the overall accuracy is about 87.5%.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.561-568
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• 2023

Various defects occur in concrete structures due to internal and external environments. If there is a defect, it is important to efficiently identify and maintain it because there is a problem with the structural safety of concrete. However, recent deep learning research has focused on cracks in concrete, and studies on exfoliation and contamination are lacking. In this study, focusing on exfoliation and contamination, which are difficult to label, four models were developed and their performance evaluated through unlabelling method, filtering method, the convergence of transfer learning based k-means clustering. As a result of the analysis, the convergence model classified the defects in the most detail and could increase the efficiency compared to direct labeling. It is hoped that the results of this study will contribute to the development of deep learning models for various types of defects that are difficult to label in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.71-82
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• 2024

With the advancement of information and communication technology (ICT), the use of electronic document types such as PDF, MS Office, and HWP files has increased. Such trend has led the cyber attackers increasingly try to spread malicious documents through e-mails and messengers. To counter such attacks, AI-based methodologies have been actively employed in order to detect malicious document files. The main challenge in detecting malicious HWP(Hangul Word Processor) files is the lack of quality dataset due to its usage is limited in Korea, compared to PDF and MS-Office files that are highly being utilized worldwide. To address this limitation, data augmentation have been proposed to diversify training data by transforming existing dataset, but as the usefulness of the augmented data is not evaluated, augmented data could end up harming model's performance. In this paper, we propose an effective semi-supervised learning technique in detecting malicious HWP document files, which improves overall AI model performance via quantifying the utility of augmented data and filtering out useless training data.