• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.98-107
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• 2017

In these days, the increase of applications that highly use network resources has revealed the limitations of the current research phase from the traffic classification for network management. Various researches have been conducted to solutions for such limitations. The representative study is automatic finding of the common pattern of traffic. However, since the study of automatic signature generation is a semi-automatic system, users should collect the traffic. Therefore, these limitations cause problems in the traffic collection step leading to untrusted accuracy of the signature verification process because it does not contain any of the generated signature. In this paper, we propose an automated traffic collection, signature management, signature generation and signature verification process to overcome the limitations of the automatic signature update system. By applying the proposed method in the campus network, actual traffic signatures maintained the completeness with no false-positive.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.602-613
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• 2023

High-quality image datasets are in high demand for various applications. With many online sources providing manually collected datasets, a persisting challenge is to fully automate the dataset collection process. In this study, we surveyed an automatic image dataset generation field through analyzing a collection of existing studies. Moreover, we examined fields that are closely related to automated dataset generation, such as query expansion, web scraping, and dataset quality. We assess how both noise and regional search engine differences can be addressed using an automated search query expansion focused on hypernyms, allowing for user-specific manual query expansion. Combining these aspects provides an outline of how a modern web scraping application can produce large-scale image datasets.

• Journal of Preventive Medicine and Public Health
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• v.41 no.4
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• pp.219-224
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• 2008

Due to heightened concerns regarding possible bioterrorist attacks, the Korea Center for Disease Control and Prevention introduced syndromic surveillance systems, which have been run by emergency departments in hospitals throughout Korea since 2002. These systems are designed to identify illness clusters before diagnoses are confirmed and reported to public health agencies, to mobilize a rapid response, and thereby to reduce morbidity and mortality. The Korea Center for Disease Control and Prevention performed drop-in syndromic surveillance successfully during the World Cup Football Games in 2002, the Universiad games in 2004, and the Asian Pacific Economic Cooperation meeting in 2005. In addition, sustainable syndromic surveillance system involving the collaborative efforts of 125 sentinel hospitals has been in operation nationwide since 2002. Because active data collection can bias decisions a physician makes, there is a need to generate an automatic and passive data collection system. Therefore, the Korea Center for Disease Control and Prevention plans to establish computerized automatic data collection systems in the near future. These systems will be used not only fur the early detection of bioterrorism but also for more effective public health responses to disease.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.4
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• pp.1-26
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• 2017

This study aims to provide the efficient sharing and utilization method of disasters scattered by each institution and develop automated collection algorithm using web crawler for disaster information in deep web accounts. To achieve these goals, this study analyzes the logical structure of the deep web and develops algorithms to collect the information. With the proposed automatic algorithm, it is expected that disaster management will be helped by sharing and utilizing disaster safety information.

• Journal of Information Processing Systems
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• v.5 no.3
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.164-171
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• 2023

To control the spread of COVID-19, it is important to identify the infection in its incipient stages so that the infected persons can be dealt with accordingly. The currently used face to face sampling method may increase the risk of infection for medical professionals as it exposes them to the asymptomatic yet infectious patients. This can result in further increases in the load on the medical system and workload of the medical staff. As a solution to this problem, in this paper, we present a robotic system for rapid non-face-to-face automatic nasopharyngeal swab sample collection. The system consists of a custom designed 7-DoF manipulator equipped with a specially developed safety mechanism for restricting the maximum force applied by the tip of the swab. During the swab sampling process, the force applied by the tip of the swab is continuously monitored in real-time by a 3-axis force sensor in order to detect contact with the nasopharynx. The possibility of using this system for automaticnasopharyngeal swab sample collection is proven through experimentation with a phantom model.

• Journal of the Korea Organic Resources Recycling Association
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• v.21 no.1
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• pp.25-35
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• 2013

• International conference on construction engineering and project management
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• 2011.02a
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• pp.187-194
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• 2011

Optimizing truck dispatching-intervals is imperative in ready mixed concrete (RMC) delivery process. Intervals shorter than optimal may induce queuing of idle trucks at a construction site, resulting in a long delivery cycle time. On the other hand, intervals longer than optimal can trigger work discontinuity due to a lack of available trucks where required. Therefore, the RMC delivery process should be systematically scheduled in order to minimize the occurrence of waiting trucks as well as guarantee work continuity. However, it is challenging to find optimal intervals, particularly in urban areas, due to variations in both traffic conditions and concrete placement rates at the site. Truck dispatching intervals are usually determined based on the concrete plant managers' intuitive judgments, without sufficient and reliable information regarding traffic and site conditions. Accordingly, the RMC delivery process often experiences inefficiency and/or work discontinuity. Automatic data collection (ADC) techniques (e.g., RFID or GPS) can be effective tools to assist plant managers in finding optimal dispatching intervals, thereby enhancing delivery performance. However, quantitative evidence of the extent of performance improvement has rarely been reported to data, and this is a central reason for a general reluctance within the industry to embrace these techniques, despite their potential benefits. To address this issue, this research reports on the development of a discrete event simulation model and its application to a large-scale building project in Abu Dhabi. The simulation results indicate that ADC techniques can reduce the truck idle time at site by 57% and also enhance the pouring continuity in the RMC delivery process.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.181-186
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• 2006

Estimation based on current data of construction performances have become one of the critical subjects which many researchers have been interested in for the past decades. In order to accomplish accurate measurement and estimation of construction performances, the method of data collection stands the highest priority. However, there are many difficulties in data collection from construction jobsite due to the characteristics of the construction industry. With developments of new technologies in other industries, several technologies has recently initiated to be applied to construction field. Electronic tags based on the identification technology, automatic volume measurement based on laser scanning technology, and Global Positioning System (GPS) have been represented the technologies which show the high opportunity for being used in construction. This study reviews specific aspects of these technologies focused on the utilization in construction jobsite. Also, the challenges which these technologies need to overcome are discussed.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.421-424
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• 2000

In this paper, We presented overview and characteristics of ITS(Intelligent Transport System) DSRC(Dedicated Short Range Communication) system that was introduced to the basic installation for ITS service and proposed system architecture and implementation methods to realize automatic collection of parking charge using DSRC system.