• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.213-220
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• 2011

This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.535-540
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• 2016

Coreference resolution is to identify mentions in documents and is to group co-referred mentions in the documents. It is an essential step for natural language processing applications such as information extraction, event tracking, and question-answering. Recently, various coreference resolution models based on ML (machine learning) have been proposed, As well-known, these ML-based models need large training data that are manually annotated with coreferred mention tags. Unfortunately, we cannot find usable open data for learning ML-based models in Korean. Therefore, we propose an efficient coreference resolution model that needs less training data than other ML-based models. The proposed model identifies co-referred mentions using random forests based on sieve-guided features. In the experiments with baseball news articles, the proposed model showed a better CoNLL F1-score of 0.6678 than other ML-based models.

• Herbal Formula Science
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• v.29 no.4
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• pp.285-295
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• 2021

Objective : This systematic review was conducted to investigate the effect of herbal medicine on Helicobacter pylori(H. pylori) infection compared to amoxicillin included synthetic drugs. Methods : Relevant randomized controlled trials(RCTs) which were published prior to December 26, 2020, were collected using PubMED database. Risk of bias evaluation and data extraction were done independently by two reviewers, and the third reviewer reassessed mismatching parts. Results : Two RCTs testing two different herbal medicines against synthetic drugs solitary treatment or synthetic drugs with placebo for herbal medicine were included. One study reported that there was no significant difference between the eradication rate of synthetic drugs and the herbal medicine. The other study did not report the eradication rate of the herbal medicine. One study reported histologic severity, the other reported dyspepsia score as efficacy indicators. There was no adverse event reported in all studies. However, the number of included RCTs was too small, the quality of reported data was not enough to verify efficacy of herbal medicine, and there were some methodological problems. Conclusion : It was difficult to conclude that solitary treatment of herbal medicine was as effective as amoxicillin included synthetic drugs for H. pylori infection.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.189-195
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• 2018

With the advent of the digital age, production and distribution of web pages has been exploding. Internet users frequently need to extract specific information they want from these vast web pages. However, it takes lots of time and effort for users to find a specific information in many web pages. While search engines that are commonly used provide users with web pages containing the information they are looking for on the Internet, additional time and efforts are required to find the specific information among extensive search results. Therefore, it is necessary to develop algorithms that can automatically extract specific information in web pages. Every year, thousands of international conference are held all over the world. Each international conference has a website and provides general information for the conference such as the date of the event, the venue, greeting, the abstract submission deadline for a paper, the date of the registration, etc. It is not easy for researchers to catch the abstract submission deadline quickly because it is displayed in various formats from conference to conference and frequently updated. This study focuses on the issue of extracting abstract submission deadlines from International conference websites. In this study, we use three machine learning models such as SVM, decision trees, and artificial neural network to develop algorithms to extract an abstract submission deadline in an international conference website. Performances of the suggested algorithms are evaluated using 2,200 conference websites.

• The Journal of Economics, Marketing and Management
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• v.8 no.2
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• pp.13-17
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• 2020

Purpose: In modern corporate management, the establishment of a crisis management system that minimizes damage through measures used to respond to corporate crises is no longer an option. The importance of corporate reputation and brand asset management in modern enterprise management cannot be overemphasized and negative events that might arise from a number of different causes can cause brand crises. Research design, data and methodology: More than half of the questionnaire respondents were female (252 or 53%). More than a fourth of the respondents were aged 20 (122 or 26%) and the number of married participants was 196 (41%). Of the participants, 32% (153) had graduated from college. Only 18% (87) were employees and the monthly household income was 121. In this study, we conducted factor analysis in order to extract the variables that may enhance the explanation capability of each variable. For the method of factor extraction, an Eigen value of at least 1 was used as was factor loading. An analysis was performed using the Cronbach's alpha coefficient to verify the reliability of the measurement scale. Results: First, the analysis of the impact of the social responsibility activities on brand image revealed that the social, economic, philanthropic, ethical, and environmental responsibility activities significantly affected brand image, but legal responsibility activities were not statistically significant. Second, the analysis of the impact of brand image on loyalty showed that brand image had a significant impact on loyalty. Third, the analysis of the impact of social responsibility activities on loyalty showed that they had a significant impact on loyalty. Conclusions: The pro-social enterprise image is not only a brand asset that can be shared, but also a heavy proposition followed by a corresponding social responsibility, it will have to practice transparent corporate management based on clear principles through the establishment of various systems and the implementation of a strict code of conduct within the enterprise.

• The korean journal of orthodontics
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• v.51 no.3
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• pp.199-216
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• 2021

Objective: The aim of this systematic review was to evaluate the complications and side effects associated with the clinical use of orthodontic miniscrews by systematically reviewing the best available evidence. Methods: A survey of articles published up to March 2020 investigating the complications associated with miniscrew insertion, in both the maxilla and mandible, was performed using 7 electronic databases. Clinical studies, case reports, and case series reporting complications associated with the use of orthodontic miniscrew implants were included. Two authors independently performed study selection, data extraction, and risk-of-bias assessment. Results: The database survey yielded 24 articles. The risk-of-bias assessment revealed low methodological quality for the included studies. The most frequent adverse event reported was root injury with an associated periradicular lesion, vitality loss, pink discoloration of the tooth, and transitory loss of pulp sensitivity. Chronic inflammation of the soft tissue surrounding the miniscrew with mucosal overgrowth was also reported. The other adverse events reported were lesion of the buccal mucosa at the insertion site, soft-tissue necrosis, and perforation of the floor of the nasal cavity and maxillary sinus. Adverse events were also reported after miniscrew removal and included secondary bleeding, miniscrew fracture, scars, and exostosis. Conclusions: These findings highlight the need for clinicians to preliminarily assess generic and specific insertion site complications and side effects.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.371-384
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• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.43-54
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• 2016

The occurrence of cyber crime is on the rise every year, and the security control center, which should play a crucial role in monitoring and early response against the cyber attacks targeting various information systems, its importance has increased accordingly. Every endeavors to prevent cyber attacks is being attempted by information security personnel of government and financial sector's security control center, threat response Center, cyber terror response center, Cert Team, SOC(Security Operator Center) and else. The ordinary method to monitor cyber attacks consists of utilizing the security system or the network security device. It is anticipated, however, to be insufficient since this is simply one dimensional way of monitoring them based on signatures. There has been considerable improvement of the security control system and researchers also have conducted a number of studies on monitoring methods to prevent threats to security. In accordance with the environment changes from ESM to SIEM, the security control system is able to be provided with more input data as well as generate the correlation analysis which integrates the processed data, by extraction and parsing, into the potential scenarios of attack or threat. This article shows case studies how to detect the threat to security in effective ways, from the initial phase of the security control system to current SIEM circumstances. Furthermore, scenarios based security control systems rather than simple monitoring is introduced, and finally methods of producing the correlation analysis and its verification methods are presented. It is expected that this result contributes to the development of cyber attack monitoring system in other security centers.

• Analytical Science and Technology
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• v.28 no.2
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• pp.117-124
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• 2015

Genetically modified (GM) papaya line 55-1, which is resistant to PRSV infection, has been marketed globally. Prompt and sensitive protocols for specific detections are essential for the traceability of this line. Here, an event- and construct-specific real-time polymerase chain reaction (RT-PCR) method was established to detect 55-1. Qualitative detection was possible for fresh papaya fruit up to dilutions of 0.005% and 0.01% for the homozygous SunUp and heterozygous Rainbow cultivars, respectively, in non-GM papaya. The method was applied in the qualitative detection of 55-1 in eight types of commercially processed papaya products. Additionally, papaya products were monitored to distinguish GM papaya using the P35S and T-nos RT-PCR detection methods. As expected, detection capacity was improved via modified sample preparation and the established RT-PCR detection method. Taking these results together, it can be suggested that a suitable method for the extraction and purification of DNA from processed papaya products was established for the detection of GM papaya.