• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.39-45
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• 2020

Recently the terrors have been attempted in the public places of the nations such as United states, England and Japan by explosive things, toxic materials and so on. It is understood that the method in which dangerous objects are put in public places is one of the difficult types in detection. While there are the cameras recording videos for many spots in public places, it is very hard for the security personnel to monitor every videos. Nowadays the smart softwares which can analyzing videos automatically are utilized to detect abandoned objects. The method by Lin et al. shows comparatively high detection rates for abandoned objects but it is not easy to obtain the shape information because there is a tendency that the number of the pixels decreases abruptly along the time goes due to the characteristics of short-term background images. In this research a novel method is proposed to successfully extract the shape of the abandoned object by analysing the characteristics of area variation. The experiment results show that the proposed method has better performance in extracting shape information in comparison with the precedent approach.

• Journal of KIISE
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• v.44 no.8
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• pp.774-781
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• 2017

Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term "mention detection" relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.

• Advances in Energy Research
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• v.4 no.2
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• pp.121-146
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• 2016

This study is concerned with the analysis of two renewable technologies for electric energy production: wind energy and photovoltaic energy. The two technologies were assessed and compared by economic point of view, by using selected indicators characterized by a clear calculation approach, requirement of information easy to be collected, clear, but even complete, interpretation of results. The used economic indicators are Levelized Cost of Energy, $CO_2$ abatement cost and fossil fuel saving specific cost; these last two specifically aimed at evaluating the different capabilities that renewable technologies have to cut down direct $CO_2$ emissions and to avoid fossil fuel extraction. The two technologies were compared also from the environmental point of view by applying Life Cycle Assessment approach and using the environmental impact categories from the Eco-indicator'95 method. The economic analysis was developed by taking into account different energy system sizes and different geographic areas in order to compare different European conditions (Italy, Germany and Denmark) in term of renewable resource availability and market trend. The environmental analysis was developed comparing two particular types of PV and wind plants, respectively residential and micro-wind turbine, located in Italy. According to the three calculated economic indicators, the wind energy emerged as more favorable than PV energy. From the environmental point of view, both the technologies are able to provide savings for almost all the considered environmental impact categories. The proposed approach, based on the use of economic and environmental indicators may be useful in supporting the policies and the decision making procedures concerned with the promotion and use of renewables, in reference to the specific geographic, economic and temporal conditions.

• Journal of The Korean Society of Clinical Toxicology
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• v.10 no.2
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• pp.103-110
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• 2012

Purpose: The aim of this study was to evaluate the cardiovascular manifestations and clinical course in patients with acute carbon monoxide poisoning. Methods: A retrospective study was conducted over a 36 month period on consecutive patients who visited an emergency medical center and were diagnosed with acute carbon monoxide poisoning. A standardized data extraction protocol was performed on the selected patients. Results: A total of 293 patients were selected during the study period. Cardiac manifestations were observed in 35.2% (n=103) of the patients: hypotension in 11 patients (3.8%), ECG abnormalities in 44 patients (15.0%) and cardiac enzyme abnormalities in 103 patients (35.2%). Echo cardiography was performed on 56 patients with cardiac toxicity: 12 patients had abnormal results (5 patients with global hypokinesia and 7 patients with regional wall akinesia). Five patients died within 3 hours after ED admission, and the remaining patients were discharged alive. At 3 months after discharge, none of these patients had died.The SOFA scores in the severe cardiac toxicity group and non-severe cardiac toxicity group at the time of arrival were $2.53{\pm}2.29$ and $2.19{\pm}2.12$, respectively (p=0.860). Conclusion: Cardiovascular manifestations occur after acute CO poisoning at arateof 35.2%. Even those with severe cardiovascular toxicity recovered well within 10 days after admission. Therefore, the importance of cardiac toxicity after acute CO poisoning is not significant in itself in the clinical course, and the short-term prognosis of cardiac toxicity is unlikely to be unfavorable in acute CO poisoning.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.137-148
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• 2008

Computerization is recognized as simple supporting tools up to recently but Information is made for the improvement of company's outcome beyond the assistant tools and is emerged as core tools of creating new opportunities for company in point of management strategy. But Information plays minor role in Korean Institute of Oriental Medicine(KIOM) yet. Information has not processed systematically because of lack of understanding and budget in KIOM. The purpose of this research is to make plan for knowledge information which is aimed to make effective supporting and strengthening the ability of KIOM's research, until 2013. Also we tried to find new project s for information and to apply the result to planned information projects. The final goal of this study is to make mid/long-term information plan of KIOM. We applied the general information strategy planning(ISP) method which is progressed as analysis of information technology, analysis of information status, design of future model and extraction of action plan order. First, we examined the information elements for the advanced management from analysis of management environment and analyzed information technology trends. Second, we list up the gaps and problems of KIOM's information status and define the future model to solve this problems. It is showed the road map of time and budget for future model to progress systematically inner-information of purpose. We hope that information of KIOM will be conducted successfully.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.4 s.310
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• pp.11-20
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• 2006

This paper presents a method for detecting tumors on chicken carcasses by fusion of hyperspectral fluorescence and reflectance images. Classification of normal skin and tumor is performed by the image obtain 어 from optimal band ratio which minimizes the overlapping area of PDFs for normal skin and tumor. This method yields four feature images, each of them represents the ratio of two intensity values from a pixel. Classification is achieved by applying ISODATA to each pixel from the feature images. For the analysis of reflectance image, band selection method is proposed based on the information quantity, many effective features are acquired for the classification by defining the linear transformation selecting the projection axis, accordingly, accurate interpretation of images is possible in the reflectance image and automatic feature selection method is realized. Feature images from reflectance images are also classified by ISODATA and combined with the result from fluorescence images. Experimental result indicates that improved performance in term of reducing false detection rate is observed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.1085-1092
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• 2017

In this paper, we propose a wind forecasting method that reflects wind characteristics to improve the accuracy of wind power prediction. The proposed method consists of extracting wind characteristics and predicting power generation. The part that extracts the characteristics of the wind uses correlation analysis of power generation amount, wind direction and wind speed. Based on the correlation between the wind direction and the wind speed, the feature vector is extracted by clustering using the K-means method. In the prediction part, machine learning is performed using the SVR that generalizes the SVM so that an arbitrary real value can be predicted. Machine learning was compared with the proposed method which reflects the characteristics of wind and the conventional method which does not reflect wind characteristics. To verify the accuracy and feasibility of the proposed method, we used the data collected from three different locations of Jeju Island wind farm. Experimental results show that the error of the proposed method is better than that of general wind power generation.

• Journal of KIISE
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• v.44 no.11
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• pp.1236-1243
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• 2017

In this paper, we propose a novel way of producing keyword networks, named LSI-based ClusterTextRank, which extracts significant key words from a set of clusters with a mutual information metric, and constructs an association network using latent semantic indexing (LSI). The proposed method reduces the dimension of documents through LSI, decomposes documents into multiple clusters through k-means clustering, and expresses the words within each cluster as a maximal spanning tree graph. The significant key words are identified by evaluating their mutual information within clusters. Then, the method calculates the similarities between the extracted key words using the term-concept matrix, and the results are represented as a keyword association network. To evaluate the performance of the proposed method, we used travel-related blog data and showed that the proposed method outperforms the existing TextRank algorithm by about 14% in terms of accuracy.

• Analytical Science and Technology
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• v.23 no.5
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• pp.477-484
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• 2010

Personal care products are a diverse group of synthetic organic chemicals such as antimicrobial compounds, UV filters and organo-phosphate flame retardants and derived from individual usages of soaps, toothpaste and cosmetics. It has been detected in municipal sewage effluent and various environmental samples such as surface water, marine, soil, sediment and aquatic biota in many countries. The occurrence of personal care products in environmental samples could negatively impact the health of the ecosystem and humans, due to persistent, long-term chronic exposure of aquatic organisms. In this study, fifteen personal care products in aquatic environmental samples were determined by gas chromatography-mass spectrometry (GC-MS) with liquidliquid extraction (LLE). Method detection limits were in the range of $0.004\sim0.273\;{\mu}g/L$. Two compounds (TCEP, TCPP) were detected in surface waters and seven compounds (triclosan, 4-MBC, EHMC, BP-3, TCEP, TPP, TBEP) were detected in sewage treatment plants (STP) influents or effluents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4467-4488
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• 2014

The bibliometric analysis is widely used for understanding research domains, trends, and knowledge structures in a particular field. The analysis has majorly been used in the field of information science, and it is currently applied to other academic fields. This paper describes the analysis of academic literatures for classifying research domains and for suggesting empty research areas in the telecommunications policy. The application software is developed for retrieving Thomson Reuters' Web of Knowledge (WoK) data via web services. It also used for conducting text mining analysis from contents and citations of publications. We used three text mining techniques: the Keyword Extraction Algorithm (KEA) analysis, the co-occurrence analysis, and the citation analysis. Also, R software is used for visualizing the term frequencies and the co-occurrence network among publications. We found that policies related to social communication services, the distribution of telecommunications infrastructures, and more practical and data-driven analysis researches are conducted in a recent decade. The citation analysis results presented that the publications are generally received citations, but most of them did not receive high citations in the telecommunications policy. However, although recent publications did not receive high citations, the productivity of papers in terms of citations was increased in recent ten years compared to the researches before 2004. Also, the distribution methods of infrastructures, and the inequity and gap appeared as topics in important references. We proposed the necessity of new research domains since the analysis results implies that the decrease of political approaches for technical problems is an issue in past researches. Also, insufficient researches on policies for new technologies exist in the field of telecommunications. This research is significant in regard to the first bibliometric analysis with abstracts and citation data in telecommunications as well as the development of software which has functions of web services and text mining techniques. Further research will be conducted with Big Data techniques and more text mining techniques.