• Journal of Korean Association for Spatial Structures
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• v.1 no.1 s.1
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• pp.125-134
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• 2001

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.489-497
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• 2007

Named entity translation plays an important role in many applications, such as information retrieval and machine translation. In this paper, we focus on translating person names, the most common type of name entity in Korean-Chinese cross language information retrieval (KCIR). Unlike other languages, Chinese uses characters (ideographs), which makes person name translation difficult because one syllable may map to several Chinese characters. We propose an effective hybrid person name translation method to improve the performance of KCIR. First, we use Wikipedia as a translation tool based on the inter-language links between the Korean edition and the Chinese or English editions. Second, we adopt the Naver people search engine to find the query name's Chinese or English translation. Third, we extract Korean-English transliteration pairs from Google snippets, and then search for the English-Chinese transliteration in the database of Taiwan's Central News Agency or in Google. The performance of KCIR using our method is over five times better than that of a dictionary-based system. The mean average precision is 0.3490 and the average recall is 0.7534. The method can deal with Chinese, Japanese, Korean, as well as non-CJK person name translation from Korean to Chinese. Hence, it substantially improves the performance of KCIR.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Structural Engineering and Mechanics
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• v.9 no.2
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• pp.169-180
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• 2000

An optimization procedure has been prescribed for the minimum weight design of symmetrical parabolic arches subjected to arbitrary loading. The cross section is assumed to be a symmetrical box section with variable depth and flange areas. The webs are unstiffened and have constant thickness. The proposed sequential, iterative search technique determines the optimum geometrical configuration of the parabolic arch which includes the optimum depth profile and the optimum lengths and areas of the required flange plates corresponding to the prescribed number of curtailments. The study shows that the optimum value of rise to span ratio (h/L) of a parabolic arch is maximum at 0.41 for uniformly distributed loading over the entire span. For any other loading, the optimum value of h/L is less than 0.41.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.963-968
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• 2006

The main purpose of this paper is to investigate the stability of water tower with a relatively small footing. The water tower is modeled that the column carrying a container is supported by a rotational spring at the base and is of constant cross-section, with a weight per unit length of column axis. The column model is based on the Bernoulli-Euler beam theory. The Runge-Kutta method and Determinant Search method are used to perform the integration of the governing differential equation and to determine the critical values(critical own weight. and critical buckling load), respectively. The critical buckling loads are calculated over a range of system parameters: the rotational stiffness parameter, the dimensionless radius of container and the own weight parameter of the column. The relation between the rotational stiffness parameter and the critical own weight parameter of the column is analyzed.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.904-907
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• 2019

Nuclear energy remains one of the world's major energy sources, making up over 10% of global electricity generation in 2017. Public acceptance of nuclear energy is essential for its adoption. From a practical perspective, it is beneficial to have a simple indicator that can predict the actual adoption of nuclear energy. Based on practical experience, the authors suggest tolerance and self-sufficiency as potential indicators that may predict the adoption of nuclear energy. By evaluating the cross-sectional data of 18 countries in 2013, this research assesses the actual impact of tolerance and self-sufficiency on public acceptance in order to identify the validity of the two variables. The results indicate that the two variables are statistically significant, while public acceptance is insignificant in explaining national adoption of nuclear energy. This may be because tolerance reflects national willingness to accept potential risk, while self-sufficiency explains a government's likelihood of developing non-carbon energy sources.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.757-772
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• 2019

The purpose of this paper is to measure the clustering change and analyze empirical results, and choose the clustering ports for Busan, Incheon, and Gwangyang ports by using Artificial Neural Network, Social Network, and Tabu Search models on 38 Asian container ports over the period 2007-2016. The models consider number of cranes, depth, birth length, and total area as inputs and container throughput as output. Followings are the main empirical results. First, the variables ranking order which affects the clustering according to artificial neural network are TEU, birth length, depth, total area, and number of cranes. Second, social network analysis shows the same clustering in the benevolent and aggressive models. Third, the efficiency of domestic ports are worsened after clustering using social network analysis and tabu search models. Forth, social network and tabu search models can increase the efficiency by 37% compared to that of the general CCR model. Fifth, according to the social network analysis and tabu search models, 3 Korean ports could be clustered with Asian ports like Busan Port(Kobe, Osaka, Port Klang, Tanjung Pelepas, and Manila), Incheon Port(Shahid Rajaee, and Gwangyang), and Gwangyang Port(Aqaba, Port Sulatan Qaboos, Dammam, Khor Fakkan, and Incheon). Korean seaport authority should introduce port improvement plans by using the methods used in this paper.

• Safety and Health at Work
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• v.11 no.3
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• pp.262-265
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• 2020

Coronavirus disease 2019 (Covid-19) poses an important occupational health risk to health workers (HWs) that has attracted global scrutiny. To date, several thousand HWs globally have been reported as infected with the severe acute respiratory syndrome coronavirus 2 virus that causes the disease. It is therefore a public health priority for policymakers to understand risk factors for this vulnerable group to avert occupational transmission. A rapid review was carried out on 20 April 2020 on Covid-19 risk factors among HWs in PubMed, Google Scholar, and EBSCOHost Web (Academic Search Complete, CINAHL Complete, MEDLINE with Full Text, CINAHL with Full Text, APA PsycInfo, Health Source-Consumer Edition, Health Source: Nursing/Academic Edition) and WHO Global Database. We also searched for preprints on the medRxiv database. We searched for reports, reviews, and primary observational studies (case control, case cross-over, cross-sectional, and cohort). The review included studies conducted among HWs with Covid-19 that reported risk factors irrespective of their sample size. Eleven studies met the inclusion criteria. Lack of personal protective equipment, exposure to infected patients, work overload, poor infection control, and preexisting medical conditions were identified as risk factors for Covid-19 among HWs. In the context of Covid-19, HWs face an unprecedented occupational risk of morbidity and mortality. There is need for rapid development of sustainable measures that protect HWs from the pandemic.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.939-948
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• 2004

In this paper. we Propose a robust and fast image registration technique for motion correction in brain CT-CT angiography obtained from same patient to be taken at different time. First, the feature points of two images are respectively extracted by 3D edge detection technique, and they are converted to locally weighted 3D distance map in reference image. Second, we search the optimal location whore the cross-correlation of two edges is maximized while floating image is transformed rigidly to reference image. This optimal location is determined when the maximum value of cross-correlation does't change any more and iterates over constant number. Finally, two images are registered at optimal location by transforming floating image. In the experiment, we evaluate an accuracy and robustness using artificial image and give a visual inspection using clinical brain CT-CT angiography dataset. Our proposed method shows that two images can be registered at optimal location without converging at local maximum location robustly and rapidly by using locally weighted 3D distance map, even though we use a few number of feature points in those images.

• ETRI Journal
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• v.34 no.1
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• pp.87-97
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• 2012

This study presents an automatic matching method for generating a dense, accurate, and discontinuity-preserved digital surface model (DSM) using multiple images acquired by an aerial digital frame camera. The proposed method consists of two main procedures: area-based multi-image matching (AMIM) and stereo-pair epipolar line matching (SELM). AMIM evaluates the sum of the normalized cross correlation of corresponding image points from multiple images to determine the optimal height of an object point. A novel method is introduced for determining the search height range and incremental height, which are necessary for the vertical line locus used in the AMIM. This procedure also includes the means to select the best reference and target images for each strip so that multi-image matching can resolve the common problem over occlusion areas. The SELM extracts densely positioned distinct points along epipolar lines from the multiple images and generates a discontinuity-preserved DSM using geometric and radiometric constraints. The matched points derived by the AMIM are used as anchor points between overlapped images to find conjugate distinct points using epipolar geometry. The performance of the proposed method was evaluated for several different test areas, including urban areas.