• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.4
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• pp.329-334
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• 2017

The structures, which are made up with the huge number of degrees-of-freedom and the assembly of substructures, have a great complexity. In order to increase the computational efficiency, the analysis models have to be simplified. Many substructuring techniques have been developed to simplify large-scale engineering problems. The techniques are very powerful for solving nonlinear problems which require many iterative calculations. In this paper, a modal derivatives-based model order reduction method, which is able to capture the stretching-bending coupling behavior in geometrically nonlinear systems, is adopted and investigated for its performance evaluation. The quadratic terms in nonlinear beam theory, such as Green-Lagrange strains, can be explained by the modal derivatives. They can be obtained by taking the modal directional derivatives of eigenmodes and form the second order terms of modal reduction basis. The method proposed is then applied to a co-rotational finite element formulation that is well-suited for geometrically nonlinear problems. Numerical results reveal that the end-shortening effect is very important, in which a conventional modal reduction method does not work unless the full model is used. It is demonstrated that the modal derivative approach yields the best compromised result and is very promising for substructuring large-scale geometrically nonlinear problems.

• Membrane Journal
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• v.27 no.6
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• pp.487-498
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• 2017

In this study, pervaporation performances of water/methanol and water/butanol mixture were evaluated using zeolite 4A membranes manufacutred by FINETECH by experimental works and numerical modeling. Permeation and separation characteristics, such as flux and separation factor, were analyzed by gas chromatography (TCD) and liquid nitrogen traps. Experiments have shown that water is selectively separated from a mixture of water and methanol (separation factor up to approximately 250) and water and butanol (separation factor up to approximately 1,500). Generalized Maxwell Stefan (GMS) theory was implemented to predict pervaporation behaviors of water/alcohol mixtures and diffusional coefficients of zeolite layer were obtained through parameter estimation using $MATLAB^{(R)}$ optimization toolbox. Since the pore size of zeolite 4A are much larger than kinetic diameter of water molecules and smaller than those of methanol and butanol, zeolite 4A membranes can be applied to in situ water removal process such as membrane reactors or hybrid reaction-dehydration process.

• Journal of Environmental Health Sciences
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• v.36 no.5
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• pp.343-350
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• 2010

This study was performed to investigate laboratory workers' exposures to airborne nanoparticles at a university laboratory where acid treatment experiments were conducted on the surfaces of engineered carbon nanotubes (CNTs). The surface area concentrations, number concentrations, and mass concentrations of airborne nanoparticles were measured at personal breathing zones (PBZs) for various tasks using direct reading instruments. For all three metrics, airborne nanoparticle concentrations during the experiments were higher than background levels measured before and after the experiments for all three metrics. Among the various tasks that were performed as part of these experiments, one task that involved filtering a mixture of acid and CNTs showed the highest concentrations in all three metrics, with concentrations of $116.6\;{\mu}m^2$/cc, 24320 pt/cc, and $9.0\;{\mu}g/m^3$, respectively. Nanoparticle surface area concentrations measured at a representative area fluctuated with those at the PBZs in the laboratory. This result indicates that nanoparticles generated during the experiments were not just limited to the PBZs of the workers but were also present throughout the room, potentially exposing co-located workers. CNTs were detected by a transmission electron microscope in an air sample collected while handling the CNTs. All the tasks were performed inside fume hoods, with the sliding sashes open to their required heights. It was noted that the capture velocities of the fume hoods were much lower than the American National Standards Institute (ANSI)'s recommendation level (0.4 to 0.6 m/s). In conclusion, this study showed that, due to inadequate control, laboratory researchers performing acid treatment experiments on surfaces of CNTs were exposed to airborne nanoparticles generated during the tasks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.49-54
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• 2017

We collected and reared Theragra chalcogramma walleye pollock brood-stock for use in natural spawning tests and undertook to obtain domestic pollock via fertilized egg capture, development of fertilized eggs, and absorption of yolk sac after hatching. Whole pollock were caught with trammel and set nets and immediately placed in a deep-sea water tank. Adults were the most common pollock age group (43.0%; n = 86) among the 254 pollock captured in March 2014 with 57.9% (n = 147) being captured off Southern Gosung, Korea. The main spawning period of pollock is February (spawning phase of 91% of pollock). From the deep-sea tank, we collected 1640 mL of naturally fertilized eggs (~820,000 eggs) from 12 spawning events occurring between February 4 and 22 2015. The floating/ live eggs were maintained in deep-sea water tanks at $5.5{\pm}0.2^{\circ}C$. Egg size was $1.5{\pm}0.03mm$. Six hours after fertilization the eggs were at the 2 cell stage, and the eggs hatched approximately 340 hours after collection. At hatching, larval length and yolk sac area were $5.2{\pm}0.25mm$ and $9.5{\pm}1.00mm^2$ (100%), respectively. Four days after hatching, the yolk sac area was $2.2{\pm}0.53mm^2$ ($23.1{\pm}5.55%$). This is the first report of collection of naturally fertilized eggs from pollock and their subsequent hatching while held in an indoor deep-sea water tank. The results suggest that such collection could assist in the recovery of pollock resources and the possibility of domestic rearing of cultivated larvae.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.5
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• pp.335-347
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• 2013

In February 2008, high storm waves due to a developed atmospheric low pressure system propagating from the west off Hokkaido, Japan, to the south and southwest throughout the East Sea (ES) caused extensive damages along the central coast of Japan and along the east coast of Korea. This study consists of two parts. In the first part, we estimate extreme storm wave characteristics in the Toyama Bay where heavy coastal damages occurred, using a non-hydrostatic meteorological model and a spectral wave model by considering the extreme conditions for two factors for wind wave growth, such as wind intensity and duration. The estimated extreme significant wave height and corresponding wave period were 6.78 m and 18.28 sec, respectively, at the Fushiki Toyama. In the second part, we perform numerical experiments on wave-structure interaction in the Fushiki Port, Toyama Bay, where the long North-Breakwater was heavily damaged by the storm waves in February 2008. The experiments are conducted using a non-linear shallow-water equation model with adaptive mesh refinement (AMR) and wet-dry scheme. The estimated extreme storm waves of 6.78 m and 18.28 sec are used for incident wave profile. The results show that the Fushiki Port would be overtopped and flooded by extreme storm waves if the North-Breakwater does not function properly after being damaged. Also the storm waves would overtop seawalls and sidewalls of the Manyou Pier behind the North-Breakwater. The results also depict that refined meshes by AMR method with wet-dry scheme applied capture the coastline and coastal structure well while keeping the computational load efficiently.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• 한국체육학회지인문사회과학편
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• v.57 no.3
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• pp.345-356
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• 2018

Objective: The purpose of this study was to investigate the local stability of the lower extremity joints and muscle activation patterns of the lower extremity during walking between falling and non-falling group in the elderly women. Method: Forty women, heel strikers, were recruited for this study. Twenty subjects (age:72.55±5.42yrs; height:154.40±4.26cm; mass:57.40±6.21kg; preference walking speed:0.52±0.17m/s; fall frequency=1.70±1.26 times) had a history falls(fall group) within two years and Twenty subjects (71.90±2..90yrs; height:155.28±4.73cm; mass:56.70±5.241kg; preference walking speed: 0.56±0.13m/s) had no history falls(non-fall group). While they were walking on a instrumented treadmill at their preference speed for a long while, kinematic and EMG signals were obtained using 3-D motion capture and wireless EMG electrodes, respectively. Local stability of the ankle and knee joint were calculated using Lyapunov Exponent (LyE) and muscles activation and their co-contraction index were also quantified. Hypotheses were tested using one-way ANOVA and Mann-Whitey. Spearman rank was also used to determine the correlation coefficients between variables. Level of significance was set at p<.05. Results: Local stability in the knee joint adduction-abduction was significantly greater in fall group than non-fall group(p<.05). Activation of anterior tibials that acts on the foot segment dorsal flexion was greater in non-fall group than fall group(p<.05). CI between gastrocnemius and anterior tibials was found to be significantly different between two groups(p<.05). In addition, there was significant correlation between CI of the leg and LyE of the ankle joint flexion-extention in the fall group(p<.05). Conclusion: In conclusion, muscles that act on the knee joint abduction-adduction as well as gastrocnemius and anterior tibials that act on the ankle joint flexion-extention need to be strengthened to prevent from potential fall during walking.

• Journal of Life Science
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• v.28 no.7
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• pp.835-840
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• 2018

Quantitative determination of the protein expression levels is one of the most important parts in assessment of the safety of foods derived from genetically modified (GM) crops. Overexpression of AtCYP78A7, a gene encoding cytochrome P450 protein, has been reported to improve tolerance to abiotic stress, such as drought and salt stress, in transgenic rice (Oryza sativa L.). In the present study, an enzyme-linked immunosorbent assay (ELISA) kit for diagnosing AtCYP78A7 protein including AtCYP78A7-specific monoclonal antibody was developed. GST-AtCYP78A7 recombinant protein was induced and purified by affinity column. Four monoclonal antibodies (mAb 6A7, mAb 4C2, mAb 11H6, and mAb 7E8) against recombinant protein were also produced and biotinylated with avidin-HRP. After pairing test using GST-AtCYP78A7 protein and lysate of rice samples, mAb 4C2 and mAb 7E8 were selected as a capture antibody and a detecting antibody, respectively, for ELISA kit. Product test using rice samples indicated that percentages of detected protein in total protein were greater than 0.1% in AtCYP78A7-overexpressing transgenic rice (Line 10B-5 and 18A-4), whereas those in negative control non-transgenic rice (Ilpum and Hwayoung) were less than 0.1%. The ELISA kit developed in this study can be useful for the rapid detection and safety assessment of transgenic rice overexpressing AtCYP78A7.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.