• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.579-585
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• 2019

Increasing the detection probability of underwater targets necessitates securing the towing stability of the active towed SONAR. In this paper, to confirm the effects of tail wing fin on towing attitude and towing stability, two scale model experiments and one sea trials were conducted and the results were analyzed. The scale model tests measured the towing behavior of each of the tail fin shapes according to towing speed in a towing tank. The shape of the tail fin used in the scale model test was tested with an I-type tail fine and four Y-type tail fins, totaling five tail fins of the two kinds. The first scale model test confirmed that the Y-type tail fin was superior to the I-type tail fin in towing attitude and towing stability. The second scale model test confirmed the characteristics of the vertical tail fin height increase and the lower horizontal tail fin inclination angle application shape based on the Y-type tail fin. The shape of the application of the lower horizontal tail fin inclination angle showed the best performance. In order to verify the results of the scale model test, a full size model was constructed, sea trials were performed, and the towing attitude was measured. The results were similar to those of the scale model test.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.2
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• pp.107-119
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• 2022

The frequent detection and occurrence of micropollutants (MPs) in aquatic ecosystems has raised public health concerns worldwide. In this study, the behavior of 50 MPs was investigated in three different domestic wastewater treatment plants (WWTPs). Furthermore, the Kruskal-Wallis test was used to assess the geographical and seasonal variation of MPs in the WWTPs. The results showed that the concentrations of 43 MPs ranged from less than 0.1 to 237.6 ㎍ L^-1, while other seven MPs including 17-ethynylestradiol, 17-estradiol, sulfathiazole, sulfamethazine, clofibric acid, simvastatin, and lovastatin were not detected in all WWTPs. Among the detected MPs, the pharmaceuticals such as metformin, acetaminophen, naproxen, and caffeine were prominent with maximum concentrations of 133.4, 237.6, 71.5, and 107.7 ㎍ L^-1, respectively. Most perfluorinated compounds and nitrosamines were found at trace levels of 1.2 to 55.3 ng L^-1, while the concentration of corrosion inhibitors, preservatives (parabens), and endocrine disruptors ranged from less than 0.1 to 4310.8 ng L^-1. Regardless of the type of biological treatment process such as MLE, A2O, and MBR, the majority of pharmaceuticals (except lincomycin, diclofenac, iopromide, and carbamazepine), parabens (except Methyl paraben), and endocrine disruptors were removed by more than 80%. However, the removal efficiencies of certain MPs such as atrazine, DEET, perfluorinated compounds (except PFHxA), nitrosamines, and corrosion inhibitors were relatively low or their concentration even increased after treatment. The results of statistical analysis reveal that there is no significant geographical difference in the removal efficacy of MPs, but there are temporal seasonal variations in all WWTPs.

• Journal of Marine Life Science
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• v.8 no.1
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• pp.68-77
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• 2023

In this study, we investigated the quantitative distribution of microplastics in the surface seawater at 8 points near the Taean Peninsula using fluorescence staining. The study revealed a detection range of microplastics from 0 to 360.5 particles/l, with an average of 149.7 ± 46.0 particles/l. When classifying the microplastics by size, it was found that particles smaller than 50 ㎛ were dominant, although there were differences at Site 3. Moreover, it was not possible to identify clear correlations when comparing the number of microplastics based on collection area and particle size. Various physical and chemical factors, including plastic material, dynamic ocean conditions (such as currents, wind, waves, tides), geological characteristics (topography, slope), sediment materials including coastal organisms, human activities (fishing, development, tourism), and weather conditions (floods, rainfall), affect the behavior of microplastics. Therefore, future efforts should focus on standardizing quantitative analysis methods and conducting fundamental research on microplastic monitoring, including the analysis of environmental factors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.245-253
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• 2021

RC slab bridges account for the largest portion of deteriorated bridges in Korea. However, most RC slabs are not included in the first and second classes of bridges, which are subject to bridge safety management and maintenance. The highest damaged components in highway bridges are the subsidiary facilities including expansion joints and bearings. In particular, leakage through expansion joints causes deterioration and cracks of concrete and exposure of reinforced bars. Therefore, this study analyzed the effect of adhesion damage at expansion joints on the response of the deck in RC slab bridges. When the spacing between the expansion joints at both ends was closely adhered, cracks occurred in the concrete at both ends of the deck due to the resistance rigidity at the expansion joints. Based on the response results, the correlation analysis between displacements in the longitudinal direction of the expansion joint and concrete stress at both ends of the deck for each damage scenario was performed to investigate the effect of the occurrence of damage on the bridge behavior. When expansion joint devices at both sides were damaged, the correlation between displacement and stress showed a low correlation of 0.18 when the vehicles proceeded along all the lanes. Compared with those in the intact state, the deflections of the deck in the damaged case at both sides showed a low correlation of 0.34 to 0.53 while the vehicle passed and 0.17 to 0.43 after the vehicle passed. This means that the occurrence of cracks in the ends of concrete changed the behavior of the deck. Therefore, data-deriven damage detection could be developed to manage the damage to expansion joints that cause damage and deterioration of the deck.

• 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.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Clinical and Experimental Pediatrics
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• v.49 no.5
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• pp.507-512
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• 2006

Purpose : Early detection and intervention of hearing impairment is believed to improve speech and language development and behavior of children. The aim of this preliminary study was to determine the prevalence of hearing impairments, and to identify the association of risk factors relating to refer response in high risk neonates who were screened using distortion product otoacoustic emissions (DPOAE). Methods : The subjects included 871 neonates who were admitted to the neonatal intensive care unit of the Pediatric Department in Soonchunhyang University Bucheon Hospital from May, 2001 to December, 2004. They were screened using DPOAE. Based on DPOAE, we divided the neonates in two groups : 'Pass' and 'Refer'. The differences in risk factors between the pass group and the refer group were analyzed. Results : The incidence of the refer group was 12.1 percent(106 out of 871). The bilateral refer rate was 5.4 percent(47 out of 871). And the unilateral refer rate was 6.7 percent(59 out of 871). Gender, birth place, family history of hearing loss, small/large for gestational age, obstetrical factor, hyperbilirubinemia and use of gentamicin were not statistically related to the refer rate. Statistically related to refer rate were birth weight, resuscitated neonates, Apgar score, craniofacial anomaly, mechanical ventilator application, sepsis, using of vancomycin(P<0.05). The prevalence of hearing impairment (${\geq}60dB$) in this study was 2 percent(18 out of 871). Conclusion : This study showed a higher prevalence of hearing impairment in high-risk neonates. Thus neonatal hearing screening should be carried out in high-risk neonates.

• Progress in Medical Physics
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• v.8 no.2
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• pp.67-76
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• 1997

We studied optical behavior of scintillation light generated in NaI(TI) crystal using Monte Carlo simulation method. The simulation was performed for the model of NaI(TI) scintillator (size: 60 mm ${\times}$ 60 mm ${\times}$ 6 mm) using an optical tracking code. The sensitivity as a function of surface treatment (Ground, Polished, Metal-0.95RC, Polished-0.98RC, Painted- 0.98RC) of the incident surface of the scintillator was compared. The effects of NaI(TI) scintillator thickness and the refractive index of light guide optically coupling between the NaI(TI) scintillator and photomultiplier tube (PMT) were simulated. We also evaluated intrinsic position resolution of the system by calculating the spread of scintillation light generated. The sensitivities of the system having the surface treatment of Ground, Polished, Metal-0.95RC, Polished-0.98RC and Painted-0.98RC were 70.9％, 73.9％, 78.6％, 80.1％ and 85.2％, respectively, and the surface treatment of Painted-0.98RC allowed the highest sensitivity. As increasing the thickness of scintillation crystal and light guide, the sensitivity of the system was decreased. As the refractive index of light guide increases, the sensitivity was increased. The intrinsic position resolution of the system was estimated to be 1.2 mm in horizontal and vertical directions. In this study, the performance of NaI(TI)-PMT detector system was evaluated using Monte Carlo simulation. Based on the results, we concluded that the NaI(TI)-PMT detector array is a favorable configuration for small gamma camera imaging breast tumor using Tc-99m labeled radiopharmaceuticals.