• Journal of Korean Society for Atmospheric Environment
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• v.33 no.3
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• pp.217-232
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• 2017

An Eulerian-Lagrangian hybrid modeling system to analyze physical and chemical processes during the transport of air parcels was developed. The Backward-tracking Model Analyzer (BMA) was designed to take advantages of both Eulerian and Lagrangian modeling approaches. Simulated trajectories from the National Oceanic and Atmospheric Administration HYSPLIT model were combined with the US Environmental Protection Agency Community Multi-scale Air Quality (CMAQ)-simulated concentrations and additional diagnostic analyses. In this study, we first introduced a generalized methodology to seamlessly match polylines (HYSPLIT) and threedimensional polygons (CMAQ), which enables mass-conservative analyses of physio-chemical processes of transporting air parcels. Two applications of the BMA were conducted: (1) a long-range transport case of pollutant plume across the Yellow Sea using CMAQ Integrated Process Rate analyses, and (2) a domestic circulation of pollutants within (and near) the South Korea based on the sulfate tracking analyzer. The first episode demonstrated a secondary formation of nitrate and ammonium during the transport over the Yellow Sea while sulfate is mostly transported after being formed over the China, and the second episode demonstrated a dominant impact of boundary condition with active sulfate formation from gas-phase oxidation near the Seoul Metropolitan Area.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.101-108
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• 2011

Construction of the Jangbogo antarctic station was planned, and it requires detailed information on topography of the area around the station. The purpose of this research is to generate the topographic map to construct the Jangbogo antarctic station using the satellite image. To do this, surveying and pre-test of equipment were conducted. In addition, for sensor modeling of the GeoEye-1 satellite image, RPC-bias correction was done, and it showed that at least two control points are required. In generating the map, a 1/2,500 scale was deemed suitable in consideration of resolution of the image and the fact that supplementary topographic surveying would be impossible. In order to provide detailed information on the topography around the Jangbogo station, the digital elevation model based on image matching was created, and compared with GPS-RTK data, accuracy of vertical location about 0.6m was exhibited.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.5
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• pp.355-361
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others, and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order, Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis(PCA) was first applied to identify a relationship between input and output. The input would be microbial densities and the treatment parameters, such as population densities of heterotrophic and lactic acid bacteria, suspended solids(SS), COD, NH$_4$(sup)+-N, ortho-phosphorus (o-P), and total-phosphorus (T-P). then multi-layer neural networks were employed to model the treatment process for each tank. PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of imput. Neural network independently trained for each treatment tank and their subsequent combined data analysis allowed a successful prediction of the treatment system for at least two days.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.10
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• pp.899-905
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• 2016

Wireless access in vehicle environment (WAVE) communication is currently being researched as core wireless communication technologies for cooperative intelligent transport systems (C-ITS). WAVE consists of both vehicle to vehicle (V2V) communication, which refers to communication between vehicles, and vehicle to infrastructure (V2I) communication, which refers to the communication between vehicles and road-side stations. V2I has a longer communication range than V2V, and its communication range and reception rate are heavily influenced by various factors such as structures on the road, the density of vehicles, and topography. Therefore, domestic environments in which there are many non-lines of sight (NLOS), such as mountains and urban areas, require optimized communication channel modeling based on research of V2I propagation characteristics. In the present study, the received signal strength indicator (RSSI) was measured on both an experience road and a test road, and the large-scale characteristics of the WAVE communication were analyzed using the data collected to assess the propagation environment of the WAVE-based V2I that is actually implemented on highways. Based on the results of this analysis, this paper proposes a WAVE communication channel model for domestic public roads by deriving the parameters of a dual-slope logarithmic distance implementing a two-ray ground-reflection model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.1-13
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• 2020

Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation's voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as "other". Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.

• Steel and Composite Structures
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• v.49 no.2
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• pp.143-159
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• 2023

Recently, the design of scaffolding systems has garnered considerable attention due to the increasing number of scaffold collapses. These incidents arise from the underestimation of imposed loads and the site-specific conditions that restrict the application of lateral restraints in scaffold assemblies. The present study is committed to augmenting the buckling resistance of vertical support members, obviating the need for supplementary lateral restraints. To achieve this objective, experimental and computational analyses were performed to assess the axial load buckling capacity of steel props, composed of two hollow steel pipes that slide into each other for a certain length. Three full-scale steel props with various geometric properties were tested to construct and validate the analytical models. The total unsupported length of the steel props is 6 m, while three pins were installed to tighten the outer and inner pipes in the distance they overlapped. Finite Element (FE) modeling is carried out for the three steel props, and the developed models were verified using the experimental results. Also, theoretical analysis is utilized to verify the FE analysis. Using the FE-verified models, a parametric study is conducted to evaluate the effect of different inserted pipe lengths on the steel props' axial load capacity and lateral displacement. Based on the results, the typical failure mode for the studied steel props is global elastic buckling. Also, the prop's elastic buckling strength is sensitive to the inserted length of the smaller pipe. A threshold of minimum inserted length is one-third of the total length, after which the buckling strength increases. The present study offers a prop with enhanced buckling resistance and introduces an equation for calculating an equivalent effective length factor (k), which can be seamlessly incorporated into Euler's buckling equation, thereby facilitating the determination of the buckling capacity of the enhanced props and providing a pragmatic engineering solution.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.5
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• pp.502-515
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• 2020

To identify the dynamic characteristics of the Auto-leveling system applied to the Tractor-Trailer type Transporter for mounting a large scale precision equipment, Dynamics Modeling & Simulation were performed using general Dynamics Analysis Program - RecurDyn(V9R2). The axial load data, transverse load data and pad trace data of leveling actuators were obtained from M&S. And they were analyzed and compared with each other by parameters, i.e. friction coefficients on the ground, landing ram speed of actuators, and direction & quantity of ground slope. It was observed that ground contact friction coefficients affected to transverse load and pad trace; the landing ram speed of actuators to both amplitude of axial & transverse load, and this phenomena was able to explain from the frequency analysis of the axial load data; the direction of ground slope to driving sequence of landing ram of actuators. But the dynamic behaviors on the two-directional slope were very different from them on the one-directional slope and more complex.

• Journal of the Ergonomics Society of Korea
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• v.33 no.5
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• pp.377-386
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• 2014

Objective: In this study, we used resting-state fMRI data to map differences in functional connectivity between a comprehensive set of 8 distinct cortical and subcortical brain regions in healthy controls and Internet addicts. We also investigated the relationship between resting state connectivity strength and the level of psychopathology (ex. score of internet addiction scale and score of Barratt impulsiveness scale). Background: There is a lot of evidence of relationship between Internet addiction and impaired inhibitory control. Clinical evidence suggests that Internet addicts have a high level of impulsivity as measured by behavioral task of response inhibition and a self report questionnaire. Method: 15 Internet addicts and 15 demographically similar non-addicts participated in the current resting-state fMRI experiment. For the connectivity analysis, regions of interests (ROIs) were defined based on the previous studies of addictions. Functional connectivity assessment for each subject was obtained by correlating time-series across the ROIs, resulting in $8{\times}8$ matrixs for each subject. Within-group, functional connectivity patterns were observed by entering the z maps of the ROIs of each subject into second-level one sample t test. Two sample t test was also performed to examine between group differences. Results: Between group, the analysis revealed that the connectivity in between the orbito frontal cortex and inferior parietal cortex, between orbito frontal cortex and putamen, between the orbito frontal cortex and anterior cingulate cortex, between the insula and anterior cingulate cortex, and between amydgala and insula was significantly stronger in control group than in the Internet addicts, while the connectivity in between the orbito frontal cortex and insula showed stronger negative correlation in the Internet addicts relative to control group (p < 0.001, uncorrected). No significant relationship between functional connectivity strength and current degree of Internet addiction and degree of impulsitivy was seen. Conclusion: This study found that Internet addicts had declined connectivity strength in the orbitofrontal cortex (OFC) and other regions (e.g., ACC, IPC, and insula) during resting-state. It may reflect deficits in the OFC function to process information from different area in the corticostriatal reward network. Application: The results might help to develop theoretical modeling of Internet addiction for Internet addiction discrimination.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.55-61
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• 2007

In this paper, we propose a three-dimensional(3D) active shape models for medical image segmentation. In order to build a 3D shape model, we need to generate a point distribution model(PDM) and select corresponding landmarks in all the training shapes. The manual determination method, two-dimensional(2D) method, and limited 3D method of landmark correspondences are time-consuming, tedious, and error-prone. In this paper, we generate a 3D statistical shape model using the 3D model generation method of a distance transform and a tetrahedron method for landmarking. After generating the 3D model, we extend the shape model training and gray-level model training of 2D active shape models(ASMs) and we use the integrated modeling process with scale and gray-level models for the appearance profile to represent the local structure. Experimental results are comparable to those of region-based, contour-based methods, and 2D ASMs.