• Korean Journal of Computational Design and Engineering
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• v.19 no.3
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• pp.253-262
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• 2014

3D city model, which consisted of the 3D building models and their geospatial position and orientation, is becoming a valuable resource in virtual reality, navigation systems, civil engineering, etc. The purpose of this research is to propose the new framework to generate the 3D city model that satisfies visual and physical requirements in ground oriented simulation system. At the same time, the framework should meet the demand of the automatic creation and cost-effectiveness, which facilitates the usability of the proposed approach. To do that, I suggest the framework that leverages the mobile mapping system which automatically gathers high resolution images and supplement sensor information like position and direction of the image. And to resolve the problem from the sensor noise and a large number of the occlusions, the fusion of digital map data will be used. This paper describes the overall framework with major process and the recommended or demanded techniques for each processing step.

• Journal of Ginseng Research
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• v.34 no.1
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• pp.59-67
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• 2010

Response surface methodology was used to predict the optimum conditions of explosive puffing process for ginseng. A central composite design was used to monitor the effect of moisture content and puffing pressure on dependent variables such as functional compounds (extract yield, crude saponin, acidic polysaccharide, and total phenolic content) and sensory properties. Correlation coefficients $(R^2)$ of models for crude saponin, acidic polysaccharide, and total phenolic content were 0.9176 (p<0.05), 0.9494 (p<0.05), and 0.9878 (p<0.001), respectively. Functional compounds increased with decreasing moisture content and increasing puffing pressure. Overall palatability was high at 15-20% moisture content and 98-294 kPa of puffing pressure. On the basis of superimposed contour maps for functional compounds and overall palatability of puffed ginseng, the optimum ranges of puffing conditions were 10-17% moisture content and 294-392 kPa puffing pressure.

• Genomics & Informatics
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• v.19 no.2
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• pp.14.1-14.6
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• 2021

Coronavirus disease 2019 (COVID-19) is an on-going pandemic disease infecting millions of people across the globe. Recent reports of reduction in antibody levels and the re-emergence of the disease in recovered patients necessitated the understanding of the pandemic at the core level. The cases of multiple organ failures emphasized the consideration of different organ systems while managing the disease. The present study employed RNA sequencing data to determine the disease associated differentially regulated genes and their related protein interactions in several organ systems. It signified the importance of early diagnosis and treatment of the disease. A map of protein interactions of multiple organ systems was built and uncovered CAV1 and CTNNB1 as the top degree nodes. A core interactions sub-network was analyzed to identify different modules of functional significance. AR, CTNNB1, CAV1, and PIK3R1 proteins were unfolded as bridging nodes interconnecting different modules for the information flow across several pathways. The present study also highlighted some of the druggable targets to analyze in drug re-purposing strategies against the COVID-19 pandemic. Therefore, the protein interactions map and the modular interactions of the differentially regulated genes in the multiple organ systems would incline the scientists and researchers to investigate in novel therapeutics for the COVID-19 pandemic expeditiously.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.34.3-34.3
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• 2019

We report our discovery of observational evidence for the coherence between galaxy rotation and the average motion of neighbors. Using the Calar Alto Legacy Integral Field Area (CALIFA) survey data analyzed with the Python CALIFA STARLIGHT Synthesis Organizer (PyCASSO) platform, and the NASA-Sloan Atlas (NSA) catalog, we estimate the angular momentum vectors of 445 CALIFA galaxies and build composite maps of their neighbor galaxies on the parameter space of velocity versus distance. The composite radial profiles of the luminosity-weighted mean velocity of neighbors show striking evidence for dynamical coherence between the rotational direction of the CALIFA galaxies and the average moving direction of their neighbor galaxies. The signal of such dynamical coherence is significant for the neighbors within 800 kpc distance from the CALIFA galaxies with a confidence level of $3.5{\sigma}$, when the angular momentum is measured at the outskirt ($Re<R{\leq}2Re$) of each CALIFA galaxy. We also find that faint or kinematically misaligned galaxies show stronger coherence with neighbor motions than bright or kinematically well-aligned galaxies do. Our results show that the rotation of a galaxy, particularly at its outskirt, may be significantly influenced by recent interactions with its neighbors.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1097-1107
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• 2020

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.

• Journal of Ocean Engineering and Technology
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• v.34 no.6
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• pp.442-453
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• 2020

Underwater color images suffer from low visibility and color cast effects caused by light attenuation by water and floating particles. This study applied single image enhancement techniques to enhance the quality of underwater images and compared their performance with real underwater images taken in Korean waters. Dark channel prior (DCP), gradient transform, image fusion, and generative adversarial networks (GAN), such as cycleGAN and underwater GAN (UGAN), were considered for single image enhancement. Their performance was evaluated in terms of underwater image quality measure, underwater color image quality evaluation, gray-world assumption, and blur metric. The DCP saturated the underwater images to a specific greenish or bluish color tone and reduced the brightness of the background signal. The gradient transform method with two transmission maps were sensitive to the light source and highlighted the region exposed to light. Although image fusion enabled reasonable color correction, the object details were lost due to the last fusion step. CycleGAN corrected overall color tone relatively well but generated artifacts in the background. UGAN showed good visual quality and obtained the highest scores against all figures of merit (FOMs) by compensating for the colors and visibility compared to the other single enhancement methods.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.112-121
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• 2021

Unlike a typical small-sized robot navigating in a free space, an autonomous vehicle has to travel in a designated road which has lanes to follow and traffic rules to obey. High-Definition (HD) maps, which include road markings, traffic signs, and traffic lights with high location accuracy, can help an autonomous vehicle avoid the need to detect such challenging road surroundings. With space constraints and a pre-built HD map, a new type of path planning algorithm can be conceived as a substitute for conventional grid-based path planning algorithms, which require substantial planning time to cover large-scale free space. In this paper, we propose an obstacle-avoiding, cost-based planning algorithm in a continuous space that aims to pursue a globally-planned path with the help of HD map information. Experimentally, the proposed algorithm is shown to outperform other state-of-the-art path planning algorithms in terms of computation complexity in a typical urban road setting, thereby achieving real-time performance and safe avoidance of obstacles.

• Smart Structures and Systems
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• v.29 no.1
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3619-3637
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• 2022

Big data analytics offers endless opportunities for operational enhancement by extracting valuable insights from complex voluminous data. Hadoop is a comprehensive technological suite which offers solutions for the large scale storage and computing needs of Big data. The performance of Hadoop is closely tied with its configuration settings which depends on the cluster capacity and the application profile. Since Hadoop has over 190 configuration parameters, tuning them to gain optimal application performance is a daunting challenge. Our approach is to extract a subset of impactful parameters from which the performance enhancing sub-optimal configuration is then narrowed down. This paper presents a statistical model to analyze the significance of the effect of Hadoop parameters on a variety of performance metrics. Our model decomposes the total observed performance variation and ascribes them to the main parameters, their interaction effects and noise factors. The method clearly segregates impactful parameters from the rest. The configuration setting determined by our methodology has reduced the Job completion time by 22%, resource utilization in terms of memory and CPU by 15% and 12% respectively, the number of killed Maps by 50% and Disk spillage by 23%. The proposed technique can be leveraged to ease the configuration tuning task of any Hadoop cluster despite the differences in the underlying infrastructure and the application running on it.