• Geotechnical Engineering
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• v.26 no.9
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• pp.33-47
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• 2010

Underground constructions continue to provide challenges to Geotechnical Engineers yet they pose the best opportunities for development and deployment of advance technologies for analysis, design and construction. The reason for this is that, by virtue of the nature of underground constructions, more data and information on ground characteristics and response become available as the construction progresses. However, due to several barriers, these data and information are rarely, if ever, utilized to modify and improve project design and construction during the construction stage. To enable the use of evolving realtime data and information, and adaptively modify and improve design and construction, the paper presents an analysis and design system, called AMADEUS, for underground projects. AMADEUS stands for Adaptive, real-time and geologic Mapping, Analysis and Design of Underground Space. AMADEUS relies on recent advances in IT (Information Technology), particularly in digital imaging, data management, visualization and computation to significantly improve analysis, design and construction of underground projects. Using IT and remote sensors, real-time data on geology and excavation response are gathered during the construction using non-intrusive techniques which do not require expensive and time-consuming monitoring. The real-time data are then used to update geological and geomechanical models of the excavation, and to determine the optimal, construction sequences and stages, and structural support. Virtual environment (VE) systems are employed to allow virtual walk-throughs inside an excavation, observe geologic conditions, perform virtual construction operations, and investigate stability of the excavation via computer simulation to steer the next stages of construction.

• Journal of Korean Academy of Nursing
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• v.36 no.6
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• pp.1042-1054
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• 2006

Purpose: Previous evaluation studies of the visiting nursing program explained an average change of the participants' health status, without considering socio-ecological characteristics and their impacts. However, these factors must affect individual health problems and lifestyles. For effective and appropriate community based programs, the Geographical Information System(GIS) can be utilized. GIS is a computer-based tool for mapping and analyzing things that happen on earth, and integrates statistical analysis with unique visualization. The purpose of this study was to evaluate visiting nursing care and to advocate the usefulness of planning and evaluating visiting nursing programs using Exploratory Spatial Data Analysis(ESDA) with GIS technology. Methods: One hundred eighty-four elderly participants with cerebrovascular risk factors who lived in 13 areas of one community received visiting nursing care. The data analyzed characteristics of pre-post change and autocorrelation by ESDA using GIS technology. Results: Visiting nursing care showed an improvement in the participants' lifestyle habits, and family management ability and stress level, while the improvements were different depending on the regions. The change of family management ability and stress level correlated with neighborhoods (Morgan's I=0.1841, 0.1675). Conclusions: Community health providers need to consider the individual participant's health status as well as socio-ecological factors. Analysis using GIS technology will contribute to the effective monitoring, evaluation and design of a visiting nursing program.

• Journal of Ecology and Environment
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• v.41 no.3
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• pp.85-92
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• 2017

Unmanned aerial vehicles (UAVs) are a new and yet constantly developing part of forest inventory studies and vegetation-monitoring fields. Covering large areas, their extensive usage has saved time and money for researchers and conservationists to survey vegetation for various data analyses. Post-processing imaging software has improved the effectiveness of UAVs further by providing 3D models for accurate visualization of the data. We focus on determining the coniferous tree coverage to show the current advantages and disadvantages of the orthorectified 2D and 3D models obtained from the image photogrammetry software, Pix4Dmapper Pro-Non-Commercial. We also examine the methodology used for mapping the study site, additionally investigating the spread of coniferous trees. The collected images were transformed into 2D black and white binary pixel images to calculate the coverage area of coniferous trees in the study site using MATLAB. The research was able to conclude that the 3D model was effective in perceiving the tree composition in the designated site, while the orthorectified 2D map is appropriate for the clear differentiation of coniferous and deciduous trees. In its conclusion, the paper will also be able to show how UAVs could be improved for future usability.

• Genomics & Informatics
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• v.15 no.1
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• pp.11-18
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• 2017

Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPseeker. 'dada2' performs trimming of the high-throughput sequencing data. 'QuasR' and 'mosaics' perform quality control and mapping of the input reads to the reference genome and peak calling, respectively. Finally, 'ChIPseeker' performs annotation and visualization of the called peaks. This workflow runs well independently of operating systems (e.g., Windows, Mac, or Linux) and processes the input fastq files into various results in one run. R code is available at github: https://github.com/ddhb/Workflow_of_Chipseq.git.

• Smart Media Journal
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• v.8 no.3
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• pp.31-40
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• 2019

To understand how a Convolutional Neural Network (CNN) model captures the features of a pattern to determine which class it belongs to, in this paper, we use Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize and analyze how well a CNN model behave on the CNU weeds dataset. We apply this technique to Resnet model and figure out which features this model captures to determine a specific class, what makes the model get a correct/wrong classification, and how those wrong label images can cause a negative effect to a CNN model during the training process. In the experiment, Grad-CAM highlights the important regions of weeds, depending on the patterns learned by Resnet, such as the lobe and limb on 미국가막사리, or the entire leaf surface on 단풍잎돼지풀. Besides, Grad-CAM points out a CNN model can localize the object even though it is trained only for the classification problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.147-165
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• 2021

In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

• Investigative Magnetic Resonance Imaging
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• v.20 no.1
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• pp.36-43
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• 2016

Visualization of the tissue loss tangent property can provide distinct contrast and offer new information related to tissue electrical properties. A method for non-invasive imaging of the electrical loss tangent of tissue using magnetic resonance imaging (MRI) was demonstrated, and the effect of loss tangent was observed through simulations assuming a hyperthermia procedure. For measurement of tissue loss tangent, radiofrequency field maps ($B_1{^+}$ complex map) were acquired using a double-angle actual flip angle imaging MRI sequence. The conductivity and permittivity were estimated from the complex valued $B_1{^+}$ map using Helmholtz equations. Phantom and ex-vivo experiments were then performed. Electromagnetic simulations of hyperthermia were carried out for observation of temperature elevation with respect to loss tangent. Non-invasive imaging of tissue loss tangent via complex valued $B_1{^+}$ mapping using MRI was successfully conducted. Simulation results indicated that loss tangent is a dominant factor in temperature elevation in the high frequency range during hyperthermia. Knowledge of the tissue loss tangent value can be a useful marker for thermotherapy applications.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.97-104
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• 2009

This paper describes comparison and analysis of methodology which enables us in order to search the projection technique of optimum for projection in the plane. For this methodology, we applies the high-dimensional facial motion capture data respectively in linear and nonlinear projection techniques. The one core element of the methodology is to applies the high-dimensional facial expression data of frame unit in PCA where is a linear projection technique and Isomap, MDS, CCA, Sammon's Mapping and LLE where are a nonlinear projection techniques. And another is to find out the methodology which distributes in this low-dimensional space, and analyze the result last. For this goal, we calculate the distance between the high-dimensional facial expression frame data of existing. And we distribute it in two-dimensional plane space to maintain the distance relationship between the high-dimensional facial expression frame data of existing like that from the condition which applies linear and nonlinear projection techniques. When comparing the facial expression data which distribute in two-dimensional space and the data of existing, we find out the projection technique to maintain the relationship of distance between the frame data like that in condition of optimum. Finally, this paper compare linear and nonlinear projection techniques to projection high-dimensional facial expression data in low-dimensional space and analyze it. And we find out the projection technique of optimum from it.

• Educational Technology International
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• v.15 no.2
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• pp.117-141
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• 2014

This qualitative study investigated different interaction patterns in an online discussion. The data was collected from asynchronous discussion occurred in a graduate course. The data analysis methods include inductive analysis and mapping strategy. The results of the study suggest three layers of interaction: response sequences, interaction amongst participants, and concept map of messages. The visualization of response sequences enabled the researcher to discover complex and dynamic interaction patterns amongst participants. The many-to-many communication feature of online discussion does not always enable direct one-on-one interaction between two participants. Rather, one message contributed to multiple threads in the stream of conversation. In terms of interaction amongst participants, the interaction amongst participants, as indicated in the data, the messages also bind each participant and consequently a group(s) of participants together. It appears that the contribution of one message may not only enable a response to one participant, but also connect many participants to each other. The concept map of messages proposes that response sequences and interaction amongst participants can also be viewed between concepts within messages in the discussion. On the surface, the messages posted by individuals are linked by the system in a linear fashion as they are posted. However, the interaction extends to collaborative conversation amongst participants. Ultimately, a conceptual network of interrelated ideas including multiple perspectives is built in asynchronous discussion.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.45-70
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• 2012

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.