• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.195-199
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• 2009

Augmented reality is a field of computer research which deals with the combination of real-world and computer-generated data, where computer graphics objects are blended into real footage in real time and it has tremendous potential in visualizing geospatial information. However, to utilize augmented reality in mobile system, many researches have undergone with GPS or marker based approaches. Localization and tracking of current position become more complex problem when it is used in indoor environments. Many proposed RF based tracking and localization. However, it does cause deployment problems of large sensors and readers. In this paper, we present a noble markerless AR approach for indoor navigation system only using a camera. We will apply this work to mobile seamless indoor/outdoor u-GIS system.

• Journal of Life Science
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• v.24 no.11
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• pp.1258-1267
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• 2014

Developing economic traits with a high growth rate, robustness, and disease resistance in livestock is an important challenge. RFLP and AFLP are the classical methods used to develop economic traits. Whole-genome-based economic traits have recently been detected with the advent of next-generation sequencing (NGS) technologies. However, NGS technologies are rather costly for use in studies, and RNA-seq, RAD-Seq, RRL, MSG, and GBS have been used to overcome the issue of high costs. In this study, recent NGS-based studies were reviewed, particularly those that focused on minimum costs and maximum effects. Then, we presented further prospects on how to apply for selection of high economic-trait livestock.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1067-1087
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• 2018

In a study for LiDAR data based building boundary extraction, usually dense point cloud was used to cluster building rooftop area and extract building outline. However, when we used DSM generated from stereo image matching to extract building boundary, it is not trivial to cluster building roof top area automatically due to outliers and large holes of point cloud. Thus, we propose a technique to extract building boundary semi-automatically from the DSM created from stereo images. The technique consists of watershed segmentation for using user input as markers and recursive MBR algorithm. Since the proposed method only inputs simple marker information that represents building areas within the DSM, it can create building boundary efficiently by minimizing user input.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.131-140
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• 2009

This paper presents a new algorithm to the segmentation of the FISH images. First, for segmentation of the cell nuclei from background, a threshold is estimated by using the gaussian mixture model and maximizing the likelihood function of gray value of cell images. After nuclei segmentation, overlapped nuclei and isolated nuclei need to be classified for exact nuclei analysis. For nuclei classification, this paper extracted the morphological features of the nuclei such as compactness, smoothness and moments from training data. Three probability density functions are generated from these features and they are applied to the proposed Bayesian networks as evidences. After nuclei classification, segmenting of overlapped nuclei into isolated nuclei is necessary. This paper first performs intensity gradient transform and watershed algorithm to segment overlapped nuclei. Then proposed stepwise merging strategy is applied to merge several fragments in major nucleus. The experimental results using FISH images show that our system can indeed improve segmentation performance compared to previous researches, since we performed nuclei classification before separating overlapped nuclei.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.369-371
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• 2011

Since users can experience new type of experiment system using augmentation technology and its data visibility, it is a media that is suited to educational purpose application. This paper developed an augmentation-based experimentation that excludes risks and ehnances immersion in chemical experiment. This virtual chemistry experiment system utilizes users' hands to control 3D objects in experiment through interface that interacts virtual objects without markers that are the cause of losing immersion in existing augmentation system. To maximize immersion effect, we propose a virtual chemistry experiment system that enables collaborative work.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.313-315
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• 2023

본 논문에서는 ¹⁸F-FDG PET과 CT에서 추출한 영상인자를 이용하여 비소세포폐암의 전이를 예측하는 머신러닝 모델을 생성하였다. ¹⁸F-FDG는 종양의 포도당 대사 시 사용되며 이를 추적하여 환자의 암 세포를 진단하는데 사용되는 의료영상 기법 중 하나이다. PET과 CT 영상에서 추출한 이미지 특징은 종양의 생물학적 특성을 반영하며 해당 ROI로부터 계산되어 정량화된 값이다. 본 연구에서는 환자의 의료영상으로부터 image texture 프절 전이 예측에 있어 유의한 인자인지를 확인하기 위하여 AUC를 계산하고 단변량 분석을 진행하였다. PET과 CT에서 각각 4개(GLRLM_GLNU, SHAPE_Compacity only for 3D ROI, SHAPE_Volume_vx, SHAPE_Volume_mL)와 2개(NGLDM_Busyness, TLG_ml)의 image texture feature를 모델의 생성에 사용하였다. 생성된 각 모델의 성능을 평가하기 위해 accuracy와 AUC를 계산하였으며 그 결과 random forest(RF) 모델의 예측 정확도가 가장 높았다. 추출된 PET과 CT image texture feature를 함께 사용하여 모델을 훈련하였을 때가 각각 따로 사용하였을 때 보다 예측 성능이 개선됨을 확인하였다. 추출된 영상인자가 림프절 전이를 나타내는 바이오마커로서의 가능성을 확인할 수 있었으며 이러한 연구 결과를 바탕으로 개인별 의료 영상을 기반으로 한 비소세포폐암의 치료 전략을 수립할 수 있을 것이라 기대된다.

• Korean Journal of Environmental Biology
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• v.36 no.3
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• pp.352-358
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• 2018

This study aimed to develop a species identification method for the egg and fry of the three Korean bitterling fishes (Pisces: Acheilognathinae), including Acheilognathus signifer, Acheilognathus yamatsutae and Rhodeus uyekii based on the PCR-based Restriction Fragment Length Polymorphism (RFLP) markers. We conducted a field survey on the Deokchicheon River from the North Han River basin, where the three Acheilognathinae species co-occur, and also analyzed the existing sequence dataset available from the GenBank. We found coexistence of the three species at the study site. The egg and fry were obtained from the host mussels (Unio douglasiae sinuolatus) by hand from May to June 2015 and in May 2017. To develop PCR-based RFLP markers for species identification of the three Acheilognathinae fish species, restriction enzymes pinpointing species-specific single nucleotide variation (SNV) sites in mitochondrial DNA COI (cytochrome oxidase I) and cyt b (cytochrome b) genes were determined. Genomic DNA was extracted from the egg and fry and RFLP experiments were carried out using restriction enzymes Apal I, Stu I and EcoR V for A. signifer, A. yamatsutae and R. uyekii, respectively. Consequently, unambiguous discrimination of the three species was possible, as could be seen in DNA band patterns from gel electrophoresis. Our developed PCR-based RFLP markers will be useful for the determination of the three species for the young and would assist in studying the spawning patterns and reproductive ecology of Acheilognathinae fishes. Furthermore, we believe the obtained information will be of importance for future maintenance, management and conservation of these natural and endangered species.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Journal of Applied Biological Chemistry
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• v.64 no.3
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• pp.245-251
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• 2021

Codonopsis lanceolata (Deoduk) was grown in East Asia, including Korea, China, Japan, and Russia, and the roots of C. lanceolata have been used as functional foods and traditional medicine to treat symptoms of cough, bronchitis, asthma, tuberculosis, and dyspepsia. The phytochemicals of C. lanceolata have been reported such as phenylpropanoids, polyacetylenes, saponins, and flavonoids that are involved in pharmacological effects such as anti-obesity, anti-inflammation, anti-tumor, anti-oxidant, and anti-microbial activities. Selecting marker substances of the main producing area by MS-based metabolomics analysis is important to ensure the beneficial effect of C. lanceolata without side-effects because differences in cultivated areas of plants were related not only to the safety of medicinal plants but also to changes in chemical composition and biological efficacy. In our present study, ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry combined with multivariate statistical analysis was applied to recognize the main producing area of C. lanceolata in South Korea. As a result of Principal Component Analysis and loading plot analysis of three groups, Inje (Kangwon-do), Hoengseong (Kangwon-do), and Muju (Jeonlabuk-do), several secondary metabolites of C. lanceolata including tangshenoside I, lancemaside A, and lancemaside G, were suggested as potential marker substances to distinguish the place of main producing area of C. lanceolata.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.192-197
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• 2012

With the advent of DNA microarray technologies, researches for disease diagnosis has been actively in progress. In typical experiments using microarray data, problems such as the large number of genes and the relatively small number of samples, the inherent measurement noise and the heterogeneity across different samples are the cause of the performance decrease. To overcome these problems, a new method using functional modules (e.g. signaling pathways) used as markers was proposed. They use the method using an activity of pathway summarizing values of a member gene's expression values. It showed better classification performance than the existing methods based on individual genes. The activity calculation, however, used in the method has some drawbacks such as a correlation between individual genes and each phenotype is ignored and characteristics of individual genes are removed. In this paper, we propose a method based on the ensemble classifier. It makes weak classifiers based on feature vectors using subsets of genes in selected pathways, and then infers the final classification result by combining the results of each weak classifier. In this process, we improved the performance by minimize the search space through a filtering process using gene-gene interaction information and the mRMR filter. We applied the proposed method to a classifying the lung cancer, it showed competitive classification performance compared to existing methods.