• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.478-481
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• 2000

In this paper, we propose a joint coding system for still images using source coding and powerful error correcting code schemes. Our system comprises an LVQ (lattice vector quantization) source coding for wavelet transformed images and turbo coding for channel coding. The parameters of the image encoder and channel encoder have been optimized for an n-D (dimension) cubic lattice (D$_{n}$, Z$_{n}$), parallel concatenation fur two simple RSC (recursive systematic convolutional code) and an interleaver. For decoding the received image in the case of the AWGN (additive white gaussian noise) channel, we used an iterative joint source-channel decoding algorithm for a SISO (soft-input soft-output) MAP (maximum a posteriori) module. The performance of transmission system has been evaluated in the PSNR, BER and iteration times. A very small degradation of the PSNR and an improvement in BER were compared to a system without joint source-channel decoding at the input of the receiver.ver.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.40.1-40.1
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• 2014

We have classified barred galaxies for 418 RC3 sample galaxies within z < 0.01 from SDSS DR7 using the visual inspection, ellipse fitting method and Fourier analysis. We found the bar fraction to be ~60%, 43% and 70% for each method and that the ellipse fitting method tends to miss the bar when a large bulge hides the transition from bar to disk in early spirals. We also confirmed that the Fourier analysis cannot distinguish between a bar and spiral arm structure. These systematic difficulties may have produced the long-time controversy about bar fraction dependence on Hubble sequence, mass and color. We designed a new method to fine bars by analyzing the ratio map of bar strength in polar coordinates, which yields the bar fraction of ~27% and ~32% for SAB and SB, respectively. The consistency with visual inspection reaches around 70%, and roughly 90% of visual strong bar are classified as SAB and SB in our classification. Although our method also has a weakness that a large bulge lowers the value of bar strength, the missing bar fraction in early spirals is reduced to the level of ~1/4 compared to the ellipse fitting method. Our method can make up for the demerits of the previous automatic classifications and provide a quantitative bar classification that agrees with visual classification.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.231-241
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• 2019

Bulk-loading an R-tree can be a good approach to build an efficient one. However, it takes a lot of time to bulk-load an R-tree for huge amount of data. In this paper, we propose a parallel R-tree construction scheme based on a Hadoop framework. The proposed scheme divides the data set into a number of partitions for which local R-trees are built in parallel via Map-Reduce operations. Then the local R-trees are merged into an global R-tree that covers the whole data set. While generating the partitions, it considers the spatial distribution of the data into account so that each partition has nearly equal amounts of data. Therefore, the proposed scheme gives an efficient index structure while reducing the construction time. Experimental tests show that the proposed scheme builds an R-tree more efficiently than the existing approaches.

• The Mathematical Education
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• v.16 no.2
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• pp.22-26
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• 1978

It is shown that a map having an extension to an open map between the Alex-androff base compactifications of its domain and range has a unique such extension. J.S. Wasileski has introduced the Alexandroff base compactifications of Hausdorff spaces endowed with Alexandroff bases. We introduce a definition of morphism between such spaces to obtain a category which we denote by ABC. We prove that the Alexandroff base compactification on objects can be extended to a functor on ABC and that the compact objects give an epireflective subcategory of ABC. For each topological space X there exists a completely regular space $\alpha$X and a surjective continuous function $\alpha$$_{x}$ : Xlongrightarrow$\alpha$X such that for each completely regular space Z and g$\in$C (X, Z) there exists a unique g$\in$C($\alpha$X, 2) with g=g$^{\circ}$$\beta$$_{x}$. Such a pair ($\alpha$$_{x}$, $\alpha$X) is called a completely regularization of X. Let TOP be the category of topological spaces and continuous functions and let CREG be the category of completely regular spaces and continuous functions. The functor $\alpha$ : TOPlongrightarrowCREG is a completely regular reflection functor. For each topological space X there exists a compact Hausdorff space $\beta$X and a dense continuous function $\beta$x : Xlongrightarrow$\beta$X such that for each compact Hausdorff space K and g$\in$C (X, K) there exists a uniqueg$\in$C($\beta$X, K) with g=g$^{\circ}$$\beta$$_{x}$. Such a pair ($\beta$$_{x}$, $\beta$X) is called a Stone-Cech compactification of X. Let COMPT$_2$ be the category of compact Hausdorff spaces and continuous functions. The functor $\beta$ : TOPlongrightarrowCOMPT$_2$ is a compact reflection functor. For each topological space X there exists a realcompact space (equation omitted) and a dense continuous function (equation omitted) such that for each realcompact space Z and g$\in$C(X, 2) there exists a unique g$\in$C (equation omitted) with g=g$^{\circ}$(equation omitted). Such a pair (equation omitted) is called a Hewitt's realcompactification of X. Let RCOM be the category of realcompact spaces and continuous functions. The functor (equation omitted) : TOPlongrightarrowRCOM is a realcompact refection functor. In [2], D. Harris established the existence of a category of spaces and maps on which the Wallman compactification is an epirefiective functor. H. L. Bentley and S. A. Naimpally [1] generalized the result of Harris concerning the functorial properties of the Wallman compactification of a T$_1$-space. J. S. Wasileski [5] constructed a new compactification called Alexandroff base compactification. In order to fix our notations and for the sake of convenience. we begin with recalling reflection and Alexandroff base compactification.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of Life Science
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• v.9 no.2
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• pp.169-175
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• 1999

Salmonella typhimurium can encounter a wide variety of environments during its life cycle. In nature, S. typhimurium can experience and survive dramatic acid stresses that occur in diverse ecological niches ranging from pond water to phagolysosomes. These survival mechanism is aquired by the Acid Tolerance Response(ATR) in Salmonella. The ATR of S. typhimurium is a complex inducible phenomenon in which exposures to slight or moderate low pH will produce a stress response capable of protecting the organism against more severe acid challenges. ATR in Salmonella has two different systems that are called RpoS dependent and independent. We found that ATR in anaerobic was showed RpoS independent because rpoS$\Omega$AP had ATR as S. typhimurium UK1. Using the P22 MudJ(Km, lacZ) operon fusion technique and a lethal selection procedure combining low pH(pH4.5) and sodium acetate(10mM, pH4.5), we isolated LF487 aatA::MudJ which showed acid sensitive in anaerobic condition. aatA locus was determined at 12 min on Salmonella Genetic Map. The survival rate of aatA mutant was showed significantly diminished at pH4.3 than virulent wild type Salmonella in anaerobic condition(5% $CO_2$, 5% H$_2$, 90% $N_2$). Therefore isolated gene was confirmed important gene for anaerobic ATR system.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.95-105
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• 2020

In order to promote efficient coastal management, it is important to continuously monitor the characteristics of the terrain, which are changed by various factors. In this study, time series UAV images were taken of Gyeokpo beach. And the standard deviation of ±11cm(X), ±10cm(Y), and ±15cm(Z) was obtained as a result of comparing with the VRS measurement performance for UAV position accuracy evaluation. Therefore, it was confirmed that the tolerance of the digital map work rule was satisfied. In addition, as a result of monitoring the erosion and sedimentation changes using the DSM(digital surface model) constructed through UAV images, an average of 0.01 m deposition occurred between June 2018 and December 2018, and in December 2018 and June 2019. It was analyzed that 0.03m of erosion occurred. Therefore, 0.02m of erosion occurred between June 2018 and June 2019. From the topographical change analysis results, the area of erosion and sediment height was analyzed, and the area of erosion and sedimentation was widely distributed in the ±0.5m section. If we continuously monitor the topographical changes in the coastal regions by using the 3D terrain modeling results using the time series UAV images presented in this study, we can support the coastal management tasks such as supplement or dredging of sand.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.132-139
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• 2015

Visual SLAM(Simultaneous Localization And Mapping) has been used widely to estimate a mobile robot's location. Visual SLAM estimates relative motions with static visual features over image sequence. Because visual SLAM methods assume generally static features in the environment, we cannot obtain precise results in dynamic situation including many moving objects: cars and human beings. This paper presents a stereo vision based SLAM method in dynamic environment. First, we extract disparity map with stereo vision and compute optical flow. We then compute disparity change that is the estimated flow field between stereo views. After examining the disparity change value, we detect ROIs(Region Of Interest) in disparity space to determine dynamic scene objects. In indoor environment, many structural planes like walls may be determined as false dynamic elements. To solve this problem, we segment the scene into planar structure. More specifically, disparity values by the stereo vision are projected to X-Z plane and we employ Hough transform to determine planes. In final step, we remove ROIs nearby the walls and discriminate static scene elements in indoor environment. The experimental results show that the proposed method can obtain stable performance in dynamic environment.

• Investigative Magnetic Resonance Imaging
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• v.4 no.2
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• pp.113-119
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• 2000

Purpose : To know the possibility of clinical application of MRI using oxygen inhalation as a perfusion MRI Materials and methods : Two healthy volunteers and three patients of one moyamoya disease, one acute infarction and one meningioma were studied using a 1.5 Tesla MRI unit. Oxygen (15 liters/min) mixed with room air was given using face mask from 8 second to 35 second during the study. Images were acquired 25 times (scan time per study were 1.6 seconds) using susceptibility contrast EPI (echo planar image) sequence. Difference maps were acquired by early (study 12-18), and late (study 19-25) O2 inhalation image groups minus pre-O2 inhalation image group (study 3-9) with a Z-score of 0.7-1.0 using VB31C program of Magneton Vision. The resulting perfusion images were created by superimposition of difference maps on corresponding T1 weighted anatomic images. On moyamoya patient, similar perfusion images were acquired after Gd-DTPA injection, and compared with O2 inhalation perfusion images. Results ; The author can get the perfusion images of the brain by oxygen inhalation with susceptibility contrast EPI sequence at the volunteers, and the patient of moyomoya disease, acute infarction and meningioma. On moyamoya patient, perfusion images with O2 inhalation are similar with perfusion images by Gd-DTPA injection. Conclusion 1 This study has demonstrated that the susceptibility contrast EPI by oxygen inhalation can be used as the clinically useful perfusion MRI technique