• Spatial Information Research
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• v.20 no.3
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• pp.95-106
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• 2012

Recently, with increase and development of the wireless access network area, u-GIS Service is supported in various fields. Especially, spatio-temporal data is used in the mobile environment for the u-GIS service. However, there is no standard for the spatio-temporal data used in different spaces, spatio-temporal data processing technology is necessary to makes interoperability among mobile u-GIS services. Furthermore, it is also necessary to develop the system of gathering, storing, and managing the spatio-temporal data in consideration of small capacity and low performance of mobile devices. Therefore, in this paper, we designed and implemented a spatio-temporal query processing system based on GML to manage spatio-temporal data efficiently in the mobile environment. The spatio-temporal query processing system based on GML can offer a structured storage method which maps a GML schema to a storage table and a binary XML storage method which uses the Fast Infoset technique, so as to support interoperability that is an important feature of GML and increase storage efficiency. we can also provide spatio-temporal operators for rapid query processing of spatio-temporal data of GML documents. In addition, we proved that this system can be utilized for the u-GIS service to implement a virtual scenario.

• Journal of the Korean GEO-environmental Society
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• v.15 no.6
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• pp.57-65
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• 2014

Recently, Construction business is changing from on the ground to underground space because of deficit of developing space, creation of green space and of incremental of land compensation expenses. Meanwhile, 3D Topographic, Marine and Cadastral maps need to have Spatial Interrelation. Also, understanding of the information is also needed. Spatial information object registration system is impossible to contact and understanding intelligence mutually because the former one is managed as automatic ID system. Therefore, 3D Object information ID System of underground space is managed based on Object Identifier. Construction of Spatial information integration ID System is required and it will offer Division Code (Ground, Index, Underground) and depth information. We are defined and classified Under Spatial Information in this paper. Moreover, we developed the integration ID System based on UFID for cadastral information Construction. We supposed underground spatial information DB Construction and a developed the way of exploiting 3D cadastral information system through the study. The research result will be the base data of Standard ID system, DB Construction and system Development of National spatial data which is considered together with spatial interrelation.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.31-39
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• 2018

In this paper, we propose an automated method for the ground-glass nodule(GGN) classification using GGN-Net based on intensity, texture, and shape-enhanced images in chest CT images. First, we propose the utilization of image that enhances the intensity, texture, and shape information so that the input image includes the presence and size information of the solid component in GGN. Second, we propose GGN-Net which integrates and trains feature maps obtained from various input images through multiple convolution modules on the internal network. To evaluate the classification accuracy of the proposed method, we used 90 pure GGNs, 38 part-solid GGNs less than 5mm with solid component, and 23 part-solid GGNs larger than 5mm with solid component. To evaluate the effect of input image, various input image set is composed and classification results were compared. The results showed that the proposed method using the composition of intensity, texture and shape-enhanced images showed the best result with 82.75% accuracy.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.495-505
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• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.944-953
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• 2020

Object detection plays a crucial role in a self-driving system. With the advances of image recognition based on deep convolutional neural networks, researches on object detection have been actively explored. In this paper, we proposed a lightweight model of the mask R-CNN, which has been most widely used for object detection, to efficiently predict location and shape of various objects on the road environment. Furthermore, feature maps are adaptively re-calibrated to improve the detection performance by applying an attention module to the neural network layer that plays different roles within the mask R-CNN. Various experimental results for real driving scenes demonstrate that the proposed method is able to maintain the high detection performance with significantly reduced network parameters.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1514-1524
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• 2021

Objective: To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma. Materials and Methods: One hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K^trans), fractional volume of vascular plasma space (V_p), and fractional volume of extravascular extracellular space (V_e) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the "radiomics risk score" groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates. Results: 16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively). Conclusion: We developed and validated the "radiomics risk score" from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.

• Smart Media Journal
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• v.12 no.11
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• pp.48-56
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• 2023

The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.4
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• pp.170-181
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• 2011

This Research will explore the meaning indicated in the landscape meaning and feature of literary group culture, focusing in Gurujeong(九老亭: pavilion for nine elders) and Samgaeseokmun(三溪石門: stone gate in three valleys) located in Dundeok-myun, Imshil-gun, and will seek to understand the implications by studying the cultural landscape spread out in the area. The place where Gurojeong and Samgaeseokmun is located is the meeting point of the three valleys, Dunnam stream, Osu stream, and Yul stream, which is the main location to view the beautiful scenery, which has the nickname as the dwelling place of a celestial being. Especially, based on the description of old maps, "Samgae(three valleys)" and "Samgaeseokmun" possesses significance as a landmark and shows a characteristic feature of landscape structures of low hills. Dangugurohwe(丹丘九老會: nine elders gathering on the dwelling of a celestial being) originated from Hyangsangurohwe(香山九老會: gathering of nine elders on a fragrant mountain), where Baekgeoi(白居易) of China was one of the main people. This group was organized by nine elders over the age of 60 desiring to view the scenery of Doyeonmyeong. The group enhanced the literary spirit on the low hill, erecting a tower, and enjoying the beautiful scenery changing every season with scholars from the same region. This phenomenon seems to have been formed upon the positive response to gatherings of elders, which were prevalent in the Joseon Dynasty. If the internal idea pursued by the group was "longevity," the external idea pursued can be summarized as "the spirit the respect for the elders." Naming the groups as 'Dangudae(place where the celestial being lives), Guseondong(valley of seeking a celestial life), Bangjangsan(mountain of a high priest), and Daecheondae(place of communicating with God) was likely a device to introspect oneself and symbolize one's life process. Furthermore, the reason Samgaeseokmun, which is an imitation of Choi, Chiwon's work, was built near Soyocheo, was probably to yearn the celestial land and based on the desire to follow Choi, Chiwon, who was the most self-fulfilling being presumed to have become a celestial being by practicing the pursuit of freedom, escaping from the reality. After tracing the symbolizing meaning of the four letters carved in the left side of the stone wall of Dangudae, the conclusion that this place was not only a place for literary gatherings of the nine elders of Saseong(four families), but was a place where the celestial being dwelled could be inferred. Corresponding with Dangudae and Gurojeong, which are places where the order of human and nature is harmonized and where its meaning associated with the location intensifies, arouses strong bond, can be said to be the symbol of the traces of celestial beings where the spirits of attachment to a certain place is embedded. The acts performed in Dangugurohwe were those of traditional leisure including strolling, viewing the scenery, drinking, composing poems, and playing instruments, and sometimes listening to stories, tea ceremony, prayers, and fishing were added, which indicates that the gathering had a strong tendency towards pastoral and hermit life.