• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.637-643
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• 2020

In this study, the information from environmental impact statements was converted into spatial data because environmental data from development sites are collected during the environmental impact assessment (EIA) process. Spatiotemporal big data were built from environmental spatial data for each environmental medium for 2,235 development sites during 2007-2018, available from public data portals. Comparing air-quality monitoring stations, 33,863 measurement points were constructed, which is approximately 75 times more measurement points than that 452 in Air Korea's real-time measurement network. Here, spatiotemporal big data from 2,677,260 EIAs were constructed. In the future, such data might be used not only for EIAs but also for various spatial plans.

• Spatial Information Research
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• v.19 no.1
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• pp.93-105
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• 2011

In recent years, large and complex three-dimensional building has been constructed by the development of building technology and advanced IT skills, and people have lived there and spent a considerable time so far. Accordingly. in this sophisticatcd three-dimensional space, emergencies services or convenient information services have been in demand. In order to provide these services efficiently, understanding of topological relationships among the complex space should be supported naturally. Not on1y each method of understanding the topological relationships but also its efficiency can be different depending on different topological data models. B-rep based data model is the most widely used for storaging and representing of topological relationships. And from early 2000s, many researches on a network based topological data model have been conducted. The purpose of this study is to verify the efficiency of performance on spatial queries. As a result, Network-based topological data model is more efficient than B-rep based data model for determining the spatial relationship.

• Journal of Digital Policy
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• v.2 no.3
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• pp.1-7
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• 2023

In this paper, I proposes a method for performing single image super resolution by separating texture-spatial domains and then classifying features based on detailed information. In CNN (Convolutional Neural Network) based super resolution, the complex procedures and generation of redundant feature information in feature estimation process for enhancing details can lead to quality degradation in super resolution. The proposed method reduced procedural complexity and minimizes generation of redundant feature information by splitting input image into two channels: texture and spatial. In texture channel, a feature refinement process with step-wise skip connections is applied for detail restoration, while in spatial channel, a method is introduced to preserve the structural features of the image. Experimental results using proposed method demonstrate improved performance in terms of PSNR and SSIM evaluations compared to existing super resolution methods, confirmed the enhancement in quality.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.394-396
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• 2005

IEEE 802.11를 비롯한 여러 무선 네트워크에서는 multi-rate을 활용한 시스템 성능향상에 관한 연구가 진행되고 있다. 그 중에 한가지 연구결과로 제안된 방법이 rDCF.이다. 만약 Mesh 네트워크에서 rDCF를 동작시킨다면, 시스템 throughput의 증가, Packet delay의 감소와 항께 채널상태에 따라 포워딩 전략을 다르게 함으로써 채널 error의 영향이 줄어들 것으로 기대해 볼 수 있다. 하지만 기존의 rDCF를 아무런 revision 없이 Mesh 네트워크에 적용하기에는 spatial reuse 측면에서 비효율적이다. Mesh 네트워크에서는 외부 네트워크와 access point 지점이 되는 portal쪽으로 traffic이 집중되는 것이 일반적이므로 portal에 가까울수록 traffic간의 contention도 가중되므로 시스템 전체 성능에 영향을 미치게 된다. 이러한 문제를 줄이기 위하여 무선 네트워크 환경에서 spatial reuse 측면을 향상시킴으로써 동시에 진행되는 communication 수를 늘리는 방법이 있다. 그러므로 본 논문에서 rDCF의 spatial reuse를 늘임으로써 좀더 Mesh Network위에서도 효율적으로 작동할 수 있는 기법을 제시하고자 한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2006.11a
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• pp.257-260
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• 2006

In Video streaming environment, we must consider terminal and network characteristics, such as display resolution, frame rate, computational resource, network bandwidth, etc. The JVT (Joint Video Team) by ISO/IEC MPEG and ITU-TVCEG is currently standardizing Scalable Video Coding (SVC). This can represent video bitstreams in different sealable layers for flexible adaptation to terminal and network characteristics. This characteristic is very useful in video streaming applications. One fully scalable video can be extracted with specific target spatial resolution, temporal frame rate and quality level to match the requirements of terminals and networks. Besides, the extraction process is fast and consumes little computational resource, so it is possible to extract the partial video bitstream online to accommodate with changing network conditions etc. With all the advantages of SVC, we design and implement a network-adaptive SVC streaming system with an SVC extractor and a streamer to extract appropriate amounts of bitstreams to meet the required target bitrates and spatial resolutions. The proposed SVC extraction is designed to allow for flexible switching from layer to layer in SVC bitstreams online to cope with the change in network bandwidth. The extraction is made in every GOP unit. We present the implementation of our SVC streaming system with experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• Journal of Korea Spatial Information System Society
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• v.5 no.1 s.9
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• pp.75-81
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• 2003

In spite of widely deploying information broadcasting services based on Internet, there are some limitations to use them due to the bound of Internet protocol. In this paper, a new Internet broadcasting technology for access network and Location Based Services are proposed by employing IP address translation function in base station. There are some advantages such as, no need to allocate IP address to receiver no need to know web site address, and reduction of traffic for server and network.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.141-148
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• 2023

During the medical data transmissions, the protection of the patient information is vital. Hence this work proposes a spatial domain watermarking algorithm that enhances the data payload (capacity) while maintaining the authentication and data hiding. The code is distributed at every pixel of the digital image and not only in the regions of non-interest pixels. But the image details are still preserved. The performance of the proposed algorithm is evaluated using several performance measures such as the mean square error (MSE), the mean absolute error (MAE), and the peak signal to noise Ratio (PSNR), the universal image quality index (UIQI) and the structural similarity index (SSIM).

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.193-199
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• 2017

Instead of using only spatial features from a single frame for person re-identification, a combination of spatial and temporal factors boosts the performance of the system. A recurrent neural network (RNN) shows its effectiveness in generating highly discriminative sequence-level human representations. In this work, we implement RNN, three Long Short Term Memory (LSTM) network variants, and Gated Recurrent Unit (GRU) on Caffe deep learning framework, and we then conduct experiments to compare performance in terms of size and accuracy for person re-identification. We propose using GRU for the optimized choice as the experimental results show that the GRU achieves the highest accuracy despite having fewer parameters than the others.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.33-42
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• 1995

A new neural network architecture for the recognition of patterns from images is proposed, which is partially based on the results of physiological studies. The proposed network is composed of multi-layers and the nerve cells in each layer are connected by spatial filters which approximate receptive fields in optic nerve fields. In the proposed method, patterns recognition for complicated images is carried out using global features as well as local features such as lines and end-points. A new generating method of matched filters representing global features is proposed in this network.