• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.92-97
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• 2001

The intra-annual and interannual variations of total, high, middle, low clouds, and cloud forcing net solar radiation flux, cloud forcing net long-wave radiation flux, and SSTs over the tropical oceans are investigated with the use of ISCP D2, NCEP/NCAR Reanalysis for January 1983-December 1993. The intra-annual variation of total cloudiness is dominated by high and middle clouds in the western Pacific and central tropical oceans, the interannual variation of total cloudiness is also dominated by high and middle clouds in the central Pacific and Atlantic. The dominant intra-annual and interannual EOFs of total cloudiness have spatially coherent link with those SSTs. For the interannual EOFs, total cloudiness and SSTs are related to E1 nino-Southern Oscillation(ENSO). The second most important intra-annual EOFs of total cloudiness are related to Inter Tropical Convergence Zone(ITCZ). The third most important intra-annual EOFs show coherent relation in the western Pacific. The correlation analysis between cloud radiative effects and SSTs show spatially coherent relation over the tropical oceans even though cloud forcing cooling effect is much higher than heating effect.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1035-1045
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• 2018

Recently, with the development of IT technology, authentication methods of users using cloud services have been diversified. However, research on providing authentication information of a user using a cloud service securely according to authority has not been make until now. In this paper, we propose a key establishment protocol which can perform split authentication using secret key and access control key according to the role authority of user in Intra cloud environment. The proposed protocol generates the access control key and secret key of the user by using the attributes of the user and the generated random number($t_1$, $t_2$), and classifies the roles according to the user's authority after generating the key. Unnecessary operation processes can be reduced. As a result of the performance evaluation, the proposed protocol guarantees the security against various type of attacks that may occur in the cloud environment because the user is authenticated by dividing the access control key and secret key. The size of the ciphertext used to establish the key could be reduced by ${\sum}+1$ more than the existing protocol.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.435-449
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• 2023

The state-of-the-art video-based point cloud compression(V-PCC) has a high efficiency of compressing 3D point cloud by projecting points onto 2D images. These images are then padded and compressed by High-Efficiency Video Coding(HEVC). Pixels in padded 2D images are classified into three groups including origin pixels, padded pixels and unoccupied pixels. Origin pixels are generated from projection of 3D point cloud. Padded pixels and unoccupied pixels are generated by copying values from origin pixels during image padding. For padded pixels, they are reconstructed to 3D space during geometry reconstruction as well as origin pixels. For unoccupied pixels, they are not reconstructed. The rate distortion optimization(RDO) used in HEVC is mainly aimed at keeping the balance between video distortion and video bitrates. However, traditional RDO is unreliable for padded pixels and unoccupied pixels, which leads to significant waste of bits in geometry reconstruction. In this paper, we propose a new RDO scheme which takes 3D-Distortion into account instead of traditional video distortion for padded pixels and unoccupied pixels. Firstly, these pixels are classified based on the occupancy map. Secondly, different strategies are applied to these pixels to calculate their 3D-Distortions. Finally, the obtained 3D-Distortions replace the sum square error(SSE) during the full RDO process in intra prediction and inter prediction. The proposed method is applied to geometry frames. Experimental results show that the proposed algorithm achieves an average of 31.41% and 6.14% bitrate saving for D1 metric in Random Access setting and All Intra setting on geometry videos compared with V-PCC anchor.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.137-148
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• 2004

In this paper, we propose a new camera calibration method for rotating multi-view cameras to generate image-based panoramic 3D Virtual Environment. Since calibration accuracy worsens with an increase in distance between camera and calibration pattern, conventional camera calibration algorithms are not proper for panoramic 3D VE generation. To remedy the problem, a geometric relationship among all lenses of a multi-view camera is used for intra-camera calibration. Another geometric relationship among multiple cameras is used for inter-camera calibration. First camera parameters for all lenses of each multi-view camera we obtained by applying Tsai's algorithm. In intra-camera calibration, the extrinsic parameters are compensated by iteratively reducing discrepancy between estimated and actual distances. Estimated distances are calculated using extrinsic parameters for every lens. Inter-camera calibration arranges multiple cameras in a geometric relationship. It exploits Iterative Closet Point (ICP) algorithm using back-projected 3D point clouds. Finally, by repeatedly applying intra/inter-camera calibration to all lenses of rotating multi-view cameras, we can obtain improved extrinsic parameters at every rotated position for a middle-range distance. Consequently, the proposed method can be applied to stitching of 3D point cloud for panoramic 3D VE generation. Moreover, it may be adopted in various 3D AR applications.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.680-691
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• 2021

Due to the recent developments of attaining 3D contents by using devices such as 3D scanners, the diversity of the contents being used in AR(Augmented Reality)/VR(Virutal Reality) fields is significantly increasing. There are several ways to represent 3D data, and using point clouds is one of them. A point cloud is a cluster of points, having the advantage of being able to attain actual 3D data with high precision. However, in order to express 3D contents, much more data is required compared to that of 2D images. The size of data needed to represent dynamic 3D point cloud objects that consists of multiple frames is especially big, and that is why an efficient compression technology for this kind of data must be developed. In this paper, a motion estimation and compensation method for dynamic point cloud objects using 3D DCT is proposed. This will lead to switching the 3D video frames into I frames and P frames, which ensures higher compression ratio. Then, we confirm the compression efficiency of the proposed technology by comparing it with the anchor technology, an Intra-frame based compression method, and 2D-DCT based V-PCC.

• Electronics and Telecommunications Trends
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• v.37 no.2
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• pp.42-52
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• 2022

Intra- and inter- datacenter data traffic is rapidly increasing due to the spread of smart devices, cloud computing, and non-face-to-face services. Recently, 400-Gbps optical transceivers based on 100-Gbps/channel have been released primarily by major overseas companies. Various solutions for next-generation datacenter interconnect are being proposed by international standardization and multiple source agreement groups. Following this trend, ETRI has developed a 400-Gbps optical transmission/reception engine using 100-Gbps/channel light sources and photodetectors as well as a silica-based AWG. In the future, technologies of optical devices and components for intra-datacenter communication are expected to be developed based on a data rate of 200-Gbps/channel. Thus, 1.6-Tbps class optical transceivers will be released.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.78-80
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• 2021

In this paper, we proposed an improved method on the picture order of coding (POC) of MPEG-5 Essential video Coding (EVC) encoder to support a short intra period for Video-based Point Cloud Compression (V-PCC). As a codec-agnostically designed standard, V-PCC claimed to be able to work with a lot of codecs. Current EVC test model software shows that the baseline profile could not provide appropriate POC calculation. The proposed method offers a solution to this POC-related problem and provides up to 44.6% coding grains for EVC based V-PCC.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.45-50
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• 2019

With the gradual advancement of IT, passive work processes are automated and the overall quality of life has greatly improved. This is made possible by the formation of an organic topology between a wide variety of real-life smart devices. To serve these diverse smart devices, businesses or users are using the cloud. The services in the cloud are divided into Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). SaaS runs on PaaS, and PaaS runs on IaaS. Since IaaS is the basis of all services, an algorithm is required to operate virtualization resources efficiently. Among them, desktop resource virtualization is used for resource high availability of unused state time of existing desktop PC. Clustering of hierarchical structures is important for high availability of these resources. In addition, it is very important to select a suitable algorithm because many clustering algorithms are mainly used depending on the distribution ratio and environment of the desktop PC. If various attempts are made to find an algorithm suitable for desktop resource virtualization in an operating environment, a great deal of power, time, and manpower will be incurred. Therefore, this paper proposes a resource clustering simulator for cluster selection of desktop virtualization. This provides a clustering simulation to properly select clustering algorithms and apply elements in different environments of desktop PCs.