• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1321-1330
• /
• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.2
• /
• pp.48-59
• /
• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.127-146
• /
• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Korean Journal of Ecology and Environment
• /
• v.38 no.1 s.110
• /
• pp.45-53
• /
• 2005

The environmental change of Yong-nup in Mt. Dae-Am, which is located at the northern part of Kangwon-Do, Korea, was assesed with peat sedimentary carbon and nitrogen isotope analysis. The surface layer of the peat (0 ${\sim}$ 5 cm) was 190 year BP, and the middle layers (30 ${\sim}$ 35 cm and 50 ${\sim}$ 55 cm) were 870 year BP and 1900 year BP, respectively. Bulk sedimentation rate was estimated to be about 0.4 mm $year^{-1}$ for 0 cm to 30 cm and 0.15 mm $year^{-1}$ for 35 cm to 50 cm. The $^{14}C$ age of the bottom sediment (75 ${\sim}$ 80 cm) collected and measured in this study was about 1900 year BP, although it was measured that the $^{14}C$ of the lowest bottom sediment in Yong-nup was 4105 ${\pm}$ 175 year BP (GX-23200). Since the $^{14}C$ ages for 50 ${\sim}$ 55 cm and 75 ${\sim}$ 80 cm layers were almost the same as 1890 ${\pm}$ 80 fear BP (NUTA 5364) and 1850 ${\pm}$ 90 year BP (NUTA 5462), respectively, we have estimated that the deep layers (55 ${\sim}$ 80 cm) in the high moor were the original forest soil. The low organic C and N contents in the deeper layers supported the inference. The sediment of 50 ${\sim}$ 55 cm layer contains much sandy material and showed very low organic content, suggesting the erosion (flooding) from the surrounding area. In this context, the Yong-nup, high moor, of Mt. Dae-Am, might have developed to the sampling site at about 1900 year BP. The ${\delta}^{13}C$ values of organic carbon and the ${\delta}^{15}N$ values of total nitrogen in the peat sediments fluctuated with the depths. The profile of ${\delta}^{13}C$ may indicate that the Yong-nup of Mt. Dae-Am have experienced the dry-wet and cool-warm period cycles during the development of the high moor. The ${\delta}^{15}N$ may indicate that the nitrogen cycling in the Yong-nup have changed from the closed (regeneration depending) system to the open (rain $NO_3\;^-$ and $N_2$ fixation depending) system during the development of the high moor.