• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1311-1314
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• 2022

DNN을 사용하여 객체 인식 과정에서 객체를 잘 분류하기 위해서는 시각적 설명성이 요구된다. 시각적 설명성은 object class에 대한 예측을 pixel-wise attribution으로 표현해 예측 근거를 해석하기 위해 제안되었다, Scale-invariant한 특징을 제공하도록 설계된 pyramidal features 기반 backbone 구조는 object detection 및 classification 등에서 널리 쓰이고 있으며, 이러한 특징을 갖는 feature pyramid를 trainable attention mechanism에 적용하고자 할 때 계산량 및 메모리의 복잡도가 증가하는 문제가 있다. 본 논문에서는 일반적인 FPN에서 객체 인식 성능과 설명성을 높이기 위한 피라미드-주의집중 계층네트워크 (FPN-Attention Layered Network) 방식을 제안하고, 실험적으로 그 특성을 평가하고자 한다. 기존의 FPN만을 사용하였을 때 객체 인식 과정에서 설명성을 향상시키는 방식이 객체 인식에 미치는 정도를 정량적으로 평가하였다. 제안된 모델의 적용을 통해 낮은 computing 오버헤드 수준에서 multi-level feature를 고려한 시각적 설명성을 개선시켜, 결괴적으로 객체 인식 성능을 향상 시킬 수 있음을 실험적으로 확인할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.461-463
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• 2023

본 연구는 실시간 MOT(Multiple-Object-Tracking)의 성능을 향상시키기 위해 다양한 기법을 적용한 MOT 모델을 설계한다. 연구에서 사용하는 Backbone 모델은 TBD(Tracking-by-Detection) 기반의 Tracking 모델을 사용한다. Heatmap Detection을 통해 객체를 검출하고 Transformer 기반의 Feature를 연결하여 Tracking 한다. 제안하는 방법은 Anchor 기반의 Detection의 장시간 문제와 추적 객체 정보 전달손실을 감소하여 실시간 객체 추적에 도움이 될 것으로 사료된다.

• Computers and Concrete
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• v.32 no.5
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.883-892
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• 2014

Free-to-play business model, which first commercialized in Korea, now becomes crucial sales drivers in the game industry, but the theoretical background is not well known that most of free-to-play content business models are developed based on the guts and trial-and-errors. In this study, we verified that the price discrimination theory in economics is the backbone of the free-to-play business model, and we also derived the three boundary conditions that should be satisfied in the business model design. We reviewed the three boundary conditions of free-to-play business model using case studies of previous games, and showed how the boundary conditions should work in the actual business. Through case studies, we tried to suggest the theoretical basis of free-to-play business model design, and sales enhancing techniques in free-to-play business.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.173-182
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• 2005

A three dimensional (3D) model for the catalytic region of Type II Pseudomonas sp. USM 4-55 PHA synthase 1 (PhaC1$_{P.sp\;USM\;4-55}$) from residue 267 to residue 484 was developed. Sequence analysis demonstrated that PhaC1$_{P.sp\;USM\;4-55}$ lacked homology with all known structural databases. PSI-BLAST and HMM Superfamily analyses demonstrated that this enzyme belongs to the ${\alpha}/{\beta}$ hydrolase fold family. Threading approach revealed that the most suitable template to use was the Human gastric lipase (1HLG). The superimposition of the predicted PhaC1$_{P.sp\;USM\;4-55}$ model with the 1HLG template structure covering 86.2% of the backbone atoms showed an RMSD of 1.15 ${\AA}$ The catalytic residues comprising of Cys296, Asp451, His452 and His479 were found to be conserved and were located adjacent to each other. We proposed that the catalytic mechanism involved the formation of two tetrahedral intermediates.

• Structural Engineering and Mechanics
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• v.46 no.2
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• pp.213-229
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• 2013

Although performance based assessment procedures are mainly developed for reinforced concrete and steel buildings, URM (Unreinforced Masonry) buildings occupy significant portion of buildings in earthquake prone areas of the world as well as in IRAN. Variability of material properties, non-engineered nature of the construction and difficulties in structural analysis of masonry walls make analysis of URM buildings challenging. Despite sophisticated finite element models satisfy the modeling requirements, extensive experimental data for definition of material behavior and high computational resources are needed. Recently, nonlinear equivalent frame models which are developed assigning lumped plastic hinges to isotropic and homogenous equivalent frame elements are used for nonlinear modeling of URM buildings. The equivalent frame models are not novel for the analysis of masonry structures, but the actual potentialities have not yet been completely studied, particularly for non-linear applications. In the present paper an effective tool for the non-linear static analysis of 2D masonry walls is presented. The work presented in this study is about performance assessment of unreinforced brick masonry buildings through nonlinear equivalent frame modeling technique. Reliability of the proposed models is tested with a reversed cyclic experiment conducted on a full scale, two-story URM building at the University of Pavia. The pushover curves were found to provide good agreement with the experimental backbone curves. Furthermore, the results of analysis show that EFM (Equivalent Frame Model) with Dolce RO (rigid offset zone) and shell element have good agreement with finite element software and experimental results.

• The KIPS Transactions:PartC
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• v.12C no.6 s.102
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• pp.839-846
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• 2005

Recently broadband convergence network technology is emerging as an integrated network of telecommunication, broadcasting and Internet. But there are various threats as side effects against the growth of information technology, and malicious codes such af Internet worms may bring about confusions to upset a national backbone network. In this paper, we survey the traditional spreading models and propose a new worm spreading model on Internet environment. We also analyze the spreading effects due to tile spread period and the response period of Internet worms. The proposed model leads to a better prediction of the scale and speed of worm spreading. It can be applied to high-speed network such as broadband convergence network.

• Journal of Communications and Networks
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• v.14 no.3
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• pp.319-335
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• 2012

In this study, we design an applicable model enabling internet protocol television (IPTV) service providers to use a virtual network (VN) for IPTV service delivery. The model addresses the guaranteed service delivery, cost effectiveness, flexible control, and scalable network infrastructure limitations of backbone or IP overlay-based content networks. There are two major challenges involved in this research: i) The design of an efficient, cost effective, and reliable virtual network topology (VNT) for IPTV service delivery and the handling of a VN allocation failure by infrastructure providers (InPs) and ii) the proper approach to reduce the cost of VNT recontruction and reallocation caused by VNT allocation failure. Therefore, in this study, we design a more reliable virtual network topology for solving a single virtual node, virtual link, or video server failure. We develop a novel optimization objective and an efficient VN construction algorithm for building the proposed topology. In addition, we address the VN allocation failure problem by proposing VNT decomposition and reconstruction algorithms. Various simulations are conducted to verify the effectiveness of the proposed VNT, as well as that of the associated construction, decomposition, and reconstruction algorithms in terms of reliability and efficiency. The simulation results are compared with the findings of existing works, and an improvement in performance is observed.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.261-285
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• 2019

Nowadays in offshore industry there are emerging hazards with vague property such as act of terrorism, act of war, unforeseen natural disasters such as tsunami, etc. Therefore industry professionals such as offshore energy insurers, safety engineers and risk managers in order to determine the failure rates and frequencies for the potential hazards where there is no data available, they need to use an appropriate method to overcome this difficulty. Furthermore in conventional risk based analysis models such as when using a fault tree analysis, hazards with vague properties are normally waived and ignored. In other word in previous situations only a traditional probability based fault tree analysis could be implemented. To overcome this shortcoming fuzzy set theory is applied to fault tree analysis to combine the known and unknown data in which the pre-combined result will be determined under a fuzzy environment. This has been fulfilled by integration of a generic bow-tie based risk analysis model into the risk assessment phase of the Risk Management (RM) cycles as a backbone of the phase. For this reason Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) are used to analyse one of the significant risk factors associated in offshore terminals. This process will eventually help the insurers and risk managers in marine and offshore industries to investigate the potential hazards more in detail if there is vagueness. For this purpose a case study of offshore terminal while coinciding with the nature of the Caspian Sea was decided to be examined.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.