• Earthquakes and Structures
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• 제10권4호
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• pp.757-768
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• 2016

In the past decades, effect of near field earthquake on the historical monuments has attracted the attention of researchers. So, many analyses in this regard have been presented. Tunnels as vital arteries play an important role in management after the earthquake crisis. However, digging tunnels and seismic effects of earthquake on the historical monuments have always been a challenge between engineers and historical supporters. So, in a case study, effect of near field earthquake on the historical monument was investigated. For this research, Finite Element Analysis (FEM) in soil environment and soil-structure interaction was used. In Plaxis 2D software, different accelerograms of near field earthquake were applied to the geometric definition. Analysis validations were performed based on the previous numerical studies. Creating a nonlinear relationship with space parameter, time, angular and numerical model outputs was of practical and critical importance. Hence, artificial Neural Network (ANN) was used and two linear layers and Tansig function were considered. Accuracy of the results was approved by the appropriate statistical test. Results of the study showed that buildings near and far from the tunnel had a special seismic behavior. Scattering of seismic waves on the underground tunnels on the adjacent buildings was influenced by their distance from the tunnel. Finally, a static test expressed optimal convergence of neural network and Plaxis.

• 한국콘텐츠학회논문지
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• 제8권1호
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• pp.9-15
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• 2008

본 연구는 재해복지 구호정책에 참여자간의 상호작용, 특히, 참여자간의 상호의존성에 초점을 두고, 재해 구호정책의 집행에 있어서 공공부문과 민간부문의 상호관계의 개선방안을 정책네트워크 분석요소를 통하여 규명하고 개선방향을 제시하고자 한다. 재해구호 집행과정에 있어서 공공부문과 민간부문의 관계는 기본적으로 상호 보완 혹은 상호의존적인 협력관계를 형성할 것이 요구된다. 또한, 이들은 각각의 재해구호 활동에 있어서 조직 자체가 동원할 수 있는 자원을 토대로 정부와 민간조직들이 상호간 재해구호에서의 역할과 기능을 협의하고 공동의 노력을 통하여 재해를 극복하는 것이 요구된다.

• 디지털융복합연구
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• 제11권11호
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• pp.693-704
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• 2013

본 연구의 목적은 협력 학습에서 소셜 네트워크 서비스를 활용했을 때, 구성원들의 협력 능력, 협력만족도, 그리고 집단내 상호작용에 어떤 영향을 미치는지를 분석하는데 있었다. 협력 과정에서 소셜 네트워크 서비스 중의 하나인 카카오톡을 활용하여 과제를 수행한 집단은 그렇지 않은 집단에 비해 협력 능력과 협력 만족도의 정도가 유의미하게 높았다(p<.05). 카카오톡을 활용한 집단을 대상으로 생성한 담화의 양과 내용을 분석한 결과 담화의 양은 협력 능력이나 협력 만족도에 영향을 미치지 않는 것으로 나타났다.

• ETRI Journal
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• 제42권2호
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• pp.230-238
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• 2020

Human-object interaction (HOI) detection is a popular computer vision task that detects interactions between humans and objects. This task can be useful in many applications that require a deeper understanding of semantic scenes. Current HOI detection networks typically consist of a feature extractor followed by detection layers comprising small filters (eg, 1 × 1 or 3 × 3). Although small filters can capture local spatial features with a few parameters, they fail to capture larger context information relevant for recognizing interactions between humans and distant objects owing to their small receptive regions. Hence, we herein propose a three-stream HOI detection network that employs a context convolution module (CCM) in each stream branch. The CCM can capture larger contexts from input feature maps by adopting combinations of large separable convolution layers and residual-based convolution layers without increasing the number of parameters by using fewer large separable filters. We evaluate our HOI detection method using two benchmark datasets, V-COCO and HICO-DET, and demonstrate its state-of-the-art performance.

• ETRI Journal
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• 제44권6호
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• pp.1004-1019
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• 2022

For autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under a generative adversarial network framework. Furthermore, we devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model for improving the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving, in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.

• Nuclear Engineering and Technology
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• 제54권11호
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• pp.4209-4214
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• 2022

Precise modelling of the interaction of ions with materials is important for many applications including material characterization, ion implantation in devices, thermonuclear fusion, hadron therapy, secondary particle production (e.g. neutron), etc. In this study, a new approach using the Geant4 toolkit in combination with the Bayesian regularization (BR) learning algorithm of the feed-forward neural network (FFNN) is developed to estimate the range of ions in materials accurately and quickly. The different incident ions at different energies are interacted with the target materials. The Geant4 is utilized to model the interactions and to calculate the range of the ions. Afterward, the appropriate architecture of the FFNN-BR with the relevant input features is utilized to learn the modelled ranges and to estimate the new ranges for the new cases. The notable achievements of the proposed approach are: 1- The range of ions in different materials is given as quickly as possible and the time required for estimating the ranges can be neglected (i.e. less than 0.01 s by a typical personal computer). 2- The proposed approach can generalize its ability for estimating the new untrained cases. 3- There is no need for a pre-made lookup table for the estimation of the range values.

• Smart Structures and Systems
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• 제31권5호
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• pp.501-516
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• 2023

Dynamic irregularity and acceleration of bridges subjected to high-speed trains provide crucial information for comprehensive evaluation of the health state of under-track structures. This paper proposes a novel approach for real-time estimation of vertical track dynamic irregularity and bridge acceleration using deep generative adversarial network (GAN) and vibration data from in-service train. The vehicle-body and bogie acceleration responses are correlated with the two target variables by modeling train-bridge interaction (TBI) through least squares generative adversarial network (LSGAN). To realize supervised learning required in the present task, the conventional LSGAN is modified by implementing new loss function and linear activation function. The proposed approach can offer pointwise and accurate estimates of track dynamic irregularity and bridge acceleration, allowing frequent inspection of high-speed railway (HSR) bridges in an economical way. Thanks to its applicability in scenarios of high noise level and critical resonance condition, the proposed approach has a promising prospect in engineering applications.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1121-1125
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• 2004

The RT(Robot Technology) has been developed as the next generation of a future technology. According to the 2002 technical report from Mitsubishi R&D center, IT(Information Technology) and RT(Robotic Technology) fusion system will grow five times larger than the current IT market at the year 2015. Moreover, a recent IEEE report predicts that most people will have a robot in the next ten years. RCP(Robotic Cellular Phone), CP(Cellular Phone) having personal robot services, will be an intermediate hi-tech personal machine between one CP a person and one robot a person generations. RCP infra consists of $RCP^{Mobility}$, $RCP^{Interaction}$, $RCP^{Integration}$ technologies. For $RCP^{Mobility}$, human-friendly motion automation and personal service with walking and arming ability are developed. $RCP^{Interaction}$ ability is achieved by modeling an emotion-generating engine and $RCP^{Integration}$ that recognizes environmental and self conditions is developed. By joining intelligent algorithms and CP communication network with the three base modules, a RCP system is constructed. Especially, the RCP interaction system is really focused in this paper. The $RCP^{interaction}$(Robotic Cellular Phone for Interaction) is to be developed as an emotional model CP as shown in figure 1. $RCP^{interaction}$ refers to the sensitivity expression and the link technology of communication of the CP. It is interface technology between human and CP through various emotional models. The interactive emotion functions are designed through differing patterns of vibrator beat frequencies and a feeling system created by a smell injection switching control. As the music influences a person, one can feel a variety of emotion from the vibrator's beats, by converting musical chord frequencies into vibrator beat frequencies. So, this paper presents the definition, the basic theory and experiment results of the RCP interaction system. We confirm a good performance of the RCP interaction system through the experiment results.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2004년도 추계 종합학술대회 논문집
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• pp.511-515
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• 2004

Post-genome 시대에는 유전체뿐만 아니라 단백질에 대한 연구의 필요성이 증대되고 있다. 특히 단백질-단백질 상호작용 및 단백질 네트워크에 대한 연구를 기반으로 전체 생물 시스템을 분석하는 연구가 중요한 이슈로 떠오르고 있다. 기존에 생물학자들이 실험을 통해서 증명한 사실들을 논문이나 기타 매체를 통해서 공개를 하고 있다. 하지만 공개된 정보의 양이 방대하므로 생물학자들이 정보를 효율적으로 이용하지 못하는 경우가 많다. 인터넷의 발달로 하루에도 수 없이 쏟아져 나오는 연구 성과들에 쉽게 접근이 가능해졌다. 이러한 매체로부터 생물학적 의미를 가지는 정보를 효과적으로 추출하는 일이 중요하게 대두되었다. 따라서 본 연구에서는 인터넷상에 공개된 다량의 논문 및 기타정보 매체로부터 단백질-단백질 상호작용 정보를 추출한 데이터베이스로부터 단백질의 네트워크를 구성하고 단백질 네트워크를 통해서 생물학적 의미를 가지는 여러 가지 경로 분석 알고리즘을 설계하고 구현한다.

• Animal Bioscience
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• 제37권4호
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.