• Geomechanics and Engineering
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• 제30권3호
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• pp.233-246
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• 2022

In this study, the city of Osijek is used as a case study area for low to medium seismicity regions with deep soil over deep geological deposits to determine horizontal PGA values. For this reason, we propose new regional attenuation equations for PGA that can simultaneously capture the effects of deep geology and local soil conditions. A micro-zoning map for the city of Osijek is constructed using the derived empirical scaling equations and compared to all prior seismic hazard estimates for the same area. The findings suggest that the deep soil atop deep geological sediments results in PGA values that are only 6 percent larger than those reported at rock soil sites atop geological rocks. Given the rarity of ground motion records for deep soils atop deep geological layers around the world, we believe this case study is a start toward defining more reliable PGA estimates for similar areas.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 추계 학술발표회
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• pp.1259-1267
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• 2008

One of the most important item for insuring the stability of ground in urban deep excavation site near by major structure such as subway is displacement control of earth retaining wall. The field monitoring system is classified by two types as manual system and automatic system. The application case of latter type of field monitoring is increased because real time measurement is possible in automatic system and that is correspondent with the recent constructional trend. Though the automatic monitoring system is more useful and advanced than manual monitoring system, accuracy of the system is not verified sufficiently. It was examined that the reliance of automatic monitoring system in this paper through the comparison of monitoring result obtained one of deep urban excavation site in which the each type of monitoring system was executed concurrently. Result of the examination is that the two types of monitoring system is generally alike in view of monitoring result, so the engineering reliance of automatic system was confirmed in case site. This study was researched in restricted one case site, so it is expected more precise analysis from security of more data monitored and progressive study.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• 지능정보연구
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• 제26권4호
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• pp.27-65
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• 2020

많은 정보통신기술 기업들은 자체적으로 개발한 인공지능 기술을 오픈소스로 공개하였다. 예를 들어, 구글의 TensorFlow, 페이스북의 PyTorch, 마이크로소프트의 CNTK 등 여러 기업들은 자신들의 인공지능 기술들을 공개하고 있다. 이처럼 대중에게 딥러닝 오픈소스 소프트웨어를 공개함으로써 개발자 커뮤니티와의 관계와 인공지능 생태계를 강화하고, 사용자들의 실험, 적용, 개선을 얻을 수 있다. 이에 따라 머신러닝 분야는 급속히 성장하고 있고, 개발자들 또한 여러가지 학습 알고리즘을 재생산하여 각 영역에 활용하고 있다. 하지만 오픈소스 소프트웨어에 대한 다양한 분석들이 이루어진 데 반해, 실제 산업현장에서 딥러닝 오픈소스 소프트웨어를 개발하거나 활용하는데 유용한 연구 결과는 미흡한 실정이다. 따라서 본 연구에서는 딥러닝 프레임워크 사례연구를 통해 해당 프레임워크의 도입 전략을 도출하고자 한다. 기술-조직-환경 프레임워크를 기반으로 기존의 오픈 소스 소프트웨어 도입과 관련된 연구들을 리뷰하고, 이를 바탕으로 두 기업의 성공 사례와 한 기업의 실패 사례를 포함한 총 3 가지 기업의 도입 사례 분석을 통해 딥러닝 프레임워크 도입을 위한 중요한 5가지 성공 요인을 도출하였다: 팀 내 개발자의 지식과 전문성, 하드웨어(GPU) 환경, 데이터 전사 협력 체계, 딥러닝 프레임워크 플랫폼, 딥러닝 프레임워크 도구 서비스. 그리고 도출한 성공 요인을 실현하기 위한 딥러닝 프레임워크의 단계적 도입 전략을 제안하였다: 프로젝트 문제 정의, 딥러닝 방법론이 적합한 기법인지 확인, 딥러닝 프레임워크가 적합한 도구인지 확인, 기업의 딥러닝 프레임워크 사용, 기업의 딥러닝 프레임워크 확산. 본 연구를 통해 각 산업과 사업의 니즈에 따라, 딥러닝 프레임워크를 개발하거나 활용하고자 하는 기업에게 전략적인 시사점을 제공할 수 있을 것이라 기대된다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2005년도 지반공학 공동 학술발표회
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• pp.15-26
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• 2005

This paper concerns the assessment of impact of deep excavation on surrounding environment with emphasis on the groundwater lowering. Fundamentals of ground excavation and groundwater interaction were reviewed and the stress-pore pressure coupled analysis approach as a tool for assessment was introduced. A case study concerning the use of coupled analysis for deep excavation design was presented. Implications of the finding from from this study were discussed.

• Journal of Information Technology Applications and Management
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• 제30권3호
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• pp.1-13
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• 2023

Demand forecasting is a crucial task for an online retail where has to manage daily fresh foods effectively. Failing in forecasting results loss of profitability because of incompetent inventory management. This study investigated the optimal performance of different forecasting models for a very short shelf-life product. Demand data of 13 perishable items with aging of 210 days were used for analysis. Our comparison results of four methods: Trivial Identity, Seasonal Naïve, Feed-Forward and Autoregressive Recurrent Neural Networks (DeepAR) reveals that DeepAR outperforms with the lowest MAPE. This study also suggests the managerial implications by employing coefficient of variation (CV) as demand variation indicators. Three classes: Low, Medium and High variation are introduced for classify 13 products into groups. Our analysis found that DeepAR is suitable for medium and high variations, while the low group can use any methods. With this approach, the case can gain benefit of better fill-rate performance.

• 소성∙가공
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• 제13권4호
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• pp.307-318
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• 2004

Rectangular drawn case with extreme aspect ratio is widely used for electrical parts such as a lithium-ion battery container, semi-conductor case and so on. Additionally, from the recent trend towards miniaturization of the multi-functional mobile device, demands for rectangular case with the narrow width are increased. In this study, numerical and experimental approaches for the multi-stage deep drawing process have been carried out. Based on the research results of the width of 5.95mm model, finite element analysis for storage case of rectangular cup type was verified to the width of 4.95mm. Also, a series of manufacturing experiments for rectangular case is conducted and the deformed configuration of the rectangular drawn case are investigated by comparing with the results of the numerical analysis. And the modification of the initial blank is performed to minimize the trimmed material amount. By the application of the modified blank, the sound shape of the deformed parts is improved.

• 소성∙가공
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• 제11권8호
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• pp.676-681
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• 2002

The square cup deep drawing simulations for hydraulic counter pressure deep drawing are carried out by the finite element method and the formability factors which affect to the formability in case of that process are investigated. As a result, it is found that the thickness distributions keep the higher quality than that of the conventional deep drawing, and the maximum pressure increased the thickness at the die profile regions of blank. But friction coefficient decreased the thickness at the same regions.

• 소성∙가공
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• 제8권2호
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• pp.152-159
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• 1999

This study is to investigate the effects of warm deep drawing with aluminum sheets of A1050-H16 and A5020-H32 for improving deep drawability. Experiments for producing circular cups and square cups were carried out for various working conditions, such as forming temperature and blank shapes. The limit drawing ratio(LDR) of 2.63 in warm deep drawing of circular cups in case of A5020-H32 sheet, whereas LDR of 2.25 in case of A1050-H16, could be obtained and the former was 1.4 times higher than the value at room temperature. The maximum relative drawing depth for square cups of A5020-H32 material was also about 1.92 times deeper than the depth drawn at room temperature. The effects of blank shape and forming temperature on drawability as well as thickness distribution of drawn cups were examined and discussed.

• 한국도로학회논문집
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• 제19권4호
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.