• 천문학회보
• /
• 제45권1호
• /
• pp.52.4-53
• /
• 2020

It is less well known that the properties, especially the mass accretion rate, of accretion flow are affected by the angular momentum of accreting gas. Park (2009) found that the mass accretion rate \dot{m}, mass accretion rate in units of Bondi accretion rate, is inversely proportional to the angular momentum of gas λ, at the Bondi radius where gas sound speed is equal to the free-fall velocity and proportional to the viscosity parameter α, and also Narayan & Fabian (2011) found a similar relation, but the dependence of the mass accretion rate of the gas angular momentum is much weaker. In this work, we investigate the global solutions for the rotating Bondi flow, i.e., polytropic flow accreting via viscosity, for various accretion parameters and the dependence of the mass accretion rate on the physical characteristics of gas. We set the outer boundary at various radius r_{out}=10^3~10^5 r_{Sch}, where r_{Sch} is the Schwarzschild radius of the black hole. For a small Bondi radius, the mass accretion rate changes steeply, as the angular momentum changes, and for a large Bondi radius, the mass accretion rate changes gradually. When the accreting gas has a near or super Keplerian rotation, we confirm that the relation between the mass accretion rate and angular momentum is roughly independent of Bondi radius as shown in Park (2009). We find that \dot{m} is determined by the gas angular momentum at the Bondi radius in units of r_{Sch}c. We also investigate the solution for the rotating Bondi flow with the outflow. The outflow affects the determination of the mass accretion rate at the outer boundary. We find that the relation between the mass accretion and the gas angular momentum becomes shallower as the outflow strengthens.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2022년도 추계학술대회
• /
• pp.496-498
• /
• 2022

초고령사회의 진입에 따라 시니어 헬스 케어를 위한 IT 기술과 보건 의료 산업의 융합형 스마트 헬스 케어가 고령화 시대 독거노인 문제의 해결책으로 급부상 중이다. AI를 활용한 비대면 돌봄 서비스의 발전은 일부 국가가 아닌 세계적 경향으로 AI 인공지능 기술을 이용한 노인 돌봄 서비스의 형태는 급격히 변화하고 있다. 국내 역시 초고령화 사회의 진입을 앞두고 있으며 1인 가구가 증가함으로 인해 독거노인 돌봄과 관련하여 노인 복지 인력 부족 문제가 사회 이슈로 대두되고 있다. IT 기술을 이용한 노인 돌봄 서비스는 편리하다는 장점뿐만 아니라 노인 돌봄 노동자의 인력난 등의 장기적인 사회적 문제점을 해소해 줄 비전으로 제시된다. 따라서 고령층을 위한 노인 돌봄 서비스의 사례 연구와 초고령화 시대의 대책으로써 노인 돌봄 서비스의 발전 방향이 제안하고자 한다.

• Structural Engineering and Mechanics
• /
• 제84권4호
• /
• pp.489-502
• /
• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.

• Asia Marketing Journal
• /
• 제14권3호
• /
• pp.185-197
• /
• 2012

Caffe Bene, one of the most notable coffeehouse chain brands in Republic of Korea, gives us some thought-provoking issues in terms of sustainable success. Despite harsh competition among various coffeehouse brands, Caffe Bene has been accomplished astonishing outcomes in domestic market and now ranked 2^nd place in sales among the global coffeehouse franchise in 2010 and 2011. These achievements were possible mainly because Caffe Bene adopted distinctive shop design, maintained aggressive marketing strategy, developed new menu, and combined the unique Korean culture with ordinary concept of café to make its place attractive. However, since Korean coffeehouse market is getting saturated and consumers are becoming savvy about coffee, Caffe Bene needs to find a new solution to overcome growth stagnation. Besides, many experts pointed out that irrational increase in the number of stores might hurt its business in the aspect of managing distribution channel and providing consistent services. Also, customers of Caffe Bene have shown that it has to complement its critical weaknesses: inferior coffee taste and relatively high price for a cup of coffee. Especially, some people view that the company is shifting its high rental fee, interior cost and PPL marketing cost to consumers by charging high price for coffee. To get over the problems, Caffe Bene is currently using C/S Consumer Management System though experts are questioning about the efficacy because of the conflict between purpose of the system and the headquarters' plan. Present CEO Kim also announced that the company will complete its logistics system in the latter half of 2012 to provide stores with more high quality coffee beans to improve taste of coffee. Thus, in this case, we describe how Caffe Bene succeeded in Korean market and enumerate its key success factors. Also, we specify the long-term goals of Caffe Bene and introduce the current policies and strategies to show how the company is working on to achieve its ultimate goal. By reading and analyzing this business case, students could get useful insights regarding franchise management and think about issues on competing in a saturated market. Also, it would be worthwhile to generate creative solutions for the problems that Caffe Bene is now facing to broaden the practical perspective.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2022년도 춘계학술대회
• /
• pp.317-318
• /
• 2022

전(全)세계적인 탈탄소화에 따라 그린에너지로의 전환 가속화를 위한 디지털트윈을 활용한 최적화 및 생산성 향상 모색하고 있으며, 주요국은 미래 핵심기술로 디지털트윈을 선정하여 선박 및 해양에너지 운용 최적화를 위한 SW 개발 등 경쟁이 가속화 되고 있음. 국제적으로 탄소 배출량에 대한 규제 강화로 선박의 운영비용 절감 및 조선 산업의 경쟁력 강화를 위해서는 선박의 탄소배출량 사전 예측 및 탄소저감 운항 솔루션을 위한 선제적 대응이 필요함. 이를 위해 선박해양시스템의 탄소 투명성 확보를 지원하는 개방형 디지털 플랫폼 기술 개발 및 환경 구축에 대한 기획을 수행하였음

• 한국정보전자통신기술학회논문지
• /
• 제16권5호
• /
• pp.307-314
• /
• 2023

슈퍼컴퓨팅 기술 및 하드웨어 기술이 발전함에 따라 기후 예측 모델도 고도화되고 있다. 한국 기상청 역시 영국 기상청으로부터 GloSea5을 도입하였고 한국 기상 환경에 맞추어 업데이트된 GloSea6를 운용 중이다. 각 대학 및 연구기관에서는 슈퍼컴퓨터보다는 사양이 낮은 중소규모 서버에서 활용하기 위해 저해상도 결합모델인 Low-GloSea6를 구축하여 사용하고 있다. 본 논문에서는 중소규모 서버에서의 기상 연구의 효율성을 위한 Low-GloSea6 소프트웨어를 분석하여 가장 많은 CPU Time을 점유하는 대기 모델의 tri_sor.F90 모듈의 tri_sor_dp_dp 서브루틴을 Hotspot으로 검출하였다. 해당 함수에 머신러닝의 한 종류인 선형 회귀 모델을 적용하여 해당 기법의 가능성을 확인한다. 이상치 데이터를 제거 후 선형 회귀 모델을 학습한 결과 RMSE는 2.7665e-08, MAE는 1.4958e-08으로 Lasso 회귀, ElasticNet 회귀보다 더욱 좋은 성능을 보였다. 이는 Low-GloSea6 수행 과정 중 Hotspot으로 검출된 tri_sor.F90 모듈에 머신러닝 기법 적용 가능성을 확인하였다.

• Steel and Composite Structures
• /
• 제49권2호
• /
• pp.143-159
• /
• 2023

Recently, the design of scaffolding systems has garnered considerable attention due to the increasing number of scaffold collapses. These incidents arise from the underestimation of imposed loads and the site-specific conditions that restrict the application of lateral restraints in scaffold assemblies. The present study is committed to augmenting the buckling resistance of vertical support members, obviating the need for supplementary lateral restraints. To achieve this objective, experimental and computational analyses were performed to assess the axial load buckling capacity of steel props, composed of two hollow steel pipes that slide into each other for a certain length. Three full-scale steel props with various geometric properties were tested to construct and validate the analytical models. The total unsupported length of the steel props is 6 m, while three pins were installed to tighten the outer and inner pipes in the distance they overlapped. Finite Element (FE) modeling is carried out for the three steel props, and the developed models were verified using the experimental results. Also, theoretical analysis is utilized to verify the FE analysis. Using the FE-verified models, a parametric study is conducted to evaluate the effect of different inserted pipe lengths on the steel props' axial load capacity and lateral displacement. Based on the results, the typical failure mode for the studied steel props is global elastic buckling. Also, the prop's elastic buckling strength is sensitive to the inserted length of the smaller pipe. A threshold of minimum inserted length is one-third of the total length, after which the buckling strength increases. The present study offers a prop with enhanced buckling resistance and introduces an equation for calculating an equivalent effective length factor (k), which can be seamlessly incorporated into Euler's buckling equation, thereby facilitating the determination of the buckling capacity of the enhanced props and providing a pragmatic engineering solution.

• 국제학술발표논문집
• /
• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
• /
• pp.299-303
• /
• 2011

Congestion is a growing concern of many global cities and the demands on Infrastructure services within a locale coupled by the rising expectations from the growing population places stress on these cities. This entails the ability to build a sustainable community that requires an understanding and recognition of Population growth, changing demographics and the ever changing urban development on both a macro and micro level. Infrastructure is an integral part of Australian economy, particularly the 'Infrastructure Assets Management' which highlights the importance towards the development of sustainable communities for Melbourne's future. Melbourne 2030 is a comprehensive representation of government's response to a wide-ranging population growth within Melbourne metropolitan and surrounding areas. Urban plan and specific Infrastructure Assets Planning needs not only to provide sufficient Infrastructure to a community, but it must also be efficient and innovative so that it produces an optimised management system. A system that incorporates engineering techniques that will be sustainable for decades to come by maintaining an acceptable level of services to its intended community in an effective manner, which also strengthens service delivery. The fundamental challenges for optimization of Infrastructure with the Melbourne urban plan is, the ability to manage and sustain maintenance of Infrastructure to provide the acceptable level of service required by the community in a most effective manner which also strengthens service delivery to contribute towards Melbourne 2030. This paper particularly investigates some of the fundamental issues within the Melbourne urban plan such as Infrastructure Asset Management, AusLink and the Australian Road Management Act 2004, which the Governments at all levels must deal with to provide an economically viable solution to the changing Infrastructure so it may suits the needs and services the strategies of a metropolis.

• 한국산업융합학회 논문집
• /
• 제26권6_3호
• /
• pp.1279-1288
• /
• 2023

Recently, domestic leisure boats have been actively researching eco-friendly product development to enter the global market. Since the hulls of existing leisure boats are mainly made of fiber reinforced plastic (FRP) or aluminum, design techniques for securing structural safety by applying related materials have been mainly studied. In this study, an initial structural design safety assessment of a trimaran pontoon leisure boat with a modular hull structure and eco-friendly high-density polyethylene (HDPE) material was conducted, and sensitivity evaluation and optimization analysis for lightweight design were performed. The initial structural design safety assessment was carried out by creating a finite element analysis model and applying the loading conditions specified in the ship classification regulation to check whether the specified allowable stresses are satisfied. For the sensitivity evaluation, the influence of stress and weight of each hull structural member was evaluated using the orthogonal array design of experiments method, and an approximate model based on the response surface method was generated using the results of the design of experiments. The optimization analysis set the thickness of the hull structural members as the design variable and considered the optimal design formulation to minimize the weight while satisfying the allowable stress. The algorithm of the optimization analysis applied the Gradient-population Based Optimizer (GBO) to improve the accuracy of the optimal solution convergence while reducing the numerical cost. Through this study, the optimal design of a newly developed eco-friendly trimaran pontoon leisure boat with a weight reduction of 10% was presented.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권4호
• /
• pp.826-842
• /
• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.