• International journal of advanced smart convergence
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• 제8권4호
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• pp.75-81
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• 2019

Though recent advancement of deep learning methods have provided satisfactory results from large data domain, somehow yield poor performance on few-shot classification tasks. In order to train a model with strong performance, i.e. deep convolutional neural network, it depends heavily on huge dataset and the labeled classes of the dataset can be extremely humongous. The cost of human annotation and scarcity of the data among the classes have drastically limited the capability of current image classification model. On the contrary, humans are excellent in terms of learning or recognizing new unseen classes with merely small set of labeled examples. Few-shot learning aims to train a classification model with limited labeled samples to recognize new classes that have neverseen during training process. In this paper, we increase the backbone depth of the embedding network in orderto learn the variation between the intra-class. By increasing the network depth of the embedding module, we are able to achieve competitive performance due to the minimized intra-class variation.

• 한국항공운항학회지
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• 제20권4호
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• pp.14-20
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• 2012

A properly determined separation minima applied in Air Traffic Management(ATM) is critical for safe and efficient aircraft operations. The separation minima is primarily determined by the accuracy performance of surveillance system, and, due to the stringent aviation safety standard, the position accuracy of the surveillance system must be estimated with a high level of reliability. This study proposed a method for estimating the position accuracy of surveillance system with a relatively small amount of data by finding upper confidence limit instead of maximum likelihood values of unknown parameters. Through the proposed method, it is possible to determine a required separation minima with a more reliability in the face of data scarcity which often occurs when we implement a new surveillance system such as Automatic Dependent Surveillance-Broadcast (ADS-B).

• Journal of Information Technology Applications and Management
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• 제24권1호
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• pp.143-156
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• 2017

As the number of people going aboard is growing and technology is developed rapidly, Chinese customers are also getting better understanding about overseas products, and they hope to get less expensive and better ones, which leads to the growth of the online overseas purchasing agent service. In this paper, we tried to analyze the factors that impact the usage of online overseas purchasing agent service using the survey data. We found that customers pursue not only the reasonable prices but also enjoyment of shopping in the online overseas purchasing agent service. In addition, product scarcity and the information literacy of a customer were positively related with the use of online overseas purchasing agent service.

• Wind and Structures
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• 제22권5호
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• pp.543-553
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• 2016

The growing complexity of modern technological systems requires more flexible and powerful reliability analysis tools. Existing tools encounter a number of limitations including lack of modeling power to address components interactions for complex systems and lack of flexibility in handling component failure distribution. We propose a reliability modeling framework based on the Bayesian network (BN). It can combine historical data with expert judgment to treat data scarcity. The proposed methodology is applied to wind turbines reliability analysis. The observed result shows that a BN based reliability modeling is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, BN provides performing several inference approaches such as smoothing, filtering, what-if analysis, and sensitivity analysis for considering system.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.605-616
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• 2020

The construction quality control of hull blocks is of great significance for shipbuilding. The total station device is predominantly employed in traditional applications, but suffers from long measurement time, high labor intensity and scarcity of data points. In this paper, the Terrestrial Laser Scanning (TLS) device is utilized to obtain an efficient and accurate comprehensive construction information of hull blocks. To address the registration problem which is the most important issue in comparing the measurement point cloud and the design model, an automatic registration approach is presented. Furthermore, to compare the data acquired by TLS device and sparse point sets obtained by total station device, a method for key point extraction is introduced. Experimental results indicate that the proposed approach is fast and accurate, and that applying TLS to control the construction quality of hull blocks is reliable and feasible.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.29-29
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• 2023

To mitigate the damaging impacts of floods, accurate prediction of runoff, streamflow and flood inundation is needed. Conventional approach of simulating hydrology and hydraulics using loosely coupled models cannot capture the complex dynamics of surface and sub-surface processes. Additionally, the scarcity of data in ungauged basins and quality of data in gauged basins add uncertainty to model predictions, which need to be quantified. In this presentation, first the role of integrated modeling on creating accurate flood simulations and inundation maps will be presented with specific focus on urban environments. Next, the use of machine learning in producing streamflow predictions will be presented with specific focus on incorporating covariate shift and the application of theory guided machine learning. Finally, a framework to quantify the uncertainty in flood models using Hierarchical Bayesian Modeling Averaging will be presented. Overall, this presentation will highlight that creating accurate information on flood magnitude and extent requires innovation and advancement in different aspects related to hydrologic predictions.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1093-1099
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• 2022

The number of buildings is increasing day by day. The next logical footstep is tackling challenges regarding scarcity of resources and sustainability, as well as shifting focus on existing building structures to renovate and retrofit. Many existing old and heritage buildings lack documentation, such as building models, despite their necessity. Technological advances allow us to use virtual reality, augmented reality, and mixed reality on mobile platforms in various aspects of the construction industry. For these purposes, having a BIM model or high detail 3D model is not always necessary, as a simpler model can serve the purpose within many mobile platforms. This paper streamlines a framework for generating a lightweight 3D model for mobile platforms. In doing so, we use an existing structure's site survey data for the foundation data, followed by mobile VR implementation. This research conducted a pilot study on an existing building. The study provides a process of swiftly generating a lightweight 3D model of a building with relative accuracy and cost savings.

• 한국수자원학회논문집
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• 제51권spc1호
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• pp.1135-1148
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• 2018

기존의 저수지 운영 연구들은 미래의 기후가 과거와 유사하다는 정상성의 가정을 전제로 하였다. 하지만 기후의 비정상성으로 인해 불확실성이 더욱 커질 경우에는 큰 불확실성에서도 안정된 최적해를 찾을 수 있는 로버스트 최적화 과정(Robust Optimization, 이하 RO)이 필요하다고 알려져 있다. RO는 입력자료의 비정상성으로 인해 야기되는 불확실성을 제어하는 로버스트 항을 목적함수에 추가하여 기존의 최적화 방법을 개선한다. 본 연구는 기후변화의 비정상성을 대비하는 저수지 운영규칙 산정을 위해 추계학적동적계획법(Stochastic Dynamic Programing, 이하 SDP)과 RO를 결합하는 Robust-SDP를 제안하였고, 이를 최근 4년간 가뭄을 겪었던 보령댐에 적용하였다. 즉, 비정상성이 반영된 미래 유입량 자료를 생성하고 이를 6가지의 평가지표와 2가지의 의사결정 지원그림을 사용하여 과거 유입량 자료로부터 산출된 저수지 운영규칙의 수행능력을 평가하였다. 그 결과, Robust-SDP가 기후의 비정상성 하에서 극단적인 물 부족 사건의 발생률과 물 부족 사건의 실패의 크기를 감소시켰지만, 작은 크기의 물 부족 발생률은 증가하는 상충관계(trade-off)를 가져옴을 확인할 수 있었다. 이를 바탕으로 의사결정자가 우선시하는 평가지표의 결과에 따라 최적화 모형을 선택할 수 있음을 제안하였다.

• 성인간호학회지
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• 제21권6호
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• pp.652-665
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• 2009

Purpose: This study was conducted to identify job experiences of male hospital nurses. Methods: Data were collected from 20 male nurses working at general hospitals, through semi-structured in-depth interviews. The interviews were recorded and subsequently transcribed verbatim. Using content analysis, data were coded and categorized. Results: The analyzed domains were motivations for choosing nursing, occupational experiences (3 subdomains), and attitudes toward the future. A total of 85 significant statements were selected from the data and classified into 32 categories. The nurses' motivations for choosing nursing were advantages of employment, their aptitude, scarcity value of men, professionalism and job security, good promotion, stable income, and family influence. In occupational experiences, they were assigned to special fields and dissatisfied with vertical relationship, promotion system, their salary, and gaps in military service time; they had difficulties in adapting to female-dominated groups and encountered gender role stereotype and preconception; they were satisfied with their distinguished performance, but had damaged self-esteem, and were stressed and disappointed in their work. In their attitudes toward the future, they considered their career changes, but tried to make professional and personal advancement. Conclusion: These findings have implications for recruiting and retaining male nurses in clinical settings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.613-631
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• 2013

Cognitive radio (CR) is proposed as a key solution to improve spectral efficiency and overcome the spectrum scarcity. Spectrum sensing is an important task in each CR system with the aim of identifying the spectrum holes and using them for secondary user's (SU) communications. Several conventional methods for spectrum sensing have been proposed such as energy detection, matched filter detection, etc. However, the main limitation of these classical methods is that the CR network is not able to communicate with its own base station during the spectrum sensing period and thus a fraction of the available primary frame cannot be exploited for data transmission. The other limitation in conventional methods is that the SU data frames should be synchronized with the primary network data frames. To overcome the above limitations, here, we propose a spectrum sensing technique based on blind source separation (BSS) that does not need time synchronization between the primary network and the CR. Moreover, by using the proposed technique, the SU can maintain its transmission with the base station even during spectrum sensing and thus higher rates are achieved by the CR network. Simulation results indicate that the proposed method outperforms the accuracy of conventional BSS-based spectrum sensing techniques.