• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.171-171
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• 2020

Decision making strategies should consider both adaptiveness and robustness in order to deal with two main characteristics of climate change: non-stationarity and deep uncertainty. Especially, robust strategies are different from traditional optimal strategies in the sense that they are satisfactory over a wider range of uncertainty and may act as a key when confronting climate change. In this study, a new framework named Robust Stochastic Dynamic Programming (R-SDP) is proposed, which couples previously developed robust optimization (RO) into the objective function and constraint of SDP. Two main approaches of RO, feasibility robustness and solution robustness, are considered in the optimization algorithm and consequently, three models to be tested are developed: conventional-SDP (CSDP), R-SDP-Feasibility (RSDP-F), and R-SDP-Solution (RSDP-S). The developed models were used to derive optimal monthly release rules in a single reservoir, and multiple simulations of the derived monthly policy under inflow scenarios with varying mean and standard deviations are undergone. Simulation results were then evaluated with a wide range of evaluation metrics from reliability, resiliency, vulnerability to additional robustness measures. Evaluation results were finally visualized with advanced visualization tools that are used in multi-objective robust decision making (MORDM) framework. As a result, RSDP-F and RSDP-S models yielded more risk averse, or conservative, results than the CSDP model, and a trade-off relationship between traditional and robustness metrics was discovered.

• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.121-126
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• 2016

In recommender systems based on collaborative filtering, measuring similarity is very critical for determining the range of recommenders. Data sparsity problem is fundamental in collaborative filtering systems, which is partly solved by Jaccard coefficient combined with traditional similarity measures. This study proposes a new coefficient for improving performance of Jaccard coefficient by compensating for its drawbacks. We conducted experiments using datasets of various characteristics for performance analysis. As a result of comparison between the proposed and the similarity metric of Pearson correlation widely used up to date, it is found that the two metrics yielded competitive performance on a dense dataset while the proposed showed much better performance on a sparser dataset. Also, the result of comparing the proposed with Jaccard coefficient showed that the proposed yielded far better performance as the dataset is denser. Overall, the proposed coefficient demonstrated the best prediction and recommendation performance among the experimented metrics.

• Industrial Engineering and Management Systems
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• 제15권1호
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• pp.99-109
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• 2016

During the last two decades, impact factor has been widely used as a journal evaluation metric that differentiates the influence of a specific journal compared with other journals. However, impact factor does not provide a reliable metric between journals in different subject categories. For example, higher impact factors are given to biology and general sciences than those assigned to other traditional engineering and social sciences. This study initially analyzes the trend of the time series of the impact factors of the journals listed in Journal Citation Reports during the last decade. This study then proposes new journal evaluation metrics that adjust the impact factors across different subject categories. The proposed metrics possibly provides a consistent measure to mitigate the differences in impact factors among subject categories. On the basis of experimental results, we recommend the most reliable and appropriate metric to evaluate journals that are less dependent on the characteristics of subject categories.

• International Journal of Advanced Culture Technology
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• 제1권2호
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• pp.1-6
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• 2013

In this paper, we evaluate performance of existing similarity measurement metric and propose a novel method using user's preferences information entropy to reduce MAE in memory-based collaborative recommender systems. The proposed method applies a similarity of individual inclination to traditional similarity measurement methods. We experiment on various similarity metrics under different conditions, which include an amount of data and significance weighting from n/10 to n/60, to verify the proposed method. As a result, we confirm the proposed method is robust and efficient from the viewpoint of a sparse data set, applying existing various similarity measurement methods and Significance Weighting.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.899-911
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• 2005

This paper conducts an explanatory analysis of the web sphere produced by National Assemblymen in South Korea, using some statistical methods. First, some descriptive metrics were employed. Next, the traditional methods of multi-variate analyses, multidimensional scaling and corresponding analysis, were applied to the data. Finally, cross-sectional data were compared to examine a change over time.

• Journal of information and communication convergence engineering
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• 제20권2호
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• Journal of Neurogastroenterology and Motility
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• 제24권4호
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• pp.570-576
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• 2018

Background/Aims Swallows with viscous or solid boluses in different body positions alter esophageal manometry patterns. Limitations of previous studies include lack of standardized viscous substrates and the need for chewing prior to swallowing solid boluses. We hypothesize that high-resolution impedance manometry (HRiM) using standardized viscous and super-viscous swallows in supine and upright positions improves sensitivity for detecting esophageal motility abnormalities when compared with traditional saline swallows. To establish normative values for these novel substrates, we recruited healthy volunteers and performed HRiM. Methods Standardized viscous and super-viscous substrates were prepared using "Thick-It" food thickener and a rotational viscometer. All swallows were administered in 5-mL increments in both supine and upright positions. HRiM metrics and impedance (bolus transit) were calculated. We used a paired two-tailed t test to compare all metrics by position and substrate. Results The 5-g, 7-g, and 10-g substrates measured 5000, 36 200, and 64 $700mPa{\cdot}sec$, respectively. In 18 volunteers, we observed that the integrated relaxation pressure was lower when upright than when supine for all substrates (P < 0.01). The 10-g substrate significantly increased integrated relaxation pressure when compared to saline in the supine position (P < 0.01). Substrates and positions also affected distal contractile integral, distal latency, and impedance values. Conclusions We examined HRiM values using novel standardized viscous and super-viscous substrates in healthy subjects for both supine and upright positions. We found that viscosity and position affected HRiM Chicago metrics and have potential to increase the sensitivity of esophageal manometry.

• 한국컴퓨터정보학회논문지
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• 제9권3호
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• pp.27-34
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• 2004

개발 패러다임이 점차 복잡한 웹 환경으로 전환되면서 복잡도에 대한 연구가 다시 활발해지고 있으나 아직 웹 어플리케이션의 구조나 복잡도 측정 매트릭에 정립된 이론이 부족한 실정이다. 또한 전통적 복잡도를 측정하는 프로그램 규모(LOC)나 순환복잡도 매트릭은 구현 후에나 알 수 있어 소프트웨어 개발주기 초기의 분석 및 설계 단계에는 큰 도움을 주지 못하고 있다. 본 연구에서는 실무에서 사용되는 6개 웹 프로젝트에 복잡도 인디케이터를 적용하여 결함 가능성이 높은 어플리케이션을 추출한다 추출한 61개의 프로그램을 대상으로 복잡도와 클래스 수 및 메소드 수에 대한 선형적 상관관계를 제안함으로써 웹어플리케이션의 복잡도를 구현 전에 미리 예측 가능하도록 하여 개발 프로세스의 인적 자원 관리나 비용 예측에 기여하고자 한다.

• Journal of Communications and Networks
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• 제8권2호
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• pp.241-252
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• 2006

Traditional service-level agreements (SLAs), defined by average delay or packet loss, often camouflage the instantaneous performance perceived by end-users. We define a set of metrics for service availability to quantify the performance of Internet protocol (IP) backbone networks and capture the impact of routing dynamics on packet forwarding. Given a network topology and its link weights, we propose a novel technique to compute the associated service availability by taking into account transient routing dynamics and operational conditions, such as border gateway protocol (BGP) table size and traffic distributions. Even though there are numerous models for characterizing topologies, none of them provide insights on the expected performance perceived by end customers. Our simulations show that the amount of service disruption experienced by similar networks (i.e., with similar intrinsic properties such as average out-degree or network diameter) could be significantly different, making it imperative to use new metrics for characterizing networks. In the second part of the paper, we derive goodness factors based on service availability viewed from three perspectives: Ingress node (from one node to many destinations), link (traffic traversing a link), and network-wide (across all source-destination pairs). We show how goodness factors can be used in various applications and describe our numerical results.

• 한국콘텐츠학회논문지
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• 제6권1호
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• pp.54-63
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• 2006

본 논문에서는 레거시 시스템에 대한 기존 도메인 분석의 문제점을 해결하기 위하여 동적 모델링을 기반으로 하는 확장된 워크플로우 메커니즘을 이용한 도메인 분석 방법론을 제안한다. 이 방법론을 WODA(Werldlow Oriented Domain Analysis)라 명명한다. 제안하는 절차를 통해 공통/비공통 컴포넌트를 식별 및 컴포넌트들의 클러스터를 추출할 수 있다. 이를 통해 새로운 시스템을 개발 시 효율적으로 재사용하고자 한다. 동적 분석으로 특정한 시스템에 발생 가능한 시나리오들을 식별한 후, 제안한 컴포넌트 테스트 플랜 매트릭스를 이용해 재사용성이 높은 컴포넌트와 컴포넌트 시나리오를 결정한다. 또한 컴포넌트 가중치 측정을 통해 재사용 가능한 컴포넌트들의 중요성과 빈도수를 인식하고 컴포넌트 시나리오들의 우선순위를 도출 할 수 있다. 구현한 자동화 모델링 도구인 WODA을 통해 UPS(Uninterrupted Power Supply)에 적용 사례를 소개한다.