• Healthcare Informatics Research
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• 제24권4호
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• pp.346-358
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• 2018

Objectives: The association between obstructive sleep apnea (OSA) and mortality or serious cardiovascular events over a long period of time is not clearly understood. The aim of this observational study was to estimate the clinical effectiveness of continuous positive airway pressure (CPAP) treatment on an outcome variable combining mortality, acute myocardial infarction (AMI), and cerebrovascular insult (CVI) during a follow-up period of 15.5 years ($186{\pm}58$ months). Methods: The data set consisted of 978 patients with an apnea-hypopnea index (AHI) ${\geq}5.0$. One-third had used CPAP treatment. For the first time, a data-driven causal Bayesian network (DDBN) and a hypothesis-driven causal Bayesian network (HDBN) were used to investigate the effectiveness of CPAP. Results: In the DDBN, coronary heart disease (CHD), congestive heart failure (CHF), and diuretic use were directly associated with the outcome variable. Sleep apnea parameters and CPAP treatment had no direct association with the outcome variable. In the HDBN, CPAP treatment showed an average improvement of 5.3 percentage points in the outcome. The greatest improvement was seen in patients aged ${\leq}55$ years. The effect of CPAP treatment was weaker in older patients (>55 years) and in patients with CHD. In CHF patients, CPAP treatment was associated with an increased risk of mortality, AMI, or CVI. Conclusions: The effectiveness of CPAP is modest in younger patients. Long-term effectiveness is limited in older patients and in patients with heart disease (CHD or CHF).

• 한국BIM학회 논문집
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• 제6권4호
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

• 한국정보과학회논문지:시스템및이론
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• 제35권1호
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• pp.44-52
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• 2008

본 논문에서는 모션켑쳐데이타를 이용한 두 캐릭터간의 빠른 충돌감지에 대한 연구를 논의한다. 본 연구의 목적이 군중 시뮬레이션이기 때문에, 제안한 알고리즘은 캐릭터를 실린더 형태로 모델링 한 후에 Rough한 충돌감지를 목표로 한다. 이를 위해 계층적인 바운딩 박스 데이타 구조인 MOBB를 제안한다. MOBB는 모션클립에 대한 시공간 바운딩 박스이며, 제안된 알고리즘에 대한 테스트 결과 2배 이상의 속도 향상이 있음을 밝힌다.

• The International Journal of Costume Culture
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• 제5권2호
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• pp.24-36
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• 2002

Prestige products, such as apparel, are infrequently purchased and require a higher level of interest and knowledge because they are strongly related to an individual's self-concept. This study was designed to conceptualize prestige seeking behaviors by investigating the underlying motives and its influence on brand loyalty. This study adapts Vigneron and Johnson's (1999) framework as a conceptualization of prestige seeking apparel consumption. A survey questionnaire was developed to measure the five underlying values of prestige consumption and brand loyalty. Data from 554 college students were used for the analysis. Results of confirmatory factor analysis using LISREL indicated that apparel prestige consumption does not consist of five distinctive dimensions. Among five theoretically driven dimensions, prestige consumption due to conspicuous, social and emotional value were highly correlated. Structural equation modeling using LISREL showed that brand loyalty was significantly influenced by prestige consumption due to conspicuous value, hedonic value, and uniqueness value.

• 한국시뮬레이션학회논문지
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• 제8권1호
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• pp.113-138
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• 1999

Driven by the explosive expansion and acceptance of the Internet and its multimedia front-end, the Web, a new generation of the modeling and simulation tools have come up with the name of Web-Based Simulation (WBS). Most of WBS libraries inherit its powerful advantages from Java. However, there are cases where explicit specification of models or interface objects is more desirable than the black-box programs. This paper presents an XML-based DEVS (Discrete Event System Specification) markup language for sharing simulation models on the Web. DEVS provides a system-theoretic formalism for the language while XML supports platform-independent data access. This paper focuses on the design of such a language.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.717-723
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• 2019

Smart systems and services aim to facilitate growing urban populations and their prospects of virtual-real social behaviors, gig economies, factory automation, knowledge-based workforce, integrated societies, modern living, among many more. To satisfy these objectives, smart systems and services must comprises of a complex set of features such as security, ease of use and user friendliness, manageability, scalability, adaptivity, intelligent behavior, and personalization. Recently, artificial intelligence (AI) is realized as a data-driven technology to provide an efficient knowledge representation, semantic modeling, and can support a cognitive behavior aspect of the system. In this paper, an integration of AI with the smart systems and services is presented to mitigate the existing challenges. Several novel researches work in terms of frameworks, architectures, paradigms, and algorithms are discussed to provide possible solutions against the existing challenges in the AI-based smart systems and services. Such novel research works involve efficient shape image retrieval, speech signal processing, dynamic thermal rating, advanced persistent threat tactics, user authentication, and so on.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.223-228
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• 2021

As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios.

• Advances in aircraft and spacecraft science
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• 제9권5호
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• pp.479-491
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• 2022

This work concerns the investigation of a Q1D methodology employed to study pressure oscillations in solid rocket motors driven by hydrodynamic instabilities. A laboratory-scale solid motor designed to develop vortex-shedding phenomena is analyzed for the whole firing time. The comparison between numerical results and experimental data shows good agreement regarding pressure oscillations signature, especially in the flute-mode behavior, the typical oscillations frequency trend present in any motor liable to hydrodynamic instabilities. Such result ensures the model capability to cope with this particular kind of pressure oscillations source, allowing the investigation of the phenomenon with a lighter and cost savings methodology than CFD simulations.

• Journal of Information Technology Applications and Management
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• 제30권4호
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• pp.65-76
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• 2023

On social media, companies create brand experiences while customers actively seek, consume, and generate brand-related content. Customer engagement plays a vital role in the marketing performance of social media-driven brands. This study explores the positive relationship between brand authenticity, aligning brand identity with image, and self-brand connection, aligning brand identity with consumers' self-concepts, on customer engagement and its subsequent impact on brand loyalty. The study surveyed 243 consumers engaged with brand-related social media content, validating hypotheses using structural equation modeling. Results confirmed that brand authenticity and self-brand connection positively affect customer engagement, which, in turn, boosts brand loyalty. These findings highlight the importance of companies enhancing brand authenticity and self-brand connection to drive customer engagement, with theoretical and practical implications provided.

• Korean Chemical Engineering Research
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• 제53권2호
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• pp.236-242
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• 2015

고분자전해질 연료전지 스택의 성능 및 주요 운전 변수를 예측하기 위해 부분최소자승법과 인공신경망의 두 가지 데이터 기반 모델링 기법을 제시한다. 30 kW급 고분자전해질 연료전지 스택 실험으로부터 확보한 데이터를 사용하여 부분최소자승 및 인공신경망 모델들을 구성한 후 각 모델의 예측 성능 및 계산 시간을 비교하였다. 모델의 복잡성을 줄이기 위해 부분최소자승법에 기초한 VIP(Variable Importance on PLS Projections) 선정기준을 모델링 절차에 포함하여, 초기 입력변수의 집합으로부터 모델링에 필요한 입력변수들을 선정하였다. 모델링 결과, 인공신경망이 스택의 평균 셀전압과 캐소드(cathode) 출구 온도를 예측하는데 있어서, 부분최소자승법 보다 우수한 성능을 보였다. 그러나 부분최소자승법 또한 입력변수와 출력변수 간에 선형적 상관관계만을 모델링 할 수 있음에도 불구하고 비교적 만족할 만한 예측 성능을 나타냈다. 모델의 정확도와 계산속도의 요구조건에 따라 두 모델링 기법은 고분자전해질 연료전지의 설계 및 운전 분야의 성능 예측, 온라인 및 오프라인 최적화, 제어 및 이상 진단을 위해 적용될 수 있을 것으로 판단된다.