• Nutrition Research and Practice
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• v.10 no.3
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• pp.352-358
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• 2016

BACKGROUND/OBJECTIVES: Nutrition is a determinant factor of health in elderly people. Independent living in elderly people can be maintained or enhanced by improvement of nutritional behavior. Hence, the present study was conducted to determine the impact of Health Belief Model (HBM)-based intervention on the nutritional behavior of elderly women. SUBJECTS/METHODS: Cluster-random sampling was used to assess the sample of this clinical trial study. The participants of this study attended a 12-week nutrition education program consisting of two (2) sessions per week. There was also a follow-up for another three (3) months. Smart PLS 3.5 and SPSS 19 were used for structural equation modeling, determination of model fitness, and hypotheses testing. RESULTS: The findings indicate that intervention had a significant effect on knowledge improvement as well as the behavior of elderly women. The model explained 5 to 70% of the variance in nutritional behavior. In addition, nutritional behavior was positively affected by the HBM constructs comprised of perceived susceptibility, self-efficacy, perceived benefits, and barriers after the intervention program. CONCLUSION: The results of this study show that HBM-based educational intervention has a significant effect in improving nutritional knowledge and behavior among elderly women.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.525-527
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• 2013

최근 IT 기업들이 자동차에 IT도입에 열을 올리고 있으며, 그 중에서도 미터 클러스터는 운전자의 편의성을 고려하여 많은 연구와 관심이 증대되고 있다. 자동차 계기판은 전자기기의 발전과 더불어 아날로그 계기판, 디지털 계기판, 이 둘을 함께 사용한 하이브리드 계기판, 그리고 보다 상세한 정보를 이미지와 함께 제공해 주는 스마트 하이브리드 계기판으로 발전하고 있다. 기존의 미터 클러스터는 단순히 차량의 제한된 정보를 표시하며, 운전자의 운전 상태에 따른 시인성이 부족하며, 차량의 상태, 그리고 운행지역의 주변 환경적 정보의 부족으로 인해 운전자의 안정성이 고려되지 못한 부분이 많아 운전자의 안전 운전에 도움이 되지 못하는 부분이 많다. 따라서 본 논문에서는 차량의 상태, 운행 환경정보 등의 통합 된 정보를 LCD 디스플레이 기반에서 운전자의 시인성과 안정성 우선으로 하는 레이아웃을 연구하고자 한다.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1778-1786
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• 2009

Many sensor network applications have been developed for smart home, disaster management, and a wide range of other applications. These applications, however, generally assume a fixed base station as well as fixed sensor nodes. Previous research on sensor networks mainly focused on efficient transmission of data from sensors to fixed sink nodes. Recently there has been active research on mobile sink nodes, sink mobility is one of the most comprehensive trends for information gathering in sensor networks, but the research of an environment where both fixed sink nodes and mobile sinks are present at the same time is rather scarce. This paper proposes a scheme for context-aware by ubiquitous devices with the sink functionality added through fixed sinks under a previously-built, cluster-based multi-hop sensor network environment. To this end, clustering of mobile devices were done based on the fixed sinks of a previously-built sensor network, and by using appropriate fixed sinks, context gathering was made possible. By mathematical comparison with TTDD routing protocol, which was proposed for mobile sinks, it was confirmed that performance increases by average 50% in energy with the number of mobile sinks, and with the number of movements by mobile devices.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.370-371
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• 2019

태양 에너지 수집형 무선 센서 네트워크는 지속해서 에너지를 수집할 수 있어 배터리 기반 센서 네트워크의 에너지 제약 문제를 완화할 수 있지만, 고정된 싱크의 사용으로 싱크 주변에 존재하는 노드들이 상대적으로 에너지 소비가 증가하는 문제, 즉 에너지 사용 불균형 문제는 해결하지 못한다. 최근의 연구에서는 클러스터링을 기반으로 한 모바일 싱크를 도입하여 이를 해결하고자 했지만, 클러스터 헤드 및 그 주변 노드들의 에너지 부담은 여전히 존재한다. 한편, 무선 전력 전송 기술 발전에 따라 무선 센서 네트워크에서 모바일 싱크를 이용한 무선 전력 전송의 연구가 활발히 이루어지고 있다. 따라서 본 논문에서는 무선 전력 전송이 가능한 모바일 싱크와 효율적인 클러스터링 기법(클러스터 헤드 선출 포함)을 이용하여 에너지 불균형 문제를 최소화하는 기법을 제안한다. 제안 기법은 클러스터 헤드 및 헤드 주변 노드의 에너지 핫 스팟이 완화됨으로, 전체 네트워크의 정전 노드들이 감소하고 수집된 데이터양이 증가한 것을 성능평가를 통해 확인할 수 있다.

• The Journal of Information Systems
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• v.28 no.3
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• pp.39-58
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• 2019

Purpose A digital twin is a digital replication of a physical system. Gartner identified the digital twin as one of the Gartner Top 10 Strategic Technology Trend for three years from 2017. The rapid development of the digital twin market is expected to bring about innovation and change throughout society, and much research has been done recently in academia. In this research, we tried to explore the main research trends for digital twin research. Design/methodology/approach We collected the digital twin research from Web of Science, and analyzed 804 articles that was published during time span of 2010-2018. A total of 41 key authors were selected based on the frequency of citation. We created a co-citation matrix for the core authors, and performed multivariate analysis such as cluster analysis and multidimensional scaling. We also conducted social network analysis to find the influential researchers in digital twin research. Findings We identified four major sub- areas of digital twin research: "Infrastructure", "Prospects and Challenges", "Security", and "Smart Manufacturing". We also identified the most influential researchers in digital twin research: Lee EA, Rajkumar R, Wan J, Karnouskos S, Kim K, and Cardenas AA. Limitation and further research suggestion were also discussed as a concluding remarks.

• Journal of International Academy of Physical Therapy Research
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• v.11 no.4
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• pp.2191-2196
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• 2020

Background: Incorrect postures of adolescents caused by the use of smart devices have been noted as a factor causing spinal diseases. Objectives: To examine the effect of joint mobilization and stretching on Cobb's angle and respiratory function in adolescent idiopathic scoliosis (AIS). Design: Cluster-randomized controlled trial. Methods: A total of 22 subjects with AIS were enrolled. They were allocated to two groups: the joint mobilization (n=11) and the stretching (n=11). All interventions were conducted for 30 minutes, three times a week for six weeks. Outcome measures were the Cobb's angle and respiratory function. The Cobb's angle and respiratory function measured using the X-ray and Micro-Quark. Results: Joint mobilization group showed significant differences in Cobb's angle and respiratory function, but stretching group showed significant differences Cobb's angle. The differences in peak expiratory flow (PEF) between the two groups were significant. Conclusion: This study proved that joint mobilization is a more effective intervention for AIS to improve Cobb's angle and respiratory function, when compared to stretching.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.2
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• pp.71-83
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• 2022

In this study, an indicator and assessment system for evaluating the monthly hydrological cycle was prepared using simple factors such as the landuse status of the watershed and topographic characteristics to the dynamic water balance model (DWBM) based on the Budyko framework. The parameters a1 of DWBM are introduced as hydrologic cycle indicators. An indicator estimation regression model was developed using watershed characteristics data for the introduced indicator, and an assessment system was prepared through K-means cluster analysis. The hydrological cycle assessment system developed in this study can assess the hydrological cycle with simple data such as land use, CN, and watershed slope, so it can quickly assess changes in hydrological cycle factors in the past and present. Because of this advantage is expected that the developed assessment system can predict changes in the hydrological cycle and use an auxiliary tool for policymaking.

• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.63-67
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• 2024

Recently, artificial intelligence (AI) workloads encompassing various industries such as smart logistics, FinTech, and entertainment are being executed on the cloud. In this paper, we address the scheduling issues of various AI workloads on a multi-tenant cloud system composed of heterogeneous GPU clusters. Traditional scheduling decreases GPU utilization in such environments, degrading system performance significantly. To resolve these issues, we present a new scheduling approach utilizing genetic algorithm-based optimization techniques, implemented within a process-based event simulation framework. Trace driven simulations with diverse AI workload traces collected from Alibaba's MLaaS cluster demonstrate that the proposed scheduling improves GPU utilization compared to conventional scheduling significantly.