• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.49-62
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• 2024

In this paper, a sustainable closed-loop supply chain (SCLSC) network model is proposed for domestic mobile phone industry. Economic, environmental and social factors are respectively considered for reinforcing the sustainability of the SCLSC network model. These three factors aim at minimizing total cost, minimizing total amount of CO₂ emission, and maximizing total social influence resulting from the establishment and operation of facilities at each stage of the SCLSC network model. Since they are used as each objective function in modeling, the SCLSC network model can be a multi-objective optimization problem. A mathematical formulation is used for representing the SCLSC network model and a hybrid meta-heuristic approach is proposed for efficiently solving it. In numerical experiment, the performance of the proposed hybrid meta-heuristic approach is compared with those of conventional meta-heuristic approaches using some scales of the SCLSC network model. Experimental results shows that the proposed hybrid meta-heuristic approach outperforms conventional meta-heuristic approaches.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.95-100
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• 2021

This paper introduces model-based meta reinforcement learning as a control for the manipulator of an underwater construction robot. Model-based meta reinforcement learning updates the model fast using recent experience in a real application and transfers the model to model predictive control which computes control inputs of the manipulator to reach the target position. The simulation environment for model-based meta reinforcement learning is established using MuJoCo and Gazebo. The real environment of manipulator control for underwater construction robot is set to deal with model uncertainties.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3717-3721
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• 2012

Purpose: Any association between the CYP1A1 Ile462Val polymorphism and endometrial cancer risk remains inconclusive. For a more precise estimate, we performed the present meta-analysis. Methods: PUBMED, OVID and EMBASE were searched for the studies which met inclusion criteria. Data in all eligible studies were evaluated and extracted by two authors independently. The meta-analysis estimated pooled odds ratio (OR) with 95% confidence interval (CI) for endometrial cancer risk attributable to the CYP1A1 Ile462Val polymorphism. Results: A total of 7 studies were included in this meta-analysis. The results indicated no association between endometrial cancer risk and the CYP1A1 Ile462Val polymorphism (for Val vs Ile allele model [OR 1.09, 95% CI 0.73-1.62]; for Val.Val vs Ile.Ile genotype model [OR 1.54, 95% CI 0.56-4.23]; for (Ile.Val + Val.Val) vs Ile.Ile genotpye model [OR 1.08, 95% CI 0.71-1.63]; for Val.Val vs (Ile.Ile + Ile.Val) genotype model [OR 1.46, 95% CI 0.53-4.04]). Conclusions: This meta-analysis suggests that there is no association between endometrial cancer risk and the CYP1A1 Ile462Val polymorphism.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.3
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• pp.74-81
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• 2019

Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.68-74
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• 2019

Along with the deeper architecture in the deep learning approaches, the need for the data becomes very big. In the real problem, to get huge data in some disciplines is very costly. Therefore, learning on limited data in the recent years turns to be a very appealing area. Meta-learning offers a new perspective to learn a model with this limitation. A state-of-the-art model that is made using a meta-learning framework, Meta-SGD, is proposed with a key idea of learning a hyperparameter or a learning rate of the fast adaptation stage in the outer update. However, this learning rate usually is set to be very small. In consequence, the objective function of SGD will give a little improvement to our weight parameters. In other words, the prior is being a key value of getting a good adaptation. As a goal of meta-learning approaches, learning using a single gradient step in the inner update may lead to a bad performance. Especially if the prior that we use is far from the expected one, or it works in the opposite way that it is very effective to adapt the model. By this reason, we propose to add a weight term to decrease, or increase in some conditions, the effect of this prior. The experiment on few-shot learning shows that emphasizing or weakening the prior can give better performance than using its original value.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.19-34
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• 2000

Impact of business process re-engineering requires the fundamental rethinking of how information systems are analyzed and designed. It is no longer sufficient to establish a monolithic system for fixed business environments. Information systems must be adaptive in nature. This demand is also applied in production domain. Enabling concept for the adaptive information system is reusability. This paper presents a new object-oriented analysis process for creating such reusable software components in production domain, especially for production planning and scheduling. Our process called MeCOMA is based on three meta-models: physical object meta-model, data object meta-model, and activity object meta-model. After the three meta-models are extended independently for a given production system, they are collaboratively integrated on the basis of integration pattern. The main advantages of MeCOMA are (1) to reduce software development time and (2) to consistently build reusable production software components.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.127-133
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• 2003

The data models which express all types of video information physically and logically. and the definition of spatiotemporal relationship of video data objects In This paper, we classifies meta-model for efficient management on spatiotemporal relationship between two objects in video image data, suggests meta-models based on Rambaugh's OMT technique, and expanded user model to apply the adaptive model, established from hyper-media or web agent to VOD. The proposed meta-model uses data's special physical feature: the effects of camera's and editing effects of shot, and 17 spatial relations on Allen's 13 temporal relations, topology and direction to include logical presentation of spatiotemporal relation for possible spatiotemporal reference and having unspecified applied mediocrity.

• Journal of Korean Academy of Nursing
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• v.52 no.4
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• pp.363-377
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• 2022

Purpose: This study aimed to test a hypothetical model of Korean nurses' patient safety management activities using meta-analytic path analysis. Methods: A systematic review, meta-analysis, and meta-analytic path analysis were conducted following the PRISMA and MOOSE guidelines. Seventy-four studies for the meta-analysis and 92 for the meta-analytic path analysis were included. The R software program (Version 3.6.3) was used for data analysis. Results: Four variables out of 49 relevant variables were selected in the meta-analysis. These four variables showed large effect sizes (ESr = .54) or median effect sizes (ESr = .33~.40) with the highest k (number of studies) in the individual, job, and organizational categories. The hypothetical model for the meta-analytic path analysis was established using these variables and patient safety management activities. Twelve hypothetical paths were set and tested. Finally, the perception of the importance of patient safety management and patient safety competency directly affected patient safety management activities. In addition, self-efficacy, the perception of the importance of patient safety management, patient safety competency, and patient safety culture, indirectly affected patient safety management activities. Conclusion: Self-efficacy, the perception of the importance of patient safety management, patient safety competency, and the organization's patient safety culture should be enhanced to improve nurses' patient safety management activities.

• Journal of Korean Academy of Nursing
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• v.50 no.6
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• pp.757-777
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• 2020

Purpose: The purpose of this study was to identify the outcome variables of nursing unit managers' transformational leadership and to test a hypothetical model using meta-analytic path analysis. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. Data analysis, conducted using R version 3.6.2 software, included 49 studies for the meta-analysis and 119 studies for meta-analytic path analysis. Results: In the meta-analysis, four out of 32 outcome variables were selected. These four variables were empowerment, nursing performance, job satisfaction, and organizational commitment, which showed larger effect sizes than the median and more than five k. The hypothetical model for the meta-analytic path analysis was established by using these four variables and transformational leadership. A total of 22 hypothetical paths including nine direct effects and 13 indirect effects were set and tested. The meta-analytic path analysis showed that transformational leadership had direct effects on the four variables. Finally, eight direct effects, 12 indirect effects, and six mediating effects were statistically significant, and the hypothetical model was verified. Conclusion: Nursing unit managers can use the transformational leadership to improve empowerment, nursing performance, job satisfaction, and organizational commitment of nurses. This study empirically showed the importance of transformational leadership of nursing managers. This finding will be used as evidence to develop strategies for enhancing transformational leadership, empowerment, nursing performance, job satisfaction, and organizational commitment in nursing science and practice.