• Journal of Communications and Networks
• /
• v.18 no.4
• /
• pp.649-657
• /
• 2016

In this paper, we investigate energy efficiency (EE) of the traditional co-located and the distributed massive multiple-input multiple-output (MIMO) systems. First, we derive an approximate EE expression for both the idealistic and the realistic power consumption models. Then an optimal energy-efficient remote access unit (RAU) selection algorithm based on the distance between the mobile stations (MSs) and the RAUs are developed to maximize the EE for the downlink distributed massive MIMO systems under the realistic power consumption model. Numerical results show that the EE of the distributed massive MIMO systems is larger than the co-located massive MIMO systems under both the idealistic and realistic power consumption models, and the optimal EE can be obtained by the developed energy-efficient RAU selection algorithm.

• Journal of the Korean Institute of Rural Architecture
• /
• v.18 no.3
• /
• pp.35-42
• /
• 2016

For the reason of economy, farmers and structural engineers prefer the vinyl house frame members that have the lightest cross sections. Therefore, in order to reach this aim, rod bracing system is the best method for multiple vinyl house frames. In this study, wire rods (tension members) are used to be bracing members in multiple vinyl house frames. The effects of additional wire rods in the frames are investigated by the variations of the bending moments, axial forces, displacements and combined stresses in the main frames that are reinforced by different shapes of rod bracing system. Vinyl house frames are usually made by steel pipe members and collapsed by the excessive wind and snow loads. Two kinds of bracing models are used for wind and snow loads separately in this study. The effective bracing models for each load are finally figured out.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.4
• /
• pp.249-255
• /
• 2022

The COVID-19 virus appeared in 2019 and is extremely contagious. Because it is very infectious and has a huge impact on people's mobility. In this paper, multiple linear regression and random forest models are used to predict the number of COVID-19 cases using COVID-19 infection status data (open source data provided by the Ministry of health and welfare) and Google Mobility Data, which can check the liquidity of various categories. The data has been divided into two sets. The first dataset is COVID-19 infection status data and all six variables of Google Mobility Data. The second dataset is COVID-19 infection status data and only two variables of Google Mobility Data: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The models' performance has been compared using the mean absolute error indicator. We also a correlation analysis of the random forest model and the multiple linear regression model.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.34.3-34
• /
• 2002

1. Introduction 2. Stereovison Algorithm 3. Superquadric Models 4. Recovery of Superquadric Models 5. Conclusions

• Communications for Statistical Applications and Methods
• /
• v.7 no.1
• /
• pp.165-178
• /
• 2000

Bayesian methods are considered for the multiple caputure-recapture data. Reference priors are developed for such model and sampling-based approach through Gibbs sampler is used for inference from posterior distributions. Furthermore approximate Bayes factors are obtained for model selection between trap and nontrap response models. Finally one methodology is implemented for a capture-recapture model in generated data and real data.

• Journal of Korea Game Society
• /
• v.3 no.2
• /
• pp.30-34
• /
• 2003

Image-based rendering is an approach to generate realistic images in real-time without modeling explicit 3D geometry, Especially, TIP(Tour Into the Picture) is preferred for its simplicity in constructing 3D background scene. However, TP has a limitation that a viewpoint cannot go far from the origin of the TIP for the lack of geometrical information. in this paper, we propose a method to interpolating the TIP images to generate smooth and realistic navigation. We construct multiple TIP models in a wide area of the virtual environment. Then we interpolate foreground objects and background object respectively to generate smooth navigation results.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.11a
• /
• pp.335-336
• /
• 2009

In this paper, I propose an automatic approach to annotating images dynamically based on MBRM(Multiple Bernoulli Relevance Models) using GLCM(Grey Level Co-occurrence Matrix). MBRM is more appropriate to annotate images compare with multinomial distribution. The model is used in limited test set, MSRC-v2 (Microsoft Research Cambridge Image Database). The results show that this model is significantly outperforms previously reported results on the task of image annotation and retrieval.

• Communications for Statistical Applications and Methods
• /
• v.3 no.2
• /
• pp.1-11
• /
• 1996

The detection of multiple outliers in nonlinear regression models can be computationally not feasible. As a compromise approach, we consider the use of simulated annealing algorithm, an approximate approach to combinatorial optimization. We show that this method ensures convergence and works well in locating multiple outliers while reducing computational time.

• Water Engineering Research
• /
• v.4 no.2
• /
• pp.59-68
• /
• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• International Journal of Highway Engineering
• /
• v.15 no.2
• /
• pp.121-129
• /
• 2013

PURPOSES : This study deals with the traffic accidents of rotary in Korea. The objective of this study is to develop the accident models by age group based on the various data of rotaries. METHODS : In pursuing the above, this study gives particular attentions to classifying the accident data of 17 rotaries by age, collecting the data of geometric structure, traffic volume and others, and developing the models using SPSS 17.0 and EXCEL. RESULTS : First, 3 multiple linear regression models which were all statistically significant were developed. The value of model of under 30-49 age group were, however, evaluated to be 0.688 and be less than those of other models. Second, the most powerful variables were analyzed to be traffic volume in the model of under 30 age group, circulatory roadway width in the model of 30-49 age group, and the number of approach lane in the model of above 50 age group. Finally, the test results of accident models using RMSE were all evaluated to be fitted to the given data. CONCLUSIONS : This study propose install streetlights, speed humps and widen Circulatory as effective improvements for reduction of accident in rotary.