• Wind and Structures
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• v.28 no.3
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• pp.191-201
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• 2019

In this paper, the aerodynamic analysis of a vertical axis wind turbine was carried out by CFD approach to optimize the turbine performance. To perform numerical simulation, SST-Transition turbulence model was used, which demonstrated more precise results compared to non-transition models. A parametric study was conducted to optimize the VAWT performance based on the selected model. The investigation of pitch angle changes showed that the highest power produced by the turbine occurs at $2^{\circ}$ angle. Considering the effect of the rotor's arm junction to the airfoil showed that by increasing the distance of the junction from the edge of the airfoil from 25 cm to 40 cm, the power of the turbine increases by 60%. However, further increase in this distance results in power decrease. Based on the proposed numerical model, a case study was conducted to consider the installation of four VAWTs in the southwest corner of the medical science building at TMU campus with a height of 42m. The results of the simulation showed that 8.27 MWh energy is obtainable annually.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.383-392
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• 2014

The research for coatings is one of the most popular and active research in the polymer industry. For the coatings, electronics industry, medical and optical fields are growing more important. In particular, the trend is the increasing of the technical requirements for the performance and accuracy of the coatings by the development of automotive and electronic parts. In addition, the industry has a need of more intelligent and automated system in the industry is increasing by introduction of the IoT and big data analysis based on the environmental information and the context information. In this paper, we propose an optimization model for the design of experiments based coating formulation data objects using the Internet technologies and big data analytics. In this paper, the coating formulation was calculated based on the best data analysis is based on the experimental design, modify the operator with respect to the error caused based on the coating formulation used in the actual production site data and the corrected result data. Further optimization model to correct the reference value by leveraging big data analysis and Internet of things technology only existing coating formulation is applied as the reference data using a manufacturing environment and context information retrieval in color and quality, the most important factor in maintaining and was derived. Based on data obtained from an experiment and analysis is improving the accuracy of the combination data and making it possible to give a LOT shorter working hours per data. Also the data shortens the production time due to the reduction in the delivery time per treatment and It can contribute to cost reduction or the like defect rate reduced. Further, it is possible to obtain a standard data in the manufacturing process for the various models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.483-503
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• 2015

This paper analyzes the optimal user density and power allocation for Device-to-Device (D2D) communication underlaying cellular networks on multiple bands with the target of maximizing the D2D transmission capacity. The entire network is modeled by Poisson point process (PPP) which based on stochastic geometry. Then in order to ensure the outage probabilities of both cellular and D2D communication, a sum capacity optimization problem for D2D system on multiple bands is proposed. Using convex optimization, the optimal D2D density is obtained in closed-form when the D2D transmission power is determined. Next the optimal D2D transmission power is obtained in closed-form when the D2D density is fixed. Based on the former two conclusions, an iterative algorithm for the optimal D2D density and power allocation on multiple bands is proposed. Finally, the simulation results not only demonstrate the D2D performance, density and power on each band are constrained by cellular communication as well as the interference of the entire system, but also verifies the superiority of the proposed algorithm over sorting-based and removal algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.9
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• pp.817-823
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• 2017

In the past, research has been conducted on the development of an adaptive cruise control algorithm considering fuel efficiency, and an adaptive cruise control system considering fuel efficiency have been developed. However, research on optimizing vehicle and adaptive cruise control parameters in order to maximize performances is insufficient. In this study, the design optimization of vehicle and control parameters considering fuel efficiency, trackability, ride comfort and safe distance is performed. This paper proposes performance measures of vehicle behavior and develops an adaptive cruise control system. In addition, based on the screening of vehicle parameters that significantly influence performances, kriging surrogate models are constructed through a sequential design of experiment, and kriging surrogate model-based design optimization is performed to maximize fuel efficiency and satisfy target performances.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.5
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• pp.459-467
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• 2012

In this work, the configuration of a haptic gripper featuring magnetorheological(MR) brakes is proposed and an optimal design of the MR brakes for the haptic griper is performed considering the required braking torque, the uncontrollable torque(zero-field friction torque) and mass of the brakes. Several configurations of MR brake is proposed such as disc-type, serpentine-type and hybrid-type. After the configurations of the MR brakes are proposed, braking torque of the brakes is analyzed based on Bingham rheological model of the MR fluid. The zero-field friction torque of the MR brakes is also analyzed. An optimization procedure based on finite element analysis integrated with an optimization toolbox is developed for the MR brakes. The purpose of the optimal design is to find optimal geometric dimensions of the MR brake structure that can produce the required braking torque and minimize the mass of the MR brakes. In addition, the uncontrollable torque of the MR brakes is constrained to be much smaller than the required braking torque. Based on the developed optimization procedure, optimal solution of the proposed MR brakes are achieved and the best MR brake is determined. The working performance of the optimized MR brake is then investigated.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.58-65
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• 2016

This paper presents optimization techniques to reduce the processing time of the dehazing method and proposes an efficient dehazing method based on them. In the proposed techniques, the atmospheric light is estimated based on the distributed sorting of the dark channel pixels, so as to reduce the computations. The normalization process required in the transmission estimation is simplified by the assumption that the atmospheric light is monochromatic. In addition, the dark channel is modified into the median dark channel in order to eliminate the transmission refinement process while achieving a comparable dehazing quality. The proposed dehazing method based on the optimization techniques is presented and its performance is investigated by developing a prototype system. When compared to the previous method, the proposed dehazing method reduces the processing time by 65% while maintaining the dehazing quality.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.45-51
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• 2006

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The proposed IG_gSOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data(gas furnace process and NOx process data).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4305-4323
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• 2014

In this paper, we investigate the degrees of freedom (DOF) of the Y channel consisting of three single-antenna users and a two-antenna common access relay, where each user intends to exchange independent messages with the other two users with the assistance of the relay. We show that the DOF of this particular scenario is 1.5. In order to prove this result, we firstly derive a DOF upper bound based on cut-set bound by allowing cooperation among users, which shows that the total DOF is upper bounded by 1.5. Then we propose a novel transmission scheme based on asymmetric signal space alignment (ASSA) to demonstrate the achievability of the upper bound. Theoretical evaluation and numerical results confirm that the upper bound can be achieved by utilizing ASSA, which also proves the optimality of the ASSA-based scheme in terms of DOF. Combining the upper bound and achievability, we conclude that the exact DOF is 1.5. Moreover, we present a novel iterative joint beamforming optimization (I-JBO) algorithm to further improve the sum rate. Numerical simulations have been provided to demonstrate the convergence speed and performance advantage of the I-JBO algorithm.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.882-897
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• 2020

Recently, the use of immersive media contents representing Panorama and 360° video is increasing. Since the viewing angle is limited to generate the content through a general camera, image stitching is mainly used to combine images taken with multiple cameras into one image having a wide field of view. However, if the parallax between the cameras is large, parallax distortion may occur in the stitched image, which disturbs the user's content immersion, thus an image stitching overcoming parallax distortion is required. The existing Seam Optimization based image stitching method to overcome parallax distortion uses energy function or object segment information to reflect the location information of objects, but the initial seam generation location, background information, performance of the object detector, and placement of objects may limit application. Therefore, in this paper, we propose an image stitching method that can overcome the limitations of the existing method by adding a weight value set differently according to the type of object to the energy value using object detection based on deep learning.