• 전기학회논문지
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• 제65권6호
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• pp.1070-1079
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• 2016

In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

• 전기학회논문지
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• 제58권12호
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• pp.2520-2528
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• 2009

In this paper, we proposed tracking detection methodology using information granulation-based fuzzy radial basis function neural networks (IG-FRBFNN). According to IEC 60112, tracking device is manufactured and utilized for experiment. We consider 12 features that can be used to decide whether tracking phenomenon happened or not. These features are considered by signal processing methods such as filtering, Fast Fourier Transform(FFT) and Wavelet. Such some effective features are used as the inputs of the IG-FRBFNN, the tracking phenomenon is confirmed by using the IG-FRBFNN. The learning of the premise and the consequent part of rules in the IG-FRBFNN is carried out by Fuzzy C-Means (FCM) clustering algorithm and weighted least squares method (WLSE), respectively. Also, Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA) is exploited to optimize the IG-FRBFNN. Effective features to be selected and the number of fuzzy rules, the order of polynomial of fuzzy rules, the fuzzification coefficient used in FCM are optimized by the HFC-PGA. Tracking inference engine is implemented by using the LabVIEW and loaded into embedded system. We show the superb performance and feasibility of the tracking detection system through some experiments.

• Earthquakes and Structures
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• 제11권5호
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• pp.841-859
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• 2016

Bridge bearings are important connection elements between bridge superstructures and substructures, whose health states directly affect the performance of the bridges. This paper systematacially presents a new method to identify the bridge bearing damage based on the neural network theory. Firstly, based on the analysis of different damage types, a description of the bearing damage is introduced, and a uniform description for all the damage types is given. Then, the feasibility and sensitivity of identifying the bearing damage with bridge vibration modes are investigated. After that, a Radial Basis Function Neural Network (RBFNN) is built, whose input and output are the beam modal information and the damage information, respectively. Finally, trained by plenty of data samples formed by the numerical method, the network is employed to identify the bearing damage. Results show that the bridge bearing damage can be clearly reflected by the modal information of the bridge beam, which validates the effectiveness of the proposed method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.170-174
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• 2004

Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO$_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO$_2$ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2010년도 제39회 하계학술대회 초록집
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• pp.314-314
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• 2010

For the optimal design of a tokamak-type reactor, self-consistent determination of a radial build of reactor systems is important and the radial build has to be determined by considering the plasma physics and engineering constraints which inter-relate various reactor systems. In a low aspect ratio (LAR) tokamak reactor with a superconducting toroidal field (TF) coil, the shield should provide sufficient protection for the superconducting TF coil and the shield plays a key role in determining the size of a reactor. To determine the radial build of a reactor, neutronic effects such as tritium breeding in the blanket, nuclear heating, and radiation damage to toroidal field (TF) coil has to be included in the systems analysis. In this work, the outboard blanket only is considered where tritium self-sufficiency is possible by using an inboard neutron reflector instead of breeding blanket. The reflecting shield should provide not only protection for the superconducting TF coil but also improved neutron economy for the tritium breeding in outboard blanket. Tungsten carbide, metal hydride such as titanium hydride and zirconium hydride can be used for improved shielding performance and thus smaller shield thickness. With the use of advanced technology in the shield, conceptual design of a compact superconducting LAR reactor with aspect ratio of less than 2 will be presented as a viable power plant.

• 설비공학논문집
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• 제23권11호
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• pp.726-732
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• 2011

The aerodynamic and aeroacoustic performance of discharge grille of outdoor unit of air-conditioner was investigated in this study. Discharge grille is one of outdoor unit's important parts to affect the flow rate and Overall Sound Pressure Level(OSPL). New type of discharge grille was suggested based on the results of numerical simulation. To simulate the flow pattern near the propeller fan, commercial flow solver FLUENT was used. Sliding mesh method was used for rotating propeller fan and initial condition for unsteady model was calculated by Multiple Reference Frame(MRF) method. To minimize the interaction noise between fan blade wake and discharge grille, new discharge grille has radial rib which is aligned with trailing edge of fan blade. And inclined radial rib was adopted for reducing flow rate drop in discharge grille. The optimization of inclined angle of radial grille was performed experimentally.

• 전기학회논문지
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• 제59권7호
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• 전기학회논문지
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• 제64권10호
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• pp.1469-1478
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• 2015

In this paper, The methodology of Type-2 fuzzy set-based Radial Basis Function Neural Network(T2RBFNN) is proposed for Sewage Treatment Process and the simulator is developed for application to the real-world sewage treatment plant by using the proposed model. The proposed model has robust characteristic than conventional RBFNN. architecture of network consist of three layers such as input layer, hidden layer and output layer of RBFNN, and Type-2 fuzzy set is applied to receptive field in contrast with conventional radial basis function. In addition, the connection weights of the proposed model are defined as linear polynomial function, and then are learned through Back-Propagation(BP). Type reduction is carried out by using Karnik and Mendel(KM) algorithm between hidden layer and output layer. Sewage treatment data obtained from real-world sewage treatment plant is employed to evaluate performance of the proposed model, and their results are analyzed as well as compared with those of conventional RBFNN.

• 한국기계가공학회지
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• 제18권7호
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• pp.8-13
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• 2019

As increasing demand for precise machining in advanced disciplines, especially in semi-conductor, aeronautical and automotive industries, the magnetic abrasive deburring(MAD) which is able to eliminate micro-sized burr on complex surface in less time has drawn the attention in the last decades. However, the performance of MAD is subject to shape and size of a tool. Therefore, this study aim to identify deburring behavior of MAD in U-type flow channel by measuring the length rate of burr removal in radial distance of the cylindrical tool under four process factors. In order to evaluate the deburring effect of MAD on the surface, finishing regions are divided based on center of the circular cutting tool. As a results, it was defined that the amount of burr removal in a downward direction moving toward flow channel from the top surface was higher than upward direction. This is because the magnetic abrasives were detached from magnetic lines of force due to geometrical shape.

• Clinics in Shoulder and Elbow
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• 제24권4호
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• pp.239-244
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• 2021

Background: Radial neck fracture in children is rare. This study attempted to evaluate the outcome of surgically treated patients and any associated complications. Methods: This study evaluated 23 children under 15 years of age with radial neck fracture who were treated with open reduction between 2006 and 2016 to determine their range of motion, postoperative complications, and radiographic outcomes. The results were assessed clinically using the Mayo clinic elbow performance score. Results: The mean follow-up duration for patients was 34.6 months. The average postoperative angulation was 3.6°. Hypoesthesia was reported in only 9% of patients, and none of the patients complained of postoperative pain. The postoperative X-ray results were excellent in 60% and good in 40%. No radiographic complications were identified. The elbow score was excellent in 87% and good in 13% (mean score, 96.74). There was a statistical relationship between range of motion limitations and age, degree of fracture, initial displacement, and surgical pin removal time. Conclusions: Although most patients accept the closed reduction method as a primary treatment, the present study suggests that an open-reduction approach has been associated with optimal therapeutic outcomes for patients in whom closed reduction was not satisfactory or indicated.