• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.511-517
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• 2016

Current oil-type potential transformers for trains are filled with insulating oil, which could have problems like explosions due to rising inner pressure during train operation. Therefore, mold and dry-type potential transformers are being developed to prevent explosions. One problem in manufacturing mold-type transformers is preventing void formation around the coiled core inside the mold during epoxy filling, which could cause an electrical spark. Micro voids can remain in the resin after filling, and macro voids can occur due to the structure shape. A transformer that is being developed has a cavity at the junction of the core and the coil for better performance, and when highly viscous epoxy flows inside the cavity channel, macro voids can form inside it. Therefore, in this study, the free-surface flow of the mold filling procedure was analyzed numerically by applying the VOF method. The results were used to understand the phenomena of void formation inside the cavity and to modify the process conditions to reduce voids.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.71-81
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• 2010

The problem of finding clusters is widely used in numerous applications, such as pattern recognition, image analysis, market analysis. The important factors that decide cluster quality are the similarity measure and the number of attributes. Similarity measures should be defined with respect to the data types. Existing similarity measures are well applicable to numerical attribute values. However, those measures do not work well when the data is described by categorical attributes, that is, when no inherent similarity measure between values. In high dimensional spaces, conventional clustering algorithms tend to break down because of sparsity of data points. To overcome this difficulty, a subspace clustering approach has been proposed. It is based on the observation that different clusters may exist in different subspaces. In this paper, we propose a new similarity measure for clustering of high dimensional categorical data. The measure is defined based on the fact that a good clustering is one where each cluster should have certain information that can distinguish it with other clusters. We also try to capture on the attribute dependencies. This study is meaningful because there has been no method to use both of them. Experimental results on real datasets show clusters obtained by our proposed similarity measure are good enough with respect to clustering accuracy.

• Tunnel and Underground Space
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• v.14 no.5
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• pp.327-338
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• 2004

The purpose of this research is to establish a concept of the factor of safety of tunnels which is a quantitative estimate of the stability of tunnels. Based on this concept, a numerical technique which calculates the factor of safety of tunnels was developed. To obtain the safety factor of a tunnel, the strength reduction technique in which a series of analyses are repeated with reduced ground strength until the tunnel collapses were employed. With this technique, the failure plane, as well as the factor of safety, can be obtained without prescribing the trial failure plane. Analyses were conducted with FLA $C^{2D}$(ver3.3), a geotechnical analysis program which is based on the finite difference method. From the result, the location of plastic zones, the maximum convergence and the maximum stress generated in the support system were also analyzed. The result shows that factors of safety are higher for the 1st and 2nd rock classes, and lower for the lower rock classes. Furthermore, factor of safety is higher when $K_{0}$ =0.5 compared to at in case of $K_{0}$ =2.0. Through this research, it is found that the factor of safety defined in this research can be used as a good quantitative index representing the stability of tunnels. Also, close examination of the results can help adjustment of the quantity and location of additional supports.s.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.211-216
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• 2013

In general, smoothing filters regularize functions by reducing differences between adjacent values. The smoothing filters, therefore, can regularize inverse solutions and produce more accurate subsurface structure when we apply it to full waveform inversion. If we apply a smoothing filter with a constant coefficient to subsurface image or velocity model, it will make layer interfaces and fault structures vague because it does not consider any information of geologic structures and variations of velocity. In this study, we develop a selective smoothing regularization technique, which adapts smoothing coefficients according to inversion iteration, to solve the weakness of smoothing regularization with a constant coefficient. First, we determine appropriate frequencies and analyze the corresponding wavenumber coverage. Then, we define effective maximum wavenumber as 99 percentile of wavenumber spectrum in order to choose smoothing coefficients which can effectively limit the wavenumber coverage. By adapting the chosen smoothing coefficients according to the iteration, we can implement multi-scale full waveform inversion while inverting multi-frequency components simultaneously. Through the successful inversion example on a salt model with high-contrast velocity structures, we can note that our method effectively regularizes the inverse solution. We also verify that our scheme is applicable to field data through the numerical example to the synthetic data containing random noise.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.53-60
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• 2013

Datamining from the large data in the form of various techniques for obtaining information have been developed. In recent years one of the most sought areas of pattern recognition and machine learning method is created with most of existing learning algorithms based on categorical attributes to a rule or decision model. However, the real-world data, it may consist of numeric attributes in many cases. In addition it contains attributes with numerical values to the normal categorical attribute. In this case, therefore, it is required processes in order to use the data to learn an appropriate value for the type attribute. In this paper, the domain of the numeric attributes are divided into several segments using learning algorithm techniques of discritization. It is described Clustering with other data mining techniques. Large amount of first cluster with characteristics is similar records from the database into smaller groups that split multiple given finite patterns in the pattern space. It is close to each other of a set of patterns that together make up a bunch. Among the set without specifying a particular category in a given data by extracting a pattern. It will be described similar grouping of data clustering technique to classify the data.

• Korean Journal of Optics and Photonics
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• v.31 no.5
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• pp.213-217
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• 2020

In this study we report a wet-etching-based fabrication method for adiabatic optical-fiber tapers (OFTs), and describe their adiabaticity and HE₁₁ mode evolution at a wavelength of 1550 nm. The profile of the fabricated system satisfies the adiabaticity properties well, and the far-field pattern from the etched OFT shows that the fundamental HE₁₁ mode is maintained without a higher-order mode coupling throughout the tapers. In addition, the measured far-field pattern agrees well with the simulated result. The proposed adiabatic OFTs can be applied to a number of photonic applications, especially fiber-chip packages. Based on the fabricated adiabatic OFT structures, the optical transmission to the inversely tapered silicon waveguide shows large spatial-dimensional tolerances for 1 dB excess loss of ~60 ㎛ (silicon waveguide angle of 1°) and insertion loss of less than 0.4 dB (silicon waveguide angle of 4°), from the numerical simulation. The proposed adiabatic coupler shows the ultrabroadband coupling efficiency over the O- and C-bands.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.63-74
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• 1999

A sliding mode fuzzy control (SMFC) algorithm is presented for vibration of large structures. Rule-base of the fuzzy inference engine is constructed based on the sliding mode control, which is one of the nonlinear control algorithms. Fuzziness of the controller makes the control system robust against the uncertainties in the system parameters and the input excitation. Non-linearity of the control rule makes the controller more effective than linear controllers. Design procedure based on the present fuzzy control is more convenient than those of the conventional algorithms based on complex mathematical analysis, such as linear quadratic regulator and sliding mode control(SMC). Robustness of presented controller is illustrated by examining the loop transfer function. For verification of the present algorithm, a numerical study is carried out on the benchmark problem initiated by the ASCE Committee on Structural Control. To achieve a high level of realism, various aspects are considered such as actuator-structure interaction, modeling error, sensor noise, actuator time delay, precision of the A/D and D/A converters, magnitude of control force, and order of control model. Performance of the SMFC is examined in comparison with those of other control algorithms such as $H_{mixed 2/{\infty}}$ optimal polynomial control, neural networks control, and SMC, which were reported by other researchers. The results indicate that the present SMFC is an efficient and attractive control method, since the vibration responses of the structure can be reduced very effectively and the design procedure is simple and convenient.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.11-20
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• 2014

For Web-based applications in IoT environment, IETF CoRE WG has standardizing the CoAP. One of limitations of CoAP is that CoAP standard does not consider the mobility management of the CoAP sensor node. In this paper, we propose the mobility management protocol of CoAP sensor node by considering the characteristics of the constrained network. The proposed mobility management protocol supports for Web client to be transmitted the sensing data from CoAP node reliably while the CoAP sensor moves into different wireless networks. To do this, we designed the architecture with the separate IP address management of CoAP sensor node and presented the mobility management protocol, which includes the holding and binding mode, in order to provide the reliable transmission. Finally, the numerical analysis and simulation with NS2 tool have been done for the performance evaluation in terms of the handover latency and packet loss with comparing the proposed mobility management protocol with other the existing mobility management protocols. The performance result shows that the proposed mobility management can provide the transmission of sensing data without the packet loss comparing with the existing mobility management protocol reliably.

• Geophysics and Geophysical Exploration
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• v.14 no.2
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• pp.164-175
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• 2011

Using natural electromagnetic (EM) fields at low frequencies, magnetotelluric (MT) surveys can investigate conductivity structures of the deep subsurface and thus are used to explore geothermal energy resources and investigate proper sites for not only geological $CO_2$ sequestration but also enhanced geothermal system (EGS). Moreover, marine MT data can be used for better interpretation of marine controlled-source EM data. In the interpretation of MT data, MT modeling schemes are important. This study improves a three dimensional (3D) MT modeling algorithm which uses edge finite elements. The algorithm computes magnetic fields by solving an integral form of Faraday's law of induction based on a finite difference (FD) strategy. However, the FD strategy limits the algorithm in computing vertical magnetic fields for a topographic model. The improved algorithm solves the differential form of Faraday's law of induction by making derivatives of electric fields, which are represented as a sum of basis functions multiplied by corresponding weightings. In numerical tests, vertical magnetic fields for topographic models using the improved algorithm overcome the limitation of the old algorithm. This study recomputes induction vectors and tippers for a 3D hill and valley model which were used for computation of the responses using the old algorithm.