• Korean Journal of Computational Design and Engineering
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• v.4 no.3
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• pp.173-179
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• 1999

In reverse engineering, when a shape containing multi-patched surfaces is digitized, the boundaries of these surfaces should be detected. The objective of this paper is to introduce a computationally efficient segmentation technique for extracting edges, ad partitioning the 3D measuring point data based on the location of the boundaries. The procedure begins with the identification of the edge points. An automatic edge-based approach is developed on the basis of local geometry. A parametric quadric surface approximation method is used to estimate the local surface curvature properties. the least-square approximation scheme minimizes the sum of the squares of the actual euclidean distance between the neighborhood data points and the parametric quadric surface. The surface curvatures and the principal directions are computed from the locally approximated surfaces. Edge points are identified as the curvature extremes, and zero-crossing, which are found from the estimated surface curvatures. After edge points are identified, edge-neighborhood chain-coding algorithm is used for forming boundary curves. The original point set is then broke down into subsets, which meet along the boundaries, by scan line algorithm. All point data are applied to each boundary loops to partition the points to different regions. Experimental results are presented to verify the developed method.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.425-433
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• 2002

After the removal of SA (Selective Availability), horizontal accuracy of 25m(2dRMS) is easily obtained using GPS (Global Positioning System). In this paper, the error characteristics without SA are analyzed and a navigation algorithm concerns this error characteristics is proposed to further improve the accuracy. The proposed method utilizes the relationship between elevation angle and errors that are remained after ionospheric and troposheric delay compensation. The relationship is derived from real measurements and used as a weighting matrix of weighted least squares estimator. Furthermore, a RAIM (Receiver Autonomous Integrity Monitoring) technique is included to remove abnormal measurements affected by multi-path or low SNR (Signal-to-Noise Ratio). It is shown that using the proposed method, more than 4 times accurate result, which is comparable with DGPS (Differential GPS), can be obtained from experiments with real data. Besides accuracy and reliability, the proposed method reduces large jumps in position and maintains better performance than a method using mask angle to completely remove satellites below this mask angle. Thus it is expected that the proposed method can be efficiently applied to land navigation where some satellites are blocked by building or forest.

• Journal of Mechanical Science and Technology
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• v.15 no.7
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• pp.847-858
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• 2001

This paper considers the control model identification and H(sub)$\infty$ controller design for a tandem cold mill (TCM). In order to improve the performance of the existing automatic gauge control (AGC) system based on the Taylor linearized model of the TCM, a new mathematical model that can complement the Taylor linearized model is constructed by using the N4SID algorithm based on subspace method and the least squares algorithm based on ARX model. It is shown that the identified model had dynamic characteristics of the TCM than the existing Taylor linearized model. The H(sub)$\infty$ controller is designed to have robust stability to the system parameters variation, disturbance attenuation and robust tracking capability to the set-up value of strip thickness. The H(sub)$\infty$ servo problem is formulated and it is solved by using LMI (linear matrix inequality) techniques. Simulation results demonstrate the usefulness and applicability of the proposed H(sub)$\infty$ controller.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.11
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• pp.1423-1428
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• 2011

This paper proposes a learning input shaper with nonlinear actuator dynamics to reduce the residual vibration of flexible systems. The controller is composed of an estimator of the time constant of the nonlinear actuator dynamics, a recursive least squares method, and an iterative updating algorithm. The updating mechanism is modified by introducing a vibration measurement function to cope with the dynamics of nonlinear actuators. The controller is numerically evaluated with respect to parameter convergence and control performance by using a benchmark pendulum system. The feasibility and applicability of the controller are demonstrated by comparing its control performance to that of an existing controller algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.563-570
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• 2003

When a fault occurs on railway feeders it is very important to detect the fault to protect trains and facilities. Because a DC railway system has low feeder voltage, The fault current can be smaller than the current of load starting. So it is important to discriminate between the small fault current and the load starting current. The load starting current increases step by step but the fault current increases at one time. So the type of $\Delta$I/ relay(50F) was developed using the different characteristics between the load starting current and the fault current. The load starting current increases step by step so the time constant of each step is much smaller than that of the fault current. First, to detect faults in DC railway systems, an algorithm using the time constant calculated by the method of least squares is presented in this paper. If a fault occurs on DC railway systems it is necessary to find a fault location to repair the faulted system as soon as possible. The second aim of the paper is to calculate the accurate fault location using Kirchhoff's voltage law.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.157-162
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• 2007

A new level set based topology optimization employing inner-front creation algorithm is presented. In the conventional level set based topology optimization, the optimum topology strongly depends on the initial level set distribution due to the incapability of inner-front creation during optimization process. In the present work, in this regard, an inner-front creation algorithm is proposed. in which the sizes. shapes. positions, and number of new inner-fronts during the optimization process can be globally and consistently identified by considering both the value of a given criterion for inner-front creation and the occupied volume (area) of material domain. To facilitate the inner-front creation process, the inner-front creation map which corresponds to the discrete valued criterion of inner-front creation is applied to the level set function. In order to regularize the design domain during the optimization process, the edge smoothing is carried out by solving the edge smoothing partial differential equation (PDE). Updating the level set function during the optimization process, in the present work, the least-squares finite element method (LSFEM) is employed. As demonstrative examples for the flexibility and usefulness of the proposed method. the level set based topology optimization considering lightweight design of 3D shell structure is carried out.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.495-503
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• 2017

Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict the progression of LOCAs in nuclear power plants. Therefore, accurate information on the LOCA break position and size should be provided to efficiently manage the accident. In this paper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of the CFNN model are the time-integrated values of each measurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.48-55
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• 1998

The main drawback of filtered-X LMS(FXLMS) algorithm for the ANC of broadband noises is its low convergence speed when the filtered reference signals are strongly correlated, producing a large eigenvalue ratio in correlation matrix. This correlation can be caused either by autocorrelation of the signals of the reference sensors, or by coupling between the error path which introduces intercorrelation in the filtered reference signals. In this paper, we introduce a transform domain FXLMS(TD-FXLMS) algorithm that has a high convergence speed by orthogonal transform's decorrelation properties.