• Geomechanics and Engineering
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• v.11 no.4
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• pp.571-585
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• 2016

The crest of the Pubugou central core rockfill dam (CCRD) cracked in the first and second impounding periods. To evaluate the safety of the Pubugou CCRD, an inversion analysis of the constitutive model parameters for rockfill materials is performed based on the in situ deformation monitoring data. The aim of this work is to truly reflect the deformation state of the Pubugou CCRD and determine the causes of the dam crest cracks. A novel real-coded genetic algorithm based upon the differences in gene fragments (DGFX) is proposed. It is used in combination with the radial based function neural network (RBFNN) to perform the parameters back analysis. The simulated settlements show good agreements with the monitoring data, illustrating that the back analysis is reasonable and accurate. Furthermore, the deformation gradient of the dam crest has been analysed. The dam crest has a great possibility of cracking due to the uncoordinated deformation, which agrees well with the field investigation. The deformation gradient decreases to the value lower than the critical one and reaches a stable state after the second full reservoir.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.429-433
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• 1995

Compound solid is maded for specially fixed object. A number of compound soild are devided as a unit germetric solid. The special case of rotation about an arbitrary axis makes according selection modelfor transformation. View plane and View region are estiblished for back face removal. After back-face removal eachedge is checked for point of intersection. The designing of of fset surface id maded and tool-path gernerted on the part surface. In tersection point is checked among the offset surfaces. The paper used an efficient algorithm for generating tool paths to apply a notion view volume. Through machining experiments with a 3-axis machining centre, the adequacy of the analysis was confirmed.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1930-1932
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• 2004

SOM(Self Organizing Map) algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. For the purpose, partial discharge data were acquired and analysed from the artificial defects in GIS. As a result, basically the pattern recognition rate of BP algorithm was found out to be better than that of SOM algorithm. However, SOM algorithm showed a great on-site-applicability such as ability of suggesting new-pattern-possibility. Therefore, through increasing pattern recognition rate it is possible to apply SOM algorithm to partial discharge analysis. Also, for the image processing method it is required the normalization of the PRPDA graph. However, due to the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• Structural Engineering and Mechanics
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• v.36 no.5
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• pp.529-544
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• 2010

Nonlinear equations of structures are generally solved numerically by the iterative solution of linear equations. However, this iterative procedure diverges when the tangent stiffness is ill-conditioned which occurs near limit points. In other words, a major challenge with simple iterative methods is failure caused by a singular or near singular Jacobian matrix. In this paper, using the Newton-Raphson algorithm based on Davidenko's equations, the iterations can traverse the limit point without difficulty. It is argued that the propose algorithm may be both more computationally efficient and more robust compared to the other algorithm when tracing path through severe nonlinearities such as those associated with structural collapse. Two frames are analyzed using the proposed algorithm and the results are compared with the previous methods. The ability of the proposed method, particularly for tracing the limit points, is demonstrated by those numerical examples.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5311-5316
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• 2012

Single Photon Emission Computed tomography(SPECT) was performed on 13 healthy adults (average age: 39) to investigate the changes of cerebral blood flow according to brain imaging analysis algorithm. The acquired images were filtered and reconstructed through Filtered Back Projection (FBP) and Ordered Subset Expectation Maximization (OSEM). The brain distribution data of radiopharmaceuticals were compared using Statistical Parametric Mapping (SPM), and the changes of blood flow was expressed in Cluster. As a result, uptake rate was increased in Sub-gyral, Sub-Lobar, Extra-Nuclear, Limbic lobe and Cingulate Gyrus, while uptake rate was decreased in Middle frontal gyrus, Inferior Frontal Gyrus and Precentral Gyrus. The discriminable SPM was shown according to cerebral blood flows in Cluster by the reconstruction algorithm.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.5
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• pp.389-399
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• 2016

Efficiency for estimation of subsea tunnel safety can be increased through reflecting back analysis algorithm to displacement measurements besides other measurement information such as stress, water pressure and ground stiffness degradation. In this study, the finite difference code FLAC3D built-in FISH language is used. In addition, the stability of the tunnel lining will be evaluated from the development of displacement-based algorithm and its expanded algorithm with conformity of several parameters such as stress measurements, water pressure measurements, tunnel lining degradation measurements and ground stiffness degradation measurements. By using additional measurement information to assess the stability of subsea tunnel, it was confirmed that the error rate is reduced to the tunnel back analysis.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.27-33
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• 1997

A neural network approach has been developed to determine the depth of a surface breaking crack in a steel plate from ultrasonic backscattering data. The network is trained by the use of feedforward three-layered network together with a back-scattering algorithm for error correction. The signal used for crack insonification is a mode converted 70$^{\circ}$transverse wave. A numerical analysis of back scattered field is carried out based on elastic wave theory, by the use of the boundary element method. The numerical data are calibrated by comparison with experimental data. The numerical analysis provides synthetic data for the training of the network. The training data have been calculated for cracks with specified increments of the crack depth. The performance of the network has been tested on other synthetic data and experimental data which are different from the training data.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.523-528
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• 2015

A new algorithm for a coordinative control method is proposed with respect to voltage control and system stabilization of local substations. This is accomplished using control cooperation between a static synchronous compensator (STATCOM) and the existing voltage compensation equipment in the steady state and emergency state of a power system. A real-time system analysis was developed by combining a system analysis program with InTouch, which has primarily been used in factory automation for verification. PSS/E was used for the load flow calculation software, Python for language, Intouch as an HMI program, and MS SQL for the database. To test this system, the system in the vicinity of the Migeum and the Migeum substations was modeled and simulated.