• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• Journal of the Korea Concrete Institute
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• v.29 no.3
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• pp.249-257
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• 2017

The purpose of this study was to contribute to the field application cost effectively and reasonably by developing the functional piles that make up for the defects of PHC piles. CFP (Concrete Filled Pretensioned Spun High Strength Concrete Pile with Ring type Composite shear connectors) piles developed in this study increases the compressive stress through enlarged cross section by rearranging composite shear connectors and filling the hollow part of PHC pile with concrete. And it improved shear and bending performance placing the rebar (H13-8ea) within the PHC pile and the hollow part of PHC pile of rebar (H19-8ea). In addition, the composite shear connectors were placed for the composite behavior between PHC pile and filled concrete. Placing Rebars (H13-8ea) of PHC pile into composite shear connector holes are sleeve-type mechanical coupling method that filling the concrete to the gap of the two members. Nonlinear finite element analyzes were performed to verify the performance of shear and bending moments and it deduced the spacing of the composite shear connectors. Through a various interpretation of CFP piles, it's proved that the CFP pile can increase the shear and bending stiffness of the PHC pile effectively. Therefore, this can be utilized usefully on the construction sites.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.471-479
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• 2024

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.

• The Journal of the Acoustical Society of Korea
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• v.35 no.4
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• pp.261-271
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• 2016

In the prediction of response of a pile in soil, numerical approaches such as a finite element method are generally applied due to complicate nonlinear behaviors of soils. However, the numerical methods based on the finite elements require heavy efforts in pile and soil modelling and also take long computing time. So their usage is limited especially in the early design stage in which principal dimensions and properties are not specified and tend to vary. On the contrary, theoretical approaches adopting linear approximations for soils are relatively simple and easy to model and take short computing time. Therefore, if they are validated to be reliable, they would be applicable in predicting responses of a pile in soil, particularly in early design stage. In case of wind turbines regarded in this study, it is required to assess their natural frequencies in early stages, and in this simulation the supporting pile inserted in soil could be replaced with a simplified elastic boundary condition at the bottom end of the wind turbine tower. To do this, analysis for a pile in soil is performed in this study to extract the spring constants at the top end of the pile. The pile in soil can be modelled as a beam on elastic spring by assuming that the soils deform within an elastic range. In this study, it is attempted to predict pile deformations and influence factors for lateral loads by means of the beam-on-spring model. As two example supporting structures for wind turbines, mono pile and suction pile models with different diameters are examined by evaluating their influence factors and validated by comparing them with those reported in literature. In addition, the deflection profiles along the depth and spring constants at the top end of the piles are compared to assess their supporting features.

• Computational Structural Engineering
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• v.7 no.3
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• pp.95-103
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• 1994

The safety related components in the nuclear power plant should be designed to withstand the seismic load. Among these components the integrity of reactor internals under earthquake load is important in stand points of safety and economics, because these are classified to Seismic Class I components. So far the modelling methods of reactor internals have been investigated by many authors. In this paper, the dynamic behaviour of reactor internals of Yong Gwang 1&2 nuclear power plants under SSE(Safe Shutdown Earthquake) load is analyzed by using of the simpled Global Beam Model. For this, as a first step, the characteristic analysis of reactor internal components are performed by using of the finite element code ANSYS. And the Global Beam Model for reactor internals which includes beam elements, nonlinear impact springs which have gaps in upper and lower positions, and hydrodynamical couplings which simulate the fluid-filled cylinders of reactor vessel and core barrel structures is established. And for the exciting external force the response spectrum which is applied to reactor support is converted to the time history input. With this excitation and the model the dynamic behaviour of reactor internals is obtained. As the results, the structural integrity of reactor internal components under seismic excitation is verified and the input for the detailed duel assembly series model could be obtained. And the simplicity and effectiveness of Global Beam Model and the economics of the explicit Runge-Kutta-Gills algorithm in impact problem of high frequency interface components are confirmed.