• Tunnel and Underground Space
• /
• v.10 no.1
• /
• pp.33-44
• /
• 2000

In order to investigate the effect of progressive collapse of underground circular opening on surface subsidence, laboratory model tests were performed. The modelling materials were sand which has been used as KS standard. Six test models which had respectively different depths of openings were produced. Surface subsidence and horizontal displacements were measured according to progressive collapse of underground opening. Some subsidence prediction method such as NCB method, profile function method and influence function method were considered to predict the subsidence of sand models. The profile function method approximated by Gaussian error function was finally suggested as the most appropriate to sand models.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2005.10a
• /
• pp.417-424
• /
• 2005

Observed field behaviors are frequently different from the behaviors predicted in the design state due to several uncertainties involved in soil properties, numerical modelling, and error of measuring system even though a sophisticated numerical analysis technique is applied to solve the consolidation behavior of drainage-installed soft deposits. In this study, genetic algorithms are applied to back-analyze the soil properties using the observed behavior of soft clay deposit composed of multi layers that shows complex consolidation characteristics. Utilizing the program, one might be able to appropriately predict the subsequent consolidation behavior from the measured data in an early stage of consolidation of multi layered soft deposits. Example analyses for drainage-installed multi-layered soft deposits are performed to examine the applicability of proposed back-analysis method.

• Proceedings of the KIEE Conference
• /
• 1999.11c
• /
• pp.812-814
• /
• 1999

In this paper, we propose a new control algorithm based on the neural network(NN) feedback compensation with a desired trajectory modification. The proposed algorithm decreases trajectory errors by a feed-forward desired torque combined with a neural network feedback torque component. And, to robustly control the tracking error, we modified the desired trajectory by variable structure concept smoothed by a fuzzy logic. For the numerical simulation, a 2-link robot manipulator model was assumed. To simulate the disturbance due to the modelling uncertainty. As a result of this simulation, the proposed method shows better trajectory tracking performance compared with the CTM and decreases the chattering in control inputs.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.5
• /
• pp.966-972
• /
• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• Steel and Composite Structures
• /
• v.9 no.1
• /
• pp.1-17
• /
• 2009

This paper is focused on the buckling and post buckling behaviour of rectangular corrugated board panels simply supported and subjected to compression load. The aim of the work is to understand the failure mechanism of investigated structure in order to quantify the effect of design parameters on the strength of a panel of given geometry. Two numerical models were developed adopting the finite element method. In the first one the corrugated board is represented by means of shell elements adopting an equivalent material, in the second the local structure is described in full detail modelling both straight and corrugated layers by means of shell elements and representing the connection between layers by special interface elements. The model correctness was checked by the comparison between out of plane central displacement predicted by the models and the experimental values found in literature. For the same case the effect of panel planarity error was evaluated. Finally a parametric analysis to investigate the effect of design parameters was carried out.

• KIEAE Journal
• /
• v.3 no.2
• /
• pp.59-66
• /
• 2003

To be the design method supporting aesthetic ability of human, CAAD system should essentially recognize architectural form in the same way of human. In this study, visual perception process of human was analyzed to search proper computational method performing similar step of perception of it. Through the analysis of visual perception, vision was separated to low-level vision and high-level vision. Edge detection and neural network were selected to model after low-level vision and high-level vision. The 24 images of building, tree and landscape were processed by edge detection and trained by neural network. And 24 new images were used to test trained network. The test shows that trained network gives right perception result toward each images with low error rate. This study is on the meaning of artificial intelligence in design process rather than on the design automation strategy through artificial intelligence.

• Proceedings of the KSR Conference
• /
• 2004.06a
• /
• pp.696-701
• /
• 2004

Electric vehicle that is manufactured present by development of electric vehicle technology were available automatic driving. Control of air breaking system for precision stopping is important at automatic driving. Current Electric vehicle is doing precision stopping using braking force control. Braking force control is difficult to take static deceleration by rail condition or change of friction coefficient. Therefore, Proposed the controller in this study is deceleration controller. Designed controller is a robust controller that take state control characteristic for modelling error.

• Spatial Information Research
• /
• v.8 no.1
• /
• pp.1-14
• /
• 2000

Zoning data such as national land use planning map, urban land use planning map, agricultural promotion zoning ap, forest land zoning map has a relationship each other in law and spatial context. But difficulties in data share and the lack of accuracy of manual work makes serious inconsistence son zoning data relationship. This causes many trial and error in land use. For resolving this problem the data modellingmethod is presented as a technical solution.

• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.223-226
• /
• 1998

The control objective of steam generator water level in the secondary circuit of a nuclear power plant is to regulate the water level at the desired set point. The dynamics of steam generators is non-linear in nature. The task of modelling such plant is very difficult and especially so when plant operating conditions change frequently. In these reasons, conventional PI gains over all pover range will not work efficiently and a manual control is generally used in low power operation. Therefore the robust H$\infty$ controller design method should be required. In this paper, we design the robust H$\infty$ controller for water level control of steam generator using a mixed H$\infty$ optimization with model-matching method. Firstly we choose the desired model that has good disturbance rejection performance. Secondly we design a stabilizing controller to keep the model-matching error small and also provide sufficiently large stability margin against additive perturbations of the nominal plant.

• Journal of Ocean Engineering and Technology
• /
• v.3 no.2
• /
• pp.559-559
• /
• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.