• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.427-432
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• 2018

This paper uses a recursive least squares method to estimate the projectile motion trajectory of an object in real time. The equations of motion of the object are obtained considering the air resistance which occurs in the actual experiment environment. Because these equations consider air resistance, parameter estimation of nonlinear terms is required. However, nonlinear recursive least squares estimation is not suitable for estimating trajectory of projectile in that it requires a lot of computation time. Therefore, parameter estimation for real-time trajectory prediction is performed by recursive least square estimation after using Taylor series expansion to approximate nonlinear terms to polynomials. The proposed method is verified through experiments by using VICON Bonita motion capture system which can get three dimensional coordinates of projectile. The results indicate that proposed method is more accurate than linear Kalman filter method based on the equations of motion of projectile that does not consider air resistance.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.641-646
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• 2010

This paper presents algorithms for real-time navigation of a humanoid robot with a stereo vision but no other sensors. Using the algorithms, a robot can recognize its 3D environment by retrieving SIFT features from images, estimate its position through the Kalman filter, and plan its path to reach a destination avoiding obstacles. Our approach focuses on estimating the robot’s central walking path trajectory rather than its actual walking motion by using an approximate model. This strategy makes it possible to apply mobile robot localization approaches to humanoid robot localization. Simple collision free path planning and motion control enable the autonomous robot navigation. Experimental results demonstrate the feasibility of our approach.

• Structural Engineering and Mechanics
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• v.63 no.1
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• pp.115-124
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• 2017

In this paper, a different technique to predict the effects of soil-structure interaction (SSI) on seismic response of building systems is investigated. The technique use a machine learning algorithm called Support Vector Regression (SVR) with technical and analytical results as input features. Normally, the effects of SSI on seismic response of existing building systems can be identified by different types of large data sets. Therefore, predicting and estimating the seismic response of building is a difficult task. It is possible to approximate a real valued function of the seismic response and make accurate investing choices regarding the design of building system and reduce the risk involved, by giving the right experimental and/or numerical data to a machine learning regression, such as SVR. The seismic response of both single-degree-of-freedom system and six-storey RC frame which can be represent of a broad range of existing structures, is estimated using proposed SVR model, while allowing flexibility of the soil-foundation system and SSI effects. The seismic response of both single-degree-of-freedom system and six-storey RC frame which can be represent of a broad range of existing structures, is estimated using proposed SVR model, while allowing flexibility of the soil-foundation system and SSI effects. The results show that the performance of the technique can be predicted by reducing the number of real data input features. Further, performance enhancement was achieved by optimizing the RBF kernel and SVR parameters through grid search.

• Steel and Composite Structures
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• v.29 no.1
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• pp.91-106
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• 2018

This paper investigates the deflection of the steel-concrete composite truss beam (SCCTB) at the serviceability limit state. A precise solution for the distributed uplift force of the SCCTB, considering five different loading types, is first derived based on the differential and equilibrium equations. Furthermore, its approximate solution is proposed for practical applications. Subsequently, the shear slip effect corresponding to the shear stiffness of the stub connectors, uplift effect corresponding to the axial stiffness of the stub connectors and shear effect corresponding to the brace deformation of the steel truss are considered in the derivation of deflection. Formulae for estimating the SCCTB deflection are proposed. Moreover, based on the proposed formulae, a practical design method is developed to provide an effective and convenient tool for designers to estimate the SCCTB deflection. Flexure tests are carried out on three SCCTBs. It is observed that the SCCTB stiffness and ultimate load increase with an increase in the shear interaction factor. Finally, the reliability of the practical design method is accurately verified based on the available experimental results.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.119-124
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• 2009

In this paper, we propose a novel approach to estimating the real-time moving trajectory of an object is proposed in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the input-output relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Composites Research
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• v.23 no.2
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• pp.40-47
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• 2010

In this paper, the hybrid response surface method(HRSM) is proposed and examined. Hybrid response surface method calculate a approximate model repeatedly based on MPP coordinates. To verify the performance, probability of failure, MPP(Most Probable failure Point) and reliability index are calculated for nonlinear function and composite laminated plate by using reliability analysis method and compared with results by using typical response surface method(RSM). Probability of failure is calculated under the assumption of the nonlinear limit state equation and given failure criterion. The results of proposed method shows performance improvement in estimating the probability of failure.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.87-93
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• 2017

A maximum likelihood (ML) approach is presented for estimating the mean of radar cross section (RCS) of a Swerling III target and its numerical solution methods are discussed. The solution methods are based on an approximate expression for implementing the expectation maximization (EM) algorithm. The methods are evaluated and compared through Monte Carlo simulations in terms of estimation accuracy and computational efficiency to obtain a most efficient method for both Swerling I and Swerling III targets. The methods are also compared with a previously reported method based on heuristics.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.3
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• pp.75-84
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• 2013

The seepage quantity analysis of reservoir embankment is very important for assessment of embankment safety. However, the conventional analysis does not consider uncertainty of soil properties. Permeability is known that the coefficient of variation is larger than other soil properties and seepage quantity is highly dependent on the permeability of embankment. Therefore, probabilistic analysis should be carried out for seepage analysis. To designers, however, the probabilistic analysis is not an easy task. In this paper, the method that can be performed probabilistic analysis easily and efficiently through the numerical analysis based commercial program is proposed. Stochastic response surface method is used for approximate the limit state function and when estimating the coefficients, the moving least squares method is applied in order to reduce local error. The probabilistic analysis is performed by LHC-MCS through the response surface. This method was applied to two type (homogeneous, core zone) earth dams and permeability of embankment body and core are considered as random variables. As a result, seepage quantity was predicted effectively by response surface and probabilistic analysis could be successfully implemented.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1078-1082
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• 1997

The distance in load parameter space to the closest voltage collapse point provides the worst case power margin and the left eigenvector identifies the most effective direction to steer the system to maximize voltage stability under contingency. This paper presents the results of the comparative performance study of the algorithms, which are applicable to a large scale power system, for computing the closest saddle node bifurcation (CSNB) point. Dobson's iterative method converges with robustness. However the slow process of updating the load increasing direction makes the algorithm less efficient. The direct method converges very quickly. But it diverges if the initial guess is not very close to CSNB. Zeng's method of estimating the approximate critical point in the pre-determined direction is attractive in the sense that it uses only using load flow equations. However, the method is found to be less efficient than Dobson's iterative method. It may be concluded from the above observation that the direct method with the initial values obtained by carrying out the iterative method twice is most efficient at this time and more efficient algorithms are needed for on-line application.

• Geomechanics and Engineering
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• v.10 no.3
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• pp.315-324
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• 2016

Frictional forces between soil and structural elements are of vital importance for the foundation engineering. Although numerous studies were performed about the soil-structure interaction in recent years, the approximate relations proposed in the first half of the 20th century are still used to determine the frictional forces. Throughout history, wood was often used as friction piles. Steel has started to be used in the last century. Today, alternatively these materials, FRP (fiber-reinforced polymer) piles are used extensively due to they can serve for long years under harsh environmental conditions. In this study, various ratios of low plasticity clays (CL) were added to the sand soil and compacted to standard Proctor density. Thus, soils with various internal friction angles (${\phi}$) were obtained. The skin friction angles (${\delta}$) of these soils with FRP, which is a composite material, steel (st37) and wood (pine) were determined by performing interface shear tests (IST). Based on the data obtained from the test results, a chart was proposed, which engineers can use in pile design. By means of this chart, the skin friction angles of the soils, of which only the internal friction angles are known, with FRP, steel and wood materials can be determined easily.