• Steel and Composite Structures
• /
• v.39 no.4
• /
• pp.471-491
• /
• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• ETRI Journal
• /
• v.42 no.1
• /
• pp.54-66
• /
• 2020

Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low-cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.

• 전자공학회논문지 IE
• /
• v.47 no.2
• /
• pp.21-26
• /
• 2010

This paper proposed an efficient algorithm to reduce the amount of calculation for Scalability Extension which takes a great deal of the operational time in H.264/AVC. This algorithm decides a search range according to the direction of predicted motion vector, and then performs an adaptive spiral search for the candidates with JM(Joint Model) FME(Fast Motion Estimation) which employs the rate-distortion optimization(RDO) method. Experimental results by applying the proposed method to various video sequences showed that the process time was decreased up to 80% comparing to the previous prediction methods. The degradation of video Quality was only from 0.05dB to 0.19dB and the compression ratio decreased as small as 0.58% in average. Therefore, we are sure that the proposed method is an efficient method for the fast inter prediction.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.969-981
• /
• 2018

Recent advances in machine learning approaches such as deep neural network and reinforcement learning offer significant performance improvements in generating detailed and varied motions in physically simulated virtual environments. The optimization methods are highly attractive because it allows for less understanding of underlying physics or mechanisms even for high-dimensional subtle control problems. In this paper, we propose an efficient learning method for stochastic policy represented as deep neural networks so that agent can generate various energetic motions adaptively to the changes of tasks and states without losing interactivity and robustness. This strategy could be realized by our novel trajectory search method motivated by the trust region policy optimization method. Our value-based trajectory smoothing technique finds stably learnable trajectories without consulting neural network responses directly. This policy is set as a trust region of the artificial neural network, so that it can learn the desired motion quickly.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.604-609
• /
• 2005

This paper presents an experimental study for optimizing the acceleration of AC servomotor using finite jerk (the first derivative of acceleration). The acceleration optimization with finite jerk aims at generating the smooth velocity profile of AC servomotor by experimentally minimizing vibration resulted from the initial friction of servomotor. The stick-slip motion of AC servomotor induced by initial friction can result in the positional errors that are not good for high-precision devices such as the assembly robot arms to be used in a 300mm wafer or a LCD (Liquid Crystal Display) stocker system. In this paper, experiments were made by using a PM (Permanent Magnet) type AC servomotor with MMC(R) (Multi Motion Controller) programmed in Visual C++(R). The experiments have been performed for finding the optimal duration time of finite jerk in terms of the minimization of vibration displacements when both the magnitude of velocity and the allowable acceleration are given. We have compared the proposed control with the conventional control with trapezoidal velocity profile by measuring vibration displacements. The effectiveness of the proposed control has been verified in that the experimental results showed the decrease of vibration displacement by about 24%.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1997.04a
• /
• pp.146-152
• /
• 1997

This paper deals with a study of striker type impact hammer drill for improving the drilling performance. The study was performed through a numerical simulation of the impact hammer mechanism, an experimental comparison of the numerical simulation results and an optimization of the impact mechanism. The numerical model of the impact hammer drill takes into account the striker motion and the effects of the pressure in the cylinder as well as the friction acting on the striker. The equation of motion is solved with the pressure equation in the cylinder and the friction force. At the moment of impact, an ideal impact model that uses restitutiion codfficient is used to calculate the sudden change of the striker motion. The impact force numerically simulated shows a good agreement with the experimental results and thus, the validity of the numerical model is proven. Based upon the proposed model, an optimization was performed to improve the impact force of the hammer drill. The objective function is to maximize the impact force and the design variables are striker mass, frequency of piston, bit guide mass, cylindrical diameter and dimensions of the mechanism components. Each design variable and some other conditions that are essential to maintain normal operation of the hammer drill are considered as constraints. The optimized result shows remarkable improvement in impact force and an experimental proof was investigated.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.28 no.1
• /
• pp.48-54
• /
• 2004

Generally, structural optimization is carried out based on external static loads. All forces have dynamic characteristics in the real world. Mathematical optimization with dynamic loads is extremely difficult in a large-scale problem due to the behaviors in the time domain. In practical applications, it is customary to transform the dynamic loads into static loads by dynamic factors, design codes, and etc. But the optimization results with the unreasonably transformed loads cannot give us good solutions. Recently, a systematic transformation has been proposed as an engineering algorithm. Equivalent static loads are made to generate the same displacement field as the one from dynamic loads at each time step of dynamic analysis. Thus, many load cases are used as the multiple loading conditions which are not costly to include in modem structural optimization. In this research, the proposed algorithm is applied to the optimization of flexible multibody dynamic systems. The equivalent static load is derived from the equations of motion of a flexible multibody dynamic system. A few examples that have been solved before are solved to be compared with the results from the proposed algorithm.

• Structural Engineering and Mechanics
• /
• v.56 no.6
• /
• pp.959-982
• /
• 2015

In this study, a non-stationary random earthquake Clough-Penzien model is used to describe earthquake ground motion. Using stochastic direct integration in combination with an equivalent linear method, a solution is established to describe the non-stationary response of lead-rubber bearing (LRB) system to a stochastic earthquake. Two parameters are used to develop an optimization method for bearing design: the post-yielding stiffness and the normalized yield strength of the isolation bearing. Using the minimization of the maximum energy response level of the upper structure subjected to an earthquake as an objective function, and with the constraints that the bearing failure probability is no more than 5% and the second shape factor of the bearing is less than 5, a calculation method for the two optimal design parameters is presented. In this optimization process, the radial basis function (RBF) response surface was applied, instead of the implicit objective function and constraints, and a sequential quadratic programming (SQP) algorithm was used to solve the optimization problems. By considering the uncertainties of the structural parameters and seismic ground motion input parameters for the optimization of the bearing design, convex set models (such as the interval model and ellipsoidal model) are used to describe the uncertainty parameters. Subsequently, the optimal bearing design parameters were expanded at their median values into first-order Taylor series expansions, and then, the Lagrange multipliers method was used to determine the upper and lower boundaries of the parameters. Moreover, using a calculation example, the impacts of site soil parameters, such as input peak ground acceleration, bearing diameter and rubber shore hardness on the optimization parameters, are investigated.

• The Journal of Korea Robotics Society
• /
• v.3 no.4
• /
• pp.287-299
• /
• 2008

People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.

• Journal of Broadcast Engineering
• /
• v.3 no.1
• /
• pp.50-60
• /
• 1998

This paper addresses a problem of extraction of parameteric motion estimation and structural motion segmentation for compact image sequence representation and object-based generic video coding. In order to extract meaningful motion structure from image sequences, a direct parameteric motion estimation based on a pre-segmentation is proposed. The pre-segmentation which considers the motion of the moving objects is canied out based on probabilistic clustering with mixture models using optical flow and image intensities. Parametric motion segmentation can be obtained by iterated estimation of motion model parameters and region reassignment according to a criterion using Gauss-Newton iterative optimization algorithm. The efficiency of the proposed methoo is verified with computer simulation using elF real image sequences.