• Steel and Composite Structures
• /
• 제39권4호
• /
• 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
• /
• 제42권1호
• /
• 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
• /
• 제47권2호
• /
• pp.21-26
• /
• 2010

본 논문에서는 H.264/AVC SE(Scalability Extension)의 부호화 효율을 향상시킬 수 있는 방법을 제안하였다. 제안한 방법은 율-왜곡 최적화 기법(Rate-Distortion Optimization, RDO)이 적용된 JM(Joint Model)의 FME(Fast Motion Estimation)를 대상으로 예측된 움직임 벡터의 방향성을 고려하여 탐색영역을 결정한 후 적용적인 후보 나선형 탐색을 수행한다. 다양한 영상들을 대상으로 실험한 결과 기존의 예측 방식에 최대 80%의 연산량을 줄일 수 있음을 확인하였다. 이에 따른 화질 열화는 평균 0.05dB-0.19dB에 불과하며, 압축률은 평균 0.58%의 미미한 감소를 보임으로써, 제안한 방법이 매우 효율적임을 확인하였다.

• 한국멀티미디어학회논문지
• /
• 제21권8호
• /
• 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
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• 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%.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 1997년도 춘계학술대회논문집; 경주코오롱호텔; 22-23 May 1997
• /
• 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.

• 대한기계학회논문집A
• /
• 제28권1호
• /
• 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
• /
• 제56권6호
• /
• 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.

• 로봇학회논문지
• /
• 제3권4호
• /
• 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.

• 방송공학회논문지
• /
• 제3권1호
• /
• pp.50-60
• /
• 1998

본 논문에서는 콤팩트한 동영상 표현과 객체기반의 generic한 동영상압축을 위한 파라미터릭 움직임 모델의 파라미터 추정과 세그맨테이션 기법에 관해서 기술한다. 동영상의 optical flow와 같은 국소적 움직임 정보와 파라미터 움직임 모델의 특징을 이용해서 영상의 콤팩트한 구조적 표현을 추출하기 위해, 본 논문에서는 2 스템의 과정 즉, 초기영역을 추출하는 과정과, 파라미터릭 움직임 파라미터의 추정과 세그맨테이션을 동시에 수행하는 과정으로 구성된 새로운 알고리즘을 제안한다. 혼합 모델이 ML 추정에 의거한 확률적 클러스터링에 의해 움직임 물체의 움직임과 형상을 반영한 초기영역을 추출하고, 파라미터릭 움직임 모델을 사용해서 각각의 초기 영역마다 움직임 파라미터를 추정하고 세그맨테이션을 수행한다. 또한, CIF 표준 동영상을 사용한 모의 실험을 통해 본 제안 알고리즘의 유효성을 평가한다.