• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2000.10a
• /
• pp.496-500
• /
• 2000

대형 구조물의 진동제어를 위하여 MR 유체 댐퍼를 사용한 반능동 제어기법에 대하여 연구하였다. 기존에 많이 사용되고 있는 수동제어기법은 일단 제어장치를 설치한 후에는 구조물에 실제로 작용하고 있는 외부 하중의 현재 특성에 대해서 적절히 반응할 수 없다는 제한을 가지고 있으며, 이를 극복하기 위하여 연구되어온 능동제어기법은 구조물이 진동을 감소시키기 위하여 구조물에 직접적으로 가해지는 커다란 제어력을 요구하며, 이로 인해 경우에 따라서는 불안정한 상태가 유발될 수도 있다는 점이 단점으로 지적되고 있다. 최근에 Spencer 등은 반능동 제어기법을 제안하였는데, 이는 수동제어장치의 제어특성을 On-Line 으로 조절하는 방식으로서 제어 가능한 수동제어기법으로도 불리운다. 구조물의 진동제어에 필요한 제어력이, 특수한 제어기구에서 발생되는 인위적인 힘이 아니라, 적절한 구조부재에서 발생되는 자연적인 부재력이므로, 무엇보다 강인하고 신뢰할 수 있는 제어기법이며, 이때 제어장치의 구조적 특성을, 측정된 구조물의 응답에 맞추어 적절히 조절함으로써 다양한 외부하중에 대해 보다 효율적인 제어가 이루어질 수 있도록 한 방법이다. 반능동제어를 위한 제어기로서는 Variable Orifice Dampers, Friction Controllable Isolators, Variable Stiffness Devices, Electro-Rheological (ER) Fluid Damper, Magneto-Rheological(MR) Fluid Damper등이 제안되고 있으며, 본 논문에서는 반응속도가 빠르고, 적은 파워만을 요구하며, 커다란 제어력을 낼 수 있는 MR Damper를 사용하여 지진하중을 받는 구조물의 반능동 제어게 대하여 연구하였다. MR Damper의 특성이 비선형이므로 이에 적합한 Sliding Mode Fuzzy Control(SMFC)기법을 사용하였으며 이때 SMFC 의 최적 설계를 위하여 Genetic Algorithm을 적용하였다. 제안된 제어기법의 실제 적용성을 검증하기 위하여 기존이 제어결과와 비교 검토하였으며, 그 결과로부터 MR Damper를 사용한 반능동 제어기법이 구조물의 진동제어에 매우 효과적임을 확인할 수 있었다.

• Structural Engineering and Mechanics
• /
• v.11 no.5
• /
• pp.485-502
• /
• 2001

Modem soft computing methods, such as neural networks, evolutionary models and fuzzy logic, are mainly inspired by the problem solving strategies the biological systems use in nature. As such, the soft computing methods are fundamentally different from the conventional engineering problem solving methods, which are based on mathematics. In the author's opinion, these fundamental differences are the key to the full understanding of the soft computing methods and in the realization of their full potential in engineering applications. The main theme of this paper is to discuss the fundamental differences between the soft computing methods and the mathematically based conventional methods in engineering problems, and to explore the potential of soft computing methods in new ways of formulating and solving the otherwise intractable engineering problems. Inverse problems are identified as a class of particularly difficult engineering problems, and the special capabilities of the soft computing methods in inverse problems are discussed. Soft computing methods are especially suited for engineering design, which can be considered as a special class of inverse problems. Several examples from the research work of the author and his co-workers are presented and discussed to illustrate the main points raised in this paper.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1181-1185
• /
• 2009

Adaptive Nuero-Fuzzy Inference System(ANFIS) 모형은 인공신경망과 퍼지모형의 특징을 가지는 모형으로 자료간의 관계가 선형이 아닌 비선형관계를 가질 경우 매우 정확한 예측 모형을 구축할 수 있는 특징이 있다. 월강수량 예측이 관측된 기상자료들과 비선형 관계에 있다고 생각되어 ANFIS 모형을 이용하여 월강수량을 예측하였다. 본 연구의 대상 지점으로는 금강유역의 대전 지점으로 선정하였다. 금강유역은 우리나라의 한가운데 위치하여 평균적인 강수형태 및 특징을 보여 좋은 실험유역으로 생각되어 선정하였다. 금강유역의 기상청에서 운영하는 지상 유인관측소 중 비교적 금강유역을 대표하고 양질의 자료가 기록되어 있다고 판단되는 대전지점을 실험지점으로 생각되어 선정하였다. 기상청 대전 유인 관측소에는 총 39년치 기상 자료가 기록되어 있다. 기상청에서는 전국 주요 도시들을 대상으로 2003년부터 월간 예보를 하고 있다. 본 연구에서는 기상청 월간예보와 기상청 대전 유인관측소에서 관측된 5년 치 기상자료를 모델의 입력자료로 구성하였다. 적절한 입력변수 조합을 구성하기 위하여 반복해법을 적용하였다. 5년 치 자료 중 절반은 학습을 시키는데 사용하였고 나머지 절반을 이용하여 모형을 검증하였다. 여러 입력변수를 이용하여 모형의 학습시킨 결과 입력변수가 3개 일 경우 가장 높은 정확도를 보였다. 입력변수가 3개로 학습 시킨 ANFIS 모형과 기상청에서 제공하는 월간예보를 비교해본 결과 ANFIS 모형을 적용하여 월 강수량을 예측하는 것이 기상청에서 제공하는 월간예보보다 높은 정확도를 보이는 것을 확인할 수 있었다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.1
• /
• pp.1-6
• /
• 2003

The Delayed Feedback Control method (DFC) proposed by Pyragas applies an input based on the difference between the current state of the system, which is generating chaos orbits, and the $\tau$-time delayed state, and stabilizes the chaos orbit into a target. In DFC, the information about a position in the state space is unnecessary if the period of the unstable periodic orbit to stabilize is known. There exists the fault that DFC cannot stabilize the unstable periodic orbit when a linearlized system around the periodic point has an odd number property. There is the chaos control method using the prediction of the $\tau$-time future state (PDFC) proposed by Ushio et al. as the method to compensate this fault. Then, we propose a method such as improving the fault of the DFC. Namely, we combine DFC and PDFC with parameter W, which indicates the balance of both methods, not to lose each advantage. Therefore, we stabilize the state into the $\tau$ periodic orbit, and ask for the ranges of Wand gain K using Jury' method, and determine the quasi-optimum pair of (W, K) using a genetic algorithm. Finally, we apply the proposed method to a discrete-time chaotic system, and show the efficiency through some examples of numerical experiments.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.27 no.4
• /
• pp.297-304
• /
• 2022

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.

• Journal of Korean Association for Spatial Structures
• /
• v.18 no.3
• /
• pp.37-44
• /
• 2018

A base isolation system is widely used to reduce seismic responses of low-rise buildings. This system cannot be effectively applied to high-rise buildings because the initial stiffness of the high-rise building with the base isolation system maintains almost the same as the building without the base isolation system to set the yield shear force of the base isolation system larger than the design wind load. To solve this problem, the mid-story isolation system was proposed and applied to many buildings. The mid-story isolation system has two major objectives; first to reduce peak story drift and second to reduce peak drift of the isolation story. Usually, these two objectives are in conflict. In this study, a hybrid mid-story isolation system for a tall building is proposed. A MR (magnetorheological) damper was used to develop the hybrid mid-story isolation system. An existing building with mid-story isolation system, that is "Shiodome Sumitomo Building" a high rise building having a large atrium in the lower levels, was used for control performance evaluation of the hybrid mid-story isolation system. Fuzzy logic controller and genetic algorithm were used to develop the control algorithm for the hybrid mid-story isolation system. It can be seen from analytical results that the hybrid mid-story isolation system can provide better control performance than the ordinary mid-story isolation system and the design process developed in this study is useful for preliminary design of the hybrid mid-story isolation system for a tall building.

• Journal of Korea Water Resources Association
• /
• v.53 no.6
• /
• pp.395-408
• /
• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.3
• /
• pp.388-397
• /
• 2009

There are many tasks which require robotic manipulators interaction with environment. It consists of three control problems, i.e., position control, impact control and force control. The position control means the way of reaching to the environment. The moment of touching to the environment yields the impact control problem and the force control is to maintain the desired force trajectory after the impact with the environment. These three control problems occur in sequence, so each control algorithm can be developed independently. Especially for redundant manipulators, each of these three control problems has been important independent research topic. For example, joint torque minimization and impulse minimization are typical techniques for such control problems. The three control problems are considered as a single task in this paper. The position control strategy is developed to improve the performance of the task, i.e., minimization of the individual joint torques and impulse. Therefore, initial conditions of the impact control problem are optimized at the previous position control algorithm. Such a control strategy yields improved result of the impact control. Similarly, the initial conditions for the force control problem are indirectly optimized by the previous position control and impact control strategies. The force control algorithm uses the individual joint torque minimization concept. It also minimizes the force disturbances. The simulation results show the proposed control strategy works well.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.1
• /
• pp.110-115
• /
• 2005

This paper proposes the method to detect contours of a face, eyes and a mouth in a color image for making an avatar automatically. First, we use the HSI color model to exclude the effect of various light condition, and we find skin regions in an input image by using the skin color is defined on HS-plane. And then, we use deformable templates and Genetic Algorithm(GA) to detect contours of a face, eyes and a mouth. Deformable templates consist of B-spline curves and control point vectors. Those can represent various shape of a face, eyes and a mouth. And GA is very useful search procedure based on the mechanics of natural selection and natural genetics. Second, an avatar is created automatically by using contours and Fuzzy C-means clustering(FCM). FCM is used to reduce the number of face color As a result, we could create avatars like handmade caricatures which can represent the user's identity, differing from ones generated by the existing methods.

• Steel and Composite Structures
• /
• v.34 no.5
• /
• pp.743-767
• /
• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.