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| 1 | Semiactive nonlinear control of a building with a magnetorheological damper system 显示文摘 | Yeesock Kim Reza Langari Stefan Hurlebaus | 2008 | Mechanical Systems and Signal Process ing2008，6，6： | 1 |
| 2 | Fuzzy model forecasting of offshore bar-shape profiles under high waves显示文摘 | Yeesock Kim Kyu Han Kim Bum-Shick Shin | 2014 | Expert Systems With Applications2014，，13： | 1 |
| 3 | Wavelet-based AR–SVM for health monitoring of smart structures显示文摘 | Yeesock Kim Jo Woon Chong Ki H Chon JungMi Kim | 2013 | Smart Materials and Structures2013，，1： | 1 |
| 4 | Nonlinear system identification of smart structures under high impact loads显示文摘 | Kemal Sarp Arsava Yeesock Kim Tahar El-Korchi Hyo Seon Park | 2013 | Smart Materials and Structures2013，，5： | 1 |
| 5 | Fragility estimates of smart structures with sensor faults显示文摘 | Yeesock Kim Jong-Wha Bai Leonard D Albano | 2013 | Smart Materials and Structures2013，，12： | 1 |
| 6 | Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures显示文摘 | Young-Jin Cha Anil K. Agrawal Yeesock Kim Anne M. Raich | 2012 | Expert Systems With Applications2012，，9： | 1 |
| 7 | Nonlinear system identification of large-scale smart pavement systems显示文摘 | Yeesock Kim Rajib Mallick Sankha Bhowmick Bao-Liang Chen | 2012 | Expert Systems With Applications2012，，： | 1 |
| 8 | System identification of smart structures using a wavelet neuro-fuzzy model显示文摘 | Ryan Mitchell Yeesock Kim Tahar El-Korchi | 2012 | Smart Materials and Structures2012，，11： | 1 |
| 9 | MIMO fuzzy identification of building-MR damper systems显示文摘 | Yeesock Kim Reza Langari Stefan Hurlebaus | 2011 | Journal of Intelligent & Fuzzy Systems2011，，4： | 1 |
| 10 | Novel bio-inspired smart control for hazard mitigation of civil structures显示文摘 | Yeesock Kim Changwon Kim Reza Langari | 2010 | Smart Materials and Structures2010，，11： | 1 |
| 11 | Active control of highway bridges subject to a variety of earthquake loads显示文摘In this paper, a wavelet-fi ltered genetic-neuro-fuzzy(WGNF) control system design framework for response control of a highway bridge under various earthquake loads is discussed. The WGNF controller is developed by combining fuzzy logic, discrete wavelet transform, genetic algorithms, and neural networks for use as a control algorithm. To evaluate the performance of the WGNF algorithm, it is tested on a highway bridge equipped with hydraulic actuators. It controls the actuators installed on the abutments of the highway bridge structure. Various earthquakes used as input signals include an artifi cial earthquake, the El-Centro, Kobe, North Palm Springs, Turkey Bolu, Chi-Chi, and Northridge earthquakes. It is proved that the WGNF control system is effective in mitigating the vibration of the highway bridge under a variety of seismic excitation. | Ryan Mitchell Young-Jin Cha Yeesock Kim Aniket Anil Mahajan | 2015 | Earthquake Engineering and Engineering Vibration2015，14，2： | 0 |
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