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RISS 인기검색어
- 검색키워드
- 저자 : Mohammad Teshnehlab (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Faults detection and identification for gas turbine using DNN and LLM
Seyyed Mohammad Emad Oliaee,Mohammad Teshnehlab,Mahdi Aliyari Shoorehdeli 국제구조공학회 2019 Smart Structures and Systems, An International Jou Vol.23 No.4
Applying more features gives us better accuracy in modeling; however, increasing the inputs causes the curse of dimensions. In this paper, a new structure has been proposed for fault detecting and identifying (FDI) of high-dimensional systems. This structure consist of two structure. The first part includes Auto-Encoders (AE) as Deep Neural Networks (DNNs) to produce feature engineering process and summarize the features. The second part consists of the Local Model Networks (LMNs) with LOcally LInear MOdel Tree (LOLIMOT) algorithm to model outputs (multiple models). The fault detection is based on these multiple models. Hence the residuals generated by comparing the system output and multiple models have been used to alarm the faults. To show the effectiveness of the proposed structure, it is tested on single-shaft industrial gas turbine prototype model. Finally, a brief comparison between the simulated results and several related works is presented and the well performance of the proposed structure has been illustrated.
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An Optimization based Method for Simultaneous Localization and Mapping
Ramazan Havangi,Mohammad Ali Nekoui,Mohammad Teshnehlab 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.4
In this study, we present an optimization based solution to the simultaneous localization and mapping (SLAM) problem. In the proposed algorithm, the SLAM problem is considered as two optimization problems. These problems are solved using forward dynamic programming. In the first problem, it is assumed that map is known perfectly and the robot path is estimated. In the second problem, the estimated robot path with their corresponding measurements is used to identify map. As optimization problem in each step of dynamic programming have high nonlinearity and also differential evolution (DE) tends to find the globally optimal solution without being trapped at local maxima, DE is de-veloped to solve dynamic programming in each step of time. Some simulations and experiments are presented to illustrate the proposed algorithm and exhibit its performance.
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A hybrid controller based on CPG and ZMP for biped locomotion
Amir Massah B.,Ali Zamani,Yaser Salehinia,Mahdi Aliyari Sh.,Mohammad Teshnehlab 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.11
Biped locomotion has attracted much attention in recent years. The most successful implemented methods in this area are based on two approaches, central pattern generator (CPG) and zero moment point (ZMP). Unfortunately, neither of these concepts can solely solve the movement challenge completely. In this study, we introduce a hybrid controller to combine the advantages of these methods. The proposed controller is based on two major approaches, CPG and ZMP. This hybrid controller is composed of a trajectory control system and a trajectory generator system. The trajectory control system applied to keep the robot stable uses ZMP as a real time control feedback. The trajectory generator system, which is composed of nonlinear oscillators, generates stable motions. The parameters of CPG are tuned by a new two-stage approach using differential evolution (DE) and bees algorithm (BA). Furthermore, performance of the proposed controller is verified using the robotic simulation software Webots.
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