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| 1 | Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping显示文摘The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks. | Delowar Hossain Genci Capi Mitsuru Jindai | 2018 | Journal of Electronic Science and Technology2018，16，1： | 2 |
| 2 | Novel Biological Based Method for Robot Navigation and Localization显示文摘The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat's brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats' strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat's decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat's decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze. | Endri Rama Genci Capi Yusuke Fujimura Norifumi Tanaka Shigenori Kawahara Mitsuru Jindai | 2018 | Journal of Electronic Science and Technology2018，16，1： | 2 |
| 3 | Three-dimensional inkjet biofabrication based on designed images显示文摘 | Kenichi Arai1 Shintaroh Iwanaga HidekiToda Capi Genci Yuichi Nishiyama Makoto Nakamura | | 0，，03： | 1 |
| 4 | Three-dimensional inkjet biofabrication based on designed images显示文摘 | Kenichi Arai Shintaroh Iwanaga Hideki Toda Capi Genci Yuichi Nishiyama Makoto Nakamura | 2011 | Biofabrication2011，，3： | 1 |
| 5 | Evolution of recurrent neural controllers using an extended parallel genetic algorithm 显示文摘 | Genci Capi Kenji Doya | 2005 | Robotics and Autonomous Systems (S0921-8890)2005，52，： | 1 |
| 6 | Application of Genetic Algorithms for Biped Robot Gait Synthesis Optimization during Walking and Going Upstairs显示文摘 | Capi Genci Nasu Yasuo Barolli Leonard | 2001 | Advanced Robotics2001，15，6： | 1 |
| 7 | Application of evolutionary computation for efficient reinforcement learning 显示文摘 | Capi Genci Doya Kenji | 2006 | Applied Artificial Intelligence2006，20，1： | 1 |
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