Self-organizing micro robotic system. (Biologically inspired immune network architecture and micro autonomous robotic system)

Author(s):  
N. Mitsumoto ◽  
T. Hattori ◽  
T. Idogaki ◽  
T. Fukuda ◽  
F. Arai
Author(s):  
Toshio FUKUDA ◽  
Guoqing XUE ◽  
Fumihito ARAI ◽  
Kazuhiro KOSUGE ◽  
Hajime ASAMA ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3215 ◽  
Author(s):  
M. A. Viraj J. Muthugala ◽  
Anh Vu Le ◽  
Eduardo Sanchez Cruz ◽  
Mohan Rajesh Elara ◽  
Prabakaran Veerajagadheswar ◽  
...  

Regular dry dock maintenance work on ship hulls is essential for maintaining the efficiency and sustainability of the shipping industry. Hydro blasting is one of the major processes of dry dock maintenance work, where human labor is extensively used. The conventional methods of maintenance work suffer from many shortcomings, and hence robotized solutions have been developed. This paper proposes a novel robotic system that can synthesize a benchmarking map for a previously blasted ship hull. A Self-Organizing Fuzzy logic (SOF) classifier has been developed to benchmark the blasting quality of a ship hull similar to blasting quality categorization done by human experts. Hornbill, a multipurpose inspection and maintenance robot intended for hydro blasting, benchmarking, and painting, has been developed by integrating the proposed SOF classifier. Moreover, an integrated system solution has been developed to improve dry dock maintenance of ship hulls. The proposed SOF classifier can achieve a mean accuracy of 0.9942 with an execution time of 8.42 µs. Realtime experimenting with the proposed robotic system has been conducted on a ship hull. This experiment confirms the ability of the proposed robotic system in synthesizing a benchmarking map that reveals the benchmarking quality of different areas of a previously blasted ship hull. This sort of a benchmarking map would be useful for ensuring the blasting quality as well as performing efficient spot wise reblasting before the painting. Therefore, the proposed robotic system could be utilized for improving the efficiency and quality of hydro blasting work on the ship hull maintenance industry.


Author(s):  
Yong-Sheng Ding ◽  
Xiang-Feng Zhang ◽  
Li-Hong Ren

Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network environments, so it is necessary to optimize the current Internet architecture and its applications. Inspired by the resemble features between the immune systems and future Internet, the authors introduce some key principles and mechanisms of the immune systems to design a bio-network architecture to address the challenges of future Internet. In the bio-network architecture, network resources are represented by various bioentities, while complex services and application can be emerged from the interactions among bio-entities. Also, they develop a bio-network simulation platform which has the capability of service emergence, evolution, and so forth. The simulation platform can be used to simulate some complex services and applications for Internet or distributed network. The simulators with different functions can be embedded in the simulation platform. As a demonstration, this chapter provides two immune network computation models to generate the emergent services through computer simulation experiments on the platform. The experimental results show that the bio-entities on the platform provide quickly services to the users’ requests with short response time. The interactions among bio-entities maintain the load balance of the bio-network and make the resources be utilized reasonably. With the advantages of adaptability, extensibility, and survivability, the bio-network architecture provides a novel way to design new intelligent Internet information services and applications.


1997 ◽  
Vol 9 (6) ◽  
pp. 1321-1344 ◽  
Author(s):  
Teuvo Kohonen ◽  
Samuel Kaski ◽  
Harri Lappalainen

The adaptive-subspace self-organizing map (ASSOM) is a modular neural network architecture, the modules of which learn to identify input patterns subject to some simple transformations. The learning process is unsupervised, competitive, and related to that of the traditional SOM (self-organizing map). Each neural module becomes adaptively specific to some restricted class of transformations, and modules close to each other in the network become tuned to similar features in an orderly fashion. If different transformations exist in the input signals, different subsets of ASSOM units become tuned to these transformation classes.


2017 ◽  
Vol 96 (4) ◽  
pp. 5603-5620 ◽  
Author(s):  
Pondi Jyothirmai ◽  
Jennifer. S. Raj ◽  
S. Smys

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