Národní úložiště šedé literatury Nalezeno 6 záznamů.  Hledání trvalo 0.01 vteřin. 
The design of Bayesian diagnostic expert system Querix and it’s engineering application
Věchet, Stanislav ; Krejsa, Jiří ; Chen, K.-S.
Expert systems have gained attention over the last two decades as they bring the possibility of using expert knowledge in various control systems. However, it has lost attraction in favor of artificial neural networks in recent years, which is mostly influenced by the availability of data to train neural network models and the availability of various frameworks to achieve fast time-to-market applications for given solutions.
Autonomous vehicles lane detection using particle filters
Věchet, Stanislav ; Krejsa, Jiří ; Chen, K.S.
Lane detection belongs among many others crucial tasks which an autonomous vehicles traveling in urban roads need to take care of. We present a lane detection method which uses particle filters combined with visual information from onboard camera to control the vehicles direction. Our initial experiments shows promising results as applied and tested in urban roads with focus on real-time data processing.
Mobile Robot in the Elevator: What Floor Am i On?
Krejsa, Jiří ; Věchet, Stanislav ; Chen, K.S. ; Havelka, M. ; Černil, M.
Wheeled mobile robots in multiple stories buildings have to use elevator to access arbitrary floor of the building. To do so, the control system must be able to detect and access elevator controls and also determine on which floor the elevator stopped. The paper deals with the latter problem, using the fusion of relative floor change detected by onboard accelerometer and absolute floor number detected by the processing of elevator information screen panel image acquired by onboard camera. Bayesian filter is used for data fusion and convolution neural network for image processing. Field tests resulted in 97% correct detection.
Control system architecture for novel experimental microalgae photobioreactor Simbios
Věchet, Stanislav ; Krejsa, Jiří ; Chen, K. S.
Photo-bioreactors for growing biomass based on various microalgae cultures gains interests nowadays due to have possible positive impact to society because of the potential to serve as an atmospheric carbon dioxide removal (CDR) device, produce food or fuel. We present an overall architecture of a control system designed with focus to keep the photo-bioreactor alive for long term continuous operation without any human supervision needed. To test overall control system stability we design a custom build spiral photo-bioreactor (PBR) with novel hyperbolic shaped illuminated part. The shape of the illuminated part was design with the goal to increase the ratio of spiral part to volume due to the fact that the light is crucial for microalgae to grow. The control system was created from scratch while adopting reliable IoT sensors combined with highly reliable custom build embedded control system including a cloud data processing and storage. As a result we have scalable and reliable solution which is suitable for photo-bioreactors of various shapes and sizes to operate in fully autonomous mode without any human supervision needed.
Determination of current floor during mobile robot elevator ride
Krejsa, Jiří ; Věchet, Stanislav ; Chen, K. S.
In order for the mobile robot to use the elevator, it must be capable to call the elevator in, detect the control panel, select desired floor and to determine what floor is the elevator located. The paper deals with determination of correct floor the mobile robot is currently at using the processing of onboard accelerometer data. Up to 5 relative floor difference the method exhibits over 95% accuracy.
Anomalies detection in time-series data for the internal diagnostics of autonomous mobile robot
Věchet, Stanislav ; Krejsa, Jiří ; Chen, K.S.
Autonomous mobile robots are complex mechatronic machines which consists of numerous hardware and software modules working asynchronously to achieve desired behaviour. As there are many frameworks which helps to overcome the flat learning curve the problem of internal diagnostics arises. While users and developers are able to focus only on solving the high level problem algorithm or methods the internal states of the system is hidden. This helps to separate the users from solving hardware issues, which is helping until everything works properly. We present an algorithm which is able to detect anomalies in time based behaviour of the robot to improve the users confidence that the internal robot framework works correctly and as desired. The algorithm is based on probabilistic patterns detection based on Bayesian probabilistic theory.

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