A Bayesian Framework for Associative Memories

1997 ◽  
pp. 125-131
Author(s):  
Edwin R. Hancock ◽  
Marcello Pelillo
Keyword(s):  
Bayesian Framework ◽  
Associative Memories

Modeling Interstation Travel Speed of Hybrid Bus Rapid Transit within A Bayesian Framework

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Ji-hua Hu ◽  
Jia-xian Liang
Keyword(s):  
Road Traffic ◽  
Travel Speed ◽  
Bayesian Framework ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Important Indicator ◽  
Main Road ◽  
Traffic Lights ◽  
Speed Distribution ◽  
Hybrid Bus

Interstation travel speed is an important indicator of the running state of hybrid Bus Rapid Transit and passenger experience. Due to the influence of road traffic, traffic lights and other factors, the interstation travel speeds are often some kind of multi-peak and it is difficult to use a single distribution to model them. In this paper, a Gaussian mixture model charactizing the interstation travel speed of hybrid BRT under a Bayesian framework is established. The parameters of the model are inferred using the Reversible-Jump Markov Chain Monte Carlo approach (RJMCMC), including the number of model components and the weight, mean and variance of each component. Then the model is applied to Guangzhou BRT, a kind of hybrid BRT. From the results, it can be observed that the model can very effectively describe the heterogeneous speed data among different inter-stations, and provide richer information usually not available from the traditional models, and the model also produces an excellent fit to each multimodal speed distribution curve of the inter-stations. The causes of different speed distribution can be identified through investigating the Internet map of GBRT, they are big road traffic and long traffic lights respectively, which always contribute to a main road crossing. So, the BRT lane should be elevated through the main road to decrease the complexity of the running state.

Applying Associative Memories To Fault Location Identification

2016 ◽  
Author(s):  
A. P. Alves da Silva ◽  
A. H. F. Insfran ◽  
P. M. da Silveira ◽  
G. Lambert-Torres
Keyword(s):  
Fault Location ◽  
Associative Memories ◽  
Location Identification

scholarly journals Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

2021 ◽  
Vol 101 (3) ◽  
Author(s):  
Korbinian Nottensteiner ◽  
Arne Sachtler ◽  
Alin Albu-Schäffer
Keyword(s):  
Autonomous Robots ◽  
Vision System ◽  
Dynamic Environments ◽  
Bayesian Framework ◽  
Experimental Results ◽  
Robotic Assembly ◽  
Tactile Sensing ◽  
Task Execution ◽  
Industrial Assembly ◽  
Assembly Tasks

AbstractRobotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

scholarly journals 1-D/2-D/3-D Hopfield Associative Memories

2021 ◽  
Vol 1049 (1) ◽  
pp. 012001
Author(s):  
Rama Murthy Garimella ◽  
Aman Singh ◽  
GC Jyothi Prasanna ◽  
Manasa Jagannadan ◽  
Vidya Sree Vankam ◽  
...  
Keyword(s):  
Associative Memories
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