Lane Change Prediction With an Echo State Network and Recurrent Neural Network in the Urban Area

2021 ◽  
pp. 1-7
Author(s):  
Karoline Griesbach ◽  
Matthias Beggiato ◽  
Karl Heinz Hoffmann
Keyword(s):  
Neural Network ◽  
Urban Area ◽  
Recurrent Neural Network ◽  
Echo State Network ◽  
Lane Change ◽  
Change Prediction

scholarly journals The color phi phenomenon: Not so special, after all?

2021 ◽  
Vol 17 (9) ◽  
pp. e1009344
Author(s):  
Lars Keuninckx ◽  
Axel Cleeremans
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Sensory Processing ◽  
Time Reversal ◽  
Echo State Network ◽  
Connectionist Models ◽  
Long Distance ◽  
Minimal Set ◽  
The Brain

We show how anomalous time reversal of stimuli and their associated responses can exist in very small connectionist models. These networks are built from dynamical toy model neurons which adhere to a minimal set of biologically plausible properties. The appearance of a “ghost” response, temporally and spatially located in between responses caused by actual stimuli, as in the phi phenomenon, is demonstrated in a similar small network, where it is caused by priming and long-distance feedforward paths. We then demonstrate that the color phi phenomenon can be present in an echo state network, a recurrent neural network, without explicitly training for the presence of the effect, such that it emerges as an artifact of the dynamical processing. Our results suggest that the color phi phenomenon might simply be a feature of the inherent dynamical and nonlinear sensory processing in the brain and in and of itself is not related to consciousness.

scholarly journals The Color Phi phenomenon: not so special, after all?

2020 ◽  
Author(s):  
Lars Keuninckx ◽  
Axel Cleeremans
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Time Reversal ◽  
Model Neuron ◽  
Nonlinear Behavior ◽  
Echo State Network ◽  
Connectionist Models ◽  
Long Distance ◽  
Minimal Set

We show how anomalous time reversal of stimuli and their associated responses can exist in very small connectionist models. The networks these models exist of, are built using a dynamical toy model neuron, and adhere to a minimal set of biologically plausible properties. The appearance of a “ghost” response, temporally and spatially located in between responses caused by actual stimuli, as in the Phi phenomenon, is demonstrated in a similar small network, where it is caused by priming and long-distance feedforward paths. We then demonstrate that the Color Phi phenomenon can be present in an Echo State Network, a dynamical recurrent neural network, markedly without explicitly training for the presence of the effect. Our results suggest that similar illusions are likely to be obtainable for any system, artificial or biological in nature, with minimal neuron-like properties, and are thus merely artifacts of the inherent dynamical and nonlinear behavior and of suchsystems.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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