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New convolutional neural network models for efficient object recognition with humanoid robots
(Taylor and Francis Inc., 2021)
Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence, it is important that the robots have the object recognition capability. However, object recognition is ...
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
(Institute of Electrical and Electronics Engineers Inc., 2021)
We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the ...
New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration
(Springer, 2021)
As the environments that human live are complex and uncontrolled, the object manipulation with humanoid robots is regarded as one of the most challenging tasks. Learning a manipulation skill from human Demonstration (LfD) ...
An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things
(Institute of Electrical and Electronics Engineers Inc., 2021)
We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and ...