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New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration

Aslan, S.N.; Ozalp, R.; Ucar, A.; Guzelis, C. (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) ...

Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection

Nakip, M.; Guzelis, C.; Yildiz, O. (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 ...

An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

Nakip, M.; Karakayali, K.; Guzelis, C.; Rodoplu, V. (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 ...

Learning to move an object by the humanoid robots by using deep reinforcement learning

Aslan, S.N.; Tasci, B.; Ucar, A.; Guzelis, C. (IOS Press, 2021)

This paper proposes an algorithm for learning to move the desired object by humanoid robots. In this algorithm, the semantic segmentation algorithm and Deep Reinforcement Learning (DRL) algorithms are combined. The semantic ...

New convolutional neural network models for efficient object recognition with humanoid robots

Aslan, S.N.; Ucar, A.; Guzelis, C. (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 ...

Subspace-Based Emulation of the Relationship between Forecasting Error and Network Performance in Joint Forecasting-Scheduling for the Internet of Things

Nakip, M.; Helva, A.; Guzelis, C.; Rodoplu, V. (IEEE, 2021)

We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology, an Artificial Neural Network (ANN) learns ...