Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning

Mechatronics ◽  
2016 ◽  
Vol 34 ◽  
pp. 1-11 ◽  
Author(s):  
Johannes Günther ◽  
Patrick M. Pilarski ◽  
Gerhard Helfrich ◽  
Hao Shen ◽  
Klaus Diepold
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Laser Welding ◽  
Deep Neural Networks ◽  
Control Learning ◽  
Prediction And Control ◽  
And Control

scholarly journals First Steps Towards an Intelligent Laser Welding Architecture Using Deep Neural Networks and Reinforcement Learning

2014 ◽  
Vol 15 ◽  
pp. 474-483 ◽  
Author(s):  
Johannes Günther ◽  
Patrick M. Pilarski ◽  
Gerhard Helfrich ◽  
Hao Shen ◽  
Klaus Diepold
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Laser Welding ◽  
Deep Neural Networks

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Yongsen Ma ◽  
Sheheryar Arshad ◽  
Swetha Muniraju ◽  
Eric Torkildson ◽  
Enrico Rantala ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Activity Recognition ◽  
Deep Neural Networks ◽  
State Machine ◽  
Recognition Algorithm ◽  
The State ◽  
Neural Architecture ◽  
Learning Agent

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition. In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi. The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.

scholarly journals Diversity oriented Deep Reinforcement Learning for targeted molecule generation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tiago Pereira ◽  
Maryam Abbasi ◽  
Bernardete Ribeiro ◽  
Joel P. Arrais
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Deep Neural Networks ◽  
Chemical Space ◽  
Biological Properties ◽  
Training Process ◽  
Training Strategy ◽  
Inhibitory Power ◽  
Exploratory Strategy

AbstractIn this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with an optimized coefficient of partition and also high inhibitory power against the Adenosine $$A_{2A}$$ A 2 A and $$\kappa$$ κ opioid receptors. The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction. More importantly, it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.

NeSS: a Simulation Environment for Behavioral Design of Neural Networks for Prediction and Control

1999 ◽  
Vol 6 (3) ◽  
pp. 223-232
Author(s):  
Cesare Alippi ◽  
Fabio Casamatta ◽  
Leonardo Furlan ◽  
Andrea Pelagotti ◽  
Vincenzo Piuri
Keyword(s):  
Neural Networks ◽  
Simulation Environment ◽  
Behavioral Design ◽  
Prediction And Control ◽  
And Control

Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

2020 ◽  
Vol 8 (4) ◽  
pp. 486-507
Author(s):  
Guo Zhou ◽  
Chao Xu ◽  
Yuan Ma ◽  
Xiao-Hao Wang ◽  
Ping-Fa Feng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Matrix Composites ◽  
Prediction And Control ◽  
And Control

scholarly journals Real-Time Object Navigation With Deep Neural Networks and Hierarchical Reinforcement Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 195608-195621
Author(s):  
Aleksey Staroverov ◽  
Dmitry A. Yudin ◽  
Ilya Belkin ◽  
Vasily Adeshkin ◽  
Yaroslav K. Solomentsev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Real Time ◽  
Deep Neural Networks ◽  
Hierarchical Reinforcement Learning
Sign in / Sign up

Export Citation Format

Share Document