BoxStacker: Deep Reinforcement Learning for 3D Bin Packing Problem in Virtual Environment of Logistics Systems.

Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the lack of human experts. Deep Reinforcement Learning is a potential solution to solve more complex problems by introducing artificial neural networks in Reinforcement Learning. In this paper, a game engine was used for Deep Reinforcement Learning training, which allows visualization of view learning and result processes more intuitively than other tools, as well as a physical engine for a more realistic problem-solving environment.

Prediction of knee adduction moment using innovative instrumented insole and deep learning neural networks in healthy female individuals.

Background: The knee adduction moment, a biomechanical risk factor of knee osteoarthritis, is typically measured in a gait laboratory with expensive equipment and inverse dynamics modeling software. We aimed to develop a framework for a portable knee adduction moment estimation for healthy female individuals using deep learning neural networks and custom instrumented insole and evaluated its accuracy compared to the standard inverse dynamics approach.

Methods: Feed-forward, convolutional, and recurrent neural networks were applied to the data extracted from five piezo-resistive force sensors attached to the insole of a shoe.

A One-Stage Ensemble Framework Based on Convolutional Autoencoder for Remaining Useful Life Estimation.

As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In legacy studies, a machine conducting a single continuous operation has been mainly observed for power estimation. However, the production machine of a modularized line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine.

GadgetArm-Automatic Grasp Generation and Manipulation of 4-DOF Robot Arm for Arbitrary Objects Through Reinforcement Learning.

Automatic robot gripper system which involves the automated object recognition of work-in-process in production line is the key technology of the upcoming manufacturing facility achieving Industry 4.0. Automatic robot gripper enables the manufacturing system to be autonomous, self-recognized, and adaptable by using artificial intelligence of robot programming dealing with arbitrary shapes of work-in-processes.