Abstract: The present disclosure relates to an anode for a lithium-metal battery, a manufacturing method of the same, and a lithium-metal battery including the anode. The anode for a lithium-metal battery includes a complex hierarchical structure current collector which includes an inverted pyramid-shaped micro hole pattern and nanostructures provided within the inverted pyramid-shaped micro hole pattern; and a lithium metal which is electrodeposited on the nanostructure of the current collector. As a result, it is possible to increase the life stability of the battery and increase the coulombic efficiency.

Abstract: Disclosed herein are a neural network model deployment method and apparatus for providing a deep learning service. The neural network model deployment method may include providing a specification wizard to a user, searching for and training a neural network based on a user requirement specification that is input through the specification wizard, generating a neural network template code based on the user requirement specification and the trained neural network, converting the trained neural network into a deployment neural network that is usable in a target device based on the user requirement specification, and deploying the deployment neural network to the target device.

Abstract: The present disclosure relates to an anode for a lithium-metal battery, a manufacturing method of the same, and a lithium-metal battery including the anode. The anode for a lithium-metal battery includes a complex hierarchical structure current collector which includes an inverted pyramid-shaped micro hole pattern and nanostructures provided within the inverted pyramid-shaped micro hole pattern; and a lithium metal which is electrodeposited on the nanostructure of the current collector. As a result, it is possible to increase the life stability of the battery and increase the coulombic efficiency.