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Detecting Design Infringement Using Multi-Modal Visual Data and Auto Encoder based on Convolutional Neural Network
Jeonggeol Kim, Jiyou Seo, Chanjae Lee, Seongmin Jo, Seungmin Kim, Seokmin Yoon, Young Yoon
http://doi.org/10.5626/JOK.2022.49.2.137
Recently, it has become very difficult to distinguish between counterfeit products and authentic goods, and the volume of these forgeries is increasing at an alarming rate. Prompt detection of these counterfeit products is challenging since only humans can identify these forgeries through trained expertise. In this paper, given the photograph and design drawing, we use convolutional neural networks and auto-encoders to detect the possible infringement of design rights without dissembling or damaging the suspected items. We have developed an easy-to-expand system that supports the constant addition of new goods to be examined. We present the result of our system tested with a set of authentic and forged goods.
Aircraft Reinforcement Learning using Curriculum Learning
Jung Ho Bae, Yun-Seong Kang, Sukmin Yoon, Yong-Duk Kim, Sungho Kim
http://doi.org/10.5626/JOK.2021.48.6.707
Diverse algorithms have been proposed to control unmanned aircrafts. However, they have limitations such as long exploration time and/or unclear behavior rules. To overcome the drawbacks and for efficient training, we propose an aircraft control technique using deep reinforcement learning applying antenna train angle (ATA) based curriculum learning. To validate the effectiveness of the proposed technique, we constructed a 3D simulation environment adapting a 6-DOF aircraft point model and conducted training with an initial setting of two fighters in the neutral position situation where they are looking back. The results showed that the proposed technique can achieve the goal of ATA 180° when the fighters are looking back without adding supplemental reward functions, while the deep reinforcement learning (DRL) without ATA curriculum could not succeed the learning over ATA 60° in a limited training time.
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