• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.463-465
• /
• 2021

Currently, the inspection method printed on food packages and boxes is to sample only a few products and inspect them with human eyes. Such a sampling inspection has the limitation that only a small number of products can be inspected. Therefore, accurate inspection using a camera is required. This paper proposes a deep learning object recognition technology model, which is an artificial intelligence technology, as a method for detecting the defects of expiration date printed on the product packaging. Using the Faster R-CNN (region convolution neural network) model, the color images, converted gray images, and converted binary images of the printed expiration date are trained and then tested, and each detection rates are compared. The detection performance of expiration date printed on the package by the proposed method showed the same detection performance as that of conventional vision-based inspection system.

• Journal of the Institute of Electronics Engineers of Korea TE
• /
• v.37 no.5
• /
• pp.71-76
• /
• 2000

A TC-16ADPSK scheme for transmitting different kinds of information simultaneously is proposed in this paper. The scheme is designed for simultaneously transmitting two kinds of Information on one channel. In signal mapping, a data of two kinds of information is used to phase modulation on Star-16APSK constellation and the other to amplitude modulation. In detection, each data independently recovers from mixing signal on each detector Therefore, we can transmit two kinds of Information on one channel can be transmitted efficiently. BER performance of the proposed scheme is analyzed on AWGN channel and Rayleigh fading channels on a computer with Matlab communication toolbox. On same SNR, the Gray code mapping has more 0.5-1.5dB coding gains than Ungerboeck's code mapping gains.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.6
• /
• pp.1463-1474
• /
• 2018

Fuzzing is one of the software testing techniques that find security flaws by inputting invalid values or arbitrary values into the program and various methods have been suggested to increase the efficiency of such fuzzing. In this paper, focusing on the existence of field with high relevance to coverage and software crash, we propose a new method for intensively fuzzing corresponding field part while performing field based fuzzing. In this case, we use a deep learning model called Variational Autoencoder(VAE) to learn the statistical characteristic of input values measured in high coverage and it showed that the coverage of the regenerated files are uniformly higher than that of simple variation. It also showed that new crash could be found by learning the statistical characteristic of the files in which the crash occurred and applying the dropout during the regeneration. Experimental results showed that the coverage is about 10% higher than the files in the queue of the AFL fuzzing tool and in the Hwpviewer binary, we found two new crashes using two crashes that found at the initial fuzzing phase.