http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
이상백(Sang-Back Lee),박태형(Tae-Hyoung Park),한경식(Kyung-Sik Han) 제어로봇시스템학회 2014 제어·로봇·시스템학회 논문지 Vol.20 No.7
This paper proposes a machine vision module for PLCs (Programmable Logic Controllers). PLC is the industrial controller most widely used in factory automation system. However most of the machine vision systems are based on PC (Personal Computer). The machine vision system embedded in PLC is required to reduce the cost and improve the convenience of implementation. In this paper, we newly propose a machine vision module based on PLC. The image processing libraries are implemented and integrated with the PLC programming tool. In order to interface the libraries with ladder programming, the ladder instruction set was also designed for each vision library. By use of the developed system, PLC users can implement vision systems easily by ladder programming. The developed system was applied to sample inspection system to verify the performance. The experimental results show that the proposed system can reduce the cost of installing as well as increase the ease-of-implementation.
백문기 ( Moon-ki Back ),윤승원 ( Seung-won Yoon ),이상백 ( Sang-baek Lee ),이규철 ( Kyu-chul Lee ) 한국정보처리학회 2021 정보처리학회 논문지 Vol.10 No.1
Although Generative Adversarial Networks (GANs) have gained great popularity in computer vision and related fields, generating audio signals independently has yet to be presented. Unlike images, an audio signal is a sampled signal consisting of discrete samples, so it is not easy to learn the signals using CNN architectures, which is widely used in image generation tasks. In order to overcome this difficulty, GAN researchers proposed a strategy of applying time-frequency representations of audio to existing image-generating GANs. Following this strategy, we propose an improved method for increasing the fidelity of synthesized audio signals generated by using GANs. Our method is demonstrated on a public speech dataset, and evaluated by Frechet Inception Distance (FID). When employing our method, the FID showed 10.504, but 11.973 as for the existing state of the art method (lower FID indicates better fidelity).