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염색가공 공장의 에너지 효율 향상을 위한 디지털트윈 구성 및 활용
박규태(Kyu Tae Park),임성주(Sung Ju Im),강용신(Yong-shin Kang),노상도(Sang Do Noh),양석곤(Suk Gon Yang),강용태(Yong Tae Kang),김동현(Dong Hyun Kim),최수영(Su Young Choi) (사)한국CDE학회 2018 한국CDE학회 논문집 Vol.23 No.4
In the textile industry, which has low energy efficiency, dyeing and finishing shop accounted for 42 percents of the total energy consumption. In addition, the dyeing and finishing shop has a low standardization level for process and setup, and is based on know-how of operator in the field according to incorrect instruction from the laboratory. To improve the energy efficiency of the dyeing and finishing shop, the process was standardized based on manufacturing data from the dyeing process. This study suggests a configuration and a utilization plan to operate digital twin to provide decision making supporting based on interoperability scenario with manufacturing data. The digital twin is a virtual factory model that reflects real-world components and interacts with the physical world and other applications. It also contains information and functional units, which are used to interact with big data from a dyeing and finishing shop with various algorithms. Thus, this study first defines the interoperability scenarios that should be used to provide a variety of decision making support information through the utilization of digital twin, and how they should be reflected the manufacturing elements and functional units of the physical world. This provides a fundamental knowledge of the process and systematical approach to improve energy efficiency.
염색가공 산업의 에너지 효율화를 위한 제조현장 빅데이터 활용에 관한 연구
박규태(Kyu Tae Park),양석곤(Suk Gon Yang),박희진(Hee Jin Park),조문빈(Wen Bin Zhao),강용신(Yong Shin Kang),노상도(Sang Do Noh),김동현(Dong Hyun Kim),최수영(Su Young Choi),강용태(Yong Tae Kang) (사)한국CDE학회 2017 한국CDE학회 논문집 Vol.22 No.4
The manufacturing industry has been actively adapting ICT (Information and Communication Technology) into the field with keywords such as 4th industrial revolution. Due to collecting information and data analytics technologies, products, machines and process in the traditional manufacturing industry have become smart. In the textile industry, an efficiency in a dyeing process greatly depends on energy usage. Thus, researchers in dyeing-finishing factories focus on energy efficiency in this area. They have been trying to make energy efficient through various experiments, but haven’t achieved remarkable result due to various problems on the site. Therefore, we collect manufacturing big data and try to improve energy efficiency based on the collected data. In this paper, we consider a method to improve the energy efficiency in dyeing process using manufacturing big data. We propose a way to achieve energy efficiency in the dyeing process with lower energy usage and repeated dyeing. As a result of this paper, it is suggested that dyeing process should be instructed and controlled based on the significant variables and learning model when energy efficiency is to be utilized by using manufacturing big data. We also verify the feasibility of this argument through a case study using ANN.
작업자 안전 관제를 위한 자세 인식 엣지-클라우드 시스템 아키텍처
최승현(Choi Seunghyun),김보배(Kim Bobae),원진영(Won JinYoung),박일하(Il-ha Park),최성수(Sung Soo Choi),강용신(Yong-Shin Kang) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
본 연구는 산업현장에서 작업자 안전 관제를 위해 실시간으로 작업자의 자세를 분석할 수 있는 엣지-클라우드 시스템 설계하고 개발한다. 이를 위해 스마트폰을 사용하여 엣지 시스템을 구축하고 가속도/관성 센서 데이터를 수집하여 실시간으로 작업자의 자세를 인식하고 원시 데이터와 함께 클라우드에 전송한다. 클라우드는 원시 데이터와 실험데이터를 사용하여 자세 인식 모델을 학습하고 이를 엣지에 전송한다.