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RISS 인기검색어
- 검색키워드
- 저자 : 노천명(Chun-myoung Noh) (검색결과 4 건)
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정규화 기법을 통한 안면 인식 알고리즘 성능 향상에 관한 연구
노천명(Chun-myoung Noh),강동훈(Dong-hoon Kang),이재철(Jae-chul Lee) (사)한국CDE학회 2020 한국CDE학회 논문집 Vol.25 No.2
Through the combination of computer vision technology and artificial intelligence, facial recognition technology is drawing attention as a new means of personal authentication in the era of the fourth industry. Facial recognition technology uses imaging equipment to photograph a person"s face and extract characteristic data. The extracted data are matched against the facial features of the stored database. Facial recognition technology is a contactless technology compared to other biometric recognition technologies, which is used in various fields due to its high hygiene, convenience and security, and in particular, safety accidents in workplaces are closely related to life, and various studies related to workplace safety management using intelligent video information are being conducted in the manufacturing industry. In this paper, a study is conducted on the development of facial recognition algorithm using deep learning to control worker access in hazardous areas. The accuracy of the recognition of the proposed facial recognition algorithm (object detection algorithm (SSD) and object recognition algorithm (ResNet)) is closely related to the safety of the operator. Therefore, the goal is to analyze the relationship between various normalization techniques (Min-Max Scaler, MaxAbs Scaler, Standard Scaler) and the recognition rate of the proposed facial recognition algorithm to propose a high-accuracy facial recognition algorithm. In the future, we will conduct research on safety issues in the manufacturing industry based on facial recognition and image recognition technologies.
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영상 인식 알고리즘을 이용한 안전 보호구(안전모) 탐지에 관한 연구
노천명(Chun-myoung Noh),김기관(Ki-Kwan Kim),이수봉(Su-bong Lee),강동훈(Dong-hoon Kang),이재철(Jae-chul Lee) (사)한국CDE학회 2020 한국CDE학회 논문집 Vol.25 No.4
Safety accidents at work sites are directly related to workers" lives, and the manufacturing industry"s interest in safety accidents is increasing every year. Safety accidents at work sites are caused by a variety of factors, and it is difficult to predict when and why they occur. In this research, an intelligent image recognition-based worker safety protection device wearing algorithm that can determine suitability of wearing safety protective devices is developed and the proposed algorithm is sought to be applied to the site. In this study, the You only look once (YOLO) algorithm is applied to analyze the presence of workers wearing safety protection equipment in real time. Accuracy of object detection for safety protection equipment is very important. Thus, this study compared/analyzed the algorithms of two YOLO systems (YOLOv2, YOLOV3) and improved the performance of the model by changing Hyperparameters, Fine-tuning and Dataset of the selected algorithms. In the future, studies will be conducted on how to improve the accuracy of object detection and complement the accuracy of object detection in the proposed YOLO series algorithm.
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해상에서 소형 객체 인식을 위한 데이터 전처리 방안 연구
김기관(Gi-Gwan Kim),노천명(Chun-Myoung Noh),이수봉(Su-Bong Lee),이순섭(Soon-Sup Lee),이재철(Jae-Chul Lee) (사)한국CDE학회 2021 한국CDE학회 논문집 Vol.26 No.4
Implementing good performance deep learning requires a large amount of high-quality data. However, in areas where the amount of data is limited, data collection takes a lot of time and cost. This study attempts to detect small object-sized submarine masts based on the environmental characteristics of the sea, which limit data collection and reduce the amount and quality of data due to the characteristics of submarine data used in the study. This study aims to improve recognition performance through preprocessing techniques with a small amount of data, and it can be seen that recognition performance has improved based on mAP.
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PLC 제어기를 이용한 아르곤 기화 시스템 자동제어에 관한 연구
배재성(Jae-Seong Bae),노천명(Chun-myoung Noh),김현수(Hyun-soo Kim),조도원(Do-Won Cho),김성수(Sung-soo Kim),이재철(Jae-chul Lee) (사)한국CDE학회 2021 한국CDE학회 논문집 Vol.26 No.2
Recently, as research on the 4th industrial revolution has been conducted in various ways, interest in smart factories is increasing. The smart factory can be divided into five stages, from the stage of not applying ICT to the stage of upgrading. In this case, automatic control should be applied even at the lowest level, where ICT is not applied. In other words, automatic control is an essential component for building a smart factory. In the past, to apply automatic control, it was directly connected and controlled according to the circuit diagram. Today, however, PLC controllers are used that can be modified/supplemented using programming logic and are highly scalable and flexible. This study uses Siemens" S7-300 PLC controller, which can collect data in real-time among PLC and provides maximum precision and speed during machine manufacturing. In-order to consider all possible situations during system operation, it automatically controls the outlet temperature of the argon vaporization system based on SILS. At this time, the automatic control tool uses PID control, which is most commonly used in industrial sites due to its simple structure and high stability.
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