• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.170-170
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• 2000

This paper deals with on-Line detection of strip movement and real time positioning of brackets of EMD connected with it. Strip movement is detected by 4 line CCD camera and measured position correction value is inputted to motor position controller to control position of brackets.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1072-1075
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• 1996

In hot rolling process at steel plant, cooling of the rolled strip at the exit of the rolling mill is one of the most important processes that would decide the quality of products. To guarantee the thermal equity over the strip, the device called an edge-masking unit is being used. That is installed between the strip and the sprayers to cover the side edge of the strip from spraying water. The accuracy of positioning the bracket is the key to this operation. A machine vision technique can be applied to measure the position of the side edges before an as-rolled strip enters into the cooling facility to rectify the error of preset position of the bracket. This paper shows the simulation result of applying the machine vision technique to measuring the position of a strip and suggests the solution for the target.

• Electronics and Telecommunications Trends
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• v.34 no.5
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• pp.58-70
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• 2019

In this paper, we present the R&D status of ETRI's Trusted Reality (TR) project and its core technologies. ETRI's TR project focuses on the next-generation paradigm of smart phones, ETRI-TR Smart Glasses, which aims to provide the same features as those of smart phones without the involvement of any handheld device. Furthermore, they are characterized by additional features enabled by trustworthy VR/AR/MR/XR, such as privacy masking/unmasking, distributed structure of thin-client computing/networking among TR-Glasses, TR-LocalEdge, and TR-RemoteEdge, with novel see-through eyes-direct communication between IoT real/virtual objects and human eyes. Based on these core technologies of the ETRI's TR project, the human-held ETRI-TR Smart Glasses is expected to aid in the realization of XR vision with particularly more XR's safe_privacy on social life in the near future.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.