• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.10
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• pp.142-146
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• 2004

Spaceborne earth observation or astronomical payloads often use Cassegrain-type telescopes due to limits in mass and volume. Precision optical alignment of such a telescope is vital to the success of the mission. This paper describes the alignment simulation and experiment of computer-aided alignment method during the assembly of MAC (Medium-sized Aperture Camera) telescope for spaceborne earth observation.

• Korean Journal of Optics and Photonics
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• v.28 no.4
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• pp.146-152
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• 2017

Alignment of the mirrors composing a space telescope is an important process for obtaining high optical resolution and performance of the camera system. The alignment of mirrors using cube mirrors requires a relative coordinate mapping between the mirror and the cube mirror before optical-system integration. Therefore, to align the spacecraft camera mirrors, the relative coordinates of the vertex of each mirror and the corresponding cube mirror must be accurately measured. This paper proposes a new method for finding the vertex position of a primary mirror, by using an optical fiber and alignment segments of a computer-generated hologram (CGH). The measurement system is composed of an optical testing interferometer and a multimode optical fiber. We used two theodolites to measure the relative coordinates of the optical fiber located at the mirror vertex with respect to the cube mirror, and achieved a measurement precision of better than $25{\mu}m$.

• Applied Microscopy
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• v.37 no.3
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• pp.199-208
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• 2007

We have made an optical device to study the wave optics phenomena, such as image and diffraction pattern, constructive and destructive interference, by direct operation of laser beam and optical lenses. It consists of laser beam, goniometer, objective lens, intermediate lens, projection lens, CCD system, and computing system. As a result of the performance test, we were able to magnify samples up to 44 times with the resolution of about $5{\mu}m$. It is expected to help EM users understanding more easily principles of transmission electron microscopy (TEM).

• Korean Journal of Optics and Photonics
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• v.17 no.6
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• pp.507-513
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• 2006

In this paper, we propose an improved image encryption and fault-tolerance decryption method using phase wrapping and phase encoding in the frequency domain. To generate an encrypted image, an encrypting key which denotes the product of a phase-encoded virtual image, not an original image, and a random phase image is zero-padded and Fourier transformed and its real-valued data is phase-encoded. The decryption process is simply performed by performing the inverse Fourier transform for multiplication of the encrypted key with the decrypting key, made of the proposed phase wrapping method, in the output plane with a spatial filter. This process has the advantages of solving optical alignment and pixel-to-pixel mapping problems. The proposed method using the virtual image, which does not contain any information from the original image, prevents the possibility of counterfeiting from unauthorized people and also can be used as a current spatial light modulator technology by phase encoding of the real-valued data. Computer simulations show the validity of the encryption scheme and the robustness to noise of the encrypted key or the decryption key in the proposed technique.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.