• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.26 no.4
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• pp.43-50
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• 2013

Objectives : This study aims to suggest preliminary standard procedure for face lift and correction of nasolabial folds using thread-embedding (Maeseon) of Korean medicine(KM). Methods : Three KM practitioners of facial correction and rejuvenation who have over hundred case of practice participated in establishment of standard procedure. Standard procedure contains preprocessing, main procedure for correction, and solution of side effects. Results : Standard procedure is comprised of twelve processes with preprocessing and postprocessing. Preprocessing has position, disinfection, and anesthesia. Main process consists of overall structure correction, face lifting, nasolabial folds correction, and mesh making on cheek. Postprocess covers disinfection, edema prevention. Conclusions : To our knowledge, this is the first work to suggest standard procedure of facial rejuvenation using Maeseon. It would contribute to standardized practice in clinical fields and future study of revealing Maeseon's effectiveness.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.10-14
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• 2015

In holographic data storage systems, data is recorded and read by page on a volume of storage medium, and it can increase transmission rate and storage capacity because of two-dimensional page-oriented data processing by charge-coupled devices. However, HDS suffers two-dimensional intersymbol interference unlike conventional data storages. In this paper, we propose a preprocessing method of decreasing ISI before read data in HDS pass to detector. This method has some advantage when we collaborate with the preprocessing for reducing misalignment error and modulation code.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1240-1242
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• 1996

In a still mill, the effective surface defect inspection algorithm is necessary. For this purpose, this paper proposed the preprocessing algorithm for surface defect inspection of cold mill strip. This consists of live steps. They are edge detection, binarizing, noise deletion, combining of fragmented defect and selecting the largest defect. Especially, binarizing is a critical problem. Bemuse the performance of the preprocessing is largely depend on the binarized image. So, we develope the adaptive thresholding method, which is multilevel thresholding. The thresholding value is varied according to the mean graylevel value of each test image. To investigate the performance of the proposed algorithm, we classified the detected defect using neural network. The test image is 20 defect images captured at German Sick Co. This algorithm is proved to have good property in cold mill strip surface inspection.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.26-42
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• 1996

The CCD Earth Images Experiment(CEIE) is one of the main payload of the KITSAT-1. Since it was launched on Oct. 10, 1992, the CEIE has taken more than 500 images on the Earth surface world-wide so far. An image from the space is very different from a feature on the real Earth surface due to various radiometric and geometric distortions. Preprocessing to remove those distortions has to take place before the images data are processed and analyzed further for various applications. This paper describes the procedure to perform preprocessing including radiometric and geometric correction.e-processing system. The GCP marking using this technique showed a sufficient accuracy for KITSAT1,2 narrow camera images.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.433-438
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• 2019

This paper proposes a method of improving deep learning based numbers and characters recognition performance on parts of drawing through image preprocessing. The proposed character recognition system consists of image preprocessing and 7 layer deep learning model. Mathematical morphological filtering is used as preprocessing to remove the lines and shapes which causes false recognition of numbers and characters on parts drawing. Further.. Further, the used deep learning model is a 7 layer deep learning model instead of VGG-16 model. As a result of the proposed OCR method, the recognition rate of characters is 92.57% and the precision is 92.82%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.69-74
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• 2022

SNN (Spiking Neural Network) operating in neuromorphic architecture was created by mimicking human neural networks. Neuromorphic computing based on neuromorphic architecture requires relatively lower power than typical deep learning techniques based on GPUs. For this reason, research to support various artificial intelligence models using neuromorphic architecture is actively taking place. This paper conducted a performance analysis of the speech recognition model based on neuromorphic architecture according to the speech data preprocessing technique. As a result of the experiment, it showed up to 84% of speech recognition accuracy performance when preprocessing speech data using the Fourier transform. Therefore, it was confirmed that the speech recognition service based on the neuromorphic architecture can be effectively utilized.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.18-27
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• 2022

Construction accidents are difficult to prevent because several different types of activities occur simultaneously. The current method of accident analysis only indicates the number of occurrences for one or two variables and accidents have not reduced as a result of safety measures that focus solely on individual variables. Even if accident data is analyzed to establish appropriate safety measures, it is difficult to derive significant results due to a large number of data variables, elements, and qualitative records. In this study, in order to simplify the analysis and approach this complex problem logically, data preprocessing techniques, such as latent class cluster analysis (LCCA) and predictor importance were used to discover the most influential variables. Finally, the correlation was analyzed using an alluvial flow diagram consisting of seven variables and fourteen elements based on accident data. The alluvial diagram analysis using reduced variables and elements enabled the identification of accident trends into four categories. The findings of this study demonstrate that complex and diverse construction accident data can yield relevant analysis results, assisting in the prevention of accidents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.617-619
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• 2021

Since Manchu characters are written vertically and are connected without spaces within a word, a preprocessing process is required to separate the character area and the units that make up the characters before recognizing the characters. In this paper, we describe a preprocessing method that extracts the character area and cuts off the unit of the character. Unlike existing research that presupposes a method of recognizing each word or character unit, or recognizing the remaining part after removing the stem of a continuous character, this method cuts the character into each recognizable unit. It can be applied to the method of recognizing letters by combining the units. Through an experiment, the effectiveness of this method was verified.

• Journal of IKEEE
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• v.27 no.4
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• pp.561-566
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• 2023

With the development of deep learning, it is possible to solve various computer non-specialized problems such as image processing. However, most image processing methods use only the visual information of the image to process the image. Text data such as descriptions and annotations related to images may provide additional tactile and visual information that is difficult to obtain from the image itself. In this paper, we intend to improve image classification accuracy through a deep learning model that analyzes images and texts using image-text pairs. The proposed model showed an approximately 11% classification accuracy improvement over the deep learning model using only image information.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.146-154
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• 2023

In nuclear power plants, ultrasonic test for pipe wall thickness measurement is used during periodic inspections to prevent pipe rupture due to pipe wall thinning. However, when measuring pipe wall thickness using ultrasonic test, a significant amount of measurement error occurs due to the on-site conditions of the nuclear power plant. If the maximum pipe wall thinning rate is decided by the measured pipe wall thickness containing a significant error, the pipe wall thinning rate data have significant uncertainty and systematic overestimation. This study proposes preprocessing of pipe wall thinning measurement data using support vector machine regression algorithm. By using support vector machine, pipe wall thinning measurement data can be smoothened and accordingly uncertainty and systematic overestimation of the estimated pipe wall thinning rate data can be reduced.