• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.50.1-50.1
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• 2018

We present the development of tools for Echelle spectrograph of NYSC 1-m telescope. The eShel spectrograph(Shelyak) has operated at Deokheung Optical Astronomy Observatory since 2016. We carried out test observation in 2016 and completed the preprocessing and wavelength calibration of the spectroscopic data using IRAF. Based on the reduction process in IRAF, PySpecW, a set of tools for spectroscopic data was developed in 2017. PySpecW was optimized for NYSC 1m telescope, and written in Python for youth to use easily on any OS. PySpecW consists of preprocessing, aperture tracing, aperture extraction, wavelength calibration, and dispersion correction for extracted spectra.

• Journal of IKEEE
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• v.22 no.3
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• pp.585-589
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• 2018

Consumer demand for high quality image/video services led to growing trend in image quality enhancement study. Therefore, recent years was a period of substantial progress in this research field. Through careful observation of the image quality after processing by image enhancement algorithms, we perceived that the dark region in the image usually suffered loss of contrast to a certain extent. In this paper, the low-light stretch preprocessing algorithm is, hence, proposed to resolve the aforementioned issue. The proposed approach is evaluated qualitatively and quantitatively against the well-known histogram equalization and Photoshop curve adjustment. The evaluation results validate the efficiency and superiority of the low-light stretch over the benchmarking methods. In addition, we also propose the 255MHz-capable hardware implementation to ease the process of incorporating low-light stretch into real-time imaging systems, such as aerial surveillance and monitoring with drones and driving aiding systems.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.545-555
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• 2020

This paper focuses on automatic image registration between EO (Electro-Optical) and IR (InfraRed) satellite images with different spectral properties using block-based approach and simple preprocessing technique to enhance the performance of feature matching. If unpreprocessed EO and IR images from Kompsat-3A satellite were applied to local feature matching algorithms(Scale Invariant Feature Transform, Speed-Up Robust Feature, etc.), image registration algorithm generally failed because of few detected feature points or mismatched pairs despite of many detected feature points. In this paper, we proposed a new image registration method which improved the performance of feature matching with block-based registration process on 9-divided image and pre-processing technique based on adaptive histogram equalization. The proposed method showed better performance than without our proposed technique on visual inspection and I-RMSE. This study can be used for automatic image registration between various images acquired from different sensors.

• Journal of the Semiconductor & Display Technology
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• v.16 no.2
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• pp.106-109
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• 2017

Recently, ancient documents are actively studied and discussed. However, ancient documents has a few problems on interpretation. The antique documents are too fragile to hand over. So, some studies have been carried out using terahertz to read ancient documents without damaging them. Three techniques are necessary to read letters using terahertz. First, PPEX algorithm, which distinguishes pages. Second, TGSI technique, which distinguishes text from paper on a page. Third, CCSC algorithm, which transforms signals to letters. In this paper, we will describe the preprocessing process to facilitate the recognition of letters before applying the post processing as we mentioned above. Histogram equalization, Histogram stretching and the Sobel filter were applied to the preprocessing.

• Journal of IKEEE
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• v.26 no.4
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• pp.639-646
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• 2022

In this paper, to solve the large dataset problem, we collect images through an image collection method called web crawling and build datasets for use in image classification models through a data preprocessing process. We also propose a lightweight model that can automatically classify images by adding category values by incorporating transfer learning into the image classification model and an image classification model that reduces training time and achieves high accuracy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.355-357
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• 2020

최근 에너지 효율의 중요성이 높아지고 에너지 공급 형태가 다변화하면서 다양한 에너지원을 효율적으로 관리할 수 있는 마이크로그리드 개념이 중요해지고 있다. 본 연구의 산업단지 마이크로그리드 열거래 플랫폼은 실증사이트의 전기 및 열에너지 모니터링 기능과 열에너지 거래 정산 기능을 가지며, 이를 위해 정확하고 안정적인 실증사이트 데이터가 필요하다. 하지만 실증사이트 데이터는 에너지 단위의 불일치, 센서 및 현장 운영상태에 따른 불안정성 등의 문제가 있어 수집 직후 열거래 플랫폼에서 활용할 수 없다. 따라서 수집된 데이터를 활용하기 위해 엔진 최대 출력량, 최대 전력 사용량 등의 변수별 특성을 고려하여 데이터 전처리 프로세스를 설계 및 적용하였다.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.87-96
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• 2019

In this paper, we propose a MapReduce-supported clustering technique for collecting and classifying distributed workflow enactment event logs as a preprocessing tool. Especially, we would call the distributed workflow enactment event logs as Workflow BIG-Logs, because they are satisfied with as well as well-fitted to the 5V properties of BIG-Data like Volume, Velocity, Variety, Veracity and Value. The clustering technique we develop in this paper is intentionally devised for the preprocessing phase of a specific workflow process mining and analysis algorithm based upon the workflow BIG-Logs. In other words, It uses the Map-Reduce framework as a Workflow BIG-Logs processing platform, it supports the IEEE XES standard data format, and it is eventually dedicated for the preprocessing phase of the ${\rho}$-Algorithm that is a typical workflow process mining algorithm based on the structured information control nets. More precisely, The Workflow BIG-Logs can be classified into two types: of activity-based clustering patterns and performer-based clustering patterns, and we try to implement an activity-based clustering pattern algorithm based upon the Map-Reduce framework. Finally, we try to verify the proposed clustering technique by carrying out an experimental study on the workflow enactment event log dataset released by the BPI Challenges.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.951-959
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• 2017

In this paper, we design and implement an Android-based Hangul food menu recognition application that recognizes characters from images captured by a smart phone. Optical Character Recognition (OCR) technology is divided into preprocessing, recognition and post-processing. In the preprocessing process, the characters are extracted using Maximally Stable Extremal Regions (MSER). In recognition process, Tesseract-OCR, a free OCR engine, is used to recognize characters. In the post-processing process, the wrong result is corrected by using the dictionary DB for the food menu. In order to evaluate the performance of the proposed method, experiments were conducted to compare the recognition performance using the actual menu plate as the DB. The recognition rate measurement experiment with OCR Instantly Free, Text Scanner and Text Fairy, which is a character recognizing application in Google Play Store, was conducted. The experimental results show that the proposed method shows an average recognition rate of 14.1% higher than other techniques.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.