• Journal of Welding and Joining
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• v.17 no.2
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• pp.53-60
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• 1999

Conventionally, CCD camera and vision sensor using the projected pattern of light is generally used to inspect the weld bead defects. But with this method, a lot of time is needed for image preprocessing, stripe extraction and thinning, etc. In this study, laser vision sensor using the scanning beam of light is used to shorten the time required for image preprocessing. The software for deciding whether the weld bead is in proper shape or not in real time is developed. The criteria are based upon the classification of imperfections in metallic fusion welds(ISO 6520) and limits for imperfections(ISO 5817).

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.904-911
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• 1994

Code Excited Linear Prediction (CELP) speech coders exhibit good performance at data rates below 4.8 kbps. The major drawback to CELP type coders is their many computation. In this paper, we propose a new pitch search method that preserves the quality of the CELP vocoder with reducing complexity. The basic idea is to apply the preprocessing technique beforehand grasping the autocorrelation property of speech waveform. By using the proposed method, we can get approximately 77% complexity reduction in the pitch search.

• Korean Journal of Remote Sensing
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• v.3 no.1
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• pp.25-39
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• 1987

The purpose of this research is to develop the Automatic Fingerprint Verfication System by digital computer based on specially in PC level. Fingerprint is used as means of personal identity verification in view of that it has the high reliability and safety. Fingerprint pattern recognition algorithm is constitute of 3 stages, namely of the preprocessing, the feature extraction and the recognition. The preprocessing stage includes smoothing, binarization, thinning and restoration. The feature extraction stage includes the extraction of minutiae and its features. The recognition stage includes the registration and the matching score calculation which measures the similarity between two images. Tests for this study with 325 pairs of fingerprint resulted in 100% of separation which which in turn is turned out to be the reliability of this algorithm.

• Journal of the Semiconductor & Display Technology
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• v.9 no.3
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• pp.65-69
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• 2010

The stereoscopic vision system is the algorithm to obtain the depth of target object of stereo vision image. This paper presents an efficient disparity matching method using nagao filter, octree color quantization and dynamic programming algorithm. we describe methods for performing color quantization on full color RGB images, using an octree data structure. This method has the advantage of saving a lot of data. We propose a preprocessing stereo matching method based on Nagao-filter algorithm using color information. using the nagao filter, we could obtain effective depth map and using the octree color quantization, we could reduce the time of computation.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.211-220
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• 2021

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.49-50
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• 2019

데이터를 기반으로 한 기계학습은 데이터의 양, 학습 모델, 그리고 데이터의 특징 등 다양한 환경에 민감한 특징을 지니고 있어, 보다 효율적인 기계학습을 위해 데이터의 전처리 과정을 필요로 한다. 데이터의 전처리 과정이란 특징 선택(Feature selection), 노이즈 데이터의 제거, 차원 감소(Demension reduction), 클러스터링(Clustering) 등 보다 효율적인 기계학습을 위한 방법이다. 따라서 본 논문에서는 다양한 환경에서 보다 효율적인 기계학습을 위한 데이터 전처리 기술의 종류 및 간단한 특징에 대해 서술한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.19-21
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• 2019

본 논문에서는 인공지능의 인식률을 높이기 위해 명암비를 적용한 전처리 방법에 대해 제안한다. 이 방법은 인공지능이 기존의 방법보다 특징점을 쉽게 얻어내기 위해서 명암비를 적용한 전처리를 진행하여 인식률을 높이기 위함에 목적을 두고 있다. 제안한 방법으로 인하여 문자인식이 얼마나 향상되었고, 기존의 성능과 비교하여 정확도가 얼마나 향상되었는지를 알아보고 있다.

• Smart Media Journal
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• v.10 no.3
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• pp.9-22
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• 2021

Basically, machine learning models use input data to produce results. Sometimes, the input data is too complicated for the models to learn useful patterns. Therefore, feature engineering is a crucial data preprocessing step for constructing a proper feature set to improve the performance of such models. One of the most efficient methods for automating feature engineering is the autoencoder, which transforms the data from its original space into a latent space. However certain factors, including the datasets, the machine learning models, and the number of dimensions of the latent space (denoted by k), should be carefully considered when using the autoencoder. In this study, we design a framework to compare two data preprocessing approaches: with and without autoencoder and to observe the impact of these factors on autoencoder. We then conduct experiments using autoencoders with classifiers on popular datasets. The empirical results provide a perspective regarding the best suited autoencoder for these factors.

• ETRI Journal
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• v.41 no.5
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.