• Title/Summary/Keyword: Gan Algorithm

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A Study on GAN Algorithm for Restoration of Cultural Property (pagoda)

  • Yoon, Jin-Hyun;Lee, Byong-Kwon;Kim, Byung-Wan
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.1
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    • pp.77-84
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    • 2021
  • Today, the restoration of cultural properties is done by applying the latest IT technology from relying on existing data and experts. However, there are cases where new data are released and the original restoration is incorrect. Also, sometimes it takes too long to restore. And there is a possibility that the results will be different than expected. Therefore, we aim to quickly restore cultural properties using DeepLearning. Recently, so the algorithm DcGAN made in GANs algorithm, and image creation, restoring sectors are constantly evolving. We try to find the optimal GAN algorithm for the restoration of cultural properties among various GAN algorithms. Because the GAN algorithm is used in various fields. In the field of restoring cultural properties, it will show that it can be applied in practice by obtaining meaningful results. As a result of experimenting with the DCGAN and Style GAN algorithms among the GAN algorithms, it was confirmed that the DCGAN algorithm generates a top image with a low resolution.

A rubber o-ring defect detection system using data augmentation based on the SinGAN and random forest algorithm (SinGAN기반 데이터 증강과 random forest알고리즘을 이용한 고무 오링 결함 검출 시스템)

  • Lee, Yong Eun;Lee, Han Sung;Kim, Dae Won;Kim, Kyung Chun
    • Journal of the Korean Society of Visualization
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    • v.19 no.3
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    • pp.63-68
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    • 2021
  • In this study, data was augmentation through the SinGAN algorithm using small image data, and defects in rubber O-rings were detected using the random forest algorithm. Unlike the commonly used data augmentation image rotation method to solve the data imbalance problem, the data imbalance problem was solved by using the SinGAN algorithm. A study was conducted to distinguish between normal products and defective products of rubber o-ring by using the random forest algorithm. A total of 20,000 image date were divided into transit and testing datasets, and an accuracy result was obtained to distinguish 97.43% defects as a result of the test.

Trading Algorithm Selection Using Time-Series Generative Adversarial Networks (TimeGAN을 활용한 트레이딩 알고리즘 선택)

  • Lee, Jae Yoon;Lee, Ju Hong;Choi, Bum Ghi;Song, Jae Won
    • Smart Media Journal
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    • v.11 no.1
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    • pp.38-45
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    • 2022
  • A lot of research is being going until this day in order to obtain stable profit in the stock market. Trading algorithms are widely used, accounting for over 80% of the trading volume of the US stock market. Despite a lot of research, there is no trading algorithm that always shows good performance. In other words, there is no guarantee that an algorithm that performed well in the past will perform well in the future. The reason is that there are many factors that affect the stock price and there are uncertainties about the future. Therefore, in this paper, we propose a model using TimeGAN that predicts future returns well and selects algorithms that are expected to have high returns based on past records of the returns of algorithms. We use TimeGAN becasue it is probabilistic, whereas LSTM method predicts future time series data is deterministic. The advantage of TimeGAN probabilistic prediction is that it can reflect uncertainty about the future. As an experimental result, the method proposed in this paper achieves a high return with little volatility and shows superior results compared to many comparison algorithms.

Broken Image Selection Algorithm based on Histogram Analysis (히스토그램 분석 기반 파손 영상 선별 알고리즘)

  • Cho, Jin-Hwan;Jang, Si-Woong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.72-74
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    • 2021
  • Recently, the spread of deep learning environments has increased the importance of dataset generation. Therefore, data is being augmented using GAN for efficient data set generation. However, several problems have been found in data generated using GAN, such as problems that occur in the early stages of learning and pixel breakage occurring in the generated image. In this paper, we intend to implement an image data selection algorithm to solve various problems arising from the existing GAN. The broken image screening algorithm was implemented to analyze the histogram distribution in the image and determine whether to store the generated image according to whether the result value satisfies the specified threshold value.

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A Broken Image Screening Method based on Histogram Analysis to Improve GAN Algorithm (GAN 알고리즘 개선을 위한 히스토그램 분석 기반 파손 영상 선별 방법)

  • Cho, Jin-Hwan;Jang, Jongwook;Jang, Si-Woong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.4
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    • pp.591-597
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    • 2022
  • Recently, many studies have been done on the data augmentation technique as a way to efficiently build datasets. Among them, a representative data augmentation technique is a method of utilizing Generative Adversarial Network (GAN), which generates data similar to real data by competitively learning generators and discriminators. However, when learning GAN, there are cases where a broken pixel image occurs among similar data generated according to the environment and progress, which cannot be used as a dataset and causes an increase in learning time. In this paper, an algorithm was developed to select these damaged images by analyzing the histogram of image data generated during the GAN learning process, and as a result of comparing them with the images generated in the existing GAN, the ratio of the damaged images was reduced by 33.3 times(3,330%).

A study of interior style transformation with GAN model (GAN을 활용한 인테리어 스타일 변환 모델에 관한 연구)

  • Choi, Jun-Hyeck;Lee, Jae-Seung
    • Journal of KIBIM
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    • v.12 no.1
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    • pp.55-61
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    • 2022
  • Recently, demand for designing own space is increasing as the rapid growth of home furnishing market. However, there is a limitation that it is not easy to compare the style between before construction view and after view. This study aims to translate real image into another style with GAN model learned with interior images. To implement this, first we established style criteria and collected modern, natural, and classic style images, and experimented with ResNet, UNet, Gradient penalty concept to CycleGAN algorithm. As a result of training, model recognize common indoor image elements, such as floor, wall, and furniture, and suitable color, material was converted according to interior style. On the other hand, the form of furniture, ornaments, and detailed pattern expressions are difficult to be recognized by CycleGAN model, and the accuracy lacked. Although UNet converted images more radically than ResNet, it was more stained. The GAN algorithm allowed us to represent results within 2 seconds. Through this, it is possible to quickly and easily visualize and compare the front and after the interior space style to be constructed. Furthermore, this GAN will be available to use in the design rendering include interior.

Applying SeqGAN Algorithm to Software Bug Repair (소프트웨어 버그 정정에 SeqGAN 알고리즘을 적용)

  • Yang, Geunseok;Lee, Byungjeong
    • Journal of Internet Computing and Services
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    • v.21 no.5
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    • pp.129-137
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    • 2020
  • Recently, software size and program code complexity have increased due to application to various fields of software. Accordingly, the existence of program bugs inevitably occurs, and the cost of software maintenance is increasing. In open source projects, developers spend a lot of debugging time when solving a bug report assigned. To solve this problem, in this paper, we apply SeqGAN algorithm to software bug repair. In detail, the SeqGAN model is trained based on the source code. Open similar source codes during the learning process are also used. To evaluate the suitability for the generated candidate patch, a fitness function is applied, and if all test cases are passed, software bug correction is considered successful. To evaluate the efficiency of the proposed model, it was compared with the baseline, and the proposed model showed better repair.

Evaluation of Suitability of Fire Images augmented using GAN Algorithm (GAN 알고리즘을 이용하여 증식된 화재 영상의 적합성 평가)

  • Son, SeongHyeok;Choi, Donggyu;Jang, Si-woong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.77-79
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    • 2022
  • A large amount of related images are required to detect images with variable shapes. Therefore, in this paper, fire images among images with variable shapes are multiplied through GAN algorithms, and detection rates when AI learning is performed using this image are compared to analyze whether the multiplied images are suitable for learning data.

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A Study on Optimization of Classification Performance through Fourier Transform and Image Augmentation (푸리에 변환 및 이미지 증강을 통한 분류 성능 최적화에 관한 연구)

  • Kihyun Kim;Seong-Mok Kim;Yong Soo Kim
    • Journal of Korean Society for Quality Management
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    • v.51 no.1
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    • pp.119-129
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    • 2023
  • Purpose: This study proposes a classification model for implementing condition-based maintenance (CBM) by monitoring the real-time status of a machine using acceleration sensor data collected from a vehicle. Methods: The classification model's performance was improved by applying Fourier transform to convert the acceleration sensor data from the time domain to the frequency domain. Additionally, the Generative Adversarial Network (GAN) algorithm was used to augment images and further enhance the classification model's performance. Results: Experimental results demonstrate that the GAN algorithm can effectively serve as an image augmentation technique to enhance the performance of the classification model. Consequently, the proposed approach yielded a significant improvement in the classification model's accuracy. Conclusion: While this study focused on the effectiveness of the GAN algorithm as an image augmentation method, further research is necessary to compare its performance with other image augmentation techniques. Additionally, it is essential to consider the potential for performance degradation due to class imbalance and conduct follow-up studies to address this issue.

Comparison Analysis on Automatic Coloring System Algorithm Using Machine Learning (머신러닝을 활용한 자동 채색 시스템 알고리즘 비교 분석)

  • Lee, song eun;Lee, Ji Yeon;Kim, Na Heon;Kim, Jin Hwan
    • Proceedings of the Korea Information Processing Society Conference
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    • 2017.11a
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    • pp.792-794
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    • 2017
  • 현재 머신러닝(Machine Learning) 기술은 기존의 머신러닝과 조합 및 변형 되어 조금 더 발전 된 형태로 연구되어지고 있다. 따라서 수많은 알고리즘이 개발되고 있는 시점이다. 본 연구는 최근 좋은 결과로 관심을 받고있는 GAN(Generative Adversarial Net)을 중심으로 IT기술의 머신러닝과 그림을 조합하여 자동채색을 목적으로 GAN 알고리즘을 비교하고 분석하고자 한다. GAN 알고리즘들 가운데서 'Conditional GAN'과 'Wasserstein GAN'을 사용하여 자동채색을 적용시켰고, 가장 부합한 알고리즘을 찾고 성능을 비교하여 어떠한 알고리즘이 '자동채색' 목적에 더 부합한지 비교하고 판단 한다.