Detection and localization enhancement for satellite images with small forgeries using modified GAN-based CNN structure

The image forgery process can be simply defined as inserting some objects of different sizes to vanish some structures or scenes. Satellite images can be forged in many ways, such as copy-paste, copy-move, and splicing processes. Recent approaches present a generative adversarial network (GAN) as an effective method for identifying the presence of spliced forgeries and identifying their locations with a higher detection accuracy of large- and medium-sized forgeries. However, such recent approaches clearly show limited detection accuracy of small-sized forgeries. Accordingly, the localization step of such small-sized forgeries is negatively impacted. In this paper, two different approaches for detecting and localizing small-sized forgeries in satellite images are proposed. The first approach is inspired by a recently presented GAN-based approach and is modified to an enhanced version. The experimental results manifest that the detection accuracy of the first proposed approach noticeably increased to 86% compared to its inspiring one with 79% for the small-sized forgeries. Whereas, the second proposed approach uses a different design of a CNN-based discriminator to significantly enhance the detection accuracy to 94%, using the same dataset obtained from NASA and the US Geological Survey (USGS) for validation and testing. Furthermore, the results show a comparable detection accuracy in large- and medium-sized forgeries using the two proposed approaches compared to the competing ones. This study can be applied in the forensic field, with clear discrimination between the forged and pristine images

구름요소 베어링 진단을 위한 딥러닝 기반 스폴 크기 분포 추정 연구

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2023. 2. 윤병동.When a rolling element bearing (REB) fails, the most common reason is the spall caused by rolling contact fatigue. In previous studies, when a ball passes through a spall, a step response with a low-frequency appears due to the effect of entering to the spall and an impulse response with a high-frequency appears when exiting the spall in the acceleration signal. Since the entry event signal is relatively weaker than the exit event signal and noise, research to date have attempted to estimate the location of the entry event using various signal processing technic such as noise reduction and strengthening the entry event features. However, in signal processing, manual parameter selection for finding the characteristics of entry event varies on bearing geometry and operating condition and since the parameter selection is empirical, the accuracy may differ accordingly. In addition, the spall size reflected in the signal also has uncertainty due to the geometry of the real spall and the uncertainty of rotation due to random slip. To overcome this difficulty, a deep learning-based approach was proposed in this study. The proposed architecture learned through analytic simulation signals which was generated by similar geometry and operating conditions to test data, the spall size was estimated without manual parameter selection. By obtaining the mean and variance from the estimated values obtained from the models trained with several kernels and strides, the spall size distribution was obtained. The proposed method was validated through experimental data. Through the performance analysis results, the proposed method was effective.구름 접촉 피로로 인한 스폴은 구름 요소 베어링 파손의 가장 일반적인 원인이며 스폴 크기 추정은 심각도를 추정하는 좋은 방법이 될 수 있다. 기존 연구에서는 구름 요소가 스폴 영역을 지나가는 과정에서, 진입할 때 저주파 단계 응답이 나타나고, 이탈할 때 고주파의 충격 응답이 나타난다고 알려져 있다. 진입이벤트 신호는 이탈이벤트 신호 및 노이즈에 비해 상대적으로 약하기 때문에 지금까지의 연구에서는 노이즈 감소, 진입이벤트 특성인자 강화 등의 다양한 신호처리 기술을 이용하여 진입이벤트의 시간적 위치를 추정하고자 하였다. 그러나 신호처리에서 진입이벤트의 특성을 찾기 위한 매개변수 선택은 베어링 형상이나 작동 조건 등에 따라 다르며, 선택이 경험적이므로 정확도가 경우에 따라 다를 수 있다. 또한 신호에 반영된 스폴의 크기도 실제 스폴의 일정하지 않은 모양에 의한 불확실성과 베어링 구름요소의 임의 미끄러짐으로 인한 회전의 불확실성을 가지고 있다. 이러한 어려움을 극복하기 위해 본 연구에서는 딥러닝 기반 접근 방식을 제안한다. 테스트 데이터와 유사한 형상 및 작동 조건인 해석적 시뮬레이션 신호를 통해 학습된 제안모델은 매개변수의 수동적 선택 없이 스폴의 크기를 추정한다. 여러 커널과 스트라이드가 선택되어 만들어진 여러 훈련모델에서 얻은 추정값을 통해 평균과 분산을 구하여 파편 크기 분포를 추정한다. 제안된 모델은 고장을 인가한 베어링을 통해 얻어진 실험 데이터로 검증한다. 성능 분석 결과는 제안된 접근 방식이 효과적임을 나타낸다.Chapter 1. Introduction 1 1.1 Introduction 1 1.2 Dissertation Layout 4 Chapter 2. Research Background 5 2.1 Spall Size Estimation Through the Time Interval 5 Chapter 3. Spall size distribution estimation for REB 7 3.1 Transformation of Input Signal 9 3.2 Signal Generation for Training 11 3.3 Denoising Autoencoder (DAE) 12 3.4 Spall Size Estimation Through the Time Interval 14 3.5 Spall Size Ensemble 17 Chapter 4. Experimental Validation 18 4.1 Experimental Setting 18 4.2 Training Signal Generation 20 4.3 Result 25 Chapter 5. Conclusions 32 Bibliography 33 국문 초록 37석

Comparing LSTM and CNN methods in case study on public discussion about Covid-19 in Twitter

This study compares two Deep Learning model methods, which include the Long Short-Term Memory (LSTM) method and the Convolution Neural Network (CNN) method. The aim of the comparison is to discover the performance of two different fundamental deep learning approaches which are based on convolutional theory (CNN) and deal with the vanishing gradient problem (LSTM). The purpose of this study is to compare the accuracy of the two methods using a dataset of 4169 obtained by crawling social media using the Twitter API. The Tweets data we've obtained are based on a specific hashtag keyword, namely "covid-19 pandemic”. This study attempts to assess the sentiment of all tweets about the Covid-19 viral epidemic to determine whether tweets about Covid-19 contain positive or negative thoughts. Before classification, the Preprocessing and Word Embedding steps are completed, and this study has determined that the epoch used is 20 and the hidden layer is 64. Following the classification process, this study concludes that the two methods are appropriate for classifying public conversation sentences against Covid-19. According to this study, the LSTM method is superior, with an accuracy of 83.3%, a precision of 85.6%, a recall of 90.6%, and an f1-score of 88.5%. While the CNN method achieved an accuracy of 81%, precision of 71.7%, recall of 72%, and f1-score of 72

Comparing LSTM and CNN methods in case study on public discussion about Covid-19 in Twitter

This study compares two Deep Learning model methods, which include the Long Short-Term Memory (LSTM) method and the Convolution Neural Network (CNN) method. The aim of the comparison is to discover the performance of two different fundamental deep learning approaches which are based on convolutional theory (CNN) and deal with the vanishing gradient problem (LSTM). The purpose of this study is to compare the accuracy of the two methods using a dataset of 4169 obtained by crawling social media using the Twitter API. The Tweets data we've obtained are based on a specific hashtag keyword, namely "covid-19 pandemic”. This study attempts to assess the sentiment of all tweets about the Covid-19 viral epidemic to determine whether tweets about Covid-19 contain positive or negative thoughts. Before classification, the Preprocessing and Word Embedding steps are completed, and this study has determined that the epoch used is 20 and the hidden layer is 64. Following the classification process, this study concludes that the two methods are appropriate for classifying public conversation sentences against Covid-19. According to this study, the LSTM method is superior, with an accuracy of 83.3%, a precision of 85.6%, a recall of 90.6%, and an f1-score of 88.5%. While the CNN method achieved an accuracy of 81%, precision of 71.7%, recall of 72%, and f1-score of 72

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks