Available Transfer Capability Assessment of Multiarea Power Systems with Conditional Generative Adversarial Network by Xiangfei Meng, Lina Zhang, Xin Tian, Hongqing Chu, Yao Wang, Qingxin Shi

“…This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. …”
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Continuous Satellite Image Generation from Standard Layer Maps Using Conditional Generative Adversarial Networks by Arminas Šidlauskas, Andrius Kriščiūnas, Dalia Čalnerytė

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Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks by Jinghuai Yao, Puyuan Du, Yucheng Zhao, Yubo Wang

“…This study presents a conditional generative adversarial networks model to generate nighttime VIS imagery with significantly enhanced accuracy and spatial resolution. …”
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Distributionally Robust Day-Ahead Dispatch Optimization for Active Distribution Networks Based on Improved Conditional Generative Adversarial Network by WEI Wei, WANG Yudong, JIN Xiaolong

“…[Methods] To effectively improve the adaptability of day-ahead dispatch plans to uncertainties， this study proposes a distributionally robust day-ahead dispatch optimization method for active distribution networks （ADN） based on an improved conditional generative adversarial network （CGAN）. First， an improved CGAN model designed by three-dimensional convolution （Conv3D） is proposed to address the problem of generating day-ahead scenarios for wind turbines （WT） and photovoltaic （PV） outputs considering spatio-temporal correlation， which effectively reduces the conservatism of the generated scenario set. …”
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Fault Recognition Method and Application Based on Generative Adversarial Network by Shuiliang Luo, Yongmei Huang, Yun Su, Shengkui Wang, Qianqian Liu, Yingqiang Qi, Fuhao Chang

“…To overcome this challenge, this study proposes an innovative solution, which uses generative adversarial network‐UNet (GAN‐UNet) to extract features from data in depth. …”
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Well log data generation and imputation using sequence based generative adversarial networks by Abdulrahman Al-Fakih, A. Koeshidayatullah, Tapan Mukerji, Sadam Al-Azani, SanLinn I. Kaka

“…This study introduces a novel framework utilizing sequence-based generative adversarial networks (GANs) specifically designed for well log data generation and imputation. …”
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A Generative Adversarial Network Approach to EstimateFinite Element Displacement by WANG Zhangjun, XU Ping, WANG Chunpeng, ZHAO Ziliang, CAI Junkun

“…In order to explore solutions other than the finite element method, the displacement response is considered as a picture generation process with given conditions, bypassing the physical method. …”
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GANCHEST: a multi-GAN-based framework for chest CXR image generation and validation by Amal A. Al-Shargabi, Jowharah F. Alshobaili, Abdulatif Alabdulatif, Naseem Alrobah, Dina M. Ibrahim

“…To address this problem, this study proposed GANCHEST, a framework that generates Chest CXR images based on two different generative adversarial networks (GANs): the basic GAN (GAN) and the conditional GAN (CGAN). …”
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A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation by Wuxin Wang, Boheng Duan, Weicheng Ni, Jingze Lu, Taikang Yuan, Dawei Li, Juan Zhao, Kaijun Ren

“…In this study, we propose a novel model called the 4DVar‐informed generative adversarial network (4DVarGAN), which combines prior knowledge from 4DVar with the conditional generative network (CGAN). …”
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Forecasting Lakes' Chlorophyll Concentrations Using Satellite Images and Generative Adversarial Networks by Nikolaos Nagkoulis, Giorgos Vasiloudis, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris

“…Then, we use this data set (∼1,000 Sentinel‐2 images) to train a Generative Adversarial Network (GAN) to recognize spatiotemporal patterns. …”
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Overall Layout Method of Frame Structure Plane Based on Generative Adversarial Network by ZHONG Yan, LEI Xin, LONG Danbing, FANG Changjian, KANG Yongjun

“…The discriminator determines whether the generated image is real or synthetic. Through adversarial training, the generator and discriminator iteratively improve until reaching a Nash equilibrium. …”
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Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study by Valeria Sorgente, Dante Biagiucci, Mario Cesarelli, Luca Brunese, Antonella Santone, Fabio Martinelli, Francesco Mercaldo

“…Background:Generative Adversarial Networks (GANs), thanks to their great versatility, have a plethora of applications in biomedical imaging with the goal of simulating complex pathological conditions and creating clinical data used for training advanced machine learning models. …”
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Sensor-Integrated Inverse Design of Sustainable Food Packaging Materials via Generative Adversarial Networks by Yang Liu, Lanting Guo, Xiaoyu Hu, Mengjie Zhou

“…This study introduces a novel framework for the inverse design of sustainable food packaging materials using generative adversarial networks (GANs) and the recently released OMat24 dataset containing 110 million DFT-calculated inorganic material structures. …”
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Leveraging generative adversarial networks for data augmentation to improve fault detection in wind turbines with imbalanced data by Subhajit Chatterjee, Yung-Cheol Byun

“…This paper utilizes a Wasserstein Conditional Generative Adversarial Network (WC-GAN), which replaces the KL divergence in CGAN with the Wasserstein distance to rectify data imbalances by generating synthetic fault samples for wind turbine fault classification. …”
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A holistic framework for intradialytic hypotension prediction using generative adversarial networks-based data balancing by Hsuan-Ming Lin, JrJung Lyu

“…This study evaluates an enhanced conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) framework to improve IDH prediction by generating high-utility synthetic data for balancing. …”
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Ancient Javanese Manuscript Reconstruction Using Generative Adversarial Network with StarGAN v2 Variations by Kukuh Cokro Wibowo, Fitri Damayanti, Fanky Abdilqoyyim

“…This paper presents a manuscript reconstruction using the Generative Adversarial Network model, using the variation of StarGAN v2. …”
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Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization by Marek Ružička, Marcel Vološin, Juraj Gazda, Taras Maksymyuk, Longzhe Han, MisCha Dohler

“…The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. …”
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Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks by Manal Abdullah Alohali, Hamed Alqahtani, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K., Jaehyuk Cho

“…This study presents an optimised deep learning approach for lung cancer classification, integrating flying fox optimization (FFXO) for feature selection and bidirectional generative adversarial networks (Bi-GAN) for classification. …”
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Quasi-Analytical Least-Squares Generative Adversarial Networks: Further 1-D Results and Extension to Two Data Dimensions by Graham W. Pulford

“…Generative adversarial networks (GANs) are notoriously difficult to analyse, necessitating empirical studies in high dimensional spaces that suffer from stochastic sampling noise. …”
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Learning From Imbalanced Data Using Triplet Adversarial Samples by Jaesub Yun, Jong-Seok Lee

“…We present a new synthetic data generation method that addresses this issue by combining adversarial sample generation with a triplet loss method. …”
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