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1. Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks

   Pinceti, A.; Sankar, L.; Kosut, O (January 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   null (Ed.)

   A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate unique synthetic profiles on demand, based on the season and type of load required. Extensive testing of the generative model is performed to verify that the synthetic data fully captures the characteristics of real loads and that it can be used for downstream power system and/or machine learning applications. 

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2. Robust Multi-Variate Temporal Features of Multi-Variate Time Series

   https://doi.org/10.1145/3152123  

   Liu, Sicong; Poccia, Silvestro Roberto; Candan, K. Selçuk; Sapino, Maria Luisa; Wang, Xiaolan (February 2018, ACM Transactions on Multimedia Computing, Communications, and Applications)

   Many applications generate and/or consume multi-variate temporal data, and experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this article, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time seriesalong with external knowledge, including variate relationships that are known a priori. Relying on these observations, we develop data models and algorithms to detectrobust multi-variate temporal(RMT) features that can be indexed for efficient and accurate retrieval and can be used for supporting data exploration and analysis tasks. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifyingrobustmulti-scale temporal features of multi-variate time series. 

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3. AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

   https://doi.org/10.3390/s19245479  

   Rahnemoonfar, Maryam; Johnson, Jimmy; Paden, John (December 2019, Sensors)

   Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network. 

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4. ARE GENERATIVE ADVERSARIAL NETWORKS CAPABLE OF GENERATING NOVEL AND DIVERSE DESIGN CONCEPTS? AN EXPERIMENTAL ANALYSIS OF PERFORMANCE

   https://doi.org/10.1017/pds.2023.64  

   Ghasemi, Parisa; Yuan, Chenxi; Marion, Tucker; Moghaddam, Mohsen (July 2023, Proceedings of the Design Society)

   Generative Adversarial Networks (GANs) have shown stupendous power in generating realistic images to an extend that human eyes are not capable of recognizing them as synthesized. State-of-the-art GAN models are capable of generating realistic and high-quality images, which promise unprecedented opportunities for generating design concepts. Yet, the preliminary experiments reported in this paper shed light on a fundamental limitation of GANs for generative design: lack of novelty and diversity in generated samples. This article conducts a generative design study on a large-scale sneaker dataset based on StyleGAN, a state-of-the-art GAN architecture, to advance the understanding of the performance of these generative models in generating novel and diverse samples (i.e., sneaker images). The findings reveal that although StyleGAN can generate samples with quality and realism, the generated and style-mixed samples highly resemble the training dataset (i.e., existing sneakers). This article aims to provide future research directions and insights for the engineering design community to further realize the untapped potentials of GANs for generative design. 

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5. HGAN: Hybrid generative adversarial network

   https://doi.org/10.3233/JIFS-201202  

   Iranmanesh, Seyed Mehdi; Nasrabadi, Nasser M. (April 2021, Journal of Intelligent & Fuzzy Systems)

   In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines. 

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