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conditional generative adversarial network (cGAN) for generating building load profiles with photovoltaics and electric vehicles

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ENERGY AND BUILDINGS 2025年 335卷

作者： Li, Yuewei Dong, Bing Qiu, Yueming Syracuse Univ Dept Mech & Aerosp Engn 263 Link Hall Syracuse NY 13244 USA Univ Maryland Sch Publ Policy College Pk MD 20742 USA

Building load profiles are essential in research on building energy management, efficiency, demand response, and grid planning. With the growing adoption of solar photovoltaics (PV) and electric vehicles (EVs), integrated building load profiles are becoming increasingly important for effective management. However, current methods for generating building load profiles focus only on buildings without considering PV and/or EV adoptions. To address this gap, we propose using the conditional generative adversarial network (cGAN), a machine learning technique that generates realistic data conditioned on specific inputs, to create building load profiles that account for PV and EVs. This approach was tested using a smart meter dataset from a major metropolitan area in the southwest United States, containing years of hourly readings from 110 households with PV and EV adoptions. We extracted the key parameters that can describe the generated and real load profiles, and compared their mean and standard deviation to validate the results. KL divergence and FID scores were also used to compare the distributions. The results showed strong alignment between the generated and actual smart meter data across all PV, EV and seasonal conditions. The data under different combinations of PV, EV and weather conditions serve as conditional inputs for the cGAN, allowing it to generate building load profiles that maintain key statistical characteristics for both cooling and heating seasons, and various installation status of PV and EV. Additionally, this method safeguards customer privacy and reduces the effort needed for analyzing occupant behavior and building physics, which are typically required in physics-based energy models.

关键词： Building load profiles Photovoltaics (PV) Electric vehicle (EV) conditional generative adversarial network (cGAN) Smart meter data

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conditional generative adversarial network Enabled Localized Stress Recovery of Periodic Composites

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 957-974页

作者： Chengkan Xu Xiaofei Wang Yixuan Li Guannan Wang He Zhang Transportation and Municipal Engineering Institute Powerchina Huadong Engineering CorporationHangzhou310014China College of Civil Engineering and Architecture Zhejiang UniversityHangzhou310058China Anhui Transport Consulting&Design Institute Co. Ltd.Hefei230088China Center for Balance Architecture Zhejiang UniversityHangzhou310058China

Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration ***,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is *** unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of *** work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted *** locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading ***,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized *** stresses predicted by cGAN are validated against the remaining true data not used for training,showing good *** work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite *** can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.

关键词： Periodic composites localized stress recovery conditional generative adversarial network

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conditional generative adversarial network based multi-task learning framework for breast lesion segmentation of ultrasound images

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JOURNAL OF ELECTRONIC IMAGING 2024年第1期33卷 013048-013048页

作者： Datta, Satyajeet Haque, Mohammad Ariful Bangladesh Univ Engn & Technol Dept Elect & Elect Engn Dhaka Bangladesh

The conditional generative adversarial network (cGAN) is widely used in image-to-image translation for natural images by implementing supervised learning loss in the GAN framework. It has also been extended to biomedical images for image segmentation. However, during the training phase, it lacks proper guidance for smooth convergence, and the model requires a large dataset, which is often unavailable for biomedical images. To address this limitation, we integrated a multi-task learning technique using a cGAN-based adversarial framework for lesion segmentation in ultrasound (US) breast images. Our proposition defines an auxiliary task of finding the mask similarity score between the input US image and the ground-truth segmentation mask, which is trained on features extracted from the available data for the segmentation task. This learning methodology is imposed within a cGAN framework to boost the original segmentation task of the generator and guide it through the auxiliary task. The proposed method shows improved convergence and stability compared to the core adversarial network, and its performance has been assessed on different datasets and model architectures, demonstrating significant improvement over the baseline.

关键词： multi-task learning ultrasound images conditional generative adversarial network

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conditional generative adversarial network-based predictive method for crack initiation in a dual-phase austenite stainless weld

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CORROSION SCIENCE 2024年 240卷

作者： Wu, Yule Wang, Jiamei Guo, Xianglong Zhang, Lefu Shanghai Jiao Tong Univ Sch Nucl Sci & Engn Shanghai 200240 Peoples R China

A novel integration approach of a conditional generative adversarial network (cGAN) with an improved electrochemo-mechanical (E-C-M) model was developed to calculate the localised electrochemistry and evaluate the stress corrosion cracking (SCC) crack initiation properties in high-temperature water. The results demonstrate the superior accuracy, computational efficiency and robust generalisation capabilities of the hybrid method. High-temperature electrochemical tests were conducted to validate and optimize the E-C-M model for calculating the stress induced corrosion behavior of austenite stainless steel (ASS) in high-temperature water. The SCC initiation test results corroborate the predicted corrosion current distribution on ASS with a dual-phase structure.

关键词： conditional generative adversarial network Austenite stainless steel Modelling studies Electrochemical calculation Stress corrosion cracking

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conditional generative adversarial network Model for Conversion of 2 Dimensional Radiographs Into 3 Dimensional Views

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IEEE ACCESS 2023年 11卷 96283-96296页

作者： Pradhan, Nitesh Dhaka, Vijaypal Singh Rani, Geeta Pradhan, Vivek Vocaturo, Eugenio Zumpano, Ester LNM Inst Informat Technol Dept Comp Sci & Engn Jaipur 302031 Rajasthan India Manipal Univ Jaipur Dept Comp & Commun Engn Jaipur 303007 India CKS Hosp Dept Orthoped Jaipur 302013 India Univ Calabria Dept Informat Modeling Elect & Syst DIMES I-87036 Arcavacata Di Rende Italy CNR CNR NANOTEC I-87036 Arcavacata Di Rende Italy

The inefficacy of 2-Dimensional techniques in visualizing all perspectives of an organ may lead to inaccurate diagnosis of a disease or deformity. This raises a need for adopting 3-Dimensional medical images. But, the high expense, exposure to a high volume of harmful radiations, and limited availability of machinery for capturing images are limiting factors in implementing 3-Dimensional medical imaging for the whole populace. Thus, the conversion of 2-Dimensional images to 3-Dimensional images gained high popularity in the field of medical imaging. However, numerous research works offer the potential for the reconstruction of 3-Dimensional images. But, none of these provides the visualization of all angles of view from 0 degrees to 360 degrees for a 2-Dimensional input image such as X-ray and dual-energy X-ray absorptiometry. Also, these techniques fail to handle noisy and deformed input images. The purpose of this research is to propose a tailored conditional adversarial network Model for the translation of 2-Dimensional images of bones into their corresponding 3-Dimensional view. The model is preceded by pre-processing techniques for dataset cleaning, noise removal, and converting the dataset to a uniform format. Further, the efficacy of the model is improved by determining the optimal values of model parameters, employing the customized activation function, and optimizers. Additionally, the visual quality of the generated 3-Dimensional images is evaluated to showcase the degree of quality degradation while translating. The experimental results obtained on the real-life datasets collected from hospitals across India prove the efficacy of the proposed model in generating 3-Dimensional images. The generated images are similar in quality to the input image and also effective in retaining the information available in an input image.

关键词： X-ray imaging Imaging Computed tomography Bones Visualization Magnetic resonance imaging generative adversarial networks Deep learning Two dimensional displays conditional generative adversarial network deep learning X-ray 3-dimensional view 2-dimensional imaging

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conditional generative adversarial network for Intrusion Detection System Based on Deep Learning 16

Conditional Generative Adversarial Network for Intrusion Det...

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16th International Conference on Computer and Automation Engineering (ICCAE)

作者： Huang, Zhen Xiang, Yong Chinese Acad Sci Shenyang Inst Comp Technol Shenyang Peoples R China Univ Chinese Acad Sci Shenyang Peoples R China

ISBN: (纸本)9798350370058;9798350370164

With the development of big data and artificial intelligence, the tremendous amount of data have led to an increase in the number of cyberattacks. Convolutional neural network combined with intrusion detection system has been widely used to improve the performance of the traditional intrusion detection system. And KDD CPU99 is a frequently used dataset with old traffic data and unbalanced data distribution. In this paper, we proposed an intrusion detection system combined with generative adversarial network and deep residual network. The model is trained with NSL-KDD dataset which is an improved version of KDD CPU99. We also use CTGAN to synthesize data to provide more training samples for unknown cyberattacks. All the data is converted into gray scale images for ResNet-18. As a result, the proposed system perfoms well in experiment and shows great generalizaion performance.

关键词： deep residual learning ResNet-18 conditional generative adversarial network SDV Intrusion detection system NSL-KDD

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conditional generative adversarial network based data augmentation for fault diagnosis of diesel engines applied with infrared thermography and deep convolutional neural network

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2024年第1期26卷

作者： Wang, Rongcai Jia, Xisheng Liu, Zichang Dong, Enzhi Li, Siyu Cheng, Zhonghua Army Engn Univ PLA Shijiazhuang Campus Shijiazhuang Peoples R China

This paper tries to introduce a new intelligent method for the early fault diagnosis of diesel engines. Firstly, infrared thermography (IRT) is introduced into diesel engine condition monitoring, then infrared images of diesel engines in four health states, such as normal condition, singlecylinder misfire, multi -cylinder misfire and air filter blockage, are collected and the region of interest (ROI) of infrared images are extracted. Next, conditional generative adversarial network (CGAN) is deployed to perform data augmentation on infrared image datasets. Then, deep convolutional neural network (DCNN) and Softmax regression (SR) classifier are used for automatically extracting infrared image fault features and pattern recognition, respectively. Finally, a comparison with three deep learning (DL) models is performed. The validation results show that the data augmentation method proposed in the paper can significantly improve the early fault diagnosis accuracy, and DCNN has the best fault diagnosis effect and resistance to temperature fluctuation interference among the four DL models.

关键词： diesel engines data augmentation conditional generative adversarial network infrared thermography deep convolutional neural network fault diagnosis

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conditional generative adversarial network with densely-connected residual learning for single image super-resolution

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第3期80卷 4383-4397页

作者： Qiao, Jiaojiao Song, Huihui Zhang, Kaihua Zhang, Xiaolu Nanjing Univ Informat Sci & Technol Jiangsu Key Lab Big Data Anal Technol B DAT Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing Peoples R China

Recently, generative adversarial network (GAN) has been widely employed in single image super-resolution (SISR), achieving favorably good perceptual effects. However, the SR outputs generated by GAN still have some fictitious details, which are quite different from the ground-truth images, resulting in a low PSNR value. In this paper, we leverage the ground-truth high-resolution (HR) image as a useful guide to learn an effective conditional GAN (CGAN) for SISR. Among it, we design the generator network via residual learning, which introduces dense connections to the residual blocks to effectively fuse low and high-level features across different layers. Extensive evaluations show that our proposed SR method performs much better than state-of-the-art methods in terms of PSNR, SSIM, and visual perception.

关键词： Super-resolution conditional generative adversarial network Residual network Deep convolutional neural network

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Modeling and simulation of three-component ground motion intensity envelope function based on conditional generative adversarial network

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 231卷

作者： Ding, Jia-Wei Lu, Da-Gang Cao, Zheng-Gang Harbin Inst Technol Key Lab Struct Dynam Behav & Control Minist Educ Harbin 150090 Peoples R China

Artificially simulated ground motions are critical to seismic analysis of engineered structures. The ground motion intensity envelope function (IEF) defines the non-stationary characteristics and controls its duration. The three-stage intensity envelope model has been widely used in engineering applications. However, its limitations in terms of consistency of trends in horizontal and vertical changes, as well as the complexity of parameterization, hinder its further application. This study proposes a three-component ground motion intensity envelope function deep generative model (IEF-DGM) based on conditional generative adversarial network (CGAN), capable of producing high-quality IEF by considering five key condition labels: moment magnitude (MW), Joyner-Boore distance (RJB), shear wave velocity (VS30), and significant durations (DS5-75, DS5-95). Five evaluation indicators are introduced to assess the model's predictive accuracy. Results indicate that the proposed model effectively captures the stochastic and temporal non-stationarity of the IEF and accurately predicts the vertical component, a rarely studied area. Additionally, the model is used to simulate three-component artificial ground motion records, which closely matches the target response spectra, validating its accuracy and applicability. In conclusion, the proposed ground motion IEF-DGM provides a novel approach to simulation and prediction of ground motions with significant implications.

关键词： Intensity envelope function conditional generative adversarial network Evaluation indicator Non-stationarity Ground motion simulation

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Style-Controlled Image Synthesis of Concrete Damages Based on Fusion of Convolutional Encoder and Attention-Enhanced conditional generative adversarial network

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2024年第6期38卷

作者： Li, Shengyuan Le, Yushan Zhao, Xuefeng China Univ Min & Technol Sch Mech & Civil Engn Xuzhou 221116 Peoples R China China Univ Min & Technol State Key Lab Geomech & Deep Underground Engn Xuzhou 221116 Peoples R China Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Dalian 116024 Peoples R China

Developing deep learning network models for computer vision applications in concrete damage detection is a challenging task due to the shortage of training images. To address this issue, this study proposes a novel style-controlled image synthesis method for concrete damages based on the fusion of a convolutional encoder and an attention-enhanced conditional generative adversarial network. This makes it possible to generate effective images that can improve the damage detection performance of deep learning networks. To achieve this, a network architecture for concrete damage image synthesis, named DamageGAN-AE, was designed by fusing a convolutional encoder and an attention-enhanced conditional generative adversarial network. The DamageGAN-AE networks with different attention modules were trained, and the training results show that the well-trained DamageGAN-AE enhanced by coordinate attention is the best model for concrete damage image synthesis. The well-trained DamageGAN-AE was compared with the current competing methods to verify its performance. The DamageGAN-AE with image encoder was trained to implement the style-controlled image synthesis. Finally, the generated concrete damage images with diverse styles by the DamageGAN-AE model with image encoder were used to train deep learning networks. The results indicate that the generated style-controlled concrete damage images by the proposed method can effectively improve the concrete damage detection performance of deep learning networks.

关键词： Concrete damage Style-controlled image synthesis conditional generative adversarial network Performance improvement of deep learning models

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