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Near-infrared spectroscopy combined with deep convolutional generative adversarial network for prediction of component content in melt-cast explosive

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CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2024年第4期52卷

作者： Liu, Qiang Zhao, Jiajing Dan, Baosong Su, Pengfei Zhang, Goa Xian Modern Chem Res Inst Xian 710065 Peoples R China

Rapid and nondestructive prediction of component content is the key to improve industrial production efficiency. However, limited data sets also result in low generalization capabilities of the model, and it is time-consuming to obtain a large amount of content reference values and costly. Here, near infrared (NIR) spectroscopy technique combined with deep convolutional generated countermeasure network (DCGAN) was used to predict the trinitrotoluene (TNT) content of the melt -cast explosive. DCGAN was used to simultaneously extend its spectral data and content data. After several iterations, fake data were produced, which was very similar to the experimental data. The partial least squares (PLS) regression model was established and the performance was compared before and after data enhancement. The results showed that this method not only improved the performance of regression model, but also solved the problem of requiring large number of training data.

关键词： Near-infrared spectroscopy Melt-cast explosive deep convolutional generative adversarial network Data augmentation Fake data

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Image synthesis of apparel stitching defects using deep convolutional generative adversarial networks

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HELIYON 2024年第4期10卷 e26466页

作者： ul-Huda, Noor Ahmad, Haseeb Banjar, Ameen Alzahrani, Ahmed Omar Ahmad, Ibrar Naeem, M. Salman Natl Text Univ Dept Comp Sci Faisalabad Pakistan Univ Jeddah Coll Comp Sci & Engn Jeddah 21959 Saudi Arabia Univ Peshawar Dept Comp Sci Peshawar Pakistan Natl Text Univ Dept Text Engn Faisalabad Pakistan

In industrial manufacturing, the detection of stitching defects in fabric has become a pivotal stage in ensuring product quality. deep learning-based fabric defect detection models have demonstrated remarkable accuracy, but they often require a vast amount of training data. Unfortunately, practical production lines typically lack a sufficient quantity of apparel stitching defect images due to limited research-industry collaboration and privacy concerns. To address this challenge, this study introduces an innovative approach based on DCGAN (deep convolutional generative adversarial Network), enabling the automatic generation of stitching defects in fabric. The evaluation encompasses both quantitative and qualitative assessments, supported by extensive comparative experiments. For validation of results, ten industrial experts marked 80% accuracy of the generated images. Moreover, Fre ' chet Inception Distance also inferred promising results. The outcomes, marked by high accuracy rate, underscore the effectiveness of proposed defect generation model. It demonstrates the ability to produce realistic stitching defective data, bridging the gap caused by data scarcity in practical industrial settings.

关键词： deep learning deep convolutional generative adversarial network Defect generation Image synthesis

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Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel

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FOOD CHEMISTRY 2022年 370卷 131047-131047页

作者： Zhang, Liu Wang, Yaqian Wei, Yaoguang An, Dong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China China Agr Univ Key Lab Agr Informat Acquisit Technol Minist Agr Beijing 100083 Peoples R China

Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for food industry. However, obtaining a large number of oil content reference values of maize kernels is time-consuming and expensive, and the limited data set also leads to low generalization ability of the model. Here, hyperspectral imaging technology and deep convolutional generative adversarial network (DCGAN) were combined to predict the oil content of single maize kernel. DCGAN was used to simultaneously expand their spectral data and oil content data. After many iterations, fake data that was very similar to the experimental data was generated. Partial least squares regression (PLSR) and support vector regression (SVR) models were established respectively, and their performance was compared before and after data augmentation. The results showed that this method not only improved the performance of two regression models, but also solved the problem of requiring a large amount of training data.

关键词： Maize kernel Oil content Hyperspectral imaging technology deep convolutional generative adversarial network Data augmentation Fake data

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Virtual generation of pavement crack images based on improved deep convolutional generative adversarial network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104376-104376页

作者： Pei, Lili Sun, Zhaoyun Xiao, Liyang Li, Wei Sun, Jing Zhang, He Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China Shihezi Univ Comp Network Ctr Xinjiang Prod & Construct Corps Shihezi 832003 Xinjiang Peoples R China Xian Thermal Power Res Inst Xian 710032 Shannxi Peoples R China Xian Xiangteng Microelect Technol Co Ltd Xian 710068 Shannxi Peoples R China

To solve the problems associated with a small sample size during intelligent road detection, a virtual image set generation method for asphalt pavement cracks is proposed based on improved deep convolutional generative adversarial networks (DCGANs). First, a small set of sample crack images is collected and used as the basic image set to perform filtering, gamma transformation, and other processes, whereby crack feature recognition is enhanced. Second, a variational autoencoder (VAE) is used to encode real crack images. The latent variable values obtained from the VAE are provided as input to the DCGAN model generator, and the model hyperparameters are optimized. Subsequently, the adaptive moment estimation (Adam) optimizer is used to reoptimize the model and thereby improve the model convergence speed and generalization ability. The proposed method has the advantages of both VAE and DCGAN. Finally, a pavement crack classification detection model based on faster region convolutional neural network (Faster R-CNN) is used to evaluate the reliability of the generated crack images. The results show that the augmented dataset of the proposed method with the detection model has an average precision of 90.32%, which is higher than that of the conventional method evaluated using the same test dataset. The proposed method generates virtual crack images that are moderately identical to real ones, thereby solving the problem of insufficient image datasets of cracks in specific road sections. The method also provides data assurance for the intelligentization of pavement crack detection and the reduction of pavement maintenance costs.

关键词： Image virtual generation deep convolutional generative adversarial networks Pavement cracks Variational auto-encoder

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Damage identification considering randomness of structural parameters based on DCGAN and transfer learning

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STRUCTURES 2025年 75卷

作者： Li, Di Yang, Mengxue Li, Youzhi Zhu, Siyu Chengdu Univ Technol Sch Environm & Civil Engn Chengdu 610059 Sichuan Peoples R China Xihua Univ Sch Energy & Power Engn Chengdu 610039 Sichuan Peoples R China

In this study, a structural damage recognition method incorporating deep convolutional generative adversarial network (DCGAN) and transfer learning is proposed, aiming at solving the challenges of traditional models' sensitivity to structural parameter randomness and insufficient generalization ability. It is found that in the laboratory model without parameter perturbation, the basic DCGAN can achieve high-precision damage recognition (accuracy >98 %). However, when the randomness of key parameters, such as elastic modulus, mass density, is considered, its false alarm rate climbs significantly, which leads to the failure of undamaged structures by misjudgment. To cope with this shortcoming, a transfer learning mechanism is innovatively introduced to make the discriminator robust under the complex conditions of multiple structural parameter randomness by adaptively learning the data distribution characteristics under multi-parameter randomness scenarios. The proposed method is experimentally verified to break through the limitation of "one model for one bridge" while retaining the advantages of DCGAN's high-precision discrimination. The research results provide a new theoretical framework and technical path to improve the engineering practicability of structural health monitoring system under the parameter variability environment.

关键词： Structural damage identification deep convolutional generative adversarial network (DCGAN) Transfer learning (TL) Model tests The randomness of structural parameters

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A hybrid approach for the detection of images generated with multi generator MS-DCGAN

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2025年 63卷

作者： Surucu, Selim Diri, Banu Cankiri Karatekin Univ Fac Engn Dept Comp Engn TR-18100 Cankiri Turkiye Yildiz Tech Univ Comp Engn Dept TR-34220 Istanbul Turkiye

Over the past few years, there have been significant advances in remote sensing technology that have considerably expanded the range of research that can be conducted using remote sensing systems. Various fields, from agriculture to defense applications, use remote sensing imagery, primarily acquired by sensors mounted on vehicles like satellites and UAVs. In addition to advances in remote sensing technology, there have also been major advancements in deep learning. In recent years, there has been a substantial increase in the studies on these two topics. generative adversarial Networks (GAN) technology, another area of artificial intelligence and deep learning research, has taken the generation of fake satellite images to anew level. Users can use these artificial images fora variety of purposes, including information concealment and data expansion. Malicious uses of the generated fake images could trigger international crises. In this paper, we propose a new method for the generation and detection of fake satellite images. The MultiSpectral deep convolutional GAN (MS-DCGAN) model is developed to generate fake multispectral images, and the TransStacking model is proposed to distinguish between fake images and real images. This model is tested both as a single generator and multi generator model. The TransStacking (DenseNet201+stacking) model showed a very high success rate achieving 100% accuracy for single generator and 98% accuracy for multi generator MS-DCGAN, respectively. The proposed model is an advanced hybrid model that provides the best results in multi-spectral images and can be applied in diverse domains. Since the TransStacking model is a modular hybrid model, it can be used with many different old and new models. Furthermore, the effect of the models in the base part of the stacking module on the results was also analyzed by performing ablation analysis on the DenseNet201+stacking model, where the best results were obtained.

关键词： generative adversarial networks Remote sensing Multi-spectral image Manipulation detection deep convolutional generative adversarial network Multi-spectral image generation

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Brain tumour segmentation and classification with reconstructed MRI using DCGAN

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 92卷

作者： Sandhiya, B. Raja, S. Kanaga Suba Shruthi, K. Kamala, S. Praveena Rachel Easwari Engn Coll Dept Informat Technol Chennai India SRM Inst Sci & Technol CSE Dept Tiruchirapalli India Easwari Engn Coll Dept Biomed & Engn Chennai India

The median lifespan of brain malignance diagnosed patients is poor, which indicates that just two out of ten persons identified with brain malignance will live lasting a minimum of a decade, resulting in numerous of individuals perish from brain malignance every year. Therefore, early diagnosis is crucial to save lives. deep learning has drastically altered and enhanced methods for effective and highly accurate diagnosis, prediction, and recognition in recent years. deep Neural Networks (DNN) are utilized to identify the malignance portion from MRI references. In order to provide a huge data set more affordably, our suggested method makes use of deep convolutional generative adversarial Network (DCGAN), a pre-processing approach that generates counterfeit pictures that deceive the discriminator into pretending they are real followed by other pre-processing techniques. Then, the separation was done by extracting different Magnetic Resonance Imaging (MRI) angles of the brain were taken into account, and several networks with variable nodes and weight ages were used with the help of an improved K-Means Clustering (KMC) Algorithm. Using statistical and texture-based characteristics that are retrieved using the Gray-Level Co-occurrence Matrix (GLCM), the objective of this work's performance evaluation is to differentiate typical and unusual pixels. The model, which separates the pre-processed data into three forms-meningioma, glioma, pituitary, and a plain is trained using an enhanced faster Region-based convolutional Neural Network (R-CNN). The faster R-CNN method obtains a classification precision of 96%, and sensitivity of 89.23% for Glioma, and a sensitivity of 96.28% for pituitary.

关键词： Brain Tumor Segmentation and Classification deep convolutional generative adversarial Network Improved K-Means Clustering Algorithm Improved Region-based convolutional Neural

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State of charge estimation for lithium-ion batteries based on data augmentation with generative adversarial network

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JOURNAL OF ENERGY STORAGE 2024年 80卷

作者： Hu, Chunsheng Cheng, Fangjuan Zhao, Yong Guo, Shanshan Ma, Liang Ningxia Univ Sch Mech Engn Yinchuan 750000 Peoples R China Wuhan Univ Sch Power & Mech Engn Wuhan 430000 Peoples R China Ningxia Univ Sch Adv Interdisciplinary Studies Yinchuan 750000 Peoples R China Weifang Univ Sch Electromech & Vehicle Engn Weifang 261000 Peoples R China Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong 2522 Australia

Ever-increasing efforts in state of charge (SOC) of lithium-ion battery estimation techniques have been centered on machine learning-based methods, and a prerequisite guaranteeing the effectiveness of these methods demands an abundance of high-quality datasets. However, performing battery-related experiments is time-consuming and expensive, and manufacturers are reluctant to release their datasets due to confidentiality constraints. This limited data availability has hindered further research in this area. This paper proposes a time series augmentation model (TS-DCGAN) based on a deep convolutional generative adversarial framework. By converting the data generation problem into image generation and utilizing the advancements in GAN-based image generation, the proposed method can produce high-quality battery data. The distributional similarity and diversity of generated datasets are evaluated quantitatively and qualitatively with several metrics. The SOC estimator trained on the synthetic datasets achieved comparable execution to the actual data. Additionally, when integrated with a few real datasets, the performance of baseline SOC estimators improved to varying degrees. Extensive experiments demonstrate that the proposed method can generate synthetic data that accurately reflects the original feature distributions and temporal dynamics. This has the potential to greatly assist researchers in developing reliable battery SOC estimators.

关键词： Lithium-ion batteries Battery management State of charge Data augmentation Time -series generation deep convolutional generative adversarial networks

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An improved DCGAN model: Data augmentation of hyperspectral image for identification pesticide residues of Hami melon

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FOOD CONTROL 2024年 157卷

作者： Tan, Haibo Hu, Yating Ma, Benxue Yu, Guowei Li, Yujie Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Peoples R China Minist Agr & Rural Affairs Key Lab Northwest Agr Equipment Shihezi 832003 Peoples R China

The increasing concern over pesticide residues on Hami melon is due to the unregulated use of pesticides, which poses a potential food safety hazard. Thus, it is urgent to propose a method for the rapid and nondestructive detection of pesticide residues on the Hami melon. This study used short-wave infrared hyperspectral imaging (SWIR-HSI) to identify pesticide residues on the Hami melon. The data augmentation method based on improved deep convolutional generative adversarial networks (DCGAN) was proposed to expand Hami melon's spectral data with different pesticide residues. To determine the optimal training epoch, the 1-nearest neighbor (1-NN) classifier was used to evaluate the quality of the generated spectra. The effectiveness of the improved DCGAN was verified by three commonly used classifiers, including the decision tree (DT), random forest (RF), and support vector machine (SVM). The results showed that the performance of all three classifiers was improved to varying degrees by the improved DCGAN. The DT, RF, and SVM accuracy was improved by 13.13%, 7.50%, and 11.25%, respectively. Moreover, the SVM model achieved the highest accuracy of 93.13%. These findings indicated that the combination of SWIR-HSI and the improved DCGAN-based data augmentation method has good promise for detecting pesticide residues on Hami melon.

关键词： Hyperspectral imaging deep convolutional generative adversarial networks Data augmentation Hami melon Pesticide residues

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An Efficient PM - Multisampling Image Filtering with Enhanced CNN Architecture for Pneumonia Classfication

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartC期86卷

作者： Nithya, T. M. Kanna, P. Rajesh Vanithamani, S. Santhi, P. K Ramakrishnan Coll Technol Dept Comp Sci & Engn Trichy 621112 Tamilnadu India Bannari Amman Inst Technol Dept Comp Sci & Engn Erode Tamil Nadu India M Kumarasamy Coll Engn Dept Master Comp Applicat Karur Tamil Nadu India Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Chennai Tamil Nadu India

Pneumonia detection using deep convolutional networks is an important application of deep learning in the field of medical image analysis. In this approach, a convolutional Neural Network (CNN) is used to classify chest X-ray images as either normal or indicating the presence of pneumonia. The CNN is trained on a dataset of labeled images, learning to recognize patterns associated with pneumonia, such as infiltrates or consolidation in the lungs. The following three problems are addressed in this research work. Initially, the images with noise are not suitable for prediction and most of the filtering techniques generate the loss in the data and over smoothening of images. It can be resolved by the proposed Perona-Malik (PM)- Multisampling technique. It preserves the edges and smoothens the different region of image parallel manner. Second, the class imbalance problem is addressed by using synthesized image generation using generative adversarial Networks (GANs). So, the equal number of train and test images considered for better accuracy. Third, the accurate prediction is enhanced by Multi piled deep convolutional generative adversarial Network (DCGAN). It produces the accuracy of the model as 96% and loss as 10% without overfitting and under fitting. The execution time of the proposed model is 7.2 s from the initial stage to final stage.

关键词： convolutional Neural Networks (CNNs) deep convolutional generative adversarial Network (DCGAN) generative adversarial Networks (GANs) Perona-Malik (PM)

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