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Emerging Trends in Engineering and Medical Sciences (ICETEMS), International Conference on

作者： Govindaraj Ramkumar M Swarna R S Vijayashanthi Hubert Mary L Pallavi Giri Ayman Amer Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of ECE Dr. MGR Educcational and Research Institute Chennai Tamil Nadu India Department of ECE S.A Engineering College Chennai Tmil Nadu India Department of ECE Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Civil Engineering Laxminarayan Innovation Technological University Nagpur India Department of Energy Engineer Zarqa University zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Wheat crop diseases pose a significant threat to agricultural productivity worldwide. Accurate and early detection of these diseases is crucial for effective management and mitigation. This paper proposes an intensive model for wheat crop disease prediction using a hybrid deep learning approach that integrates MobileN et for feature extraction and XGBoost for classification. High-resolution images of wheat crops, obtained from remote sensing and IoT -based field sensors, are used as the primary dataset. The model leverages Local Binary Patterns (LBP) for texture feature extraction, K-means clustering for image segmentation, and Gaussian filtering for noise reduction. Experimental results demonstrate that the proposed model achieves the highest accuracy of 94.67%, significantly outperforming the other models. Additionally, the model shows superior performance in precision, recall, Fl-score, and AUC-ROC, making it a robust solution for accurate disease prediction. This hybrid approach enhances disease detection efficiency and is suitable for deployment in real-world agricultural environments.

关键词： deep learning Productivity Accuracy Prevention and mitigation Crops Predictive models Feature extraction Sensors Remote sensing Diseases

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Two-subnet network for real-world image denoising

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第5期83卷 14757-14773页

作者： Zhou, Lianmin Zhou, Dongming Yang, Hao Yang, Shaoliang Yunnan Univ Sch Informat Sci & Engn Kunming 650504 Peoples R China

Previous researches in synthetic noise image denoising have performed well. However, while these methods remove real-world noise, they result in loss of image detail. To solve the problem, this article proposes a two-subnet network for real-world image denoising (TSIDNet). The proposed TSIDNet consists of two subnets, each subnet is designed with independent purpose. The data processing subnet is used to fit the current training data for denoising. We design a cross fusion module in data processing subnet to fuse the encoder information well and then pass the fusion result to the decoder. To decode the context well, we also design a residual attention block based on polarized self-attention as the decoder. The feature extracting subnet based on transfer learning is used to obtain global robust features of the degraded images. By fusing the information from both subnets, high-quality noise-free images can be obtained. Quantitative and qualitative experimental results on four real-world noisy datasets demonstrate the excellent generalization and denoising performance of our method.

关键词： image denoising real-world deep learning Fusion mechanism Transfer learning

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BSMO: Enhancing Multitask learning Through Batch Swapping Optimization

BSMO: Enhancing Multitask Learning Through Batch Swapping Op...

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Conference on real-time image processing and deep learning

作者： Spencer, Zephaniah Schirner, Gunar Tabkhi, Hamed UNC Charlotte 9201 Univ City Blvd Charlotte NC 28223 USA Northeastern Univ 360 Huntington Ave Boston MA 02115 USA

ISBN: (纸本)9781510673878;9781510673861

With the advent of deep learning, there has been an ever-growing list of applications to which deep Convolutional Neural Networks (DCNNs) can be applied. The field of Multi-Task learning (MTL) attempts to provide optimizations to many-task systems, improving performance by optimization algorithms and structural changes to these networks. However, we have found that current MTL optimization algorithms often impose burdensome computation overheads, require meticulously labeled datasets, and do not adapt to tasks with significantly different loss distributions. We propose a new MTL optimization algorithm: Batch Swapping with Multiple Optimizers (BSMO). We utilize single-task labeled data to train on a multi-task hard parameter sharing (HPS) network through swapping tasks at the batch level. This dramatically increases the flexibility and scalability of training on an HPS network by allowing for per-task datasets and augmentation pipelines. We demonstrate the efficacy of BSMO versus current SO TA algorithms by benchmarking across contemporary benchmarks & networks.

关键词： deep learning Multi-Task learning real-time Vision

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deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers

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NPJ COMPUTATIONAL MATERIALS 2025年第1期11卷 1-10页

作者： Lee, Sung Yun Cho, Do Hyung Jung, Chulho Sung, Daeho Nam, Daewoong Kim, Sangsoo Song, Changyong POSTECH Dept Phys Pohang South Korea POSTECH Photon Sci Ctr Pohang South Korea Max Planck POSTECH Korea Res Initiat Ctr Ultrafast Sci Quantum Matter Pohang South Korea POSTECH XFEL Beamline Div Pohang Accelerator Lab Pohang South Korea

Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing, especially in X-ray methodologies, where advanced light sources and detection technologies produce vast amounts of data that exceed meticulous human inspection capabilities. Despite the increasing demands, the full application of machine learning has been hindered by the need for data-specific optimizations. In this study, we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data. This method provides robust phase retrieval for simulated data and performs well on partially damaged and noisy single-pulse diffraction data from X-ray free-electron lasers. Moreover, the method significantly reduces data processing time, facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition. This approach offers a reliable solution to the phase problem to be widely adopted across various research areas confronting the inverse problem.

关键词： Pulse repetition rate

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real-time Classification of Chicken Parts in the Packaging Process Using Object Detection Models Based on deep learning

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PROCESSES 2025年第4期13卷 1005-1005页

作者： Sahin, Dilruba Torkul, Orhan Sisci, Merve Diren, Deniz Demircioglu Yilmaz, Recep Kibar, Alpaslan Sakarya Univ Ind Engn Dept TR-54050 Sakarya Turkiye Kutahya Dumlupinar Univ Ind Engn Dept TR-43300 Kutahya Turkiye Sakarya Univ Dept Informat Syst & Technol TR-54050 Sakarya Turkiye Sakarya Univ Business Sch TR-54050 Sakarya Turkiye Sakarya Univ Dept Management Informat Syst TR-54050 Sakarya Turkiye

Chicken meat plays an important role in the healthy diets of many people and has a large global trade volume. In the chicken meat sector, in some production processes, traditional methods are used. Traditional chicken part sorting methods are often manual and time-consuming, especially during the packaging process. This study aimed to identify and classify the chicken parts for their input during the packaging process with the highest possible accuracy and speed. For this purpose, deep-learning-based object detection models were used. An image dataset was developed for the classification models by collecting the image data of different chicken parts, such as legs, breasts, shanks, wings, and drumsticks. The models were trained by the You Only Look Once version 8 (YOLOv8) algorithm variants and the real-time Detection Transformer (RT-DETR) algorithm variants. Then, they were evaluated and compared based on precision, recall, F1-Score, mean average precision (mAP), and Mean Inference time per frame (MITF) metrics. Based on the obtained results, the YOLOv8s model outperformed the other models developed with other YOLOv8 versions and the RT-DETR algorithm versions by obtaining values of 0.9969, 0.9950, and 0.9807 for the F1-score, mAP@0.5, and mAP@0.5:0.95, respectively. It has been proven suitable for real-time applications with an MITF value of 10.3 ms/image.

关键词： chicken parts deep learning image processing object detection reducing waste and costs RT-DETR YOLOv8

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real-time scheduling of power grid digital twin tasks in cloud via deep reinforcement learning

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2024年第1期13卷 1-12页

作者： Qi, Daokun Xi, Xiaojuan Tang, Yake Zheng, Yuesong Guo, Zhengwei State Grid Henan Econ & Technol Res Inst Zhengzhou Peoples R China

As energy demand continues to grow, it is crucial to integrate advanced technologies into power grids for better reliability and efficiency. Digital Twin (DT) technology plays a key role in this by using data to monitor and predict real-time operations, significantly enhancing system efficiency. However, as the power grid expands and digitization accelerates, the data generated by the grid and the DT system grows exponentially. Effectively handling this massive data is crucial for leveraging DT technology. Traditional local computing faces challenges such as limited hardware resources and slow processing speeds. A viable solution is to offload tasks to the cloud, utilizing its powerful computational capabilities to support the stable operation of the power grid. To address the need, we propose GD-DRL, a task scheduling method based on deep Reinforcement learning (DRL). GD-DRL considers the characteristics of computational tasks from the power grid and DT system and uses a DRL agent to schedule tasks in real-time across different computing nodes, optimizing for processing time and cost. We evaluate our method against several established real-time scheduling techniques, including deep Q-Network (DQN). Our experimental results show that the GD-DRL method outperforms existing strategies by reducing response time, lowering costs, and increasing success rates.

关键词： Digital twin Power grid Cloud computing Task scheduling deep reinforcement learning

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Comparison of deep generative models for real-time generation of synthesized defective wafer maps

Comparison of deep generative models for real-time generatio...

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Conference on real-time image processing and deep learning

作者： Alam, Lamia Kehtarnavaz, Nasser Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

ISBN: (纸本)9781510673878;9781510673861

Modern wafer inspection systems in Integrated Circuit (IC) manufacturing utilize deep neural networks. The training of such networks requires the availability of a very large number of defective or faulty die patterns on a wafer called wafer maps. The number of defective wafer maps on a production line is often limited. In order to have a very large number of defective wafer maps for the training of deep neural networks, generative models can be utilized to generate realistic synthesized defective wafer maps. This paper compares the following three generative models that are commonly used for generating synthesized images: Generative Adversarial Network (GAN), Variational Auto-Encoder (VAE), and CycleGAN which is a variant of GAN. The comparison is carried out based on the public domain wafer map dataset WM-811K. The quality aspect of the generated wafer map images is evaluated by computing the five metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), inception score (IS), Frechet inception distance (FID), and kernel inception distance (KID). Furthermore, the computational efficiency of these generative networks is examined in terms of their deployment in a real-time inspection system.

关键词： Synthesized Wafer Maps Generative Adversarial Network Variational Autoencoder CycleGAN real-time image Synthesis

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image Quality Assessment in Visual Reinforcement learning for Fast-moving Targets

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第11期22卷 3303-3313页

作者： Ryoo, Sanghyun Jeong, Jiseok Han, Soohee POSTECH Grad Sch Artificial Intelligence 77 Cheongam Ro Pohang Si 37673 Gyeongsangbuk D South Korea POSTECH Dept Elect Engn 77 Cheongam Ro Pohang Si 37673 Gyeongsangbuk D South Korea POSTECH Dept Elect Engn & Convergence IT Engn 77 Cheongam Ro Pohang Si 37673 Gyeongsangbuk D South Korea

Visual reinforcement learning (RL) enables agents to develop optimal control strategies directly from image data. However, most existing research primarily concentrates on numerical simulations for learning algorithms, often neglecting the challenges encountered in real-world scenarios. To address this gap, this study introduces a semi-real environment that combines MuJoCo Gym simulation with a real camera sensor, aiming to create a more realistic augmented simulation for state-of-the-art visual RL algorithms. The usefulness of this semi-real environment was initially demonstrated through conventional camera-free learning, revealing that general RL experiences substantial performance degradation, especially with fast-moving objects, due to motion blur effects. Building on this semi-real environment, the study also presents the deceleration visual RL (DVRL) algorithm, which incorporates a novel deep learning-based image quality assessment to evaluate the suitability of the acquired data for learning policies. The DVRL algorithm performs real-time image quality assessment and manages fast-moving targets by adjusting their speed, thereby balancing speed and image quality to optimize policy learning and achieve superior performance compared to baseline models.

关键词： deep learning representation learning visual reinforcement learning visual servoing

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DGImNet: A deep learning model for photovoltaic soiling loss estimation

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APPLIED ENERGY 2024年第PartB期376卷

作者： Fang, Mingyu Qian, Weixing Qian, Tao Bao, Qiwei Zhang, Haocheng Qiu, Xiao Nanjing Normal Univ Sch Elect & Automat Engn 2 Xuelin Rd Nanjing 210023 Jiangsu Peoples R China Southeast Univ Sch Elect Engn 2 Sipailou Nanjing 210096 Jiangsu Peoples R China

deep learning models have recently been introduced to photovoltaic (PV) soiling loss estimation tasks. Most PV soiling loss (PVSL) estimation models are based on a single image and the environmental factors at a specific time point, while the temporal characteristic of environmental factors is less utilized. DGImNet, a PVSL estimation model utilizing both PV panel images and time series environmental factors (TSEFs), is proposed. DGImNet takes a PV panel image and TSEFs of 50 continuous time points to estimate the PVSL. The TSEFs are processed by gate recurrent units to produce a 96D feature, while the image is extracted to generate another 96D feature by a set of analysis units. The multi-modal features are fused to yield estimation results. It is proved that the exploitation of TSEFs is beneficial to improve PVSL prediction performance, and the engagement of compact bilinear pooling is useful for better fusion of image and TSEF features. Cooperating with a real-time data collection system, the proposed model is able to run on edge computing devices and be employed for real-time PVSL estimation tasks in actual PV power stations.

关键词： Photovoltaic soiling loss estimation deep learning Multi-modal data fusion

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Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning

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WASTE MANAGEMENT & RESEARCH 2024年第9期42卷 788-796页

作者： Kronenwett, Felix Maier, Georg Leiss, Norbert Gruna, Robin Thome, Volker Laengle, Thomas Inst Optron Fraunhofer IOSB Syst Technol & Image Exploitat Karlsruhe Germany Fraunhofer IBP Inst Bldg Phys Holzkirchen Germany Inst Optron Fraunhofer IOSB Syst Technol & Image Exploitat Fraunhoferstr 1 D-76131 Karlsruhe Germany

Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the basis for sorting. To enable economical use, it is necessary to ensure robust detection of individual objects even with high material throughput. Conventional algorithms struggle with resulting high occupancy densities and object overlap, making deep learning object detection methods more promising. In this study, different deep learning architectures for object detection (Region-based CNN/Region-based Convolutional Neural Network (Faster R-CNN), You only look once (YOLOv3), Single Shot MultiBox Detector (SSD)) are investigated with respect to their suitability for CDW characterization. A mixture of brick and sand-lime brick is considered as an exemplary waste stream. Particular attention is paid to detection performance with increasing occupancy density and particle overlap. A method for the generation of synthetic training images is presented, which avoids time-consuming manual labelling. By testing the models trained on synthetic data on real images, the success of the method is demonstrated. Requirements for synthetic training data composition, potential improvements and simplifications of different architecture approaches are discussed based on the characteristic of the detection task. In addition, the required inference time of the presented models is investigated to ensure their suitability for use under real-time conditions.

关键词： Machine learning object detection synthetic data sensor-based sorting construction and demolition waste circular economy

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