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Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Computers, Materials & Continua 2023年第1期74卷 1437-1455页

作者： S.Prabhakaran R.Annie Uthra J.Preetharoselyn Department of Computational Intelligence SRM Institute of Science and TechnologyChengalpattu603203India Department of Electrical Engineering SRM Institute of Science and TechnologyChengalpattu603203India

Photovoltaic(PV)boards are a perfect way to create eco-friendly power from *** defects in the PV panels are caused by various conditions;such defective PV panels need continuous *** recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV *** general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power *** existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming *** increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is ***,in the present work,a new real-time Multi Variant deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high *** defects that arise in the PV panels are identified by the CNN based RMVDM using RGB *** biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a *** technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time *** results show that 98%of the accuracy and recall values in the fault detection and classification process.

关键词： Photovoltaic panels deep learning defect feature extraction RMVDM

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Accelerating the deep learning based Brachial Plexus Nerve Trunks Recognition in Ultrasound images using Edge Computing 16

Accelerating the Deep Learning based Brachial Plexus Nerve T...

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

作者： Sudara, Maleesha Madhushani, Nisansala De Silva, Udara Malavipathirana, Hasara Kondarage, Achintha I. Univ Ruhuna Dept Elect & Informat Engn Matara Sri Lanka Imec USA Kissimmee FL USA Univ Colombo Postgrad Inst Med Colombo Sri Lanka

ISBN: (纸本)9798350370058;9798350370164

The brachial plexus block is an ultrasound image-guided regional anesthesia technique. It involves injecting local anesthetics into or around the brachial plexus to induce numbness in the upper limb, facilitating painless surgeries. Due to the difficulty in achieving clear visualizations of nerves in ultrasound images, this procedure is typically administered by highly trained anesthetists. Therefore, the limited availability of such skilled professionals constrain the widespread performance of these anesthesia procedures. Recent advancements in deep learning-based image segmentation methods, such as U-Net architecture, can potentially solve the segmentation of nerves in ultrasound images. This paper proposes an improved deep learning-based approach to segment brachial plexus nerve trunks in ultrasound images. We improved U-Net architecture by adding a residual block and a naive inception block, achieving a dice coefficient of 0.66. However, the computational complexity of deep learning methods presents a challenge to achieve real-time segmentation using edge computing. As a solution, we deployed the model in Xilinx KV260 multi-processor system on chip (MPSOC) to accelerate the image segmentation.

关键词： Brachial Plexus Hardware Acceleration deep learning image Segmentation Medical image processing

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deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy

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MEDICAL PHYSICS 2024年第6期51卷 4271-4282页

作者： Fu, Yabo Zhang, Pengpeng Fan, Qiyong Cai, Weixing Pham, Hai Rimner, Andreas Cuaron, John Cervino, Laura Moran, Jean M. Li, Tianfang Li, Xiang Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY USA Mem Sloan Kettering Canc Ctr Dept Radiat Oncol New York NY USA Room 1251 650 Commack Rd Smithtown NY 11725 USA

BackgroundIn radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is *** introduce a novel method for markerless kV image-based tracking of lung tumors via deep learning-based target *** utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient-specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real-time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior-inferior, anterior-posterior, and left-right directions during the DRR and DTI generation process. We performed real-time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real-time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the *** sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8 +/- 0.7 mm in the superior-inferior (SI) direction and 0.9 +/- 0.8 mm in the in-plane left-right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2 mm in the SI direction, is 92.2% on the 4312 tested images. The patient-specific model took approximately 12 h to train. During testing, i

关键词： markerless tracking x-ray

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DeLTran15: A deep Lightweight Transformer-Based Framework for Multiclass Classification of Disaster Posts on X

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IEEE ACCESS 2024年 12卷 153676-153693页

作者： Saleem, Saima Hasan, Nabeela Khattar, Anuradha Jain, Priti Rai Gupta, Tarun Kumar Mehrotra, Monica Jamia Millia Islamia Dept Comp Sci New Delhi 110025 India New Delhi Inst Management Dept Artificial Intelligence & Machine Learning New Delhi 110062 India Univ Delhi Dept Comp Sci Miranda House New Delhi 110007 India

During disasters, timely and accurate information is paramount for effective decision-making and resource allocation. Social media (SM) platforms, particularly X platform (formerly Twitter), have become crucial sources of real-time data during catastrophic events, offering insights into evolving situations, needs, and responses. However, efficiently detecting and extracting meaningful content from the massive amounts of unstructured user-generated data swiftly is challenging. While large transformer models like BERT perform exceptionally well across a range of natural language processing tasks, including SM text processing, their computational demands make them impractical for real-time, low-resource applications. Therefore, developing lightweight yet effective models to detect and categorize relevant SM information during catastrophic events is essential for rapid response efforts. To address this need, a novel lightweight framework "DeLTran15" is introduced, designed specifically for classifying posts on X shared during disasters into multiple humanitarian information categories. The framework uses the "Obtain Scrub Explore Model iNterpret" (OSEMN) methodology, which enhances the reliability and effectiveness of the classification process, leading to a robust and high-quality final model. Six lightweight pretrained language models: BERT-tiny, BERT-mini, ALBERT-base, XtremeDistil, Electra small discriminator (ESD), and Electra small generator (ESG), all under 15 million parameters are finetuned. Extensive experimentation on a benchmark real-world disaster dataset indicates XtremeDistil's superior performance among all lightweight models. It is further optimized using post-training quantization techniques, enabling deployment on resource-constrained devices. Contrary to the state-of-the-art larger transformer language models with over 100 million parameters, DeLTran15 achieves high classification accuracy with enhanced computational efficiency, making it suitable fo

关键词： Transformers Computational modeling Context modeling Quantization (signal) Performance evaluation Bidirectional control Encoding Analytical models Natural language processing deep learning Disaster management Decision making Resource management Classification algorithms transfer learning lightweight transformers deep learning social media OSEMN disaster management

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Comparative study of cloud evolution for rainfall nowcasting using AI-based deep learning algorithms

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JOURNAL OF HYDROLOGY 2024年 639卷

作者： Jiang, Xianqi Chen, Ji Chen, Xunlai Wong, Wai-kin Wang, Mingjie Wang, Shuxin Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China Meteorol Bur Shenzhen Municipal Shenzhen Key Lab Severe Weather South China Shenzhen Peoples R China Hong Kong Observ Hong Kong Peoples R China

It is a critical need to provide timely and valuable alerts of rainstorms and floods to the public. However, it still remains a world-class challenge to achieve serviceable nowcasting rainstorms with even a short lead time of one hour. Different deep learning algorithms have been adopted to improve nowcasting accuracy. Unfortunately, it is still a question which algorithm is more suitable and how to interpret the rainstorm nowcasting results from deep learning. To this end, this paper focuses on modelling the evolution of rainstorm clouds using deep learning algorithms that can be applied to nowcast rainstorms for the next few hours. Adopting three deep learning algorithms, the study provides a detailed analysis of the nowcasting results of three typical cases of different rainfall intensities from a radar echo mosaic image dataset. The dataset was collected in Guangdong, China, and the analysis interprets the performance differences. The analysis further discloses that an AI-based method can provide more skilful nowcasting for medium and strong rainfall cases than for weak ones. Moreover, a deep learning algorithm trained by the dataset for one region can be skilfully used to nowcast rainfall for another region with a similar weather system. This explains the nowcasting capability of deep learning algorithms as well as their robustness. Besides, experiments on the number of iterations reveal that more iterations do not achieve higher nowcasting accuracy. With improved interpretability of deep learning from the perspective of real-world application in the study, it is expected that the algorithms producing higher accuracy and longer lead time nowcasts will be made possible.

关键词： Zhang Associate Editor?

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R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement

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OPTICS EXPRESS 2024年第1期32卷 907-921页

作者： Yang, Jiashuai Qian, Kemao Wang, Lianpo Northwestern Polytech Univ Sch Software Xian 710129 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518063 Peoples R China

Digital image correlation (DIC) is an optical metrology method for measuring object deformation and has been widely used in many fields. Recently, the deep learning based DIC methods have achieved good performance, especially for small and complex deformation measurements. However, the existing deep learning based DIC methods with limited measurement range cannot satisfy the needs of real-world scenarios. To tackle this problem, a recursive iterative residual refinement DIC network (R3-DICnet) is proposed in this paper, which mimics the idea of the traditional method of two-step method, where initial value estimation is performed on deep features and then iterative refinement is performed on shallow features based on the initial value, so that both small and large deformations can be accurately measured. R3-DICnet not only has high accuracy and efficiency, but also strong generalization ability. Synthetic image experiments show that the proposed R3-DICnet is suitable for both small and large deformation measurements, and it has absolute advantages in complex deformation measurement. The accuracy and generalization ability of the R3-DICnet for practical measurement experiments were also verified by uniaxial tensile and wedge splitting tests.

关键词： Computational imaging deep learning Digital image processing Frequency measurement Segmentation Spatial resolution

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Self-attention residual network-based spatial super-resolution synthesis for time-varying volumetric data

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IET image processing 2024年第6期18卷 1579-1597页

作者： Ma, Ji Ye, Yuhao Chen, Jinjin Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou Peoples R China Commun Univ Zhejiang Sch Design & Art Hangzhou Peoples R China Macau Univ Sci & Technol Fac Humanities & Arts Macau Peoples R China

In the field of scientific visualization, the upscaling of time-varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large-scale, time-varying simulation data. This paper proposes self-attention residual network-based spatial super-resolution (SARN-SSR), a spatial super-resolution model based on self-attention residual networks that can generate time-varying data with temporal coherence. SARN-SSR consists of two components: a generator and a discriminator. The generator takes the low-resolution volume sequences as the input and gives the corresponding high-resolution volume sequences as the output. The discriminator takes both synthesized and real high-resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN-SSR, four sets of time-varying volume datasets are applied from scientific simulation. In addition, SARN-SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning-based techniques and one traditional technique. The experimental results show that by using this method, the closest time-varying data to the ground truth can be obtained. This paper proposes a novel self-attention residual network-based spatial super-resolution (SARN-SSR) framework for upscaling time-varying volume data in scientific visualization. It utilizes a generator and discriminator based on generative adversarial networks to generate high-resolution volume sequences. Comparative evaluations demonstrate that SARN-SSR outperforms state-of-the-art techniques in generating accurate time-varying volume datasets. image

关键词： computer graphics data visualisation image processing

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Self-FuseNet: Data Free Unsupervised Remote Sensing image Super-Resolution

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 1710-1727页

作者： Mishra, Divya Hadar, Ofer Ben Gur Univ Negev Sch Elect & Comp Engn Beer Sheva Israel

real-world degradations deviate from ideal degradations, as most deep learning-based scenarios involve the ideal synthesis of low-resolution (LR) counterpart images by popularly used bicubic interpolation. Moreover, supervised learning approaches rely on many high-resolution (HR) and LR image pairings to reconstruct missing information based on their association, developed by complex long hours of deep neural network training. Additionally, the trained model's generalizability on various image datasets with various distributions is not guaranteed. To overcome this challenge, we proposed our novel Self-FuseNet, particularly for extremely poor-resolution satellite images. Also, the network exhibits strong generalization performance on additional datasets (both "ideal " and "nonideal " scenarios). The network is especially for those image datasets suffering from the following two significant limitations: 1) nonavailability of ground truth HR images;2) limitation of a large count of the unpaired dataset for deep neural network training. The benefit of the proposed model is threefold: 1) it does not require any significant extensive training data, either paired or unpaired but only a single LR image without prior knowledge of its distribution;2) it is a simple and effective model for super-resolving very poor-resolution images, saving computational resources and time;3) using UNet, the processing of data are accelerated by the network's wide skip connections, allowing image reconstruction with fewer parameters. Rather than using an inverse approach, as common in most deep learning scenarios, we introduced a forward approach to super-resolve exceptionally LR remote sensing images. This demonstrates its supremacy over recently proposed state-of-the-art methods for unsupervised single real-world image blind super-resolution.

关键词： Feature extraction Superresolution Satellites Training image reconstruction Spatial resolution deep learning Blind image super-resolution (SR) data fusion deep learning self-fusion unsupervised image super-resolution (SR)

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Smart Traffic Monitoring Through real-time Moving Vehicle Detection Using deep learning via Aerial images for Consumer Application

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第4期70卷 7302-7309页

作者： Singh, Avaneesh Rahma, Mohammad Zia Ur Rani, Preeti Agrawal, Navin Kumar Sharma, Rohit Kariri, Elham Aray, Daniel Gavilanes Indian Inst Technol Kanpur Dept Comp Sci & Engn Kanpur 208016 India Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Vaddeswaram 522302 India SRM Inst Sci & Technol Fac Engn & Technol Dept Elect & Commun Engn Ghaziabad 201204 India GLA Univ Dept Comp Engn & Applicat Mathura 281406 India ABES Engn Coll Dept Elect & Commun Engn Ghaziabad 201009 India Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Informat Syst Al Kharj 11942 Saudi Arabia Univ Europea Atlant Engn Res & Innovat Grp Santander 39011 Spain Univ Int Iberoamericana Engn Res & Innovat Grp Campeche 24560 Mexico Fdn Univ Int Colombia Dept Project Management Bogota 11171 Colombia

This paper presents a novel deep-learning method for detecting and tracking vehicles in autonomous driving scenarios, with a focus on vehicle failure situations. The primary objective is to enhance road safety by accurately identifying and monitoring vehicles. Our approach combines YOLOv8 models with Transformers-based convolutional neural networks (CNNs) to address the limitations of traditional CNNs in capturing high-level semantic information. A key contribution is the integration of a modified pyramid pooling model for real-time vehicle detection and kernelized filter-based techniques for efficient vehicle tracking with minimal human intervention. The proposed method demonstrates significant improvements in detection accuracy, with experimental results showing increases of 4.50%, 4.46%, and 3.59% on the DLR3K, VEDAI, and VAID datasets, respectively. Our qualitative and quantitative analysis highlights the model's robustness in handling shadows and occlusions in traffic scenes, outperforming several existing methods. This research contributes a more effective solution for real-time multi-vehicle detection and tracking in autonomous driving systems.

关键词： Feature extraction Vehicle detection deep learning Accuracy Lighting image color analysis real-time systems imaging technologies object detection tracking intelligent transportation systems consumer application

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deep learning-based clustering of processes and their visual exploration: An industry 4.0 use case for small, medium-sized enterprises

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EXPERT SYSTEMS 2024年第2期41卷 e13139-e13139页

作者： Mehdiyev, Nijat Mayer, Lea Lahann, Johannes Fettke, Peter German Res Ctr Artif Intelligence DFKI Saarbrucken Germany Saarland Univ Saarbrucken Germany

This paper proposes a multi-stage approach consisting of deep learning-based image classification, process trace clustering, and visual/statistical knowledge discovery of process data. The proposed decision augmentation solution aims to facilitate the production planners in estimating the process-specific production parameters such as activity duration, idle time, or machine utilization. This study focuses on 'one-of-a-kind production' (OKP). Planning in OKP is especially challenging due to the increasing individualization of customer requirements. Furthermore, the uniqueness of products adds complexity to data and information structuring. To tackle this issue, we first train deep convolutional neural networks (CNN) with image data of production parts obtained from computer-aided design (CAD) systems to extract meaningful features. After cross-validation, uncertainty, and robustness assessment of the adopted deep learning approach, we use the data representation from the penultimate layer as input for clustering production parts. The goodness of clustering results is evaluated using a series of internal clustering validation indices. Finally, process event log data provided by manufacturing execution systems (MES) is mapped to each production part, allowing us to conduct statistical and visual knowledge discovery of process parameters for each cluster. The relevance of our proposed approach has been validated by studying a real-world use case in a small, medium-sized enterprise (SME) operating in the fixture and jig manufacturing industry.

关键词： deep learning industry 4 0 one-of-a-kind production process clustering visual and statistical knowledge discovery

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