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Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

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Frontiers of Structural and Civil Engineering 2024年第4期18卷 516-535页

作者： Than v.TRAN H.NGUYEN-XUAN Xiaoying ZHUANG Institute of Photonics Leibniz University HannoverHannover 30167Germany CIRTech Institute HUTECH UniversityHo Chi Minh City 700000Vietnam College of Civil Engineering Tongji UniversityShanghai 200092China

Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering *** traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing *** address this issue,we explore the potential of deep learning(DL)to increase the efficiency of crack detection and forecasting crack ***,there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks *** the paper,we present DL models for identifying cracks,especially on concrete surface images,and for predicting crack ***,SegNet and U-Net networks are used to identify concrete *** gradient descent(SGD)and adaptive moment estimation(Adam)algorithms are applied to minimize loss function during ***,time series algorithms including gated recurrent unit(GRU)and long short-term memory(LSTM)are used to predict crack *** experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding *** evaluation of crack propagation,GRU and LSTM are used as DL models and results show good agreement with the experimental data.

关键词： deep learning crack segmentation crack propagation encoder−decoder recurrent neural network

Formation of diffuse and spark discharges between two needle electrodes with the scattering of particles

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Plasma Science and Technology 2024年第9期26卷 32-39页

作者： victor F TARASENKO Dmitry v BELOPLOTOv Alexei N PANCHENKO Dmitry A SOROKIN Institute of High Current Electronics Siberian BranchRussian Academy of SciencesTomsk 634055Russia National Research Tomsk State University Tomsk 634050Russia

The development of a nanosecond discharge in a pin-to-pin gap filled with air at atmospheric pressure has been studied with high temporal and spatial resolutions from a breakdown start to the spark *** and negative nanosecond voltage pulses with an amplitude of tens of kilovolts were ***-resolved images of the discharge development were taken with a fourchannel Intensified Charge Coupled Device(ICCD)*** minimum delay between the camera channels could be as short as≈0.1 *** made it possible to study the gap breakdown process with subnanosecond *** was observed that a wide-diameter streamer develops from the high-voltage pointed *** ionization processes near the grounded pin electrode started when the streamer crossed half of the *** bridging the gap by the streamer,a diffuse discharge was *** development of spark leaders from bright spots on the surface of the pointed electrodes was observed at the next *** was found that the rate of development of the spark leader is an order of magnitude lower than that of the wide-diameter *** thin luminous tracks were observed against the background of a discharge plasma *** has been established that the tracks are adjacent to brightly glowing spots on the electrodes and are associated with the flight of small particles.

关键词： needle electrodes short voltage pulses atmospheric air streamer breakdown diffuse and spark discharges scattering metal particles

维普期刊数据库

Joint Rain Streaks & Haze Removal Network for Object Detection

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Computers, Materials & Continua 2024年第6期79卷 4683-4702页

作者： Ragini Thatikonda Prakash Kodali Ramalingaswamy Cheruku Eswaramoorthy K.v Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Department of Computer Science and Engineering National Institute of TechnologyWarangal506004India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKurnool518008India

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

关键词： Image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)

Hybrid Optimization Algorithm for Handwritten Document Enhancement

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Computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

Heavy metal pollution of river water and eco-friendly remediation using potent microalgal species

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Water Science and Engineering 2024年第1期17卷 41-50页

作者： Amudham Radha Amal Raj Prabhakaran Mylsamy v.Sivasankar B.Sathish Kumar Kiyoshi Omine T.G.Sunitha Phycological Research Laboratory Research Department of BotanyPachaiyappa's College(Affiliated to University of Madras)Chennai 600030India Research Department of Chemistry Pachaiyappa's College(Affiliated to University of Madras)Chennai 600030India Department of Botany Sri Vidya Mandir Arts&Science College(Autonomous)KatteriUthangarai 636902India Department of Civil Engineering School of Engineering.Nagasaki UniversityNagasaki-Daigaku1-14 Bunkyo-machiNagasaki 8528521Japan

Pollution of rivers is mainly caused by anthropogenic activities such as discharge of effluent from industrial facilities,maintenance of sewage/effluent treatment plants,and dumping of solid waste on river *** study dealt with the pollution issues of the Cooum River in the well-known city of Chennai in South *** samples from 27 locations were collected and analyzed for 12 elements,including Ba,B,and Al,as well as heavy metals such as Pb,Cr,Mn,Fe,Co,Ni,Cu,Zn,and *** samples showed levels of these elements that exceeded World Health Organization *** correlation analysis revealed the inter-dependency among elements,and the contribution of each element based on factor loadings showed its percentage contribution compared to *** samples from six significant locations were chosen for remediation with three algae:Chlorella vulgaris,Scenedesmus dimorphus,and Phormedium *** uptake of pollutants led to the continuous growth of algae during the incubation period of 15 d,effectively removing heavy metals from the river *** increasing levels of algal counts and the chlorophyll a content confirmed the algal growth during the incubation period,followed by a declining stage after the incubation *** scanning electron microscopic images of algae before and after the remediation showed no remarkable modification of morphological *** study showed that the uptake of heavy metals using algae is an effective water pollution remediation measure,making the process practicable in the field on a large scale in the near future.

关键词： Heavy metal Cooum river Phycoremediation Microalgae Factor analysis

Paper Analytical Device for the Colourimetric Detection of Alkaline Phosphatase in Serum and Saliva

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Journal of Analysis and Testing 2024年第3期8卷 385-394页

作者： D.Alageswari A.LakshmiDevi P.E.Resmi P.v.Suneesh AarathiPradeep T.G.SatheeshBabu Department of Chemistry Amrita School of Physical Sciences Coimbatore Amrita Vishwa Vidyapeetham Amrita Biosensor Research Lab Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Amrita Biomedical Engineering Centre Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham

A paper analytical device (PAD) was developed for the colourimetric estimation of alkaline phosphatase (ALP). ALP catalyzes the hydrolysis of disodium phenyl phosphate (DSPP) to phenol, which then undergoes oxidative coupling with4-aminoantipyrine (4-AAP) producing a reddish brown-coloured quinone imine. The colourimetric reaction tested in solution has been translated onto a PAD for the point of care (PoC) testing of ALP. A uniform colour was obtained in 25 min with the introduction of ALP onto the reagent drop cast paper device. The images obtained by scanning the PAD were processed using ImageJ software. The colour intensity obtained by image processing was proportional to the concentration of ALP present in the sample. ALP in serum and saliva samples were tested using the PAD in the range of 0 to 528 U/L and0 to 187 U/L, respectively. The interference studies revealed that the PAD was selective to ALP in the presence of other biomolecules. The PAD exhibited a limit of detection (LOD) of 3.3 U/L and a sensitivity of 48.36 a.u/Log(U/L) for serum ALP and LOD of 1.69 U/L and a sensitivity of 28.5 a.u/Log (U/L) for salivary ALP. The PAD showed good agreement with the clinical methods for real samples.

关键词： Alkaline phosphatase Colourimetry ImageJ Paper analytical device Point of care testing

Enhancing Wildlife Monitoring Through Infrared Imaging: A Deep Learning Approach for Improved Animal Region Segmentation and Species Identification

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Journal of Harbin Institute of Technology(New series) 2024年

作者： v Janani C Shanthi Department of Computer Science， School of Computing Sciences， vels Institute of Science Technology and Advanced Studies

Infrared imaging has proven to be a powerful tool in various fields， with particular significance in wildlife monitoring. In this paper， we present an extension of our previous research， focusing on advancing the segmentation of animal regions in enhanced infrared images and expanding the scope to include species identification. Our proposed methodology builds upon the success of the R-CNN (Region-based Convolutional Neural Network) object detection to improve the accuracy and robustness of animal region segmentation， while simultaneously extending our model’s capabilities to identify and classify the species within those regions. By fine-tuning the R-CNN model on a larger dataset that includes annotated infrared images and species labels， we enhance its capacity to not only accurately segment animal regions but also classify them into specific species categories. To assess the performance of our extended model， we employ a comprehensive set of evaluation metrics， including pixel-based metrics like Intersection over Union (IoU)， as well as species classification accuracy. Our results demonstrate significant improvements in both region segmentation accuracy and species identification compared to our previous work and existing methods. This research showcases the potential of deep learning techniques， combined with transfer learning， to advance wildlife monitoring applications using infrared imaging.

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Brain Tumor Retrieval in MRI images with Integration of Optimal Features

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Journal of Harbin Institute of Technology(New series) 2024年

作者： N v Shamna B Aziz Musthafa Department of Computer Science and Engineering， P A College of Engineering Department of Computer Science and Engineering， Bearys Institute of Technology

This paper presents an approach to improve medical image retrieval， particularly for brain tumors， by addressing the gap between low-level visual and high-level perceived contents in MRI， X-ray， and CT scans. Traditional methods based on color， shape， or texture are less effective. The proposed solution uses machine learning to handle high-dimensional image features， reducing computational complexity and mitigating issues caused by artifacts or noise. It employs a genetic algorithm for feature reduction and a hybrid residual UNet (HResUNet) model for region-of-interest (ROI) segmentation and classification， with enhanced image preprocessing. The study examines various loss functions， finding that a hybrid loss function yields superior results， and the GA-HResUNet model outperforms the HResUNet. Comparative analysis with state-of-the-art models shows a 4% improvement in retrieval accuracy.

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Delineation of groundwater potential zones using remote sensing and Geographic Information Systems(GIS)in Kadaladi region,Southern India

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Journal of Groundwater Science and Engineering 2024年第2期12卷 147-160页

作者： Stephen Pitchaimani v Narayanan MSS Abishek RS Aswin SK Jerin Joe RJ PG and Research Department of Geology V.O.Chidambaram CollegeThoothukudi 628008India Research Scholar Manonmaniam Sundaranar UniversityTirunelveli 627012India

The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram,Tamil Nadu,India,using a combination of remote sensing and Geographic Information Systems(GIS)with the Analytical Hierarchical Process(AHP).various factors such as geology,geomorphology,soil,drainage,density,lineament density,slope,rainfall were analyzed at a specific *** layers were evaluated for quality and relevance using Saaty's scale,and then inte-grated using the weighted linear combination *** weights assigned to each layer and features were standardized using AHP and the Eigen vector technique,resulting in the final groundwater potential zone *** AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point ***-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various *** groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted *** layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images.2 Results indicate that approximately 21.8 km of the total area exhibits high potential for groundwater *** recharge is viable in areas with moderate slopes,particularly in the central and southeastern regions.

关键词： Groundwater Satellite image Remote sensing GIS techniques Analytical Hierarchy Process(AHP)