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Comparisons of{100}texture improvement and formability in hot-rolled non-oriented electrical steel by austenite-ferrite phase transformation and shear deformation

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Journal of Iron and Steel Research International 2025年第1期32卷 171-185页

作者： Chi-hao Yu Hong-jiang Pan Yan-ping Zhao De-ting Tang Hai-jun Wang Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093YunnanChina School of Electronics and Information Engineering Anshun UniversityAnshun561000GuizhouChina School of Metallurgical Engineering Anhui University of TechnologyMa’anshan243032AnhuiChina

Over the years,the high magnetic induction of industrial Mn-added electrical steel is assumed to be the enhancement of{100}texture derived from its austenite-ferrite phase transformation during hot rolling(phase transformation(PT)method).However,it is still undetermined without straightforward experimental *** reason for{100}texture improvement of Mn-added electrical steel is experimentally confirmed due to the recrystallization induced by the austenite-ferrite phase transformation during hot ***,a more promising methodology to further improve{100}texture and formability of hot-rolled electrical steel is promoted by the control of hot rolling deformation condition(shear deformation(SD)method).The results show that the nucleation mechanisms of{100}oriented recrystallized grains are different in the samples by SD and PT methods,which are in-depth shear deformation and austenite-ferrite phase transformation,*** this case,coarse{100}oriented recrystallized grains and low residual stress are obtained in the sample by SD method,which is responsible for its superior{100}texture and *** contrast,the sample by PT method forms fine recrystallized grains with random orientations and accumulates severe residual stress.

关键词： Non-oriented electrical steel {100}texture Formability Austenite-ferrite phase transformation Shear deformation

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Numerical and spatiotemporal features fusion for video summarization

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Multimedia Tools and Applications 2025年 1-16页

作者： Aboelenien, Mohamed Salem, Mohammed A.-M. Faculty of Media Engineering and Technology German University In Cairo Cairo11865 Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo11566 Egypt

In this paper, we propose a numerical spatiotemporal approach for video summarization. The current solutions leverage deep learning techniques to tackle this task. However, existing methods do not employ the video shots’ length data in their networks. We first introduce a new ground truth labelling for video summarization. This ground truth tackles multiple users’ annotations and is inclusive of video shot length information. We then propose a novel network architecture Numerical SpatioTemporal Fusion Net, NSTFNet. The proposed architecture leverages the temporal modelling ability of the self-attention mechanism to model video’s vision data to be fused with structured numerical features of video shots. We evaluate our results on TVSum and SumMe datasets. Experimental results show that the proposed model outperforms state-of-the-art performance on the SumMe dataset with 51.29% and 54.42% f-scores on the canonical and augmented configurations, demonstrating the effectiveness of our proposed model compared to state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Video analysis

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DiffuSAR: Frequency Domain-Aware Diffusion Model for SAR Image Generation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11851-11866页

作者： Ying, Zilu Ke, Wenyu Zhai, Yikui Long, Zhihao Zhou, Jianhong Zhu, Hufei Chen, C. L. Philip Wuyi University School of Electronics and Information Engineering Guangdong Jiangmen529020 China South China University of Technology School of Electronics and Information Engineering Guangzhou510000 China South China University of Technology Faculty of Computer Science and Engineering Guangdong Guangzhou510000 China

In recent years, deep learning-based Synthetic Aperture Radar (SAR) image detection, recognition, and segmentation models achieve remarkable accuracy when trained on large amounts of SAR image samples. However, the acquisition of SAR images tends to be costly. A practical approach to address this issue involves the use of artificially generated training samples to supplement the dataset. Current SAR image generation methods based on Generative Adversarial Networks have issues with generation fidelity and are challenge to converge. Moreover, most of these methods only consider the spatial domain of SAR images without exploring the characteristics of their frequency domain. To tackle the aforementioned problems, we proposed a lightweight frequency domain-aware SAR image generation model based on the Denoising Diffusion Probabilistic Model. The proposed generative model is capable of producing highly realistic artificial SAR image samples while converging stably. Meanwhile, our research reveals that high-frequency components of SAR images play a crucial role in the generation process, and a frequency adjustment module based on the Fast Fourier Transform is implemented in our generation model to prevent interference of high-frequency components by low-frequency components. Tests on the MSTAR dataset highlight our model's advantages in both generation quality and parameter efficiency compared to other cutting-edge image generation models. An additional ablation study also confirms the effectiveness of our proposed frequency adjustment module, which reduces the Fréchet Inception Distance by 2.85%, confirming our hypothesis that high-frequency components are more crucial to SAR image generation. Code is available at https://***/yikuizhai/DiffuSAR. © 2025 IEEE.

关键词： Fast Fourier transforms

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Adaptive isomap feature extractive gradient deep belief network classifier for diabetic retinopathy identification

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Multimedia Tools and Applications 2025年第9期84卷 6349-6370页

作者： Singh, Alka Kumar, Rakesh Gandomi, Amir H. Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur India Faculty of Engineering & Information Technology University of Technology Sydney SydneyNSW Australia Óbuda University Budapest1034 Hungary

Diabetic retinopathy (DR) is an infection that bases eternal visualization loss in patients with diabetes mellitus. With DR, the glucose level in the blood increases, as well as its viscosity, this results in fluid leakage into surrounding tissues in the retina. In other words, DR represents the pathology of capillaries and venules in the retina with leakage effects, being the main acute retinal disorder caused by diabetes. Many DR detection methods have been previously discussed by different researchers;however, accurate DR detection with a reduced execution time has not been achieved by existing methods. The proposed method, the Shape Adaptive box linear filtering-based Gradient Deep Belief network classifier (SAGDEB) Model, is performed to enhance the accuracy of DR detection. The objective of the SAGDEB Model is to perform an efficient DR identification with a higher accuracy and lower execution time. This model comprises three phases: pre-processing, feature extraction, and classification. The shape adaptive box linear filtering image pre-processing is carried out to reduce the image noise without removing significant parts of image content. Then, an isomap geometric feature extraction is performed to compute features of different natures, like shape, texture, and color, from the pre-processed images. After that, the Adaptive gradient Tversky Deep belief network classifier is to perform classification. The deep belief network is probabilistic and generative graphical model that consists of multiple layers such as one input unit, three hidden units, and one output unit. The extracted image featuresare considered in the input layer and these images are sent to hidden layers. Tversky similarity index is applied in hidden layer 1 to analyze the extracted features with testing features. Regarding the similarity value, the sigmoid activation function is determined in hidden layer 2 so different levels of DR can be identified. Finally, the adaptive gradient method is

关键词： Feature extraction

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YOLOCSP-PEST for Crops Pest Localization and Classification

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Computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Multi-band terahertz graphene-based anisotropic metamaterial absorber comprised of two circular split ring resonator arrays with two gaps and a connection rod

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Optical and Quantum Electronics 2025年第3期57卷 1-18页

作者： Asgari, Somayyeh Fabritius, Tapio Optoelectronics and Measurement Techniques Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland

A multi-band, graphene-based anisotropic metamaterial absorber designed to operate in the terahertz (THz) range features two circular split ring resonator arrays, each with two gaps and a connecting rod. This metamaterial is simulated using the frequency domain in CST Software. In addition, equivalent circuit models (ECMs) were derived to provide alternative approach to assess the characteristics of transverse electric (TE) and transverse magnetic (TM) modes using MATLAB code. The absorber is dynamically tunable, exhibiting a strong linear dichroism (LD) response of 98% within the 0.5–5.75 THz range. It achieves a single absorption peak with a 99.9% rate in TM mode and three absorption bands with an average rate of 97.1% in TE mode. This absorber has potential applications in THz polarization-sensitive devices and systems. © The Author(s) 2025.

关键词： MATLAB

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Efficient OpenMP Based Z-curve Encoding and Decoding Algorithms

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Computers, Materials & Continua 2025年第1期82卷 1313-1327页

作者： Zicheng Zhou Shaowen Sun Teng Liang Mengjuan Li Fengling Xia School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China School of Communications Information Engineering Yunnan Communications Vocational and Technical CollegeKunming650500China Library Yunnan Normal UniversityKunming650500China Faculty of Civil Aviation and Aeronautics Kunming University of Science and TechnologyKunming650500China

Z-curve’s encoding and decoding algorithms are primely important in many Z-curve-based *** bit interleaving algorithm is the current state-of-the-art algorithm for encoding and decoding *** simple,its efficiency is hindered by the step-by-step coordinate shifting and bitwise *** tackle this problem,we first propose the efficient encoding algorithm LTFe and the corresponding decoding algorithm LTFd,which adopt two optimization methods to boost the algorithm’s efficiency:1)we design efficient lookup tables(LT)that convert encoding and decoding operations into table-lookup operations;2)we design a bit detection mechanism that skips partial order of a coordinate or a Z-value with consecutive 0s in the front,avoiding unnecessary iterative *** propose order-parallel and point-parallel OpenMP-based algorithms to exploit the modern multi-core *** results on discrete,skewed,and real datasets indicate that our point-parallel algorithms can be up to 12.6×faster than the existing algorithms.

关键词： Z-curve lookup table OpenMP bit detection mechanism

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Robotic artistic writing — reproduction of signs drawn on the surface of plastically deformable materials

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IEEE Sensors Journal 2025年第10期25卷 18090-18105页

作者： He, Xin Zielinska, Teresa Matsumaru, Takafumi Dutta, Vibekananda Waseda University Graduate School of Information Production and Systems Fukuoka Kitakyushu Japan Warsaw University of Technology Faculty of Power and Aeronautical Engineering Warsaw Poland Warsaw University of Technology Faculty of Mechatronics Warsaw Poland

Existing surface shaping methods focus on hard materials with stable physical properties. This means that the approaches developed are insufficient for shaping soft materials. The article describes a cheap method of reproducing plastic deformations using a robotic manipulator. The shape recording and reproduction system consists of two RGB-D cameras, two containers with kinetic sand, and a manipulator with 6 degrees of freedom. Volunteers create templates by drawing on the surface of kinetic sand with a wooden stylus or finger. Some pressure is exerted while writing. The resulting shapes recorded by the RGB-D camera have the form of ribbon ditches (grooves). The obtained point cloud is processed to create a sand deformation model. In the first stage, a dedicated local smoothing technique is used. Then, special algorithms are implemented to create a description of the main curvatures and key dimensions of recorded signs. A spline-based approach is used. The method allows for the representation of various shapes in a unified form. In the final stage, modulated sinusoidal functions define the robot’s trajectory. The effects of the robot’s operation are recorded to assess the reproduction quality. The Point Cloud Structural Similarity Measure (PointSSIM) evaluates the results. Experimental research takes into account many different shapes. Copies of shapes created by humans and robots are compared with the originals. The outcomes show that the quality of reproduction achieved by humans and robots is comparable. The median-based curvature similarity measure obtained for the human was only 1.92% higher than the robot’s result, and the covariance-based geometric similarity measure was only 0.74% higher than the robot’s score. The system can be used to mass-produce souvenirs or special implants. © 2001-2012 IEEE.

关键词： Wooden containers

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Calibration and validation of the CMADS-Driven SWAT model in the distributed hydrological simulation of the Baihe River Basin in Nanyang

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ISH Journal of Hydraulic engineering 2025年第1期31卷 97-107页

作者： BiYing, Xu Abd Rahman, Nor Faiza Tai, Vin Cent Faculty of Automation Engineering Henan Polytechnic Institute Henan Nanyang China Centre for Sustainable Design Modelling and Simulation Faculty of Engineering Built Environment & Information Technology SEGi University Petaling Jaya Selangor Malaysia

The China Meteorological Assimilation Driving Dataset for the SWAT model (CMADS) has gained widespread use for its accuracy. This study focuses on the Baihe River Basin in Nanyang, using the SWAT tool and CMADS dataset to construct two distributed hydrological models of different scales. The accuracy and effectiveness of these models were evaluated. Parameter uncertainty analysis on a monthly and annual scale was conducted usingSUFI-2 algorithm. The goal was to assess the applicability of the CMADS-driven SWAT model in theBasin by calibrating and validating flow data at both monthly and annual scales. The results form the calibration period (2009–2013) showedmonthly R2 and NSE values were 0.81 and 0.8, while the annual values were 0.91 and 0.9. During validation(2014–2016), the annual R2 and NSE values were 0.89 and 0.86, and the monthly values were 0.76 and 0.76, with reasonable values for the P-factor, R-factor, and percentage bias (PBIAS), indicating good model applicability. Seasonal variations in rainfall, surface runoff, water yield, evapotranspiration (ET), and potential evapotranspiration (PET) were analyzed. The results demonstrate that surface runoff and water yield is closely related to precipitation, while ET is largely controlled by land use types, providing insights for water resource management. © 2024 Indian Society for Hydraulics.

关键词： SWAT

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Metaverse technology for Public Services in Smart Cities: Opportunities and Challenges 14

Metaverse Technology for Public Services in Smart Cities: Op...

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14th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2025

作者： Ngemba, Hajra Rasmita Jambari, Dian Indrayani Ahmad, Kamsuriah Faculty of Information Science and Technology National University of Malaysia Selangor Bangi43600 Malaysia Department of Information Technology Faculty of Engineering Tadulako University Palu Indonesia

ISBN: (纸本)9789897587511

This study investigates the viability of metaverse technology as a digital public service in smart cities and explores the prospects and challenges of implementing it in developed and developing countries. This study employs a conceptual exploration through a review of the contemporary literature concerning the deployment of metaverse technology, the construction of smart cities, and the revolutionization of public services. The analysis compares readiness factors of technology, infrastructure, and policy between developed and developing countries. Metaverse provides immense value to smart cities, including improved accessibility of public services, real-time interaction, and operational efficiency. However, key challenges in deploying such networks include limited infrastructure in the developing world, issues with data privacy, and high energy consumption in developed countries. Conceptual research needs further empirical studies focusing on implementing metaverse technology in smart cities. This research is beneficial for policymakers when designing smart-metaverse implementations. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Smart city

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