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A multi-physical coupling NURBS-based isogeometric analysis for nonlinear electrodynamics of three-directional poroelastic functionally graded circular nanoplate: Introducing deep neural network algorithm for nonlinear electrodynamics problems

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AEROSPACE SCIENCE AND TECHNOLOGY 2025年 157卷

作者： Gao, Jinglong Chang, Lei Mahmoud, Haitham A. Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Anhui Univ Sci & Technol Sch Carbon Neutral Sci & Engn Huainan 232001 Peoples R China King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia

For the right design of nanosystems with integrated piezoelectric transducers and materials, it is crucial to understand the electro-mechanical coupling factor. Considering this, this work determines the influence of placement and dimension of piezoelectric patch on the vibrations of circular sandwich sector nanoplate coupled with an electrically layer. The core of the current nanostructure is made of three-directional poroelastic functionally graded material (3D-PFGM). The quasi-3D sinusoidal shear deformation theory (Q-3DSSDT) considering the effect of thickness stretching, compatibility conditions, and Hamilton's principle are coupled with each other regarding discovering the general motion equations and boundary domains related to the circular sector sandwich nanoplate. For considering the size effect, nonlocal quasi-3D sinusoidal strain gradient theory (NQSSGT) by employing both hardening and softening effects is considered. The NURBS-based isogeometric analysis is applied to answer the partial differential coupled equations (PDCE). In addition, the finite element method is implemented for more verification and presenting important outcomes. The novelties of this work are considering the effects of NQSSGT, placement, and dimension of the piezoelectric patch in addition to considering 3D-PFGM of the circular sector sandwich nanoplate. After obtaining the datasets of the mathematics simulation, a deep neural network algorithm is presented to test, train, and validate the presented nonlinear electrodynamics response of the current circular sector sandwich nanoplate. The results of the current nanostructure can be used in related industries of nano-robots and nano-electronic devices for future works. The findings highlight the significant influence of material gradation, poroelastic effects, and piezoelectric coupling on the vibration characteristics, offering valuable insights for the design and optimization of smart materials and structures in microelectromechani

关键词： Nonlinear electrodynamics Poroelastic circular sector sandwich nanoplate Finite element modeling deep neural network algorithm NURBS-based isogeometric analysis

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deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

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The Crop Journal 2020年第1期8卷 87-97页

作者： Xiuliang Jin Zhenhai Li Haikuan Feng Zhibin Ren Shaokun Li Institute of Crop Sciences Chinese Academy of Agricultural SciencesKey Laboratory of Crop Physiology and EcologyMinistry of AgricultureBeijing 100081China Beijing Research Center for Information Technology in Agriculture Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Northeast Institute of Geography and Agroecology Chinese Academy of SciencesChangchun 130102JilinChina

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop *** is also a key trait in increasing grain yield by crop *** aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)*** results showed that biomass was associated with all vegetation *** three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%*** was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and *** estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of *** provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

关键词： Biomass estimation Maize Vegetation indices deep neural network algorithm LAI

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Probabilistic deep neural network price forecasting based on residential load and wind speed predictions

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IET RENEWABLE POWER GENERATION 2019年第11期13卷 1840-1848页

作者： Afrasiabi, Mousa Mohammadi, Mohammad Rastegar, Mohammad Kargarian, Amin Shiraz Univ Sch Elect & Comp Engn Shiraz Iran Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA

Precise price forecasting can lessen the risk of participation in the deregulated electricity market. On account of a large amount of historical data, deep learning based methods can be a promising solution to achieve an accurate forecast. This study presents a deep neural network algorithm to estimate the probability density function of price, incorporating the prediction of wind speed and residential load as two other high volatile parameters. To this end, first, a combination of convolution neural network (CNN) and gated recurrent unit (GRU) is utilised to predict wind speed and residential load. Then, the results are integrated into historical price information to form the input dataset for price forecasting. The proposed price forecast procedure consists of CNN, GRU, and adaptive kernel density estimator (AKDE). AKDE is used as a numerical algorithm to capture probabilistic characterisation of real-time and day-ahead prices. Several deep and shallow networks and the proposed algorithm are implemented, and the results are compared. Furthermore, the effectiveness of AKDE in providing complete statistical information is verified through comparison with conventional and fixed smooth KDEs. In addition, the gradient boosting tree method is incorporated to verify the dependence of the price to the wind and the residential loads.

关键词： pricing power system economics power markets neural nets probability power engineering computing forecasting theory learning (artificial intelligence) load forecasting AKDE day-ahead prices deep networks shallow networks residential loads prediction residential load probabilistic deep neural network price forecasting wind speed predictions precise price forecasting deregulated electricity market historical data deep learning based methods accurate forecast deep neural network algorithm probability density function high volatile parameters convolution neural network CNN GRU historical price information price forecast procedure adaptive kernel density estimator

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Low-quality fingerprint classification using deep neural network

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IET BIOMETRICS 2018年第6期7卷 550-556页

作者： Tertychnyi, Pavlo Ozcinar, Cagri Anbarjafari, Gholamreza Univ Tartu Inst Comp Sci Tartu Estonia Trinity Coll Dublin Sch Comp Sci & Stat Dublin 2 Ireland Univ Tartu Inst Technol iCV Lab Tartu Estonia Hasan Kalyoncu Univ Dept Elect & Elect Engn Gaziantep Turkey GoSwift Inc Tallinn Estonia

Fingerprint recognition systems mainly use minutiae points information. As shown in many previous research works, fingerprint images do not always have good quality to be used by automatic fingerprint recognition systems. To tackle this challenge, in this work, the authors are focusing on very low-quality fingerprint images, which contain several well-known distortions such as dryness, wetness, physical damage, presence of dots, and blurriness. They develop an efficient, with high accuracy, deep neural network algorithm, which recognises such low-quality fingerprints. The experimental results have been obtained from the real low-quality fingerprint database, and the achieved results show the high performance and robustness of the introduced deep network technique. The VGG16-based deep network achieves the highest performance of 93% for dry and the lowest performance of 84% for blurred fingerprint classes.

关键词： neural nets fingerprint identification image classification high performance robustness blurred fingerprint classes minutiae points information automatic fingerprint recognition systems deep neural network algorithm VGG16 based deep network distortions low quality fingerprint classification low-quality fingerprint database low-quality fingerprint images

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Computer Vision and Pattern Recognition Technology on Account of deep neural network 4th

Computer Vision and Pattern Recognition Technology on Accoun...

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4th International Conference on Machine Learning, Image Processing, network Security and Data Sciences (MIND)

作者： Ren, Yiming Han, Jiake Chen, Chongyu Xu, Yang Bao, Tingxuan Shenzhen MSU BIT Univ Shenzhen 518172 Guangdong Peoples R China Shanghai Univ Shanghai 201900 Peoples R China Tianjin Univ Sci & Technol 1038 Dagu South Rd Tianjin Hexi Tianjin 300222 Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. Computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. This article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. Data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.

关键词： deep neural network algorithm Computer vision Pattern recognition Technical application

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A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants

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EUROPEAN JOURNAL OF REMOTE SENSING 2022年第1期55卷 372-387页

作者： Fundisi, Emmanuel Tesfamichael, Solomon G. Ahmed, Fethi Univ Johannesburg Dept Geog Environm Management & Energy Studies Johannesburg South Africa Univ Witwatersrand Geog Archaeol & Environm Studies Johannesburg South Africa

The co-existence of diverse plant forms in densely vegetated savanna environments presents a challenge when mapping species diversity using single remotely sensed data type that carries either optical or structural information. In the present study, Sentinel-1 RADAR and Sentinel-2 multispectral data were combined to classify morphologically similar woody plant species (n =27) and three coexisting land cover types using deep neural network (DNN). The fused image recorded a higher overall classification accuracy (76%) than the sole use of Sentinel-2 (72%) and Sentinel-1 RADAR data (71%). Slightly more species (15) recorded accuracies exceeding 75% using fused image compared to Sentinel-2 and Sentinel-1 data (13 species >75%). Analysis of relative band contributions resulted in high importance from Sentinel-1 C-band ratio of VH/VV polarization (8.6%) as well as a select Sentinel-2 bands (Near infrared (9.86%), Shortwave (9.5%), and Vegetation red edge (8%)). Parallel to continual efforts to improve the accuracies of fused RADAR-optical data, the services of such data for regional-scale applications should be explored to inform timely biodiversity assessments.

关键词： Savanna woody plant species diversity data fusion Sentinel-1 C-band Sentinel-2 deep neural network algorithm

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Hybrid deep learning and machine learning approach for passive image forensic

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IET IMAGE PROCESSING 2020年第10期14卷 1952-1959页

作者： Thakur, Abhishek Jindal, Neeru Thapar Inst Engn & Technol Elect & Commun Engn Dept Patiala Punjab India

Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.

关键词： Gabor filters image classification iris recognition feature extraction learning (artificial intelligence) neural nets image forensics image segmentation image coding machine learning-based approach passive image forgery detection DL algorithm forged forged categories not forged categories CMFD image manipulation dataset machine learning approach passive image forensic forgeries emerging methods deep neural network algorithm

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Augmenting the communication and engagement toolkit for CO2 capture and storage projects

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INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL 2021年 107卷 103269-103269页

作者： Buah, Eric Linnanen, Lassi Wu, Huapeng LUT Univ Sch Energy Syst FI-53851 Lappeenranta Finland

This paper revisits the Communication and Engagement Toolkit for CO2 Capture and Storage (CCS) projects proposed by Ashworth and colleagues in collaboration with the Global CCS Institute. The paper proposes a new method for understanding the social context where CCS will be deployed based on the toolkit. In practice, the proposed method can be used to harness social data collected on the CCS project. The outcome of this application is a development of a predictive tool for gaining insight into the future, to guide strategic decisions that may enhance deployment. Methodologically, the proposed predictive tool is an artificial intelligence (AI) tool. It uses fuzzy deep neural network to develop computational ability to reason about the social behavior. The hybridization of fuzzy logic and deep neural network algorithms make the predictive tool an explainable AI system. It means that the prediction of the algorithm is interpretable using fuzzy logical rules. The practical feasibility of the proposed system has been demonstrated using an experimental sample of 198 volunteers. Their perceptions, emotions and sentiments were tested using a standard questionnaire from the literature, on a hypothetical CCS project based on 26 predictors. The generalizability of the algorithm to predict future reactions was tested on, 84 out-of-sample respondents. In the simulation experiment, we observed an approximately 90 % performance. This performance was measured when the algorithm?s predictions were compared to the self- reported reactions of the out of sample subjects. The implication of the proposed tool to enhance the predictive power of the conventional CCS Communication and Engagement tool is discussed ? 2020 xx. Hosting by Elsevier B.V. All rights reserved.

关键词： Artificial intelligence CCS communication and engagement CCS toolkit CCS SWOT deep neural network algorithm Fuzzy logic Fuzzy deep learning

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Emotional responses to energy projects: A new method for modeling and prediction beyond self-reported emotion measure

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ENERGY 2020年 190卷 1页

作者： Buah, Eric Linnanen, Lassi Wu, Huapeng LUT Univ Sch Energy Syst FI-53851 Lappeenranta Finland

A considerable amount of studies report that negative emotions evoked by Wind Energy, Nuclear Energy and CO2 Capture and Storage (CCS) can lead to cancellation of the energy project or a delay in policy decisions for its implementation if not adequately addressed. Earlier studies have attempted to study this problem using self-reported emotion measurements to identify the emotions the participants felt. As an alternative, we propose the use of an emotional artificial intelligence (Al) algorithm for improved modeling and prediction of the participants' emotional behaviour to guide decision-making. We have validated the system using emotional responses to a hypothetical CCS project as a case study. Running our simulation on the experimental dataset (thus 40% of the 72,105), we obtained an average validation accuracy of 98.81%. We challenged the algorithm further with 84 test samples (unseen cases), and it predicted 75 feelings correctly when the stakeholders took a definite position on how they felt. Although there are few limitations to this study, we did find, in a sensitivity experiment, that it was challenging for the algorithm to predict indecisive feelings. The method is adaptable to study emotional responses to other projects, including Wind Energy, Nuclear Energy and Hydrogen Technology. (C) 2019 Published by Elsevier Ltd.

关键词： Artificial intelligence CO2 capture and stoarge deep neural network algorithm Environmental social science Fuzzy logic Fuzzy deep learning

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Digital twin-driven clamping force control for thin-walled parts

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ADVANCED ENGINEERING INFORMATICS 2022年 51卷

作者： Wang, Gang Cao, Yansheng Zhang, Yingfeng Northwestern Polytech Univ Sch Mech Engn Key Lab Ind Engn & Intelligent Mfg Xian Shaanxi Peoples R China State Key Lab Intelligent Mfg Syst Technol Beijing 100854 Peoples R China CNSA Innovat Ctr Liquid Rocket Engine Digital Res & De Beijing Peoples R China

Clamping quality is one of the main factors that will affect the deformation of thin-walled parts during their processing, which can then directly affect parts' performance. However, traditional clamping force settings are based on manual experience, which is a random and inaccurate manner. In addition, dynamic clamping force adjustment according to clamping deformation is rarely considered in clamping force control process, which easily causes large clamping deformation and low machining accuracy. To address these issues, this study proposes a digital twin-driven clamping force control approach to improve the machining accuracy of thin walled parts. The total factor information model of clamping system is built to integrate the dynamic information of the clamping process. The virtual space model is constructed based on finite element simulation and deep neural network algorithm. To ensure bidirectional mapping of physical-virtual space, the workflow of clamping force control and interoperability method between digital twin models are elaborated. Finally, a case study is used to verify the effectiveness and feasibility of the proposed method.

关键词： Clamping force control Digital twin Clamping deformation Finite element simulation deep neural network algorithm

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