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A new Fermatean fuzzy Spearman-like correlation coefficient and its application in evaluating insecurity problem via multi-criteria decision-making approach

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Heliyon 2024年第22期10卷 e40403页

作者： Ejegwa, Paul Augustine Kausar, Nasreen Aydin, Nezir Feng, Yuming Olanrewaju, Oludolapo Akanni Department of Mathematics Joseph Sarwuan Tarka University Makurdi Nigeria Department of Mathematics Faculty of Arts and Science Yildiz Technical University Esenler Istanbul 34220 Turkey College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Industrial Engineering Yildiz Technical University Besiktas Istanbul 34349 Turkey Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing 404100 China Department of Industrial Engineering Durban University of Technology Durban 4001 South Africa

The problem of insecurity is a global crisis with adverse effects on lives and properties. Fermatean fuzzy correlation coefficient is a dependable method for handling imprecision, which is the main bottleneck of insecurity assessment. A number of Fermatean fuzzy correlation coefficient methods have been developed. Based on Spearman's correlation coefficient, an innovative Fermatean fuzzy correlation coefficient method is built to enhance trustworthy insecurity assessment. The existing Fermatean fuzzy correlation coefficient methods are evaluated, and their shortcomings are identified in order to validate the construction of a new Fermatean fuzzy correlation coefficient method. The drawbacks of the existing methods lead us into building a new Fermatean fuzzy correlation coefficient method by using the Spearman's correlation coefficient approach, which has the potential of overcoming the drawbacks of the existing Fermatean fuzzy correlation coefficient methods. In addition, some theoretical findings are provided to support the strength of the novel Fermatean fuzzy correlation coefficient method and it is shown that the new method satisfies the Fermatean fuzzy correlation coefficient requirements. Furthermore, the novel Fermatean fuzzy correlation coefficient method is applied to assess the insecurity situation in the North-Central Region of Nigeria to furnish intended tourists with relevant travel advice. To demonstrate the inherent significance of the novel Fermatean fuzzy correlation coefficient method, we compare its effectiveness to that of the extant Fermatean fuzzy correlation coefficient methods. The results of the comparison show the superiority of the novel Fermatean fuzzy correlation coefficient method over the existing ones in terms of reliability, consistency, precision and compliance with the Fermatean fuzzy correlation coefficient axioms. Ultimately, it is discovered that the new method can effectively address hesitations related to insecurity assessment

关键词： Correlation coefficient Fermatean fuzzy sets Insecurity assessment Multi-criteria decision-making North central region of Nigeria Security crises

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Fractional Modeling and Dynamic Analysis of Two Kinds of Viscoelastic Thin Plates with Gaussian White Noise

SSRN

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SSRN 2023年

作者： Deng, Jinna Liu, Qiumei Yang, Aimin Chen, Yiming College of Science North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Tangshan063210 China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Tangshan063210 China School of Science Yanshan University Qinhuangdao066004 China LE STUDIUM Loire Valley Institute for Advanced Studies Orléans & Tours Orléans45000 France

According to fractional order model, the governing equation of viscoelastic plate is established. Using the properties of the shifted Bernstein polynomials, the fractional order equation of viscoelastic plate is resolved, the conversion of fractional differential equations to algebraic equations, and operator matrices represented by Kronecker products are solved in the domain. The convergence study validates the algorithm's efficacy and precision in solving differential equations. The displacement of viscoelastic metal thin plate under different external loads (uniform distribution and linear distribution), the effect of white Gaussian noise on the thin metal plate displacement, the effect of different boundary conditions on thin metal plate displacement under harmonic load are studied, the displacements of metal and polymer plates under different loads are contrasted and stress and strain relationship of two kinds of plates are discussed. Based on the outcomes, it can be concluded that the shift Bernstein polynomial approach is useful for numerically simulating viscoelastic plates. © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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TSW-FD: A Novel Temporal and Spatial Domain Weight Analysis of Feature Difference for Micro-Expression Spotting

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Zhihao Zhang Fan Mo Ke Zhao Tong Chen Xiaolan Fu State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences Beijing 100101 China Department of Psychology University of Chinese Academy of Sciences Beijing 100049 China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University Chongqing 400715 China Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology Chongqing 400715 China

The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.

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FDTD model of photonic crystal laser with four-energy-level gain media

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Jisuan Wuli/Chinese Journal of Computational Physics 2007年第4期24卷 452-456页

作者： Wang, Hong Ouyang, Zhengbiao Han, Yanling Meng, Qingsheng Center of THz Technology Shenzhen University Shenzhen 518060 China College of Engineering and Technology Shenzhen University Shenzhen 518060 China School of Physical Electronics University of Electronic Science and Technology of China Chengdu 610054 China Mathematics and Physics Department China University of Geosciences Wuhan 430074 China State Education Commission Laboratory for Image Processing and Intelligent Control Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

A model of one-dimensional photonic crystal laser with four-energy-level gain media is presented. The model consists of Maxwell-Bloch equations coupled with rate equations of electronic population in a photonic crystal laser system. The discretization of coupled equations is accomplished with finite different time domain (FDTD). Spectra, lasing mode, lasing threshold and dynamics of the photonic crystal laser are obtained. The simulation shows good agreement with theoretical predictions.

关键词： Photonic crystals

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Vehicle detection based on point cloud intensity and distance clustering

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zhao Wang Xing Wang Bin Fang Kun Yu Jie Ma School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National key laboratory of multispectral information processing technology Huazhong University of Science and Technology Wuhan 430074 China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing 100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.

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Just Use Epigallocatechin-3-Gallate for Stable Nanoparticle Assembly of Hydrophobic Polyphenols

SSRN

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SSRN 2023年

作者： Lin, Ruge Wang, Yi Cheng, Huan Chen, Shiguo Ye, Xingqian Pan, Haibo College of Biosystems Engineering and Food Science National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang Key Laboratory for Agro-Food Processing Integrated Research Base of Southern Fruit and Vegetable Preservation Technology Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control Zhejiang University Hangzhou310058 China Innovation Center of Yangtze River Delta Zhejiang University Jiaxing314102 China Fuli Institute of Food Science Zhejiang University Hangzhou310058 China Ningbo Research Institute Zhejiang University Ningbo315100 China Zhejiang University Zhongyuan Institute Zhengzhou450000 China Institute of Modern Agriculture Zhejiang University Linyi276000 China

Curcumin holds significant potential for physiological efficacy, but its practical application is hindered by its extremely low water solubility. In this study, we just added EGCG to form nanoparticles with curcumin using the self-assembly method, with a particle size of 120-150 nm. UV-Vis analysis and docking simulations indicated π-π stacking between curcumin and EGCG. Curcumin encapsulated with EGCG showed exceptional dispersibility in aqueous solutions, with 90.92% remaining after 60 days. The electrostatic screening effect from the charge imparted to the nanoparticles by EGCG, resulting in increased curcumin retention across varying pH, temperature, and ionic strength.. EGCG also interacts with other hydrophobic polyphenols, such as hesperidin and resveratrol, improving their dispersibility and stability in aqueous system. Leveraging this principle, we created a flavorful beverage combining turmeric extract and green tea. These EGCG-encapsulated nanoparticles hold promise for enhancing curcumin's application in aqueous food systems. © 2023, The Authors. All rights reserved.

关键词： Nanoparticles

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AIM 2024 Challenge on Video Saliency Prediction: Methods and Results

arXiv

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arXiv 2024年

作者： Zhan, Gen Yang, Li Tang, Yunlong Liao, Yiting Lin, Jiongzhi Huang, Baitao Moradi, Morteza Moradi, Mohammad Rundo, Francesco Spampinato, Concetto Borji, Ali Palazzo, Simone Zhu, Yuxin Sun, Yinan Duan, Huiyu Cao, Yuqin Jia, Ziheng Hu, Qiang Min, Xiongkuo Zhai, Guangtao Fang, Hao Cong, Runmin Lu, Xiankai Zhou, Xiaofei Zhang, Wei Zhao, Chunyu Mu, Wentao Deng, Tao Tavakoli, Hamed R. Moskalenko, Andrey Bryncev, Alexey Vatolin, Dmitry Timofte, Radu ByteDance China ByteDance United States University of Rochester United States College of Electronic and Information Engineering Shenzhen University China Shenzhen Key Laboratory of Digital Creative Technology China Guangdong Key Laboratory of Intelligent Information Processing China University of Catania Italy STMicrolectronics ADG Central R&D Italy Quintic AI San Francisco United States Shanghai Jiao Tong University China School of Control Science and Engineering Shandong University China School of Software Shandong University China School of Automation Hangzhou Dianzi University China School of Information Science and Technology Southwest Jiaotong University China Nokia Technologies Finland Lomonosov Moscow State University Russia AIRI Moscow Russia MSU Institute for Artificial Intelligence Russia University of Würzburg Germany

This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://***/challenges/***. © 2024, CC BY.

关键词： Video analysis

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Deep learning models for EEG-based rapid serial visual presentation event classification

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Journal of Information Hiding and Multimedia Signal processing 2018年第1期9卷 177-187页

作者： Zhang, Fu-Quan Mao, Zi-Jing Huang, Yu-Fei Xu, Lin Ding, Gang Yi Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Software Beijing Institute of Technology Room A9-301 Juyuan Jinshan Jiyuan Fuzhou350008 China Department of Electronic Engineering University of Texas at San Antonio One UTSA Circle San Antonio Arrow Oaks 126 San AntonioTX78249 United States Innovative Information Industry Research Institute Fujian Normal University Fuqing Brunch of Fujian Normal University Campus County 1 Longjiang Road FuqingFuzhou350300 China School of Software Beijing Institute of Technology Room 611 Software Building 5 South Zhongguancun Street Haidian Beijing100081 China

We consider deep learning (DL) for event classification using electroencephalogram (EEG) measurements of brain activities. We proposed HDNN or hierarchical deep neural network, and CNN4EEG, a new convolution neural network (CNN). Both DL models are designed to improve the representation of spatial and local temporal correlations inherent in EEG data. These models were tested for image target prediction in a time-locked rapid serial visual presentation (RSVP) experiment. The performances were compared with the state-of-the-art RSVP classification algorithms including HDCA and XDAWN and with other popular machine learning algorithms. The results show that global spatial local temporal CNN (CNN4EEG) achieved a 13% improvement over the best competing non-DL algorithm and a 9% improvement over canonical CNN for image processing, and a 6% over deep neural network (DNN). Our results suggest that the unique design of temporal and spatial filters in CNN4EEG can improve the representation of EEG data, hence the prediction performance. © 2018, Ubiquitous International. All rights reserved.

关键词： Deep neural networks

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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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