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Fully Completed Spherical Fuzzy Approach-Based Z Numbers(PHIModel)for Enhanced Group Expert Consensus

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Computers, Materials & Continua 2024年第7期80卷 1655-1675页

作者： Phi-hung Nguyen Research Center of Applied Sciences Faculty of BusinessFPT UniversityHanoi100000Vietnam

This study aims to establish an expert consensus and enhance the efficacy of decision-making processes by integrating Spherical Fuzzy Sets(SFSs)and Z-Numbers(SFZs).A novel group expert consensus technique,the PHImodel,is developed to address the inherent limitations of both SFSs and the traditional Delphi technique,particularly in uncertain,complex *** such contexts,the accuracy of expert knowledge and the confidence in their judgments are pivotal *** study provides the fundamental operational principles and aggregation operators associated with SFSs and Z-numbers,encompassing weighted geometric and arithmetic operators alongside fully developed operators tailored for SFZs ***,a case study and comparative analysis are conducted to illustrate the practicality and effectiveness of the proposed operators and *** the PHI model with SFZs numbers represents a significant advancement in decision-making frameworks reliant on expert ***,this combination serves as a comprehensive tool for decision-makers,enabling them to achieve heightened levels of consensus while concurrently assessing the reliability of expert *** case study results demonstrate the PHI model’s utility in resolving complex decision-making scenarios,showcasing its ability to improve consensus-building processes and enhance decision ***,the comparative analysis highlights the superiority of the integrated approach over traditional methodologies,underscoring its potential to revolutionize decision-making practices in uncertain environments.

关键词： Spherical fuzzy sets Delphi method Z-numbers expert consensus PHI model uncertainty

Identifying Brand Consistency by Product Differentiation Using CNN

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 685-709页

作者： hung-Hsiang Wang Chih-Ping Chen Department of Industrial Design National Taipei University of TechnologyTaipeiTaiwan Department of Product Design Ming Chuan UniversityTaoyuanTaiwan

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance *** Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and *** show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various *** investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late *** show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real *** relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style *** addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred *** study provides insights into practical problems for designers,manufacturers,and marketers in product *** not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand *** can use these techniques to find features that influence brand ***,capture these features as innovative design elements and maintain core brand values.

关键词： Machine learning product differentiation brand consistency principal component analysis convolutional neural network computer mouse

Exponentially Bounded C-semigroup and the Cauchy Initial Value Problems in Complete Random Normed Modules

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Acta Mathematica Sinica,English Series 2024年第9期40卷 2195-2212页

作者： Son TA CONG Thang Dang hung Oanh Le THI Department of Mathematics University of ScienceVietnam National University 334 Nguyen TraiThanh XuanHanoiVietnam Faculty of Natural Sciences Hong Duc University565 Quang TrungDong VeThanh HoaVietnam

In this paper,the notion of C-semigroup of continuous module homomorphisms on a complete random normal(RN)module is introduced and *** existence and uniqueness of solution to the Cauchy problem with respect to exponen... 详细信息

关键词： C-semigroup Cauchy problem random normed modules L^(0)-Lipschitz

Data-Driven Modeling of Partially Observed Biological Systems

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Communications on Applied Mathematics and Computation 2024年第1期6卷 739-754页

作者： Wei-hung Su Ching-Shan Chou Dongbin Xiu Department of Mathematics The Ohio State UniversityColumbusOH43221USA

We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical *** an extension of the recently developed deep neural network(DNN)learning methods,our approach is particularly suitable for practical situations when(i)measurement data are available for only a subset of the state variables,and(ii)the system parameters cannot be observed or measured at *** demonstrate that,with a properly designed DNN structure with memory terms,effective DNN models can be learned from such partially observed data containing hidden *** learned DNN model serves as an accurate predictive tool for system *** a few representative biological problems,we demonstrate that such DNN models can capture qualitative dynamical behavior changes in the system,such as bifurcations,even when the parameters controlling such behavior changes are completely unknown throughout not only the model learning process but also the system prediction *** learned DNN model effectively creates a“closed”model involving only the observables when such a closed-form model does not exist mathematically.

关键词： Deep neural network(DNN) Governing equation discovery Biological system Partial observation

Study of relationship between motion of mechanisms in gas operated weapon and its shock absorber

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Defence Technology（防务技术） 2024年第3期33卷 42-54页

作者： Jiri Balla Roman Vitek Dung Nguyen Van Zbynek Krist hung Nguyen Van Department of Weapons and Ammunition University of Defence BrnoCzech Republic Department of Weapons Le Quy Don Technical UniversityHanoiViet Nam Department of Design and Special Engineering Alexander Dubcek University of TrencinSlovakia

The article deals with the motion of the breech block carrier and the weapon casing of an automatic weapon mounted on a flexible carriage and the base of the *** works,which did not consider the dynamic properties of the base of the weapon,did not allow to reconcile the calculated and experimental results of the weapon casing displacement when shooting from firing *** the analysis of the motion of individual parts,the methods of mathematical modelling and firing experiments using a high-speed camera were *** show the best accord with experiment when modelling the system with 4 degrees of *** oscillation of the system regarding the movement of the breech block carrier and the weapon casing was investigated under changed conditions of rate of fire,the use of a muzzle brake and different types of shock *** velocities and displacements of the weapon casing and the breech block carrier at different values of the impulse of the gases to the breech block carrier were determined.

关键词： Shock absorber Gas-operated weapon Force-impulse diagram Recoil system Breech block carrier

Heterogeneous decentralised machine unlearning with seed model distillation

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 608-619页

作者： Guanhua Ye Tong Chen Quoc Viet hung Nguyen Hongzhi Yin School of Information Technology and Electrical Engineering The University of QueenslandSt LuciaQueenslandAustralia School of Information and Communication Technology Griffith UniversityBrisbaneQueenslandAustralia

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model,personalised IoT service pro-viders have to put unlearning functionality into their *** most straight-forward method to unlearn users'contribution is to retrain the model from the initial state,which is not realistic in high throughput applications with frequent unlearning *** some machine unlearning frameworks have been proposed to speed up the retraining process,they fail to match decentralised learning scenarios.A decentralised unlearning framework called heterogeneous decentralised unlearning framework with seed(HDUS)is designed,which uses distilled seed models to construct erasable en-sembles for all ***,the framework is compatible with heterogeneous on-device models,representing stronger scalability in real-world *** experiments on three real-world datasets show that our HDUS achieves state-of-the-art performance.

关键词： data mining data privacy machine learning

Forecasting measured responses of structures using temporal deep learning and dual attention

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 832-850页

作者： Viet-hung DANG Trong-Phu NGUYEN Thi-Lien PHAM Huan X.NGUYEN Department of Structural Mechanics Hanoi University of Civil EngineeringHanoi 11600Vietnam Faculty of Applied Science University of Economics-Technology for IndustriesHanoi 11600Vietnam London Digital Twin Research Centre Faculty of Science and TechnologyMiddlesex UniversityLondon NW44BTUK International School Vietnam National UniversityHanoi 11600Vietnam

The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random *** key idea is to design a deep learning architecture to leverage the relationships,between external excitations and structure's vibration signals,and between historical values and future values,within multiple time-series *** proposed method consists of two main steps:the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture;the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future *** viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional(3D)reinforced concrete structure and experimental data from an 18-story steel ***,comparison and robustness studies are carried out,showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.

关键词： structural dynamic time-varying excitation deep learning signal processing response forecasting

A Projection and Decomposition Approach for Multi-agent Coordinated Scheduling in Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 991-996页

作者： Haifeng Qiu Zhigang Li Hongjun Gao hung Dinh Nguyen Veerapandiyan Veerasamy Hoay Beng Gooi the School of Electrical and Electronic Engineering Nanyang Technological University639798 Singapore the School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China the College of Electrical Engineering Sichuan UniversityChengdu 610065China

Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty,a generic projection and decomposition(P&D)approach is proposed in this *** canonical min-max-min two-stage robust optimization(TSRO)model with coupling constraints is equivalent to a concise robust optimization(RO)model in the version of mixed-integer linear programming(MILP)via feasible region *** decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm,which ensures the fast convergence to a high-quality solution in the distributed *** tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.

关键词： Coordinated scheduling multi-agent system distributed robust optimization projection and decomposition

Autolysosomal acidification impairment as a mediator for TNFR1 induced neuronal necroptosis in Alzheimer’s disease

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Neural Regeneration Research 2024年第9期19卷 1869-1870页

作者： Evridiki Asimakidou Richard Reynolds Anna M.Barron Chih hung Lo Department of Clinical Neurosciences University of CambridgeCambridgeUK Department of Brain Sciences Faculty of MedicineImperial College LondonLondonUK Lee Kong Chian School of Medicine Nanyang Technological UniversitySingaporeSingapore

Neuronal necroptosis-an emerging form of regulated cell death associated with neuroinflammatory signaling:Alzheimer’s disease(AD)is characterized by the presence of extracellular amyloid-β(Aβ)plaques and intracellular tau neurofibrillary tangles as well as progressive neuronal *** evidence has suggested that prolonged neuroinflammation with increased levels of cytokines,arising from neuronal injury,innate immune responses from glial cells,and peripheral inflammation,leads to neuronal death and AD progression.

关键词： Alzheimer death tau Aβ