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Cognitive-emotional feasibility of the effect of visual quality of building form on promoting the sense of place attachment(Case study:Cultural iconic buildings of Iran’s contemporary architecture)

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Frontiers of Architectural Research 2024年第1期13卷 37-56页

作者： Narmin Ariannia Negar Naseri Mansour Yeganeh Digital Architecture and Artificial Intelligence Lab Department of ArchitectureTarbiat Modares UniversityTehranIran Architectural design Modeling and Fabrication LabDepartment of ArchitectureTarbiat Modares UniversityTehranIran

The present research studies the relationship between place attachment and the perception of form’s visual quality in fifteen outstanding contemporary Iranian architectural cultural *** study puts forward the hypothesis that there is a correlation between the quality of building form and the sense of place attachment,in the sense that creating high visual quality through enhancing the quality of building form increases citizens’initial satisfaction with and subsequent attachment to the *** visual quality influences people’s experience of the environment and improves the quality of *** attachment highlights how people,on a personal level,recreate a sense of place for *** present study adopts the descriptive-analytical method as its theoretical framework and the survey as the empirical *** were developed using the Likert scale and distributed among experts and ordinary *** analysis using SPSS and the adoption of descriptive-analytical statistics,correlation analysis,and regression showed the relationship among the characteristics of *** results show a positive correlation between form and place attachment mediated through visual quality,and they are causal conditions for one *** addition,only some of the buildings under study evoke the same level of place attachment.

关键词： Visual preferences Environmental quality Environmental psychology Mental schemas Contemporary architecture

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

On the superconvergence of a WG method for the elliptic problem with variable coefficients

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Science China Mathematics 2024年第8期67卷 1899-1910页

作者： Junping Wang Xiaoshen Wang Xiu Ye Shangyou Zhang Peng Zhu Division of mathematical Sciences National Science FoundationAlexandriaVA 22314USA department of Mathematics University of Arkansas at Little RockLittle RockAR 72204USA department of mathematical Sciences University of DelawareNewarkDE 19716USA College of Data Science Jiaxing UniversityJiaxing 314001China

This article extends a recently developed superconvergence result for weak Galerkin(WG)approximations for modeling partial differential equations from constant coefficients to variable *** superconvergence features a rate that is two orders higher than the optimal-order error estimates in the usual energy and L^(2)*** extension from constant to variable coefficients for the modeling equations is highly *** underlying technical analysis is based on a sequence of projections and *** results confirm the superconvergence theory for second-order elliptic problems with variable coefficients.

关键词： weak Galerkin finite element methods superconvergence second-order elliptic problems stabilizerfree

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

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Advances in Atmospheric Sciences 2024年第7期41卷 1299-1312页

作者： Ya WANG Gang HUANG Baoxiang PAN Pengfei LIN Niklas BOERS Weichen TAO Yutong CHEN BO LIU Haijie LI State Key Laboratory of Numerical modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Earth System Modelling School of Engineering and DesignTechnical University of MunichOttobrunn 85521Germany Potsdam Institute for Climate Impact Research Potsdam 14412Germany department of Mathematics and Global Systems Institute University of ExeterExeter EX44QEUK CMA Earth System modeling and Prediction Centre(CEMC) Chinese Academy of Meteorological SciencesBeijing 100081China

Climate models are vital for understanding and projecting global climate change and its associated ***,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future *** these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning ***,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST ***,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to ***,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to *** enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales *** study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

关键词： generative adversarial networks model bias deep learning El Niño-Southern Oscillation marine heatwaves

modeling the Physical Parameters of Solenoids for MEMS Applications

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Journal of Applied Mathematics and Physics 2024年第5期12卷 1819-1834页

作者： Mykhaylo Andriychuk Bohdan Karkulovskyi Yarko Kuleshnyk department of Computer design System Lviv Polytechnic National University Lviv Ukraine department of Numerical Methods of mathematical Physics Pidstryhach Institute for Applied Problems of Mechanics and Mathematics NASU Lviv Ukraine

The paper is devoted to study of the electrical parameters of the motion parts of the MEMS such as solenoids. The analytical background is given in order to describe the influence of the electrical field components on the forces, which are result of interaction of the electromagnetic (EM) field components with the parts of motion devices of MEMS. The given analytical formulas open the ability to calculate the self-inductance of the microsolenoids of the different kind, as well as the stored energy of such motion devices, that could be used for the modeling and optimization of parameters of running devices of MEMS such as actuators, sensors etc.

关键词： EM Description Motion Microdevice Microsolenoid Stored Energy Computational Data

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 141-156页

作者： Luigi D’Alfonso Francesco Giannini Giuseppe Franzè Giuseppe Fedele Francesco Pupo Giancarlo Fortino IEEE the department of Computer Engineering ModelingElectronics and SystemsUniversitàdella CalabriaVia Pietro BucciCubo 42-CRende(CS)87036Italy the department of Mechanical Engineering Energy Engineering and ManagementUniversitàdella CalabriaVia Pietro BucciCubo 42-CRende(CS)87036Italy

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the a

关键词： Distributed model predictive control distributed reinforcement learning routing decisions urban road networks

In search of optimal building behavior models for model predictive control in the context of flexibility

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Building Simulation 2024年第1期17卷 71-91页

作者： Arash Erfani Tohid Jafarinejad Staf Roels Dirk Saelens KU Leuven Department of Civil EngineeringBuilding Physics and Sustainable Design SectionKasteelpark Arenberg 40 Bus 24473001 HeverleeBelgium EnergyVille Thor Park 8310Genk 3600Belgium

Model predictive control(MPC)is an advanced control *** has been deployed to harness the energy flexibility of a *** requires a dynamic model of the building to achieve such an ***,developing a suitable predictive model is the main challenge in MPC implementation forflexibility *** studyfocuses on the application of key performance indicators(KPls)to evaluate the suitability of MPC models via feature *** this end,multiple models were developed for two houses.A feature selection method was developed to select an appropriate feature space to train the *** predictive models were then quantified based on one-step ahead prediction error(OSPE),a standard KPI used in multiple studies,and a less-often KPl:multi-step ahead prediction error(MSPE).An MPC workflow was designed where different models can serve as the predictive *** showed that MSPE better demonstrates the performance of predictive models used for flexibility *** revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.

关键词： building flexibility model predictive control for building building behaviour identification smart home

Combining Deep Learning with Knowledge Graph for design Knowledge Acquisition in Conceptual Product design

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工程与科学中的计算机建模(英文) 2024年第1期138卷 167-200页

作者： Yuexin Huang Suihuai Yu Jianjie Chu Zhaojing Su Yangfan Cong Hanyu Wang Hao Fan Key Laboratory of Industrial design and Ergonomics Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi’an710072China School of Industrial design Engineering Delft University of TechnologyDelft2628 CEThe Netherlands department of Industrial design College of ArtsShandong University of Science and TechnologyTsingtao266590China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product *** study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge ***,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product ***,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction *** comparison verified the effectiveness and accuracy of the proposed knowledge extraction *** case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

关键词： Conceptual product design design knowledge acquisition knowledge graph entity extraction relation extraction

Analysis of Progressively Type-Ⅱ Inverted Generalized Gamma Censored Data and Its Engineering Application

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Computer modeling in Engineering & Sciences 2024年第10期141卷 459-489页

作者： Refah Alotaibi Sanku Dey Ahmed Elshahhat department of mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of Statistics St.Anthony’s CollegeShillong793001India Faculty of Technology and Development Zagazig UniversityZagazig44519Egypt

A novel inverted generalized gamma(IGG)distribution,proposed for data modelling with an upside-down bathtub hazard rate,is *** many real-world practical situations,when a researcher wants to conduct a comparative study of the life testing of items based on cost and duration of testing,censoring strategies are frequently *** this point of view,in the presence of censored data compiled from the most well-known progressively Type-Ⅱ censoring technique,this study examines different parameters of the IGG *** a classical point of view,the likelihood and product of spacing estimation methods are *** Fisher information and the deltamethod are used to obtain the approximate confidence intervals for any unknown parametric function of the *** the Bayesian paradigm,the same traditional inferential approaches are used to estimate all unknown ***-Chain with Monte-Carlo steps are considered to approximate all Bayes’*** numerical comparisons are presented to examine the performance of the proposed methodologies using various criteria of ***,using several optimality criteria,the optimumprogressive censoring design is *** highlight how the proposed estimators can be used in practice and to verify the flexibility of the proposed model,we analyze the failure times of twenty mechanical components of a diesel engine.

关键词： Inverted generalized gamma censoring spacing function likelihood Bayesian optimal plan