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A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using grey Relation Analysis Coupled with Principal Component Analysiss

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Journal of Wuhan University of Technology(Materials Science) 2024年第1期39卷 160-166页

作者： A Visagan P Ganesh Department of Production Technology Madras Institute of TechnologyChennai-600044India

We investigated the parametric optimization on incremental sheet forming of stainless steel using grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

关键词： single point incremental forming AISI 316L taguchi grey relation analysis principal component analysis surface roughness scanning electron microscopy

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

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Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

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China Ocean Engineering 2024年第2期38卷 326-337页

作者： LI Hao-jie CAI Bao-ping YUAN Xiao-bing KONG Xiang-di LIU Yong-hong Javed Akbar KHAN CHU Zheng-de YANG Chao TANG An-bang College of Mechanical and Electronic Engineering China University of PetroleumQingdao 266580China Shenzhen Urban Public Safety Technology institute Shenzhen 518000China institute of Hydrocarbon Recovery Universiti Teknologi PETRONASBandar Seri IskandarPerak Darul Ridzuan 32610Malaysia

Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.

关键词： grey wolf optimizer variational modal decomposition mean envelope entropy correlation coefficient time difference of arrival

An improved interval model updating method via adaptive Kriging models

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Applied Mathematics and Mechanics(English Edition) 2024年第3期45卷 497-514页

作者： Sha WEI Yifeng CHEN Hu DING Liqun CHEN Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Frontier Science Center of MechanoinformaticsShanghai Institute of Applied Mathematics and MechanicsSchool of Mechanics and Engineering ScienceShanghai UniversityShanghai 200072China Shanghai institute of Aircraft Mechanics and Control Shanghai 200092China

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

关键词： interval model updating(IMU) non-probabilistic uncertainty adaptive Kriging model surrogate model grey number

Categorizing methods for integrating machine learning with executable specifications

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Science China(Information Sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF Department of Computer Science and Applied Mathematics Weizmann Institute of Science Department of Software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite readily.That promise is already delivering extremely impressive results in a variety of areas.However,while DL-enabled systems achieve excellent performance,they are far from perfect.It has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto encountered.Furthermore,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human engineers.At the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system behavior.In this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered here.We then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with BP.We conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

Multi-scale approach to understanding climate effects on offspring size at birth and date of birth in a reptile

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Integrative Zoology 2010年第2期5卷 164-175页

作者： ChloéD.CADBY Geoffrey M.WHILE Alistair J.HOBDAY Tobias ULLER Erik WAPSTRA School of Zoology University of Tasmania Climate Adaptation Flagship CSIRO Marine and Atmospheric ResearchHobartAustralia edward grey institute Department of ZoologyUniversity of OxfordUK

Climate change is already impacting species around the world.Although most focus has been on the effect of temperature,changes in climatic variables other than temperature are also expected to drive biological change.Current models suggest that ectotherms,such as reptiles,will be strongly affected by climate change;however,data from natural populations are rare.Here,we use extensive data from 2 populations of a viviparous lizard(Niveoscincus ocellatus Gray,1845)at the climatic extreme of the species distribution.We examine the effects of climate at a local,a regional and a global scale(thus,integrating a suite of variables at different spatial and temporal scales)on 2 key life history traits:offspring date of birth and size at birth.Overall,our results show that across 9 years of study,local temperature had strong effects on the offspring date of birth but not on the size at birth.Therefore,a rapid increase in local temperature throughout the species range(as predicted under global warming scenarios)is likely to affect phenological processes with potential concomitant effects on offspring fitness and survival.

关键词： atmospheric index climate change lizard phenology sea surface temperature

Consilience and unity in ocular anterior segment research

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International Journal of Ophthalmology(English edition) 2024年第7期17卷 1173-1183页

作者： Houmam Araj Department of Health and Human Services National Eye Institute/National Institutes of Health(NEI/NIH)BethesdaMaryland 20892USA

In his beautiful book,Consilience:The Unity of Knowledge,the eminent biologist edward O Wilson,advocates the need for integration and reconciliation across the sciences.He defines consilience as“literally a‘jumping together’of knowledge with a linking of facts…to create a common groundwork of explanation”.It is the premise of this paper that as much as basic biomedical research is in need of data generation using the latest available techniques–unifying available knowledge is just as critical.This involves the necessity to resolve contradictory findings,reduce silos,and acknowledge complexity.We take the cornea and the lens as case studies of our premise.Specifically,in this perspective,we discuss the conflicting and fragmented information on protein aggregation,oxidative damage,and fibrosis.These are fields of study that are integrally tied to anterior segment research.Our goal is to highlight the vital need for Wilson’s consilience and unity of knowledge which in turn should lead to enhanced rigor and reproducibility,and most importantly,to greater understanding and not simply knowing.

关键词： anterior segment ocular surface cornea lens cataract posterior capsular opacification protein aggregation oxidative damage antioxidants fibrosis wound healing consilience

Energy Planning of Beijing Towards Low-carbon,Clean and Efficient Development in 2035

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 913-928页

作者： Chengzhou Li Ningling Wang Xiaohu Shen Yumeng Zhang Zhiping Yang Xiaofeng Tong François Maréchal Ligang Wang Yongping Yang institute of Energy Power Innovation North China Electric Power UniversityBeijing 102206China School of Energy Power and Mechanical Engineering and National Research Center for Thermal Power Engineering and TechnologyNorth China Electric Power UniversityBeijing 102206China Industrial Process and Energy Systems Engineering Swiss Federal Institute of Technology in LausanneSionSwitzerland

Energy transition towards clean,efficient energy supply has been a common sense of the government and public in China.However,lacking reasonable planning will lead to undisciplined development,resource waste,and excessive investment.In this context,this paper investigates potential pathways of Beijing energy transition towards a high-level low-carbon,clean and efficient energy system in 2035 with an extended energysocpe model.Firstly,based on available data,future energy demands are predicted by a newly proposed hybrid forecasting method,which combines the traditional regression model,grey model,and support vector machine model with an entropy-based weighted factor.Secondly,the superstructure-based optimization model is employed to investigate the system configuration and operation strategy of the future Beijing energy system.Finally,the uncertainty impact of electricity price,natural gas price,hydrogen price,and the capital expenditures of electrolyzer and steam methane reforming for hydrogen applications are studied.The forecasting results show that all walks of life will witness a continuously increasing energy demand in multiple sectors of Beijing towards 2035.The planning results suggest that the imported electricity and natural gas will dominate the energy supply of Beijing in 2035 with a contribution of 86%of the energy resources consumption of 384 TWh.Moreover,the energy system presents a high end-use electrification level of 65%and high penetration of efficient technologies,which supply 119 TWh via combined heat and power,26 TWh via heat pump and 95 TWh via district heating network.The energy use of various sectors of energy resources,technologies and end-use are closely related.Hydrogen will have an increased penetration in the private mobility sector,but the locally generated hydrogen is mainly from steam methane reforming technology.

关键词： Energy transition energy planning carbonneutral demand forecasting energyscope

Assessing efficacy of standard impregnation techniques on die-cast aluminum alloys using X-ray micro-CT

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China Foundry 2024年第3期21卷 276-286页

作者： Ajith Bandara Koichi Kan Katanaga Yusuke Natsuto Soga Akifumi Koike Toru Aoki Hamamatsu TSC Chuo Hatsumei Institute322 lida-choMinami-kuHamamatsu435-0028Japan Graduate School of Medical Photonics ShizuokaUniversityJohoku 3-5-1HamamatsuShizuoka432-8561Japan ANSeeN Inc Johoku 3-5-1HamamatsuShizuoka432-8561Japan Research institute of Electronics ShizuokaUniversityJohoku 3-5-1HamamatsuShizuoka432-8561Japan

Utilizing lightweight Al alloys in various industrial applications requires achieving precise pressure tightness and leak requirements.Vacuum pressure impregnation(VPI)with thermosetting polymers is commonly used to address leakage defects in die-cast Al alloys.In this study,the efficacy of the VPI technique in sealing alloy parts was investigated using a combination of nondestructive micro X-ray computed tomography(micro XCT)and a standard leak test.The results demonstrate that the commonly used water leak test is insufficient for determining the sealing performance.Instead,micro XCT shows distinct advantages by enabling more comprehensive analysis.It reveals the presence of a low atomic number impregnates sealant within casting defects,which has low grey contrast and allows for visualizing primary leakage paths in 3D.The effective atomic number of impregnated resin is 6.75 and that of Al alloy is 13.69 by dual-energy X-ray CT.This research findings will contribute to enhancing the standard VPI process parameters and the properties of impregnating sealants to improve quality assurance for impregnation in industrial metals.

关键词： nondestructive testing Al alloy die-casting vacuum pressure impregnation micro X-ray computed tomography duel-energy X-ray CT