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CFP:A Coherence-Free Processor Design

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Journal of Computer Science & Technology 2024年第1期39卷 99-102页

作者：杨光诺 mount Gilead OH 43338U.S.A

This paper presents the design of a Coherence-Free Processor(CFP)that enables a scalable multiprocessor by eliminating cache coherence operations in both hardware and *** CFP uses a coherence-free cache(CFC)that can improve the cost-effectiveness and performance-effectiveness of the existing multiprocessors for commonly used *** CFC is feasible because not all program data that reside in a multiprocessor cache need to be accessed by other processors,and private caches at level 1(L1)and level 2(L2)facilitate this method of *** programs are specifically designed to protect their data from modification by other *** data that are modified but not shared with other tasks do not require a coherence *** processors reduces the multitasking queue,reducing elapsed *** execution replaces concurrent execution.

关键词： multiprocessor multitasking cache coherence snoopy

machine learning-guided accelerated discovery of structure-property correlations in lean magnesium alloys for biomedical applications

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Journal of magnesium and Alloys 2024年第6期12卷 2267-2283页

作者： Sreenivas Raguraman maitreyee Sharma Priyadarshini Tram Nguyen Ryan mcGovern Andrew Kim Adam J.Griebel Paulette Clancy Timothy P.Weihs Department of materials Science and Engineering Johns Hopkins University3400 N Charles StBaltimore21218MDU.S.A Hopkins Extreme materials Institute Johns Hopkins University3400 N Charles StBaltimore21218MDU.S.A Department of Chemical and Biomolecular Engineering Johns Hopkins University3400 N Charles StBaltimore21218MDU.S.A Translational Tissue Engineering Center Johns Hopkins School of Medicine400 N BroadwayBaltimore21231MDU.S.A Department of Biomedical Engineering Johns Hopkins University3400 N Charles StBaltimore21218MDU.S.A Research and Development Fort Wayne Metals Research ProductsLLC9609 Ardmore AveFort Wayne46809INU.S.A Department of mechanical Engineering Johns Hopkins University3400 N Charles StBaltimore21218MDU.S.A

magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability,biocompatibility,and impressive mechanical ***,their rapid in-vivo degradation presents challenges,notably in upholding mechanical integrity over *** study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean mg-Zn-Ca-mn alloy,*** rapid,cost-efficient characterization methods like X-ray diffraction and optical microscopy,we swiftly examine microstructural changes post-thermal *** Pearson correlation coefficient analysis,we unveil the relationship between microstructural properties and critical targets(properties):hardness and corrosion ***,leveraging the least absolute shrinkage and selection operator(LASSO),we pinpoint the dominant microstructural factors among closely correlated *** findings underscore the significant role of grain size refinement in strengthening and the predominance of the ternary Ca_(2)mg_(6)Zn_(3)phase in corrosion *** suggests that achieving an optimal blend of strength and corrosion resistance is attainable through fine grains and reduced concentration of ternary *** thorough investigation furnishes valuable insights into the intricate interplay of processing,structure,and properties in magnesium alloys,thereby advancing the development of superior biodegradable implant materials.

关键词： magnesium alloys machine learning Corrosion mechanical properties Rapid characterization

A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering Algorithms

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Journal of Computer Science & Technology 2024年第3期39卷 610-636页

作者： Jagat Sesh Challa Navneet Goyal Amogh Sharma Nikhil Sreekumar Sundar Balasubramaniam Poonam Goyal Advanced Data Analytics and Parallel Technologies Laboratory Birla Institute of Technology and Science Pilani 333031India uber New York 11101U.S.A. Computer Science and Engineering Department University of MinnesotaMinneapolis 55455U.S.A.

The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as mPI,mapReduce,and *** important step for any parallel clustering algorithm is the distribution of data amongst the cluster *** step governs the methodology and performance of the entire *** typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the ***,these strategies are generic and are not tailor-made for any specific parallel clustering *** this paper,we give a very comprehensive literature survey of mPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they *** also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution *** of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load *** experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

关键词： parallel data mining data distribution parallel clustering spatial locality preservation

Assimilation of GOES-R Geostationary Lightning mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

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Advances in Atmospheric Sciences 2024年第2期41卷 263-277页

作者： Rong KONG ming XuE Edward R.mANSELL Chengsi LIu Alexandre O.FIERRO Center for Analysis and Prediction of Storms Norman Oklahoma 73072U.S.A university of Oklahoma Norman Oklahoma 73072U.S.A NOAA/National Severe Storms Laboratory NormanOklahoma 73072U.S A.4Zentralanstalt für meteorologie und Geodynamik Department of Forecasting Models-ZAMGVienna 1190Austria

Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning mapper(GLm) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(mCS) case. In this study, such capabilities are further developed to assimilate GOES GLm FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLm FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.

关键词： GOES-R lightning data assimilation EnKF EnVar

VPI:Vehicle Programming Interface for Vehicle Computing

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Journal of Computer Science & Technology 2024年第1期39卷 22-44页

作者：吴宝福仲任王昱心万健张纪林施巍松 School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Department of Computer Science Wayne State UniversityDetroitMI 48202U.S.A Department of Computer and Information Sciences University of DelawareNewarkDE 19716U.S.A

The emergence of software-defined vehicles(SDVs),combined with autonomous driving technologies,has en-abled a new era of vehicle computing(VC),where vehicles serve as a mobile computing ***,the interdisci-plinary complexities of automotive systems and diverse technological requirements make developing applications for au-tonomous vehicles *** simplify the development of applications running on SDVs,we propose a comprehen-sive suite of vehicle programming interfaces(VPIs).In this study,we rigorously explore the nuanced requirements for ap-plication development within the realm of VC,centering our analysis on the architectural intricacies of the Open Vehicu-lar Data Analytics Platform(OpenVDAP).We then detail our creation of a comprehensive suite of standardized VPIs,spanning five critical categories:Hardware,Data,Computation,Service,and management,to address these evolving pro-gramming *** validate the design of VPIs,we conduct experiments using the indoor autonomous vehicle,Ze-bra,and develop the OpenVDAP prototype *** comparing it with the industry-influential AuTOSAR interface,our VPIs demonstrate significant enhancements in programming efficiency,marking an important advancement in the field of SDV application *** also show a case study and evaluate its *** work highlights that VPIs significantly enhance the efficiency of developing applications on *** meet both current and future technologi-cal demands and propel the software-defined automotive industry toward a more interconnected and intelligent future.

关键词： software-defined vehicle(SDV) vehicle computing(VC) vehicle programming interface(VPI) au-tonomous system

Goal-Oriented Control Systems(GOCS):From HOW to WHAT

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 816-819页

作者： Wen-Hua Chen the Department of Aeronautical and Automotive Engineering Loughborough UniversityLeicestershireLE113TUU.K.

Brief:New control theory is required to underpin safe design and deployment of future highly automated systems to deal with uncertain environments and complicated tasks,enabled by AI and other advanced ***-Oriented Control Systems offer potential to transform the control system design from currently instructing a control system how to perform a task to specifying what is to be achieved.

关键词： automated instru system

A hybrid machine-learning model for solar irradiance forecasting

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CLEAN ENERGY 2024年第1期8卷 100-110页

作者： Almarzooqi, Ameera m. maalouf, maher El-Fouly, Tarek H. m. Katzourakis, Vasileios E. El moursi, mohamed S. Chrysikopoulos, Constantinos, V Khalifa univ Dept Ind & Syst Engn POB 127788 Abu Dhabi U Arab Emirates Khalifa univ Abu Dhabi Elect Engn & Comp Sci Dept Abu Dhabi U Arab Emirates Khalifa univ Dept Civil Infrastruct & Environm Engn POB 127788 Abu Dhabi U Arab Emirates Tech univ Crete Sch Chem & Environm Engn Khania 73100 Greece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and medford, uSA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons. A novel prediction method for solar irradiance, targeting both short-term and medium-term forecast horizons, is proposed to improve PV power production. The hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. Graphical Abstract

关键词： solar power generation kernel ridge regression hybrid model forecasting BATTERY ENERGY-STORAGE POWER PV RADIATION PREDICTION CELLS

Co-Optimization of Behind-the-meter and Front-of-meter Value Streams in Community Batteries

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Journal of modern Power Systems and Clean Energy 2024年第2期12卷 334-345页

作者： Carmen Bas Domenech Antonella maria De Corato Pierluigi mancarella university of melbourne Parkville 2010VictoriaAustralia Department of Electrical and Electronic Engineering The University of ManchesterManchesterU.K. IEEE

Community batteries(CBs)are emerging to support and even enable energy communities and generally help consumers,especially space-constrained ones,to access potential techno-economic benefits from storage and support local grid ***,the economic viability of CB projects is often *** this regard,typical feasibility studies assess CB value for behind-the-meter(BTm)operation or whole-sale market participation,i.e.,front-of-meter(FOm).This work proposes a novel techno-economic operational framework that allows systematic assessment of the different options and introduces a two-meter architecture that co-optimizes both BTm and FOm benefits.A real CB project application in Australia is used to demonstrate the significant two-meter co-optimization opportunities that could enhance the business case of CB and energy communities by multi-service provision and value stacking.

关键词： Behind-the-meter community battery distributed energy resource(DER) energy community front-of-meter value stacking

Analysis of China's Electricity Price and Electricity Burden of Basic Industries under the Carbon Peak Target before 2030

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 481-491页

作者： Yue Qiu Suyang Zhou Wei Gu Xiao-Ping Zhang School of Electrical Engineering Southeast UniversityNanjingChina School of Electronic Electrical and System EngineeringUniversity of BirminghamU.K.

The Chinese government is deepening reformation of electricity prices during the 14th Five Year Plan period and has set a carbon emission reduction target of reaching carbon peak before *** this context,will the carbon emission target influence electricity pricing and will electricity price influence competitiveness of Chinese main industries are two questions needing to be *** paper compares China's electricity price level with the selected major countries in the world,and four typical industries are selected to evaluate their electricity burden ***,the correlation between residential electricity price and industrial electricity price and the influencing factors is analyzed,from the perspectives of scale,structure and *** to the model obtained by regression analysis,the electricity price level and corresponding residential and industrial electricity burden in 2025 and 2030 are *** Terms-Electricity burden,industrial electricity price,regression analysis,residential electricity price.

关键词： Electricity burden industrial electricity price regression analysis residential electricity price