In today’s fast-paced,information-driven world,data centers can offer high-speed,intricate capabilities on a larger scale owing to the ever-growing demand for networks and information systems.Because data centers pro...In today’s fast-paced,information-driven world,data centers can offer high-speed,intricate capabilities on a larger scale owing to the ever-growing demand for networks and information systems.Because data centers process and transmit information,stability and reliability are important.Data center power supply architectures rely heavily on isolated bidirectional DC-DC converters to ensure safety and stability.For the smooth operation of a data center,the power supply must be reliable and uninterrupted.In this study,we summarize the basic principle,topology,switch conversion strategy,and control technology of the existing isolated bidirectional DC-DC converters.Subsequently,existing research results and problems with isolated bidirectional DC-DC converters are reviewed.Finally,future trends in the development of isolated bidirectional DC-DC converters for data centers are presented,which offer valuable insights for solving engineering obstacles and future research directions in the field.展开更多
传统的无线电能传输技术主要面向单向能量传输,随着无线电能传输技术应用领域的拓展,迫切需要双向无线电能传输(bidirectional wireless power transfer,BWPT)技术以实现无线充电设备间的能量交互。首先简述BWPT系统的基本工作原理,主要...传统的无线电能传输技术主要面向单向能量传输,随着无线电能传输技术应用领域的拓展,迫切需要双向无线电能传输(bidirectional wireless power transfer,BWPT)技术以实现无线充电设备间的能量交互。首先简述BWPT系统的基本工作原理,主要从BWPT系统的典型双向变换拓扑、谐振网络、同步控制技术、功率控制策略、软开关运行及其应用场景等方面论述其研究成果,分析电容式双向无线电能传输系统的发展现状和该技术亟待解决的关键问题,最后对BWPT系统未来值得关注的研究方向进行展望。展开更多
Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched An...Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO 展开更多
Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysi...Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in 展开更多
基金Supported by the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province(2022B1515020002).
文摘In today’s fast-paced,information-driven world,data centers can offer high-speed,intricate capabilities on a larger scale owing to the ever-growing demand for networks and information systems.Because data centers process and transmit information,stability and reliability are important.Data center power supply architectures rely heavily on isolated bidirectional DC-DC converters to ensure safety and stability.For the smooth operation of a data center,the power supply must be reliable and uninterrupted.In this study,we summarize the basic principle,topology,switch conversion strategy,and control technology of the existing isolated bidirectional DC-DC converters.Subsequently,existing research results and problems with isolated bidirectional DC-DC converters are reviewed.Finally,future trends in the development of isolated bidirectional DC-DC converters for data centers are presented,which offer valuable insights for solving engineering obstacles and future research directions in the field.
文摘传统的无线电能传输技术主要面向单向能量传输,随着无线电能传输技术应用领域的拓展,迫切需要双向无线电能传输(bidirectional wireless power transfer,BWPT)技术以实现无线充电设备间的能量交互。首先简述BWPT系统的基本工作原理,主要从BWPT系统的典型双向变换拓扑、谐振网络、同步控制技术、功率控制策略、软开关运行及其应用场景等方面论述其研究成果,分析电容式双向无线电能传输系统的发展现状和该技术亟待解决的关键问题,最后对BWPT系统未来值得关注的研究方向进行展望。
文摘Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO
基金ASAP 16 project call,project title:SemantiX-A cross-sensor semantic EO data cube to open and leverage essential climate variables with scientists and the public,Grant ID:878939ASAP 17 project call,project title:SIMS-Soil sealing identification and monitoring system,Grant ID:885365.
文摘Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in
