Francis Bacon’s famous quote“knowledge is power,”has long been misunderstood,for his real intention was precisely to make humankind aware of the limitations of knowledge.His concept of potestas(power)is not about c...Francis Bacon’s famous quote“knowledge is power,”has long been misunderstood,for his real intention was precisely to make humankind aware of the limitations of knowledge.His concept of potestas(power)is not about conquest,but about action,aiming to clarify the nature of knowledge,to get rid of the empty and shallow contemplation of antiquity,and thus to bring the spirit of the real world back to the earth,as Socrates did.Bacon emphasized the unity of knowledge and action while valuing action over knowledge.Nature in Bacon’s time was no longer sacred and was degraded to a poor substance that revealed its secrets after being tortured by scientific technology.As a result,natural teleology was completely abandoned.Bacon put man in increasing tension with nature,heralding Kant’s argument that human reason prescribed lawfulness to nature.But Bacon,after all,lived in an era not far from antiquity,so he agreed the limitations of knowledge and action and considered technology to be a labyrinth prone to divest one’s identity.Bacon thought that knowledge could be venom that made humankind swell,and the antidote was charity.Bacon’s quote is not so much an encouragement to take from nature as it is a way to learn from nature and to take a practical approach to happiness.展开更多
Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power system...Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.展开更多
This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data represent...This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data representation of infrastructure data,socio-demographic data,areal attributes like demand,and models describing power systems.The knowledge graph links the data with a hierarchical representation of administrative regions,supporting geospatial queries to retrieve information about the population within the vicinity of a power plant,the number of power plants,total generation capacity,and demand within specific areas.Computational agents were developed to operate on the knowledge graph.The agents performed tasks including data uploading,updating,retrieval,processing,model construction and scenario analysis.A derived information framework was used to track the provenance of information calculated by agents involved in each scenario.The knowledge graph was populated with data describing the UK power system.Two alternative models of the transmission grid with different levels of structural resolution were instantiated,providing the foundation for the power system simulation and optimisation tasks performed by the agents.The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.展开更多
Nuclear knowledge management integration has become very important in the current nuclear sector scenario of Pakistan.Pakistan Atomic Energy came into being in 1956 and large scale human resource was hired in 1957 and...Nuclear knowledge management integration has become very important in the current nuclear sector scenario of Pakistan.Pakistan Atomic Energy came into being in 1956 and large scale human resource was hired in 1957 and in 1973 to implement the program of peaceful uses of energy.The appropriate human resource consisting of appropriate scientist&engineers than hired and trained from the best laboratories of USA and UK has now been retired leaving a knowledge gap.Simultaneously on the other side the Civil Nuclear Power Program of Pakistan is expanding tremendously and PAEC the only organization has to fulfill its first target of 8,800 MW of nuclear energy by 2030 and of 40,000 MW by 2050.Besides having five power reactors producing 1,335 MWe,Pakistan is building three 1,000 MW reactors,02 at Karachi,01 at Chashma,and planned 02 at Mufargrah.This increasing civil nuclear power program requires skilled and trained human resource for the nuclear cycle,management of ageing nuclear installations and their safely decommissioning.There is a dire need of effective and efficient institutional memory for these new nuclear power projects,their efficient operation,environmental remediation around them and implementation of nuclear safety regulations.Scientists and engineers should be able to communicate effectively to promote understanding,its wider relevance and to encourage more informed decision making at all levels.The increased scientific knowledge among masses will result in effective sustainable development.Pakistan Nuclear Society feels that its mandate has to manage and disseminate nuclear knowledge in the scientific community,politicians,and public to develop confidence in the practices and safety procedures of the nuclear program.展开更多
For management of power system on the bases of distributed complex network （DCN-network） authors developed a new structure of knowledge base： at macro-nodes of network are located a database and a logical conclusio...For management of power system on the bases of distributed complex network （DCN-network） authors developed a new structure of knowledge base： at macro-nodes of network are located a database and a logical conclusion making machine, and on the arcs are located their connecting properties. Such structure excludes transitions from a logical conclusion making machine to database. It provides opportunity of fast and efficient processing of information, modeling of complex processes etc.展开更多
The electric power industry is the key to achieving the goals of carbon peak and neutrality.Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation ...The electric power industry is the key to achieving the goals of carbon peak and neutrality.Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation policies and the early achievement of carbon reduction targets.This study proposes a new approach that combines the decomposition-ensemble paradigm with knowledge distillation to forecast daily carbon emissions.First,seasonal and trend decomposition using locally weighted scatterplot smoothing(STL)is used to decompose the data into three subcomponents.Second,two heterogeneous deep neural network models are jointly trained to predict each subcomponent based on online knowledge distillation.During training,the two models learn and provide feedback to each other.The first model-ensemble stage is performed by synthesizing the predictions for each subcomponent of the two models.Finally,the second model-ensemble stage is performed.The predictions for each subcomponent are integrated using linear addition to obtain the final results.In addition,to avoid leakage of test data caused by decomposing the entire time series,a recursive forecasting strategy is applied.Multistep predictions are obtained by forecasting 7,15,and 30 days in the future.Experimental results using metaheuristic algorithms to optimize hyperparameters show that the proposed method evaluated on the daily carbon emissions dataset has better forecasting performance than all baselines.展开更多
基金the phased achievement of a program supported by the National Social Science Fund of China called“Translation and Research of Bacon’s Collected Works”(18BZX093)。
文摘Francis Bacon’s famous quote“knowledge is power,”has long been misunderstood,for his real intention was precisely to make humankind aware of the limitations of knowledge.His concept of potestas(power)is not about conquest,but about action,aiming to clarify the nature of knowledge,to get rid of the empty and shallow contemplation of antiquity,and thus to bring the spirit of the real world back to the earth,as Socrates did.Bacon emphasized the unity of knowledge and action while valuing action over knowledge.Nature in Bacon’s time was no longer sacred and was degraded to a poor substance that revealed its secrets after being tortured by scientific technology.As a result,natural teleology was completely abandoned.Bacon put man in increasing tension with nature,heralding Kant’s argument that human reason prescribed lawfulness to nature.But Bacon,after all,lived in an era not far from antiquity,so he agreed the limitations of knowledge and action and considered technology to be a labyrinth prone to divest one’s identity.Bacon thought that knowledge could be venom that made humankind swell,and the antidote was charity.Bacon’s quote is not so much an encouragement to take from nature as it is a way to learn from nature and to take a practical approach to happiness.
基金supported by the National Key R&D Program of China(2018AAA0101502)the Science and Technology Project of SGCC(State Grid Corporation of China):Fundamental Theory of Human-in-the-Loop Hybrid-Augmented Intelligence for Power Grid Dispatch and Control。
文摘Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.
基金supported by the National Research Foundation,Prime Minister’s Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)programme.Part of this work was also supported by Towards Turing 2.0 under the EPSRC Grant EP/W037211/1.
文摘This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data representation of infrastructure data,socio-demographic data,areal attributes like demand,and models describing power systems.The knowledge graph links the data with a hierarchical representation of administrative regions,supporting geospatial queries to retrieve information about the population within the vicinity of a power plant,the number of power plants,total generation capacity,and demand within specific areas.Computational agents were developed to operate on the knowledge graph.The agents performed tasks including data uploading,updating,retrieval,processing,model construction and scenario analysis.A derived information framework was used to track the provenance of information calculated by agents involved in each scenario.The knowledge graph was populated with data describing the UK power system.Two alternative models of the transmission grid with different levels of structural resolution were instantiated,providing the foundation for the power system simulation and optimisation tasks performed by the agents.The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.
文摘Nuclear knowledge management integration has become very important in the current nuclear sector scenario of Pakistan.Pakistan Atomic Energy came into being in 1956 and large scale human resource was hired in 1957 and in 1973 to implement the program of peaceful uses of energy.The appropriate human resource consisting of appropriate scientist&engineers than hired and trained from the best laboratories of USA and UK has now been retired leaving a knowledge gap.Simultaneously on the other side the Civil Nuclear Power Program of Pakistan is expanding tremendously and PAEC the only organization has to fulfill its first target of 8,800 MW of nuclear energy by 2030 and of 40,000 MW by 2050.Besides having five power reactors producing 1,335 MWe,Pakistan is building three 1,000 MW reactors,02 at Karachi,01 at Chashma,and planned 02 at Mufargrah.This increasing civil nuclear power program requires skilled and trained human resource for the nuclear cycle,management of ageing nuclear installations and their safely decommissioning.There is a dire need of effective and efficient institutional memory for these new nuclear power projects,their efficient operation,environmental remediation around them and implementation of nuclear safety regulations.Scientists and engineers should be able to communicate effectively to promote understanding,its wider relevance and to encourage more informed decision making at all levels.The increased scientific knowledge among masses will result in effective sustainable development.Pakistan Nuclear Society feels that its mandate has to manage and disseminate nuclear knowledge in the scientific community,politicians,and public to develop confidence in the practices and safety procedures of the nuclear program.
文摘For management of power system on the bases of distributed complex network （DCN-network） authors developed a new structure of knowledge base： at macro-nodes of network are located a database and a logical conclusion making machine, and on the arcs are located their connecting properties. Such structure excludes transitions from a logical conclusion making machine to database. It provides opportunity of fast and efficient processing of information, modeling of complex processes etc.
基金This work is supported by the National Natural Science Foundation of China(Grant Nos.:71971089 and 72001083)the Natural Science Foundation of Guangdong Province(Grant No.:2022A1515011612).
文摘The electric power industry is the key to achieving the goals of carbon peak and neutrality.Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation policies and the early achievement of carbon reduction targets.This study proposes a new approach that combines the decomposition-ensemble paradigm with knowledge distillation to forecast daily carbon emissions.First,seasonal and trend decomposition using locally weighted scatterplot smoothing(STL)is used to decompose the data into three subcomponents.Second,two heterogeneous deep neural network models are jointly trained to predict each subcomponent based on online knowledge distillation.During training,the two models learn and provide feedback to each other.The first model-ensemble stage is performed by synthesizing the predictions for each subcomponent of the two models.Finally,the second model-ensemble stage is performed.The predictions for each subcomponent are integrated using linear addition to obtain the final results.In addition,to avoid leakage of test data caused by decomposing the entire time series,a recursive forecasting strategy is applied.Multistep predictions are obtained by forecasting 7,15,and 30 days in the future.Experimental results using metaheuristic algorithms to optimize hyperparameters show that the proposed method evaluated on the daily carbon emissions dataset has better forecasting performance than all baselines.
