A Model, called 'Entity-Roles' is proposed in this paper in which the world of Interest is viewed as some mathematical structure. With respect to this structure, a First order (three-valued) Logic Language is ...A Model, called 'Entity-Roles' is proposed in this paper in which the world of Interest is viewed as some mathematical structure. With respect to this structure, a First order (three-valued) Logic Language is constructured.Any world to be modelled can be logically specified in this Language. The integrity constraints on the database and the deducing rules within the Database world are derived from the proper axioms of the world being modelled.展开更多
Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve ...Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.展开更多
The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quan...The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.展开更多
Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpe...Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.展开更多
With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. Fir...With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then au tonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to in corporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data ,source by ontology. Experiments suggest that the framework should be useful in guiding the continuous mining process.展开更多
A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterpr...A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.展开更多
In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can ...In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can realize intelligent knowledge retrieval and automatic accomplishment of DM tasks by means of ontology services. Its key features include：①Describing DM ontology and meta-data using ontology based on Web ontology language （OWL）.② Ontology reasoning function. Based on the existing concepts and relations, the hidden knowledge in ontology can be obtained using the reasoning engine. This paper mainly focuses on the construction of DM ontology and the reasoning of DM ontology based on OWL DL（s）.展开更多
Using the advantages of web crawlers in data collection and distributed storage technologies,we accessed to a wealth of forestry-related data.Combined with the mature big data technology at its present stage,Hadoop...Using the advantages of web crawlers in data collection and distributed storage technologies,we accessed to a wealth of forestry-related data.Combined with the mature big data technology at its present stage,Hadoop's distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data.The forestry data contains a wealth of information,and mining this information is of great significance for guiding the development of forestry.We conducts co-word and cluster analyses on the keywords of forestry data,extracts the rules hidden in the data,analyzes the research hotspots more accurately,grasps the evolution trend of subject topics,and plays an important role in promoting the research and development of subject areas.The co-word analysis and clustering algorithm have important practical significance for the topic structure,research hotspot or development trend in the field of forestry research.Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms.Therefore,the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.展开更多
Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challeng...Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.展开更多
In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfe...In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.展开更多
Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters...Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new knowledge.Geological knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or processes.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.In this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling study.Based on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic relations.Finally,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph representation.The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
Multiple efforts have been performed worldwide around diverse aspects of land administra-tion.However,land administration data and systems’notorious heterogeneity remains a longstanding challenge to develop a harmoni...Multiple efforts have been performed worldwide around diverse aspects of land administra-tion.However,land administration data and systems’notorious heterogeneity remains a longstanding challenge to develop a harmonized vision.In this sense,the traditional Spatial Data Infrastructures adoption is not enough to overcome this challenge since data sources’heterogeneity implies needs related to harmonization interoperability,sharing,and integration in land administration development.This paper proposes a graph-based represen-tation of knowledge for integrating multiple and heterogeneous data sources(tables,shape-files,geodatabases,and WFS services)belonging to two Colombian agencies within a decentralized land administration scenario.These knowledge graphs are developed on an ontology-based knowledge representation using national and international standards for land administration.Our approach aims to prevent data isolation,enable cross-datasets integration,accomplish machine-processable data,and facilitate the reuse and exploitation of multi-jurisdictional datasets in a single approach.A real case study demonstrates the applicability of the land administration data cycle deployed.展开更多
This study presents preliminary results of tidal-induced magnetic field signals extracted from 9 months of data collected by the Macao Science Satellite-1(MSS-1) from November 2023 to July 2024. Tidal signals were iso...This study presents preliminary results of tidal-induced magnetic field signals extracted from 9 months of data collected by the Macao Science Satellite-1(MSS-1) from November 2023 to July 2024. Tidal signals were isolated using sequential modeling techniques by subtracting non-tidal field model predictions from observed magnetic data. The extracted MSS-1 results show strong agreement with those from the Swarm and CryoSat satellites. MSS-1 effectively captures key large-scale tidal-induced magnetic anomalies, mainly due to its unique 41-degree low-inclination orbit, which provides wide coverage of local times. This finding underscores the strong potential of MSS-1 to recover high-resolution global tidal magnetic field models as more MSS-1 data become available.展开更多
Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the pr...Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients.This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients:subscription pricing and pay-per-use pricing.We find that:(1)the subscription price of big data knowledge has no effect on the optimal time of knowledge transaction in the same pricing scheme,but the usage ratio of the big data knowledge affects the optimal time of knowledge transaction,and the smaller the usage ratio of big data knowledge the earlier the big data knowledge transaction conducts;(2)big data knowledge with a higher update rate can bring greater profits to the firm both in subscription pricing scheme and pay-per-use pricing scheme;(3)a knowledge recipient will choose the knowledge that can bring a higher market share growth rate regardless of what price scheme it adopts,and firms can choose more efficient knowledge in the pay-per-use pricing scheme by adjusting the usage ratio of knowledge usage according to their economic conditions.The model and findings in this paper can help knowledge recipient firms select optimal pricing method and enhance future new product development performance.展开更多
To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under...To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under the condition of known background knowledge, the algorithm can not only greatly improve the efficiency of attribute reduction, but also avoid the defection of information entropy partial to attribute with much value. The experimental result verifies that the algorithm is effective. In the end, the algorithm produces better results when applied in the classification of the star spectra data.展开更多
A new algorithm for the knowledge discovery based on statistic inductionlogic is proposed, and the validity of the methods is verified by examples. The method is suitablefor a large range of knowledge discovery applic...A new algorithm for the knowledge discovery based on statistic inductionlogic is proposed, and the validity of the methods is verified by examples. The method is suitablefor a large range of knowledge discovery applications in the studying of causal relation,uncertainty knowledge acquisition and principal factors analyzing. The language filed description ofthe state space makes the algorithm robust in the adaptation with easier understandable results,which are isomotopy with natural language in the topologic space.展开更多
BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on ...BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on the knowledge,attitudes,and preventive practices regarding breast cancer and the associated factors among females in Wollo,Ethiopia,remain limited.AIM To assess the impact of family history(FH)of breast disease on knowledge,attitudes,and breast cancer preventive practices among reproductive-age females.METHODS A community-based cross-sectional study was conducted in May and June 2022 in Northeast Ethiopia and involved 143 reproductive-age females with FH of breast diseases and 209 without such a history.We selected participants using the systematic random sampling technique.We analyzed the data using Statistical Package for Social Science version 25 software,and logistic regression analysis was employed to determine odds ratios for variable associations,with statistical significance set at P<0.05.RESULTS Among participants with FH of breast diseases,the levels of knowledge,attitudes,and preventive practices were found to be 83.9%[95%confidence interval(CI):77.9-89.9],49.0%(95%CI:40.8-57.1),and 74.1%(95%CI:66.9-81.3),respectively.In contrast,among those without FH of breast diseases,these levels were significantly decreased to 10.5%(95%CI:6.4-14.7),32.1%(95%CI:25.7-38.4),and 16.7%(95%CI:11.7-21.8),respectively.This study also indicated that knowledge,attitudes,and preventive practices related to breast cancer are significantly higher among participants with FH of breast diseases compared to those without HF breast diseases.CONCLUSION Educational status,monthly income,and community health insurance were identified as significant factors associated with the levels of knowledge,attitudes,and preventive practices regarding breast cancer among reproductive-age females.展开更多
Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate...Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.展开更多
In order to archive and utilize the information from Chinese polar expeditions to the greatest extent, we design a novel knowledge repository, in which an automatic query model based on neural networks is proposed and...In order to archive and utilize the information from Chinese polar expeditions to the greatest extent, we design a novel knowledge repository, in which an automatic query model based on neural networks is proposed and a data call trigger is established to keep data consistent between polar data-sharing platforms. And in this repository, anybody can make contributions to the repository by creating or updating entries with version control and an authority control mechanism. In this paper, the data sources,data processes and network structure of this repository are described, and the keywords extraction and decision support operation are detailed. The analysis of this design＇s feasibility and applicability indicates that this knowledge repository is open, sole and authoritative for Chinese polar expeditions.展开更多
文摘A Model, called 'Entity-Roles' is proposed in this paper in which the world of Interest is viewed as some mathematical structure. With respect to this structure, a First order (three-valued) Logic Language is constructured.Any world to be modelled can be logically specified in this Language. The integrity constraints on the database and the deducing rules within the Database world are derived from the proper axioms of the world being modelled.
基金the National Natural Science Foundation of China (Grants No. 12072090 and No.12302056) to provide fund for conducting experiments。
文摘Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.
基金financially supported by the Innovative Research Group Project of the National Natural Science Foundation of China (22021004)Sinopec Major Science and Technology Projects (321123-1)
文摘The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.
基金supported in part by the National Key Research and Development Program of China under Grant 2024YFE0200600in part by the National Natural Science Foundation of China under Grant 62071425+3 种基金in part by the Zhejiang Key Research and Development Plan under Grant 2022C01093in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR23F010005in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01601in part by the Big Data and Intelligent Computing Key Lab of CQUPT under Grant BDIC-2023-B-001.
文摘Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.
基金Supported by the National Natural Science Foun-dation of China (60173058 ,70372024)
文摘With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then au tonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to in corporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data ,source by ontology. Experiments suggest that the framework should be useful in guiding the continuous mining process.
基金supported by NSFC(Grant No.71373032)the Natural Science Foundation of Hunan Province(Grant No.12JJ4073)+3 种基金the Scientific Research Fund of Hunan Provincial Education Department(Grant No.11C0029)the Educational Economy and Financial Research Base of Hunan Province(Grant No.13JCJA2)the Project of China Scholarship Council for Overseas Studies(201208430233201508430121)
文摘A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.
基金the Natural Science Foundation of Chongqing (CSTC2005BB2190)
文摘In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can realize intelligent knowledge retrieval and automatic accomplishment of DM tasks by means of ontology services. Its key features include：①Describing DM ontology and meta-data using ontology based on Web ontology language （OWL）.② Ontology reasoning function. Based on the existing concepts and relations, the hidden knowledge in ontology can be obtained using the reasoning engine. This paper mainly focuses on the construction of DM ontology and the reasoning of DM ontology based on OWL DL（s）.
基金grants from the Fundamental Research Funds for the Central Universities(Grant No.2572018BH02)Special Funds for Scientific Research in the Forestry Public Welfare Industry(Grant Nos.201504307-03)。
文摘Using the advantages of web crawlers in data collection and distributed storage technologies,we accessed to a wealth of forestry-related data.Combined with the mature big data technology at its present stage,Hadoop's distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data.The forestry data contains a wealth of information,and mining this information is of great significance for guiding the development of forestry.We conducts co-word and cluster analyses on the keywords of forestry data,extracts the rules hidden in the data,analyzes the research hotspots more accurately,grasps the evolution trend of subject topics,and plays an important role in promoting the research and development of subject areas.The co-word analysis and clustering algorithm have important practical significance for the topic structure,research hotspot or development trend in the field of forestry research.Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms.Therefore,the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.
基金supported by Natural Science Foundation of Sichuan,China(Grant No.:2024ZDZX0019).
文摘Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.
基金supported by the National Natural Science Foundation ofChina (Grant No. 71704016,71331008, 71402010)the Natural Science Foundation of HunanProvince (Grant No. 2017JJ2267)+1 种基金the Educational Economy and Financial Research Base ofHunan Province (Grant No. 13JCJA2)the Project of China Scholarship Council forOverseas Studies (201508430121, 201208430233).
文摘In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.
基金the IUGS Deep-time Digital Earth(DDE)Big Science Programfinancially supported by the National Key R&D Program of China(No.2022YFF0711601)+4 种基金the Natural Science Foundation of Hubei Province of China(No.2022CFB640)the Opening Fund of Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering(No.2022SDSJ04)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB 2023ZR01)the Fundamental Research Funds for the Central UniversitiesFunded by Joint Fund of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains,Henan Province and Key Laboratory of Spatiotemporal Perception and Intelligent processing,Ministry of Natural Resources(No.212205)。
文摘Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new knowledge.Geological knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or processes.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.In this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling study.Based on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic relations.Finally,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph representation.The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
基金supported by Colfuturo and Ministerio de Tecnologías de la Información y las Comunicaciones de Colombia,CYTED program-520RT0010[Red GeoLIBERO-Consolidación de una red de geomática libre aplicada a las necesidades de Iberoamérica],and SIP-IPN 20210677[Generación de grafos de conocimiento sobre eventos meteorológicos urbanos].
文摘Multiple efforts have been performed worldwide around diverse aspects of land administra-tion.However,land administration data and systems’notorious heterogeneity remains a longstanding challenge to develop a harmonized vision.In this sense,the traditional Spatial Data Infrastructures adoption is not enough to overcome this challenge since data sources’heterogeneity implies needs related to harmonization interoperability,sharing,and integration in land administration development.This paper proposes a graph-based represen-tation of knowledge for integrating multiple and heterogeneous data sources(tables,shape-files,geodatabases,and WFS services)belonging to two Colombian agencies within a decentralized land administration scenario.These knowledge graphs are developed on an ontology-based knowledge representation using national and international standards for land administration.Our approach aims to prevent data isolation,enable cross-datasets integration,accomplish machine-processable data,and facilitate the reuse and exploitation of multi-jurisdictional datasets in a single approach.A real case study demonstrates the applicability of the land administration data cycle deployed.
基金financially supported by the National Natural Science Foundation of China(42250102,42250101)the Macao Foundation and Macao Science and Technology Development Fund(0001/2019/A1)the Pre-research Project on Civil Aerospace Technologies funded by China National Space Administration(D020303)。
文摘This study presents preliminary results of tidal-induced magnetic field signals extracted from 9 months of data collected by the Macao Science Satellite-1(MSS-1) from November 2023 to July 2024. Tidal signals were isolated using sequential modeling techniques by subtracting non-tidal field model predictions from observed magnetic data. The extracted MSS-1 results show strong agreement with those from the Swarm and CryoSat satellites. MSS-1 effectively captures key large-scale tidal-induced magnetic anomalies, mainly due to its unique 41-degree low-inclination orbit, which provides wide coverage of local times. This finding underscores the strong potential of MSS-1 to recover high-resolution global tidal magnetic field models as more MSS-1 data become available.
基金This research was funded by(the National Natural Science Foundation of China)Grant Number(71704016),(the Key Scientific Research Fund of Hunan Provincial Education Department of China)Grant Number(19A006),and(the Enterprise Strategic Management and Investment Decision Research Base of Hunan Province)Grant Number(19qyzd03).
文摘Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients.This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients:subscription pricing and pay-per-use pricing.We find that:(1)the subscription price of big data knowledge has no effect on the optimal time of knowledge transaction in the same pricing scheme,but the usage ratio of the big data knowledge affects the optimal time of knowledge transaction,and the smaller the usage ratio of big data knowledge the earlier the big data knowledge transaction conducts;(2)big data knowledge with a higher update rate can bring greater profits to the firm both in subscription pricing scheme and pay-per-use pricing scheme;(3)a knowledge recipient will choose the knowledge that can bring a higher market share growth rate regardless of what price scheme it adopts,and firms can choose more efficient knowledge in the pay-per-use pricing scheme by adjusting the usage ratio of knowledge usage according to their economic conditions.The model and findings in this paper can help knowledge recipient firms select optimal pricing method and enhance future new product development performance.
基金Supported by the National Natural Science Foundation of China（No. 60573075）, the National High Technology Research and Development Program of China （No. 2003AA133060） and the Natural Science Foundation of Shanxi Province （No. 200601104）.
文摘To improve the efficiency of the attribute reduction, we present an attribute reduction algorithm based on background knowledge and information entropy by making use of background knowledge from research fields. Under the condition of known background knowledge, the algorithm can not only greatly improve the efficiency of attribute reduction, but also avoid the defection of information entropy partial to attribute with much value. The experimental result verifies that the algorithm is effective. In the end, the algorithm produces better results when applied in the classification of the star spectra data.
基金[This work was financially supported by the National Natural Science Foundation of China (No. 69835001).]
文摘A new algorithm for the knowledge discovery based on statistic inductionlogic is proposed, and the validity of the methods is verified by examples. The method is suitablefor a large range of knowledge discovery applications in the studying of causal relation,uncertainty knowledge acquisition and principal factors analyzing. The language filed description ofthe state space makes the algorithm robust in the adaptation with easier understandable results,which are isomotopy with natural language in the topologic space.
文摘BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on the knowledge,attitudes,and preventive practices regarding breast cancer and the associated factors among females in Wollo,Ethiopia,remain limited.AIM To assess the impact of family history(FH)of breast disease on knowledge,attitudes,and breast cancer preventive practices among reproductive-age females.METHODS A community-based cross-sectional study was conducted in May and June 2022 in Northeast Ethiopia and involved 143 reproductive-age females with FH of breast diseases and 209 without such a history.We selected participants using the systematic random sampling technique.We analyzed the data using Statistical Package for Social Science version 25 software,and logistic regression analysis was employed to determine odds ratios for variable associations,with statistical significance set at P<0.05.RESULTS Among participants with FH of breast diseases,the levels of knowledge,attitudes,and preventive practices were found to be 83.9%[95%confidence interval(CI):77.9-89.9],49.0%(95%CI:40.8-57.1),and 74.1%(95%CI:66.9-81.3),respectively.In contrast,among those without FH of breast diseases,these levels were significantly decreased to 10.5%(95%CI:6.4-14.7),32.1%(95%CI:25.7-38.4),and 16.7%(95%CI:11.7-21.8),respectively.This study also indicated that knowledge,attitudes,and preventive practices related to breast cancer are significantly higher among participants with FH of breast diseases compared to those without HF breast diseases.CONCLUSION Educational status,monthly income,and community health insurance were identified as significant factors associated with the levels of knowledge,attitudes,and preventive practices regarding breast cancer among reproductive-age females.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB0740000National Key Research and Development Program of China,No.2022YFB3904200,No.2022YFF0711601+1 种基金Key Project of Innovation LREIS,No.PI009National Natural Science Foundation of China,No.42471503。
文摘Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.
基金Supported by the Basic Condition Platform of the Chinese Ministry of Science and Technology-Data Sharing Infrastructure of Earth System Science(2005DKA32300)the Youth Innovation Fund of the State Oceanic Administration(2012621)+2 种基金China Polar Science Strategy Research Fund Project(20120106)the State Oceanic Administration Polar Science Key Lab Open Research Fund(KP201110)Key Laboratory of Digital Ocean,SOA(KLD0201 408)
文摘In order to archive and utilize the information from Chinese polar expeditions to the greatest extent, we design a novel knowledge repository, in which an automatic query model based on neural networks is proposed and a data call trigger is established to keep data consistent between polar data-sharing platforms. And in this repository, anybody can make contributions to the repository by creating or updating entries with version control and an authority control mechanism. In this paper, the data sources,data processes and network structure of this repository are described, and the keywords extraction and decision support operation are detailed. The analysis of this design＇s feasibility and applicability indicates that this knowledge repository is open, sole and authoritative for Chinese polar expeditions.
