Machine knowledge refers to the knowledge contained in artificial intelligence.This article discusses how to acquire machine knowledge,with a particular focus on the acquisition of causal knowledge.The latter is the p...Machine knowledge refers to the knowledge contained in artificial intelligence.This article discusses how to acquire machine knowledge,with a particular focus on the acquisition of causal knowledge.The latter is the process of interpreting machine knowledge.Through the analysis of certain research methods in the fields of physics and artificial intelligence,we propose principles and models for interpreting machine knowledge,and discuss specific methods including the automation of the interpretation process and local linearization.展开更多
Background:Effective knowledge translation allows the optimisation of access to and utilisation of research knowledge in order to inform and enhance public health policy and practice.In low-and middle-income countries...Background:Effective knowledge translation allows the optimisation of access to and utilisation of research knowledge in order to inform and enhance public health policy and practice.In low-and middle-income countries,there are substantial complexities that affect the way in which research can be utilised for public health action.This review attempts to draw out concepts in the literature that contribute to defining some of the complexities and contextual factors that influence knowledge translation for public health in low-and middle-income countries.Methods:A Critical Interpretive Synthesis was undertaken,a method of analysis which allows a critical review of a wide range of heterogeneous evidence,through incorporating systematic review methods with qualitative enquiry techniques.A search for peer-reviewed articles published between 2000 and 2016 on the topic of knowledge translation for public health in low-and middle-income countries was carried out,and 85 articles were reviewed and analysed using this method.Results:Four main concepts were identified:1)tension between‘global’and‘local’health research,2)complexities in creating and accessing evidence,3)contextualising knowledge translation strategies for low-and middle-income countries,and 4)the unique role of non-government organisations in the knowledge translation process.Conclusion:This method of review has enabled the identification of key concepts that may inform practice or further research in the field of knowledge translation in low-and middle-income countries.展开更多
Most of KBE systems applied by previous researchers are dependent on some CAD software, which makes knowledge hard to be reused to other CAD software. Independent knowledge based system is independent of CAD software;...Most of KBE systems applied by previous researchers are dependent on some CAD software, which makes knowledge hard to be reused to other CAD software. Independent knowledge based system is independent of CAD software; therefore knowledge can be reused freely. This paper describes independent knowledge based system for mechanical design. A detailed discussion about typical design is put forward including design process implementation based on knowledge engineering, independent knowledge based design architecture. The main principal of knowledge driven engineering is explained. The implementation of KBE on the design of worm reducer is studied as a case. Independent knowledge based reducer design system is realized. The usage of independent knowledge based system makes KBE system work independent of CAD software, which enhances their portability and fertilizes the collaborative work of heterogeneous CAD systems.展开更多
Accumulation of vocabulary, knowledge and experience is the foundation of comprehension and expression in simultaneous interpretation. This paper suggests the importance of accumulation in the development of a success...Accumulation of vocabulary, knowledge and experience is the foundation of comprehension and expression in simultaneous interpretation. This paper suggests the importance of accumulation in the development of a successful interpreter.展开更多
Deep learning technologies are increasingly used in the fi eld of geophysics,and a variety of algorithms based on shallow convolutional neural networks are more widely used in fault recognition,but these methods are u...Deep learning technologies are increasingly used in the fi eld of geophysics,and a variety of algorithms based on shallow convolutional neural networks are more widely used in fault recognition,but these methods are usually not able to accurately identify complex faults.In this study,using the advantage of deep residual networks to capture strong learning features,we introduce residual blocks to replace all convolutional layers of the three-dimensional(3D)UNet to build a new 3D Res-UNet and select appropriate parameters through experiments to train a large amount of synthesized seismic data.After the training is completed,we introduce the mechanism of knowledge distillation.First,we treat the 3D Res-UNet as a teacher network and then train the 3D Res-UNet as a student network;in this process,the teacher network is in evaluation mode.Finally,we calculate the mixed loss function by combining the teacher model and student network to learn more fault information,improve the performance of the network,and optimize the fault recognition eff ect.The quantitative evaluation result of the synthetic model test proves that the 3D Res-UNet can considerably improve the accuracy of fault recognition from 0.956 to 0.993 after knowledge distillation,and the eff ectiveness and feasibility of our method can be verifi ed based on the application of actual seismic data.展开更多
On the whole, the requirements on business interpreters are almost the same with other interpreters. However, the characteristics of business activities requires that the interpreter should have wide knowledge of the ...On the whole, the requirements on business interpreters are almost the same with other interpreters. However, the characteristics of business activities requires that the interpreter should have wide knowledge of the business proper names and phrases, the sensitivity against numbers and the awareness of the different cultures in trade. To be an interpreter in business, one should pay special attention to these aspects.展开更多
In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS ...In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy.展开更多
In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models...In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.展开更多
To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new lig...To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.展开更多
Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpret...Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.展开更多
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predict...Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.展开更多
As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since e...As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing DTC.In the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction process.To solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information.An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,respectively.We also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s effectiveness.The results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.展开更多
文摘Machine knowledge refers to the knowledge contained in artificial intelligence.This article discusses how to acquire machine knowledge,with a particular focus on the acquisition of causal knowledge.The latter is the process of interpreting machine knowledge.Through the analysis of certain research methods in the fields of physics and artificial intelligence,we propose principles and models for interpreting machine knowledge,and discuss specific methods including the automation of the interpretation process and local linearization.
基金CM received financial support through the Australian Government Research Training Program Scholarship.
文摘Background:Effective knowledge translation allows the optimisation of access to and utilisation of research knowledge in order to inform and enhance public health policy and practice.In low-and middle-income countries,there are substantial complexities that affect the way in which research can be utilised for public health action.This review attempts to draw out concepts in the literature that contribute to defining some of the complexities and contextual factors that influence knowledge translation for public health in low-and middle-income countries.Methods:A Critical Interpretive Synthesis was undertaken,a method of analysis which allows a critical review of a wide range of heterogeneous evidence,through incorporating systematic review methods with qualitative enquiry techniques.A search for peer-reviewed articles published between 2000 and 2016 on the topic of knowledge translation for public health in low-and middle-income countries was carried out,and 85 articles were reviewed and analysed using this method.Results:Four main concepts were identified:1)tension between‘global’and‘local’health research,2)complexities in creating and accessing evidence,3)contextualising knowledge translation strategies for low-and middle-income countries,and 4)the unique role of non-government organisations in the knowledge translation process.Conclusion:This method of review has enabled the identification of key concepts that may inform practice or further research in the field of knowledge translation in low-and middle-income countries.
文摘Most of KBE systems applied by previous researchers are dependent on some CAD software, which makes knowledge hard to be reused to other CAD software. Independent knowledge based system is independent of CAD software; therefore knowledge can be reused freely. This paper describes independent knowledge based system for mechanical design. A detailed discussion about typical design is put forward including design process implementation based on knowledge engineering, independent knowledge based design architecture. The main principal of knowledge driven engineering is explained. The implementation of KBE on the design of worm reducer is studied as a case. Independent knowledge based reducer design system is realized. The usage of independent knowledge based system makes KBE system work independent of CAD software, which enhances their portability and fertilizes the collaborative work of heterogeneous CAD systems.
文摘Accumulation of vocabulary, knowledge and experience is the foundation of comprehension and expression in simultaneous interpretation. This paper suggests the importance of accumulation in the development of a successful interpreter.
基金supported by the National Natural Science Foundation of China(No.42072169)。
文摘Deep learning technologies are increasingly used in the fi eld of geophysics,and a variety of algorithms based on shallow convolutional neural networks are more widely used in fault recognition,but these methods are usually not able to accurately identify complex faults.In this study,using the advantage of deep residual networks to capture strong learning features,we introduce residual blocks to replace all convolutional layers of the three-dimensional(3D)UNet to build a new 3D Res-UNet and select appropriate parameters through experiments to train a large amount of synthesized seismic data.After the training is completed,we introduce the mechanism of knowledge distillation.First,we treat the 3D Res-UNet as a teacher network and then train the 3D Res-UNet as a student network;in this process,the teacher network is in evaluation mode.Finally,we calculate the mixed loss function by combining the teacher model and student network to learn more fault information,improve the performance of the network,and optimize the fault recognition eff ect.The quantitative evaluation result of the synthetic model test proves that the 3D Res-UNet can considerably improve the accuracy of fault recognition from 0.956 to 0.993 after knowledge distillation,and the eff ectiveness and feasibility of our method can be verifi ed based on the application of actual seismic data.
文摘On the whole, the requirements on business interpreters are almost the same with other interpreters. However, the characteristics of business activities requires that the interpreter should have wide knowledge of the business proper names and phrases, the sensitivity against numbers and the awareness of the different cultures in trade. To be an interpreter in business, one should pay special attention to these aspects.
基金supported by the National Science Foundation of China Grant No.61762092“Dynamic multi-objective requirement optimization based on transfer learning”,No.61762089+2 种基金“The key research of high order tensor decomposition in distributed environment”the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province,Grant No.2017SE204,”Research on extracting software feature models using transfer learning”.
文摘In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy.
文摘In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.
基金support provided by the National Natural Science Foundation of China(22122802,22278044,and 21878028)the Chongqing Science Fund for Distinguished Young Scholars(CSTB2022NSCQ-JQX0021)the Fundamental Research Funds for the Central Universities(2022CDJXY-003).
文摘To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.
基金Supported in part by Science Center for Gas Turbine Project(Project No.P2022-DC-I-003-001)National Natural Science Foundation of China(Grant No.52275130).
文摘Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230685)the National Science Foundation of China(Grant No.42277161).
文摘Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.
基金supported by the National Key R&D Program of China(No.2023YFB2904803)the National Natural Science Foundation of China(Nos.62341128,62201087,and 62101069)+2 种基金the National Science Fund for Distinguished Young Scholars,China(No.61925102)the Beijing Natural Science Foundation,China(No.L243002)the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing DTC.In the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction process.To solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information.An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,respectively.We also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s effectiveness.The results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.
