Physicians gather a vast amount of information about patients’ medical procedures, treatments, insurance coverage, and other clinical data. Such information is crucial in formulating diagnosis or treatment plans for ...Physicians gather a vast amount of information about patients’ medical procedures, treatments, insurance coverage, and other clinical data. Such information is crucial in formulating diagnosis or treatment plans for patients with similar traits. A Case-Based Reasoning (CBR) system has been developed to address the effective organization and retrieval of vital patient information to aid physicians in making decisions. Integers are used to uniquely represent various medical procedures, treatments, etc. In this research, a new algorithm is presented to retrieve suitable cases to recommend to physicians. The system is tested in a simulated environment and the results prove that the system can adapt to changes such as new medical procedures or treatments that take place in the medical field.展开更多
This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal...This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.展开更多
Knowledge graph,which is a rapidly developing technology,provides strong support in business and engineering.Knowledge graph plays an important role in recommendations and decision-making,while in the electric power i...Knowledge graph,which is a rapidly developing technology,provides strong support in business and engineering.Knowledge graph plays an important role in recommendations and decision-making,while in the electric power industry,there would be more possibilities for knowledge graph to be utilized.However,as a complex cause-and-effect network,the electric power domain knowledge graph has massive nodes,heterogeneous edges,and sparse structures.Thus,it requires human effort to process data,while quality and accuracy cannot be guaranteed.We propose a novel graph computing-based knowledge reasoning method that takes into account the sparsity of the electric power domain knowledge graph to solve the aforementioned problems and achieve improved accuracy of graph classification and knowledge reasoning tasks.The Haar basis is constructed to realize fast calculation,while the multiscale network structure is introduced to assure classification accuracy and generalization.We evaluate the proposed algorithm on the NCI-1,CEPRI UHVP,and CEPRI EQUIP databases.Simulation results demonstrate its superior performance in terms of accuracy and loss.展开更多
Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal ...Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.展开更多
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati...Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.展开更多
Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independent...Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independently to achieve specific goals.Despite its transformative potential,comprehensive information on agentic AI remains scarce in the literature.This paper provides the first comprehensive review of agentic AI,focusing on its evolution and three core aspects:patterns,types,and environments.The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents,driven by advancements in reinforcement learning,neural networks,and large language models(LLMs).Five key patterns:tool use,reflection,ReAct,planning,and multi-agent collaboration(MAC)define how agentic AI systems interact and process tasks.These systems are categorized into seven categories,each tailored for specific operational styles and autonomy in decision making.The environments in which these agents operate are classified as static,dynamic,fully observable,partially observable,deterministic,stochastic,single-agent,and multiagent,emphasizing the impact of environmental complexity on agent behavior.Agentic AI has revolutionized systems through autonomous decision making and resource optimization,yet challenges persist in aligning AI with human values,ensuring adaptability,and addressing ethical constraints.Future research focuses on multidomain agents,human–AI collaboration,and self-improving systems.This work provides researchers,practitioners,and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems.展开更多
Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowled...Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance.展开更多
Aiming at practical demands of manufacturing enterprises to the CAPP system in the Internet age, the CAPP model is presented based on Web and featured by open, universality and intelligence. A CAPP software package is...Aiming at practical demands of manufacturing enterprises to the CAPP system in the Internet age, the CAPP model is presented based on Web and featured by open, universality and intelligence. A CAPP software package is developed with three layer structures （the database, the Web server and the client server） to realize CAPP online services. In the CAPP software package, a new process planning method called the successive casebased reasoning is presented. Using the method, process planning procedures are divided into three layers （the process planning, the process procedure and the process step）, which are treated with the successive process reasoning. Process planning rules can be regularly described due to the granularity-based rule classification. The CAPP software package combines CAPP software with online services. The process planning has the features of variant analogy and generative creation due to adopting the successive case-based reasoning, thus improving the universality and the practicability of the process planning.展开更多
The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of K...The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.展开更多
The composition of the modern aerospace system becomes more and more complex.The performance degradation of any device in the system may cause it difficult for the whole system to keep normal working states.Therefore,...The composition of the modern aerospace system becomes more and more complex.The performance degradation of any device in the system may cause it difficult for the whole system to keep normal working states.Therefore,it is essential to evaluate the performance of complex aerospace systems.In this paper,the performance evaluation of complex aerospace systems is regarded as a Multi-Attribute Decision Analysis(MADA)problem.Based on the structure and working principle of the system,a new Evidential Reasoning(ER)based approach with uncertain parameters is proposed to construct a nonlinear optimization model to evaluate the system performance.In the model,the interval form is used to express the uncertainty,such as error in testing data and inaccuracy in expert knowledge.In order to analyze the subsystems that have a great impact on the performance of the system,the sensitivity analysis of the evaluation result is carried out,and the corresponding maintenance strategy is proposed.For a type of Inertial Measurement Unit(IMU)used in a rocket,the proposed method is employed to evaluate its performance.Then,the parameter sensitivity of the evaluation result is analyzed,and the main factors affecting the performance of IMU are obtained.Finally,the comparative study shows the effectiveness of the proposed method.展开更多
The design of the two-step gear reducer is a tedious and time-consuming process. For the purpose of improving the efficiency and intelligence of design process, case-based reasoning（CBR） technology was applied to th...The design of the two-step gear reducer is a tedious and time-consuming process. For the purpose of improving the efficiency and intelligence of design process, case-based reasoning（CBR） technology was applied to the design of the two-step gear reducer. Firstly, the current design method for the two-step gear reducer was analyzed and the principle of CBR was described. Secondly, according to the characteristics of the reducer, three key technologies of CBR were studied and the corresponding methods were provided, which are as follows： （a） an object-oriented knowledge representation method, （b） a retrieval method combining the nearest neighbor with the induction indexing, and （c） a case adaptation algorithm combining the revision based on rule with artificial revision. Also, for the purpose of improving the credibility of case retrieval, a new method for determining the weights of characteristics and a similarity formula were presented, which is a combinatorial weighting method with the analytic hierarchy process（AHP） and roughness set theory. Lastly, according to the above analytic results, a design system of the two-step gear reducer on CBR was developed by VC＋＋, UG and Access 2003. A new method for the design of the two-step gear reducer is provided in this study. If the foregoing developed system is applied to design the two-step gear reducer, design efficiency is improved, which enables the designer to release from the tedious design process of the gear reducer so as to put more efforts on innovative design. The study result fully reflects the feasibility and validity of CBR technology in the process of the design of the mechanical parts.展开更多
This paper presents the construction of a pneumatic active suspension system for a one-wheel car model using fuzzy reasoning and a disturbance observer. The one-wheel car model can be approximately described as a nonl...This paper presents the construction of a pneumatic active suspension system for a one-wheel car model using fuzzy reasoning and a disturbance observer. The one-wheel car model can be approximately described as a nonlinear two degrees of freedom system subject to excitation from a road profile. The active control is composed of fuzzy and disturbance controls, and the active control force is constructed by actuating a pneumatic actuator. A phase lead-lag compensator is inserted to counter the performance degradation due to the delay of the pneumatic actuator. The experimental result indicates that the proposed active suspension improves much the vibration suppression of the car model.展开更多
Syllogistic fuzzy reasoning is introduced into fuzzy system, and the new Cascaded Fuzzy System(CFS) is presented. The thoroughly theoretical analysis and experimental results show that syllogistic fuzzy reasoning is m...Syllogistic fuzzy reasoning is introduced into fuzzy system, and the new Cascaded Fuzzy System(CFS) is presented. The thoroughly theoretical analysis and experimental results show that syllogistic fuzzy reasoning is more robust than all other implication inferences for noise data and that CFS has better robustness than conventional fuzzy systems, which provide the solid foundation for CFS's potential application in fuzzy control and modeling and so on.展开更多
In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is appli...In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is applied in an IDS （Intrusion Detection System） following several steps. Firstly, the initial abnormal behaviours sample set is optimized through the combining of the AIS （Artificial Immune System） and the genetic algorithm. Then, the abnormity probability algorithm is raised considering the two sides of abnormality and normality. Finally, an intrusion detection system model is established based on the above algorithms and models.展开更多
An integration-centric approach is proposed to handle inadequate information in the system readiness level(SRL)assessment using the evidential reasoning(ER)algorithm.Current SRL assessment approaches cannot be applied...An integration-centric approach is proposed to handle inadequate information in the system readiness level(SRL)assessment using the evidential reasoning(ER)algorithm.Current SRL assessment approaches cannot be applied to handle inadequate information as the input.The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity.Two case studies are performed to validate the efficiency of the proposed approach.And these studies are also performed to study how the inadequate information will affect the assessment result.And the differences caused by the system's structure.The importance of the system's structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.展开更多
Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing met...Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high risk.To address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage.Specifically,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic.Then,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem.Based on the results of decisions,the diagnostic rules are established to identify the failure modes.Finally,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.展开更多
This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distr...This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.展开更多
An evaluation support system involving complicated decision making problems during engineering design of products is introduced by first describng and modeling complicated decision making problems, and then constructi...An evaluation support system involving complicated decision making problems during engineering design of products is introduced by first describng and modeling complicated decision making problems, and then constructing and describing the architecture and functional structure of an evaluation support system, based on knowledge-based reasoning. Knowledge contains important experience of field-expert and can be classified and stored in knowledge bases, and therefore, the system suggests information-processing tools based on information resources including data knowledge bases and methods bases, which can be used to evaluate the designs against the multi-criteria decision framework thereby providing decision-makers with rational and scientific information.展开更多
Computer aided process planning system played a key role for integrating design and manufacturing or assembly systems properly considering available resources and design constraints.To take advantage of the enterprise...Computer aided process planning system played a key role for integrating design and manufacturing or assembly systems properly considering available resources and design constraints.To take advantage of the enterprise resource,the web CAPP framework was established.Case based reasoning and multi agent system were integrated in the system.The multi agent mecha-nism was discussed in the paper.And an instance of case base was introduced.They made the system run independently and contin-uously in the network environment of process planning problems.展开更多
With the development of information technology, DSS can be used to resolve the complex process of the feasible reasoning and scientific decision-making of projects. This paper offers 7 exploiting principles for the co...With the development of information technology, DSS can be used to resolve the complex process of the feasible reasoning and scientific decision-making of projects. This paper offers 7 exploiting principles for the computer support system on feasible reasoning and scientific decision-making of projects, that is, the principles of standardization, procedure, specification, agility, currency, practicability and development. On the basis of analysis on systematic procedure, the computer support system on feasible reasoning and scientific decision-making of projects is formed based on WEB, and its general structure, system function and the methods to be realized are introduced. The data composition of this system is analyzed following the principles of integrality, development, perspicuity and consistency. Also, the model-base management system is designed for the management of model storage and management of model operation.展开更多
文摘Physicians gather a vast amount of information about patients’ medical procedures, treatments, insurance coverage, and other clinical data. Such information is crucial in formulating diagnosis or treatment plans for patients with similar traits. A Case-Based Reasoning (CBR) system has been developed to address the effective organization and retrieval of vital patient information to aid physicians in making decisions. Integers are used to uniquely represent various medical procedures, treatments, etc. In this research, a new algorithm is presented to retrieve suitable cases to recommend to physicians. The system is tested in a simulated environment and the results prove that the system can adapt to changes such as new medical procedures or treatments that take place in the medical field.
基金supported by the National Key Research&Development Program of China(2021YFB3301100)the National Natural Science Foundation of China(52004014)the Fundamental Research Funds for the Central Universities(ZY2406).
文摘This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.
基金supported by National Key R&D Program of China(2020YFB0905900).
文摘Knowledge graph,which is a rapidly developing technology,provides strong support in business and engineering.Knowledge graph plays an important role in recommendations and decision-making,while in the electric power industry,there would be more possibilities for knowledge graph to be utilized.However,as a complex cause-and-effect network,the electric power domain knowledge graph has massive nodes,heterogeneous edges,and sparse structures.Thus,it requires human effort to process data,while quality and accuracy cannot be guaranteed.We propose a novel graph computing-based knowledge reasoning method that takes into account the sparsity of the electric power domain knowledge graph to solve the aforementioned problems and achieve improved accuracy of graph classification and knowledge reasoning tasks.The Haar basis is constructed to realize fast calculation,while the multiscale network structure is introduced to assure classification accuracy and generalization.We evaluate the proposed algorithm on the NCI-1,CEPRI UHVP,and CEPRI EQUIP databases.Simulation results demonstrate its superior performance in terms of accuracy and loss.
文摘Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.
文摘Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independently to achieve specific goals.Despite its transformative potential,comprehensive information on agentic AI remains scarce in the literature.This paper provides the first comprehensive review of agentic AI,focusing on its evolution and three core aspects:patterns,types,and environments.The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents,driven by advancements in reinforcement learning,neural networks,and large language models(LLMs).Five key patterns:tool use,reflection,ReAct,planning,and multi-agent collaboration(MAC)define how agentic AI systems interact and process tasks.These systems are categorized into seven categories,each tailored for specific operational styles and autonomy in decision making.The environments in which these agents operate are classified as static,dynamic,fully observable,partially observable,deterministic,stochastic,single-agent,and multiagent,emphasizing the impact of environmental complexity on agent behavior.Agentic AI has revolutionized systems through autonomous decision making and resource optimization,yet challenges persist in aligning AI with human values,ensuring adaptability,and addressing ethical constraints.Future research focuses on multidomain agents,human–AI collaboration,and self-improving systems.This work provides researchers,practitioners,and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems.
基金supported byNationalNatural Science Foundation of China(GrantNos.62071098,U24B20128)Sichuan Science and Technology Program(Grant No.2022YFG0319).
文摘Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance.
文摘Aiming at practical demands of manufacturing enterprises to the CAPP system in the Internet age, the CAPP model is presented based on Web and featured by open, universality and intelligence. A CAPP software package is developed with three layer structures （the database, the Web server and the client server） to realize CAPP online services. In the CAPP software package, a new process planning method called the successive casebased reasoning is presented. Using the method, process planning procedures are divided into three layers （the process planning, the process procedure and the process step）, which are treated with the successive process reasoning. Process planning rules can be regularly described due to the granularity-based rule classification. The CAPP software package combines CAPP software with online services. The process planning has the features of variant analogy and generative creation due to adopting the successive case-based reasoning, thus improving the universality and the practicability of the process planning.
基金supported by the Key Science and Technology R&D Project of Sichuan Province under Grants No. 2022YFG0038 and No. 2021YFG0018
文摘The knowledge graph(KG) that represents structural relations among entities has become an increasingly important research field for knowledge-driven artificial intelligence. In this survey, a comprehensive review of KG and KG reasoning is provided. It introduces an overview of KGs, including representation, storage, and essential technologies. Specifically, it summarizes several types of knowledge reasoning approaches, including logic rules-based, representation-based, and neural network-based methods. Moreover, this paper analyzes the representation methods of knowledge hypergraphs. To effectively model hyper-relational data and improve the performance of knowledge reasoning, a three-layer knowledge hypergraph model is proposed. Finally, it analyzes the advantages of three-layer knowledge hypergraphs through reasoning and update algorithms which could facilitate future research.
基金supported by the National Natural Science Foundation of China(Nos.61773388,61751304,61833016,and 61702142)the Shaanxi Outstanding Youth Science Foundation(No.2020JC-34)the Key Research and Development Plan of Hainan(No.ZDYF2019007)。
文摘The composition of the modern aerospace system becomes more and more complex.The performance degradation of any device in the system may cause it difficult for the whole system to keep normal working states.Therefore,it is essential to evaluate the performance of complex aerospace systems.In this paper,the performance evaluation of complex aerospace systems is regarded as a Multi-Attribute Decision Analysis(MADA)problem.Based on the structure and working principle of the system,a new Evidential Reasoning(ER)based approach with uncertain parameters is proposed to construct a nonlinear optimization model to evaluate the system performance.In the model,the interval form is used to express the uncertainty,such as error in testing data and inaccuracy in expert knowledge.In order to analyze the subsystems that have a great impact on the performance of the system,the sensitivity analysis of the evaluation result is carried out,and the corresponding maintenance strategy is proposed.For a type of Inertial Measurement Unit(IMU)used in a rocket,the proposed method is employed to evaluate its performance.Then,the parameter sensitivity of the evaluation result is analyzed,and the main factors affecting the performance of IMU are obtained.Finally,the comparative study shows the effectiveness of the proposed method.
基金This project is supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2008AA04Z115)Science and Technology Program of the Ministry of Construction of China (Grant No. 2008-K8-2)+1 种基金Jiangsu Provincial Natural Science Foundation of China (Grant No. BK2007042)Open Fund of State Key Lab of CAD&CG, Zhejiang University, China (Grant No. A0914)
文摘The design of the two-step gear reducer is a tedious and time-consuming process. For the purpose of improving the efficiency and intelligence of design process, case-based reasoning（CBR） technology was applied to the design of the two-step gear reducer. Firstly, the current design method for the two-step gear reducer was analyzed and the principle of CBR was described. Secondly, according to the characteristics of the reducer, three key technologies of CBR were studied and the corresponding methods were provided, which are as follows： （a） an object-oriented knowledge representation method, （b） a retrieval method combining the nearest neighbor with the induction indexing, and （c） a case adaptation algorithm combining the revision based on rule with artificial revision. Also, for the purpose of improving the credibility of case retrieval, a new method for determining the weights of characteristics and a similarity formula were presented, which is a combinatorial weighting method with the analytic hierarchy process（AHP） and roughness set theory. Lastly, according to the above analytic results, a design system of the two-step gear reducer on CBR was developed by VC＋＋, UG and Access 2003. A new method for the design of the two-step gear reducer is provided in this study. If the foregoing developed system is applied to design the two-step gear reducer, design efficiency is improved, which enables the designer to release from the tedious design process of the gear reducer so as to put more efforts on innovative design. The study result fully reflects the feasibility and validity of CBR technology in the process of the design of the mechanical parts.
文摘This paper presents the construction of a pneumatic active suspension system for a one-wheel car model using fuzzy reasoning and a disturbance observer. The one-wheel car model can be approximately described as a nonlinear two degrees of freedom system subject to excitation from a road profile. The active control is composed of fuzzy and disturbance controls, and the active control force is constructed by actuating a pneumatic actuator. A phase lead-lag compensator is inserted to counter the performance degradation due to the delay of the pneumatic actuator. The experimental result indicates that the proposed active suspension improves much the vibration suppression of the car model.
文摘Syllogistic fuzzy reasoning is introduced into fuzzy system, and the new Cascaded Fuzzy System(CFS) is presented. The thoroughly theoretical analysis and experimental results show that syllogistic fuzzy reasoning is more robust than all other implication inferences for noise data and that CFS has better robustness than conventional fuzzy systems, which provide the solid foundation for CFS's potential application in fuzzy control and modeling and so on.
基金Supported by the National Natural Science Foundation ofChina (60563002) Scientific Research Programof the Higher EducationInstitution of Xinjiang (XJEDU2004I03)
文摘In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is applied in an IDS （Intrusion Detection System） following several steps. Firstly, the initial abnormal behaviours sample set is optimized through the combining of the AIS （Artificial Immune System） and the genetic algorithm. Then, the abnormity probability algorithm is raised considering the two sides of abnormality and normality. Finally, an intrusion detection system model is established based on the above algorithms and models.
基金supported by the National Natural Science Foundation of China(7090107471001104)
文摘An integration-centric approach is proposed to handle inadequate information in the system readiness level(SRL)assessment using the evidential reasoning(ER)algorithm.Current SRL assessment approaches cannot be applied to handle inadequate information as the input.The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity.Two case studies are performed to validate the efficiency of the proposed approach.And these studies are also performed to study how the inadequate information will affect the assessment result.And the differences caused by the system's structure.The importance of the system's structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.
基金supported by National Key R&D Program of China(2022YFB2602203)Talent Fund of Beijing Jiaotong University(2021RC274,I22L00131)National Natural Science Foundation of China(U1934219,52202392,52022010,U22A2046,52172322,62271486,62120106011,52172323)。
文摘Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high risk.To address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage.Specifically,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic.Then,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem.Based on the results of decisions,the diagnostic rules are established to identify the failure modes.Finally,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.
文摘This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.
文摘An evaluation support system involving complicated decision making problems during engineering design of products is introduced by first describng and modeling complicated decision making problems, and then constructing and describing the architecture and functional structure of an evaluation support system, based on knowledge-based reasoning. Knowledge contains important experience of field-expert and can be classified and stored in knowledge bases, and therefore, the system suggests information-processing tools based on information resources including data knowledge bases and methods bases, which can be used to evaluate the designs against the multi-criteria decision framework thereby providing decision-makers with rational and scientific information.
文摘Computer aided process planning system played a key role for integrating design and manufacturing or assembly systems properly considering available resources and design constraints.To take advantage of the enterprise resource,the web CAPP framework was established.Case based reasoning and multi agent system were integrated in the system.The multi agent mecha-nism was discussed in the paper.And an instance of case base was introduced.They made the system run independently and contin-uously in the network environment of process planning problems.
文摘With the development of information technology, DSS can be used to resolve the complex process of the feasible reasoning and scientific decision-making of projects. This paper offers 7 exploiting principles for the computer support system on feasible reasoning and scientific decision-making of projects, that is, the principles of standardization, procedure, specification, agility, currency, practicability and development. On the basis of analysis on systematic procedure, the computer support system on feasible reasoning and scientific decision-making of projects is formed based on WEB, and its general structure, system function and the methods to be realized are introduced. The data composition of this system is analyzed following the principles of integrality, development, perspicuity and consistency. Also, the model-base management system is designed for the management of model storage and management of model operation.
