Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel perf...Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.展开更多
The knowledge representation mode and inference control strategy were analyzed according to the specialties of air-conditioning cooling/heating sources selection. The constructing idea and working procedure for knowle...The knowledge representation mode and inference control strategy were analyzed according to the specialties of air-conditioning cooling/heating sources selection. The constructing idea and working procedure for knowledge base and inference engine were proposed while the realization technique of the C language was discussed. An intelligent decision support system (IDSS) model based on such knowledge representation and inference mechanism was developed by domain engineers. The model was verified to have a small kernel and powerful capability in list processing and data driving, which was successfully used in the design of a cooling/heating sources system for a large-sized office building.展开更多
The expert system for judging blast furnace condition is built to realize automatic judgment of blast furnace conditions and standardization of blast furnace operation. This paper discusses the design and implementati...The expert system for judging blast furnace condition is built to realize automatic judgment of blast furnace conditions and standardization of blast furnace operation. This paper discusses the design and implementation of the inference engine of blast furnace expert system. The satisfactory simulation results have been obtained.展开更多
Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwat...Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwater wet- lands within the lake and at the mouths of neighboring rivers, due to disturbance, primarily from human activities. The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them. In the paper, an integrated method, which combined per-pixel and per-field classifi- cation, was used for mapping wetlands of Hongze Lake and their neighboring upland types. Firstly, Landsat ETM＋ imagery was segmented and classified by using spectral and textural features. Secondly, ETM＋ spectral bands, textural features derived from ETM＋ Pan imagery, relative relations between neighboring classes, shape fea^xes, and elevation were used in a decision tree classification. Thirdly, per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context. The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies, but also has im- proved the accuracy of identification by nearly 5%.展开更多
In the field of engineering,LabVIEW and MATLAB are the languages most commonly used by program developers.Howev-er,they have their respective advantages and disadvantages.The combination of the two will undoubtedly fa...In the field of engineering,LabVIEW and MATLAB are the languages most commonly used by program developers.Howev-er,they have their respective advantages and disadvantages.The combination of the two will undoubtedly facilitate program develop-ment.Design of inference engine is the key point and difficulty in the design of fault diagnosis expert system.This paper combinesLabVIEW and MATLAB to design the inference engine of fault diagnosis expert system based on the advantages of the graphical pro-gramming environment and signal analysis toolkit of LabVIEW without the defect of neural network toolkit.In addition,it introducesthe implementation methods and precautions for combination of LabVIEW and BP network so as to make LabVIWE amd BP benefitfrom each other,which is of great pragmatic value.展开更多
Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-len...Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-length fields,and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance.Hence,this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty.Specifically,we design four related constructions between entropy changes and protocol field segmentation,introduce random variables,and construct joint probability distributions with traffic sample observations.Probabilistic inference is then performed to identify the possible protocol segmentation points.Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.展开更多
Research on practical and verifiable prediction methods for the service life of bearings plays a critical role in improving the reliability and safety of aircraft engines. The concept of grade-life （GL） is introduce...Research on practical and verifiable prediction methods for the service life of bearings plays a critical role in improving the reliability and safety of aircraft engines. The concept of grade-life （GL） is introduced to de- scribe the service life of bearings. A GL prognostic model for aircraft engine bearings is proposed based on sup- port vector machine （SVM） and fuzzy logic inference. Firstly, the mathematical model is discussed to predict the physics-based GL （PGL）. Then, the diagnostic estimation model based on SVM is presented in detail to predict the empirical GL （EPL）. Thirdly, a fuzzy logic inference is adopted to fuse two GL predicted results. Finally, the GL prognostic model is verified by the run-to-failure data acquired from an accelerated life test of an aircraft bearing. The results show that the model provides a more practical and reliable prediction for the service life of bearings.展开更多
Rock mechanical parameters and their uncertainties are critical to rock stability analysis,engineering design,and safe construction in rock mechanics and engineering.The back analysis is widely adopted in rock enginee...Rock mechanical parameters and their uncertainties are critical to rock stability analysis,engineering design,and safe construction in rock mechanics and engineering.The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass,but this does not consider the uncertainty.This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data,then integrating the monitored data,prior knowledge of geotechnical parameters,and a mechanical model of a rock tunnel using Markov chain Monte Carlo(MCMC)simulation.The proposed approach is illustrated by a circular tunnel with an analytical solution,which was then applied to an experimental tunnel in Goupitan Hydropower Station,China.The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables.The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements.It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically.Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data.Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.展开更多
Prolog is one of the most important candidates to build expert systems and AI-related programs and has potential applications in embedded systems. However, Prolog is not suitable to develop many kinds of components, s...Prolog is one of the most important candidates to build expert systems and AI-related programs and has potential applications in embedded systems. However, Prolog is not suitable to develop many kinds of components, such as data acquisition and task scheduling, which are also crucial. To make the best use of the advantages and bypass the disadvantages, it is attractive to integrate Prolog with programs developed by other languages. In this paper, an IPC-based method is used to integrate backward chaining inference implemented by Prolog into applications or embedded systems. A Prolog design pattern is derived from the method for reuse, whose principle and definition are provided in detail. Additionally, the design pattern is applied to a target system, which is free software, to verify its feasibility. The detailed implementation of the application is given to clarify the design pattern. The design pattern can be further applied to wide range applications and embedded systems and the method described in this paper can also be adopted for other logic programming languages.展开更多
In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and...In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.展开更多
Inferring protocol state machines from observable information presents a significant challenge in protocol reverse engineering(PRE),especially when passively collected traffic suffers from message loss,resulting in an...Inferring protocol state machines from observable information presents a significant challenge in protocol reverse engineering(PRE),especially when passively collected traffic suffers from message loss,resulting in an incomplete protocol state space.This paper introduces an innovative method for actively inferring protocol state machines using the minimally adequate teacher(MAT)framework.By incorporating session completion and deterministic mutation techniques,this method broadens the range of protocol messages,thereby constructing a more comprehensive input space for the protocol state machine from an incomplete message domain.Additionally,the efficiency of active inference is improved through several optimizations for the L_(M)^(+)algorithm,including traffic deduplication,the construction of an expanded prefix tree acceptor(EPTA),query optimization based on responses,and random counterexample generation.Experiments on the real-time streaming protocol(RTSP)and simple mail transfer protocol(SMTP),which use Live555 and Exim implementations across multiple versions,demonstrate that this method yields more comprehensive protocol state machines with enhanced execution efficiency.Compared to the L_(M)^(+) algorithm implemented by AALpy,Act_Infer achieves an average reduction of approximately 40.7%in execution time and significantly reduces the number of connections and interactions by approximately 28.6%and 46.6%,respectively.展开更多
文摘Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
文摘The knowledge representation mode and inference control strategy were analyzed according to the specialties of air-conditioning cooling/heating sources selection. The constructing idea and working procedure for knowledge base and inference engine were proposed while the realization technique of the C language was discussed. An intelligent decision support system (IDSS) model based on such knowledge representation and inference mechanism was developed by domain engineers. The model was verified to have a small kernel and powerful capability in list processing and data driving, which was successfully used in the design of a cooling/heating sources system for a large-sized office building.
文摘The expert system for judging blast furnace condition is built to realize automatic judgment of blast furnace conditions and standardization of blast furnace operation. This paper discusses the design and implementation of the inference engine of blast furnace expert system. The satisfactory simulation results have been obtained.
基金Under the auspices of Natural Science Foundation of Jiangsu Province (No. BK2008360)Foundamental Research Funds for the Central Universities (No. 2009B12714,2009B11714)
文摘Inland freshwater lake wetlands play an important role in regional ecological balance. Hongze Lake is the fourth biggest freshwater lake in China. In the past three decades, there has been significant loss of freshwater wet- lands within the lake and at the mouths of neighboring rivers, due to disturbance, primarily from human activities. The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them. In the paper, an integrated method, which combined per-pixel and per-field classifi- cation, was used for mapping wetlands of Hongze Lake and their neighboring upland types. Firstly, Landsat ETM＋ imagery was segmented and classified by using spectral and textural features. Secondly, ETM＋ spectral bands, textural features derived from ETM＋ Pan imagery, relative relations between neighboring classes, shape fea^xes, and elevation were used in a decision tree classification. Thirdly, per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context. The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies, but also has im- proved the accuracy of identification by nearly 5%.
文摘In the field of engineering,LabVIEW and MATLAB are the languages most commonly used by program developers.Howev-er,they have their respective advantages and disadvantages.The combination of the two will undoubtedly facilitate program develop-ment.Design of inference engine is the key point and difficulty in the design of fault diagnosis expert system.This paper combinesLabVIEW and MATLAB to design the inference engine of fault diagnosis expert system based on the advantages of the graphical pro-gramming environment and signal analysis toolkit of LabVIEW without the defect of neural network toolkit.In addition,it introducesthe implementation methods and precautions for combination of LabVIEW and BP network so as to make LabVIWE amd BP benefitfrom each other,which is of great pragmatic value.
文摘Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-length fields,and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance.Hence,this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty.Specifically,we design four related constructions between entropy changes and protocol field segmentation,introduce random variables,and construct joint probability distributions with traffic sample observations.Probabilistic inference is then performed to identify the possible protocol segmentation points.Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.
基金Supported by the China Postdoctoral Science Foundation(20100481500)~~
文摘Research on practical and verifiable prediction methods for the service life of bearings plays a critical role in improving the reliability and safety of aircraft engines. The concept of grade-life （GL） is introduced to de- scribe the service life of bearings. A GL prognostic model for aircraft engine bearings is proposed based on sup- port vector machine （SVM） and fuzzy logic inference. Firstly, the mathematical model is discussed to predict the physics-based GL （PGL）. Then, the diagnostic estimation model based on SVM is presented in detail to predict the empirical GL （EPL）. Thirdly, a fuzzy logic inference is adopted to fuse two GL predicted results. Finally, the GL prognostic model is verified by the run-to-failure data acquired from an accelerated life test of an aircraft bearing. The results show that the model provides a more practical and reliable prediction for the service life of bearings.
基金support from the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.Z020006)the National Natural Science Foundation of China(Grant Nos.U1765206 and 51874119).
文摘Rock mechanical parameters and their uncertainties are critical to rock stability analysis,engineering design,and safe construction in rock mechanics and engineering.The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass,but this does not consider the uncertainty.This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data,then integrating the monitored data,prior knowledge of geotechnical parameters,and a mechanical model of a rock tunnel using Markov chain Monte Carlo(MCMC)simulation.The proposed approach is illustrated by a circular tunnel with an analytical solution,which was then applied to an experimental tunnel in Goupitan Hydropower Station,China.The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables.The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements.It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically.Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data.Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.
基金supported by the National Natural Science Foundation of China (No.61304111)National Basic Research Program of China (No. 2014CB744904)Fundamental Research Funds for the Central Universities of China (Nos. YWF-14-KKX-001 and YWF-13-JQCJ)
文摘Prolog is one of the most important candidates to build expert systems and AI-related programs and has potential applications in embedded systems. However, Prolog is not suitable to develop many kinds of components, such as data acquisition and task scheduling, which are also crucial. To make the best use of the advantages and bypass the disadvantages, it is attractive to integrate Prolog with programs developed by other languages. In this paper, an IPC-based method is used to integrate backward chaining inference implemented by Prolog into applications or embedded systems. A Prolog design pattern is derived from the method for reuse, whose principle and definition are provided in detail. Additionally, the design pattern is applied to a target system, which is free software, to verify its feasibility. The detailed implementation of the application is given to clarify the design pattern. The design pattern can be further applied to wide range applications and embedded systems and the method described in this paper can also be adopted for other logic programming languages.
文摘In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.
基金supported by the National Natural Science Foundation of China(61471343)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2014BAK14B03)
基金Project supported by the Key JCJQ Program of China(Nos.2020-JCJQ-ZD-021-00 and 2020-JCJQ-ZD-024-12)。
文摘Inferring protocol state machines from observable information presents a significant challenge in protocol reverse engineering(PRE),especially when passively collected traffic suffers from message loss,resulting in an incomplete protocol state space.This paper introduces an innovative method for actively inferring protocol state machines using the minimally adequate teacher(MAT)framework.By incorporating session completion and deterministic mutation techniques,this method broadens the range of protocol messages,thereby constructing a more comprehensive input space for the protocol state machine from an incomplete message domain.Additionally,the efficiency of active inference is improved through several optimizations for the L_(M)^(+)algorithm,including traffic deduplication,the construction of an expanded prefix tree acceptor(EPTA),query optimization based on responses,and random counterexample generation.Experiments on the real-time streaming protocol(RTSP)and simple mail transfer protocol(SMTP),which use Live555 and Exim implementations across multiple versions,demonstrate that this method yields more comprehensive protocol state machines with enhanced execution efficiency.Compared to the L_(M)^(+) algorithm implemented by AALpy,Act_Infer achieves an average reduction of approximately 40.7%in execution time and significantly reduces the number of connections and interactions by approximately 28.6%and 46.6%,respectively.
