Aiming at the problems of unreliable data transmission,poor steadiness,nonsupport of complex data types,direct couple between data transmission and exchange,a high-level method based on advanced message queuing protoc...Aiming at the problems of unreliable data transmission,poor steadiness,nonsupport of complex data types,direct couple between data transmission and exchange,a high-level method based on advanced message queuing protocol( AMQP) is proposed to integrate naval distributed tactical training simulation system after serious consideration with current information exchange features of military combat system. Transferring layer in traditional user datagram protocol is implemented by publishing and subscribing scheme of message middleware. By creating message model to standardize message structure,integration architecture is formulated to resolve potential information security risks from inconsistent data type and express data transmission. Meanwhile,a communication model is put forward based on AMQP,which is in the center position of the whole transmission framework and responsible for reliably transferring battlefield data among subsystems. Experiments show that the method can accurately post amounts of data to the subscriber without error and loss,and can get excellent real-time performance of data exchange.展开更多
Naval Vessels Combat System is a kind of complex system.The modeling of combat system has become hot issues in the past years.This paper proposed a new method to establish models of combat system based on the theory o...Naval Vessels Combat System is a kind of complex system.The modeling of combat system has become hot issues in the past years.This paper proposed a new method to establish models of combat system based on the theory of Complex Network.The method of modeling considered the operational entities as nodes.It considered flow of information,substance and energy as edges in a network.The research also carries on a simulation to prove the applicability.Ultimately,the paper concluded that this method is applicable and accurate.展开更多
The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration ...The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration Systems (LAS). LAS services involve requests and responses concerning public and private cadastral data, including credentials of parties, ownership, and spatial parcels. This study explores the integration of CTI in LAS to enhance cyber resilience, focusing on the unique vulnerabilities of LAS, such as sensitive data management and interconnection with other critical systems related to spatial data uses and changes. The approach employs a case study of a typical country-specific LAS to analyse structured vulnerabilities and their attributes to determine the degree of vulnerability of LAS through a quantitative inductive approach. The analysis results indicate significant improvements in identifying and mitigating potential threats through CTI integration, thus enhancing cyber resilience. These findings are crucial for policymakers and practitioners to develop robust cybersecurity strategies for LAS.展开更多
In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the t...In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the teaching with the OBE concept,carry out the teaching goal planning of unmanned systems application based on the OBE concept,innovate teaching methods,reconstruct course content,revitalize the teaching process,improve the evaluation model,and stimulate learning motivation to enhance the quality of course teaching and achieve the teaching goal of“knowledge+ability.”This has a certain reference value for the reform practice of unmanned systems courses.展开更多
The widespread adoption of the internet has provided new platforms and possibilities for Chinese language instruction.Students can utilize online resources or mobile devices for learning outside the classroom,while te...The widespread adoption of the internet has provided new platforms and possibilities for Chinese language instruction.Students can utilize online resources or mobile devices for learning outside the classroom,while teachers can shift the“intensive instruction”component of comprehensive Chinese courses to extracurricular settings.This approach enables increased practice time during class sessions,truly placing the learner at the center of the educational process.The flipped classroom model aligns with this philosophy and complements the disciplinary characteristics of comprehensive Chinese courses.In practice,implementing the O-PIRTAS universal flipped classroom model revealed its effectiveness in enhancing oral proficiency and overall competency.However,it is essential to concurrently address students’writing skills and cultivate their awareness of the flipped classroom approach.展开更多
This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle(UAV)imagery by integrating the Visible Atmospherically Resistant Index(VARI)with deep learning models.The primary chall...This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle(UAV)imagery by integrating the Visible Atmospherically Resistant Index(VARI)with deep learning models.The primary challenge addressed is the detection of bananas interplanted with betel nuts,a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap.The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector(SSD),You Only Look Once version 3(YOLOv3),and Faster Region-Based Convolutional Neural Network(Faster RCNN)—using Red,Green,Blue(RGB)and VARI images for banana detection.Results show that VARI significantly improves detection accuracy,with YOLOv3 achieving the best performance,achieving a precision of 73.77%,recall of 100%,and reduced training time by 95 seconds.Additionally,the average Intersection over Union(IoU)increased by 4%–25%across models with VARI-enhanced images.This study confirms that incorporating VARI improves the performance of deep learning models,offering a promising solution for precise crop detection in complex agricultural environments.展开更多
Modern shipboard microgrids(SMGs)incorporating distributed energy resources(DERs)enhance energy resilience and reduce carbon emissions.However,the hierarchical control schemes of DERs bring challenges to the tradition...Modern shipboard microgrids(SMGs)incorporating distributed energy resources(DERs)enhance energy resilience and reduce carbon emissions.However,the hierarchical control schemes of DERs bring challenges to the traditional power flow methods.This paper devises a generalized three-phase power flow approach for SMGs that integrate hierarchically controlled DERs.The main contributions include:(1)a droop-controlled three-phase Newton power flow algorithm that automatically incorporates the droop characteristics of DERs;(2)a secondary-controlled three-phase power flow method for power sharing and voltage regulation;and(3)modified Jacobian matrices to incorporate various hierarchical control modes.Numerical results demonstrate the effectiveness of the devised approach in both balanced and unbalanced three-phase hierarchically controlled SMG systems with arbitrary config-urations.展开更多
Efficient banana crop detection is crucial for precision agriculture;however,traditional remote sensing methods often lack the spatial resolution required for accurate identification.This study utilizes low-altitude U...Efficient banana crop detection is crucial for precision agriculture;however,traditional remote sensing methods often lack the spatial resolution required for accurate identification.This study utilizes low-altitude Unmanned Aerial Vehicle(UAV)images and deep learning-based object detection models to enhance banana plant detection.A comparative analysis of Faster Region-Based Convolutional Neural Network(Faster R-CNN),You Only Look Once Version 3(YOLOv3),Retina Network(RetinaNet),and Single Shot MultiBox Detector(SSD)was conducted to evaluate their effectiveness.Results show that RetinaNet achieved the highest detection accuracy,with a precision of 96.67%,a recall of 71.67%,and an F1 score of 81.33%.The study further highlights the impact of scale variation,occlusion,and vegetation density on detection performance.Unlike previous studies,this research systematically evaluates multi-scale object detection models for banana plant identification,offering insights into the advantages of UAV-based deep learning applications in agriculture.In addition,this study compares five evaluation metrics across the four detection models using both RGB and grayscale images.Specifically,RetinaNet exhibited the best overall performance with grayscale images,achieving the highest values across all five metrics.Compared to its performance with RGB images,these results represent a marked improvement,confirming the potential of grayscale preprocessing to enhance detection capability.展开更多
Many attempts have been made to identify barriers to blockchain adoption in supply chain;however,barriers to blockchain adoption in supply chain finance(SCF)are underexplored.This study prioritizes barriers to blockch...Many attempts have been made to identify barriers to blockchain adoption in supply chain;however,barriers to blockchain adoption in supply chain finance(SCF)are underexplored.This study prioritizes barriers to blockchain adoption in SCF and evaluates the barrier level of each alternative participant.We propose an integrated decision model to prioritize the barriers and evaluate their levels of alternative participants.To determine the barriers,we conducted a literature review.We then introduce an integrated weight calculation method by combining interval-valued Fermatean fuzzy(IVFF)-optimistic-pessimistic-utility values-based and IVFF-RS(ranking sum)methods to determine the barrier weights.To evaluate the barrier level of each alternative participant in SCF,the integrated IVFF-RAFSI(Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval)model is presented to rank the barrier,which uses a power-weighted aggregation operator to fuse experts’opinions.A case study demonstrates the practicality of the integrated IVFF-RAFSI model.The results show that uncertain and competitive markets(weighted at 0.0676)are the most significant barriers.This finding also suggests that small and medium-sized processing enterprises have the highest barriers to blockchain adoption.Sensitivity and comparative analyses validate the steadiness and competency of the proposed model.These results indicate that the proposed methodology provides a systematic technique for analyzing barriers to blockchain applications in SCF.展开更多
The function of the air target threat evaluation(TE)is the foundation for weapons allocation and senor resources management within the surface air defense.The multi-attribute evaluation methodology is utilized to addr...The function of the air target threat evaluation(TE)is the foundation for weapons allocation and senor resources management within the surface air defense.The multi-attribute evaluation methodology is utilized to address the issue of the TE in which the tactic features of the detected target are treated as evaluation attributes.Meanwhile,the intuitionistic fuzzy set(IFS)is employed to deal with information uncertainty in the TE process.Furthermore,on the basis of the entropy weight and inclusion-comparison probability,a hybrid TE method is developed.In order to accommodate the demands of naturalistic decision making,the proposed method allows air defense commanders to express their intuitive opinions besides incorporating into the threat features of the detected target.An illustrative example is provided to indicate the feasibility and advantage of the proposed method.展开更多
Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune s...Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune system （BIS） according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system （MAS）, the vaccine and the danger theory （DT）. Moreover a new immune multi-agent model using vaccine based on DT （IMMUVBDT） for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.展开更多
In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, the...In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.展开更多
Paris law can reflect the failure mechanism of materials and is usually used to be a method to predict fatigue life or residual fatigue life.But the variable which can represent the health of machine is hardly measure...Paris law can reflect the failure mechanism of materials and is usually used to be a method to predict fatigue life or residual fatigue life.But the variable which can represent the health of machine is hardly measured on line.To a degree,the difficulty of on-line application restricts the scope of application of Paris law.The relationship between characteristic values of vibration signals and the variable in the Paris equation which can describe the health of machine is investigated by taking ball bearings as investigative objects.Based on 6205 deep groove ball bearings as a living example,historical lives and vibration signals are analyzed.The feasibility of describing that variable in the Paris equation by the characteristic value of vibration signals is inspected.After that vibration signals decomposed by empirical mode decomposition（EMD）,root mean square（RMS） of intrinsic mode function（IMF） involving fault characteristic frequency has a consistent trend with the diameter of flaws.Based on the trend,two improved Paris models are proposed and the scope of application of them is inspected.These two Paris Models are validated by fatigue residual life data from tests of rolling element bearings and vibration signals monitored in the process of operation of rolling element bearings.It shows that the first improved Paris Model is simple and plain and it can be easily applied in actual conditions.The trend of the fatigue residual life predicted by the second improved Paris model is close to the actual conditions and the result of the prediction is slightly greater than the truth.In conclusion,after the appearance of detectable faults,these improved models based on RMS can predict residual fatigue life on line and a new approach to predict residual fatigue life of ball bearings on line without disturbing the machine running is provided.展开更多
WTA （weapon-target allocation） of air defense operation is a very complicated problem and current models focus on static and restricted WTA problem mostly. Based on the dynamic characteristics of air defense operati...WTA （weapon-target allocation） of air defense operation is a very complicated problem and current models focus on static and restricted WTA problem mostly. Based on the dynamic characteristics of air defense operational command and decision of warships＇ formation, a dynamic WTA model is established. Simulation results show that switch fire and repetition fire of anti-air weapon system affect the result of the air defense operation remarkably and the dynamic model is more satisfying than static ones. Related results are gained based on the analysis of the simulation results and the results are accordant with the intuitionistic tactical judgment. The model is some reference for the research of air defense C^3I system of warships＇ formation.展开更多
According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm ...According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.展开更多
This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedba...This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital port for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases： Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.展开更多
With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved ...With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.展开更多
A degradation model with a random failure threshold is presented for the assessment of reliability by the Bayesian approach. This model is different from others in that the degradation process is proceeding under pre-...A degradation model with a random failure threshold is presented for the assessment of reliability by the Bayesian approach. This model is different from others in that the degradation process is proceeding under pre-specified periodical calibrations. And here a random threshold distribution instead of a constant threshold which is difficult to determine in practice is used. The system reliability is defined as the probability that the degradation signals do not exceed the random threshold. Based on the posterior distribution estimates of degradation performance, two models for Bayesian reliability assessments are presented in terms of the degradation performance and the distribution of random failure threshold. The methods proposed in this paper are very useful and practical for multi-stage system with uncertain failure threshold. This study perfects the degradation modeling approaches and plays an important role in the remaining useful life estimation and maintenance decision making.展开更多
基金Supported by the National Natural Science Foundation of China(No.61401496)
文摘Aiming at the problems of unreliable data transmission,poor steadiness,nonsupport of complex data types,direct couple between data transmission and exchange,a high-level method based on advanced message queuing protocol( AMQP) is proposed to integrate naval distributed tactical training simulation system after serious consideration with current information exchange features of military combat system. Transferring layer in traditional user datagram protocol is implemented by publishing and subscribing scheme of message middleware. By creating message model to standardize message structure,integration architecture is formulated to resolve potential information security risks from inconsistent data type and express data transmission. Meanwhile,a communication model is put forward based on AMQP,which is in the center position of the whole transmission framework and responsible for reliably transferring battlefield data among subsystems. Experiments show that the method can accurately post amounts of data to the subscriber without error and loss,and can get excellent real-time performance of data exchange.
基金supported by Science Foundation of Dalian Naval Academy
文摘Naval Vessels Combat System is a kind of complex system.The modeling of combat system has become hot issues in the past years.This paper proposed a new method to establish models of combat system based on the theory of Complex Network.The method of modeling considered the operational entities as nodes.It considered flow of information,substance and energy as edges in a network.The research also carries on a simulation to prove the applicability.Ultimately,the paper concluded that this method is applicable and accurate.
文摘The current global cybersecurity landscape, characterized by the increasing scale and sophistication of cyberattacks, underscores the importance of integrating Cyber Threat Intelligence (CTI) into Land Administration Systems (LAS). LAS services involve requests and responses concerning public and private cadastral data, including credentials of parties, ownership, and spatial parcels. This study explores the integration of CTI in LAS to enhance cyber resilience, focusing on the unique vulnerabilities of LAS, such as sensitive data management and interconnection with other critical systems related to spatial data uses and changes. The approach employs a case study of a typical country-specific LAS to analyse structured vulnerabilities and their attributes to determine the degree of vulnerability of LAS through a quantitative inductive approach. The analysis results indicate significant improvements in identifying and mitigating potential threats through CTI integration, thus enhancing cyber resilience. These findings are crucial for policymakers and practitioners to develop robust cybersecurity strategies for LAS.
文摘In response to the problems existing in the teaching of unmanned systems courses,such as being confined to traditional teaching models and insufficient focus on practical application,this paper proposes to guide the teaching with the OBE concept,carry out the teaching goal planning of unmanned systems application based on the OBE concept,innovate teaching methods,reconstruct course content,revitalize the teaching process,improve the evaluation model,and stimulate learning motivation to enhance the quality of course teaching and achieve the teaching goal of“knowledge+ability.”This has a certain reference value for the reform practice of unmanned systems courses.
文摘The widespread adoption of the internet has provided new platforms and possibilities for Chinese language instruction.Students can utilize online resources or mobile devices for learning outside the classroom,while teachers can shift the“intensive instruction”component of comprehensive Chinese courses to extracurricular settings.This approach enables increased practice time during class sessions,truly placing the learner at the center of the educational process.The flipped classroom model aligns with this philosophy and complements the disciplinary characteristics of comprehensive Chinese courses.In practice,implementing the O-PIRTAS universal flipped classroom model revealed its effectiveness in enhancing oral proficiency and overall competency.However,it is essential to concurrently address students’writing skills and cultivate their awareness of the flipped classroom approach.
文摘This study aims to enhance automated crop detection using high-resolution Unmanned Aerial Vehicle(UAV)imagery by integrating the Visible Atmospherically Resistant Index(VARI)with deep learning models.The primary challenge addressed is the detection of bananas interplanted with betel nuts,a scenario where traditional image processing techniques struggle due to color similarities and canopy overlap.The research explores the effectiveness of three deep learning models—Single Shot MultiBox Detector(SSD),You Only Look Once version 3(YOLOv3),and Faster Region-Based Convolutional Neural Network(Faster RCNN)—using Red,Green,Blue(RGB)and VARI images for banana detection.Results show that VARI significantly improves detection accuracy,with YOLOv3 achieving the best performance,achieving a precision of 73.77%,recall of 100%,and reduced training time by 95 seconds.Additionally,the average Intersection over Union(IoU)increased by 4%–25%across models with VARI-enhanced images.This study confirms that incorporating VARI improves the performance of deep learning models,offering a promising solution for precise crop detection in complex agricultural environments.
基金supported in part by the Department of Navy award N00014-24-1-2287 and N00014-23-1-2124。
文摘Modern shipboard microgrids(SMGs)incorporating distributed energy resources(DERs)enhance energy resilience and reduce carbon emissions.However,the hierarchical control schemes of DERs bring challenges to the traditional power flow methods.This paper devises a generalized three-phase power flow approach for SMGs that integrate hierarchically controlled DERs.The main contributions include:(1)a droop-controlled three-phase Newton power flow algorithm that automatically incorporates the droop characteristics of DERs;(2)a secondary-controlled three-phase power flow method for power sharing and voltage regulation;and(3)modified Jacobian matrices to incorporate various hierarchical control modes.Numerical results demonstrate the effectiveness of the devised approach in both balanced and unbalanced three-phase hierarchically controlled SMG systems with arbitrary config-urations.
文摘Efficient banana crop detection is crucial for precision agriculture;however,traditional remote sensing methods often lack the spatial resolution required for accurate identification.This study utilizes low-altitude Unmanned Aerial Vehicle(UAV)images and deep learning-based object detection models to enhance banana plant detection.A comparative analysis of Faster Region-Based Convolutional Neural Network(Faster R-CNN),You Only Look Once Version 3(YOLOv3),Retina Network(RetinaNet),and Single Shot MultiBox Detector(SSD)was conducted to evaluate their effectiveness.Results show that RetinaNet achieved the highest detection accuracy,with a precision of 96.67%,a recall of 71.67%,and an F1 score of 81.33%.The study further highlights the impact of scale variation,occlusion,and vegetation density on detection performance.Unlike previous studies,this research systematically evaluates multi-scale object detection models for banana plant identification,offering insights into the advantages of UAV-based deep learning applications in agriculture.In addition,this study compares five evaluation metrics across the four detection models using both RGB and grayscale images.Specifically,RetinaNet exhibited the best overall performance with grayscale images,achieving the highest values across all five metrics.Compared to its performance with RGB images,these results represent a marked improvement,confirming the potential of grayscale preprocessing to enhance detection capability.
基金supported in part by the National Natural Science Foundation of China(Grant No.72101004)the Humanity and Social Science Research Project of the Anhui Educational Committee(2023AH030053).
文摘Many attempts have been made to identify barriers to blockchain adoption in supply chain;however,barriers to blockchain adoption in supply chain finance(SCF)are underexplored.This study prioritizes barriers to blockchain adoption in SCF and evaluates the barrier level of each alternative participant.We propose an integrated decision model to prioritize the barriers and evaluate their levels of alternative participants.To determine the barriers,we conducted a literature review.We then introduce an integrated weight calculation method by combining interval-valued Fermatean fuzzy(IVFF)-optimistic-pessimistic-utility values-based and IVFF-RS(ranking sum)methods to determine the barrier weights.To evaluate the barrier level of each alternative participant in SCF,the integrated IVFF-RAFSI(Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval)model is presented to rank the barrier,which uses a power-weighted aggregation operator to fuse experts’opinions.A case study demonstrates the practicality of the integrated IVFF-RAFSI model.The results show that uncertain and competitive markets(weighted at 0.0676)are the most significant barriers.This finding also suggests that small and medium-sized processing enterprises have the highest barriers to blockchain adoption.Sensitivity and comparative analyses validate the steadiness and competency of the proposed model.These results indicate that the proposed methodology provides a systematic technique for analyzing barriers to blockchain applications in SCF.
基金supported by the National Natural Science Foundation of China(7087111770571086)the Development Foundation of Dalian Naval Academy
文摘The function of the air target threat evaluation(TE)is the foundation for weapons allocation and senor resources management within the surface air defense.The multi-attribute evaluation methodology is utilized to address the issue of the TE in which the tactic features of the detected target are treated as evaluation attributes.Meanwhile,the intuitionistic fuzzy set(IFS)is employed to deal with information uncertainty in the TE process.Furthermore,on the basis of the entropy weight and inclusion-comparison probability,a hybrid TE method is developed.In order to accommodate the demands of naturalistic decision making,the proposed method allows air defense commanders to express their intuitive opinions besides incorporating into the threat features of the detected target.An illustrative example is provided to indicate the feasibility and advantage of the proposed method.
文摘Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune system （BIS） according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system （MAS）, the vaccine and the danger theory （DT）. Moreover a new immune multi-agent model using vaccine based on DT （IMMUVBDT） for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.
基金supported by the China Postdoctoral Science Foundation(20100471451)the Science and Technology Foundation of State Key Laboratory of Underwater Measurement&Control Technology(9140C2603051003)
文摘In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.
基金supported by National Natural Science Foundation of China (Grant No. 50705096)National Science and Technology Major Project of China(Grant No. 2009zx04014-014)
文摘Paris law can reflect the failure mechanism of materials and is usually used to be a method to predict fatigue life or residual fatigue life.But the variable which can represent the health of machine is hardly measured on line.To a degree,the difficulty of on-line application restricts the scope of application of Paris law.The relationship between characteristic values of vibration signals and the variable in the Paris equation which can describe the health of machine is investigated by taking ball bearings as investigative objects.Based on 6205 deep groove ball bearings as a living example,historical lives and vibration signals are analyzed.The feasibility of describing that variable in the Paris equation by the characteristic value of vibration signals is inspected.After that vibration signals decomposed by empirical mode decomposition（EMD）,root mean square（RMS） of intrinsic mode function（IMF） involving fault characteristic frequency has a consistent trend with the diameter of flaws.Based on the trend,two improved Paris models are proposed and the scope of application of them is inspected.These two Paris Models are validated by fatigue residual life data from tests of rolling element bearings and vibration signals monitored in the process of operation of rolling element bearings.It shows that the first improved Paris Model is simple and plain and it can be easily applied in actual conditions.The trend of the fatigue residual life predicted by the second improved Paris model is close to the actual conditions and the result of the prediction is slightly greater than the truth.In conclusion,after the appearance of detectable faults,these improved models based on RMS can predict residual fatigue life on line and a new approach to predict residual fatigue life of ball bearings on line without disturbing the machine running is provided.
文摘WTA （weapon-target allocation） of air defense operation is a very complicated problem and current models focus on static and restricted WTA problem mostly. Based on the dynamic characteristics of air defense operational command and decision of warships＇ formation, a dynamic WTA model is established. Simulation results show that switch fire and repetition fire of anti-air weapon system affect the result of the air defense operation remarkably and the dynamic model is more satisfying than static ones. Related results are gained based on the analysis of the simulation results and the results are accordant with the intuitionistic tactical judgment. The model is some reference for the research of air defense C^3I system of warships＇ formation.
基金Supported by the National Natural Science Foundation of China （No.40067116）, the Research Development Foundation of Dalian Naval Academy （No.K200821）.
文摘According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.
基金Foundation item： the National Natural Science Foundation of China （No. 61203337）
文摘This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital port for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases： Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.
基金supported by the National Natural Science Foundation of China(61703228)
文摘With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.
基金the National Natural Science Foundation of China(No.71371031)
文摘A degradation model with a random failure threshold is presented for the assessment of reliability by the Bayesian approach. This model is different from others in that the degradation process is proceeding under pre-specified periodical calibrations. And here a random threshold distribution instead of a constant threshold which is difficult to determine in practice is used. The system reliability is defined as the probability that the degradation signals do not exceed the random threshold. Based on the posterior distribution estimates of degradation performance, two models for Bayesian reliability assessments are presented in terms of the degradation performance and the distribution of random failure threshold. The methods proposed in this paper are very useful and practical for multi-stage system with uncertain failure threshold. This study perfects the degradation modeling approaches and plays an important role in the remaining useful life estimation and maintenance decision making.
