For natural water, method of water quality evaluation based on improved fuzzy matter-element evaluation method is presented. Two important parts are improved, the weights determining and fuzzy membership functions. Th...For natural water, method of water quality evaluation based on improved fuzzy matter-element evaluation method is presented. Two important parts are improved, the weights determining and fuzzy membership functions. The coefficient of variation of each indicator is used to determine the weight instead of traditional calculating superscales method. On the other hand, fuzzy matter-elements are constructed, and normal membership degrees are used instead of traditional trapezoidal ones. The composite fuzzy matter-elements with associated coefficient are constructed through associated transformation. The levels of natural water quality are determined according to the principle of maximum correlation. The improved fuzzy matter-element evaluation method is applied to evaluate water quality of the Luokou mainstream estuary at the first ten weeks in 2011 with the coefficient of variatiola method determining the weights. Water quality of Luokou mainstream estuary is dropping from level I to level II. The results of the improved evaluation method are basically the same as the official water quality. The variation coefficient method can reduce the workload, and overcome the adverse effects from abnormal values, compared with the traditional calculating superscales method. The results of improved fuzzy matter- element evaluation method are more credible than the ones of the traditional evaluation method. The improved evaluation method can use information of monitoring data more scientifically and comprehensively, and broaden a new evaluation method for water quality assessment.展开更多
An evaluation model of an international venture investment project on the basis of fuzzy matter-element and combined weight methods is introduced.First,the compound fuzzy matter-element of optimal subordinate degree i...An evaluation model of an international venture investment project on the basis of fuzzy matter-element and combined weight methods is introduced.First,the compound fuzzy matter-element of optimal subordinate degree is constructed on the principle of the bigger-more-optimal or the less-more-optimal depending on the actual evaluation indicators,and combined with standard fuzzy matter-element to form a difference-square fuzzy matter-element.Secondly,a combined weight is calculated by both information entropy and the expert grading method.Finally,the compound fuzzy matter-element of Euclidian approach degree by M(·,+)method is constituted and used to classify venture investment projects.Based on the model above,six venture investment projects in a company are evaluated,and the results show that the projects are all good,which is demonstrated by the good income of the projects.Therefore,the coincidence of evaluation results and actual operation status indicates that the model is of great value in practical application.展开更多
[Objective]The study aimed to assess the health state of rivers by using fuzzy matter-element model.[Method]Based on fuzzy matter-element analysis theory,the assessment model of river health was established,then a mod...[Objective]The study aimed to assess the health state of rivers by using fuzzy matter-element model.[Method]Based on fuzzy matter-element analysis theory,the assessment model of river health was established,then a modified method to calculate the superior subordinate degree was put forward according to Hamming distance.Afterwards,a multi-level evaluation model,which contained the assessment indicators about hydrological features,ecological characteristics,environmental traits and service function,was set up based on this method above.Finally,the model was applied in the health assessment of Qinhuai River.[Result]The health state of Qinhuai River was at medium level.This assessment result was consistent with that of comprehensive index method,and it showed that the multi-level fuzzy matter-element model was effective in the assessment of river health.[Conclusion]The research provided an effective method to evaluate the state of river health.展开更多
It＇ s a necessary selection to support the maneuver across Yangtze River by floating bridge constructed by portable steel bridge and civilian ships. It is a comprehensive index for the scheme of bridge raft, containi...It＇ s a necessary selection to support the maneuver across Yangtze River by floating bridge constructed by portable steel bridge and civilian ships. It is a comprehensive index for the scheme of bridge raft, containing a variety of technical factors and uncertainties. The optimization is the selection in the constructing time, quantity of equipments and man power. Based on the calculation result of bridge rafts, an evaluating system is established, consisting of index of spacing between interior bays, raft length, truss numbers, operation difficulty and maximal bending stress. A fuzzy matter element model of optimizing selection of bridge rafts was built up by combining quantitative analysis with qualitative analysis. The method of combination weighting was used to calculate the value of weights index to reduce the subjective randomness. The sequence of schemes and the optimization resuh were gained finally based on euclid approach degree. The application result shows that it is simple and practical.展开更多
This paper puts forward a new integrated design met ho d based on fuzzy matter-element optimization.On the based of analyzing the mod el of multi-objective fuzzy matter-element , the paper defines the m atter-element ...This paper puts forward a new integrated design met ho d based on fuzzy matter-element optimization.On the based of analyzing the mod el of multi-objective fuzzy matter-element , the paper defines the m atter-element weightily and changes solving multi-objective fuzzy optimization into solving dependent function K(x) of the single-objective optimization according to the optimization criterion. The paper particularly describes the realization approach of GA process of multi -objective fuzzy matter-element optimization: encode, produce initial populati on, confirm fitness function, select operator, etc. In the process, the adaptive macro genetic algorithms (AMGA) is applied to enhancing the evolution speed. Th e paper improves the two genetic operators: crossover and mutation operator. The modified adaptive macro genetic algorithms (MAMGA) is put forward simultane ously. It is adopted to solve the optimization problem. Three optimization methods, namely fuzzy matter-element optimization method, li nearity weighted method and fuzzy optimization method, are compared by using the table and figure, it shows that not only MAMGA is a little better than the AMGA , but also it reaches the extent to which the effective iteration generation is 62.2% of simple genetic algorithms (SGA). By the calculation of optimum exam ple, the improved method of genetic in the paper is much better than the method in reference of paper.展开更多
Owing to overcoming the characteristics that there are many economic and technical indexes which are fuzzy and incompatibility to each other in evaluating investment project,a new method is proposed.The method is base...Owing to overcoming the characteristics that there are many economic and technical indexes which are fuzzy and incompatibility to each other in evaluating investment project,a new method is proposed.The method is based on the matter-element analysis and combined with the concepts of fuzzy mathematics,which is called the method of fuzzy matter-element analysis.It constructs the compound fuzzy matter element with the investment projects,evaluation factors and their fuzzy value.Through establishing the best subjection degree (fuzzy value),complex fuzzy matter element of relational coefficient and weight aggregation of fuzzy matter-element model,the writer achieves on optimum order of the investment projects according to the calculated relational degree and finds the best project.In this paper,the calculation of weight adopts the analytical hierarchy process method(AHP).Through the actual example,it shows that the model is simple and its calculation is reliable.It is very significant for the engineering evaluated bid and investment decision.展开更多
Based on the fuzzy characters of eco-environmental quality conception and classification standards, the incompatibility of evaluation indexes, the statistical fluctuation of index values, an information entropy fuzzy ...Based on the fuzzy characters of eco-environmental quality conception and classification standards, the incompatibility of evaluation indexes, the statistical fluctuation of index values, an information entropy fuzzy matter-element model for evaluating regional eco-environmental quality is proposed by way of comprehensively utilizing such theories as information theory, fuzzy sets and matter-element theory, etc. As a case, the model established here is used to evaluate the eco-environmental quality of Lake Chaohu basin. In the case, the eco-environmental quality standards and the evaluated schemes are indicated as matter-elements, together. Through constructing compound fuzzy matter-element, probability compound fuzzy matter-element and self-information compound fuzzy matter- element, the information entropy of each matter-element （including evaluated schemes and classification standards） is calculated in the end. According to these obtained information entropy values, the evaluated schemes can be not only arranged in quality state order but also classified by classification standards .Study result shows that information entropy fuzzy matter-element model is suitable for regional eco-environmental quality assessment.展开更多
Taking account of the fuzzy results of the seepage monitoring analysis of roller compacted concrete(RCC)dam and uncertainties of the individual indicator evaluation,the fuzzy matter-element model of seepage monitoring...Taking account of the fuzzy results of the seepage monitoring analysis of roller compacted concrete(RCC)dam and uncertainties of the individual indicator evaluation,the fuzzy matter-element model of seepage monitoring of RCC dam analysis has been established with the use of the fuzzy matter-element analysis theory and the concept of euclid approach degree.The use of entropy theory can calculate the weighting factor through the disorder utility values of the information reflected by the data itself,which can effectively avoid the problems of weight distribution and uncertainties of subjective judgments of the seepage monitoring analysis of roller compacted concrete dam.And further the example shows that the analysis of entropy-based fuzzy matter-element analysis model of the seepage monitoring of roller compacted concrete dam is in accordance with the actual situation,which verifies the effectiveness of the method.展开更多
Sponge city(SPC)is proposed to solve the issues such as the degradation of urban water ecosystem environment,imbalanced water resource allocation,urban water logging,and water contamination.The PPP(Public Private Part...Sponge city(SPC)is proposed to solve the issues such as the degradation of urban water ecosystem environment,imbalanced water resource allocation,urban water logging,and water contamination.The PPP(Public Private Partnership)model is combined to release the government pressure of SPC project construction.The development of the SPC-PPP model makes significant contributions to the sustainable development and the enhancement of urban resilience against water-related disasters.However,there is no scientific performance evaluation system on its operation period has been conducted.Therefore,the SPC-PPP Evaluation model aims to objectively and reasonably assess project effectiveness,promote its development and refine the evaluation framework.This paper has set up the MEE model for performance evaluation,with improved Matter-Element Extension method to assign values to the evaluation indices.The research results show that:(1)The MEE model is more accurate in the performance evaluation and its effectiveness is reflected in its ability to capture the correlation among different indices in the same membership,rather than merely focusing on individual indices.(2)The proposed approach provided a new aspect for performance evaluation,improving the accuracy of evaluation and promoting the development of SPC-PPP project.展开更多
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.展开更多
The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt ...The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.展开更多
For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-crit...For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.展开更多
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous...Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.展开更多
基金supported by the National Natural Science Foundation of China (No. 41071322, 71031001)
文摘For natural water, method of water quality evaluation based on improved fuzzy matter-element evaluation method is presented. Two important parts are improved, the weights determining and fuzzy membership functions. The coefficient of variation of each indicator is used to determine the weight instead of traditional calculating superscales method. On the other hand, fuzzy matter-elements are constructed, and normal membership degrees are used instead of traditional trapezoidal ones. The composite fuzzy matter-elements with associated coefficient are constructed through associated transformation. The levels of natural water quality are determined according to the principle of maximum correlation. The improved fuzzy matter-element evaluation method is applied to evaluate water quality of the Luokou mainstream estuary at the first ten weeks in 2011 with the coefficient of variatiola method determining the weights. Water quality of Luokou mainstream estuary is dropping from level I to level II. The results of the improved evaluation method are basically the same as the official water quality. The variation coefficient method can reduce the workload, and overcome the adverse effects from abnormal values, compared with the traditional calculating superscales method. The results of improved fuzzy matter- element evaluation method are more credible than the ones of the traditional evaluation method. The improved evaluation method can use information of monitoring data more scientifically and comprehensively, and broaden a new evaluation method for water quality assessment.
文摘An evaluation model of an international venture investment project on the basis of fuzzy matter-element and combined weight methods is introduced.First,the compound fuzzy matter-element of optimal subordinate degree is constructed on the principle of the bigger-more-optimal or the less-more-optimal depending on the actual evaluation indicators,and combined with standard fuzzy matter-element to form a difference-square fuzzy matter-element.Secondly,a combined weight is calculated by both information entropy and the expert grading method.Finally,the compound fuzzy matter-element of Euclidian approach degree by M(·,+)method is constituted and used to classify venture investment projects.Based on the model above,six venture investment projects in a company are evaluated,and the results show that the projects are all good,which is demonstrated by the good income of the projects.Therefore,the coincidence of evaluation results and actual operation status indicates that the model is of great value in practical application.
基金Supported by National Natural Science Foundation of China(50879018)Innovation Project of Jiangsu Province in 2008+1 种基金Special Fee for Scientific Research in Public Welfare Industry of Ministry of Water Resources(201001030)Special Fee of Key National Laboratories(1069-50987112)
文摘[Objective]The study aimed to assess the health state of rivers by using fuzzy matter-element model.[Method]Based on fuzzy matter-element analysis theory,the assessment model of river health was established,then a modified method to calculate the superior subordinate degree was put forward according to Hamming distance.Afterwards,a multi-level evaluation model,which contained the assessment indicators about hydrological features,ecological characteristics,environmental traits and service function,was set up based on this method above.Finally,the model was applied in the health assessment of Qinhuai River.[Result]The health state of Qinhuai River was at medium level.This assessment result was consistent with that of comprehensive index method,and it showed that the multi-level fuzzy matter-element model was effective in the assessment of river health.[Conclusion]The research provided an effective method to evaluate the state of river health.
文摘It＇ s a necessary selection to support the maneuver across Yangtze River by floating bridge constructed by portable steel bridge and civilian ships. It is a comprehensive index for the scheme of bridge raft, containing a variety of technical factors and uncertainties. The optimization is the selection in the constructing time, quantity of equipments and man power. Based on the calculation result of bridge rafts, an evaluating system is established, consisting of index of spacing between interior bays, raft length, truss numbers, operation difficulty and maximal bending stress. A fuzzy matter element model of optimizing selection of bridge rafts was built up by combining quantitative analysis with qualitative analysis. The method of combination weighting was used to calculate the value of weights index to reduce the subjective randomness. The sequence of schemes and the optimization resuh were gained finally based on euclid approach degree. The application result shows that it is simple and practical.
文摘This paper puts forward a new integrated design met ho d based on fuzzy matter-element optimization.On the based of analyzing the mod el of multi-objective fuzzy matter-element , the paper defines the m atter-element weightily and changes solving multi-objective fuzzy optimization into solving dependent function K(x) of the single-objective optimization according to the optimization criterion. The paper particularly describes the realization approach of GA process of multi -objective fuzzy matter-element optimization: encode, produce initial populati on, confirm fitness function, select operator, etc. In the process, the adaptive macro genetic algorithms (AMGA) is applied to enhancing the evolution speed. Th e paper improves the two genetic operators: crossover and mutation operator. The modified adaptive macro genetic algorithms (MAMGA) is put forward simultane ously. It is adopted to solve the optimization problem. Three optimization methods, namely fuzzy matter-element optimization method, li nearity weighted method and fuzzy optimization method, are compared by using the table and figure, it shows that not only MAMGA is a little better than the AMGA , but also it reaches the extent to which the effective iteration generation is 62.2% of simple genetic algorithms (SGA). By the calculation of optimum exam ple, the improved method of genetic in the paper is much better than the method in reference of paper.
基金Project supported by the National High-Tech Research and Development program of China (863 Program ) (No.2 0 0 2 AA2 Z42 5 1-2 10 0 41) Postdoctoral Scientific Foundation of Northeast Agricultural U niversity. (No. 2 40 0 0 9) and postdoctoral Scien
文摘Owing to overcoming the characteristics that there are many economic and technical indexes which are fuzzy and incompatibility to each other in evaluating investment project,a new method is proposed.The method is based on the matter-element analysis and combined with the concepts of fuzzy mathematics,which is called the method of fuzzy matter-element analysis.It constructs the compound fuzzy matter element with the investment projects,evaluation factors and their fuzzy value.Through establishing the best subjection degree (fuzzy value),complex fuzzy matter element of relational coefficient and weight aggregation of fuzzy matter-element model,the writer achieves on optimum order of the investment projects according to the calculated relational degree and finds the best project.In this paper,the calculation of weight adopts the analytical hierarchy process method(AHP).Through the actual example,it shows that the model is simple and its calculation is reliable.It is very significant for the engineering evaluated bid and investment decision.
文摘Based on the fuzzy characters of eco-environmental quality conception and classification standards, the incompatibility of evaluation indexes, the statistical fluctuation of index values, an information entropy fuzzy matter-element model for evaluating regional eco-environmental quality is proposed by way of comprehensively utilizing such theories as information theory, fuzzy sets and matter-element theory, etc. As a case, the model established here is used to evaluate the eco-environmental quality of Lake Chaohu basin. In the case, the eco-environmental quality standards and the evaluated schemes are indicated as matter-elements, together. Through constructing compound fuzzy matter-element, probability compound fuzzy matter-element and self-information compound fuzzy matter- element, the information entropy of each matter-element （including evaluated schemes and classification standards） is calculated in the end. According to these obtained information entropy values, the evaluated schemes can be not only arranged in quality state order but also classified by classification standards .Study result shows that information entropy fuzzy matter-element model is suitable for regional eco-environmental quality assessment.
基金supported by the National Science and Technology Support Plan of China(Nos.2006BAC14B03,2008BAB29B06,2008BAB29B03)the Jiangsu Province 333Training High-Level Talents Projects(No.2017-B08037)the National Natural Science Foundation of China(Grant Nos.50539010,50539110,50809025,50539030-1-3)。
文摘Taking account of the fuzzy results of the seepage monitoring analysis of roller compacted concrete(RCC)dam and uncertainties of the individual indicator evaluation,the fuzzy matter-element model of seepage monitoring of RCC dam analysis has been established with the use of the fuzzy matter-element analysis theory and the concept of euclid approach degree.The use of entropy theory can calculate the weighting factor through the disorder utility values of the information reflected by the data itself,which can effectively avoid the problems of weight distribution and uncertainties of subjective judgments of the seepage monitoring analysis of roller compacted concrete dam.And further the example shows that the analysis of entropy-based fuzzy matter-element analysis model of the seepage monitoring of roller compacted concrete dam is in accordance with the actual situation,which verifies the effectiveness of the method.
基金supported by the Open Fund of Hubei Key Laboratory of Construction Management in Hydropower Engineering(Grant No.2016KSD04)the Open Fund of Engineering Research Center of Eco-environment in Three Gorges Reservoir Region,Ministry of Education(Grant No.KF2016-11).
文摘Sponge city(SPC)is proposed to solve the issues such as the degradation of urban water ecosystem environment,imbalanced water resource allocation,urban water logging,and water contamination.The PPP(Public Private Partnership)model is combined to release the government pressure of SPC project construction.The development of the SPC-PPP model makes significant contributions to the sustainable development and the enhancement of urban resilience against water-related disasters.However,there is no scientific performance evaluation system on its operation period has been conducted.Therefore,the SPC-PPP Evaluation model aims to objectively and reasonably assess project effectiveness,promote its development and refine the evaluation framework.This paper has set up the MEE model for performance evaluation,with improved Matter-Element Extension method to assign values to the evaluation indices.The research results show that:(1)The MEE model is more accurate in the performance evaluation and its effectiveness is reflected in its ability to capture the correlation among different indices in the same membership,rather than merely focusing on individual indices.(2)The proposed approach provided a new aspect for performance evaluation,improving the accuracy of evaluation and promoting the development of SPC-PPP project.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
基金supported by NARI Relays Electric Co.,Ltd.under the Project“Research on Evaluation of Clearing Results and Switching Criteria for Primary-Backup Systems in Electricity SpotMarkets”(Project No.CGSQ240800443).
文摘The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.
基金supported by the National Natural Science Foundation of China(62073267,61903305)the Fundamental Research Funds for the Central Universities(HXGJXM202214)。
文摘For mission-oriented unmanned aerial vehicle(UAV)swarms,mission capability assessment provides an important reference in the design and development process,and is a precondition for mission success.For this multi-criteria decisionmaking(MCDM)problem,the current literature lacks a way to unambiguously present criteria and the popular fuzzy analytic network process(ANP)approaches neglect the hesitancy of subjective judgments.To fill these research gaps,an MCDM method based on unified architecture framework(UAF)and interval-valued spherical fuzzy ANP(IVSF-ANP)is proposed in this paper.Firstly,selected viewpoints in UAF are extended to construct criteria models with standardized representation.Secondly,interval-valued spherical fuzzy sets are introduced to ANP to weight interdependent criteria,handling fuzziness and hesitancy in pairwise comparisons.A method of adjusting weights of experts based on their decision similarities is also included in this process to reduce ambiguity brought by multiple experts.Next,performance characteristics are non-linearly transformed regarding to expectations to get final results.This proposition is applied to assess the mission capability of UAV swarms to search and strike surface vessels.Comparative analysis shows that the proposed method is valid and reasonable.
基金supported by the Deanship of Research at the King Fahd University of Petroleum&Minerals,Dhahran,31261,Saudi Arabia,under Project No.EC241001.
文摘Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.
