Based on Lyapunov stability theory, an adaptive controller is designed for a class of chaotic systems. Globally exponential synchronization and parameter regulation for couple chaotic systems can be carried out simult...Based on Lyapunov stability theory, an adaptive controller is designed for a class of chaotic systems. Globally exponential synchronization and parameter regulation for couple chaotic systems can be carried out simultaneously. The controller and the regulating law of parameters are directly constructed by analytic formula. Simulation results with some chaotic systems show the effectiveness of the proposed controller.展开更多
Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult f...Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.展开更多
This paper proposes a personalized comprehensive cloud-based method for heterogeneous multi-attribute groupdecision-making (MAGDM), in which the evaluations of alternatives on attributes are represented by LTs (lingui...This paper proposes a personalized comprehensive cloud-based method for heterogeneous multi-attribute groupdecision-making (MAGDM), in which the evaluations of alternatives on attributes are represented by LTs (linguisticterms), PLTSs (probabilistic linguistic term sets) and LHFSs (linguistic hesitant fuzzy sets). As an eective toolto describe LTs, cloud model is used to quantify the qualitative evaluations. Firstly, the regulation parameters ofentropy and hyper entropy are dened, and they are further incorporated into the transformation process from LTsto clouds for reecting the dierent personalities of decision-makers (DMs). To tackle the evaluation informationin the form of PLTSs and LHFSs, PLTS and LHFS are transformed into comprehensive cloud of PLTS (C-PLTS)and comprehensive cloud of LHFS (C-LHFS), respectively. Moreover, DMs’ weights are calculated based on theregulation parameters of entropy and hyper entropy. Next, we put forward cloud almost stochastic dominance(CASD) relationship and CASD degree to compare clouds. In addition, by considering three perspectives, acomprehensive tri-objective programing model is constructed to determine the attribute weights. Thereby, apersonalized comprehensive cloud-based method is put forward for heterogeneous MAGDM. The validity ofthe proposed method is demonstrated with a site selection example of emergency medical waste disposal inCOVID-19. Finally, sensitivity and comparison analyses are provided to show the eectiveness, stability, exibilityand superiorities of the proposed method.展开更多
With advances in modern agricultural parks,the rural energy structure has undergone profound change,leading to the emergence of an agricultural energy internet.This integrated system combines agricultural energy utili...With advances in modern agricultural parks,the rural energy structure has undergone profound change,leading to the emergence of an agricultural energy internet.This integrated system combines agricultural energy utilization,the information internet,and agricultural production.Accordingly,this study proposes a regulation flexibility assessment approach and optimal aggregation strategy of greenhouse loads(GHLs)for modern agricultural parks.First,taking into account the operational characteristics of typical GHLs,refined load demand models for lighting,humidification,and temperature-controlled loads are established.Secondly,the recursive least squares method-based parameter identification method is designed to accurately determine key GHL model parameters.Finally,based on the regulation flexibility of quantitatively evaluated GHLs,GHLs are optimally aggregated into multiple flexible aggregators considering minimal operational cost and greenhouse environmental constraints.The results indicate that the proposed regulation flexibility assessment approach and optimal aggregation strategy of GHLs can alleviate the peak regulation pressure on power grids by flexibly shifting the load demands of GHLs.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60474011) Foundation of Yong Bone Teacher of Henan Province (No. 2004240).
文摘Based on Lyapunov stability theory, an adaptive controller is designed for a class of chaotic systems. Globally exponential synchronization and parameter regulation for couple chaotic systems can be carried out simultaneously. The controller and the regulating law of parameters are directly constructed by analytic formula. Simulation results with some chaotic systems show the effectiveness of the proposed controller.
基金the financial support by the National Natural Science Foundation of China(Grant No.U24B2029)the Key Projects of the National Natural Science Foundation of China(Grant No.52334001)+1 种基金the Strategic Cooperation Technology Projects of CNPC and CUPB(Grand No.ZLZX2020-02)the China University of Petroleum,Beijing(Grand No.ZX20230042)。
文摘Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.
基金supported by the National Natural Science Foundation of China(Nos.62141302,11861034 and 71964014)the Humanities Social Science Programming Project of Ministry of Education of China(No.20YJA630059)+1 种基金the Natural Science Foundation of Jiangxi Province of China(No.20212BAB201011)the Postgraduate Innovation Fund Project of Jiangxi Province(No.YC2020-S290).
文摘This paper proposes a personalized comprehensive cloud-based method for heterogeneous multi-attribute groupdecision-making (MAGDM), in which the evaluations of alternatives on attributes are represented by LTs (linguisticterms), PLTSs (probabilistic linguistic term sets) and LHFSs (linguistic hesitant fuzzy sets). As an eective toolto describe LTs, cloud model is used to quantify the qualitative evaluations. Firstly, the regulation parameters ofentropy and hyper entropy are dened, and they are further incorporated into the transformation process from LTsto clouds for reecting the dierent personalities of decision-makers (DMs). To tackle the evaluation informationin the form of PLTSs and LHFSs, PLTS and LHFS are transformed into comprehensive cloud of PLTS (C-PLTS)and comprehensive cloud of LHFS (C-LHFS), respectively. Moreover, DMs’ weights are calculated based on theregulation parameters of entropy and hyper entropy. Next, we put forward cloud almost stochastic dominance(CASD) relationship and CASD degree to compare clouds. In addition, by considering three perspectives, acomprehensive tri-objective programing model is constructed to determine the attribute weights. Thereby, apersonalized comprehensive cloud-based method is put forward for heterogeneous MAGDM. The validity ofthe proposed method is demonstrated with a site selection example of emergency medical waste disposal inCOVID-19. Finally, sensitivity and comparison analyses are provided to show the eectiveness, stability, exibilityand superiorities of the proposed method.
基金the Science and Technology Project of State Grid Corporation of China(No.1400-202224249A-1-1-ZN)the National Natural Science Foundation of China(No.52077075 and No.72271068)+2 种基金the Foundations of Shenzhen and Technology Committee(No.GJHZ20210705141811036 and No.GXWD20220811151845006)the Major Science and Technology Special Projects in Xinjiang Autonomous Region(No.2022A01007)the Fundamental Research Funds for the Central Universities(No.2023JC001).
文摘With advances in modern agricultural parks,the rural energy structure has undergone profound change,leading to the emergence of an agricultural energy internet.This integrated system combines agricultural energy utilization,the information internet,and agricultural production.Accordingly,this study proposes a regulation flexibility assessment approach and optimal aggregation strategy of greenhouse loads(GHLs)for modern agricultural parks.First,taking into account the operational characteristics of typical GHLs,refined load demand models for lighting,humidification,and temperature-controlled loads are established.Secondly,the recursive least squares method-based parameter identification method is designed to accurately determine key GHL model parameters.Finally,based on the regulation flexibility of quantitatively evaluated GHLs,GHLs are optimally aggregated into multiple flexible aggregators considering minimal operational cost and greenhouse environmental constraints.The results indicate that the proposed regulation flexibility assessment approach and optimal aggregation strategy of GHLs can alleviate the peak regulation pressure on power grids by flexibly shifting the load demands of GHLs.
