Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the...Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the vitality of innovation. Aiming at the shortcomings of the traditional high-value patent assessment method, which is relatively simple and seldom considers the influence of patentees, this paper proposes a high-quality patent method HMFM (High-Value Patent Multi-Feature Fusion Method) that fuses multi-dimensional features. A weighted node importance assessment method in complex network called GLE (Glob-Local-struEntropy) based on improved structural entropy is designed to calculate the influence of the patentee to form the patentee’s features, and the patent text features are extracted by BERT-DPCNN deep learning model, which is supplemented to the basic patent indicator system. Finally a machine learning algorithm is used to assess the value of patents. Experiment results show that our method can identify high-value patents more effectively and accurately.展开更多
This article proposes a synthesis and contribution at three levels: generation of dynamic equations of shell structures interacting with fluids, reduction of implicit resolution, and cross-applications to aerospace ta...This article proposes a synthesis and contribution at three levels: generation of dynamic equations of shell structures interacting with fluids, reduction of implicit resolution, and cross-applications to aerospace tanks and living systems. The synthesis of the equations is proposed around the four principles of thermodynamics at the level of discrete, structural and digitized systems. The implicit approach envisages an innovative analysis in terms of condensation and digitization, with in particular a perspective towards singular and integral methods. Some illustrations are proposed, in the field of performed research models and also in the fields of educational applications in biodynamics. The proposed bridge links, on one hand, the analytical Lagrange-Feynman’s approach, and on the other hand experimental results obtained in laboratory and numerical experiments obtained with multiphysics software. Finally, the realized models concern conservative and dissipative models for the active and passive control of complex systems, in a unified approach.展开更多
The production and trade of primary products had a growing impact on the economic security of all countries and regions,and the strategic position of these products in the global trade network was becoming increasingl...The production and trade of primary products had a growing impact on the economic security of all countries and regions,and the strategic position of these products in the global trade network was becoming increasingly prominent.Based on complex network theory,this paper explored the spatial pattern and complex structural evolution of the global primary product trade network(GPPTN)during 1985-2015 by using index methods,such as centrality,Sankey diagram,and structure entropy,focusing on the diversified spatial structure of China’s import and export markets for primary products(with exceptions of Taiwan of China,Hong Kong of China,and Macao of China due to a lack of data)and their geographical implications for China’s energy security.The research offered the following key findings.The GPPTN showed an obvious spatial heterogeneity pattern,and the area of import consumption was more concentrated;however,the overall trend was decentralized.The trade center of gravity shifted eastwards and reflected the rise of emerging markets.The overall flow of the GPPTN was from west to east and from south to north.In terms of the community detection of the GPPTN,North America,Europe,and Asia increasingly presented an unbalanced“tripartite confrontation”.China’s exports of primary products were mainly concentrated in the Association of Southeast Asian Nations(ASEAN)and other peripheral regions of Asia,and its imports undergone a major transformation,gradually expanding from the peripheral regions of Asia to Africa,the Middle East,Latin America,and other parts of the world.Energy fuels also became the largest imported primary products.Based on the changing trend of structural entropy and main market share,the analysis showed that the stable supply of China’s energy diversification was gradually realized.In particular,the cooperation dividend proposed by the Belt and Road initiative became an important turning point and a strong support for the expansion of China’s energy market diversification pattern and guarantee of energy security.展开更多
The economic potential induced by environmental changes in the Arctic region garnered substantial interest,which positions Arctic trade as a crucial indicator in forecasting the impacts of climate change on the global...The economic potential induced by environmental changes in the Arctic region garnered substantial interest,which positions Arctic trade as a crucial indicator in forecasting the impacts of climate change on the global economy.Nevertheless,attention devoted to the evolving dynamics of trade in the Arctic region remains scarce.In this study,we constructed export trade network in the Arctic region(including Denmark,Finland,Sweden,Norway,Iceland,the Canadian Arctic,the Russian Arctic,Alaska State of the USA,and Greenland)from 1990 to 2019 and analyzed its topology and evolutionary characteristics through complex network theory.We used a structural entropy index based on the distribution of the number of trading partners and the degree of trade concentration to assess export diversity,while we also utilized a revealed comparative advantage index to evaluate product export competitiveness using the share of trade volume of each type of product.The results indicate that the total export trade in the Arctic region increased by 53.4%during 1990-2019,with the most significant growth observed in the exports of chemical products and mineral fuels.The increasing complexity of trade network in the Arctic region resulted in the region’s export destinations no longer being concentrated on a few major countries and regions.The proportion of exports from the Arctic region to Europe decreased by 13.5%,while the proportion of exports from the Arctic region to Asia and North America increased by 6.8%and 3.1%,respectively.The Arctic region exhibited clear distinctions in the range of flows of different products,and its export trade was becoming increasingly diversified.Although differences in comparative advantages between products within individual countries or regions have narrowed,substantial gaps persist.The findings of this study can enhance the comprehensive understanding of the significance and function of Arctic trade activities within the global economy,providing a scientific basis for addressing the associated challenges and opportunities in the context of climate change.展开更多
Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes.However,there is high nonlinearity and association coupling among massive,complicated multisource pr...Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes.However,there is high nonlinearity and association coupling among massive,complicated multisource process data,resulting in a low accuracy of existing prediction technology.For that reason,a real-time risk prediction method for chemical processes based on the attention-based bidirectional long short-term memory(Attention-based Bi-LSTM)is proposed in this study.First,multisource process data,such as temperature,pressure,flow rate,and liquid level,are preprocessed for denoising.Data correlation is analyzed in time windows by setting time windows and moving step lengths to explore correlations,thus establishing a complex network model oriented to the chemical production process.Second,network structure entropy is introduced to reduce the dimensions of the multisource process data.Moreover,a 1D relative risk sequence is acquired by maxemin deviation standardization to judge whether the chemical process is in a steady state.Finally,an Attention-based Bi-LSTM algorithm is established by integrating the attention mechanism and the Bi-LSTM network to fit and train 1D relative risk sequences.In that way,the proposed algorithm achieves real-time prediction and intelligent perception of risk states during chemical production.A case study based on the Tennessee Eastman process(TEP)is conducted.The validity and reasonability of the proposed method are verified by analyzing distribution laws of relative risks under normal and fault conditions.Also,the proposed algorithm importantly improves the prediction accuracy of chemical process risks relative to that of existing prediction technologies.展开更多
In this paper, we review the magnetic properties and magnetocaloric effects（MCE） of binary R–T（R = Pr, Gd, Tb,Dy, Ho, Er, Tm; T = Ga, Ni, Co, Cu） intermetallic compounds（including RGa series, RNi series, R_（12...In this paper, we review the magnetic properties and magnetocaloric effects（MCE） of binary R–T（R = Pr, Gd, Tb,Dy, Ho, Er, Tm; T = Ga, Ni, Co, Cu） intermetallic compounds（including RGa series, RNi series, R_（12）Co_7 series, R_3 Co series and RCu_2series）, which have been investigated in detail in the past several years. The R–T compounds are studied by means of magnetic measurements, heat capacity measurements, magnetoresistance measurements and neutron powder diffraction measurements. The R–T compounds show complex magnetic transitions and interesting magnetic properties.The types of magnetic transitions are investigated and confirmed in detail by multiple approaches. Especially, most of the R–T compounds undergo more than one magnetic transition, which has significant impact on the magnetocaloric effect of R–T compounds. The MCE of R–T compounds are calculated by different ways and the special shapes of MCE peaks for different compounds are investigated and discussed in detail. To improve the MCE performance of R–T compounds,atoms with large spin（S） and atoms with large total angular momentum（J） are introduced to substitute the related rare earth atoms. With the atom substitution, the maximum of magnetic entropy change（？SM）, refrigerant temperature width（Twidth）or refrigerant capacity（RC） is enlarged for some R–T compounds. In the low temperature range, binary R–T（R = Pr, Gd,Tb, Dy, Ho, Er, Tm; T = Ga, Ni, Co, Cu） intermetallic compounds（including RGa series, RNi series,R_（12）Co_7 series, R_3 Co series and RCu_2series） show excellent performance of MCE, indicating the potential application for gas liquefaction in the future.展开更多
Micro triadic structure is an important motif and serves the building block of complex networks.In this paper,the authors define structure entropy for a social network and explain this concept by using the coded triad...Micro triadic structure is an important motif and serves the building block of complex networks.In this paper,the authors define structure entropy for a social network and explain this concept by using the coded triads proposed by Davis and Leinhardt in 1972.The proposed structure entropy serves as a new macro-evolution index to measure the network’s stability at a given timestamp.Empirical analysis of real-world network structure entropy discloses rich information on the mechanism that yields given triadic motifs frequency distribution.This paper illustrates the intrinsic link between the micro dyadic/triadic motifs and network structure entropy.Importantly,the authors find that the high proportion of reciprocity and transitivity results in the emergence of hierarchy,order,and cooperation of online social networks.展开更多
Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic r...Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.展开更多
We define the topological tail pressure and the conditional pressure for asymptotically sub-additive continuous potentials on topological dynamical systems and obtain a variational principle for the topological tail p...We define the topological tail pressure and the conditional pressure for asymptotically sub-additive continuous potentials on topological dynamical systems and obtain a variational principle for the topological tail pressure without any additional assumptions.展开更多
The Chinese Materials Research Society（C-MRS）Conference（2015）was held in the Guizhou Park Hotel International Conference Center,Guiyang,China,from July 10-14,2015.This conference consists of 30symposia,including 4...The Chinese Materials Research Society（C-MRS）Conference（2015）was held in the Guizhou Park Hotel International Conference Center,Guiyang,China,from July 10-14,2015.This conference consists of 30symposia,including 4international symposia.As one of 4international symposia,＂Serration and noise behavior in advanced materials＂展开更多
The entropy balance equation that describes the entropy budget of atmospheric systems is derived from the Gibbs relation.The distribution of the entropy flows of a west-Pacific typhoon and a Bengal-Bay cyclone is calc...The entropy balance equation that describes the entropy budget of atmospheric systems is derived from the Gibbs relation.The distribution of the entropy flows of a west-Pacific typhoon and a Bengal-Bay cyclone is calculated and thus the dissipativity of the atmospheric systems is revealed.展开更多
Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between moni...Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.展开更多
文摘Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the vitality of innovation. Aiming at the shortcomings of the traditional high-value patent assessment method, which is relatively simple and seldom considers the influence of patentees, this paper proposes a high-quality patent method HMFM (High-Value Patent Multi-Feature Fusion Method) that fuses multi-dimensional features. A weighted node importance assessment method in complex network called GLE (Glob-Local-struEntropy) based on improved structural entropy is designed to calculate the influence of the patentee to form the patentee’s features, and the patent text features are extracted by BERT-DPCNN deep learning model, which is supplemented to the basic patent indicator system. Finally a machine learning algorithm is used to assess the value of patents. Experiment results show that our method can identify high-value patents more effectively and accurately.
文摘This article proposes a synthesis and contribution at three levels: generation of dynamic equations of shell structures interacting with fluids, reduction of implicit resolution, and cross-applications to aerospace tanks and living systems. The synthesis of the equations is proposed around the four principles of thermodynamics at the level of discrete, structural and digitized systems. The implicit approach envisages an innovative analysis in terms of condensation and digitization, with in particular a perspective towards singular and integral methods. Some illustrations are proposed, in the field of performed research models and also in the fields of educational applications in biodynamics. The proposed bridge links, on one hand, the analytical Lagrange-Feynman’s approach, and on the other hand experimental results obtained in laboratory and numerical experiments obtained with multiphysics software. Finally, the realized models concern conservative and dissipative models for the active and passive control of complex systems, in a unified approach.
基金financially supported by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences, China (20YJCZH057)the Hubei Province Social Science Fund General Project, China (2021147)the Xiangyang City Science and Technology Planning Project, Hubei Province, China (2021rkx04)
文摘The production and trade of primary products had a growing impact on the economic security of all countries and regions,and the strategic position of these products in the global trade network was becoming increasingly prominent.Based on complex network theory,this paper explored the spatial pattern and complex structural evolution of the global primary product trade network(GPPTN)during 1985-2015 by using index methods,such as centrality,Sankey diagram,and structure entropy,focusing on the diversified spatial structure of China’s import and export markets for primary products(with exceptions of Taiwan of China,Hong Kong of China,and Macao of China due to a lack of data)and their geographical implications for China’s energy security.The research offered the following key findings.The GPPTN showed an obvious spatial heterogeneity pattern,and the area of import consumption was more concentrated;however,the overall trend was decentralized.The trade center of gravity shifted eastwards and reflected the rise of emerging markets.The overall flow of the GPPTN was from west to east and from south to north.In terms of the community detection of the GPPTN,North America,Europe,and Asia increasingly presented an unbalanced“tripartite confrontation”.China’s exports of primary products were mainly concentrated in the Association of Southeast Asian Nations(ASEAN)and other peripheral regions of Asia,and its imports undergone a major transformation,gradually expanding from the peripheral regions of Asia to Africa,the Middle East,Latin America,and other parts of the world.Energy fuels also became the largest imported primary products.Based on the changing trend of structural entropy and main market share,the analysis showed that the stable supply of China’s energy diversification was gradually realized.In particular,the cooperation dividend proposed by the Belt and Road initiative became an important turning point and a strong support for the expansion of China’s energy market diversification pattern and guarantee of energy security.
基金supported by the National Natural Science Foundation of China(42471309)the National Key Research and Development Program of China(2020YFA0608504).
文摘The economic potential induced by environmental changes in the Arctic region garnered substantial interest,which positions Arctic trade as a crucial indicator in forecasting the impacts of climate change on the global economy.Nevertheless,attention devoted to the evolving dynamics of trade in the Arctic region remains scarce.In this study,we constructed export trade network in the Arctic region(including Denmark,Finland,Sweden,Norway,Iceland,the Canadian Arctic,the Russian Arctic,Alaska State of the USA,and Greenland)from 1990 to 2019 and analyzed its topology and evolutionary characteristics through complex network theory.We used a structural entropy index based on the distribution of the number of trading partners and the degree of trade concentration to assess export diversity,while we also utilized a revealed comparative advantage index to evaluate product export competitiveness using the share of trade volume of each type of product.The results indicate that the total export trade in the Arctic region increased by 53.4%during 1990-2019,with the most significant growth observed in the exports of chemical products and mineral fuels.The increasing complexity of trade network in the Arctic region resulted in the region’s export destinations no longer being concentrated on a few major countries and regions.The proportion of exports from the Arctic region to Europe decreased by 13.5%,while the proportion of exports from the Arctic region to Asia and North America increased by 6.8%and 3.1%,respectively.The Arctic region exhibited clear distinctions in the range of flows of different products,and its export trade was becoming increasingly diversified.Although differences in comparative advantages between products within individual countries or regions have narrowed,substantial gaps persist.The findings of this study can enhance the comprehensive understanding of the significance and function of Arctic trade activities within the global economy,providing a scientific basis for addressing the associated challenges and opportunities in the context of climate change.
基金supported by the National Natural Science Foundation of China(52004014)the Fundamental Research Funds for the Central Universities(ZY2406)the National Key Research&Development Program of China(2021YFB3301100).
文摘Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes.However,there is high nonlinearity and association coupling among massive,complicated multisource process data,resulting in a low accuracy of existing prediction technology.For that reason,a real-time risk prediction method for chemical processes based on the attention-based bidirectional long short-term memory(Attention-based Bi-LSTM)is proposed in this study.First,multisource process data,such as temperature,pressure,flow rate,and liquid level,are preprocessed for denoising.Data correlation is analyzed in time windows by setting time windows and moving step lengths to explore correlations,thus establishing a complex network model oriented to the chemical production process.Second,network structure entropy is introduced to reduce the dimensions of the multisource process data.Moreover,a 1D relative risk sequence is acquired by maxemin deviation standardization to judge whether the chemical process is in a steady state.Finally,an Attention-based Bi-LSTM algorithm is established by integrating the attention mechanism and the Bi-LSTM network to fit and train 1D relative risk sequences.In that way,the proposed algorithm achieves real-time prediction and intelligent perception of risk states during chemical production.A case study based on the Tennessee Eastman process(TEP)is conducted.The validity and reasonability of the proposed method are verified by analyzing distribution laws of relative risks under normal and fault conditions.Also,the proposed algorithm importantly improves the prediction accuracy of chemical process risks relative to that of existing prediction technologies.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11274357,51501005,51590880,and 11674008)the Fundamental Research Funds for the Central Universities,China(Grant No.FRF-TP-15-010A1)+1 种基金the China Postdoctoral Science Foundation(Grant No.2016M591071)the Key Research Program of the Chinese Academy of Sciences(Grant No.KJZD-EW-M05)
文摘In this paper, we review the magnetic properties and magnetocaloric effects（MCE） of binary R–T（R = Pr, Gd, Tb,Dy, Ho, Er, Tm; T = Ga, Ni, Co, Cu） intermetallic compounds（including RGa series, RNi series, R_（12）Co_7 series, R_3 Co series and RCu_2series）, which have been investigated in detail in the past several years. The R–T compounds are studied by means of magnetic measurements, heat capacity measurements, magnetoresistance measurements and neutron powder diffraction measurements. The R–T compounds show complex magnetic transitions and interesting magnetic properties.The types of magnetic transitions are investigated and confirmed in detail by multiple approaches. Especially, most of the R–T compounds undergo more than one magnetic transition, which has significant impact on the magnetocaloric effect of R–T compounds. The MCE of R–T compounds are calculated by different ways and the special shapes of MCE peaks for different compounds are investigated and discussed in detail. To improve the MCE performance of R–T compounds,atoms with large spin（S） and atoms with large total angular momentum（J） are introduced to substitute the related rare earth atoms. With the atom substitution, the maximum of magnetic entropy change（？SM）, refrigerant temperature width（Twidth）or refrigerant capacity（RC） is enlarged for some R–T compounds. In the low temperature range, binary R–T（R = Pr, Gd,Tb, Dy, Ho, Er, Tm; T = Ga, Ni, Co, Cu） intermetallic compounds（including RGa series, RNi series,R_（12）Co_7 series, R_3 Co series and RCu_2series） show excellent performance of MCE, indicating the potential application for gas liquefaction in the future.
基金supported by the Natural Science Foundation of China under Grant Nos.71661001 and 71971190the project of Yunnan Key Laboratory of Smart City and Cyberspace Security under Grant No.202105AG070010。
文摘Micro triadic structure is an important motif and serves the building block of complex networks.In this paper,the authors define structure entropy for a social network and explain this concept by using the coded triads proposed by Davis and Leinhardt in 1972.The proposed structure entropy serves as a new macro-evolution index to measure the network’s stability at a given timestamp.Empirical analysis of real-world network structure entropy discloses rich information on the mechanism that yields given triadic motifs frequency distribution.This paper illustrates the intrinsic link between the micro dyadic/triadic motifs and network structure entropy.Importantly,the authors find that the high proportion of reciprocity and transitivity results in the emergence of hierarchy,order,and cooperation of online social networks.
基金Project supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.81701346 and 61603198)Qinglan Team of Universities in Jiangsu Province(Jiangsu Teacher Letter[2020]10 and Jiangsu Teacher Letter[2021]11).
文摘Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.
基金The NSF(11471114,11671208,11431012 and 11271191)of Chinathe National Basic Research Program(2013CB834100)of China(973 Program)
文摘We define the topological tail pressure and the conditional pressure for asymptotically sub-additive continuous potentials on topological dynamical systems and obtain a variational principle for the topological tail pressure without any additional assumptions.
文摘The Chinese Materials Research Society（C-MRS）Conference（2015）was held in the Guizhou Park Hotel International Conference Center,Guiyang,China,from July 10-14,2015.This conference consists of 30symposia,including 4international symposia.As one of 4international symposia,＂Serration and noise behavior in advanced materials＂
文摘The entropy balance equation that describes the entropy budget of atmospheric systems is derived from the Gibbs relation.The distribution of the entropy flows of a west-Pacific typhoon and a Bengal-Bay cyclone is calculated and thus the dissipativity of the atmospheric systems is revealed.
基金supported by the National Natural Science Foundation of China(Grant No.51375375)
文摘Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.
基金supported by National Science CenterPoland(Grant No.2018/30/M/ST1/00061)+1 种基金the Wroc law University of Science and Technology(Grant No.049U/0052/19)supported by National Natural Science Foundation of China(Grants Nos.11671094,11722103 and 11731003)。
文摘In this survey we will present the symbolic extension theory in topological dynamics,which was built over the past twenty years.
