The identification result of operational mode is eurychoric while operational mode identification is investigated under ambient excitation,which is influenced by the signal size and the time interval.The operational m...The identification result of operational mode is eurychoric while operational mode identification is investigated under ambient excitation,which is influenced by the signal size and the time interval.The operational mode identification method,which is based on the sliding time window method and the eigensystem realization algorithm(ERA),is investigated to improve the identification accuracy and stability.Firstly,the theory of the ERA method is introduced.Secondly,the strategy for decomposition and implementation is put forward,including the sliding time window method and the filtration method of modes.At last,an example is studied,where the model of a cantilever beam is built and the white noise exciting is input.Results show that the operational mode identification method can realize the modes,and has high robustness to the signal to noise ratio and signal size.展开更多
The fracture modes of low alloy steels and cast irons under tensile and fatigue conditions were identified by electron back-scattered diffraction（EBSD） misorientation analysis in this research. The curves of grain r...The fracture modes of low alloy steels and cast irons under tensile and fatigue conditions were identified by electron back-scattered diffraction（EBSD） misorientation analysis in this research. The curves of grain reference orientation deviation（GROD） distribution perpendicular to the fracture surface were obtained by EBSD observation, and the characteristics of each fracture mode were identified. The GROD value of the specimen fractured in tension decreases to a constant related to the elongation of corresponding specimen in the far field（farther than 5 mm away from the fracture surface）. The peak exhibits in GROD curves of two smooth specimens and a notched specimen near the fracture surface（within 5 mm away from the fracture surface）, and the formation mechanisms were discussed in detail based on the influences of specimen geometries（smooth or notched） and material toughness. The GROD value of fatigue fractured specimen is close to that at undeformed condition in the whole field, except the small area near the crack path. The loading conditions（constant stress amplitude loading or constant stress intensity factor range K loading） and the EBSD striation formation during fatigue crack propagation were also studied by EBSD observation parallel to the crack path.展开更多
The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such...The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.展开更多
Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is...Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is severely constrained by the absence of efficient methods for rapidly predicting crystal packing modes from molecular structures,impeding the high-throughput rational design of such materials.In this study,we employed quantified indicators,such as hydrogen bond dimension and maximum planar separation,to quickly screen 172DEM and 16 non-2DEM crystal structures from a crystal database.They were subsequently compared and analyzed,focusing on hydrogen bond donor-acceptor combinations,skeleton features,and intermolecular interactions.Our findings suggest that theπ-πpacking interaction energy is a key determinant in the formation of layered packing modes by planar energetic molecules,with its magnitude primarily influenced by the strongest dimericπ-πinteraction(π-π2max).Consequently,we have delineated a critical threshold forπ-π2max to discern layered packing modes and formulated a theoretical model for predictingπ-π2max,grounded in molecular electrostatic potential and dipole moment analysis.The predictive efficacy of this model was substantiated through external validation on a test set comprising 31 planar energetic molecular crystals,achieving an accuracy of 84%and a recall of 75%.Furthermore,the proposed model shows superior classification predictive performance compared to typical machine learning methods,such as random forest,on the external validation samples.This contribution introduces a novel methodology for the identification of crystal packing modes in 2DEMs,potentially accelerating the design and synthesis of high-energy,low-sensitivity 2DEMs.展开更多
Leveraging extensive trajectory data to analyze the operation modes of agricultural machinery for gathering precise spatial information is an important fundamental task for subsequent agricultural machinery trajectory...Leveraging extensive trajectory data to analyze the operation modes of agricultural machinery for gathering precise spatial information is an important fundamental task for subsequent agricultural machinery trajectory research.However,complex algorithm models hinder nonspecialized researchers from further processing agricultural machinery trajectory data.In the present application note,ChatGPT is taken as an example and a complete prompt guide for large language models(LLMs)is provided for autonomously identifying the operation mode of agricultural machinery.This guide provides low-cost workflows for processing agricultural machinery trajectory data when computer science or data science expertise is lacking.It even possesses the capability to utilize newly learned algorithms such as the random forest model,which has not been previously explored in the literature for operation mode identification,to accomplish the task.To the best of our knowledge,this is the first attempt to apply LLMs to identifying agricultural machinery operation mode based on trajectory data.The complete prompt guide is publicly available at https://github.com/kakushuu/prompt-guide/.展开更多
The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system mo...The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.展开更多
An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiabilit...An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.展开更多
For microseisimic monitoring it is difficult to determine wave modes and their propagation velocity. In this paper, we propose a new method for automatically inverting in real time the source characteristics of micros...For microseisimic monitoring it is difficult to determine wave modes and their propagation velocity. In this paper, we propose a new method for automatically inverting in real time the source characteristics of microseismic events in mine engineering without wave mode identification and velocities. Based on the wave equation in a spherical coordinate system, we derive a tomographic imaging equation and formulate a scanning parameter selection criterion by which the microseisimic event maximum energy and corresponding parameters can be determined. By determining the maximum energy positions inside a given risk district, we can indentify microseismic events inside or outside the risk districts. The synthetic and field examples demonstrate that the proposed tomographic imaging method can automatically position microseismic events by only knowing the risk district dimensions and range of velocities without identifying the wavefield modes and accurate velocities. Therefore, the new method utilizes the full wavefields to automatically monitor microseismic events.展开更多
By inspecting and analyzing the debris, which is the most direct and important information units in the lubricating oil, we can monitor the machine condition to predict its failure. The debris monitoring and analyzing...By inspecting and analyzing the debris, which is the most direct and important information units in the lubricating oil, we can monitor the machine condition to predict its failure. The debris monitoring and analyzing system (DMAS) is developed from the traditional iron spectrum technology, and has such characteristics as ease for debris separating, forecasting machine failure automatically and accurately in time and so on. The fundamental theory, components and its application in aeroengine health monitoring of DMAS are presented.展开更多
Methodology for the reliability analysis of hydraulic gravity dam is the key technology in current hydropower construction.Reliability analysis for the dynamical dam safety should be divided into two phases:failure mo...Methodology for the reliability analysis of hydraulic gravity dam is the key technology in current hydropower construction.Reliability analysis for the dynamical dam safety should be divided into two phases:failure mode identification and the calculation of the failure probability.Both of them are studied based on the mathematical statistics and structure reliability theory considering two kinds of uncertainty characters(earthquake variability and material randomness).Firstly,failure mode identification method is established based on the dynamical limit state system and verified through example of Koyna Dam so that the statistical law of progressive failure process in dam body are revealed; Secondly,for the calculation of the failure probability,mathematical model and formula are established according to the characteristics of gravity dam,which include three levels,that is element failure,path failure and system failure.A case study is presented to show the practical application of theoretical method and results of these methods.展开更多
Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional prom...Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional promise for discerning fine-grained characteristics of residents'travel behaviors,owing to its superior spatial and temporal resolution.This study focuses on identifying the most consistent commuting patterns of residents in the Qiaoxi District of Shijiazhuang,China,over the course of a month,using these patterns as the basis for transport mode identification.Leveraging the high-precise geographical coordinates of individuals'workplaces and homes,along with actual commuting durations derived from the high-frequency positioning of HMPSD,and comparing these with the predicted commuting durations for four transport modes from a navigational map,we have developed a novel approach for identifying individual transport modes,incorporating time matching,frequency ranking,and speed threshold assessments.This approach swiftly and effectively identifies the commuting modes for each resident—namely,driving,public transportation,walking,bicycling,and electric biking—along with their respective commuting distances and durations.Furthermore,to support urban planning and transportation management efforts,we aggregated individual commuting data—including flows,modes,distances,and durations—at a parcel level.This aggregation method effectively reveals favorable commuting characteristics within the central area of Qiaoxi District,highlights the commuting needs and irrational commuting conditions in peripheral parcels,and informs tailored strategies for adjusting planning layouts and optimizing facility configurations.This study facilitates an in-depth exploration of fine-grained travel patterns through integrated air-land transportation resources,providing new insights and methodologies for refined urban transportation planning and travel management through advanced data applications and identification methods.展开更多
Vortex optical communication has been a hot research field in recent years. A key step is mode recognition in the orbital angular momentum(OAM) free-space optical(FSO) communication system. In this article, we propose...Vortex optical communication has been a hot research field in recent years. A key step is mode recognition in the orbital angular momentum(OAM) free-space optical(FSO) communication system. In this article, we propose an OAM mode recognition method based on image recognition technology, which uses the interferogram between the vortex beam and the Gaussian beam to identify the OAM mode. In order to resist the influence of atmospheric turbulence on the recognition accuracy, we added a Gaussian smoothing filter into the recognition process. Moreover, we used random phase screens to generate interferogram sets at distances of 1 km and 2 km. The verification result shows that the proposed scheme produces high identification accuracy for the distorted optical field. The average accuracy can reach 100% and 87.78% under the conditions of medium-and strong-turbulence levels, respectively. It is anticipated that these results might be helpful for improving the reliability of the OAM-FSO communication system in the future.展开更多
基金supported in part by the National Basic Research Program of China (No. JCKY2016203B032)
文摘The identification result of operational mode is eurychoric while operational mode identification is investigated under ambient excitation,which is influenced by the signal size and the time interval.The operational mode identification method,which is based on the sliding time window method and the eigensystem realization algorithm(ERA),is investigated to improve the identification accuracy and stability.Firstly,the theory of the ERA method is introduced.Secondly,the strategy for decomposition and implementation is put forward,including the sliding time window method and the filtration method of modes.At last,an example is studied,where the model of a cantilever beam is built and the white noise exciting is input.Results show that the operational mode identification method can realize the modes,and has high robustness to the signal to noise ratio and signal size.
基金financially supported by Mitsubishi Heavy Industries,Ltd.,Japanthe National Natural Science Foundation of China(Nos.11572171,11632010 and U1533134)the opening project(No.KFJJ15-12M)of State Key Laboratory of Explosion Science and Technology(Beijing Institute of Technology)
文摘The fracture modes of low alloy steels and cast irons under tensile and fatigue conditions were identified by electron back-scattered diffraction（EBSD） misorientation analysis in this research. The curves of grain reference orientation deviation（GROD） distribution perpendicular to the fracture surface were obtained by EBSD observation, and the characteristics of each fracture mode were identified. The GROD value of the specimen fractured in tension decreases to a constant related to the elongation of corresponding specimen in the far field（farther than 5 mm away from the fracture surface）. The peak exhibits in GROD curves of two smooth specimens and a notched specimen near the fracture surface（within 5 mm away from the fracture surface）, and the formation mechanisms were discussed in detail based on the influences of specimen geometries（smooth or notched） and material toughness. The GROD value of fatigue fractured specimen is close to that at undeformed condition in the whole field, except the small area near the crack path. The loading conditions（constant stress amplitude loading or constant stress intensity factor range K loading） and the EBSD striation formation during fatigue crack propagation were also studied by EBSD observation parallel to the crack path.
文摘The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.
基金support from National Natural Science Foundation of China(Grant Nos.22275145,22305189and 21875184)Natural Science Foundation of Shaanxi Province(Grant Nos.2022JC-10 and 2024JC-YBQN-0112).
文摘Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is severely constrained by the absence of efficient methods for rapidly predicting crystal packing modes from molecular structures,impeding the high-throughput rational design of such materials.In this study,we employed quantified indicators,such as hydrogen bond dimension and maximum planar separation,to quickly screen 172DEM and 16 non-2DEM crystal structures from a crystal database.They were subsequently compared and analyzed,focusing on hydrogen bond donor-acceptor combinations,skeleton features,and intermolecular interactions.Our findings suggest that theπ-πpacking interaction energy is a key determinant in the formation of layered packing modes by planar energetic molecules,with its magnitude primarily influenced by the strongest dimericπ-πinteraction(π-π2max).Consequently,we have delineated a critical threshold forπ-π2max to discern layered packing modes and formulated a theoretical model for predictingπ-π2max,grounded in molecular electrostatic potential and dipole moment analysis.The predictive efficacy of this model was substantiated through external validation on a test set comprising 31 planar energetic molecular crystals,achieving an accuracy of 84%and a recall of 75%.Furthermore,the proposed model shows superior classification predictive performance compared to typical machine learning methods,such as random forest,on the external validation samples.This contribution introduces a novel methodology for the identification of crystal packing modes in 2DEMs,potentially accelerating the design and synthesis of high-energy,low-sensitivity 2DEMs.
基金supported by the National Natural Science Foundation of China(Grant No.32301691)the National Key R&D Program of China and Shandong Province,China(Grant No.2025YFE0103600 and 2021YFB3901300)+2 种基金the National Precision Agriculture Application Project(Grant No.JZNYYY001)the National Key R&D Program of China(Grant No.2021YFD13000500,2023YFB3904905 and 2022YFC3301605)the Project of Research and Demonstration of a Water Intelligent Monitoring System based on Domestic High-resolution Remote Sensing Multi-satellite Fusion(24-1-2-QLJH-7-GX).
文摘Leveraging extensive trajectory data to analyze the operation modes of agricultural machinery for gathering precise spatial information is an important fundamental task for subsequent agricultural machinery trajectory research.However,complex algorithm models hinder nonspecialized researchers from further processing agricultural machinery trajectory data.In the present application note,ChatGPT is taken as an example and a complete prompt guide for large language models(LLMs)is provided for autonomously identifying the operation mode of agricultural machinery.This guide provides low-cost workflows for processing agricultural machinery trajectory data when computer science or data science expertise is lacking.It even possesses the capability to utilize newly learned algorithms such as the random forest model,which has not been previously explored in the literature for operation mode identification,to accomplish the task.To the best of our knowledge,this is the first attempt to apply LLMs to identifying agricultural machinery operation mode based on trajectory data.The complete prompt guide is publicly available at https://github.com/kakushuu/prompt-guide/.
基金Supported by the China Scholarship Council,National Natural Science Foundation of China(Grant No.11402022)the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office(DYSCO)+1 种基金the Fund for Scientific Research–Flanders(FWO)the Research Fund KU Leuven
文摘The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.
基金This project is supported by National Natural Science Foundation of China (No.10302019).
文摘An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.
基金support jointly by projects of the National Natural Science Fund Project (40674017 and 50774012)the National Key Basic Research and Development Plan 973 (2010CB226803)
文摘For microseisimic monitoring it is difficult to determine wave modes and their propagation velocity. In this paper, we propose a new method for automatically inverting in real time the source characteristics of microseismic events in mine engineering without wave mode identification and velocities. Based on the wave equation in a spherical coordinate system, we derive a tomographic imaging equation and formulate a scanning parameter selection criterion by which the microseisimic event maximum energy and corresponding parameters can be determined. By determining the maximum energy positions inside a given risk district, we can indentify microseismic events inside or outside the risk districts. The synthetic and field examples demonstrate that the proposed tomographic imaging method can automatically position microseismic events by only knowing the risk district dimensions and range of velocities without identifying the wavefield modes and accurate velocities. Therefore, the new method utilizes the full wavefields to automatically monitor microseismic events.
文摘By inspecting and analyzing the debris, which is the most direct and important information units in the lubricating oil, we can monitor the machine condition to predict its failure. The debris monitoring and analyzing system (DMAS) is developed from the traditional iron spectrum technology, and has such characteristics as ease for debris separating, forecasting machine failure automatically and accurately in time and so on. The fundamental theory, components and its application in aeroengine health monitoring of DMAS are presented.
基金Projects(51021004,51379141)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China
文摘Methodology for the reliability analysis of hydraulic gravity dam is the key technology in current hydropower construction.Reliability analysis for the dynamical dam safety should be divided into two phases:failure mode identification and the calculation of the failure probability.Both of them are studied based on the mathematical statistics and structure reliability theory considering two kinds of uncertainty characters(earthquake variability and material randomness).Firstly,failure mode identification method is established based on the dynamical limit state system and verified through example of Koyna Dam so that the statistical law of progressive failure process in dam body are revealed; Secondly,for the calculation of the failure probability,mathematical model and formula are established according to the characteristics of gravity dam,which include three levels,that is element failure,path failure and system failure.A case study is presented to show the practical application of theoretical method and results of these methods.
基金supported by the Key Research Base of Philosophy and Social Sciences in Sichuan Province—Modern Design and Culture Research Center(Grant No.MD24C003)the National Natural Science Foundation of China(Grant No.52308084)+1 种基金China Postdoctoral Science Foundation(Grant No.2022M712877)Key R&D and Promotion Projects of Henan Province(Grant No.222102110125)。
文摘Due to the widespread use of navigational satellites,the ubiquity of mobile phones,and the rapid advancement of mobile communication technologies,high-precision mobile phone signaling data(HMPSD)holds exceptional promise for discerning fine-grained characteristics of residents'travel behaviors,owing to its superior spatial and temporal resolution.This study focuses on identifying the most consistent commuting patterns of residents in the Qiaoxi District of Shijiazhuang,China,over the course of a month,using these patterns as the basis for transport mode identification.Leveraging the high-precise geographical coordinates of individuals'workplaces and homes,along with actual commuting durations derived from the high-frequency positioning of HMPSD,and comparing these with the predicted commuting durations for four transport modes from a navigational map,we have developed a novel approach for identifying individual transport modes,incorporating time matching,frequency ranking,and speed threshold assessments.This approach swiftly and effectively identifies the commuting modes for each resident—namely,driving,public transportation,walking,bicycling,and electric biking—along with their respective commuting distances and durations.Furthermore,to support urban planning and transportation management efforts,we aggregated individual commuting data—including flows,modes,distances,and durations—at a parcel level.This aggregation method effectively reveals favorable commuting characteristics within the central area of Qiaoxi District,highlights the commuting needs and irrational commuting conditions in peripheral parcels,and informs tailored strategies for adjusting planning layouts and optimizing facility configurations.This study facilitates an in-depth exploration of fine-grained travel patterns through integrated air-land transportation resources,providing new insights and methodologies for refined urban transportation planning and travel management through advanced data applications and identification methods.
基金This work was supported in part by the National Natural Science Foundation of China(NSFC)(Nos.61875057 and 61475049)the Guangdong Natural Science Foundation(No.2021A1515012652).
文摘Vortex optical communication has been a hot research field in recent years. A key step is mode recognition in the orbital angular momentum(OAM) free-space optical(FSO) communication system. In this article, we propose an OAM mode recognition method based on image recognition technology, which uses the interferogram between the vortex beam and the Gaussian beam to identify the OAM mode. In order to resist the influence of atmospheric turbulence on the recognition accuracy, we added a Gaussian smoothing filter into the recognition process. Moreover, we used random phase screens to generate interferogram sets at distances of 1 km and 2 km. The verification result shows that the proposed scheme produces high identification accuracy for the distorted optical field. The average accuracy can reach 100% and 87.78% under the conditions of medium-and strong-turbulence levels, respectively. It is anticipated that these results might be helpful for improving the reliability of the OAM-FSO communication system in the future.
