An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The method...An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.展开更多
With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat systems.Aiming at the problems of insufficient ...With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat systems.Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches,this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop.Firstly,a combat network model is given that takes into account the capability properties of combat nodes.Then,based on the transition matrix between combat nodes,an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search.Given the mission-capability satisfaction of nodes,the effectiveness evaluation indexes for operation loops and combat network are proposed,followed by node importance measure.Through a case study of the combat scenario involving space-based support against surface ships under different strategies,the effectiveness of the proposed method is verified.The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network,whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.展开更多
A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic response...A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.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
Power plant performance can decrease along with its life span,and move away from the design and commissioning targets.Maintenance issues,operational practices,market restrictions,and financial objectives may lead to t...Power plant performance can decrease along with its life span,and move away from the design and commissioning targets.Maintenance issues,operational practices,market restrictions,and financial objectives may lead to that behavior,and the knowledge of appropriate actions could support the system to retake its original operational performance.This paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency conditions.The selected operational variables are evaluated in respect to their impact on the system performance,quantified by the Variable Importance Index.That metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation,and should be controlled with more attention.Principal Component Analysis(PCA)and k-means++clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power plant.The adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones,to finally group recommended settings to achieve the target conditions.Results show performance gains in respect to the average historical values of 73.5%and the lowest efficiency condition records of 68%,to the target steam generator efficiency of 76%.展开更多
基金The National High Technology Research and Development Program of China(863Program)(No.2006AA04Z416)
文摘An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.
文摘With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat systems.Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches,this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop.Firstly,a combat network model is given that takes into account the capability properties of combat nodes.Then,based on the transition matrix between combat nodes,an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search.Given the mission-capability satisfaction of nodes,the effectiveness evaluation indexes for operation loops and combat network are proposed,followed by node importance measure.Through a case study of the combat scenario involving space-based support against surface ships under different strategies,the effectiveness of the proposed method is verified.The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network,whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.
基金Supported by the National Natural Science Foundation of China（51079027）
文摘A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.
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Authors acknowledge Energy of Portugal EDP for the financial and technical support to this projectJ.Duarte acknowledges the financial support from CNPq 154147/2020-6 for her undergraduate scholarship+2 种基金L.W.Vieira acknowledges the INCT-GD and the financial support from CAPES 23038.000776/2017-54 for her Ph.D.grantA.D.Marques ac-knowledges the financial support from CNPq 132422/2020-4 for his MSc grantP.S.Schneider acknowledges CNPq for his research grant(PQ 301619/2019-0).T.S.Prass acknowledges the support of FAPERGS(ARD 01/2017,Processo 17/2551-0000826-0).
文摘Power plant performance can decrease along with its life span,and move away from the design and commissioning targets.Maintenance issues,operational practices,market restrictions,and financial objectives may lead to that behavior,and the knowledge of appropriate actions could support the system to retake its original operational performance.This paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency conditions.The selected operational variables are evaluated in respect to their impact on the system performance,quantified by the Variable Importance Index.That metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation,and should be controlled with more attention.Principal Component Analysis(PCA)and k-means++clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power plant.The adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones,to finally group recommended settings to achieve the target conditions.Results show performance gains in respect to the average historical values of 73.5%and the lowest efficiency condition records of 68%,to the target steam generator efficiency of 76%.
