Modal analysis,which provides modal parameters including frequencies,damping ratios,and mode shapes,is essential for assessing structural safety in structural health monitoring.Automated operational modal analysis(AOM...Modal analysis,which provides modal parameters including frequencies,damping ratios,and mode shapes,is essential for assessing structural safety in structural health monitoring.Automated operational modal analysis(AOMA)offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment.However,estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions.To address this issue,we propose an automated modal identification approach comprising three key aspects:(1)identification of modal parameters using covariance-driven stochastic subspace identification;(2)automated interpretation of the stabilization diagram;(3)an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure(OPTICS)combined with k-nearest neighbors(KNN).The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention.A simulated 10-story shear frame was used to verify the methodology.Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice.The proposed approach can automatically identify modal parameters with high accuracy,making it suitable for a real-time structural health monitoring framework.展开更多
Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are ...Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are used to localize only a single fault.However,existing clustering algorithms cannot always obtain completely correct clustering results,which is a potential threat for bug isolation based MFL approaches.To address this issue,we first analyze the influence of the accuracy of the clustering on the performance of MFL,and the results of a controlled study indicate that using the clustering algorithm with the highest accuracy can achieve the best performance of MFL.Moreover,previous studies on clustering algorithms also show that the elements in a higher density cluster have a higher similarity.Based on the above motivation,we propose a novel approach FATOC(One-Fault-at-a-Time via OPTICS Clustering).In particular,FATOC first leverages the OPTICS(Ordering Points to Identify the Clustering Structure)clustering algorithm to group failed test cases,and then identifies a cluster with the highest density.OPTICS clustering is a density-based clustering algorithm,which can reduce the misgrouping and calculate a density value for each cluster.Such a density value of each cluster is helpful for finding a cluster with the highest clustering effectiveness.FATOC then combines the failed test cases in this cluster with all passed test cases to localize a single-fault through the traditional spectrum-based fault localization(SBFL)formula.After this fault is localized and fixed,FATOC will use the same method to localize the next single-fault,until all the test cases are passed.Our evaluation results show that FATOC can significantly outperform the traditional SBFL technique and a state-of-the-art MFL approach MSeer on 804 multi-faulty versions from nine real-world programs.Specifically,FATOC’s performance is 10.32%higher than that of traditional SBFL when using Ochiai formula in terms of metric A-EXAM.Besides,the results also indicate that,when checking 1%,3%and 5%statements of all subject programs,FATOC can locate 36.91%,48.50%and 66.93%of all faults respectively,which is also better than the traditional SBFL and the MFL approach MSeer.展开更多
With the frame of the time-dependent local density approximation, an efficient description of the optical response of clusters has been used to study the photo-absorption cross section of Na2 and Na4 clusters. It is s...With the frame of the time-dependent local density approximation, an efficient description of the optical response of clusters has been used to study the photo-absorption cross section of Na2 and Na4 clusters. It is shown that our calculated results are in good agreement with the experiment. In addition, our calculated spectrum for the Na4 cluster is in better agreement with experiment than the GW absorption spectrum.展开更多
The local solid flow structure of a bubbling fluidized bed of sand particles was investigated m three different columns to characterize the properties of clusters. The experiments were performed using a reflective opt...The local solid flow structure of a bubbling fluidized bed of sand particles was investigated m three different columns to characterize the properties of clusters. The experiments were performed using a reflective optical fiber probe. The variations in size, velocity, and void fraction of the clusters due to changes in the superficial gas velocity, particle size, and radial positions were studied. The results indicate that the velocity of the clusters remained unchanged while their size increased as the column diameter increased. In addition, the radial profile of the clusters＇ velocity did not depend on the radial position. The results indicate that larger particles form larger clusters, which move slower.展开更多
The chiral clusters (m3-S)MCoW(CO)8[h5-C5H4C(O)OCH3] [M=Ru (2), Fe (3)] were synthesized by asymmetric induction of N-benzylcinchonium chloride as phase-transfer catalyst (PTC). The most suitable amount of PTC is 70 m...The chiral clusters (m3-S)MCoW(CO)8[h5-C5H4C(O)OCH3] [M=Ru (2), Fe (3)] were synthesized by asymmetric induction of N-benzylcinchonium chloride as phase-transfer catalyst (PTC). The most suitable amount of PTC is 70 mol%. Cluster 3 was determined by single crystal X-ray diffraction analysis. The best ee of the chiral cluster is over 20%.展开更多
基金supported by the National Natural Science Foundation of China(No.52408200)the Natural Science Foundation of Jiangsu Province(No.BK20240996)+1 种基金China,the Suzhou Science and Technology Plan(Basic Research)Project(No.SJC2023002)China,and the Natural Science Research Projects of Colleges and Universities in Jiangsu Province(No.24KJB560022),China.
文摘Modal analysis,which provides modal parameters including frequencies,damping ratios,and mode shapes,is essential for assessing structural safety in structural health monitoring.Automated operational modal analysis(AOMA)offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment.However,estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions.To address this issue,we propose an automated modal identification approach comprising three key aspects:(1)identification of modal parameters using covariance-driven stochastic subspace identification;(2)automated interpretation of the stabilization diagram;(3)an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure(OPTICS)combined with k-nearest neighbors(KNN).The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention.A simulated 10-story shear frame was used to verify the methodology.Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice.The proposed approach can automatically identify modal parameters with high accuracy,making it suitable for a real-time structural health monitoring framework.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61902015,61872026,and 61672085the Nantong Application Research Plan under Grant No:JC2019106the Open Project of State Key Laboratory of Information Security(Institute of Information Engineering,Chinese Academy of Sciences)under Grant No.2020-MS-07.
文摘Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are used to localize only a single fault.However,existing clustering algorithms cannot always obtain completely correct clustering results,which is a potential threat for bug isolation based MFL approaches.To address this issue,we first analyze the influence of the accuracy of the clustering on the performance of MFL,and the results of a controlled study indicate that using the clustering algorithm with the highest accuracy can achieve the best performance of MFL.Moreover,previous studies on clustering algorithms also show that the elements in a higher density cluster have a higher similarity.Based on the above motivation,we propose a novel approach FATOC(One-Fault-at-a-Time via OPTICS Clustering).In particular,FATOC first leverages the OPTICS(Ordering Points to Identify the Clustering Structure)clustering algorithm to group failed test cases,and then identifies a cluster with the highest density.OPTICS clustering is a density-based clustering algorithm,which can reduce the misgrouping and calculate a density value for each cluster.Such a density value of each cluster is helpful for finding a cluster with the highest clustering effectiveness.FATOC then combines the failed test cases in this cluster with all passed test cases to localize a single-fault through the traditional spectrum-based fault localization(SBFL)formula.After this fault is localized and fixed,FATOC will use the same method to localize the next single-fault,until all the test cases are passed.Our evaluation results show that FATOC can significantly outperform the traditional SBFL technique and a state-of-the-art MFL approach MSeer on 804 multi-faulty versions from nine real-world programs.Specifically,FATOC’s performance is 10.32%higher than that of traditional SBFL when using Ochiai formula in terms of metric A-EXAM.Besides,the results also indicate that,when checking 1%,3%and 5%statements of all subject programs,FATOC can locate 36.91%,48.50%and 66.93%of all faults respectively,which is also better than the traditional SBFL and the MFL approach MSeer.
基金The project supported by National Natural Science Foundation of China under Grant Nos. 10405025, 10575012, 10435020, and 10535010
文摘With the frame of the time-dependent local density approximation, an efficient description of the optical response of clusters has been used to study the photo-absorption cross section of Na2 and Na4 clusters. It is shown that our calculated results are in good agreement with the experiment. In addition, our calculated spectrum for the Na4 cluster is in better agreement with experiment than the GW absorption spectrum.
文摘The local solid flow structure of a bubbling fluidized bed of sand particles was investigated m three different columns to characterize the properties of clusters. The experiments were performed using a reflective optical fiber probe. The variations in size, velocity, and void fraction of the clusters due to changes in the superficial gas velocity, particle size, and radial positions were studied. The results indicate that the velocity of the clusters remained unchanged while their size increased as the column diameter increased. In addition, the radial profile of the clusters＇ velocity did not depend on the radial position. The results indicate that larger particles form larger clusters, which move slower.
文摘The chiral clusters (m3-S)MCoW(CO)8[h5-C5H4C(O)OCH3] [M=Ru (2), Fe (3)] were synthesized by asymmetric induction of N-benzylcinchonium chloride as phase-transfer catalyst (PTC). The most suitable amount of PTC is 70 mol%. Cluster 3 was determined by single crystal X-ray diffraction analysis. The best ee of the chiral cluster is over 20%.
