• Journal of Information Processing Systems
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• 제12권1호
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• pp.129-148
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• 2016

Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi-modal medical images with flat and unstructured data. It has a lack of semantic information between multi-modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.

• Smart Structures and Systems
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• 제10권4_5호
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• pp.393-410
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• 2012

The 610 m high Canton Tower (formerly named Guangzhou New Television Tower) is currently considered as a benchmark problem for structural health monitoring (SHM) of high-rise slender structures. In the benchmark study task I, a set of 24-hour ambient vibration measurement data has been available for the output-only system identification study. In this paper, the vector autoregressive models (ARV) method is adopted in the operational modal analysis (OMA) for this TV tower. The identified natural frequencies, damping ratios and mode shapes are presented and compared with the available results from some other research groups which used different methods, e.g., the data-driven stochastic subspace identification (SSI-DATA) method, the enhanced frequency domain decomposition (EFDD) algorithm, and an improved modal identification method based on NExT-ERA technique. Furthermore, the environmental effects on the estimated modal parameters are also discussed.

• Earthquakes and Structures
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• 제15권3호
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• pp.263-273
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• 2018

Operational modal analysis is being widely used in aerospace, mechanical and civil engineering. Common research fields include optimal design and rehabilitation under dynamic loads, structural health monitoring, modification and control of dynamic response and analytical model updating. In many practical cases, influence of noise contamination in the recorded data makes it difficult to identify the modal parameters accurately. In this paper, an improved frequency domain method called Enhanced Least Square Complex Frequency (eLSCF) is developed to extract modal parameters from noisy recorded data. The proposed method makes the use of pre-defined approximate mode shape vectors to refine the cross-power spectral density matrix and extract fundamental frequency for the mode of interest. The efficiency of the proposed method is illustrated using an example five story shear frame loaded by random excitation and different noise signals.

• 소음진동
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• 제6권1호
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• pp.97-106
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• 1996

This paper presents systematic study of the experimental application of a free-interfaced component mode synthesis method. In the free-interfaced component mode synthesis method, an error the to truncated higher modes and neglected ineria loadings on a component from the connected component is inherent. Also, it is difficult to directly use experimental modal data in a modal synthesis method which links experimental model to finite-element model because of many inconsistencies between experimentally obtained and analytically obtained modal vectors and missing degrees-of-freedom (DOFs) such as rotational DOFs. In order to solve these problems, three methods, the first one based on attaching auxiliary weights to the connection points, the second one utillizing the normalization of experimental modal vector, and the third one generating smoothed and expanded experimental mode shapes, are studied in this paper. Finally, the study is illustrated for a flat-plate structure by using simulated and measured experimental data.

• Earthquakes and Structures
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• 제16권3호
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• pp.321-327
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• 2019

This study investigated the possibility of using the recorded micro tremor data on ground level as ambient vibration input excitation data for investigation and application Operational Modal Analysis (OMA) on the bench-scale earthquake simulator (The Quanser Shake Table) for model chimney. As known OMA methods (such as EFDD, SSI and so on) are supposed to deal with the ambient responses. For this purpose, analytical and experimental modal analysis of a model chimney for dynamic characteristics was performed. 3D Finite element model of the chimney was evaluated based on the design drawing. Ambient excitation was provided by shake table from the recorded micro tremor ambient vibration data on ground level. Enhanced Frequency Domain Decomposition is used for the output only modal identification. From this study, best correlation is found between mode shapes. Natural frequencies and analytical frequencies in average (only) 1.996% are different.

• Smart Structures and Systems
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• 제31권2호
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• pp.171-181
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• 2023

Using the most up-to-date system identification methods in both time and frequency domains, the dynamic monitoring data from the reinforced concrete Egebaekvej Bridge near Holte, Denmark, is examined in this investigation. The bridge was erected in the 1960s and was still standing during test campaign before demolishing. The ARTeMIS Modal was adopted to derive the modal parameters from ambient vibration data. Several Operational Modal Analysis (OMA) approaches were applied, including Enhanced Frequency Domain Decomposition (EFDD), Curve-fit Frequency Domain Decomposition (CFDD), and Frequency Domain Decomposition (FDD). Afterward, Principal Component (SSI-PC), Unweighted Principal Component (SSI-UPC) Stochastic Subspace Identification methods were utilized. Danish engineering consulting company, COWI with the allowance of the bridge contractor BARSLUND, allow the researcher for this experimental test to demonstrate the impact of OMA applications.

• 산업기술연구
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• 제29권A호
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• pp.19-25
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• 2009

The proper orthogonal decomposition (POD) analysis of vibration of a steel frame structure is performed to extract modal parameters. The theoretical background of the POD method is introduced briefly, and this technique is further applied to free vibration displacements of one bay-two story steel frame structure to extract the modal parameters. From the POD analysis of the steel frame structure, it is found that important modal parameters such as true mode shapes, modal kinematic energy, natural frequencies, and damping ratios can be obtained for the building efficiently and in detail. Therefore, it is concluded that the POD method could be one of the useful techniques in analysis of vibration of structures.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1997년도 춘계학술대회논문집; 경주코오롱호텔; 22-23 May 1997
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• pp.184-191
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• 1997

A method, termed as the substructural sensitivity synthesis method, which utilizes the computational merits of the component mode synthesis technique is proposed to calculate design sensitivity of modal parameters of substructurally combined structures. In this method, the sensitivity analysis is combined with component mode synthesis thchnique. thus the degrees of freedom of a combined structure can be dramatically reduced. Free-interface mode method including the residual attachment modes among the component mode synthesis methods is used to calculate the modal sensitivity of the combined structure. For the design sensitivities of modal properties of structure, the Nelson's method, which is exact solving method is used. It is shown that the modal sensitivities of the entire structure can be obtained by synthesizing the substructural modal data, and the sensitivities of the modal data about the design variables of modifiable substructure. Using the proposed method, the final degrees of freedom of entire structure can be remarkably reduced to calculate the modal parameter sensitivities. With a structure composed of beams and plates, as an example, the sensitivities of the eigenvalues and eigenvectors obtained by this proposed method were compared with the exact solutions in terms of accuracy.

• Smart Structures and Systems
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• 제30권1호
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• pp.107-119
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• 2022

Modal parameters are widely used in bridge damage detection, finite element model (FEM) updating and design optimization. However, the conventional modal identification approaches require large number of sensors, enormous data processing workload, but normally result in mode shapes with low accuracy. This paper proposes a modal identification method of time-varying vehicle-bridge system using a single sensor. Firstly, the essential physical relationship between the instantaneous frequency of the vehicle-bridge system and the bridge mode shapes are derived. Subsequently, based on the synchroextracting transform, the instantaneous frequency of the system is tracked through the dynamic response collected by a single sensor, and further the modal parameters are estimated by using the derived physical relationship. Then numerical and experimental examples are conducted to examine the feasibility and effectiveness of the proposed method. Finally, the modal parameters identified by the proposed method are applied in bridge FEM updating. The results manifest that the proposed method identifies the modal parameters with high accuracy via a single sensor, and can provide reliable data for the FEM updating.

• Smart Structures and Systems
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• 제10권4_5호
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• pp.471-483
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• 2012

In this paper, the structural health monitoring (SHM) benchmark problem of the Canton tower is studied. Based on the field monitoring data from the 20 accelerometers deployed on the tower, some modal frequencies and mode shapes at measured degrees of freedom of the tower are identified. Then, these identified incomplete modal data are used to update the reduced finite element (FE) model of the tower by a novel algorithm. The proposed algorithm avoids the problem of subjective selection of updated parameters and directly updates model stiffness matrix without model reduction or modal expansion approach. Only the eigenvalues and eigenvectors of the normal finite element models corresponding to the measured modes are needed in the computation procedures. The updated model not only possesses the measured modal frequencies and mode shapes but also preserves the modal frequencies and modes shapes in their normal values for the unobserved modes. Updating results including the natural frequencies and mode shapes are compared with the experimental ones to evaluate the proposed algorithm. Also, dynamic responses estimated from the updated FE model using remote senor locations are compared with the measurement ones to validate the convergence of the updated model.