• Smart Structures and Systems
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• v.32 no.1
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• pp.1-7
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• 2023

In modal analysis, the mode shape reflects the vibration characteristics of the structure, and thus it is widely performed for finite element model updating and structural health monitoring. Generally, the acceleration-based mode shape is suitable to express the characteristics of structures for the translational vibration; however, it is difficult to represent the rotational mode at boundary conditions. A tilt sensor and gyroscope capable of measuring rotational mode are used to analyze the overall behavior of the structure, but extracting its mode shape is the major challenge under the small vibration always. Herein, we conducted a feasibility study on a multi-mode shape estimating approach utilizing a single physical quantity signal. The basic concept of the proposed method is to receive multi-metric dynamic responses from two sensors and obtain mode shapes through bridge loading test with relatively large deformation. In addition, the linear transformation matrix for estimating two mode shapes is derived, and the mode shape based on the gyro sensor data is obtained by acceleration response using ambient vibration. Because the structure's behavior with respect to translational and rotational mode can be confirmed, the proposed method can obtain the total response of the structure considering boundary conditions. To verify the feasibility of the proposed method, we pre-measured dynamic data acquired from five accelerometers and five gyro sensors in a lab-scale test considering bridge structures, and obtained a linear transformation matrix for estimating the multi-mode shapes. In addition, the mode shapes for two physical quantities could be extracted by using only the acceleration data. Finally, the mode shapes estimated by the proposed method were compared with the mode shapes obtained from the two sensors. This study confirmed the applicability of the multi-mode shape estimation approach for accurate damage assessment using multi-dimensional mode shapes of bridge structures, and can be used to evaluate the behavior of structures under ambient vibration.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.397-401
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• 1994

This paper proposes an identification method for a thin plate where multiple actuators and sensors are bonded. Since a thin plate has small damping ratios of all modes, each mode can be identified seperately with a bandpass filter for each modal signal. With the bandpass filter and the characteristics of the plate, the Multi-Input Multi-Output (MIMO) model of the plate can be converted to several Multi-Input Single-Output(MISO) models of second order linear difference equations of the modes. Parameters for each mode are obtained by using the Least Square method. Form there MISO models, the MIMO model is obtained in the form of the state space. Experiments were performed for an all-clamped plate with two pairs of piezoelectric actuators and sensors. The outputs of the identified model and the experimental data match well.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.351-360
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• 2018

Human emotion recognition is a research topic that is receiving continuous attention in computer vision and artificial intelligence domains. This paper proposes a method for classifying human emotions through multiple neural networks based on multi-modal signals which consist of image, landmark, and audio in a wild environment. The proposed method has the following features. First, the learning performance of the image-based network is greatly improved by employing both multi-task learning and semi-supervised learning using the spatio-temporal characteristic of videos. Second, a model for converting 1-dimensional (1D) landmark information of face into two-dimensional (2D) images, is newly proposed, and a CNN-LSTM network based on the model is proposed for better emotion recognition. Third, based on an observation that audio signals are often very effective for specific emotions, we propose an audio deep learning mechanism robust to the specific emotions. Finally, so-called emotion adaptive fusion is applied to enable synergy of multiple networks. The proposed network improves emotion classification performance by appropriately integrating existing supervised learning and semi-supervised learning networks. In the fifth attempt on the given test set in the EmotiW2017 challenge, the proposed method achieved a classification accuracy of 57.12%.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.132-146
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• 2020

Biometric technologies have become widely available in many different fields. However, biometric technologies using existing physical features such as fingerprints, facial features, irises, and veins must consider forgery and alterations targeting them through fraudulent physical characteristics such as fake fingerprints. Thus, a trend toward next-generation biometric technologies using behavioral biometrics of a living person, such as bio-signals and walking characteristics, has emerged. Accordingly, in this study, we developed a bio-signal authentication algorithm using electrocardiogram (ECG) signals, which are the most uniquely identifiable form of bio-signal available. When using ECG signals with our system, the personal identification and authentication accuracy are approximately 90% during a state of rest. When using fingerprints alone, the equal error rate (EER) is 0.243%; however, when fusing the scores of both the ECG signal and fingerprints, the EER decreases to 0.113% on average. In addition, as a function of detecting a presentation attack on a mobile phone, a method for rejecting a transaction when a fake fingerprint is applied was successfully implemented.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.526-534
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• 2019

Localization methods using radio signals should obtain range measurements from three or more anchors. However, a typical building consists of narrow, long hallways and corners, making it difficult to secure more than three light of sight anchors. The result is a multi-modal solution that makes it difficult to estimate the user's location. In order to overcome this problem, this paper proposes a method for estimating the location using artificial neural networks. Using the artificial neural network, even if a multi-modal solution occurs, the position can be estimated by acquiring user movement pattern information based on accumulated range measurements. The method does not require any additional equipment or sensors, and only anchor-based range measurements can estimate the user's location. In order to verify the proposed method, location estimation tests were performed in situations where the multi-modal solution occurred by installing an insufficient number of anchors in a building. As a result, it was confirmed that the location can be estimated even when the number of anchors is insufficient.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.266-270
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• 2006

Noise reduction the noise of a high precision multi-stage gearbox applied at the industrial robot is investigated by experiment such as the modal test and the signal analysis. The signal analysis performed with the waterfall plot representing the power spectrum as a function of the rotating speed. An eccentric load is installed at the gearbox in order to organize similar condition used in the industrial robot. Exciting sources are found out by the waterfall plot, and then the main factor to make the noise is distinguish. For the low-noise gearbox, the gear design parameter is modified and this gearbox is experimented in the same procedure. The results of the test show the noise level of gearbox reduced.

• Smart Structures and Systems
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• v.2 no.2
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• pp.141-154
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• 2006

Recently, the Hilbert-Huang transform (HHT) has gained considerable attention as a novel technique of signal processing, which shows promise for the system identification and damage detection of structures. This study investigates the effectiveness and accuracy of the HHT method for the system identification and damage detection of structures through a series of experiments. A multi-degree-of-freedom (MDOF) structural model has been constructed with modular members, and the columns of the model can be replaced or removed to simulate damages at different locations with different severities. The measured response data of the structure due to an impulse loading is first decomposed into modal responses using the empirical mode decomposition (EMD) approach with a band-pass filter technique. Then, the Hilbert transform is subsequently applied to each modal response to obtain the instantaneous amplitude and phase angle time histories. A linear least-square fit procedure is used to identify the natural frequencies and damping ratios from the instantaneous amplitude and phase angle for each modal response. When the responses at all degrees of freedom are measured, the mode shape and the physical mass, damping and stiffness matrices of the structure can be determined. Based on a comparison of the stiffness of each story unit prior to and after the damage, the damage locations and severities can be identified. Experimental results demonstrate that the HHT method yields quite accurate results for engineering applications, providing a promising tool for structural health monitoring.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.300-307
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• 2002

Damage estimation methods are classified into two groups according to the dependence on the FE model : signal-based and model-based methods. Signal-based damage estimation methods are generally appropriate for detection of damage location, whereas not effective for estimation of damage severities. Model-based damage estimation methods are difficult to apply directly to the structures with a large number of the probable damaged members. It is difficult to obtain the exact model representing the real bridge behavior due to the modeling errors. The modeling errors even may exceed the modal sensitivity on damage. In this study, Model-based damage detection method which can effectively consider the modeling errors is suggested. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the presented method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.9 s.90
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• pp.819-826
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• 2004

A multi-step optimum strategy for the selection of the locations and directions of strain gauges is proposed in this paper to capture at best the modal response of blade in a series of modes on fan blades. It is consist of three steps including two pass reduction step, genetic algorithm and fine optimization to find the locations-directions of strain gauges. The optimization is based upon the maximum signal-to-noise ratio(SNR) of measured strain values with respect to the inherent system measurement noise, the mispositioning of the gauge in location and gauge failure. Optimal gauge positions for a fan blade is analyzed to prove the effectiveness of the multi-step optimum methodology and to investigate the effects of the considering parameters such as the mispositioning level, the probability of gauge failure, and the number of gauges on the optimal strain gauge position.