• Journal of Drive and Control
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• v.16 no.2
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• pp.80-90
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• 2019

Fuel cell hybrid electric vehicle is an attractive solution to reduce pollutants, such as noise and carbon dioxide emission. This study presents an approach for energy management and control algorithm based on energetic macroscopic representation for a fuel cell hybrid electric vehicle that is powered by proton exchange membrane fuel cell, battery and supercapacitor. First, the detailed model of the fuel cell hybrid electric vehicle, including fuel cell, battery, supercapacitor, DC-DC converters and powertrain system, are built on the energetic macroscopic representation. Next, the power management strategy was applied to manage the energy among the three power sources. Moreover, the control scheme that was based on back-stepping sliding mode control and inversed-model control techniques were deduced. Simulation tests that used a worldwide harmonized light vehicle test procedure standard driving cycle showed the effectiveness of the proposed control method.

• Journal of Power Electronics
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• v.19 no.1
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• pp.108-118
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• 2019

This paper proposes a DC-link voltage balance controller using the fourth-phase of a three-level neutral-point clamped (NPC) PWM converter with medium vector selection (MVS) PWM for common-mode voltage reduction. MVS PWM makes the voltage reference by synthesizing the voltage vectors that cannot generate common-mode voltage. This PWM method is effective for reducing the EMI noise emitted from converter systems. However, the DC-link voltage imbalance problem is caused by the use of limited voltage vectors. Therefore, in this paper, the effect of MVS PWM on the DC-link voltage of a three-level NPC converter is analyzed. Then a proportional-derivative (PD) controller for the DC-link voltage balance is designed from the DC-link modeling. In addition, feedforward compensation of the neutral point current is included in the proposed PD controller. The effectiveness of the proposed controller is verified by experimental results.

• Journal of Drive and Control
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• v.18 no.3
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• pp.8-15
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• 2021

A rear axle steering (RAS) system is attached to the rear of medium and large commercial vehicles that transport large cargo. The existing RAS systems are driven by electro-hydraulic actuator (EHA), and most commercialized EHAs consist of electric motors, hydraulic pumps, relief valves, prefill valves and cylinders. The prefill valve required for such EHAs is a type of check valve with extremely low cracking pressure that should not allow RAS to have noise or vibration, and the prefill valve prevents system negative pressure as well as unstable operation. Most papers on this topic rely on experiments to predict valve performance, and theoretically detailed modeling of valves or pipelines is performed, but it is very rare to evaluate hydraulic vibration characteristics by analysing everything from hydraulic pumps to valves comprehensively. In this study, we proposed an experimental circuit that can predict the performance of the prefill valve. The study also analysed the pressure-flow pulsation that is transmitted to the valve through the pipeline, and how the transmitted pressure-flow pulsation affects the valve vibration.

• New & Renewable Energy
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• v.16 no.4
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• pp.33-40
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• 2020

The research topics of more than 1,900 wind energy papers registered in the Korean Journal Citation Index (KCI) were modeled into 25 topics using latent directory allocation (LDA), and their consistency was cross-validated through principal component analysis (PCA) of the document word matrix. Key research topics in the wind energy field were identified as "offshore, wind farm," "blade, design," "generator, voltage, control," 'dynamic, load, noise," and "performance test." As a new method to determine the similarity between research topics in journals, a systematic evaluation method was proposed to analyze the correlation between topics by constructing a journal-topic matrix (JTM) and clustering them based on topic similarity between journals. By evaluating 24 journals that published more than 20 wind energy papers, it was confirmed that they were classified into meaningful clusters of mechanical engineering, electrical engineering, marine engineering, and renewable energy. It is expected that the proposed systematic method can be applied to the evaluation of the specificity of subsequent journals.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.139-151
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• 2021

By the end of December 2020, an earthquake with Mw about 6.4 hit Sisak-Moslavina County, Croatia. The town of Petrinja was the most affected region with major power outage and many buildings collapsed. The damage also affected neighbor countries such as Bosnia and Herzegovina and Slovenia. As a light of this devastating event, a deformation map due to this earthquake could be generated by using remote sensing imagery from Sentinel-1 SAR data. InSAR could be used as deformation map but still affected with noise factor that could problematize the exact deformation value for further research. Thus in this study, 17 SAR data from Sentinel-1 satellite is used in order to generate the multi-temporal interferometry utilize Stanford Method for Persistent Scatterers (StaMPS). Mean deformation map that has been compensated from error factors such as atmospheric, topographic, temporal, and baseline errors are generated. Okada model then applied to the mean deformation result to generate the modeled earthquake, resulting the deformation is mostly dominated by strike-slip with 3 meter deformation as right lateral strike-slip. The Okada sources are having 11.63 km in length, 2.45 km in width, and 5.46 km in depth with the dip angle are about 84.47° and strike angle are about 142.88° from the north direction. The results from this modeling can be used as learning material to understand the seismic activity in the latest 2020 Petrinja, Croatia Earthquake.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.814-822
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• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• Smart Structures and Systems
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• v.31 no.5
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• pp.501-516
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• 2023

Dynamic irregularity and acceleration of bridges subjected to high-speed trains provide crucial information for comprehensive evaluation of the health state of under-track structures. This paper proposes a novel approach for real-time estimation of vertical track dynamic irregularity and bridge acceleration using deep generative adversarial network (GAN) and vibration data from in-service train. The vehicle-body and bogie acceleration responses are correlated with the two target variables by modeling train-bridge interaction (TBI) through least squares generative adversarial network (LSGAN). To realize supervised learning required in the present task, the conventional LSGAN is modified by implementing new loss function and linear activation function. The proposed approach can offer pointwise and accurate estimates of track dynamic irregularity and bridge acceleration, allowing frequent inspection of high-speed railway (HSR) bridges in an economical way. Thanks to its applicability in scenarios of high noise level and critical resonance condition, the proposed approach has a promising prospect in engineering applications.

• Steel and Composite Structures
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• v.42 no.5
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• pp.685-697
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• 2022

In this paper, a vibration-based method using the change ratios of modal data and the experience-based learning algorithm is presented for quantifying the position, size, and interface layer of delamination in laminated composites. Three types of objective functions are examined and compared, including the ones using frequency changes only, mode shape changes only, and their combination. A fine three-dimensional FE model with constraint equations is utilized to extract modal data. A series of numerical experiments is carried out on an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam for investigating the influence of the objective function, the number of modal data, the noise level, and the optimization algorithms. Numerical results confirm that the frequency-and-mode-shape-changes-based technique yields excellent results in all the three delamination variables of the composites and the addition of mode shape information greatly improves the accuracy of interface layer prediction. Moreover, the EBL outperforms the other three state-of-the-art optimization algorithms for vibration-based delamination detection of composites. A laboratory test on six CFRP beams validates the frequency-and-mode-shape-changes-based technique and confirms again its superiority for delamination detection of composites.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Geophysics and Geophysical Exploration
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• v.20 no.1
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• pp.33-42
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• 2017

Recently, the grounded electrical-source airborne transient electromagnetic (GREATEM) system with high power source was introduced to achieve deeper investigation depth and to overcome high noise level. Although the GREATEM is a transient electromagnetic system using a long grounded wire as the transmitter, GREATEM data have been interpreted with 1D earth models because 2D or 3D modeling and inversion of vast airborne data are complicated and expensive to calculate. Generally, 1D inversion is subsequently applied to every survey point and combining 1D images together forms the stitched conductivity-depth image. However, the stitched models often result in abrupt variations in neighboring models. To overcome this problem, laterally constrained inversion (LCI) has been developed in inversion of ATEM data, which can yield layered sections with lateral smooth transitions. In this study, we analysed the GREATEM data through 1D numerical modeling for a curved grounded wire source. Furthermore, we developed a laterally constrained inversion scheme for continuous GREATEM data based on a layered earth model. All 1D data sets and models are inverted as one system, producing layered sections with lateral smooth transitions. Applying the developed LCI technique to the GREATEM data, it was confirmed that the laterally constrained inversion can provide laterally smooth model sections that reflect the layering of the survey area effectively.