• Journal of Power Electronics
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• v.19 no.4
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• pp.1000-1010
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• 2019

The use of internal-model-based linear controller, such as resonant controller, is a well-established technique for the current control of grid-connected systems. Attractive properties for resonant controllers include their two-sequence tracking ability, the simple control structure, and the reduced computational burden. However, in the case of continuous-designed resonant controller, the transient performance is inevitably degraded at a low switching frequency. Moreover, available design methods for resonant controller is not able to realize the direct design of transient performances, and the anticipated transient performance is mainly achieved through trial and error. To address these problems, the zero-order-hold (ZOH) characteristic and inherent time delay in digital control systems are considered comprehensively in the design, and a corresponding hold-equivalent discrete model of the grid-connected converter is then established. The relationship between the placement of closed-loop poles and the corresponding transient performance is comprehensively investigated to realize the direct mapping relationship between the control gain and the transient response time. For the benefit of automatic tuning and real-time adaption, analytical expressions for controller gains are derived in detail using the required transient response time and system parameters. Simulation and experimental results demonstrate the validity of the proposed method.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.327-332
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• 2022

This experiment presents a precise analysis method using a mass spectrometer in the biopharmaceutical market, where utility is expanding. Among various techniques for analyzing the protein drug, somatotropin, the peptide fragments through biochemical sample preparation was analyzed by LC-MS/MS characterization. The analysis process was performed by separation analysis using nanoUPLC and MS/MS analysis using Orbitrap. In the case of somatotropin with 21 tryptic peptides, 13 of them were consistent with theoretical predictions within an average of 1 ppm error.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.769-779
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• 2023

This paper describes an hp-angular adaptivity algorithm in the discrete ordinates method for Boltzmann transport applications with strong angular effects. This adaptivity uses discontinuous finite element quadrature sets with different degrees, which updates both angular mesh and the degree of the underlying discontinuous finite element basis functions, allowing different angular local refinement to be applied in space. The regular and goal-based error metrics are considered in this algorithm to locate some regions to be refined. A mapping algorithm derived by moment conservation is developed to pass the angular solution between spatial regions with different quadrature sets. The proposed method is applied to some test problems that demonstrate the ability of this hp-angular adaptivity to resolve complex fluxes with relatively few angular unknowns. Results illustrate that a reduction to approximately 1/50 in quadrature ordinates for a given accuracy compared with uniform angular discretization. This method therefore offers a highly efficient angular adaptivity for investigating difficult particle transport problems.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.29-41
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• 2023

The 3D geospatial modeling of geotechnical information can aid in understanding the geotechnical characteristic values of the continuous subsurface at construction sites. In this study, a geostatistical optimization model for the three-dimensional (3D) mapping of subsurface stratification and the SPT-N value based on a trial-and-error rule was developed and applied to a dam emergency spillway site in South Korea. Geospatial database development for a geotechnical investigation, reconstitution of the target grid volume, and detection of outliers in the borehole dataset were implemented prior to the 3D modeling. For the site-specific subsurface stratification of the engineering geo-layer, we developed an integration method for the borehole and geophysical survey datasets based on the geostatistical optimization procedure of ordinary kriging and sequential Gaussian simulation (SGS) by comparing their cross-validation-based prediction residuals. We also developed an optimization technique based on SGS for estimating the 3D geometry of the SPT-N value. This method involves quantitatively testing the reliability of SGS and selecting the realizations with a high estimation accuracy. Boring tests were performed for validation, and the proposed method yielded more accurate prediction results and reproduced the spatial distribution of geotechnical information more effectively than the conventional geostatistical approach.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.317-322
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• 2019

The purpose of this study was to develop a self-management program for patients suffering from coronary artery disease (CAD), based on the self-determination theory and subsequently perform a heuristic evaluation by professionals and a quality assessment by users. The program consisted of 6 main menus and 20 submenus. Heuristic evaluation was conducted using eight principles, and as a result, a score of 1 was assigned by a professional for five principles: consistency and mapping, good ergonomics and minimalist design, flexibility and efficiency, anesthetics, and error management. Two professionals gave the principles of ease of input, screen readability, and glanceability a score of 1. In the quality assessment by the users, the system quality category had the highest score of 4.6 out of 5, and information quality had the lowest score of 3.87 out of 5. The overall average score was 4.08, which indicated the general satisfaction regarding the quality of the application. We have reflected on all the recommendations provided by the professionals based on their heuristic evaluation and incorporated them in the program.

• ETRI Journal
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• v.46 no.2
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• pp.227-237
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• 2024

This study proposes a novel, conditionally applied neural network technique to reduce the overall peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) system while maintaining an acceptable bit error rate (BER) level. The main purpose of the proposed scheme is to adjust only those subcarriers whose peaks exceed a given threshold. In this respect, the developed C-ANN algorithm suppresses only the peaks of the targeted subcarriers by slightly shifting the locations of their corresponding frequency samples without affecting their phase orientations. In turn, this achieves a reasonable system performance by sustaining a tolerable BER. For practical reasons and to cover a wide range of application scenarios, the threshold for the subcarrier peaks was chosen to be proportional to the saturation level of the nonlinear power amplifier used to pass the generated OFDM blocks. Consequently, the optimal values of the factor controlling the peak threshold were obtained that satisfy both reasonable PAPR reduction and acceptable BER levels. Furthermore, the proposed system does not require a recovery process at the receiver, thus making the computational process less complex. The simulation results show that the proposed system model performed satisfactorily, attaining both low PAPR and BER for specific application settings using comparatively fewer computations.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.395-418
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• 2024

The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (R_e), maximum fault distance (R_f), or maximum hypocentral distance (R_h), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (R_e, R_f, or R_h) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.65-72
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• 2010

In this study, the performances of a medium grade IMU which is aimed for Mobile Mapping System and a low grade IMU for pedestrian navigation are analyzed through simulations under GPS signal blockage. In addition, an analysis on the accuracy improvement of barometer, electronic compass, or multi-sensor(combination of barometer and electronic compass) to correct medium grade or low grade IMU errors in the situation of GPS signal blockage is performed. With the medium grade IMU, the three dimensional positioning error from INS exceeds the demanded accuracy of 5m when the block time is over 30 seconds. When we correct IMU with barometer, compass, or multi-sensor, however, the demanded accuracy is maintained up to 60 seconds. In addition, barometer is more effective than the electronic compass when they are combined. In case of low grade IMU like MEMS IMU, the three dimensional positioning error from INS exceeds the demanded accuracy of 20m when the block time is over 15 seconds. When we correct INS with barometer, compass, or multi-sensor, however, the demanded accuracy is maintained up to 15 seconds in simulation results. On the contrary to medium grade IMU, electronic compass is more effective than the barometer in case of low velocity such as pedestrian navigation. It is expected that the analysis suggested a method to decrease position or attitude error using aided sensor integration when MMS or pedestrian navigation is operated under 1he environment of GPS signal blockage.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.525-533
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• 2019

There are two feature collection methods in digital mapping using the UAV (Unmanned Aerial Vehicle) Photogrammetry: vectorization and stereo plotting. In vectorization, planar information is extracted from orthomosaics and elevation value obtained from a DSM (Digital Surface Model) or a DEM (Digital Elevation Model). However, the exact determination of the positional accuracy of 3D features such as ground facilities and buildings is very ambiguous, because the accuracy of vectorizing results has been mainly analyzed using only check points placed on the ground. Thus, this study aims to review the possibility of 3D spatial information acquisition and digital map production of vectorization by analyzing the corner point coordinates of different layers as well as check points. To this end, images were taken by a Phantom 4 (DJI) with 3.6 cm of GSD (Ground Sample Distance) at altitude of 90 m. The outcomes indicate that the horizontal RMSE (Root Mean Square Error) of vectorization method is 0.045 cm, which was calculated from residuals at check point compared with those of the field survey results. It is therefore possible to produce a digital topographic (plane) map of 1:1,000 scale using ortho images. On the other hand, the three-dimensional accuracy of vectorization was 0.068~0.162 m in horizontal and 0.090~1.840 m in vertical RMSE. It is thus difficult to obtain 3D spatial information and 1:1,000 digital map production by using vectorization due to a large error in elevation.