• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.59.2-59.2
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• 2019

Event Horizon Telescope (EHT) aims to resolve the innermost region to the super massive black hole (SMBH) with its extremely high angular resolution (~20-25 uas) and enhanced sensitivity (down to 1-10 mJy) in concert with the Atacama Large Millimeter/submillimeter Array (ALMA) at 1.3 mm wavelength. This has a great importance as the first observational probe of the black hole shadow which has been theoretically predicted as a ring-like emission affected by the general relativistic effect under a strong gravitational field of SMBH. During the 2017 April 5-11, four nights of EHT observing campaign were carried out towards its primary targets, M87 and $SgrA{\ast}$. To robustly ensure the data processing, independent pipelines for various radio data calibration softwares (e.g., AIPS, HOPS, CASA) have been developed and cross-compared each other. The EHT has also been developing newer interferometric imaging techniques (e.g., eht-imaging-library, SMILI, dynamical imaging), as well as using an established method (CLEAN). With these, the EHT has designed various strategies which will be adopted for convincing imaging results. In this talk, I review how the robustness of EHT data processing and imaging will be validated so that the results can be ensured against well known uncertainties or biases in the interferometric data calibration and imaging.

• Journal of Power Electronics
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• v.19 no.6
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• pp.1527-1535
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• 2019

Because of the shortcomings of the PID controllers and traditional drive systems of permanent magnet synchronous motors (PMSMs), a PMSM passivity-based control (PBC) drive system based on a quasi-Z source matrix converter (QZMC) is proposed in this paper. The traditional matrix converter is a buck converter with a maximum voltage transmission ratio of only 0.866, which limits the performance of the driven motor. Therefore, in this paper a quasi-Z source circuit is added to the input side of the two-stage matrix converter (TSMC) and its working principle has also been verified. In addition, the controller of the speed loop and current loop in the conventional vector control of a PMSM is a PID controller. The PID controller has the problem since its parameters are difficult to adjust and its anti-interference capability is limited. As a result, a port controlled dissipative Hamiltonian model (PCHD) of a PMSM is established. Thereafter a passivity-based controller based on the interconnection and damping assignment (IDA) of a QZMC-PMSM is designed, and the stability of the equilibrium point is theoretically verified. Simulation and experimental results show that the designed PBC control system of a PMSM based on a QZMC can make the PMSM run stably at the rated speed. In addition, the system has strong robustness, as well as good dynamic and static performances.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.177-187
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• 2021

The global economy has been very difficult due to the recent impact of COVID-19. Korea is also pushing for strong quarantine policies such as K- quarantine and social distancing, but the economy is hardly recovering. In particular, the economic situation began to change rapidly depending on the export and domestic market, the public's interest in the economy increased, and companies became more sensitive. In order to estimate this rapidly changing economic situation, major advanced countries have also developed models that can periodically monitor the economy at the government level. Through this, by periodically reporting the economic trends, the public and companies can be aware of the economic trends to some extent. This study analyzed the cases of weekly business trends in advanced countries and developed a model of weekly economic activity suitable for Korea. To verify this, indices closely related to the economy such as mobility, industrial activity, face-to-face consumption, and psychology were discovered and estimated. As a result of the study, the weekly economic activity index was judged to be very useful in capturing short-term real economic activity. In the future, in order to secure the robustness and stability of the index and to increase the reflection of reality, model improvement and parameter estimation should be performed regularly.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2627-2647
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• 2022

The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.571-578
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• 2022

Modularization in a high-rise building is different from a small building, as it is exposed to more lateral forces like wind and earthquakes. The integrity, robustness, and overall stability of the modules and their performance is based on the joining techniques and strong structural systems. High lateral stiff construction structures like concrete shear walls and frames, braced steel frames, and steel moment frames are used for the stability of high-rise modular buildings. Similarly, high-rise stick-built buildings have concrete cores and perimeter frames for lateral load strength and stiffness. Methods for general steel-concrete connections are available in many works of literature. However, there are few modular-related papers describing this connection system in modular buildings. This paper aims to review the various research and practice adopted for steel-to-concrete connections in construction and compare the methods between stick-built buildings and modular buildings. The literature review shows that the practice of steel module-to-concrete core connection in high-rise modular buildings is like outrigger beams-to-concrete core connection in stick-built framed buildings. This paper concludes that further studies are needed in developing proper guidelines for a steel module-to-concrete core connection system in high-rise modular buildings.

• FOCUS: LIFE SCIENCE
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• no.1
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• pp.6.1-6.9
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• 2024

The document is a white paper on Hydrophilic Interaction Liquid Chromatography (HILIC) analysis method development. HILIC is a type of chromatography that uses an organic/aqueous mobile phase and a polar stationary phase. In HILIC, water is a strong solvent, and unlike in Reversed Phase Liquid Chromatography (RPLC), increasing the proportion of water in the mobile phase reduces the retention time of the analyte. The paper discusses when to consider HILIC analysis methods, the advantages of HILIC, and the challenges often encountered due to the lack of understanding of HILIC mechanisms compared to RPLC. It also provides a systematic flowchart for intelligent solutions for HILIC analysis method development, which includes a three-step approach for chromatography analysis method development. The first step involves gathering as much information as possible about the analyte (e.g., pKa, log P, log D). The second step involves analyzing the sample under different pH conditions using three HILIC columns in either isocratic or gradient mode to identify the suitable column/pH combination for the analyte. The third step involves optimizing the separation by investigating other parameters such as temperature and ionic strength, and assessing the robustness of the method. The paper emphasizes that the selection of the appropriate stationary/mobile phase combination, based on the differences between the HILIC stationary phases and the mobile phase pH, can provide high selectivity in the analysis. This step-by-step approach can help users develop an efficient analysis method.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.

• Structural Engineering and Mechanics
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• v.91 no.2
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• pp.227-238
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• 2024

Sparse regularization methods have proven effective in addressing the ill-posed equations encountered in moving force identification (MFI). However, the complexity of vehicle loads is often ignored in existing studies aiming at enhancing MFI accuracy. To tackle this issue, a double 𝑙₁ regularization method is proposed for MFI based on a response spectrum-based weighted dictionary in this study. Firstly, the relationship between vehicle-induced responses and moving vehicle loads (MVL) is established. The structural responses are then expanded in the frequency domain to obtain the prior knowledge related to MVL and to further construct a response spectrum-based weighted dictionary for MFI with a higher accuracy. Secondly, with the utilization of this weighted dictionary, a double 𝑙₁ regularization framework is presented for identifying the static and dynamic components of MVL by the alternating direction method of multipliers (ADMM) method successively. To assess the performance of the proposed method, two different types of MVL, such as composed of trigonometric functions and driven from a 1/4 bridge-vehicle model, are adopted to conduct numerical simulations. Furthermore, a series of MFI experimental verifications are carried out in laboratory. The results shows that the proposed method's higher accuracy and strong robustness to noises compared with other traditional regularization methods.