• Advances in aircraft and spacecraft science
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• v.9 no.2
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• pp.95-102
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• 2022

The paper investigates the process of pulsation of a spherical cavity (bubble) in a liquid under the influence of a source of ultrasonic vibrations. The process of pulsation of a cavitation pocket in liquid is investigated. The Kirkwood-Bethe model was used to describe the motion. A numerical solution algorithm based on the Runge-Kutta-Felberg method of 4-5th order with adaptive selection of the integration step has been developed and implemented. It was revealed that if the initial bubble radius exceeds a certain value, then the bubble will perform several pulsations until the moment of collapse. The same applies to the case of exceeding the amplitude of ultrasonic vibrations of a certain value. The proposed algorithm makes it possible to fully describe the process of cavitation pulsations, to carry out comprehensive parametric studies and to evaluate the influence of various process parameters on the intensity of cavitation.

• Journal of Space Technology and Applications
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• v.3 no.1
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• pp.1-25
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• 2023

In this paper, I briefly introduce recently terminated, current, and future scientific spacecraft missions for in situ and remote-sensing observations of Earth's and other planetary magnetospheres as of February 2023. The spacecraft introduced here are Geotail, Cluster, Time History of Events and Macroscale Interactions during Substorms / Acceleration, Reconnection, Turbulence, and Electrodynamics of the Moon's Interaction with the Sun (THEMIS / ARTEMIS), Magnetospheric Multiscale (MMS), Exploration of energization and Radiation in Geospace (ERG), Cusp Plasma Imaging Detector (CuPID), and EQUilibriUm Lunar-Earth point 6U Spacecraft (EQUULEUS) for recently terminated or currently operated missions for Earth's magnetosphere; Lunar Environment Heliospheric X-ray Imager (LEXI), Gateway, Solar wind Magneto-sphere Ionosphere Link Explorer (SMILE), HelioSwarm, Solar-Terrestrial Observer for the Response of the Magnetosphere (STORM), Geostationary Transfer Orbit Satellite (GTOSat), GEOspace X-ray imager (GEO-X), Plasma Observatory, Magnetospheric Constellation (MagCon), self-Adaptive Magnetic reconnection Explorer (AME), and COnstellation of Radiation BElt Survey (CORBES) approved for launch or proposed for future missions for Earth's magnetosphere; BepiColombo for Mercury and Juno for Jupiter for current missions for planetary magnetospheres; Jupiter Icy Moons Explorer (JUICE) and Europa Clipper for Jupiter, Uranus Orbiter and Probe (UOP) for Uranus, and Neptune Odyssey for Neptune approved for launch or proposed for future missions for planetary magnetospheres. I discuss the recent trend and future direction of spacecraft missions as well as remaining challenges in magnetospheric research. I hope this paper will be a handy guide to the current status and trend of magnetospheric missions.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.237-240
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• 2020

The fine-tuned neural network (NN) model for a whole temporal portion in a video does not always yield the best quality (e.g., PSNR) performance over all regions of each frame in the temporal period. For certain regions (usually homogeneous regions) in a frame for super-resolution (SR), even a simple bicubic interpolation method may yield better PSNR performance than the fine-tuned NN model. When there are multiple NN models available at the receivers where each NN model is trained for a group of images having a specific category of image characteristics, the performance of Quality enhancement can be improved by selectively applying an appropriate NN model for each image region according to its image characteristic category to which the NN model was dedicatedly trained. In this case, it is necessary to signal which NN model is applied for each region. This is very advantageous for image restoration and quality enhancement (IRQE) applications at user terminals with limited computing capabilities.

• Smart Structures and Systems
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• v.31 no.1
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• pp.69-78
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• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2
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• pp.69-74
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• 2007

In signal processing applications with highly correlated input signals, subband affine projection algorithm and step size controlling is a good solution for improving the slow convergence rate and large computational complexity of LMS-type algorithms. This paper proposes a subband affine projection algorithm using a variable step size. The proposed method achieves fast convergence rate and small steady-state error with a small computational complexity by combining the SAP and step size controlling in a subband structure. Experimental results on highly correlated input signal show that the proposed method is superior to the conventional methods.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.149-163
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• 2024

As conversational AI systems such as ChatGPT have become more advanced, researchers are exploring ways to use them in education. However, we need effective ways to evaluate these systems before allowing them to help teach students. This study proposes a detailed framework for testing conversational AI across three important criteria as follow. First, specialized benchmarks that measure skills include giving clear explanations, adapting to context during long dialogues, and maintaining a consistent teaching personality. Second, adaptive standards check whether the systems meet the ethical requirements of privacy, fairness, and transparency. These standards are regularly updated to match societal expectations. Lastly, evaluations were conducted from three perspectives: technical accuracy on test datasets, performance during simulations with groups of virtual students, and feedback from real students and teachers using the system. This framework provides a robust methodology for identifying strengths and weaknesses of conversational AI before its deployment in schools. It emphasizes assessments tailored to the critical qualities of dialogic intelligence, user-centric metrics capturing real-world impact, and ethical alignment through participatory design. Responsible innovation by AI assistants requires evidence that they can enhance accessible, engaging, and personalized education without disrupting teaching effectiveness or student agency.

• BMB Reports
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• v.57 no.1
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• pp.40-49
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• 2024

Prokaryotes encode clustered regularly interspaced short palindromic repeat (CRISPR) arrays and CRISPR-associated (Cas) genes as an adaptive immune machinery. CRISPR-Cas systems effectively protect hosts from the invasion of foreign enemies, such as bacteriophages and plasmids. During a process called 'adaptation', non-self-nucleic acid fragments are acquired as spacers between repeats in the host CRISPR array, to establish immunological memory. The highly conserved Cas1-Cas2 complexes function as molecular recorders to integrate spacers in a time course manner, which can subsequently be expressed as crRNAs complexed with Cas effector proteins for the RNA-guided interference pathways. In some of the RNA-targeting type III systems, Cas1 proteins are fused with reverse transcriptase (RT), indicating that RT-Cas1-Cas2 complexes can acquire RNA transcripts for spacer acquisition. In this review, we summarize current studies that focus on the molecular structure and function of the RT-fused Cas1-Cas2 integrase, and its potential applications as a directional RNA-recording tool in cells. Furthermore, we highlight outstanding questions for RT-Cas1-Cas2 studies and future directions for RNA-recording CRISPR technologies.