• Earthquakes and Structures
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• v.21 no.6
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• pp.577-585
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• 2021

In this research, a neural network (NN) method was developed, which combines H-infinity and fuzzy control for the purpose of stabilization and stability analysis of nonlinear systems. The H-infinity criterion is derived from the Lyapunov fuzzy method, and it is defined as a fuzzy combination of quadratic Lyapunov functions. Based on the stability criterion, the nonlinear system is guaranteed to be stable, so it is transformed to be a linear matrix inequality (LMI) problem. Since the demo active vibration control system to the tuning of the algorithm sequence developed a controller in a manner, it could effectively improve the control performance, by reducing the wind's excitation configuration in response to increase in the cost efficiency, and the control actuator.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.83-90
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• 2020

A protocol HMNR, was proposed to utilize control information of every other process piggybacked on each sent message for minimizing the number of forced checkpoints. Then, an improved protocol, called Lazy-HMNR, was presented to lower the possibility of taking forced checkpoints incurred by the asymmetry between checkpointing frequencies of processes. Despite these two different minimization techniques, if the high message interaction traffic occurs, Lazy-HMNR may considerably lower the probability of knowing whether there occurs no Z-cycle due to its shortcomings. Also, we recognize that no previous work has smart procedures to be able to utilize network infrastructures for highly decreasing the number of forced checkpoints with dependency information carried on every application message. We introduce a novel Lazy-HMNR protocol for group communication-based distributed computing systems to cut back the number of forced checkpoints in a more effective manner. Our simulation outcomes showed that the proposed protocol may highly lessen the frequency of forced checkpoints by comparison to Lazy-HMNR.

• Smart Structures and Systems
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• v.29 no.5
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• pp.693-703
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• 2022

This study investigates an effective approach to stabilize nonlinear systems. To ensure the asymptotic nonlinear stability in nonlinear discrete-time systems, the present study presents controller for an EBA (Evolved Bat Algorithm) NN (fuzzy neural network) in the algorithm. In fuzzy evolved NN modeling, the auxiliary circuit with high frequency LDI (linear differential inclusions) and NN model representation is developed for the nonlinear arbitrary dynamics. An example is utilized to demonstrate the system more robust compared with traditional control systems.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.3-78.3
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• 2019

We develop a novel technique that can constrain the evolutionary track of the epoch of reionization (EoR) by applying the convolutional neural network (CNN) to the 21-cm differential brightness temperature. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm map between z=6-13. We design a CNN architecture that predicts the volume-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction has a good agreement with its truth value even after smoothing the 21-cm map with somewhat realistic choices of beam size and the frequency bandwidth of the Square Kilometre Array (SKA). Our technique could be further utilized to denoise the 21-cm map or constrain the properties of the radiation sources.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.66.2-66.2
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• 2020

We propose a deep learning analysis technique with a convolutional neural network (CNN) to predict the evolutionary track of the Epoch of Reionization (EoR) from the 21-cm differential brightness temperature tomography images. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm maps between z = 6 ~ 13. We then apply two observational effects, such as instrumental noise and limit of (spatial and depth) resolution somewhat suitable for realistic choices of the Square Kilometre Array (SKA), into the 21-cm maps. We design our deep learning model with CNN to predict the sliced-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction from our CNN model has great agreement with the true value even after coarsely smoothing with broad beam size and frequency bandwidth and heavily covered by noise with narrow beam size and frequency bandwidth. Our results show that the deep learning analyzing method has the potential to reconstruct the EoR history efficiently from the 21-cm tomography surveys in future.

• International Journal of Concrete Structures and Materials
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• v.7 no.3
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• pp.225-238
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• 2013

In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of $SiO_2$ particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.63-74
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• 2022

Repressive measures in the Crimea against the Ukrainian media and the ban on the entry of international and Ukrainian monitoring missions created the conditions for the function of providing information to be performed by representatives of civil society. Such a phenomenon was called Crimean citizen journalism and became a post-occupation phenomenon characteristic of the Crimean information sphere. The journalists' activities are aimed at reporting on human rights violations and repression against Ukrainian citizens who find themselves in conditions of information bans and restrictions. Crimean citizen journalism, which connects the peninsula with the mainland of Ukraine, is monothematic in nature, and its emergence has become a form of nonviolent resistance to the occupation of Crimea. The purpose of the study is to cover the characteristic features, the development of common Crimean citizen journalistic movement features as a social phenomenon, a phenomenon that arose after the occupation through the identification of a modern journalist portrait. The study uses the general scientific method of empirical research as the main one, the sociological method of a questionnaire survey, as well as the methods of classification, generalisation, observation, statistical calculation. An analysis of a survey of Crimean citizen journalists demonstrates the existence of an active, mobile community in Crimea that seeks to provide information and human rights nonviolent resistance to the occupation.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2334-2340
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• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9B
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• pp.832-844
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• 2008

Distributed localization algorithms are necessary for large-scale wireless sensor network applications. In this paper, we introduce an efficient node distribution based localization algorithm that emphasizes simple refinement and low system load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighbor nodes for sensors, update its position estimate by minimizing a local cost function and then passes this update to the neighbor nodes. The update process considers a distribution of nodes for large-scale networks which have same density in a unit area for optimizing the system performance. Neighbor nodes are selected within a range which provides the smallest received signal strength error based on the real experiments. MATLAB simulation showed that the proposed algorithm is more accurate than trilateration and les complex than multidimensional scaling. The implementation on MicaZ using TinyOS-2.x confirmed the practicality of the proposed algorithm.

• Journal of the Korean Magnetics Society
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• v.17 no.2
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• pp.55-59
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• 2007

Co-based amorphous magnetic wire with a diameter of $125{\mu}m$ and a length of 40 mm was used as an inner conductor of a coaxial cable to construct a magnetic sensor. Sensor characteristics was measured up to 3 GHz with applied up to 60 Oe by using network analyzer. Frequency dependence of impedance for this sensor was very close to the impedance resonant pattern of transmission line and 250 MHz was obtained as a 1/4 wavelength without external magnetic field. Large impedance change was measured in the magnetic field range between 0 Oe and 1 Oe, which was influenced by permeability change of magnetic amorphous wire. Because ${\Delta}Z/{\Delta}H$ value of $300{\Omega}/Oe$ was obtained at 0.1 Oe, this coaxial cable with amorphous wire can be useful as a magnetic sensor.