• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.742-756
• /
• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• Journal of Communications and Networks
• /
• v.8 no.2
• /
• pp.220-227
• /
• 2006

In this paper, we present a novel approach to analyze the throughput of direct-sequence spread spectrum multiple access (DS/SSMA) unslotted ALOHA system. In the unslotted system, the departure rate of interfering transmissions is proportional to the number of current interferers that can be regarded as the system state. In order to model this state-dependency, we introduce a two-dimensional state transition model that describes the state transition of the system. This model provides a more rigorous analysis tool for the DS/SSMA unslotted ALOHA systems with both fixed and variable packet lengths. Numerical results reveal that this analysis yields an accurate system performance that coincides with the simulation results. Throughout the analysis we have discovered that the state-dependency of the departure rate causes interference averaging effect in the unslotted system and that this effect yields a higher throughput for the unslotted system than for the slotted system when supported by a strong channel coding.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.679-684
• /
• 1993

This article presents an approach to estimate the general 3D motion of a polyhedral object using multiple, sensory data some of which may not provide sufficient information for the estimation of object motion. Motion can be estimated continuously from each sensor through the analysis of the instantaneous state of an object. We have introduced a method based on Moore-Penrose pseudo-inverse theory to estimate the instantaneous state of an object. A linear feedback estimation algorithm is discussed to estimate the object 3D motion. Then, the motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown object. The techniques of multisensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision.

• Journal of KSNVE
• /
• v.6 no.4
• /
• pp.439-446
• /
• 1996

This paper introduces a newly designed pendulum system that enables the more accurate boservation of dynamic behaviour arising from both horizontal and vertical(i.e. two dimension) excitation. First, experiments were carried out to examine the frequency responses of the devised pendulum system. Interestingly, experimental results for the three excitation angles of 22, 32 and 48 degree show 'new' jump phenomena. For the further understanding of these phenomena, experimental investigationhas been made to identify the equation of motion of the pendulum system from experimental data. This attempt has revealed that the viscous, coulomb and aerodynamic damping factors are involved in the equation of motion. By applying the Ritz averaging method to the equation, it becomes apparent that the jumping phenomena of the pendulum system in this work is more theoretically understood.

• Proceedings of the KSME Conference
• /
• 2004.04a
• /
• pp.1857-1862
• /
• 2004

EK pumps packed with particles inside capillaries are involved in the mixed electroosmotic flow and pressure driven flow. For analysis in the porous EK pumps, the volume-averaging technique is applied to derive the volume-averaged equations for momentum and electrical potential. By using the volume-averaged equations, analytical solutions for electric potential and velocity distribution due to the mixed electroosmotic and pressure driven flows are obtained. The present analysis is validated by comparison with numerical and experimental results for the case of microchannel EK pumps.

• Journal of information and communication convergence engineering
• /
• v.19 no.3
• /
• pp.180-187
• /
• 2021

In this paper, we present a computational volumetric reconstruction method for three-dimensional (3D) photon counting imaging with enhanced visual quality when low-resolution elemental images are used under photon-starved conditions. In conventional photon counting imaging with low-resolution elemental images, it may be difficult to estimate the 3D scene correctly because of a lack of scene information. In addition, the reconstructed 3D images may be blurred because volumetric computational reconstruction has an averaging effect. In contrast, with our method, the pixels of the elemental image rearrangement technique and a Bayesian approach are used as the reconstruction and estimation methods, respectively. Therefore, our method can enhance the visual quality and estimation accuracy of the reconstructed 3D images because it does not have an averaging effect and uses prior information about the 3D scene. To validate our technique, we performed optical experiments and demonstrated the reconstruction results.

• Honam Mathematical Journal
• /
• v.44 no.3
• /
• pp.402-418
• /
• 2022

Noise is a fundamental factor to increased validity and regularity of spike propagation and neuronal firing in the nervous system. In this paper, we examine the stochastic version of the Izhikevich-FitzHugh neuron dynamical model. This approach is based on techniques presented by Luo and Guo, which provide a general framework for the bifurcation and stability analysis of two dimensional stochastic dynamical system as an Itô averaging diffusion system. By using largest lyapunov exponent, local and global stability of the stochastic system at the equilibrium point are investigated. We focus on the two kinds of stochastic bifurcations: the P-bifurcation and the D-bifurcations. By use of polar coordinate, Taylor expansion and stochastic averaging method, it is shown that there exists choices of diffusion and drift parameters such that these bifurcations occurs. Finally, numerical simulations in various viewpoints, including phase portrait, evolution in time and probability density, are presented to show the effects of the diffusion and drift coefficients that illustrate our theoretical results.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.14 no.3
• /
• pp.235-243
• /
• 2016

In the near future, many countries, including the Republic of Korea, will face a significant increase in low level radioactive waste (LLW) from nuclear power plant decommissioning. The purpose of this paper is to look at blending as a method for enhancing disposal options for low-level radioactive waste from the decommissioning of nuclear reactors. The 2007 U.S. Nuclear Regulatory Commission strategic assessment of the status of the U.S. LLW program identified the need to move to a risk-informed and performance-based regulatory approach for managing LLW. The strategic assessment identified blending waste of varying radionuclide concentrations as a potential means of enhancing options for LLW disposal. The NRC's position is that concentration averaging or blending can be performed in a way that does not diminish the overall safety of LLW disposal. The revised regulatory requirements for blending LLW are presented in the revised NRC Branch Technical Position for Concentration Averaging and Encapsulation (CA BTP 2015). The changes to the CA BTP that are the most significant for NPP operation, maintenance and decommissioning are reviewed in this paper and a potential application is identified for decommissioning waste in Korea. By far the largest volume of LLW from NPPs will come from decommissioning rather than operation. The large volumes in decommissioning present an opportunity for significant gains in disposal efficiency from blending and concentration averaging. The application of concentration averaging waste from a reactor bio-shield is also presented.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.13 no.2
• /
• pp.72-77
• /
• 1988

Subjective scoring by different groups on different status of morbidity are compared to objective data obtained from legal awards. A power law is tested between subjective and objective scores. Regression analysis by means of a power function provides a measure of consistency in its regression coefficient. Power functions fitting also leads to a justified use of geometric averaging of individual scores into group scores.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.4
• /
• pp.205-211
• /
• 2014

This paper reports an efficient algorithm for accurate rolling shutter distortion removal. A hierarchical global motion estimation approach for a group of blocks reduces the level of computation by three orders of magnitude. In addition, the motion of each scanline is determined accurately by averaging two candidates obtained through cubic spline interpolation. The experimental results show that the proposed method produces accurate motion information with significant computation reduction and corrects the rolling shutter distortion effectively.