• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1125-1128
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• 2004

사용자 고유번호와 패스워드 기반의 사용자 인증 매커니즘을 수행하는 네트워크 시스템 환경에서는 스니퍼 프로그램 등을 이용하여 불법 도청함으로써 쉽게 사용자의 패스워드를 알아낼 수 있다. 이러한 불법적인 도청에 의한 패스워드 노출 문제를 해결하는 방법으로 일회용 패스워드, Challenge-Response 인증 방식이 유용하게 사용되며, 클라이언트/서버 환경에서는 별도 동기가 필요 없는 시간을 이용한 일회용 패스워드 방식이 특히 유용하게 사용될 수 있다. 안전성은 Square root problem에 기초를 두고 있고, 프리 플레이 공격, 오프라인 사전적 공격 그리고 서버 등을 포함하여 지금까지 잘 알려진 공격(해킹)들에 대해서 안전성을 높이기 위한 OPI을 제안하고자한다. OPI는 패스워드를 생성하는데 특별한 키를 생성할 필요가 없다는 것이다. OPI는 승인된 자를 확인하는데 걸리는 시간이 적게 소요되며, 특출하다.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.241-247
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• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.261-267
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• 2023

A common approach to the problem of predicting student test scores is based on the student's previous educational history. In this study, high school transcripts of about two thousand candidates, who took the High-school Student Assessment (HSA) were collected. The data were estimated through building a regression model - Random Forest and optimizing the model's parameters based on Genetic Algorithm (GA) to predict the HSA scores. The RMSE (Root Mean Square Error) measure of the predictive models was used to evaluate the model's performance.

• Smart Structures and Systems
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• v.9 no.3
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• pp.231-251
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• 2012

The stochastic optimal control for a piezoelectric spherically symmetric shell subjected to stochastic boundary perturbations is constructed, analyzed and evaluated. The stochastic optimal control problem on the boundary stress output reduction of the piezoelectric shell subjected to stochastic boundary displacement perturbations is presented. The electric potential integral as a function of displacement is obtained to convert the differential equations for the piezoelectric shell with electrical and mechanical coupling into the equation only for displacement. The displacement transformation is constructed to convert the stochastic boundary conditions into homogeneous ones, and the transformed displacement is expanded in space to convert further the partial differential equation for displacement into ordinary differential equations by using the Galerkin method. Then the stochastic optimal control problem of the piezoelectric shell in partial differential equations is transformed into that of the multi-degree-of-freedom system. The optimal control law for electric potential is determined according to the stochastic dynamical programming principle. The frequency-response function matrix, power spectral density matrix and correlation function matrix of the controlled system response are derived based on the theory of random vibration. The expressions of mean-square stress, displacement and electric potential of the controlled piezoelectric shell are finally obtained to evaluate the control effectiveness. Numerical results are given to illustrate the high relative reduction in the root-mean-square boundary stress of the piezoelectric shell subjected to stochastic boundary displacement perturbations by the optimal electric potential control.

• Computers and Concrete
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• v.26 no.2
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• pp.107-114
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• 2020

In this study, a two dimensional model of receding contact problem has been analyzed using finite element method (FEM) based software ANSYS and ABAQUS. For this aim finite element modeling of elastic layer and two homogeneous, isotropic and symmetrical elastic quarter planes pressed by means of a rigid circular punch has been presented. Mass forces and friction are neglected in the solution. Since the problem is examined for the plane state, the thickness along the z-axis direction is taken as a unit. In order to check the accuracy of the present models, the obtained results are compared with the available results of the open literature as well as the results of two software are compared using Root Mean Square Error (RMSE) and good agreements are found. Numerical analyses are performed considering different values of the external load, rigid circular radius, quarter planes span length and material properties. The contact lengths and contact stresses of these values are examined, and their results are presented. Consequently, it is concluded that the considered non-dimensional quantities have noteworthy influence on the contact lengths and contact stress distributions, additionally if FEM analysis is used correctly, it can be an efficient alternative method to the analytical solutions that need time.

• Journal of Power Electronics
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• v.18 no.1
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• pp.45-55
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• 2018

It is usually difficult for dual-active-bridge (DAB) dc-dc converters to operate efficiently at light loads. This paper presents an in-depth analysis of a DAB with triple-phase-shift (TPS) control under the light load condition to overcome this problem. A kind of operating mode which is suitable for light load operation is analyzed in this paper. First, an analysis of the zero-voltage-switching (ZVS) constraints for the DAB converter has been carried out and a reasonable dead-band setting method has been proposed. Secondly, the basic operating characteristics of the converter are analyzed. Third, under the condition of satisfying the ZVS constraints, both the reactive power and the root mean square (RMS) value of the current are simultaneously minimized and a particle swarm optimization (PSO) algorithm is employed to analyze and solve this optimization problem. Lastly, both simulations and experiments are carried out to verify the effectiveness of the proposed method. The experimental results show that the converter can effectively achieve ZVS and improved efficiency.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3388-3400
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• 2023

Detailed analysis of the neutron pathway through matter inside the nuclear reactor core is exceedingly needed for safety and economic considerations. Due to the constant development of high-performance computing technologies, neutronics analysis using computer codes became more effective and efficient to perform sophisticated neutronics calculations. In this work, a commercial pressurized water reactor (PWR) presented by Virtual Environment for Reactor Applications (VERA) Core Physics Benchmark are modeled and simulated using a high-fidelity simulation of OpenMC code in terms of criticality and fuel pin power distribution. Various problems have been selected from VERA benchmark ranging from a simple two-dimension (2D) pin cell problem to a complex three dimension (3D) full core problem. The development of the code capabilities for reactor physics methods has been implemented to investigate the accuracy and performance of the OpenMC code against VERA SCALE codes. The results of OpenMC code exhibit excellent agreement with VERA results with maximum Root Mean Square Error (RMSE) values of less than 0.04% and 1.3% for the criticality eigenvalues and pin power distributions, respectively. This demonstrates the successful utilization of the OpenMC code as a simulation tool for a whole core analysis. Further works are undergoing on the accuracy of OpenMC simulations for the impact of different fuel types and burnup levels and the analysis of the transient behavior and coupled thermal hydraulic feedback.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2163-2173
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• 2024

This study introduces a transient analysis module developed for RAST-V and validates it using the Kalinin-3 benchmark problem. For the benchmark analysis, RAST-V standalone and STREAM/RAST-V calculations were performed. STREAM supplies the few-group constants and RAST-V conducts a 3D core simulation utilizing few-group cross-sectional data. To improve accuracy, the main solver was developed based on the advanced semi-analytic nodal method. To evaluate the computational capability of the transient analysis module in RAST-V, Kalinin-3 benchmark is employed. Kalinin-3 represents a coolant transient benchmark that offers experimental data during the deactivation of the Main Circulation Pumps. Consequently, the transient calculations reflected the changes in the reactor flow rate. Benchmark comprising steady-state and transient calculations. During the steady state, the STREAM/RAST-V combination demonstrated a 30 ppm root mean square difference from 0 to 128.50 EFPD. For the transient calculations, STREAM/RAST-V showed power differences within ±7 % over a range of 0-300 s. Axial offset differences were within ±3 %, and the RMS difference in radial power ranged within 2.596 % at both 0 and 300 s. Overall, this study effectively demonstrated the newly developed transient solver in RAST-V and validated it using the Kalinin-3 benchmark problem.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.