• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.896-905
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• 2021

In the field of SIEM(Security information and event management), many studies try to use a feedback system to solve lack of completeness of training data and false positives of new attack events that occur in the actual operation. However, the current feedback system requires too much human inputs to improve the running model and even so, those feedback from inexperienced analysts can affect the model performance negatively. Therefore, we propose "active model improving feedback technology" to solve the shortage of security analyst manpower, increasing false positive rates and degrading model performance. First, we cluster similar predicted events during the operation, calculate feedback priorities for those clusters and select and provide representative events from those highly prioritized clusters using XAI (eXplainable AI)-based event visualization. Once these events are feedbacked, we exclude less analogous events and then propagate the feedback throughout the clusters. Finally, these events are incrementally trained by an existing model. To verify the effectiveness of our proposal, we compared three distinct scenarios using PKDD2007 and CSIC2012. As a result, our proposal confirmed a 30% higher performance in all indicators compared to that of the model with no feedback and the current feedback system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.9
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• pp.722-730
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• 2018

This paper presents an analysis technique for estimating the properties of a target's power-spill patterns observed in reconstructed SAR images, which in turn depend on the setup squint angle of the FMCW signal-based SAR system. The target responses observed in the reconstructed SAR images were affected by the range-direction and azimuth-direction of a wave projected on the ground, and the obtained results were analyzed by applying three-dimensional squinted SAR geometry. Furthermore, the rotation patterns were verified through simulations based on the FMCW signal model and back-projection algorithm. This paper summarizes the obtained evaluation results as a function of SAR geometry and squint angle.

• Composites Research
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• v.24 no.1
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• pp.37-44
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• 2011

In this paper, Particle Swarm Optimization(PSO) is applied to the design of the Frequency Selective Surface(FSS) and residual stresses of hybrid radome is predicted. An equivalent circuit model with Square Loops arrays was derived and then PSO was applied for acquiring the optimized geometrical parameters with proper resonant frequency. Residual stresses occur in the FSS embedded composite structures after cocuring and have a great influence on the strength of the FSS embedded composite structures. They also effect transmission quality because of delamination. Therefore, the thermal residual stresses of FSS embedded composite structures were analyzed using finite element analysis with considering the effects of FSS pattern, and composite stacking sequence.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.364-370
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• 2020

Rocket noise generated from the exhaust plume produces the enormous acoustic loading, which adversely affects the integrity of the electronic components and payload (satellite) at liftoff. The prediction of rocket noise consists of two steps: the supersonic jet exhaust is simulated by a method of the Computational Fluid Dynamics (CFD), and an acoustic transport method, such as the Helmholtz-Kirchhoff integral, is applied to predict the noise field. One of the difficulties in the CFD step is to remove the boundary reflection artifacts from the finite computation boundary. In general, artificial damping, known as a sponge layer, is added nearby the boundary to attenuate these reflected waves but this layer demands a large computational area and an optimization procedure of related parameters. In this paper, a cost-efficient way to separate the reflected waves based on the two microphone method is firstly introduced and applied to the computation result of a laboratory-scale supersonic jet noise without sponge layers.

• Journal of Korean Tunnelling and Underground Space Association
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• v.6 no.1
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• pp.25-40
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• 2004

The supersonic shock wave generated by fully coupled explosion will change into subsonic shock wave, plastic wave, and elastic wave consecutively as the wave propagates through rock mass. While the estimation of the blast-induced peak pressure was the main aim of the companion paper, this paper will concentrate on the estimation of the rise time of blast-induced pressure. The rise time can be expressed as a function of explosive density, isentropic exponent, detonation velocity, exponential coefficient of the peak pressure attenuation, dynamic yield stress, plastic wave velocity, elastic wave velocity, rock density, Hugoniot parameters, etc. Parametric analysis was performed to pinpoint the most influential parameter that affects the rise time and it was found that rock properties are more sensitive than explosive properties. The probabilistic distribution of the rise time is evaluated by the Rosenblueth'S point estimate method from the probabilistic distributions of explosive properties and rock properties. Numerical analysis was performed to figure out the effect of rock properties and explosive properties on the uncertainty of blast-induced vibration. Uncertainty analysis showed that uncertainty of rock properties constitutes the main portion of blast-induced vibration uncertainty rather than that of explosive properties. Numerical analysis also showed that the loading rate, which is the ratio of the peak blasting pressure to the rise time, is the main influential factor on blast-induced vibration. The loading rate is again more influenced by rock properties than by explosive properties.

• The Journal of the Acoustical Society of Korea
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• v.37 no.5
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• pp.263-270
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• 2018

In most industry fields concerning external flow noise problems, the hybrid computational aeroacoustic techniques based on the FW-H (Ffowcs Williams and Hawkings) equation are widely used for its numerical efficiency. However, when the surface integral form of FW-H equation is used without volume quadrupole sources, it is known to generate significant non-physical noise in a certain case. Especially, in the case of a flow in which the tip vortex cavitation is formed in the distant downstream direction such as flow driven by an underwater propeller, the accuracy in noise prediction becomes poor unless it is not properly modelled. Therefore, in this study, the nonphysical acoustic waves caused by the surface integral form of FW-H equation is reduced by adding the quadrupole correction term. First, to verify the accuracy of the in-house code of FW-H equation, the noise by an axial fan used in the outdoor unit of air conditioner was calculated and compared with the results of ANSYS Fluent. In order to verify the effects of the quadrupole correction term, the noise prediction for isentropic vortex convection is performed and it is confirmed that the error is reduced by the quadrupole correction term. Finally, the noise prediction is performed for the flow field generated by the Clark-Y hydrofoil in underwater. It is confirmed that the error caused by the cavitation passing through the integral surface can be reduced by the quadrupole correction term.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.81-92
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• 2014

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.559-570
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• 2017

A partial least squares regression (PLSR) model was developed to map the internal soluble solids content (SSC) of apples using a ground-based hyperspectral scanner that could simultaneously acquire outdoor data and capture images of large quantities of apples. We evaluated the applicability of various preprocessing techniques to construct an optimal prediction model and calculated the optimal band through a variable importance in projection (VIP)score. From the 515 bands of hyperspectral images extracted at wavelengths of 360-1019 nm, 70 reflectance spectra of apples were extracted, and the SSC ($^{\circ}Brix$) was measured using a digital photometer. The optimal prediction model wasselected considering the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP) and coefficient of determination of prediction $r_p^2$. As a result, multiplicative scatter correction (MSC)-based preprocessing methods were better than others. For example, when a combination of MSC and standard normal variate (SNV) was used, RMSECV and RMSEP were the lowest at 0.8551 and 0.8561 and $r_c^2$ and $r_p^2$ were the highest at 0.8533 and 0.6546; wavelength ranges of 360-380, 546-690, 760, 915, 931-939, 942, 953, 971, 978, 981, 988, and 992-1019 nm were most influential for SSC determination. The PLSR model with the spectral value of the corresponding region confirmed that the RMSEP decreased to 0.6841 and $r_p^2$ increased to 0.7795 as compared to the values of the entire wavelength band. In this study, we confirmed the feasibility of using a hyperspectral scanner image obtained from outdoors for the SSC measurement of apples. These results indicate that the application of field data and sensors could possibly expand in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1742-1754
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• 1999

In this paper, we propose a rate control scheme, using histogram based rate-distortion (R-D) estimation, which produces a consistent picture quality between consecutive frames. The histogram based R-D estimation used in our rate control scheme offers a closed-form mathematical model that enable us to predict the bits and the distortion generated from an encoded frame at a given quantization parameter (QP) and vice versa. The most attractive feature of the R-D estimation is low complexity of computing the R-D data because its major operation is just to obtain a histogram or weighted histogram of DCT coefficients from an input picture. Furthermore, it is accurate enough to be applied to the practical video coding. Therefore, the proposed rate control scheme using this R-D estimation model is appropriate for the applications requiring low delay and low complexity, and controls the output bit-rate ad quality accurately. Our rate control scheme ensures that the video buffer do not underflow and overflow by satisfying the buffer constraint and, additionally, prevents quality difference between consecutive frames from exceeding certain level by adopting the distortion constraint. In addition, a consistent considering the maximum tolerance BER of the voice service. Also in Rician fading channel of K=6 and K=10, considering CLP=$10^{-3}$ as a criterion, it is observed that the performance improment of about 3.5 dB and 1.5 dB is obtained, respectively, in terms of $E_b$/$N_o$ by employing the concatenated FEC code with pilot symbols.