• Journal of Ocean Engineering and Technology
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• 제7권2호
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• pp.114-120
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• 1993

Responses of marine vehicles and ocean structures in a seaway can be predicted by applying the probabilistic approach. When we consider a linear system, the responses in a random seaway can be evaluated through spectral analysis in the frequency domain. But when we treat nonlinear system in irregular waves, it is necessary to get time history of waves. In the previous study we introduced one-directional waves (long crested waves)as wave environment and carried out calculations and experiments in the waves. But the real sea in which marine vehicles and structures are operated has multi-directional waves (short crested waves). It is important to get a simulated random sea and analyse dynamic problems in the sea. We need entire sample function or probabillty density function to infer statistical value of random process. However if the process are ergodic process, we can get statistical values by analysis of one sample function. In this paper, we developed the simulation technique of multi-directional waves and proved that the time history given by this method keep ergodic characteristics by the statistical analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제48권6호
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• pp.124-132
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• 2011

In a modern electronic warfare, radar signal environments become more denser and complex. Therefor the capability of reliable signal analysis techniques is required for ES(Electronic warfare Support) system to identify and analysis individual emitter signals from received signals. In this paper, we propose the new signal grouping algorithm to ensure the reliable signal analysis and to reduce the cost of the signal processing steps in the ES. The proposed grouping algorithm uses KDE(Kernel Density Estimator) and its CDF(Cumulative Distribution Function) to compose windows considering the statistical distribution characteristics based on the radar frequency modulation type. Simulation results show the good performance of the proposed technique in the signal grouping.

• Structural Engineering and Mechanics
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• 제45권6호
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• pp.759-777
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• 2013

In this paper, a topology optimization method based on the element independent nodal density (EIND) is developed for continuum solids with multiple load cases and multiple constraints. The optimization problem is formulated ad minimizing the volume subject to displacement constraints. Nodal densities of the finite element mesh are used a the design variable. The nodal densities are interpolated into any point in the design domain by the Shepard interpolation scheme and the Heaviside function. Without using additional constraints (such ad the filtering technique), mesh-independent, checkerboard-free, distinct optimal topology can be obtained. Adopting the rational approximation for material properties (RAMP), the topology optimization procedure is implemented using a solid isotropic material with penalization (SIMP) method and a dual programming optimization algorithm. The computational efficiency is greatly improved by multithread parallel computing with OpenMP to run parallel programs for the shared-memory model of parallel computation. Finally, several examples are presented to demonstrate the effectiveness of the developed techniques.

• Journal of the Korean Society of Clothing and Textiles
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• 제29권8호
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• pp.1102-1113
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• 2005

The propose of this study is to develop the optimal sizing system of ready-to-wear f3r elementary school girls using a newly invented statistical technique. The body measurements was classified by the method that equalizes the distribution of the subjects using the probability density function, to theoretically systemize a method to determine a size range of ready-to-wear for elementary school girls between 6 to 12 years old. The statistical method were 1) The total of 11 height groups, which size interval from one another is 6 cm that is an average height gap between each age. 2) In order to determine an approximate figure (m ${\times}$ n) to establish the appropriate sizes far each height group that fit to the combinations of bust and hip girth, which based on their means and standard deviations on the probability density curve to produce the standard normal distribution. 3) m and n were aligned by 4cm -the grading increments used for patterns making- and determined the size ranges by confirming the approximate figures of m and n. 4) The representative values were determined by an area ratio calculated by dividing the area determined from the range of bust and hip girth with the representative value. Considering the characteristics of subjects' distribution, the area ratios was used. 5) Weight was calculated by seeking a growth exponent for each age and multiplying it by the number of girls that fit to each size range. As sections that show the highest weight are more likely sought by the consumers, these sections were determined as the optimal size standards. 6) This optimal sizing system consists of sizes determined by the optimal size standards and its sizes are marked with height, bust and hip girth.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• 제8권4호
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• Journal of Internet Computing and Services
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• 제8권6호
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• pp.43-53
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• 2007

Intrusion detection system(IDS) has recently evolved while combining signature-based detection approach with anomaly detection approach. Although signature-based IDS tools have been commonly used by utilizing machine learning algorithms, they only detect network intrusions with already known patterns, Ideal IDS tools should always keep the signature database of your detection system up-to-date. The system needs to generate the signatures to detect new possible attacks while monitoring and analyzing incoming network data. In this paper, we propose a new outlier cluster detection algorithm with density (or influence) function, Our method assumes that an outlier is a kind of cluster with similar instances instead of a single object in the context of network intrusion, Through extensive experiments using KDD 1999 Cup Intrusion Detection dataset. we show that the proposed method outperform the conventional outlier detection method using Euclidean distance function, specially when attacks occurs frequently.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제21권1호
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• pp.161-174
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• 1996

Addressed in this paper is performance analysis of the maximum-likelihood code acquisition technique for slotted-mode preamble search in the CDMA reverse link. The probabilities of detection, miss, and false alarm are derived analytically for a multiple $H_{1}$ cell case in a frequency-selective Rayleigh fading channel, based on the statics of the CDMA noncoherent demodulator output. the probability density function of the decision variable consisting of successive demodulator outputs is also derived by considering the fading characteristics of the received signal for both single and dual antenna cases. The performance of the code acquisition technique is evaluated numerically with an emphasis on investigating the effects of post-detection integration, fading rate, and antenna diversity on the detection performance.

• Journal of Korean Society of Forest Science
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• 제102권4호
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• pp.477-483
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• 2013

The purpose of this study was to calculate uncertainty of emission factor collected data and to evaluate the applicability of Monte Carlo simulation technique. To estimate the distribution of emission factors (Such as Basic wood density, Biomass expansion factor, and Root-to-shoot ratio), four probability density functions (Normal, Lognormal, Gamma, and Weibull) were used. The two sample Kolmogorov-Smirnov test and cumulative density figure were used to compare the optimal probability density function. It was observed that the basic wood density showed the gamma distribution, the biomass expansion factor results the log-normal distribution, and root-shoot ratio showd the normal distribution for Pinus densiflora in the Gangwon region; the basic wood density was the normal distribution, the biomass expansion factor was the gamma distribution, and root-shoot ratio was the gamma distribution for Pinus densiflora in the central region, respectively. The uncertainty assessment of emission factor were upper 62.1%, lower -52.6% for Pinus densiflora in the Gangwon region and upper 43.9%, lower -34.5% for Pinus densiflora in the central region, respectively.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• Journal of the Korean Society of Radiology
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• 제16권1호
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• pp.53-59
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• 2022

DEXA, as the standard areal bone mineral density (aBMD) measurement method, often shows an insuficient correlation between aBMDs of the measured bones and referring bones and is inaccurate due to the mass effect. In contrast, quantitative computer tomography (QCT), as a volumetric BMD (vBMD) measurement method, is being advanced so that it uses less radiation before, owing to improved CT device and computer imaging technology. Because dual-energy CTs can modulate the image signals showing tumor or specific chemicals as well as the ability to measure vBMD, they are expanding their application. For pre-checking vBMD of surgeon-specific bone volume at implantation candidate sites, a finite element creation-based local vBMD measurement technique was developed. The local vBMD measurement function for surgeon-specific shape volumes will be added to clinical imaging systems.