• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1600-1604
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• 2017

A new Complex Image Method based on Genetic Algorithm (GA) is proposed to calculate the Green's functions of CPW (coplanar waveguide)-type microwave components and antennas. The closed-forms of the spectral-domain integrals are obtained by the GA, avoiding the conventional procedures of the tedious linear algebra and the sampling conditions sensitive to the complex-variable sampling paths adopted in the Prony's and GPOF methods. The proposed method is compared with the numerical Sommerfeld Integral, which results in good agreement.

• Bulletin of the Korean Chemical Society
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• v.33 no.11
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• pp.3597-3600
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• 2012

Thrombin binding aptamter (TBA-15) is a 15-mer guanine-rich oligonucleotide. This DNA apamer specifically binds to the thrombin protein involved in blood coagulation. Using extensive umbrella sampling molecular dynamics simulation method at all atom level, we investigated the potential of mean force (PMF) upon pulling the DNA aptamer from the binding mode of aptamer/thrombin complex. From this calculation, the free energy cost for a full dissociation of this aptamer/protein complex is 17 kcal/mol, indicating a substantial binding affinity of TBA-15. Interestingly, this PMF reveals noticeable plateau regions along the pulling coordinate. Possible structural changes of this complex in the plateau were investigated in details.

• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.166-171
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• 2010

In this paper, the front-end of a digital receiver with a 25 kHz carrier frequency, 5 kHz symbol rate, and any excess-bandwidth is designed using two basic facts. The first is known as the uniform sampling theorem, which states that the sampled sequence might not suffer from aliasing even if its sampling rate is lower than the Nyquist sampling rate if the analog signal is a bandpass one. The other fact is that if the sampling rate is 4 times the center frequency of the sampled sequence, the front-end processing complexity can be dramatically reduced due to the half of the sampled sequence to be multiplied by zero in the demixing process. Furthermore, the designed front-end is simplified by introducing sub-filters and sub-sampling sequences. The designed front-end is composed of an A/D converter, which takes samples of a bandpass filtered signal at a 20 kHz rate; a serial-to-parallel converter, which converts a sampled bandpass sequence to 4 parallel sub-sample sequences; 4 sub-filter blocks, which act as a frequency shifter and lowpass filter for a complex sequence; 4 synchronized switches; and 2 adders. The designed front-end dramatically reduces the computational complexity by more than 50% for frequency shifting and lowpass filtering operations since a conventional front-end requires a frequency shifting and two lowpass filtering operations to get one lowpass complex sample, while the proposed front-end requires only four filtering operation to get four lowpass complex samples, which is equivalent to one filtering operation for one sample.

• Journal of Environmental Health Sciences
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• v.42 no.3
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• pp.224-233
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• 2016

Objectives: The purpose of this study was to evaluate the relationship between residential surroundings, such as a power plant, steel mill and petrochemical facilities, and urinary arsenic concentrations in Chungcheongnam-do Province, Korea. Methods: Stratified by fish consumption and residential district, median and maximum block sampling was applied. A total of 346 spot urine samples were speciated for $As^{5+}$, $As^{3+}$, monomethylarsonic acid(MMA), dimethylarsonic acid (DMA) and arsenobetaine (AsB). Exposure assessment was based on questionnaires including data on sex, age, current tobacco use, fish consumption, type of water consumed, and occupational category. Results: Urinary $As^{5+}+As^{3+}+MMA+DMA$ concentrations of people living in the vicinity of a power plant ($GM=50.39{\mu}g/g$) were 61% higher than those of people living in the inland area according to median block sampling. Urinary $As^{5+}+As^{3+}+MMA+DMA+AsB$ concentrations of people living in the vicinity of industrial complex area were higher than those of people living in the inland area according to block sampling by median and maximum. Conclusion: Urinary arsenic concentrations of people living in vulnerable areas such as around industrial complexes, especially power plants, were higher than those of people living in an inland area.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.233-239
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• 2022

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

• Journal of Integrative Natural Science
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• v.5 no.3
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• pp.157-162
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• 2012

Measurement error is one of main source of error in survey. It is generally defined as the difference between an observed value and an underlying true value. An observed value with error may be expressed as a function of the true value plus error term. In some cases, the measurement error variance may be also a function of the unknown true value. The error variance function can be rewritten as a function of true value multiplied by a scale factor. This research explore methods for estimation of the measurement error variance based on the data from complex sampling design. We consider the case in which the variance of mesurement error is a linear function of unknown true value, and the error variance scale factor is small. We applied our results to the U.S. Third National Health and Nutrition Examination Survey (the U.S. NHANES III) data for empirical analyses, which has replicate measurements for relatively small subset of initial respondents's group.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.2
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• pp.147-157
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• 2007

This study was carried out to investigate the characteristics of fourteen odor compounds from a total of 10 sampling sites in residential areas around Banwol-Sihwa industrial complex. The measurement data are analyzed and compared with sensation of odor unit. Only a hydrogen sulfide out of four sulfur compounds was quantified above the detection limit (0.06 ppb) in the residential area around Banwol industrial complex with leather companies and a sewage treatment plant. The concentrations of VOCs were higher than those measured from a big city, and styrene showed the relatively high concentration from all sampling sites ($2.1{\sim}37.8\;ppb$). In the case of carbonyl compounds, acetaldehyde was found most frequently with the mean of 3.97 ppb, and its concentration difference was not significant between Banwol and Sihwa industrial complex. Of the nitrogen compounds, ammonia was measured at the relatively high concentration from all the sampling sites ($12{\sim}707\;ppb$), and a trimethylamine was found at the odor threshold level (0.1 ppb). The concentrations of styrene and ammonia showed relatively seasonal variation, the concentration of styrene in summer was five times higher than that in autumn, the concentration of ammonia in autumn was two times higher than that in summer. However other odorous compounds did not show such strong seasonal variation. Odor-concentration relationship between odor unit and $H_{2}S$ concentrations from industrial sources was examined and used as odor sensation evaluation, and thus the neighbourhood odor complaints maybe caused during the four seasons from the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2468-2483
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• 2017

The construction of completely random sensing matrices of Compressive Sensing requires a large number of random numbers while that of deterministic sensing operators often needs complex mathematical operations. Thus both of them have difficulty in acquiring large signals efficiently. This paper focuses on the enhancement of the practicability of the structurally random matrices and proposes a semi-deterministic sensing matrix called Partial Kronecker product of Identity and Hadamard (PKIH) matrix. The proposed matrix can be viewed as a sub matrix of a well-structured, sparse, and orthogonal matrix. Only the row index is selected at random and the positions of the entries of each row are determined by a deterministic sequence. Therefore, the PKIH significantly decreases the requirement of random numbers, which has a complex generating algorithm, in matrix construction and further reduces the complexity of sampling. Besides, in order to process large signals, the corresponding fast sampling algorithm is developed, which can be easily parallelized and realized in hardware. Simulation results illustrate that the proposed sensing matrix maintains almost the same performance but with at least 50% less random numbers comparing with the popular sampling matrices. Meanwhile, it saved roughly 15%-35% processing time in comparison to that of the SRM matrices.

• International Journal of Concrete Structures and Materials
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• v.18 no.2E
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• pp.89-95
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• 2006

This paper proposes a modified method of Latin Hypercube sampling to reduce the variance of statistical parameters in uncertainty analysis of concrete structures. The proposed method is a modification of Latin Hypercube sampling method. This analysis method uses specifically modified tables of random permutations of ranked numbers. In addition, the Spearman coefficient is used to make modified tables. Numerical analysis is carried out to predict the uncertainty of axial shortening in prestressed concrete bridge. Statistical parameters obtained from modified Latin Hypercube sampling method and conventional Latin Hypercube sampling method are compared and evaluated by a numeric analysis. The results show that the proposed method results in a decrease in the variance of statistical parameters. This indicates the method is efficient and effective in the uncertainty analysis of complex structural system such as prestressed concrete bridges.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1127-1135
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• 2014

Genome-wide association studies (GWAS) are designed to discover genetic variants such as single nucleotide polymorphisms (SNPs) that are associated with human complex traits. Although there is an increasing interest in the application of GWAS methodologies to population-based cohorts, many published GWAS have adopted a case-control design, which raise an issue related to a sampling bias of both case and control samples. Because of unequal selection probabilities between cases and controls, the samples are not representative of the population that they are purported to represent. Therefore, non-random sampling in case-control study can potentially lead to inconsistent and biased estimates of SNP-trait associations. In this paper, we proposed inverse-probability of sampling weights based on disease prevalence to eliminate a case-control sampling bias in estimation and testing for association between SNPs and quantitative traits. We apply the proposed method to a data from the Korea Association Resource project and show that the standard estimators applied to the weighted data yield unbiased estimates.