• Korean Journal of Food Science and Technology
• /
• v.29 no.6
• /
• pp.1319-1321
• /
• 1997

The effect of cooking peanut kernels before grinding on the hexanal content of peanut milk was investigated. Hexanal which is thought to be one of the major compounds contributing to the beany flavor of peanut milk, was quantified using a simplified headspace gas chromatographic method. Four cooking times (0, 10, 20 and 30 min) were evaluated. The concentration of hexanal in peanut milk was one-third by cooking peanut kernels for 10 min or longer. Protein content of peanut milk gradually decreased by heat treatments.

• Nuclear Engineering and Technology
• /
• v.49 no.6
• /
• pp.1240-1249
• /
• 2017

An essential component of the neutron transport solver is the resonance self-shielding calculation used to determine equivalence cross sections. The neutron transport code, MPACT, is currently using the subgroup self-shielding method, in which the method of characteristics (MOC) is used to solve purely absorbing fixed-source problems. Recent efforts incorporating multigroup kernels to the MOC solvers in MPACT have reduced runtime by roughly $2{\times}$. Applying the same concepts for self-shielding and developing a novel lumped parameter approach to MOC, substantial improvements have also been made to the self-shielding computational efficiency without sacrificing any accuracy. These new multigroup and lumped parameter capabilities have been demonstrated on two test cases: (1) a single lattice with quarter symmetry known as VERA (Virtual Environment for Reactor Applications) Progression Problem 2a and (2) a two-dimensional quarter-core slice known as Problem 5a-2D. From these cases, self-shielding computational time was reduced by roughly $3-4{\times}$, with a corresponding 15-20% increase in overall memory burden. An azimuthal angle sensitivity study also shows that only half as many angles are needed, yielding an additional speedup of $2{\times}$. In total, the improvements yield roughly a $7-8{\times}$ speedup. Given these performance benefits, these approaches have been adopted as the default in MPACT.

• Advances in aircraft and spacecraft science
• /
• v.7 no.3
• /
• pp.215-228
• /
• 2020

As is shown in the paper, the Koltunov-Rzhanitsyn singular kernel of heredity (when constructing mathematical models of the dynamics problem of the hereditary theory of viscoelasticity) adequately describes real mechanical processes, best approximates experimental data for a long period of time. A mathematical model of the problem of the flutter of viscoelastic plates moving in a gas with a high supersonic velocity is given. Using the Bubnov-Galerkin method, discrete models of the problem of the flatter of viscoelastic plates flowed over by supersonic gas flow are obtained. A numerical method is developed to solve nonlinear integro-differential equations (IDE) for the problem of the hereditary theory of viscoelasticity with weakly singular kernels. A general computational algorithm and a system of application programs have been developed, which allow one to investigate the nonlinear dynamic problems of the hereditary theory of viscoelasticity with weakly singular kernels. On the basis of the proposed numerical method and algorithm, nonlinear problems of the flutter of viscoelastic plates flowed over in a gas flow at an arbitrary angle are investigated. In a wide range of changes in various parameters of the plate, the critical velocity of the flutter is determined. It is shown that the singularity parameter α affects not only the oscillations of viscoelastic systems, but the critical velocity of the flutter as well.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.3055-3073
• /
• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.1
• /
• pp.92-98
• /
• 2019

In this paper, we propose a deep learning system based on morphological neural network(MNN). The deep learning layers are morphological operation layer, pooling layer, ReLU layer, and the fully connected layer. The operations used in morphological layer are erosion, dilation, and edge detection, etc. Unlike CNN, the number of hidden layers and kernels applied to each layer is limited in MNN. Because of the reduction of processing time and utility of VLSI chip design, it is possible to apply MNN to various mobile embedded systems. MNN performs the edge and shape detection operations with a limited number of kernels. Through experiments using database images, it is confirmed that MNN can be used as a deep learning system and its performance.

• Genomics & Informatics
• /
• v.20 no.2
• /
• pp.17.1-17.11
• /
• 2022

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.54 no.4
• /
• pp.357-365
• /
• 2009

On the purpose to improve the milling efficiency as well as head-rice percentage after milling, an experiment to improve the removal ability of immature kernels in the immature brown rice separator (IBRS) was performed focused on varietal characteristics. The removal ability of immature grains by the IBRS was absolutely depending on kernel thickness of brown rice. The kernel thickness of the tested rice varieties distributed from 1.79 mm in Nonganbyeo to 2.16 mm in Daeribbyeo 1. Although there were some variation among rice varieties, it was roughly suggested that the suitable sieve-slit widths for good separation of the immature kernels were 1.9 mm for the varieties thicker than 2.08 mm in thickness, 1.8 mm for the varieties with 2.00-2.08 mm thickness, 1.7 mm for the varieties with 1.90-2.00 mm thickness, and 1.60-1.65 mm for the varieties thinner than 1.7 mm. It was found out that the higher the proportions of immature kernels in brown rice, the more conspicuous the improvement of milling efficiency as well as head rice rates by their removals. With increasing the sieve slit-widths beyond an optimum range, the losses of mature grains increased sharply. For effective separation of immature kernels, it was suggested that the optimum sieve-slit width should be set up depending on both of the kernel thickness and the critical loss limit of mature kernel.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.167-183
• /
• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.37 no.5
• /
• pp.476-485
• /
• 1992

M1 plants which were produced from seed soaking in chemical mutagen, EMS or NaN$_3$, appeared wide morphorogical variations such as dwarf, albino, twisted leaf, white streaked leaf, and purpled stem. In mutants of reproductive organs, there were monoecious plants such as female-flower plant and male-flower plant, multiple spikes, and steriled plants among M1 plants. Also, barren stalk was increased significantly in M1 plants. Ear bagging at ear initiation stage prevented seed set on cob in normal plants. In spite of ear bagging, M1 plants which had cobs with seed set was 3.9-11.2% of stalks developed from seeds soaking with mutagens, but only three or four kernels could be matured on a cob. Ear bagging after mutagen injection into initiating ear produced 5.1-10% in cobs with seed set, but only 1.7-6.3 kernels could be matured. Cobs removed silk at four hours after artificial pollination increased the rate of cobs with seed set to 27%. Microscopic observation confirmed that ontogeny of kernels matured from ear bagging and mutagen treatment would be both adventitious and diplosporous apomictic reproduction. Chromosome set of M2 seedling was found to be diploid type in chromosomal counting of root tip. As M$_2$ plants showed an uniform appearence within each lines and their CV of plant height were ranged 4-6% in each lines, we concluded that they were apomictic progeny. But we could not find any marker traits combined with apomixis.

• Journal of KIISE:Software and Applications
• /
• v.37 no.2
• /
• pp.85-94
• /
• 2010

This paper introduces a novel way to leverage convolution parse tree kernel to extract the interaction information between two proteins in a sentence without multiple features, clues and complicated kernels. Our approach needs only the parse tree alone of a candidate sentence including pairs of protein names which is potential to have interaction information. The main contribution of this paper is two folds. First, we show that for the PPI, it is imperative to execute parse tree pruning removing unnecessary context information in deciding whether the current sentence imposes interaction information between proteins by comparing with the latest existing approaches' performance. Secondly, this paper presents that tree kernel decay factor can play an pivotal role in improving the extraction performance with the identical learning conditions. Consequently, we could witness that it is not always the case that multiple kernels with multiple parsers perform better than each kernels alone for PPI extraction, which has been argued in the previous research by presenting our out-performed experimental results compared to the two existing methods by 19.8% and 14% respectively.