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

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.595-604
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• 2004

Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.22-27
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• 2007

Phosphorylation is one of the most important post translational modifications which regulate the activity of proteins. The problem of predicting phosphorylation sites is the first step of understanding various biological processes that initiate the actual function of proteins in each signaling pathway. Although many prediction methods using single or multiple features extracted from protein sequences have been proposed, systematic data integration approach has not been applied in order to improve the accuracy of predicting general phosphorylation sites. In this paper, we propose an optimal way of integrating multiple features in the framework of multiple kernel learning. We optimally combine seven kernels extracted from sequence, physico-chemical properties, pairwise alignment, and structural information. Using the data set of Phospho. ELM, the accuracy evaluated by 5-fold cross-validation reaches 85% for serine, 85% for threonine, and 81% for tyrosine. Our computational experiments show significant improvement in the performance of prediction relative to a single feature, or to the combined feature with equal weights. Moreover, our systematic integration method significantly improves the prediction preformance compared with the previous well-known methods.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.103-112
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• 2007

In this paper, we implement a hypervisor that runs multiple uC/OS-II real-time kernels on one microprocessor. The hypervisor virtualizes microprocessor and memory that are main resources managed by uC/OS-II kernel. Microprocessor is virtualized by controlling interrupts that uC/OS-II real-time kernel handles and memory is virtualized by partitioning physical memory. The hypervisor consists of three components: interrupt control routines that virtualize timer interrupt and software interrupt, a startup code that initializes the hypervisor and uC/OS-II kernels, and an API that provides communication between two kernels. The original uC/OS-II kernel needs to be modified slightly in source-code level to run on the hypervisor. We performed a real-time test and an independent computation test on Jupiter 32-bit EISC microprocessor and showed that the virtualized kernels run without problem. The result of our research can reduce the hardware cost, the system space and weight, and system power consumption when the hypervisor is applied in embedded applications that require many embedded microprocessors.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.645-652
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• 2023

The aleurone layer in maize is crucial as it contains essential nutrients such as minerals, vitamins, and high-quality proteins. While most of the maize varieties are known to possess a single aleurone layer, several multi-aleurone layer mutants and landraces have been suggested for hierarchical genetic control of aleurone development. Conventional microscopy analysis often involves using immature seeds or sampling only a portion of the kernel sample, and whole kernel section analysis using a microtome is technically difficult and time-consuming. Additionally, the larger size of maize kernels posed challenges for comprehensive cross-sectional analysis compared to other cereal crops. Consequently, this study aimed to develop an efficient method to comprehensively understand the aleurone layer characteristics of the entire cross-section in maize. Through observations of diverse maize genetic resources, we confirmed irregular aleurone layer patterns in those with multiple aleurone layers, and we discovered a landrace having multiple aleurone layers. By selectively identifying genetic resources with multiple aleurone layers, this method may contribute to efficient breeding processes in maize.

• Journal of the Korea Computer Graphics Society
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• v.21 no.4
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• pp.11-17
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• 2015

As the needs of real-time visual object tracking are increasing in various kinds of application fields such as surveillance, entertainment, etc., kernel-based mean-shift tracking has received more interests. One of major issues in kernel-based mean-shift tracking is to be robust under partial or full occlusion status. This paper presents a real-time mean-shift tracking which is robust in partial occlusion by applying multi-mode local kernel weight. In the proposed method, a kernel is divided into multiple sub-kernels and each sub-kernel has a kernel weight to be determined according to the location of the sub-kernel. The experimental results show that the proposed method is more stable than the previous methods with multi-mode kernels in partial occlusion circumstance.

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

• ETRI Journal
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• v.38 no.6
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• pp.1124-1134
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• 2016

The tone reservation method is one of the most effective pre-distortion methods for peak-to-average power ratio reduction in orthogonal frequency division multiplexing (OFDM) systems. Its direct application to OFDM systems with offset quadrature amplitude modulation (OQAM) is, however, not effective. In this paper, two novel TR-based methods are proposed, specifically designed for OFDM/OQAM systems by taking into consideration the overlapping nature of OQAM signals. These two methods have different approaches to the generation of the peak-cancelling signal. The first one (overlapped scaling tone reservation) generates the peak-cancelling signal using a least squares approximation algorithm with possible adjacent symbol overlap; the second one (multi-kernel tone reservation) generates the peak-cancelling signal by using multiple impulse-like time domain kernels. It is shown by simulation that, when used in OFDM/OQAM systems, the proposed methods can provide better performance than the direct application of the existing controlled clipping tone reservation method, and even outperform the multi-block tone reservation method.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.525-528
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• 2019

Atmospheric particle removal is a challenging task and attacks wide interests in computer vision filed. In this paper, we proposed a single image snow removal framework based on deep residual networks. According to the fact that there are various snow sizes in a snow image, the inception module which consists of different filter kernels was adopted to extract multiple resolution features of the input snow image. Except the traditional mean square error loss, the perceptual loss and total variation loss were employed to generate more clean images. Experimental results on synthetic and realistic snow images indicated that the proposed method achieves superior performance in respect of visual perception and objective evaluation.

• Journal of the Chungcheong Mathematical Society
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• v.24 no.4
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• pp.745-758
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• 2011

Generalizing the Burchnall-Chaundy operator method, the authors are aiming at presenting certain decomposition formulas for the chosen six Exton functions expressed in terms of Appell's functions $F_3$ and $F_4$, Horn's functions $H_3$ and $H_4$, and Gauss's hypergeometric function F. We also give some integral representations for the Exton functions $X_i$ (i = 6, 8, 14) each of whose kernels contains the Horn's function $H_4$.