• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.156-156
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• 2019

In this study, a gated recurrent unit (GRU) network is constructed based on a deep neural network (DNN) with the aim of restoring the missing daily flow data in river basins. Lai Chau hydrological station is located upstream of the Da river basin (Vietnam) is selected as the target station for this study. Input data of the model are data on observed daily flow for 24 years from 1961 to 1984 (before Hoa Binh dam was built) at 5 hydrological stations, in which 4 gauge stations in the basin downstream and restoring - target station (Lai Chau). The total available data is divided into sections for different purposes. The data set of 23 years (1961-1983) was employed for training and validation purposes, with corresponding rates of 80% for training and 20% for validation respectively. Another data set of one year (1984) was used for the testing purpose to objectively verify the performance and accuracy of the model. Though only a modest amount of input data is required and furthermore the Lai Chau hydrological station is located upstream of the Da River, the calculated results based on the suggested model are in satisfactory agreement with observed data, the Nash - Sutcliffe efficiency (NSE) is higher than 95%. The finding of this study illustrated the outstanding performance of the GRU network model in recovering the missing flow data at Lai Chau station. As a result, DNN models, as well as GRU network models, have great potential for application within the field of hydrology and hydraulics.

• Journal of Integrative Natural Science
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• v.5 no.1
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• pp.32-37
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• 2012

Chemokine receptor antagonists have potential applications in field of drug discovery. Although the chemokine receptors are G-protein-coupled receptors, their cognate ligands are small proteins (8 to 12 kDa), and so inhibiting the ligand/receptor interaction has been challenging. The application of structure-based in-silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human CCR2 and CCR5, the most important members of the chemokine receptor family and also a potential drug target. Herein, we review the success stories of combined receptor modeling/mutagenesis approach to probe the allosteric nature of chemokine receptor binding by small molecule antagonists for CCR2 and CCR5 using Rhodopsin as template. We also urged the importance of recently available ${\beta}2$-andrenergic receptor as an alternate template to guide mutagenesis. The results demonstrate the usefulness and robustness of in-silico 3D models. These models could also be useful for the design of novel and potent CCR2 and CCR5 antagonists using structure based drug design.

• Journal of Korean Neurosurgical Society
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• v.62 no.3
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• pp.272-287
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• 2019

The mechanistic target of rapamycin (mTOR) pathway coordinates the metabolic activity of eukaryotic cells through environmental signals, including nutrients, energy, growth factors, and oxygen. In the nervous system, the mTOR pathway regulates fundamental biological processes associated with neural development and neurodegeneration. Intriguingly, genes that constitute the mTOR pathway have been found to be germline and somatic mutation from patients with various epileptic disorders. Hyperactivation of the mTOR pathway due to said mutations has garnered increasing attention as culprits of these conditions : somatic mutations, in particular, in epileptic foci have recently been identified as a major genetic cause of intractable focal epilepsy, such as focal cortical dysplasia. Meanwhile, epilepsy models with aberrant activation of the mTOR pathway have helped elucidate the role of the mTOR pathway in epileptogenesis, and evidence from epilepsy models of human mutations recapitulating the features of epileptic patients has indicated that mTOR inhibitors may be of use in treating epilepsy associated with mutations in mTOR pathway genes. Here, we review recent advances in the molecular and genetic understanding of mTOR signaling in epileptic disorders. In particular, we focus on the development of and limitations to therapies targeting the mTOR pathway to treat epileptic seizures. We also discuss future perspectives on mTOR inhibition therapies and special diagnostic methods for intractable epilepsies caused by brain somatic mutations.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.561-573
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• 2018

Oomycetes are known to secrete a vast arsenal of effectors that modulate the host defense system as well as facilitate establishing a parasitic infection in plants. In recent years, tremendous progress has been made in the field of effectromics based on studies of oomycetes, especially the cytoplasmic family of RXLR effectors. Yet, the biology of the RXLR effector family is still poorly understood. There has been a consensus regarding the structure of the RXLR motif in the mycologist community. However, the function of the RXLR motif is still unclear. First, different models have suggested that the role of the RXLR motif is either in translocation to a target destination inside a host cell or in the cleavage of itself followed by secretion. Second, recent studies have suggested different functional models for the RXLR motif. According to a widely accepted model, the RXLR motif is directly involved in the translocation of effectors to target sites. In contrast, a new study has proposed that the RXLR motif is involved in secretion rather than translocation. Thus, this review is an attempt to summarize the recent advances made in the functional analysis of the N-terminal domain of RXLR effectors.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2222-2229
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• 2023

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.3
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• pp.21-27
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• 1997

In this paper, the hysteretic characteristics of traditional wooden house frame,which is fabricated by Sagaemachum, under cyclic lateral load are presented. Full scale frame models are used in the tests. The skeleton curves of traditional wooden frame are quite different from those of wooden frames which are fabricated using nails or bracings. The equivalent viscous damping ratios of the frame system are about 27% and 13% for ordinary and high-column frames, respectively. The nonlinear hysteretic characteristics of the frame is modeled by the so called Modified Double Target model.

• MALSORI
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• no.59
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• pp.27-51
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• 2006

For speech recognition systems, rejection function as well as decoding function is necessary to improve the reliability. There have been many research efforts on out-of-vocabulary word rejection, however, little attention has been paid on non-target sentence rejection. Recently pronunciation approaches using speech recognition increase the need for non-target sentence rejection to provide more accurate and robust results. In this paper, we proposed filler model method and word/phoneme detection ratio method to implement non-target sentence rejection system. We made performance evaluation of filler model along to word-level, phoneme-level, and sentence-level filler models respectively. We also perform the similar experiment using word-level and phoneme-level word/phoneme detection ratio method. For the performance evaluation, the minimized average of FAR and FRR is used for comparing the effectiveness of each method along with the number of words of given sentences. From the experimental results, we got to know that word-level method outperforms the other methods, and word-level filler mode shows slightly better results than that of word detection ratio method.

• International Journal of Contents
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• v.11 no.3
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• pp.47-53
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• 2015

Visual object tracking is a fundamental problem in the field of computer vision, as it needs a proper model to account for drastic appearance changes that are caused by shape, textural, and illumination variations. In this paper, we propose a feature-based visual-object-tracking method with a sparse representation. Generally, most appearance-based models use the gray-scale pixel values of the input image, but this might be insufficient for a description of the target object under a variety of conditions. To obtain the proper information regarding the target object, the following combination of features has been exploited as a corresponding representation: First, the features of the target templates are extracted by using the HOG (histogram of gradient) and LBPs (local binary patterns); secondly, a feature-based sparsity is attained by solving the minimization problems, whereby the target object is represented by the selection of the minimum reconstruction error. The strengths of both features are exploited to enhance the overall performance of the tracker; furthermore, the proposed method is integrated with the particle-filter framework and achieves a promising result in terms of challenging tracking videos.

• BMB Reports
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• v.41 no.12
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• pp.833-839
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• 2008

Angiogenesis in tumors is driven by multiple growth factors that activate receptor tyrosine kinases. An important driving force of angiogenesis in solid tumors is signaling through vascular endothelial growth factor (VEGF) and its receptors (VEGFRs). Angiogenesis inhibitors that target this signaling pathway are now in widespread use for the treatment of cancer. However, when used alone, inhibitors of VEGF/VEGFR signaling do not destroy all blood vessels in tumors and do not slow the growth of most human cancers. VEGF/VEGFR signaling inhibitors are, therefore, used in combination with chemotherapeutic agents or radiation therapy. Additional targets for inhibiting angiogenesis would be useful for more efficacious treatment of cancer. One promising target is the signaling pathway of hepatocyte growth factor (HGF) and its receptor (HGFR, also known as c-Met), which plays important roles in angiogenesis and tumor growth. Inhibitors of this signaling pathway have been shown to inhibit angiogenesis in multiple in vitro and in vivo models. The HGF/c-Met signaling pathway is now recognized as a promising target in cancer by inhibiting angiogenesis, tumor growth, invasion, and metastasis.

• Journal of National Security and Military Science
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• s.1
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• pp.191-229
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• 2003

Multi-sensor data fusion techniques combine evidences from multiple sensors in order to get more accurate and efficient meaningful information through several process levels that may not be possible from a single sensor alone. One of the most important parts in the data fusion system is the identification fusion, and it can be categorized into physical models, parametric classification and cognitive-based models, and parametric classification technique is usually used in multi-sensor data fusion system by its characteristic. In this paper, we propose a novel heuristic identification fusion method in which we adopt desirable properties from not only parametric classification technique but also cognitive-based models in order to meet the realtime processing requirements.