• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2664-2669
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• 2014

Recently, many research efforts have paid attention to spectrum pooling meshanism that provides efficient way to manage transient spectrum requests of secondary users in cognitive radio networks. Spectrum pooling is maintained by WSP (wireless service provider). WSP leases the spectrums stored in her spectrum pool to secondary users with being paid for it. It is natural that WSP tries to get profits as much as possible, which implies the WSP tries to minimize the cost required for maintaining her spectrum pool. In this paper, we model the spectrum pool into a probabilistic inventory model. Assuming secondary users' spectrum requests follow normal distribution, we give a strategic way that minimizes the maintenance cost of the spectrum pool. By a series of simulations, we show that WSP can reduce the total maintenance cost through our inventory model-based spectrum pooling than maintaining fixed inventory level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.338-341
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• 2014

Recently, many research efforts have paid attention to spectrum pooling meshanism that provides efficient way to manage transient spectrum requests of secondary users in cognitive radio networks. Spectrum pooling is maintained by WSP (wireless service provider). WSP leases the spectrums stored in her spectrum pool to secondary users with being paid for it. It is natural that WSP tries to get profits as much as possible, which implies the WSP tries to minimize the cost required for maintaining her spectrum pool. In this paper, we model the spectrum pool into a probabilistic inventory model. Assuming secondary users' spectrum requests follow normal distribution, we give a strategic way that minimizes the maintenance cost of the spectrum pool. By a series of simulations, we show that WSP can reduce the total maintenance cost through our inventory model-based spectrum pooling than maintaining fixed inventory level.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1179-1191
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• 2019

We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its "learning" ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.495-500
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• 2012

A standard paradigm for the allocation of wireless resources in communication demands symmetry, whereby all users are assumed to be on equal footing and hence get equal shares of communication capabilities. However, there are situations in which "prime users" should be given higher priority, as for example in the transmission of emergency messages. In this paper, we examine a prioritization policy that can be implemented at the physical layer. In particular, we evaluate the performance of a prioritized transmission scheme based on spectrum pooling and on the assignment of higher signal-to-noise ratio channels to higher-priority users. This performance is compared to that of unprioritized (or "symmetric") schemes, and the impact of prioritization on the unprioritized users is discussed.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.544-551
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• 2023

Speech enhancement used to improve the perceptual quality and intelligibility of noise speech has been studied as a method using a complex-valued spectrum that can improve both magnitude and phase in a method using a magnitude spectrum. In this paper, a study was conducted on how to apply attention mechanism to complex-valued spectrum-based speech enhancement systems to further improve the intelligibility and quality of noise speech. The attention is performed based on additive attention and allows the attention weight to be calculated in consideration of the complex-valued spectrum. In addition, the global average pooling was used to consider the importance of the feature map. Complex-valued spectrum-based speech enhancement was performed based on the Deep Complex U-Net (DCUNET) model, and additive attention was conducted based on the proposed method in the Attention U-Net model. The results of the experiments on noise speech in a living room environment showed that the proposed method is improved performance over the baseline model according to evaluation metrics such as Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short Time Object Intelligence (STOI), and consistently improved performance across various background noise environments and low Signal-to-Noise Ratio (SNR) conditions. Through this, the proposed speech enhancement system demonstrated its effectiveness in improving the intelligibility and quality of noisy speech.

• Smart Structures and Systems
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• v.29 no.3
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• Molecules and Cells
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• v.25 no.2
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• pp.205-210
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• 2008

To develop molecular markers linked to the $L^4$ locus conferring resistance to tobamovirus pathotypes in pepper plants, we performed AFLP with 512 primer combinations for susceptible (S pool) and resistant (R pool) DNA bulks against pathotype 1.2 of pepper mild mottle virus. Each bulk was made by pooling the DNA of five homozygous individuals from a T10 population, which was a near-isogenic $BC_4F_2$ generation for the $L^4$ locus. A total of 19 primer pairs produced scorable bands in the R pool. Further screening with these primer pairs was done on DNA bulks from T102, a $BC_{10}F_2$ derived from T10 by back crossing. Three AFLP markers were finally selected and designated L4-a, L4-b and L4-c. L4-a and L4-c each underwent one recombination event, whereas no recombination for L4-b was seen in 20 individuals of each DNA bulk. Linkage analysis of these markers in 112 $F_2$ T102 individuals showed that they were each within 2.5 cM of the $L^4$ locus. L4-b was successfully converted into a simple 340-bp SCAR marker, designated L4SC340, which mapped 1.8 cM from the $L^4$ locus in T102 and 0.9 cM in another $BC_{10}F_2$ population, T101. We believe that this newly characterized marker will improve selection of tobamovirus resistance in pepper plants by reducing breeding cost and time.

• Journal of Web Engineering
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• v.14 no.2
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• pp.265-283
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• 2022

Bauhinia scandens L. (Family: Fabaceae) is commonly used to treat cholera, diarrhea, asthma, and diabetes disorder in integrative medicine. This study aimed to screen the presence of phytochemicals (preliminary and UPLC-QTOF-M.S. analysis) and to examine the pharmacological activities of Bauhinia scandens L. stems (MEBS) stem extracts. Besides, in silico study was also implemented to elucidate the binding affinity and drug capability of the selected phytochemicals. In vivo anti diarrheal activity was investigated in mice models. In vitro, antibacterial and antifungal properties of MEBS against several pathogenic strains were evaluated using the disc diffusion method. In addition, in silico study has been employed using Discovery studio 2020, UCFS Chimera, PyRx autodock vina, and online tools. In the anti-diarrheal investigation, MEBS showed a significant dose-dependent inhibition rate in all three methods. The antibacterial and antifungal screening showed a remarkable zone of inhibition, of the diameter 14-26 mm and 12-28 mm, by MEBS. The present study revealed that MEBS has remarkable anti-diarrheal potential and is highly effective in wide-spectrum bacterial and fungal strains. Moreover, the in silico study validated the results of biological screenings. To conclude, MEBS is presumed to be a good source in treating diarrhea, bacterial and fungal infections.