• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1620-1634
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• 2023

This paper studies the task of embedding nodes with multiple graphs representing multiple information channels, which is useful in a large volume of network clustering tasks. By learning a node using multiple graphs, various characteristics of the node can be represented and embedded stably. Existing studies using multi-channel networks have been conducted by integrating heterogeneous graphs or limiting common nodes appearing in multiple graphs to have similar embeddings. Although these methods effectively represent nodes, it also has limitations by assuming that all networks provide the same amount of information. This paper proposes a method to overcome these limitations; The proposed method gives different weights according to the source graph when embedding nodes; the characteristics of the graph with more important information can be reflected more in the node. To this end, a novel method incorporating a multi-channel gate layer is proposed to weigh more important channels and ignore unnecessary data to embed a node with multiple graphs. Empirical experiments demonstrate the effectiveness of the proposed multi-channel-based embedding methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• Journal of Korea Water Resources Association
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• v.44 no.11
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• pp.875-887
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• 2011

The objective of this study is to develop and evaluate a Korean threshold runoff computation method. The selected study area is the Han-River basin and the stream channels in the study area are divided into 3 parts; natural channel and artificial manmade channel for small mountainous catchments, and main channel for master stream. The threshold runoff criteria for small streams is decided to 0.5 m water level increase from the channel bottom, which is the level that mountain climbers and campers successfully escape from natural flood damage. Threshold runoff values in natural channel of small mountainous area are computed by the results from the regional regression analysis between parameters of basin and stream channel, while those in artificial channel of small mountainous area are obtained from the data of basin and channel characteristics parameter. On the other hand, the threshold runoff values for master channel are used the warning flood level that is useful information for escaping guideline for riverside users. For verification of the threshold runoff computation method proposed in this study, three flash flood cases are selected and compared with observed values, which is obtained from SCS effective rainfall computation. The 1, 3, 6-hour effective rainfall values are greater than the corresponding threshold runoff values represents that the proposed computation results are reasonable.

• Journal of The Geomorphological Association of Korea
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• v.27 no.1
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• pp.1-19
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• 2020

A river bed which is submerged in water at high flow and becomes part of the river at low flow, serves as a bridge between the river and the land. The channel bar creates a unique ecosystem with vegetation adapted to the particular environment and the water pool forms a wetland that plays a very important role in the environment. To evaluate anthropogenic impacts on the river bed in the Middle Yeongsangang River, the fluvial landforms in the stream channel were analyzed using multi-temporal remotely-sensed images. In the aerial photograph of 2005 taken before the construction of the large weirs, oxbow lakes, mid-channel bars, point bars, and natural wetlands between the artificial levees were identified. Multiple bars divided the flow of stream water to cause the braided pattern in a particular section. After the construction of the Seungchon weir, aerial photographs of 2013 and 2015 revealed that most of the fluvial landforms disappeared due to the dredging of its riverbed and water level control(maintenance at 7.5El.m). Sentinel-2 images were analyzed to identify differences between before and after the opening of weir gate. Change detection was performed with the near infrared and shortwave infrared spectral bands to effectively distinguish water surfaces from land. As a result, water surface area of the main stream of the Yeongsangang River decreased by 40% from 1.144km² to 0.692km². A large mid-channel bar that has been deposited upstream of the weir was exposed during low water levels, which shows the obvious influence of weir on the river bed. Newly formed unvegetated point bars that were deposited on the inside of a meander bend were identified from the remotely sensed images. As the maintenance period of the weir gate opening was extended, various habitats were created by creating pools and riffles around the channel bars. Considering the ecological and hydrological functions of the river bed, it is expected that the increase in bar areas through weir gate opening will reduce the artificial interference effect of the weir.

• Steel and Composite Structures
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• v.33 no.3
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• pp.319-332
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• 2019

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.67-72
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• 2020

The fifth generation (5G) mobile communication has an impact on the human life over the whole world, nowadays, through the artificial intelligence (AI) and the internet of things (IoT). The low latency of the 5G new radio (NR) access is implemented by the state-of-the art technologies, such as non-orthogonal multiple access (NOMA). This paper investigates a practical issue that in NOMA, for the practical channel models, such as fading channel environments, the successive interference cancellation (SIC) should be performed on the stronger channel users with low power allocation. Only if the SIC is performed on the user with the stronger channel gain, NOMA performs better than orthogonal multiple access (OMA). Otherwise, NOMA performs worse than OMA. Such the superiority requirement can be easily implemented for the channel being static or slow varying, compared to the block interval time. However, most mobile channels experience fading. And symbol by symbol channel estimations and in turn each symbol time, selections of the SIC-performing user look infeasible in the practical environments. Then practically the block of symbols uses the single channel estimation, which is obtained by the training sequence at the head of the block. In this case, not all the symbol times the SIC is performed on the stronger channel user. Sometimes, we do perform the SIC on the weaker channel user; such cases, NOMA performs worse than OMA. Thus, we can say that by what percent NOMA is better than OMA. This paper calculates analytically the percentage by which NOMA performs better than OMA in the practical mobile communication systems. We show analytically that the percentage for NOMA being better than OMA is only the function of the ratio of the stronger channel gain variance to weaker. In result, not always, but almost time, NOMA could perform better than OMA.

• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.59-63
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• 2006

Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.05a
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• pp.54.1-54.1
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• 2011

Porous hydroxyapatite has been widely used as clinical implanted material. However, it has poor mechanical properties. To increase the strength as well as the biocompatibility of the porous HAp based artificial bone, it was fabricated by multi-extrusion process. Hydroxyapatite and graphite powders were mixed separately with ethylene vinely acetate and steric acid by shear mixing process. Hydroxyapatite composites containing porous microstructure were fabricated by arranging it in the die and subject it to extrusion process. Burn-out and sintering processes were performed to remove the binder and graphite as well as increase the density. The external and internal diameter of cylindrical hollow core were approximately 10.4 mm and 4.2 mm, respectively. The size of pore channel designed to increase bone growth (osteconduction) was around 150 ${\mu}m$ in diameter. X-ray diffraction analysis and SEM observation were performed to identity the crystal structure and the detailed microstructure, respectively.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.1027-1035
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• 1994

This paper aims to make an artificial arm control strategy. For this, we propose a new feature extraction method and design artificial neural network for the functional classification of myoelectric signal(MES). We first transform the two channel myoelectric signals (MES) for biceps and triceps into frequency domain using fast Fourier transform (FFT). And features were obtained by comparing the magnitudes of ensemble spectrum data and used as inputs to the three-layer neural network for the learning. By changing the number of units in hidden layer of neural network we observed the improvement of classification performance. To observe the effeciency of the proposed scheme we performed experiments for classification of six arm functions to the three subjects. And we obtained on average 94[%] the ratio of classification.

• Journal of Distribution Science
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• v.21 no.3
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• pp.113-121
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• 2023

Purpose: Online distribution channel alludes to the many different digital channels utilized in marketing and distributing goods and services to end users. The present research aims to explore and provide various retail strategy for the success of the art market within online distribution channel. Research design, data and methodology: The current author has conducted and investigate the qualitative textual methodology to take a look at carefully the current and prior literature dataset to achieve the purpose of the present research so that the present author could obtain total 27 relevant prior studies. Results: According to the comprehensive literature investigation, this research has found that there are six kinds of retail strategy for the prosperity of the art market within online distribution channel as follows: (1) Blockchain Technology, (2) Artificial Intelligence (AI), (3) Virtual Reality (VR), (4) Online Market Places, (5) Social Media, and (6) Regulations. Conclusions: The results of this analysis of the relevant literature show that the art market industry needs to adjust to keep up with the quickly shifting landscape of the digital world. In addition, although these technologies can be helpful in addressing difficulties linked to authenticity and transparency, they cannot eliminate the hazards of fraud and misrepresentation.