• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.849-852
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• 2014

Reverse engineering of gene regulatory network is a challenging task in computational biology. To detect a regulatory relationship among genes from time series data is called reverse engineering. Reverse engineering helps to discover the architecture of the underlying gene regulatory network. Besides, it insights into the disease process, biological process and drug discovery. There are many statistical approaches available for reverse engineering of gene regulatory network. In our paper, we propose pairwise mutual information for the reverse engineering of a gene regulatory network from time series data. Firstly, we create random boolean networks by the well-known $Erd{\ddot{o}}s-R{\acute{e}}nyi$ model. Secondly, we generate artificial time series data from that network. Then, we calculate pairwise mutual information for predicting the network. We implement of our system on java platform. To visualize the random boolean network graphically we use cytoscape plugins 2.8.0.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.22-24
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• 2017

The Korean e-Navigation operating system is designed to provide various services related to marine safety. These services are not configured to be provided in one software, but are made in separate software. In order to integrate the service software installed in the e-Navigation operating system, it is necessary to define the data message which can exchange data between the service software, Depending on the operating concept of the service, many messages are expected to be transmitted over the network in a short time. There is a need for a buffer or message queue to store messages as a way to manage messages efficiently. Therefore, in this paper, we analyze the types and characteristics of Advanced Message Queuing Protocol(AMQP) suitable for Korean e-Navigation operating system and shows that's result.

• Applied Microscopy
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• v.49
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• pp.1.1-1.10
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• 2019

Joints of three-dimensional (3D) rutile-type (r) tin dioxide ($SnO_2$) nanowire networks, produced by the flame transport synthesis (FTS), are formed by coherent twin boundaries at $(101)^r$ serving for the interpenetration of the nanowires. Transmission electron microscopy (TEM) methods, i.e. high resolution and (precession) electron diffraction (PED), were utilized to collect information of the atomic interface structure along the edge-on zone axes $[010]^r$, $[111]^r$ and superposition directions $[001]^r$, $[101]^r$. A model of the twin boundary is generated by a supercell approach, serving as base for simulations of all given real and reciprocal space data as for the elaboration of three-dimensional, i.e. relrod and higher order Laue zones (HOLZ), contributions to the intensity distribution of PED patterns. Confirmed by the comparison of simulated and experimental findings, details of the structural distortion at the twin boundary can be demonstrated.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.395-418
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• 2024

The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (R_e), maximum fault distance (R_f), or maximum hypocentral distance (R_h), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (R_e, R_f, or R_h) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.

• Journal of Korea Technology Innovation Society
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• v.17 no.2
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• pp.334-354
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• 2014

This study aims to analyze the research trend of Korean medicine based on social network analysis. To do this, a dataset has been collected from KCI (Korea Citation Index) database. According to the results, we have identify the longitudinal trend of the number of papers, journals, organizations and key words in this field. Moreover, based on the nodes' centrality of co-author network, we have found a core journal (i.e. Korean Journal of Oriental Physiology and Pathology), a hub institution (i.e. Kyunghee university) and two main key words (i.e. anti-oxidation and acupuncture) in the research network. In conclusion, integrating field experts' tacit knowledge in Korean medicine studies with the results of the explicit social network analysis on the research trend, we put forward further policy implications with regard to R&D strategies in this field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4301-4319
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• 2017

Adopting mobile chargers (MC) in rechargeable wireless sensors network (R-WSN) to recharge sensors can increase network efficiency (e.g., reduce MC travel distance per tour, reduce MC effort, and prolong WSN lifetime). In this study, we propose a mechanism to split the sensing field into partitions that may be equally spaced but differ in distance to the base station. Moreover, we focus on minimizing the MC effort by providing a new charging mechanism called the sector-based charging schedule (SBCS), which works to dispatch the MC in charging trips to the sector that sends many charging requests and suggesting an efficient sensor-charging algorithm. Specifically, we first utilize the high ability of the BS to divide the R-WSN field into sectors then it select the cluster head for each sector to reduce the intra-node communication. Second, we formulate the charging productivity as NP-hard problem and then conduct experimental simulations to evaluate the performance of the proposed mechanism. An extensive comparison is performed with other mechanisms. Experimental results demonstrate that the SBCS mechanism can prolong the lifetime of R-WSNs by increasing the charging productivity about 20% and reducing the MC effort by about 30%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.681-688
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• 2016

Several VHF bands for the use in analog communications has been changed to those for the use in new maritime digital communications by WRC-12. ITU-R M. 1842-1 has been also standardized, recommending the characteristics of maritime digital communication systems. In addition, a Shipborne Ad-hoc Network (SANET) has been introduced by IMO in order to provide ships, which cannot be connected to a shore directly, with maritime digital data exchange services with the help of ad-hoc communication. In this paper, several functionalities of the SANET, including channel access, route determination to a shore, and data exchange, are verified via sea trials. It is expected that the SANET can be applicable to collecting and analyzing maritime information, facilitating the entry and departure of vessels, and the communication infrastructure of e-navigation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1081-1094
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• 2016

KREONET is a principal national R&E network running by KISTI in Korea. It uniquely provides production research network services for around 200 non-profit research and educational organizations, based on hybrid (IP and non-IP) network infrastructure. However, KREONET is limited to meet various needs of new network services for advanced Science & Technology (S&T) users because its infrastructure is inherently derived form classical hardware-based, fixed and closed environments. So, KREONET-S is designed to provide advanced S&T services to catch up with time-to-research and time-to-collaboration. In this paper, we present a system architecture of KREONET-S based on network infrastructure that consists of data and control planes separately. Furthermore, we propose and describe VDN service which is capable of building a virtual dedicate & bandwidth-guaranteed network for S&T group dynamically. we implement VDN application on KREONET-S and then perform performance analysis for proving that KREONET-S system and VDN application can be a good solutions to cope with new network paradigms for various advanced S&T applications and users.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.205-211
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• 2021

Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems.

• Design & Manufacturing
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• v.13 no.3
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• pp.53-58
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• 2019

This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.