• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.114-123
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• 2007

As a promising technology that enables ubiquitous computing and will lead the information technology industries of the next generation, the new field of sensor networks is one of the most active research topics today. From now on, each node, the network formation, and even the sensor network itself will interact with the generic network and evolve dynamically according to environmental changes, in a process of continual creation and extinction. In this paper, we propose a address-Internetworking scheme for interactive networking between a sensor network and the Internet based on the TCP port-numbers. The proposed scheme enables internetworking between a sensor network address scheme based on Zigbee and the Internet address scheme based on the Internet Protocol version 6 (IPv6). We implement the proposed address-Internetworking scheme using Berkeley TinyOS, Mica Motes, and IP. In addition we verify the proposed scheme by an interconnection experiment, which involves wireless sensor networks and the Internet, using IPv4/IPv6.

• Applied Chemistry for Engineering
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• v.30 no.1
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• pp.74-80
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• 2019

$Nafion/TiO_2$ composite membranes were prepared via an in-situ sol-gel process with different immersing periods from 1 day to 7 days for the low humidifying proton exchange membrane fuel cell. As the immersing time increased, the $TiO_2$ content within the Nafion membrane increased. The contact angle decreased with the increased $TiO_2$ content in the composite membrane due to the increased hydrophilicity. The water uptake and proton conductivity reached to the highest level for 4 day immersing period, then decreased as the immersing period increased. A 7 days of immersing time was shown to be too long because too much $TiO_2$ aggregates were formed on the membrane surface as well as interior of the membrane, interfering the proton transfer from anode to cathode. Cell performance results were in good agreement with those of the water uptake and proton conductivity; current densities under a relative humidity (RH) of 40% were 0.54, 0.6, $0.63A/cm^2$ and $0.49A/cm^2$ for the immersing time of 1, 3, 4 and 7 days, respectively at a 0.6 V. The composite membrane prepared via the in-situ sol-gel process exhibited the enhancement in the cell performance under of RH 40% by a maximum of about 66% compared to those of using the recasting composite membrane and Nafion 115.

• Korean Journal of Cognitive Science
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• v.29 no.3
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• pp.161-192
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• 2018

Retrieving temporal information of encoded events is one of the core control processes in episodic memory. Despite much prior neuroimaging research on episodic retrieval, little is known about how large-scale connectivity patterns are involved in the retrieval of sequentially organized episodes. Task-related functional connectivity multivariate pattern analysis was used to distinguish the different sequential retrieval. In this study, participants performed temporal episodic memory tasks in which they were required to retrieve the encoded items in either the forward or backward direction. While separately parsed local networks did not yield substantial efficiency in classification performance, the large-scale patterns of interactivity across the cortical and sub-cortical brain regions implicated in both the cognitive control of memory and goal-directed cognitive processes encompassing lateral and medial prefrontal regions, inferior parietal lobules, middle temporal gyrus, and caudate yielded high discriminative power in classification of temporal retrieval processes. These findings demonstrate that mnemonic control processes across cortical and subcortical regions are recruited to re-experience temporally-linked series of memoranda in episodic memory and are mirrored in the qualitatively distinct global network patterns of functional connectivity.

• Journal of Technology Innovation
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• v.29 no.1
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• pp.39-73
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• 2021

The cooperative network structure influences the academic performance of the research institute. In particular, South Korea's Government-Funded Research Institutes(GRI) need to establish an efficient cooperative system as a leading national R&D implementer. This study applied the Small World structure, which has been discussed as an efficient network structure, and the centrality of representing the characteristics of nodes to the cooperative network of GRI in Korea. Based on the SCIE published data from 2010 to 2019, we analyze how the Small World characteristics and centrality of GRI contribute to academic performance using a network analysis and Feasible GLS regression. The GRI cooperative network has shown that the Small World network structure facilitates the academic performance. In addition, centrality indicating the degree of direct connection showed positive significance, but centrality indicating the degree of intermediary was not significant or negative. The results of this study explain that the higher the number of institutions that exchange and cooperate, the higher the academic performance, and the higher the performance of the institutions that serve as the center of cooperation. In addition, it was established that the stronger the cooperative network of GRIs have the characteristics of Small World, the more effective it is to create research results. This study applies centrality and Small World previously discussed as an efficient network structure to the GRI cooperation network and provide implications for establishing policies and strategies related to R&D cooperation among GRIs.

• Membrane Journal
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• v.32 no.6
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• pp.427-441
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• 2022

Polymer electrolyte membrane (PEM) serving as a separator that can prevent the permeation of unreacted fuels as well as an electrolyte that selectively transports protons from the anode to the cathode has been considered a key component of polymer electrolyte membrane fuel cell (PEMFC). The perfluorinated sulfonic acid-based PEMs, represented by Nafion®, have been commercialized in PEMFC systems due to their high proton conductivity and chemical stability. Nevertheless, these PEMs have several inherent drawbacks including high manufacturing costs by the complex synthetic processes and environmental problems caused by producing the toxic gases. Although numerous studies are underway to address these drawbacks including the development of sulfonated hydrocarbon polymer-based PEMs (SHP-PEMs), which can easily control the polymer structures, further improvement of PEM performances and durability is necessary for practical PEMFC applications. Therefore, this study focused on the various strategies for the development of SHP-PEMs with outstanding performance and durability by 1) introducing cross-linked structures, 2) incorporating organic/inorganic composites, and 3) fabricating reinforced-composite membranes using porous substrates.

• Journal of the Korean Geotechnical Society
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• v.22 no.6
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• pp.15-26
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• 2006

Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.3
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• pp.155-163
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• 2023

Real-time monitoring technology is critical for ensuring the safety and reliability of nuclear power plant structures. However, the current seismic monitoring system has limited system identification capabilities such as modal parameter estimation. To obtain global behavior data and dynamic characteristics, multiple sensors must be optimally placed. Although several studies on optimal sensor placement have been conducted, they have primarily focused on civil and mechanical structures. Nuclear power plant structures require robust signals, even at low signal-to-noise ratios, and the robustness of each mode must be assessed separately. This is because the mode contributions of nuclear power plant containment buildings are concentrated in low-order modes. Therefore, this study proposes an optimal sensor placement methodology that can evaluate robustness against noise and the effects of each mode. Indicators, such as auto modal assurance criterion (MAC), cross MAC, and mode shape distribution by node were analyzed, and the suitability of the methodology was verified through numerical analysis.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.487-494
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• 2023

In this study, performance comparison was performed on R and TensorFlow, which are free deep learning tools. In the experiment, six types of deep neural networks were built using each tool, and the neural networks were trained using the 10-year Korean temperature dataset. The number of nodes in the input layer of the constructed neural network was set to 10, the number of output layers was set to 5, and the hidden layer was set to 5, 10, and 20 to conduct experiments. The dataset includes 3600 temperature data collected from Gangnam-gu, Seoul from March 1, 2013 to March 29, 2023. For performance comparison, the future temperature was predicted for 5 days using the trained neural network, and the root mean square error (RMSE) value was measured using the predicted value and the actual value. Experiment results shows that when there was one hidden layer, the learning error of R was 0.04731176, and TensorFlow was measured at 0.06677193, and when there were two hidden layers, R was measured at 0.04782134 and TensorFlow was measured at 0.05799060. Overall, R was measured to have better performance. We tried to solve the difficulties in tool selection by providing quantitative performance information on the two tools to users who are new to machine learning.

• Geophysics and Geophysical Exploration
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• v.2 no.1
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• pp.26-32
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• 1999

Among the various seismic data processing sequences, the velocity analysis is the most time consuming and man-hour intensive processing steps. For the production seismic data processing, a good velocity analysis tool as well as the high performance computer is required. The tool must give fast and accurate velocity analysis. There are two different approches in the velocity analysis, batch and interactive. In the batch processing, a velocity plot is made at every analysis point. Generally, the plot consisted of a semblance contour, super gather, and a stack pannel. The interpreter chooses the velocity function by analyzing the velocity plot. The technique is highly dependent on the interpreters skill and requires human efforts. As the high speed graphic workstations are becoming more popular, various interactive velocity analysis programs are developed. Although, the programs enabled faster picking of the velocity nodes using mouse, the main improvement of these programs is simply the replacement of the paper plot by the graphic screen. The velocity spectrum is highly sensitive to the presence of the noise, especially the coherent noise often found in the shallow region of the marine seismic data. For the accurate velocity analysis, these noise must be removed before the spectrum is computed. Also, the velocity analysis must be carried out by carefully choosing the location of the analysis point and accuarate computation of the spectrum. The analyzed velocity function must be verified by the mute and stack, and the sequence must be repeated most time. Therefore an iterative, interactive, and unified velocity analysis tool is highly required. An interactive velocity analysis program, xva(X-Window based Velocity Analysis) was invented. The program handles all processes required in the velocity analysis such as composing the super gather, computing the velocity spectrum, NMO correction, mute, and stack. Most of the parameter changes give the final stack via a few mouse clicks thereby enabling the iterative and interactive processing. A simple trace indexing scheme is introduced and a program to nike the index of the Geobit seismic disk file was invented. The index is used to reference the original input, i.e., CDP sort, directly A transformation techinique of the mute function between the T-X domain and NMOC domain is introduced and adopted to the program. The result of the transform is simliar to the remove-NMO technique in suppressing the shallow noise such as direct wave and refracted wave. However, it has two improvements, i.e., no interpolation error and very high speed computing time. By the introduction of the technique, the mute times can be easily designed from the NMOC domain and applied to the super gather in the T-X domain, thereby producing more accurate velocity spectrum interactively. The xva program consists of 28 files, 12,029 lines, 34,990 words and 304,073 characters. The program references Geobit utility libraries and can be installed under Geobit preinstalled environment. The program runs on X-Window/Motif environment. The program menu is designed according to the Motif style guide. A brief usage of the program has been discussed. The program allows fast and accurate seismic velocity analysis, which is necessary computing the AVO (Amplitude Versus Offset) based DHI (Direct Hydrocarn Indicator), and making the high quality seismic sections.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.57-79
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• 2016

It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.