• Composites Research
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• v.19 no.5
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• pp.20-27
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• 2006

Self-sensing and interfacial evaluation of Ni nanowire/polymer composites were investigated using electro-macromechanical technique, which can be used fur a feasible sensing measurement on tensile and compressive loading/consequent unloading, temperature, and humidity. Mechanical properties of Ni nanowire with different aspect ratio and adding contents in either epoxy or silicone composites were measured indirectly using electro-pullout test under uniform and non-uniform cyclic loadings. Comparing apparent modulus with the conventional mechanical tensile modulus of Ni nanowire/epoxy composites, the trends were consistent with each other. Ni nanowire/epoxy composites showed the sensing response on humidity and temperature. Self-sensing on applied tensile and compressive loading/unloading was also responded for Ni nanowire/silicone composites via electrical contact resistivity showing the opposite trend between tension and compression. It can be due to the different electrically-interconnecting mechanisms of dispersed Ni nanowires embedded in silicone matrix.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.153-158
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• 2011

This paper presents subjective experimental results to understand how audio should be changed when a video clip is watched in 3D than 2D. This paper divided auditory perceptual information into two categories; distance and azimuth that a sound source contributes mostly, and spaciousness that scene or environment contribute mostly. According to the experiment for distance and azimuth, i.e. sound localization, we found that distance and azimuth of sound sources were magnified when heard with 3D than 2D video. This lead us to conclude 3D sound for localization should be designed to have more distance and azimuth than 2D sound. Also we found 3D sound are preferred to be played with not only 3D video clip but also 2D video clip. According to the experiment for spaciousness, we found people prefer sound with more reverberation when they watch 3D video clips than 2D video clips. This can be understood that 3D video provides more spacial information than 2D video. Those subjective experimental results can help audio engineer familiar with 2D audio to create 3D audio, and be fundamental information of future research to make 2D to 3D audio conversion system. Furthermore when designing 3D broadcasting system with limited bandwidth and with 2D TV supportive, we propose to consider transmitting stereoscopic video, audio with enhanced localization, and metadata for TV sets to generate reverberation for spaciousness.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.127-131
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• 2024

SW education was conducted for slow youth learners. 6 learners participatd in 8 sessions of an introductory course using several plays and 3 learners who more interested in introductory course participated in deeper course using normal method. After education, we survey and interview from learners, instructors and heads of welfare organizations. Learners showed interest and participated in the fact that they were participating in SW education, which was widely talked about. Learners were found to be more satisfied with introductory course education using play such as board games, and although they initially appeared to participate in unfamiliar learning content with low efficacy, it was observed that their efficacy increased with repetition. Additionally, it was observed that young people with an IQ of 80 or higher had a higher level of interest or interest in SW education than those with an IQ of 80 or lower. we discussed that there were not many opportunities to directly use the SW education content for youth who are slow learners in work or real life. We suggest this should be a focus education on the use of digital media - online meeting apps, office SW etc.- to improve digital literacy for life and work and that research on this should continue.

• Composites Research
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• v.18 no.4
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• pp.21-26
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• 2005

Nondestructive damage sensing and load transfer mechanisms of carbon nanotube (CNT) and nanofiber (CNF)/epoxy composites have been investigated by using electro-micromechanical technique. The electrospun PVDF nanofibers were also prepared as a piezoelectric sensor. The electro-micromechanical techniques were applied to evaluate sensing response of carbon nanocomposites by measuring electrical resistance under an uniform cyclic loading. Composites with higher volume content of CNT showed significantly higher tensile properties than neat and low volume$\%$ CNT composites. CNT composites showed humidity sensing within limited temperature range. CNT composites with smaller aspect ratio showed higher apparent modulus due to high volume content in case of shorter aspect ratio. Thermal treated electrospun PVDF nanofiber showed higher mechanical properties than the untreated case due to crystallinity increase, whereas load sensing decreased in heat treated case. Electrospun PVDF nanofiber web also showed sensing effect on humidity and temperature as well as stress transferring. Nanocomposites and electrospun PVDF nanofiber web can be applicable for sensing application.

• Informatization Policy
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• v.23 no.2
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• pp.54-72
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• 2016

This article analyzes the latest cases of Digital Social Innovation such as crowdsourcing and online petitions for public trouble-shooting in oder to demonstrate that public engagement of the citizens on decision making can enhance the quality of democracy. Digital Social Innovation contributes to citizen's participation on decision making and policy implementation with taking advantage of digital technologies of crowdsourcing and online petitions. Active civic engagement for decision making literally helps to improve and democratize government policy. These series of processes not only improve quality and efficiency of policy governance by building up transparency and effectiveness of policy itself but also enhance the throughput legitimacy. With this article, I quote and analyze various practices of Digital Social Innovation which we had substantiated to demonstrate the effectiveness of civic engagement for decision making to improve and enhance democracy. The hypothesis that the Digital Social Innovation attempted in various ways is a principal factor of democratization could be verified. Moreover, the practices of Digital Social Innovation helps the civic participation in policy making in modern society. Finally, this article suggests an implication of Digital Social Innovation as part of efforts to ensure the involvement of throughput legitimacy for the development of democracy.

• Cross-Cultural Studies
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• v.52
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• pp.277-312
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• 2018

First-order cybernetics during the 1940s and 1950s aimed for control of an observed system, while second-order cybernetics during the mid-1970s aspired to address the mechanism of an observing system. The former pursues an objective, subjectless, approach to a system, whereas the latter prefers a subjective, personal approach to a system. Second-order observation must be noted since a human observer is a living system that has its unique cognition. Maturana and Varela place the autopoiesis of this biological system at the core of second-order cybernetics. They contend that an autpoietic system maintains, transforms and produces itself. Technoscientific recreation of biological autopoiesis opens up to a new step in cybernetics: what I describe as third-order cybernetics. The formation of technoscientific autopoiesis overlaps with the Fourth Industrial Revolution or what Erik Brynjolfsson and Andrew McAfee call the Second Machine Age. It leads to a radical shift from human centrism to posthumanity whereby humanity is mechanized, and machinery is biologized. In two versions of the novel Demon Seed, American novelist Dean Koontz explores the significance of technoscientific autopoiesis. The 1973 version dramatizes two kinds of observers: the technophobic human observer and the technology-friendly machine observer Proteus. As the story concludes, the former dominates the latter with the result that an anthropocentric position still works. The 1997 version, however, reveals the victory of the techno-friendly narrator Proteus over the anthropocentric narrator. Losing his narrational position, the technophobic human narrator of the story disappears. In the 1997 version, Proteus becomes the subject of desire in luring divorcee Susan. He longs to flaunt his male egomaniac. His achievement of male identity is a sign of technological autopoiesis characteristic of third-order cybernetics. To display self-producing capabilities integral to the autonomy of machinery, Koontz's novel demonstrates that Proteus manipulates Susan's egg to produce a human-machine mixture. Koontz's demon child, problematically enough, implicates the future of eugenics in an era of technological autopoiesis. Proteus creates a crossbreed of humanity and machinery to engineer a perfect body and mind. He fixes incurable or intractable diseases through genetic modifications. Proteus transfers a vast amount of digital information to his offspring's brain, which enables the demon child to achieve state-of-the-art intelligence. His technological editing of human genes and consciousness leads to digital standardization through unanimous spread of the best qualities of humanity. He gathers distinguished human genes and mental status much like collecting luxury brands. Accordingly, Proteus's child-making project ultimately moves towards technologically-controlled eugenics. Pointedly, it disturbs the classical ideal of liberal humanism celebrating a human being as the master of his or her nature.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.