• Proceedings of the Materials Research Society of Korea Conference
• /
• 2003.03a
• /
• pp.73-73
• /
• 2003

나노 크기의 기공이 규칙적으로 배열되어 있는 실리케이트 메조포러스 분말 재료는 넓은 표면적과 화합물에 대한 선택적 흡착 등이 가능하여 많은 연구가 진행퇴고 있다. 최근에는 실리케이트 메조포러스 재료를 박막으로 제조하여 전자소자 혹은 광소자의 제작에 응용하기 위한 연구가 많이 진행되고 있다. 이러한 실리케이트 기공 내부의 표면에 소수성, 극성, 광전자 활성 등, 특정한 기공 표면 특성을 부여하기 위해서 grafting 방법과 co condensation 방법을 이용하고 있다. 특히, co-condensation 방법을 이용하여 tetraalkoxysilane 과 organo-trialkoxysilane을 함께 반응시키는 경우, 유기성분의 양을 더욱 증가시킬 수 있고 물질 내부에 균일한 유기성분의 분포를 얻을 수 있다. 메조포러스 무기 network에 fluorine을 포함하는 그룹이 공유결합으로 결합되어져 있는 물질은 소수성, 흡착성 및 광학적으로 응용 가능성을 가질 것으로 기대된다.

• Proceedings of the Materials Research Society of Korea Conference
• /
• 2003.11a
• /
• pp.106-106
• /
• 2003

최근 집적형 광소자, 레이저 재료, 자료 저장 또는 통신 기술부문에서 제어된 광학적 성질을 갖는 유기-무기 나노 복합체를 만드는 연구가 많은 관심과 주목을 받고 있다. 유기물인 PEG는 대다수의 금속염을 고정시키는 용매 역할을 하는 polymer로써 액체와 같은 특징을 나타내며 무기물인 silica의 network는 순수한 PEG 시스템보다 좋은 기계적 물성을 나타내며, 투명한 물질을 얻을 수 있게 해 준다. 이에 본 연구에서는 SiO2-PEG의 matrix에 우수한 광학적 성질을 지닌 europium을 doping하여 유기-무기 나노 복합막을 합성하여 europium의 농도와 PEG 분자량에 따른 구조적 및 광학적 성질을 알아보고자 한다.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.34 no.3
• /
• pp.29-41
• /
• 2009

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate Its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss Its methodological characteristics in comparison with other existing classification methods. Also, we conduct a series of experiments employing survey data of consumer choices of MP3 players to assess the prediction power of the model. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• International Journal of Advanced Culture Technology
• /
• v.8 no.3
• /
• pp.325-333
• /
• 2020

In the past, eating outside was usually the purpose of eating. However, it has recently expanded into a restaurant culture market. In particular, a dessert culture is being established where people can talk and enjoy. Each consumer has a different tendency to buy chocolate such as health, taste, and atmosphere. Therefore, it is time to recommend chocolate according to consumers' tendency to eat out. In this paper, we propose a chocolate recommendation application based on the tendency to eat out using data on social networks. To collect keyword-based chocolate information, Textom is used as a text mining big data analysis solution.Text mining analysis and related topics are extracted and modeled. Because to shorten the time to recommend chocolate to users. In addition, research on the propensity of eating out is based on prior research. Finally, it implements hybrid app base.

• Korean Chemical Engineering Research
• /
• v.46 no.1
• /
• pp.15-22
• /
• 2008

Polyolefin block copolymer has been taking a great deal of attention due to their great potential in polymer industry since a new metallocene catalytic method for producing polyolefin block copolymer was developed by Dow Chemicals. However, so far, there was no systematic study of olefin block copolymer. In this review, Linear polyolefin block copolymers, containing semicrystalline poly (ethylene) (E) blocks and a rubbery block as a thermoplastic elastomer, were investigated in the viewpoint of microphase separation mode, microstructure, deformation behavior, and molecular architecture.

• Journal of Information Processing Systems
• /
• v.14 no.6
• /
• pp.1301-1306
• /
• 2018

Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution. This paper introduces the diverse kinds of approaches to subjects that tackle diverse kinds of research fields such as model-based MS approach, deep neural network model, image edge detection approach, cross-layer optimization model, LSSVM approach, screen design approach, CPU-GPU hybrid approach and so on. The research on Superintelligence and superconnection for IoT and big data is also described such as 'superintelligence-based systems and infrastructures', 'superconnection-based IoT and big data systems', 'analysis of IoT-based data and big data', 'infrastructure design for IoT and big data', 'artificial intelligence applications', and 'superconnection-based IoT devices'.

• International Journal of Computer Science & Network Security
• /
• v.24 no.9
• /
• pp.30-40
• /
• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Journal of KIISE:Information Networking
• /
• v.36 no.1
• /
• pp.34-42
• /
• 2009

In this paper, we propose a practical hybrid transport error control scheme to provide SVC video streaming service over error-prone IP networks. Many error control mechanisms for various video coding standards have been proposed in the literature. However, there is little research result which can be practically applicable to the multilayered coding structure of SVC(the scalable extension of H.264/AVC). We present a new hybrid transport error control scheme that efficiently combines layered Forward Error Correction(FEC) and Automatic Repeat Request(ARQ) for better packet-loss resilience. In the proposed hybrid error control, we adopt ACK-based ARQ instead of NACK-based ARQ to maximize throughput which is the amount of effective data packets delivered over a physical link per time unit. In order to prove the effectiveness of the proposed hybrid error control scheme, we adopt NIST-Net network emulator which is a general-purpose tool for emulating performance dynamics in IP networks. It is shown by simulations over the NIST-Net that the proposed hybrid error control scheme shows improved packet-loss resilience even with much less number of overhead packets compared to various conventional error control schemes.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.127-138
• /
• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• Journal of Broadcast Engineering
• /
• v.10 no.4 s.29
• /
• pp.548-556
• /
• 2005

In wireless network environments, wireless channels are characterized by time-varying fading and interference conditions, which may lead to burst packet corruptions and delay variation. This can cause severe quality degradation of streaming media. To guarantee successful transmission of media over the hostile wireless networks, where channel conditions are highly fluctuating, a flexible and network-adaptive transport method is required. Thus, we propose a network-adaptive transport error control consisting of packet-level interleaved FEC and delay-constrained ARQ, which acts as an application-level transport method of streaming media to alleviate burst packet losses while adapting to the changing channel condition in wireless networks. The performances of the proposed network-adaptive transport error control, general error control schemes, and hybrid schemes are evaluated by a developed simulator at the transport-level and video quality of streaming media. Simulation results show that the proposed mechanism provides the best overall performance among compared other schemes in terms of the transport-level performance of error control and the performance of video quality for streaming media.