• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2305-2315
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• 2015

Frequency resource value is growing more and more with the development of the wireless communication. With the advent of the current information society comes a serious shortage of frequency resource, as the amount of supply is far from meeting its demands. Thus, cognitive radio (CR) technique is receiving more attention as a way to make use of the temporarily unoccupied frequency resource. In this paper we propose a novel out-of-band spectrum sensing and dynamic channel access scheme for frequency hopping-based cognitive radio ad-hoc networks. At the beginning of each current channel hopping time, member nodes perform spectrum sensing for the next hopping channel. Based on the proposed collision free primary detection notification, member nodes can determine whether they should execute a hopping time extension procedure of the current channel or not. When the primary detected hopping channel is re-idled, the hopping pattern recovery procedure is performed. In this paper we evaluated the performance of the proposed dynamic sensing and hopping channel extension mechanism for the various wireless network conditions. As a result, we show that the proposed method can increase channel utilization and provide reliable channel management operation.

• IMMUNE NETWORK
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• v.5 no.2
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• pp.124-129
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• 2005

Background: CM1 (centrocyte/-blast marker 1) is originally defined as a germinal center B cell marker. It is known that CM1 plays a critical role on B cell development in germinal center. In addition, we have found that CM1 is expressed on lymphoma cell lines, such as Raji, Ramos and IM-9. This means that CM1 might be served as a tumor marker as well. In the present study, we examined the expression of CM1 on the surface of the other tumors and the possibility of the development of tumor screening ELISA kit by using CM1. Methods: First, we have examined the expression of CM1 on stomach cancer and hepatoma, which are predominantly (discovered) occurred in Korean, by flow cytometry analysis. After purifying of CM1 antigen from Raji and Ramos, the optimal ELISA condition was determined. And then we compared the level of CM1 between normal individuals and cancer patients by ELISA. To decrease the non-specific binding of anti-CM1 mAb with serum components except CM1 and to enhance the diagnostic accuracy, albumin depletion spin column was used. Results: CM1 was highly expressed on stomach cancer and hepatoma cell lines. In addition, we have also confirmed the increased CM1 expression on cancer patients. The difference of CM1 expression between normal individuals and cancer patients were more clearly observed, after deletion of serum albumin by using albumin depletion spin column. Conclusion: Based on the results from this study, CM1 might be a useful molecule for the early diagnosis of cancer. In addition, further studies for the increase of ELISA sensitivity and appropriate albumin depletion methods should be needed.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.571-576
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• 2002

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.

• Journal of Broadcast Engineering
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• v.14 no.5
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• pp.573-588
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• 2009

In this paper, we propose a novel video quality degradation monitoring scheme titled VR-VQMS(Visual Rhythm based Video Quality Monitoring Scheme) over an IPTV service prone to packet losses during network transmission. Proposed scheme quantifies the amount of quality degradation due to packet losses, and can be classified into a RR(reduced-reference) based quality measurement scheme exploiting visual rhythm data of H.264-encoded video frames at a media server and reconstructed ones at an Set-top Box as feature information. Two scenarios, On-line and Off-line VR-VQMS, are proposed as the practical solutions. We define the NPSNR(Networked Peak-to-peak Signal-to-Noise Ratio) modified by the well-known PSNR as a new objective quality metric, and several additional objective and subjective metrics based on it to obtain the statistics on timing, duration, occurrence, and amount of quality degradation. Simulation results show that the proposed method closely approximates the results from 2D video frames and gives good estimation of subjective quality(i.e.,MOS(mean opinion score)) performed by 10 test observers. We expect that the proposed scheme can play a role as a practical solution to monitor the video quality experienced by individual customers in a commercial IPTV service, and be implemented as a small and light agent program running on a resource-limited set-top box.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.205-214
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• 2015

In this study, a novel land-cover classification framework for multi-temporal SAR data is presented that can combine multiple features extracted through data transforms and multiple classifiers. At first, data transforms using principle component analysis (PCA) and 3D wavelet transform are applied to multi-temporal SAR dataset for extracting new features which were different from original dataset. Then, three different classifiers including maximum likelihood classifier (MLC), neural network (NN) and support vector machine (SVM) are applied to three different dataset including data transform based features and original backscattering coefficients, and as a result, the diverse preliminary classification results are generated. These results are combined via a majority voting rule to generate a final classification result. From an experiment with a multi-temporal ENVISAT ASAR dataset, every preliminary classification result showed very different classification accuracy according to the used feature and classifier. The final classification result combining nine preliminary classification results showed the best classification accuracy because each preliminary classification result provided complementary information on land-covers. The improvement of classification accuracy in this study was mainly attributed to the diversity from combining not only different features based on data transforms, but also different classifiers. Therefore, the land-cover classification framework presented in this study would be effectively applied to the classification of multi-temporal SAR data and also be extended to multi-sensor remote sensing data fusion.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.10a
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• pp.7-7
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• 2011

Quantum beam technology has been expected to develop breakthroughs for nanotechnology during the third basic plan of science and technology (2006~2010). Recently, Green- or Life Innovations has taken over the national interests in the fourth basic science and technology plan (2011~2015). The NIMS (National Institute for Materials Science) has been conducting the corresponding mid-term research plans, as well as other national projects, such as nano-Green project (Global Research for Environment and Energy based on Nanomaterials science). In this lecture, the research trends in Japan and NIMS are firstly reviewed, and the typical achievements are highlighted over key nanotechnology fields. As one of the key nanotechnologies, the quantum beam research in NIMS focused on synchrotron radiation, neutron beams and ion/atom beams, having complementary attributes. The facilities used are SPring-8, nuclear reactor JRR-3, pulsed neutron source J-PARC and ion-laser-combined beams as well as excited atomic beams. Materials studied are typically fuel cell materials, superconducting/magnetic/multi-ferroic materials, quasicrystals, thermoelectric materials, precipitation-hardened steels, nanoparticle-dispersed materials. Here, we introduce a few topics of neutron scattering and ion beam nanofabrication. For neutron powder diffraction, the NIMS has developed multi-purpose pattern fitting software, post RIETAN2000. An ionic conductor, doped Pr2NiO4, which is a candidate for fuel-cell material, was analyzed by neutron powder diffraction with the software developed. The nuclear-density distribution derived revealed the two-dimensional network of the diffusion paths of oxygen ions at high temperatures. Using the high sensitivity of neutron beams for light elements, hydrogen states in a precipitation-strengthened steel were successfully evaluated. The small-angle neutron scattering (SANS) demonstrated the sensitive detection of hydrogen atoms trapped at the interfaces of nano-sized NbC. This result provides evidence for hydrogen embrittlement due to trapped hydrogen at precipitates. The ion beam technology can give novel functionality on a nano-scale and is targeting applications in plasmonics, ultra-fast optical communications, high-density recording and bio-patterning. The technologies developed are an ion-and-laser combined irradiation method for spatial control of nanoparticles, and a nano-masked ion irradiation method for patterning. Furthermore, we succeeded in implanting a wide-area nanopattern using nano-masks of anodic porous alumina. The patterning of ion implantation will be further applied for controlling protein adhesivity of biopolymers. It has thus been demonstrated that the quantum beam-based nanotechnology will lead the innovations both for nano-characterization and nano-fabrication.

• Journal of KIISE:Databases
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• v.37 no.5
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• pp.258-266
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• 2010

In this paper, we introduce a novel method to predict next update of blogs. The number of RSS feeds registered on meta-blogs is on the order of several million. Checking for updates is very time consuming and imposes a heavy burden on network resources. Since blog search engine has limited resources, there is a fix number of blogs that it can visit on a day. Nevertheless we need to maximize chances of getting new data, and the proposed method which predicts update probability on blogs could bring better chances for it. Also this work is important to avoid distributed denial-of-service attack for the owners of blogs. Furthermore, for the internet as whole this work is important, too, because our approach could minimize traffic. In this study, we assumed that there is a specific pattern to when a blogger is actively posting, in terms of days of the week and, more specifically, hours of the day. We analyzed 15,119 blogs to determine a blogger's posting preference. This paper proposes a method to predict the update probability based on a blogger's posting history and preferred days of the week. We applied proposed method to 12,115 blogs to check the precision of our predictions. The evaluation shows that the model has a precision of 0.5 for over 93.06% of the blogs examined.

• Journal of KIISE:Information Networking
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• v.35 no.3
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• pp.192-206
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• 2008

Multihop is the main communication style for wireless sensor networks composed of tiny sensor nodes. Until now, most applications have treated the periodic small sized sensing data. Recently, the burst traffic with the transient and continuous nature is increasingly introduced due to the advent of wireless multimedia sensor networks. Therefore, the efficient communication protocol to support this trend is required. In this paper, we propose a novel PIGAB(Packet Interval Gap based on Adaptive Backoff) protocol to efficiently transmit the burst data in multihop wireless sensor networks. The contention-based PIGAB protocol consists of the PIG(Packet Interval Gap) control algorithm in the source node and the MF(MAC-level Forwarding) algorithm in the relay node. The PIGAB is on basis of the newly proposed AB(Adaptive Backoff), CAB(Collision Avoidance Backoff), and UB(Uniform Backoff). These innovative algorithms and schemes can achieve the performance of network by adjusting the gap of every packet interval, recognizing the packet transmission of the hidden node. Through the simulations and experiments, we identify that the proposed PIGAB protocol considerably has the stable throughput and low latency in transmitting the burst data in multihop wireless sensor networks.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.299-309
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• 2006

Due to the rapid development in mobile communication technologies, the usage of mobile devices such as cell phone or PDA becomes increasingly popular. As different devices require different applications, various new services are being developed to satisfy the needs. One of the popular services under heavy demand is the Location-based Service (LBS) that exploits the spatial information of moving objects per temporal changes. In order to support LBS efficiently, it is necessary to be able to index and query well a large amount of spatio-temporal information of moving objects. Therefore, in this paper, we investigate how such location information of moving objects can be efficiently stored and indexed. In particular, we propose a novel location encoding method based on hierarchical administrative district information. Our proposal is different from conventional approaches where moving objects are often expressed as geometric points in two dimensional space, (x,y). Instead, in ours, moving objects are encoded as one dimensional points by both administrative district as well as road information. Our method is especially useful for monitoring traffic situation or tracing location of moving objects through approximate spatial queries.

• Childhood Kidney Diseases
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• v.15 no.1
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• pp.38-48
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• 2011

Purpose: Focal segmental glomerulosclerosis (FSGS) is the most common glomerulopathy causing pediatric renal failure. Since specific treatment targeting the etiology and pathophysiology of primary FSGS is yet elusive, the authors explored the pathophysiology of FSGS by transcriptome analysis of the disease using an animal model. Methods: FGS/kist strain, a mouse model of primary FSGS, and RFM/kist strain, as control and the parent strain of FGS/kist, were used. Kidney tissues were harvested and isolated renal cortex was used to extract mRNA, which was run on AB 1700 mouse microarray chip after reverse transcription to get the transcriptome profile. Results: Sixty two genes were differentially expressed in FGS/kist kidney tissue compared to the control. Those genes were related to cell cycle/cell death, immune reaction, and lipid metabolism/vasculopathy, and the key molecules of their networks were TNF, IL-6/4, IFN${\gamma}$, TP53, and PPAR${\gamma}$. Conclusion: This study confirmed that renal cell death, immune system activation with subsequent fibrosis, and lipid metabolism-related early vasculopathy were involved in the pathophysiology of FSGS. In addition, the relevance of methodology used in this study, namely transcriptome profiling, and Korean animal model of FGS/kist was validated. Further study would reveal novel pathophysiology of FSGS for new therapeutic targets.