• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.134-136
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• 1986

In this paper, a new power spectral estimation technique is presented. At first, by transforming the original data with the Karhunen-Loeve Transform(KLT), we can reduce the amount of the redundant information. Next, by modeling the transformed data by means of the autoregressive(AR) model and then applying the least-squares parameter estimation algorithm to this model, even more accurate spectrum estimates can be obtained. The KLT is the optimum transform for signal representation with respect to the mean-square error criterion. And the least-squares method is used to overcome the inherent shortcomings of popular burg algorithm.

• Proceedings of the KSR Conference
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• 2002.05a
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• pp.530-534
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• 2002

This paper show serial communication method in order to design how to interface between fault tolerant systems with redundancy. Problem has been in the method that fault tolerant system had switched of serial data with common switching device. This problem degrade reliability in itself and total system which is interfaced with that serial communication system. So Arbitration module of serial communication which is suggested in this paper can improve the reliability using voter algorithm which fault is detected passively.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.75-84
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• 2017

It becomes more and more more important for the storage that supports high speed recording and stable access from network environment. As one field of basic science which produces massive astronomical data, VLBI(: Very Long Baseline Interferometer) is now demanding more data writing performance and which is directly related to astronomical observation with high resolution and sensitivity. But most of existing storage are cloud model based for the high throughput of general IT, finance, and administrative service, and therefore it not the best choice for recording of big stream data. Therefore, in this study, we design storage system optimized for high performance of I/O and concurrency. To solve this problem, we implement packet read and writing module through the use of libpcap and pf_ring API on the multi core CPU environment, and build a scalable storage based on software RAID(: Redundant Array of Inexpensive Disks) for the efficient process of incoming data from external network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4268-4290
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• 2015

Future mobile communications face enormous challenges as traditional voice services are replaced with increasing mobile multimedia and data services. To address the vast data traffic volume and the requirement of user Quality of Experience (QoE) in the next generation mobile networks, it is imperative to develop efficient content distribution technique, aiming at significantly reducing redundant data transmissions and improving content delivery performance. On the other hand, in recent years cloud computing as a promising new content-centric paradigm is exploited to fulfil the multimedia requirements by provisioning data and computing resources on demand. In this paper, we propose a cooperative caching framework which implements State based Content Distribution (SCD) algorithm for future mobile networks. In our proposed framework, cloud service providers deploy a plurality of cloudlets in the network forming a Distributed Cloud Service Network (DCSN), and pre-allocate content services in local cloudlets to avoid redundant content transmissions. We use content popularity and content state which is determined by content requests, editorial updates and new arrivals to formulate a content distribution optimization model. Data contents are deployed in local cloudlets according to the optimal solution to achieve the lowest average content delivery latency. We use simulation experiments to validate the effectiveness of our proposed framework. Numerical results show that the proposed framework can significantly improve content cache hit rate, reduce content delivery latency and outbound traffic volume in comparison with known existing caching strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2484-2498
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• 2017

Various types of social media make the people share their personal experience in different ways. In some social networking sites. Some users post their reviews, some users can support these reviews with comments, and some users just rate the reviews as kind of support or not. Unfortunately, there is rare explicit negative comments towards other reviews. This means if there is a link between two users, it must be positive link. Apparently, the negative link is invisible in these social network. Or in other word, the negative links are redundant to positive links. In this work, we first discuss the feature extraction from social media data and propose new method to compute the distance between each pair of comments or reviews on social media. Then we investigate whether we can predict negative links via regression analysis when only positive links are manifested from social media data. In particular, we provide a principled way to mathematically incorporate multi-facet data in a novel framework, Constructing Negative Links, CsNL to predict negative links for discovering the hidden information. Additionally, we investigate the ways of solution to general negative link predication problems with CsNL and its extension. Experiments are performed on real-world data and results show that negative links is predictable with multi-facet of social media data by the proposed framework CsNL. Essentially, high prediction accuracy suggests that negative links are redundant to positive links. Further experiments are performed to evaluate coefficients on different kernels. The results show that user generated content dominates the prediction performance of CsNL.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.177-189
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• 2020

The OECD has assessed Korea as the third highest in trade facilitation worldwide. The paperless trade of Korea is world class based on uTradeHub : national e-trade service's infrastructure for trade community. Over 800 trade-related document standards provide interoperability of message exchange and trade process automation among exporters, importers, banks, customs, airlines, shippers, forwarders and trade authorities. Most one-to-one unit processes are perfectly paperless & online; however, from the perspective of process flow, there is a lack of streamlining end-to-end trade processes spread over many different parties. This situation causes the trade community to endure repetitive-redundant load for handling trade documents. The trade community has a strong demand for seamless trade flow. For streamlining the trade process, processes with data should flow seamlessly to multilateral parties. Flowing data with an optimized process is the critical success factor to accomplish seamless trade. This study proposes four critical digital trade infrastructures as a platform service : (1) data-centric Intelligent Document Recognition(IDR), (2) data-driven Digital Document Flow (DDF), (3) platform based Digital Collaboration & Communication(DCC), and (4) new digital Trade Facilitation Index (dTFI) for precise assessment of K-Trade Digital Trade Framework. The results of new dTFI analyses showed that redundant reentry load was reduced significantly over the whole trade and logistics process. This study leads to the belief that if put into real-world application can provide huge economic gains by building a new global value chain of the K-trade eco network. A new digital trade framework will be invaluable in promoting national soft power for enhancing global competitiveness of the trade community. It could become the advanced reference model of next trade facilitation infrastructure for developing countries.

• Journal of Korean Neurosurgical Society
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• v.52 no.3
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• pp.215-220
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• 2012

Objective : There were previous reports of redundant nerve roots (RNRs) focused on their clinical significance and pathogenesis. In this study, we investigated the significant radiologic findings that correlate with RNRs occurrence. These relations would provide an advanced clue for clinical significance and pathogenesis of RNRs. Methods : Retrospective research was performed with data from 126 patients who underwent surgery for central lumbar spinal stenosis (LSS). Finally, 106 patients with common denominators (inter-observer accuracy : 84%) were included on this study. We divided the patients into two groups by MRI, patients with RNRs and those with no RNRs (NRNRs). Comparative analyses were performed with clinical and radiologic parameters. Results : RNRs were found in 45 patients (42%) with central LSS. There were no statistically significant differences between the two groups in severity of symptoms. On the other hand, we found statistically significant differences in duration of symptom and number of level included (p<0.05). In the maximal stenotic level, ligamentum flavum (LF) thickness, LF cross-sectional area (CSA), dural sac CSA, and segmental angulation are significantly different in RNRs group compared to NRNRs group (p<0.05). Conclusion : RNRs patients showed clinically longer duration of symptoms and multiple levels included. We also confirmed that wide segmental angulation and LF hypertrophy play a major role of the development of RNRs in central LSS. Together, our results suggest that wide motion in long period contribute to LF hypertrophy, and it might be the key factor of RNRs formation in central LSS.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.345-354
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• 2021

Data quality issue in information systems is analyzed with focus on conceptual data modeling. Extensive investigation through triangulation of case studies is attempted to find how much extent inappropriate data modeling practices exercised in real workplace environment. It is revealed that more than 40 percent of data adversely contributed to unnecessary data redundancy, i.e., the level of data obesity is over 40 percent. Another contribution of this paper lies in excavation of all the categories of inappropriate data modeling practices, which has been previously only partially uncovered in the literature. New findings in this paper prove that the extent of inappropriate modeling is more serious that previously reported.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.8
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• pp.957-965
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• 1999

그룹간 공동 작업이나 화상 회의와 같은 그룹 통신의 수요가 늘어나면서 멀티캐스팅을 이용한 효율적인 데이타 전송에 대한 요구가 증가하고 있다. 특히 오디오나 비디오 데이타와는 달리 공동 문서 작업이나 그룹간 문서 전송을 위한 데이타 처리를 위해서는 어느 정도의 시간 손실이 있더라도 신뢰성을 보장할 수 있는 멀티캐스트 프로토콜이 요구된다. 그러나 멀티캐스트 전송에서의 신뢰성을 보장하기 위하여 손실 패킷에 대한 재전송 패킷이 전체 그룹으로 멀티캐스트 되는 것은 네트워크 상에 트래픽을 폭증시키는 요인이 된다. 이에 본 논문에서는 지역 그룹에서의 오류 회복을 위한 재전송 제한 기법을 제안하여 손실 패킷의 재전송 과정에서 발생하는 트래픽의 폭주를 제어하고자 한다. 이것은 재전송 패킷이 중복없이 다중 수신자에게 전송될 수 있도록 하여 그룹 내의 재전송 패킷의 양을 줄이고 필요없는 중복 패킷이 네트워크의 효율을 저하시키는 것을 방지하고자 하는 데 그 목적이 있다. Abstract As the size and the geographic span of communication groups increases, efficient data transmission schemes using Multicast service become more and more essential. Especially, unlike audio and video applications, for some collaborative applications and other data delivery components which require ordered and lossless delivery of data, Reliable Multicast Service is needed to ensure consistent presentation across multiple views. Thus error recovery by retransmission of loss data is provided in order to guarantee the reliability of multicast transmission protocol. However, redundant retransmission packets by multicast may cause traffic implosion on the Internet and it can be aggravated with continuous retransmission.This paper describes a Restricted Retransmission Mechanism as an error recovery method of multicast service in a local group, which can handle traffic implosion in retransmission process. It reduces redundant retransmission packets flowing into a local group and supports reliable multicast transmission. The goal of this mechanism is to reduce retransmission packets and decrease the load for group members and networks.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2029-2032
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• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.