• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.511-522
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• 2002

WBI (Web-Based Instruction), a web-based tool for teaching the students at a long distance, makes possible to Interact between learners and instructors, provides a wide variety of learning materials, has an advantage of overcoming spatial constraints. In this paper, as a model of using the web for education, a web-based education-evaluation system has been designed and implemented. Web-based education-evaluation system has to be equipped with both of the online setting question mode and the upload setting question mode, the former makes questions on web and the latter uploads the setted questions on offline with settling a defeat of the existing systems on setting questions. And the system has to be equipped with the function of analyzing the questions that gives teacher several kinds of analysis information and makes possible to feedback to questions by adjusting the difficulty and revising the questions. In this paper, a system that reflects the above requirements has been designed and implemented with PHP script language and MySQL database system.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.15-26
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• 2007

In this study, a SOHO (Small Office Home Office) bankruptcy prediction model is proposed using Modified Bagging Predictors which is modification of traditional Bagging Predictors. There have been several studies on bankruptcy prediction for large and middle size companies. However, little studies have been done for SOHOs. In commercial banks, loan approval processes for SOHOs are usually less structured than those for large and middle size companies, and largely depend on partial information such as credit scores. In this study, we use a real SOHO loan approval data set of a Korean bank. First, decision tree induction techniques and artificial neural networks are applied to the data set, and the results are not satisfactory. Bagging Predictors which has been not previously applied for bankruptcy prediction and Modified Bagging Predictors which is proposed in this paper are applied to the data set. The experimental results show that Modified Bagging Predictors provides better performance than decision tree inductions techniques, artificial neural networks, and Bagging Predictors.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.1
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• pp.73-85
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• 2001

Electronic check schemes are more efficient than electronic coin scheme with respect to computational costs and the amount of information exchanged. In spite of these, difficulties in making a refund reusable and in representing the face value of a check have discouraged its development. In this paper, a new online electronic check system is presented, which solves the above problems. This system uses the partially blind signature to provide user anonymity and to represent the face value of a check. The partially blind signature enables us to make the format of refunds and initially withdrawn checks identical. Thus, it allows refunds to be reused to buy goods without any limitatiosn. Both initially withdrawn checks and refunds in our system guarantee untraceability as well as unlinkability. We also use a one-time secret key as the serial number of a check to increase the efficiency of payments. The presented check system also provides multiple offline shopping sessions to minimize the number of online messages handled by a bank. During the multiple offline shopping session, we use a one-way accumulator to provide non-repudiation service. We also analyze our new systems our new system\`s security, efficiency, and atomicity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.331-340
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• 2006

In this Paper, we propose to extend the TSS-LS(Two-Step Search scheme with Linear search based Second step) scheme which was already proposed by the authors for coherent UWB(Ultra Wide Band) systems, to rapid and reliable acquisition of noncoherent UWB systems in multipath channels. The proposed noncoherent TSS-LS employing simple energy window banks utilizes two different thresholds and search windows to achieve fast acquisition. Furthermore, the linear search is adopted for the second step in the proposed scheme to correctly find the starting point in the range of effective delay spread of the multipath channels, and to obtain reliable BER(Bit Error Rate) performance of the noncoherent UWB systems. Simulation results with multipath channel models by IEEE 802.15.3a show that the proposed two-step search scheme can achieve significant reduction of the required mean acquisition time as compared to general search schemes. ]n addition, the proposed scheme achieves quite good BER performance for large signal-to-noise ratios, which is favorably comparable to the case of ideal perfect timing.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.542-546
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• 2008

Recently dual-port SDRAM (DPSDRAM) architecture tailored for dual-processor based mobile embedded systems has been announced where a single memory chip plays the role of the local memories and the shared memory for both processors. In order to maintain memory consistency from simultaneous accesses of both ports, every access to the shared memory should be protected by a synchronization mechanism, which can result in substantial access latency. We propose two optimization techniques by exploiting the communication patterns of target applications: lock-priority scheme and static-copy scheme. Further, by dividing the shared bank into multiple blocks, we allow simultaneous accesses to different blocks thus achieve considerable performance gain. Experiments on a virtual prototyping system show a promising result - we could achieve about 20-50% performance gain compared to the base DPSDRAM architecture.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.133-146
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• 2015

Ever sophisticated e-finance fraud techniques have led to an increasing number of reported phishing incidents. Financial authorities, in response, have recommended that we enhance existing Fraud Detection Systems (FDS) of banks and other financial institutions. FDSs are systems designed to prevent e-finance accidents through real-time access and validity checks on client transactions. The effectiveness of an FDS depends largely on how fast it can analyze and detect abnormalities in large amounts of customer transaction data. In this study we detect fraudulent transaction patterns and establish detection rules through e-finance accident data analyses. Abnormalities are flagged by comparing individual client transaction patterns with client profiles, using the ruleset. We propose an effective flagging method that uses decision trees to normalize detection rules. In demonstration, we extracted customer usage patterns, customer profile informations and detection rules from the e-finance accident data of an actual domestic(Korean) bank. We then compared the results of our decision tree-normalized detection rules with the results of a sequential detection and confirmed the efficiency of our methods.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.179-192
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• 2008

This case study shows constructing the knowledge-based system using a rule-based approach for detecting illegal transactions regarding money laundering in the Korea Financial Intelligence Unit (KoFIU). To better manage the explosive increment of low risk suspicious transactions reporting from financial institutions, the adoption of a knowledge-based system in the KoFIU is essential. Also since different types of information from various organizations are converged into the KoFIU, constructing a knowledge-based system for practical use and data management regarding money laundering is definitely required. The success of the financial information system largely depends on how well we can build the knowledge-base for the context. Therefore we designed and constructed the knowledge-based system for anti-money laundering by committing domain experts of each specific financial industry co-worked with a knowledge engineer. The outcome of the knowledge base implementation, measured by the empirical ratio of Suspicious Transaction Reports (STRs) reported to law enforcements, shows that the knowledge-based system is filtering STRs in the primary analysis step efficiently, and so has made great contribution to improve efficiency and effectiveness of the analysis process. It can be said that establishing the foundation of the knowledge base under the entire framework of the knowledge-based system for consideration of knowledge creation and management is indeed valuable.

• International Commerce and Information Review
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• v.17 no.2
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• pp.3-18
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• 2015

This thesis examines the performance and improvement strategy of SWIFTNet TSU's BPO for computerization and suggests the following results: First, the URBPO should be legally complemented, and the SWIFT and banks need to keep improving the systems to meet trade parties' diverse needs. Second, the SWIFTNet TSU's BPO should have an institutionally unified sharing platform with security, stability and convenience. In other words, it is needed to develop services which meet e-payment paradigm and international and regional environments through continued analysis on market changes and flow. Third, in order for the SWIFTNet TSU BPO to evolve into a perfect global system, there should be an innovative payment solution which can meet all trade parties all over the world. For this, technology standardization for a worldwide e-trade payment system is essential. Lastly, based on the results derived from this study, an analysis framework with which more diverse and practical environmental variables can be analyzed should be developed.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.755-764
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• 2004

Recently, the rapid development of biotechnology brings the explosion of biological data and biological data host. Moreover, these data are highly distributed and heterogeneous, reflecting the distribution and heterogeneity of the Molecular Biology research community. As a consequence, the integration and interoperability of molecular biology databases are issue of considerable importance. But, up to now, most of the integrated systems such as link based system, data warehouse based system have many problems which are keeping the data up to date when the schema and data of the data source are changed. For this reason, the integrated system using web service technology that allow biological data to be fully exploited have been proposed. In this paper, we built the integrated system if the bio sequence information bated on the web service technology. The developed system allows users to get data with many format such as BSML, GenBank, Fasta to traverse disparate data resources. Also, it has better retrieval performance because the retrieval modules of the external database proceed in parallel.