• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.115-122
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• 2011

Adaptive arrays suffer from severe performance degradation when there are errors in the steering vector. The DCRCB (doubly constrained robust Capon beamformer) overcomes such a problem, introducing a spherical uncertainty set of the steering vector together with a norm constraint. However, in the standard DCRCB, it is a difficult task to determine the bound for the uncertainty, the radius of the spherical set, such that a near best solution is obtained. A novel beamforming method is presented which has no difficulty of the uncertainty bound setting, employing a recursive search for the steering vector. Though the basic idea of recursive search has been known, the conventional recursive method needs to set a parameter for the termination of the search. The proposed method terminates it by using distances to the signal subspace, without the need for parameter setting. Simulation demonstrates that the proposed method has better performance than the conventional recursive method and than the non-recursive standard DCRCB, even the one with the optimum uncertainty bound.

• The KIPS Transactions:PartD
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• v.8D no.3
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• pp.203-210
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• 2001

These days data is being collected and accumulated in a wide variety of fields. Stored data itself is to be an information system which helps us to make decisions. An information system includes many kinds of necessary and unnecessary attribute. So many algorithms have been developed for finding useful patterns from the data and reasoning approximately new objects. We are interested in the simple and understandable rules that can represent useful patterns. In this paper we propose an algorithm which can reduce the information in the system to a minimum, based on a probabilistic rough set theory. The proposed algorithm uses a value that tolerates accuracy of classification. The tolerant value helps minimizing the necessary attribute which is needed to reason a new object by reducing conditional attributes. It has the advantage that it reduces the time of generalizing rules. We experiment a proposed algorithm with the IRIS data and Wisconsin Breast Cancer data. The experiment results show that this algorithm retrieves a small reduct, and minimizes the size of the rule under the tolerant classification rate.

• Journal of the Korea Society for Simulation
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• v.26 no.2
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• pp.19-29
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• 2017

System Entity Structure/Model Base (SES/MB) enhances organizing model families and storing and reusing model components in the multifaceted system modeling. However, the real world can be described not only an individual system but also a collection of those systems, which is called system of systems (SoS). Because SES/MB has a limitation to simulate the SoS using HLA/RTI, an extended framework is required to simulate it. Therefore, this paper proposes System of Systems Entity Structure/Federate Base (SoSES/FB) for simulation in a distributed environment (HLA/RTI). The proposed method provides the library of federates (FB) and System of System Entity Structure (SoSES) formalism, which represents structural knowledge of a collection of simulators. It also provides a methodology for the development process of distributed simulation. The paper introduces the anti-missile defense simulation using the proposed SoSES/FB.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.413-420
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• 2024

RISC-V is an open-source instruction set architecture, used in various hardware implementations, and can be flexibly expanded to meet system requirements through the RV64I base instruction set and 16 standard extensions. Currently, the RISC-V architecture employs the shadow stack technique to protect return addresses. This paper compares the performance of the compact shadow stack mechanism and the parallel shadow stack mechanism in the RISC-V architecture using the SPEC CPU 2017 and beebs benchmarks. Experimental results show that the parallel shadow stack mechanism exhibits higher overhead than the compact shadow stack mechanism. This suggests that the efficiency of the parallel mechanism is reduced due to the limitations of the RISC-V architecture, making the compact shadow stack more suitable for RISC-V. Additionally, this paper identifies the security limitations of the existing RISC-V shadow stack and proposes directions for enhancing the performance and security of shadow stack mechanisms to ensure a secure execution environment for RISC-V.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.99-118
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• 2015

Object-oriented programming languages have been widely selected for developing modern information systems. The use of concepts relating to object-oriented (OO, in short) programming has reduced efforts of reusing pre-existing codes, and the OO concepts have been proved to be a useful in interpreting system requirements. In line with this, we have witnessed that a modern conceptual modeling approach supports features of object-oriented programming. Unified Modeling Language or UML becomes one of de-facto standards for information system designers since the language provides a set of visual diagrams, comprehensive frameworks and flexible expressions. In a modeling process, UML users need to consider relationships between classes. Based on an explicit and clear representation of classes, the conceptual model from UML garners necessarily attributes and methods for guiding software engineers. Especially, identifying an association between a class of part and a class of whole is included in the standard grammar of UML. The representation of part-whole relationship is natural in a real world domain since many physical objects are perceived as part-whole relationship. In addition, even abstract concepts such as roles are easily identified by part-whole perception. It seems that a representation of part-whole in UML is reasonable and useful. However, it should be admitted that the use of UML is limited due to the lack of practical guidelines on how to identify a part-whole relationship and how to classify it into an aggregate- or a composite-association. Research efforts on developing the procedure knowledge is meaningful and timely in that misleading perception to part-whole relationship is hard to be filtered out in an initial conceptual modeling thus resulting in deterioration of system usability. The current method on identifying and classifying part-whole relationships is mainly counting on linguistic expression. This simple approach is rooted in the idea that a phrase of representing has-a constructs a par-whole perception between objects. If the relationship is strong, the association is classified as a composite association of part-whole relationship. In other cases, the relationship is an aggregate association. Admittedly, linguistic expressions contain clues for part-whole relationships; therefore, the approach is reasonable and cost-effective in general. Nevertheless, it does not cover concerns on accuracy and theoretical legitimacy. Research efforts on developing guidelines for part-whole identification and classification has not been accumulated sufficient achievements to solve this issue. The purpose of this study is to provide step-by-step guidelines for identifying and classifying part-whole relationships in the context of UML use. Based on the theoretical work on Meta-model Formalization, self-check forms that help conceptual modelers work on part-whole classes are developed. To evaluate the performance of suggested idea, an experiment approach was adopted. The findings show that UML users obtain better results with the guidelines based on Meta-model Formalization compared to a natural language classification scheme conventionally recommended by UML theorists. This study contributed to the stream of research effort about part-whole relationships by extending applicability of Meta-model Formalization. Compared to traditional approaches that target to establish criterion for evaluating a result of conceptual modeling, this study expands the scope to a process of modeling. Traditional theories on evaluation of part-whole relationship in the context of conceptual modeling aim to rule out incomplete or wrong representations. It is posed that qualification is still important; but, the lack of consideration on providing a practical alternative may reduce appropriateness of posterior inspection for modelers who want to reduce errors or misperceptions about part-whole identification and classification. The findings of this study can be further developed by introducing more comprehensive variables and real-world settings. In addition, it is highly recommended to replicate and extend the suggested idea of utilizing Meta-model formalization by creating different alternative forms of guidelines including plugins for integrated development environments.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.173-193
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• 2010

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component-based software development to promote application interaction and integration within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web services repositories not only be well-structured but also provide efficient tools for an environment supporting reusable software components for both service providers and consumers. As the potential of Web services for service-oriented computing is becoming widely recognized, the demand for an integrated framework that facilitates service discovery and publishing is concomitantly growing. In our research, we propose a framework that facilitates Web service discovery and publishing by combining clustering techniques and leveraging the semantics of the XML-based service specification in WSDL files. We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the Web service domain. We have developed a Web service discovery tool based on the proposed approach using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web services repositories. We believe that both service providers and consumers in a service-oriented computing environment can benefit from our Web service discovery approach.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.33-54
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• 2010

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component based software development to promote application interaction both within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web service repositories not only be well-structured but also provide efficient tools for developers to find reusable Web service components that meet their needs. As the potential of Web services for service-oriented computing is being widely recognized, the demand for effective Web service discovery mechanisms is concomitantly growing. A number of public Web service repositories have been proposed, but the Web service taxonomy generation has not been satisfactorily addressed. Unfortunately, most existing Web service taxonomies are either too rudimentary to be useful or too hard to be maintained. In this paper, we propose a Web service taxonomy generation framework that combines an artificial neural network based clustering techniques with descriptive label generating and leverages the semantics of the XML-based service specification in WSDL documents. We believe that this is one of the first attempts at applying data mining techniques in the Web service discovery domain. We have developed a prototype system based on the proposed framework using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web service repositories. We report on some preliminary results demonstrating the efficacy of the proposed approach.

• Resources Recycling
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• v.27 no.1
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• pp.84-91
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• 2018

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.

• Journal of Energy Engineering
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• v.24 no.1
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• pp.149-163
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• 2015

In this study, we conducted study on the confirmation of thermal-hydraulic safety for Mast assembly with Computational Fluid Dynamics(CFD) analysis. Before performing the natural convection analysis for the Mast assembly by using CFD code, we validated the CFD code against two benchmark natural convection data for the evaluation of turbulence models and confirmation of its applicability to the natural convection flow. From the first benchmark test which was performed by Betts et al. in the simple rectangular channel, we selected standard k-omega turbulence model for natural convection. And then, calculation performance of CFD code was also investigated in the sub-channel of rod bundle by comparing with PNL(Pacific Northwest Laboratory) experimental data and prediction results by MATRA and Fluent 12.0 which were performed by Kwon et al.. Finally, we performed main natural convection analysis for fuel assembly inside the Mast assembly by using validated turbulence model. From the calculation, we observed stable natural circulation flow between the mast assembly and pool side and evaluated the thermal-hydraulic safety by calculating the departure from nucleate boiling ratio.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.6
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• pp.503-513
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• 2005

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity($77\%$) and specificity($95\%$) are achieved for test protein pairs containing common domains with teaming sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.