• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2070-2079
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• 1997

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.738-745
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• 2001

In this paper, when the moving object move to the three-dimentional space, the tracking system track the moving object using the fuzzy reasoning. The joint angle el of the manipulator rotate from $0^{\circ}\; to\; 360^{\circ}$ , and the joint angle $\theta_2$rotate from$0^{\circ}\; to\; 360^{\circ}$. The fuzzy singleton is used for fuzzification and the control rule is twenty five and the fuzzy inference method is simplified Mamdani's reasoning and the defuzzification is the SCOG(Simplified Center Of Gravity) of the fuzzy controller To measure of the performance of the designed system, the fuzzy controller is compared with the CTM(Computed Torque Method) controller at the same condition. when the disturbance torque is ON, the both of CTM and fuzzy controller tracked object without error, However, the disturbance torque changed 0.4N, the CTM controller is 10 times greater than fuzzy controller at the sum of absolute error difference. The designed system is showed it's robustness against with disturbance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3407-3411
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• 2015

In the process of extending the UML model for a various domain, comply with the UML metamodel and it is possible to obtain a number of advantages. However, the UML metamodel is necessary to redefine the diagram must be defined via the Profile in order to expand and transformation the UML metamodel from a variety of sources using the UML metamodel is becoming increase massive scale. it is necessary to use only those extracts the element relative to the target to be used to extend a UML metamodel. Used to re-define the extension of the UML Metamodel and Profile based UML modeling tools and metadata repositories by analysis tools, can develop more quickly and easily, by utilizing these tools can improve the quality of development in the SW industry. In this paper, we present an algorithm that of the profile through the expansion of the UML metamodel and shows the results in actually applying e-government standards framework.

• Journal of KIISE
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• v.42 no.5
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• pp.580-590
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• 2015

Recently, the issue of efficient power management in datacenters as a part of green computing has gained prominence. For realizing efficient power management, effective power monitoring and analysis must be conducted for servers in a datacenter. However, an administrator should know the exact structure of the datacenter and its associated databases, and is required to analyze relationships among the observed data. This is because according to previous monitoring approaches, servers are usually managed using only databases. In addition, it is not possible to monitor data that are not indicated in databases. To overcome these drawbacks, we proposed an ontology-based monitoring approach. We constructed domain ontology for management servers at a datacenter, and mapped observed data onto the constructed domain ontology by using semantic annotation. Moreover, we defined query creation rules and server state rules. To demonstrate the proposed approach, we designed an ontology based monitoring system architecture, and constructed a knowledge system. Subsequently, we implemented the pilot system to verify its effectiveness.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.165-179
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• 2005

Close collaboration among businesses is required in B2B EC environment. Furthermore, emergency requests or critical information among businesses should be processed in an immediate mode. Most current systems, however, due to firewalls for the systems' security and autonomy, can not handle these requirements appropriately, but handle them in an ad hoc manner In this paper a method of timely collaboration among businesses and an active functionality component to support it in B2B EC environment are proposed. Since the active functionality component supports high level ECA rule patterns and event-based immediate processing, system administrators and programmers can easily program and maintain the timely collaboration independently to the application logic. The proposed active functionality component uses HTTP protocol to be applied through firewalls and is designed using a commercial DBMS for practical purpose.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5135-5142
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• 2011

A flexible mechanism is proposed in this paper to improve the dynamic response performance of a traffic flow control system in an urban area. The roads, vehicles, and traffic control systems are all modeled as intelligent systems, wherein a wireless communication network is used as the medium of communication between the vehicles and the roads. The necessary sensor networks are installed in the roads and on the roadside upon which reinforcement learning is adopted as the core algorithm for this mechanism. A traffic policy can be planned online according to the updated situations on the roads, based on all the information from the vehicles and the roads. This improves the flexibility of traffic flow and offers a much more efficient use of the roads over a traditional traffic control system. The optimum intersection signals can be learned automatically online. An intersection control system is studied as an example of the mechanism using Q-learning based algorithm, and simulation results showed that the proposed mechanism can improve the traffic efficiency and the waiting time at the signal light by more than 30% in various conditions compare to the traditional signaling system.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.9
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• pp.465-474
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• 2005

For high density SoC design, on-chip communication based on bus interconnection encounters bandwidth limitation while an NoC(Network-on-Chip) approach suffers from unacceptable complexity in its Implementation. This paper introduces a new on-chip communication protocol, SNP (SoC Network Protocol) to overcome these problems. In SNP, conventional on-chip bus signals are categorized into three groups, control, address, and data and only one set of wires is used to transmit all three groups of signals, resulting in the dramatic decrease of the number of wires. SNP efficiently supports master-master communication as well as master-slave communication with symmetric channels. A sequencing rule of signal groups is defined as a part of SNP specification and a phase-restoration feature is proposed to avoid redundant signals transmitted repeatedly over back-to-back transactions. Simulation results show that SNP provides about the same bandwidth with only $54\%$ of wires when compared with AMBA AHB.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.561-570
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• 2009

In order to manage repair and maintenance efficiently in the mobile environment, the system structure to manage service as a policy and the policy description language are needed. This research defined the structure of PEP, which is the executioner of policy in the IETF policy framework, and proposed the policy description language which can be carried out under the PEP structure. The proposed policy description language derived demand matters based on documentary data and the characteristics of mobile and the policy information model was designed with the three stage approaches and was defined as policy description language. The three stage approaches are made up of the policy domain that decides the scope to which the policy applies, the policy rules which distinguish the kinds of policy application and control, and policy grammar which contextualizes the policy structure. In order to verify the efficiency of the policy description language, scenarios are defined with the policy description language and verified it by using policy tool and proved the expansive nature by comparing and analyzing other policy description language.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.41-49
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• 2019

We are to build an unsupervised machine learning-based language model which can estimate the amount of information that are in need to process words consisting of subword-level morphemes and syllables. We are then to investigate whether the reading times of words reflecting their morphemic and syllabic structures are predicted by an information-theoretic measure such as surprisal. Specifically, the proposed Morfessor-based unsupervised machine learning model is first to be trained on the large dataset of sentences on Sejong Corpus and is then to be applied to estimate the information-theoretic measure on each word in the test data of Korean words. The reading times of the words in the test data are to be recruited from Korean Lexicon Project (KLP) Database. A comparison between the information-theoretic measures of the words in point and the corresponding reading times by using a linear mixed effect model reveals a reliable correlation between surprisal and reading time. We conclude that surprisal is positively related to the processing effort (i.e. reading time), confirming the surprisal hypothesis.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.