• The Journal of Information Technology
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• v.10 no.4
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• pp.11-26
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• 2007

EDS is a typical convergence service performing convergence between functions owned by multiple SPs. EDS provides users with the relevant information and the communication by the various personal conditional environment. An alternative terminology for events for net- work related applications is 'triggers'-these are assumed to be included in the present description. Events could be defined as any activities which occur within a service avail ability, signals. Events can be managed, for example, as part of a context-aware application. EDS carry out a procedure for processing user's requested information about mobil network-based entities, such as the combination with various applications, such as a LBS (Location Based Service) and area information. In AS (Application Service) network environments, the user has his customized EDS in the network, the EDS automatically connects the appropriate Terminals consistent with a user profile. EDS can also send the collected information to other requesting users by way of event information and using the procedures of combination with other services. The user can record/retrieve the user-related information to from the information base through EDS.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.133-140
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• 2019

In this paper, we present an algorithm for adjusting degree of belief for consistency on the evidence dependency network where various sets of evidence support different sets of hypotheses. It is common for experts to assign higher degree of belief to a hypothesis when there is more evidence over the hypothesis. Human expert without knowledge of uncertainty handling may not be able to cope with how evidence is combined to produce the anticipated belief value. Belief in a hypothesis changes as a series of evidence is known to be true. In non-monotonic reasoning environments, the belief retraction method is needed to clearly deal with uncertain situations. We create evidence dependency network from rules and apply the evidence retraction algorithm to refine belief values on the hypothesis set. We also introduce negative belief values to reflect the reverse effect of evidence combination.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2432-2434
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.37-45
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• 2004

In this paper we propose Clustered Group Multicast by Clustering Algorithm in Wireless Mobile Ad-hoc Network. The proposed scheme applies to Weighted Cluster Algorithm Ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration or reliable support services such as wired network and base station. In ad hoc network muting protocol because of limited bandwidth and high mobility robust, simple and energy consume minimal. WCGM method uses a base structure founded on combination weighted value and applies combination weight value to cluster header keeping data transmission by seeped flooding, which is the advantage of the exiting FGMP method. Because this method has safe and reliable data transmission, it shows the effect to decrease both overhead to preserve transmission structure and overhead for data transmission.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.109-112
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• 2005

As the degree of use of network is increasing the need for building the basic of the stable network service is being needed a lot. Also the engineers require the traffic control for the best application and management and bandwidth management urgently. To solve these problems, there were no other ways of increasing the bandwidth of lines until now. However, this solution has the limit that the cost is too high and the traffic problem is growing continuously. On this study, it is suggested that through the planning the wired /wireless combination network design offering the service of QoS, Multicast, Mobile, realtime multimedia traffic delay without packet loss will be dealth with.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.699-705
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• 2009

This paper reviews methods used to perform reliability and availability analysis of the network system composed by nodes and links. The combination of nodes and links forms virtual connections (VC). The failure of several VCs cause failure of whole network system. Petri Net models are used to analyze the reliability and availability. Stochastic reward nets (SRN) is an extension of stochastic Petri nets provides modelling facilities for network system analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.96.2-96
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• 2001

This paper presents a control method for the experimental mobile vehicle. By merging the advantages of neural network, adaptive and fuzzy control, neural network-based adaptive fuzzy control is proposed. It can deal with a large amount of training data by neural network, from these data producing more accurate fuzzy rules by adaptive control, and then controlling the object by fuzzy control. This is not the simple combination of the three methods, but merging them into one control system Experiments and some future considerations are given.

• Journal of Industrial Technology
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• v.16
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• pp.197-205
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• 1996

In this paper, we propose a new neural architecture. We synthesize the architecture from a combination of structures known as MRCCN (Multi-resolution Radial-basis Competitive and Cooperative Network) and BPN (Backpropagation Network). The proposed neural network is able to improve the learning speed of MRCCN and the mapping capability of BPN. The ability and effectiveness of identifying a ninlinear dynamic system using the proposed architecture will be demonstrated by computer simulation.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.119-127
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• 2018

Training a very large deep neural network can be painfully slow and prone to overfitting. Many researches have done for overcoming the problem. In this paper, a combination of early stopping and ADAM based deep neural network was presented. This form of deep network is useful for handling the big data because it automatically stop the training before overfitting occurs. Also generalization ability is better than pure deep neural network model.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1489-1502
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• 2019

This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.