• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.62-66
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• 2017

In this paper, we present the development of communication and data processing strategy for the electromagnetic wave precipitation gauge system. The electromagnetic wave precipitation gauge system is a small system for deriving area rainfall rates within 1 km radius through dual polarization radar observation at 24GHz band. It is necessary to take consider for measurement of accurate precipitation under limited computing resources originating from small systems and to minimize the use of network for the unattended operation and remote management. To overcome computational resource limitations, we adopted the fuzzy logic for quality control to eliminate non-precipitation echoes and developed the method by weighted synthesis of various rain rate fields using multiple radar QPE formulas. Also we have designed variable data packets rules to minimize the network traffic.

• KSCI Review
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• v.14 no.2
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• pp.235-244
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• 2006

This paper attacked the unknown DoS which mixed a DoS attack, Worm and the Trojan horse which used IP Source Address Spoofing and Smurf through the SYN Flooding way that UDP, ICMP, Echo, TCP Syn packet operated. the applications that used TCP/UDP in VoIP service networks. Define necessity of a Dynamic Update Engine for a prevention, and measure Miss traffic at RT statistics of inbound and outbound parts in case of designs of an engine at IPS regarding an Self-learning module and a statistical attack spread. and design a logic engine module. Three engines judge attack grades (Attack Suspicious, Normal), and keep the most suitable filtering engine state through AND or OR algorithms at Footprint Lookup modules. A Real-Time Dynamic Engine and Filter updated protected VoIP service from DoS attacks, and strengthened Ubiquitous Security anger, and were turned out to be.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.165-174
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• 2006

This paper attacked the unknown DoS which mixed a DoS attack, Worm and the Trojan horse which used IP Source Address Spoofing and Smurf through the SYN Flooding way that UDP, ICMP, Echo, TCP Syn packet operated, the applications that used TCP/UDP in VoIP service networks. Define necessity of a Dynamic Update Engine for a prevention, and measure Miss traffic at RT statistics of inbound and outbound parts in case of designs of an engine at IPS regarding an Self-learning module and a statistical attack spread, and design a logic engine module. Three engines judge attack grades (Attack, Suspicious, Normal), and keep the most suitable filtering engine state through AND or OR algorithms at Footprint Lookup modules. A Real-Time Dynamic Engine and Filter updated protected VoIP service from DoS attacks, and strengthened Ubiquitous Security anger, and were turned out to be.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.55-61
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• 2009

This paper proposes a position estimation of a mobile robot based on USN(Ubiquitous Sensor Network) and encoder, and development of tele-operation system using Internet. USN used in experiments is based on ZigBee protocol and has location estimation engine which uses RSSI signal to estimate distance between nodes. By distortion the estimated distance using RSSI is not correct, compensation method is needed. We obtained fuzzy model to calculate more accurate distance between nodes and use encoder which is built in robot to estimate accurate position of robot. Based on proposed position estimation method, tele-operation system was developed. We show by experiment that proposed method is more appropriate for estimation of position and remote navigation of mobile robot through Internet.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.9
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• pp.55-65
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• 2004

ATM was recommended by the ITU and ATM Forum as a means of transportation for B-ISDN. At this time, due to the comprehensive mature of ATM protocol, ATM has been adapted as the backbone system for carrying Internet traffi $c^{[1,2,3,4]}$. But major conceptsregarding the ATN protocol will be used on future technology. This paper presents preventive congestion control mechanisms for detecting HTR(Hard-To Reach) code in ATM systems, in particular for an improved HTR call registration method using network signaling information will discussed. In high speed circuit switching system environments, a fast HTR control mechanism is necessary. We present research results for improving HTR call registration and control methods using network signaling information and fuzzy control mechanisms. We concluded that it showed fast congestion avoidance mechanisms with a fewer system load maximized the efficiency of network resources by restricting ineffective machine attempts.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.1-15
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• 2006

In Korea, a resident registration card has various personal information such as a present address, a resident registration number, a face picture and a fingerprint. A plastic-type resident card currently used is easy to forge or alter and tricks of forgery grow to be high-degree as time goes on. So, whether a resident card is forged or not is difficult to judge by only an examination with the naked eye. This paper proposed an automatic recognition method of a resident card which recognizes a resident registration number by using a refined ART2-based RBF network newly proposed and authenticates a face picture by a template image matching method. The proposed method, first, extracts areas including a resident registration number and the date of issue from a resident card image by applying Sobel masking, median filtering and horizontal smearing operations to the image in turn. To improve the extraction of individual codes from extracted areas, the original image is binarized by using a high-frequency passing filter and CDM masking is applied to the binaried image fur making image information of individual codes better. Lastly, individual codes, which are targets of recognition, are extracted by applying 4-directional contour tracking algorithm to extracted areas in the binarized image. And this paper proposed a refined ART2-based RBF network to recognize individual codes, which applies ART2 as the loaming structure of the middle layer and dynamicaly adjusts a teaming rate in the teaming of the middle and the output layers by using a fuzzy control method to improve the performance of teaming. Also, for the precise judgement of forgey of a resident card, the proposed method supports a face authentication by using a face template database and a template image matching method. For performance evaluation of the proposed method, this paper maked metamorphoses of an original image of resident card such as a forgey of face picture, an addition of noise, variations of contrast variations of intensity and image blurring, and applied these images with original images to experiments. The results of experiment showed that the proposed method is excellent in the recognition of individual codes and the face authentication fur the automatic recognition of a resident card.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.45-51
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• 2006

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The proposed IG_gSOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data(gas furnace process and NOx process data).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).