• Journal of Information Technology Services
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• v.16 no.2
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• pp.97-110
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• 2017

Due to development of IT, various Internet services via the non-face-to-face are increasing rapidly. In the past, the resident registration numbers (RRN) was used a mean of personal identification, but the use of RRN is prohibited by the relevant laws, and the personal identification services using alternative means are activated. According to the prohibition policy of RRN, i-PIN service appeared as an alternative means to identify a person. However, the user's knowledge-based i-PIN service continues to cause fraudulent issuance, account hijacking, and fraud attempts due to hacking accidents. Due to these problems, the usage rate of i-PIN service which performs a nationwide free personal identification service, is rapidly decreasing. Therefore, this paper proposes a technical safety enhancement method for security enhancement in the i-PIN-based personal identification service. In order to strengthen the security of i-PIN, this paper analyzes the encryption key exposure, key exchange and i-PIN authentication model problems of i-PIN and suggests countermeasures. Through the proposed paper, the i-PIN can be expected to be used more effectively as a substitution of RRN by suggesting measures to enhance the safety of personal identification information. Secured personal identification services will enable safer online non-face-to-face transactions. By securing the technical, institutional, and administrative safety of the i-PIN service, the usage rate will gradually increase.

• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.85-93
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• 2018

This study investigates the suitability of the blockchain and FIDO among non-face-to-face authentication means in order to secure diversity of identfication means operated in South Korea. In order to do this, the study selected and analyzed seven conformance criteria (universality, persistence, uniqueness, convenience, security, applicability, and economics), and the results were appropriate. Accordingly, in order to apply the blockchain and FIDO as the identification means, the related regulations and notices should be revised to improve the identification procedure. In addition, differentiated certification standards should be established for each service field to apply various authentication means as well as existing identification means, and the authentication means should be continuously developed and linked with the service. In the future, the identification means will bring security of the information circulation environment in the IoT, so it should be implemented in a variety of services by supporting application of identification means.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.146-148
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• 2005

In this paper, we propose a genetically optimized identification of information granulation(IG)-based fuzzy model. To optimally design the IG-based fuzzy model we exploit a hybrid identification through genetic alrogithms(GAs) and Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the seleced input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the inital parameters are tuned effectively with the aid of the genetic algorithms and the least square method. And also, we exploite consecutive identification of fuzzy model in case of identification of structure and parameters. Numerical example is included to evaluate the performance of the proposed model.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.43-52
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• 1994

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

• Structural Engineering and Mechanics
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• v.39 no.6
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• pp.751-765
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• 2011

The present paper deals with the identification of a concentrated damage in an elastic parabolic arch through the minimization of an objective function which measures the differences between numerical and experimental values of static displacements. The damage consists in a notch that reduces the height of the cross section at a given abscissa and therefore causes a variation in the flexural stiffness of the structure. The analytical values of static displacements due to applied loads are calculated by means of the principle of virtual work for both the undamaged and damaged arch. First, pseudo-experimental data are used to study the inverse problem and investigate whether a unique solution can occur or not. Various damage intensities are considered to assess the reliability of the identification procedure. Then, the identification procedure is applied to an experimental case, where displacements are measured on a prototype arch. The identified values of damage parameters, i.e., location and intensity, are compared to those obtained by means of a dynamic identification technique performed on the same structure.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.33-37
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• 2014

As personal information is utilized as an important user authentication means, a trustable certification means is being required. The face identification technology using characteristics of the personal face among several biometrics technologies is easy in extracting features. In this paper, we implement a face identification robot for unattended reception. The robot is performed by face identification. To assess the effectiveness of the robot, it was tested and experimental results show that the proposed method is applicable for unattended reception interface.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2885-2887
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• 2005

We introduce a hybrid identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally design the IG-based fuzzy model we exploit a hybrid identification through genetic alrogithms(GAs) and Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the seleced input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of HCM clustering help determine the initial paramters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the inital parameters are tuned effectively with the aid of the GAs and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.3
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• pp.40-49
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• 1992

In this paper, an approach to identify fuzzy rules is proposed. The decision of the optimal number of fuzzy rule is made by means of fuzzy c-means clustering. The identification of the parameters of fuzzy implications is carried out by use of genetic algorithms. For the efficinet and fast parameter identification, the reduction thechnique of search areas of genetica algorithms is proposed. The feasibility of the proposed approach is evaluated through the identification of the fuzzy model to describe an input-output relation of Gas Furnace. Despite the simplicity of the propsed apprach the accuracy of the identified fuzzy model of gas furnace is superior as compared with that of other fuzzy modles.