• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.1-8
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• 2023

This paper analyzes the trends of identification proofing services(PIPSs) to identify and authenticate users online and proposes a method to improve PIPS based on alternative means of resident registration numbers in Korea. Digital identity proofing services play an important role in modern society, but there are some problems. Since they handle sensitive personal information, there is a risk of information leakage, hacking, or inappropriate access. Additionally, online service providers may incur additional costs by applying different PIPSs, which results in online service users bearing the costs. In particular, in these days of globalization, different PIPSs are being used in various countries, which can cause difficulties in international activities due to lack of global consistency. Overseas online PIPSs include expansion of biometric authentication, increase in mobile identity proofing, and distributed identity proofing using blockchain. This paper analyzes the trend of PIPSs that prove themselves when identifying users of online services in non-face-to-face overseas situations, and proposes improvements by comparing them with alternative means of Korean resident registration numbers. Through the proposed method, it will be possible to strengthen the safety of Korea's PIPS and expand the provision of more reliable identification services.

• The Journal of the Acoustical Society of Korea
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• v.24 no.3
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• pp.141-149
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• 2005

Tn general. Gaussian mixture model (GMM) is used to estimate the speaker model from the speech for speaker identification. The parameter estimates of the GMM are obtained by using the Expectation-Maximization (EM) algorithm for the maximum likelihood (ML) estimation. However the EM algorithm has such drawbacks that it depends heavily on the initialization and it needs the number of mixtures to be known. In this paper, to solve the above problems of the EM algorithm. we propose an EM algorithm with the initialization based on incremental ${\cal}k-means$ for GMM. The proposed method dynamically increases the number of mixtures one by one until finding the optimum number of mixtures. Whenever adding one mixture, we calculate the mutual relationship between it and one of other mixtures respectively. Finally. based on these mutual relationships. we can estimate the optimal number of mixtures which are statistically independent. The effectiveness of the proposed method is shown by the experiment for artificial data. Also. we performed the speaker identification by applying the proposed method comparing with other approaches.

• Computational Structural Engineering : An International Journal
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• v.3 no.1
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• pp.1-7
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• 2003

For system identification of large structures, it is not practical to identify the entire structure due to the prohibitive computational time and difficulty in numerical convergence. This paper explores the possibility of performing system identification at substructure level, taking advantage of reduction in both the number of unknowns and the number of degrees of freedom involved. Another advantage is that different portions (substructures) of a structural system can be identified independently and even concurrently with parallel computing. Two substructural identification methods are formulated on the basis whether substructural approach is used to obtain first-order or second-order model. For substructural first-order model, identification at the substructure level will be performed by means of the Observer/Kalman filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA) whereas identification at the global level will be performed to obtain second-order model in order to evaluate the system's stiffness and mass parameters. In the case of substructural second-order model, identification will be performed at the substructure level throughout the identification process. The efficiency of the proposed technique is shown by numerical examples for multi-storey shear buildings subjected to random forces, taking into consideration the effects of noisy measurement data. The results indicate that both the proposed methods are effective and efficient for damage identification of large structures.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.686-691
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• 2011

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.207-209
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• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• The Journal of Fisheries Business Administration
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• v.38 no.1 s.73
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• pp.115-132
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• 2007

Implementing the automatic identification in supply chain management is essential for effective and efficient process control. Though the GTIN based bar code system is generally used as an automatic identification method in most industries, it can not identify individual item, and is not appropriated for products' reliability and safety management. So the RFID system with EPC is considered as a better solution for resolving those problems. This study reviewed automatic identification code systems and the attributes and characteristics of RFID Gen 2 which became a global standard recently for supply chain management. Particularly, this study analyzed RFID Gen 2 systems' read rates on various conditions including distances between tags and readers and between antennas, condensation, and several packing materials in practical supply chain environment. The results of this study showed that the RFID Gen 2 had high read ratio in practical application and would be adopted as a new automatic identification means for traceability systems.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.97-99
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• 2013

Although UCI Identification metadata is not represented in the UCI syntax, it means a set of elements that enable users to easily and quickly identify. Against this backdrop, we research on how to design identification metadata for UCI. First of all, we check ISO/IEC 11179 and compare this with UCI properties. And then we defines nine components (such as UCI, Identifier, Title, Type, Mode, Format, Contributor, ContributorEntitiy, and ContributorRole) as elements of the identification metadata and establish encoding scheme with several parts (such as List of Encoding Scheme, Encoding Scheme of Identifier, Encoding Scheme of Type, Encoding Scheme of Mode, Encoding Scheme of Format, and Encoding Scheme of ContributorRole).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.61-65
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• 2003

In this paper, we analysed the standardized techniques for radio frequency identification systems. RFID system is to carry data in suitable transponders, generally known as tags, and to retrieve data, by machinable means, at a suitable time and place to satisfy particular application needs. The paper has discussed on international standardization trends and its techniques and provided with understanding the technical regulations for activating and harmonizing internationally domestic RFID industries.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.215-220
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• 1998

In order to optimize fuzzy model, we use the optimal algorithm with a hybrid type in the identification of premise parameters and standard least square method in the identification of consequence parameters of a fuzzy model. The hybrid optimal identification algorithm is carried out using a genetic algorithm and improved complex method. Also, the performance index with weighting factor is proposed to achieve a balance between the insults of performance for the training and testing data. Several numerical examples are used to evaluate the performance of the proposed model.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.