• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1416-1432
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• 2022

The access control system is a system that allows users to selectively enter the building by granting an access key to the user for security. Access keys with weak security are easily exposed to attackers and cannot properly perform the role that authenticates users. Access code keys should be protected from forgery or spoofing. For this reason, access key verification service models is important in security. However, most models manage all access keys on one central server. This method not only interrupts all services due to server errors, but also risks forgery and spoofing in the process of transmitting access keys. Therefore, blockchain algorithms are used to reduce this risk. This paper proposes a blockchain-based access key verification service model that used distributed stored blockchain gateways on storing access keys and authenticates the user's identity based on them. To evaluate the performance of this model, an experiment was conducted to confirm the performance of the access key forgery recovery rate and the blockchain network performance. As a result, the proposed method is 100% forgery recovery rate, and the registration and verification process is evaluated at 387.58 TPS and 136.66 TPS.

• MALSORI
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• no.51
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• pp.71-84
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• 2004

In this paper, we propose the automatic speech database verification method(or called automatic verification) based on confidence measure for a large speech database. This method verifies the consistency between given transcription and speech using the confidence measure. The automatic verification process consists of two stages : the word-level likelihood computation stage and multi-level likelihood ratio computation stage. In the word-level likelihood computation stage, we calculate the word-level likelihood using the viterbi decoding algorithm and make the segment information. In the multi-level likelihood ratio computation stage, we calculate the word-level and the phone-level likelihood ratio based on confidence measure with anti-phone model. By automatic verification, we have achieved about 61% error reduction. And also we can reduce the verification time from 1 month in manual to 1-2 days in automatic.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.257-267
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• 2009

This paper presents a training method for neural networks and the employment of MSE (mean scare error) values as the basis of a decision regarding the identity claim of a speaker in a recurrent neural networks based speaker verification system. Recurrent neural networks (RNNs) are employed to capture temporally dynamic characteristics of speech signal. In the process of supervised learning for RNNs, target outputs are automatically generated and the generated target outputs are made to represent the temporal variation of input speech sounds. To increase the capability of discriminating between the true speaker and an impostor, a discriminative training method for RNNs is presented. This paper shows the use and the effectiveness of the MSE value, which is obtained from the Euclidean distance between the target outputs and the outputs of networks for test speech sounds of a speaker, as the basis of speaker verification. In terms of equal error rates, results of experiments, which have been performed using the Korean speech database, show that the proposed speaker verification system exhibits better performance than a conventional hidden Markov model based speaker verification system.

• ETRI Journal
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• v.33 no.3
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• pp.423-433
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• 2011

This paper introduces an adaptive and integrated utterance verification (UV) framework using minimum verification error (MVE) training as a new set of solutions suitable for real applications. UV is traditionally considered an add-on procedure to automatic speech recognition (ASR) and thus treated separately from the ASR system model design. This traditional two-stage approach often fails to cope with a wide range of variations, such as a new speaker or a new environment which is not matched with the original speaker population or the original acoustic environment that the ASR system is trained on. In this paper, we propose an integrated solution to enhance the overall UV system performance in such real applications. The integration is accomplished by adapting and merging the target model for UV with the acoustic model for ASR based on the common MVE principle at each iteration in the recognition stage. The proposed iterative procedure for UV model adaptation also involves revision of the data segmentation and the decoded hypotheses. Under this new framework, remarkable enhancement in not only recognition performance, but also verification performance has been obtained.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.109-116
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• 2022

Recently, a model-based development method centered on highly readable and standardized UML (Unified Modeling Language) models has been applied to solve unclear communications in large-scale software development. However, it is difficult to apply consistent UML models depending on software developers' proficiency, understanding of models and modeling tools. In this paper, we propose a method for developing a Model Verification System to apply an consistent UML model to software development. Then, the developed Model Verification System is partially applied to the Naval Combat System Software development to prove its function. The Model Verification System provides automatic verification of models created by developers according to domain characteristics. If the Model Verification System proposed in this paper is used, It has the advantage of being able to apply the consistent UML model more easily to Naval Combat System Software Development.

• MALSORI
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• no.47
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• pp.131-140
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• 2003

We propose a new method of generating client models for HMM based text-dependent speaker verification system with only a small amount of training data. To make a client model, statistical methods such as segmental K-means algorithm are widely used, but they do not guarantee the quality or reliability of a model when only limited data are avaliable. In this paper, we propose a blind speech segmentation based on level building DTW algorithm as an alternative method to make a client model with limited data. In addition, considering the fact that voiced sounds have much more speaker-specific information than unvoiced sounds and energy of the former is higher than that of the latter, we also propose a new score evaluation method using the observation probability raised to the power of weighting factor estimated from the normalized log energy. Our experiment shows that the proposed methods are superior to conventional HMM based speaker verification system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.425-433
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• 2016

The construction of a light rail transit (LRT) system is a large and complex infrastructure project involving hundreds of billions of won in construction costs for a single route, and it is very important to carry out such a project from a life-cycle perspective because of its long-term operation. Systems engineering is a means and methodology to successfully implement customers' needs, and it is useful in large projects such as light rail transit. An application model called Systems Engineering for Light Rail Transit (SELRT) was developed to support systems engineering activities in light rail transit projects. In order to utilize SELRT, it is necessary to ensure that system requirements are met. As such, in this paper, we present a verification procedure and architecture based on a systems engineering-based methodology, thereby identifying the system requirements and deriving the verification requirements to confirm the SELRT model for the proposed method. The results show that the traceability of the system requirements and verification requirements, the verification method for each requirement, and the demonstration results for computerized tools are mutually connected, and that the initial requirements are clearly implemented in the SELRT. The proposed method is valid for verifying the SELRT, which can also be utilized in a LRT project.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8753-8759
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• 2015

Establishment of analysis procedures and validated performance measurements for generator output is required to maintain stable management of generator output in turbine power generation cycle. We developed turbine expansion model and measurement validation model for the performance calculation of generator using turbine output based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). We also developed verification model for uncertain measurement data related to the turbine and generator output. Although the model in previous researches was developed using artificial neural network and kernel regression, the verification model in this paper was based on algorithms through Support Vector Machine (SVM) model to overcome the problems of unmeasured data. The selection procedures of related variables and data window for verification learning was also developed. The model reveals suitability in the estimation procss as the learning error was in the range of about 1%. The learning model can provide validated estimations for corrective performance analysis of turbine cycle output using the predictions of measurement data loss.

• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.77-90
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• 2000

Real-Time verification is a procedure that verifies the correctness of specification related to requirement in time as well as in logic. One serious problem encountered in the verification task is that the state space grows exponentially owing to the unboundedness of time, which is termed the state space explosion problem. In this paper, we propose a real-time verification technique checking the correctness of specification by showing that a system model described in timed automata is equivalent to the characteristic of system property specified in timed modal-mu calculus. For this, we propose a local model checking method based on the value of the formula in initial state with constructing product graph concerned to only the nodes needed for verification process. Since this method does not search for every state of system model, the state space is reduced drastically so that the proposed method can be applied effectively to real-time system verification.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.381-386
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• 2014

A highly reliable safety class controller for NPPs (Nuclear Power Plants) is mandatory as even a minor malfunction can lead to disastrous consequences for people, the environment or the facility. In order to enhance the reliability of a safety class digital controller for NPPs, we employed a diversity approach, in which a PLC-type controller and a PLD-type controller are to be operated in parallel. We built and used structured testbenches based on the classes supported by UVM for functional verification of the PLD-type controller designed for NPPs. We incorporated a UVM register model into the testbenches in order to increase the controllability and the observability of the DUT(Device Under Test). With the increased testability, we could easily verify the datapaths between I/O ports and the register sets of the DUT, otherwise we had to perform black box tests for the datapaths, which is very cumbersome and time consuming. We were also able to perform constrained random verification very easily and systematically. From the study, we confirmed the various advantages of using the UVM register model in verification such as scalability, reusability and interoperability, and set some design guidelines for verification of the NPP controllers.