• Proceedings of the IEEK Conference
• /
• 2003.11b
• /
• pp.167-170
• /
• 2003

In this paper, a design and experiment of PCI Express Core verification Model is described. The model targeting Endpoint core verification is designed by using newly-emerging SystemC which is a system design language based on a new C++ class library and simulation engine. In the verification model, we developed a SystemC Host System model which act as a Root Complex and Device Driver dedicated to the PCI Express Endpoint RTL Core. The test of Host System Model is guided by scenarios which implements and acts point of Device Driver and Root Complex and shows the result of simulation. Also, We present the full structure of verification model and Host model.

• Proceedings of the IEEK Conference
• /
• summer
• /
• pp.285-288
• /
• 2004

In this paper, we present a design and experiment of PCI Express core verification model. The model targeting Endpoint core based on Verilog HDL is designed by newly-emerging SystemC, which is a new C++ class library based system design approach. In the verification model, we designed and implemented a SystemC host system model which acted as Root Complex and device driver dedicated to the PCI Express Endpoint RTL core. The verification process is scheduled by scenarios which are implemented in host model. We show that the model is useful especially for verifying the RTL model which has dependencies on system software.

• Journal of Advanced Navigation Technology
• /
• v.23 no.5
• /
• pp.361-366
• /
• 2019

In developing the avionics system, a system integration laboratory (SIL) is established to verify the function and interworking of individual components. In case of individual verification of SIL's components and system integration, a SIL model that simulates the function and interworking of each equipment is developed and used. A SIL model shall be pre-verified against all data defined in the interface control document (ICD) before interworking with the actual equipment and reverified even when the ICD changes or functions change. However, if the verification of the SIL model is performed manually, the verification of the individual SIL model takes considerable time. For this reason, selective regression tests are often performed to determine a impact of SIL models on ICD changes and some functional changes. In this paper, we designed SIL model verification automation method to perform regession test by reducing verification time of SIL model and verify the usefulness of verification automation design by developing SIL model verification automation tool.

• KEPCO Journal on Electric Power and Energy
• /
• v.7 no.1
• /
• pp.115-123
• /
• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

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

• Speech Sciences
• /
• v.10 no.2
• /
• pp.35-43
• /
• 2003

General speaker verification systems improve their recognition performances by normalizing log likelihood ratio, using a speaker model and its background speaker model that are required to be verified. So these systems rely heavily on the availability of much speaker independent databases for background speaker model design. This constraint, however, may be a burden in practical and portable devices such as palm-top computers or wireless handsets which place a premium on computations and memory. In this paper, new approach for the GMM-based background model design used in portable speaker verification system is presented when the enrollment data is available. This approach is to modify three parameters of GMM speaker model such as mixture weights, means and covariances along with reduced mixture order. According to the experiment on a 20 speaker population from YOHO database, we found that this method had a promise of effective use in a portable speaker verification system.

• Speech Sciences
• /
• v.9 no.2
• /
• pp.135-146
• /
• 2002

Generally a speaker verification system improves its system recognition ratio by regularizing log likelihood ratio, using a speaker model and its background speaker model that are required to be verified. The speaker-based cohort method is one of the methods that are widely used for selecting background speaker model. Recently, Gaussian-based cohort model has been suggested as a virtually synthesized cohort model, and unlike a speaker-based model, this is the method that chooses only the probability distributions close to basic speaker's probability distribution among the several neighboring speakers' probability distributions and thereby synthesizes a new virtual speaker model. It shows more excellent results than the existing speaker-based method. This study compared the existing speaker-based background speaker models and virtual speaker models and then constructed new virtual background speaker model groups which combined them in a certain ratio. For this, this study constructed a speaker verification system that uses GMM (Gaussin Mixture Model), and found that the suggested method of selecting virtual background speaker model shows more improved performance.

• Journal of the Korean Society of Systems Engineering
• /
• v.2 no.2
• /
• pp.33-38
• /
• 2006

It is well known that efficient management and thorough implementation of stakeholder requirements is vital for a successful development of a large-scale and complex system. Equally important is to make sure that all the requirements be correctly realized in the developed system. For the purpose, verification requirements are derived with traceability from the system requirements. This paper discusses a step by step process for constructing the requirements verification model which includes : 1) the schema modeling both requirements and their traceability; 2) the template documenting the verification requirements; 3) the verification model constructed from the schema; and 4) the test and evaluation plan that can be printed automatically.

• MALSORI
• /
• no.63
• /
• pp.125-137
• /
• 2007

Recently, many techniques have been proposed to improve the noise robustness for speaker verification. In this paper, we consider the feature recombination technique in multi-band approach. In the conventional feature recombination for speaker verification, to compute the likelihoods of speaker models or universal background model, whole feature components are used. This computation method is not effective in a view point of multi-band approach. To deal with non-effectiveness of the conventional feature recombination technique, we introduce a subband likelihood computation, and propose a modified feature recombination using subband likelihoods. In decision step of speaker verification system in noise environments, a few very low likelihood scores of a speaker model or universal background model cause speaker verification system to make wrong decision. To overcome this problem, a reliable feature selection method is proposed. The low likelihood scores of unreliable feature are substituted by likelihood scores of the adaptive noise model. In here, this adaptive noise model is estimated by maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. The proposed method using subband-based reliable feature selection obtains better performance than conventional feature recombination system. The error reduction rate is more than 31 % compared with the feature recombination-based speaker verification system.

• MALSORI
• /
• no.49
• /
• pp.145-158
• /
• 2004

This paper is aiming at implementation of real-time speaker verification system using DSP board. Dialog/4, which is based on microprocessor and DSP processor, is selected to easily control telephone signals and to process audio/voice signals. Speaker verification system performs signal processing and feature extraction after receiving voice and its ID. Then through computing the likelihood ratio of claimed speaker model to the background model, it makes real-time decision on acceptance or rejection. For the verification experiments, total 15 speaker models and 6 background models are adopted. The experimental results show that verification accuracy rates are 99.5% for using telephone speech-based speaker models.