• 음성과학
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• 제12권4호
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• pp.31-42
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• 2005

Recently, Gaussian Mixture Model (GMM), a special form of CHMM, has been applied to speaker identification and it has proved that performance of GMM is better than CHMM. Therefore, in this paper the speaker models based on GMM and a new GMM using the modified EM algorithm are introduced and evaluated for text-independent speaker identification. Various experiments were performed to evaluate identification performance of two algorithms. As a result of the experiments, the GMM speaker model attained 94.6% identification accuracy using 40 seconds of training data and 32 mixtures and 97.8% accuracy using 80 seconds of training data and 64 mixtures. On the other hand, the new GMM speaker model achieved 95.0% identification accuracy using 40 seconds of training data and 32 mixtures and 98.2% accuracy using 80 seconds of training data and 64 mixtures. It shows that the new GMM speaker identification performance is better than the GMM speaker identification performance.

• Journal of information and communication convergence engineering
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• 제10권1호
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• pp.33-39
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• 2012

In this paper, we compare the accuracy of most representative radio frequency identification (RFID) tag estimation functions in the context of minimizing RFID tag identification delay. Before the comparisons, we first evaluate the accuracy of Schoute's estimation function, which has been widely adopted in many RFID tag identification processes, and show that its accuracy actually depends on the number of tags to be identified and frame size L used for dynamic frame slotted Aloha cycles. Through computer simulations, we show how the accuracy of estimation functions is related to the actual tag read performance in terms of identification delay.

• 한국해양공학회지
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• 제16권6호
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• pp.25-31
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• 2002

A system identification method is introduced to increase the prediction accuracy of a ship's maneuverability in PMM test, analysis. To improve the accuracy of linear hydrodynamic coefficients, the analysis techniques of pure sway and yaw tests are developed, and confirmed. In the analysis of sway tests, accuracy to linear hydrodynamic coefficients depends on the frequency of sway motion. To obtain nonlinear hydrodynamic coefficients for large drift angles, a combined yaw test is introduced. Using this system identification method, runs of PMM test can be reduced while retaining sufficient accuracy, compared to the Fourier integration method. Through the comparisons with sea trial results and the Fourier integration method, the accuracy and efficiency of the newly proposed system identification method, based on least square method, has been validated.

• 품질경영학회지
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• 제43권4호
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• pp.521-532
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• 2015

Purpose: The purpose of this study is to compare two multivariate cumulative sum (MCUSUM) charts designed for spatio-temporal surveillance in terms of not only temporal detection performance but also spatial detection performance. Method: Experiments under various configurations are designed and performed to test two CUSUM charts, namely SMCUSUM and RMCUSUM. In addition to average run length(ARL), two measures of spatial identification accuracy are reported and compared. Results: The RMCUSUM chart provides higher level of spatial identification accuracy while two charts show comparable performance in terms of ARL. Conclusion: The RMCUSUM chart has more flexibility, robustness, and spatial identification accuracy when compared to those of the SMCUSUM chart. We recommend to use the RMCUSUM chart if control limit calibration is not an urgent task.

• Smart Structures and Systems
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• 제15권1호
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• pp.15-40
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• 2015

This paper proposes a structural damage identification approach based on the power spectral density transmissibility (PSDT), which is developed to formulate the relationship between two sets of auto-spectral density functions of output responses. The accuracy of response reconstruction with PSDT is investigated and the damage identification in structures is conducted with measured acceleration responses from the damaged state. Numerical studies on a seven-storey plane frame structure are conducted to investigate the performance of the proposed damage identification approach. The initial finite element model of the structure and measured acceleration measurements from the damaged structure are used for the identification with a dynamic response sensitivity-based model updating method. The simulated damages can be identified accurately without and with a 5% noise effect included in the simulated responses. Experimental studies on a steel plane frame structure in the laboratory are performed to further verify the accuracy of response reconstruction with PSDT and validate the proposed damage identification approach. The locations of the introduced damage are detected accurately and the stiffness reductions in the damaged elements are identified close to the true values. The identification results demonstrated the accuracy of response reconstruction as well as the correctness and efficiency of the proposed damage identification approach.

• Journal of information and communication convergence engineering
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• 제16권4호
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• pp.228-234
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• 2018

In software development, the quality of a product often depends on whether its developers can rapidly find and contribute to the proper tasks. Currently, the word data of projects to which newcomers have previously contributed are mainly utilized to find appropriate source files in an ongoing project. However, because of the vocabulary gap between software projects, the accuracy of source file identification based on information retrieval is not guaranteed. In this paper, we propose a novel source file identification method to reduce the vocabulary gap between software projects. The proposed method employs DBPedia Spotlight to identify proper source files based on semantic similarity between source files of software projects. In an experiment based on the Spring Framework project, we evaluate the accuracy of the proposed method in the identification of contributable source files. The experimental results show that the proposed approach can achieve better accuracy than the existing method based on comparison of word vocabularies.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.269-274
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• 2022

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum N_c = 100~10000 and the signal to noise ratio SNR = 0.05-1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the N_c>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of ²³⁵U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

• 한국멀티미디어학회논문지
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• 제11권6호
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• pp.764-776
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• 2008

Image identification is the process of checking whether the query image is the transformed version of the specific original image or not. In this paper, image identification method based on feature composition is proposed. Used features include color distance, texture information and average pixel intensity. We extract color characteristics using color distance and texture information by Modified Generalized Symmetry Transform as well as average intensity of each pixel as features. Individual feature is quantized adaptively to be used as bins of histogram. The histogram is normalized according to data type and it is used as the signature in comparing the query image with database images. In matching part, Manhattan distance is used for measuring distance between two signatures. To evaluate the performance of the proposed method, independent test and accuracy test are achieved. In independent test, 60,433 images are used to evaluate the ability of discrimination between different images. And 4,002 original images and its 29 transformed versions are used in accuracy test, which evaluate the ability that the proposed algorithm can find the original image correctly when some transforms was applied in original image. Experiment results show that the proposed identification method has good performance in accuracy test. And the proposed method is very useful in real environment because of its high accuracy and fast matching capacity.

• 대한의용생체공학회:의공학회지
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• 제43권6호
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• pp.441-447
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• 2022

In this paper, we propose a pill identification model using engraved text feature and image feature such as shape and color, and compare it with an identification model that does not use engraved text feature to verify the possibility of improving identification performance by improving recognition rate of the engraved text. The data consisted of 100 classes and used 10 images per class. The engraved text feature was acquired through Keras OCR based on deep learning and 1D CNN, and the image feature was acquired through 2D CNN. According to the identification results, the accuracy of the text recognition model was 90%. The accuracy of the comparative model and the proposed model was 91.9% and 97.6%. The accuracy, precision, recall, and F1-score of the proposed model were better than those of the comparative model in terms of statistical significance. As a result, we confirmed that the expansion of the range of feature improved the performance of the identification model.

• Journal of Electrical Engineering and Technology
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• 제11권3호
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• pp.769-774
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• 2016

In the design of sensorless control system for induction motor, high-precision speed estimation is one of the most difficult problems. To solve this problem, the common method is model reference adaptive method (MRAS). MRAS requires accurate motor parameters to estimate rotor speed precisely. However, when motor is running, the variety of temperature and magnetic saturation will lead to the change of motor parameters such as stator resistance and rotor resistance, which will lower the accuracy of the speed estimation. To improve the accuracy and rapidity of speed estimation, this paper analyses the mutual MRAS speed identification based on rotor flux linkage, and proposes an improved mutual MRAS speed identification based on back-EMF. The improved method is verified by Simulink simulation and motor experimental platform based on DSP2812. The results of simulation and experiment indicate that the method proposed by this paper can significantly improve the accuracy of speed identification, and speed up the response of identification.