• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.532-544
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• 2023

Purpose: To identify and evaluate the risk of chemical fire causative substances by using thermal analysis methods (DSC, TGA) for the hazards and physical property changes that occur when newly used biofuels are mixed with existing fuels It is to use it for identification and evaluation of the cause of fire by securing data related to the method and the hazards of the material according to it. Method: The research method used in this experiment is the differential scanning calorimeter (DSC: Difference in heat flux) through quantitative information on the caloric change from the location, shape, number, and area of peaks. flux) was measured, and the weight change caused by decomposition heat at a specific temperature was continuously measured by performing thermogravimetric analyzer (TGA: Thermo- gravimetric Analyzer). Result: First, in the heat flux graph, the boiling point of the material and the intrinsic characteristic value of the material or the energy required for decomposition can be checked. Second, as the content of biodiesel increased, many peaks were identified. Third, it was confirmed through analysis that substances with low expected boiling points were contained. Conclusion: It was shown that the physical risk of the material can be evaluated by using the risk of biodiesel, which is currently used as a new energy source, through various physical and chemical analysis techniques (DSC + TGA).In addition, it is expected that the comparison of differences between test methods and the accumulation and utilization of know-how on experiments in this study will be helpful in future studies on physical properties of hazardous materials and risk assessment of materials.