• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.576-582
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• 2001

The Multi-Band Excitation(MBE) speech coder uses a different approach for the representation of the excitation signal. It replaces the frame-based single voiced/unvoiced classification of a classical speech coder with a set of such decision over harmonic intervals in the frequency domain. This enables each speech segment to be a mixture of voiced and unvoiced, and improves the synthetic speech quality by reducing decision errors that might occur on the frame-based single voiced and unvoiced decision process when input speech is degraded with noise. The IMBE-LP, improved version of MBE with linear prediction, represents the spectral information of MBE model with linear prediction coefficients to obtain low bit rate of 2.4 kbps. In this Paper, we proposed a variable rate IMBE-LP vocoder that has lower bit rate than IMBE-LP without degrading the synthetic speech quality. To determine the LP order, it uses the spectral band information of the MBE model that has something to do with he input speech's characteristics. Experimental results are riven with our findings and discussions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.182-188
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.4
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• pp.363-368
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• 2015

This paper deals with an improved current source using full-bridge converter type for thyristor valve test of HVDC system. The conventional high-current and low-voltage source of synthetic test circuit requires additional auxiliary power supply to provide the reverse voltage for the auxiliary thyristor valve during turn-off process. The proposed circuit diagram to provide the reverse voltage is extremely simple because no additional component is required. The reverse voltage can be obtained from the input DC voltage of the high-current and low-voltage power supply. The operation principle and design method of the proposed system are described. Simulation and experimental results in scaled down STC of 200 V, 30 A demonstrate the validity of the proposed scheme.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.247-258
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• 2022

A field velocity resistivity probe (FVRP) can measure compressional waves, shear waves and electrical resistivity in boreholes. The objective of this study is to perform the soil classification through a machine learning technique through elastic wave velocity and electrical resistivity measured by FVRP. Field and laboratory tests are performed, and the measured values are used as input variables to classify silt sand, sand, silty clay, and clay-sand mixture layers. The accuracy of k-nearest neighbors (KNN), naive Bayes (NB), random forest (RF), and support vector machine (SVM), selected to perform classification and optimize the hyperparameters, is evaluated. The accuracies are calculated as 0.76, 0.91, 0.94, and 0.88 for KNN, NB, RF, and SVM algorithms, respectively. To increase the amount of data at each soil layer, the synthetic minority oversampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN) are applied to overcome imbalance in the dataset. The CTGAN provides improved accuracy in the KNN, NB, RF and SVM algorithms. The results demonstrate that the measured values by FVRP can classify soil layers through three kinds of data with machine learning algorithms.

• Water for future
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• v.18 no.4
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• pp.335-345
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• 1985

The purpose of stochastic models for synthetic generation of river flows based on the short-term observed data is to provide abundant input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. Among many of such models the Monte Carlo Method of synthetic generation, which is usually known to be appropriate for annual data generation, is employed to check if it can be applied for the generation of monthly flows. For the purpose of comparisons the statistical parameters of the generated monthly flows by Monte Carlo model based on the appropriate probability distribution for each month were compared with those of the generated flows by Thoms-Fiering multiseason model and with those of the observed monthly flows. On the other hand, the statistical parameters of the annual river flows obtained by adding the generated monthly flows year by year based on the Monte Carlo and Thomas-Fiering models were compared with those of the annual flows generated directly by annual Monte Carlo model with reference to those for the observed annual river flows. Based on the above comparative studies, the discussions are made and conclusions derived.

• Journal of Life Science
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• v.14 no.6 s.67
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• pp.997-1002
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• 2004

This paper deals with the WASPIFA (Web-based Assistant System for Protein-protein Interaction and Function Analysis) system that can provide the comprehensive information on Protein-protein interaction and function concerned with function analysis. Different from existing systems for protein function and protein-protein interaction analysis, which provide fragmentary information restricted to specific field, our system furnishes end-user with comprehensive and synthetic information on the input sequence to be analyzed, including function and annotation information, domain information, and interaction relationship information. The synthetic information that our system contains as local databases has been extracted from many resources related to function, annotation, motif and domain by various pre-processing. Employing our system, end-users can evaluate and judge the synthetic results to do protein interaction and function analysis effectively. In addition, the WASPIFA system is equipped with automatic system management and data update function that facilitates system manager to maintain and manage it efficiently.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.8
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• pp.997-1004
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• 2007

High resolution synthetic aperture radar image could be sensitive to the various parameters of the payload, platform, and ground system. In this paper, a parameter based SAR simulator is presented for two-dimensional image formation and image quality analysis. Functional modules are implemented by Matalb code and GUI for the flexibility and expandability. Main function of this simulator includes the SAR input signal generation, range-doppler algorithm(RDA) based SAR image formation, and the SAR image quality analysis which is relevant to the SAR system design parameters. This simulator can effectively be used for the SAR image quality performance evaluation, which can be applicable to the airborne as well as spaceborne SAR system design and analysis.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.1
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• pp.57-68
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• 2010

The purpose of this study is to analyze the characteristics of environment-friendliness of waterproof materials based on comprehensive experiments on waterproofness in terms of coefficients of permeability, harmfulness of waterproof materials and fish toxicity of Oryzias latipes mortality to verify eco-toxicity of each method of construction and waterproof material, which are to be applied by taking eco-toxicity into account when building ecological flows in upper areas on natural and artificial grounds. As a result, the following conclusions have been reached in this study: 1. In regard of the harmfulness analyzed, each material showed a different result of analytical value in each lab tank. Compared to input water, pH, COD, SS, T-P, and T-N values increased a little, but DO value decreased. The value of turbidity analyzed independent of the water quality standard of aquatic ecosystem set forth by the Ministry of Environment increased a little compared to the value in input water. 2. In the experiment of fish toxicity, compacted quicklime, cement fluid waterproof material, cement mortar waterproof material and bentonite powder were found to have 100% of fish mortality, respectively, and membrane waterproof material showed 83.3% of mortality, indicating strong fish toxicity. Improved asphalt sheet (63.3%) and synthetic rubber sheet (53.3%) were analyzed to have medium fish toxicity, while bentonite sheet (6.7%), Hwang-toh (6.7%) and clay (3.3%) showed relatively lower mortality and fish toxicity. 3. Regarding the analysis on waterproofness in terms of the coefficient of permeability of each waterproof material, improved asphalt sheet, synthetic rubber sheet, membrane waterproof material, cement fluid and mortar waterproof material and bentonite sheet were found impervious in case no leakage takes place in construction. Bentonite powder was found practically impervious based on the analytical results from the experiment done in compliance with weight ratios. So were the clay and Hwang-toh from the experimental results. To sum up such results as found in the experiment mentioned so far, the values of harmfulness and waterproofness analyzed were different in each lab tank, but there was absolutely little correlation with the mortality gained from the experiment on fish toxicity. In the experiment of fish toxicity, environment-friendly waterproof materials were analyzed, and it was found that clay, Hwang-toh and bentonite sheet are highly environment-friendly. In contrast, synthetic rubber and improved asphalt sheets were found to have medium-level environment-friendliness. Also, membrane water-proof materials, compacted quicklime, cement fluid and mortar waterproof material and bentonite powder were analyzed to have low environment-friendliness.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1709-1722
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• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.