In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.
The effects of Si impurity on electronic structures and magnetism of bcc Fe are investigated by using a first-principles method by considering spin-orbit coupling. In order to describe the Si impurity, a 27 atomic bcc Fe supercell has been considered. The Kohn-Sham equation was solved in terms of the all-electron full-potential linearized augmented plane wave (FLAPW) method within the generalized gradient approximation (GGA). The effects of spin-orbit coupling were calculated self-consistently by considering spin-diagonal terms based on second variation method. For the ferromagnetic (FM) state without considering SOC, the spin magnetic moment of the Si impurity was calculated to be $-0.143{\mu}B$, while the magnetic moments of Fe atoms were calculated to be $2.214{\mu}B$, $2.327{\mu}B$, and $2.354{\mu}B$ in away from the Si atom, respectively. However, the FM state with considering SOC, the spin magnetic moment of the Si impurity was calculated to be $-0.144{\mu}B$, which is not affected significantly by SOC, but the spin magnetic moments of Fe atoms were calculated $2.189{\mu}B$, $2.310{\mu}B$, and $2.325{\mu}B$, respectively, which are much reduced value compared to those of the FM state without SOC. Comparing the total charge density and spin density, those features are thought to be originated by the screening distortions of the Fe $t_{2g}$ orbital, which can be obtained by considering SOC.
In order to visualize participating media in 3D space, they usually calculate the incoming radiance by subdividing the ray path into small subintervals, and accumulating their respective light energy due to direct illumination, scattering, absorption, and emission. Among these light phenomena, scattering behaves in very complicated manner in 3D space, often requiring a great deal of simulation efforts. To effectively simulate the light scattering effect, several approximation techniques have been proposed. Volume photon mapping takes a simple approach where the light scattering phenomenon is represented in volume photon map through a stochastic simulation, and the stored information is explored in the rendering stage. While effective, this method has a problem that the number of necessary photons increases very fast when a higher variance reduction is needed. In an attempt to resolve such problem, we propose a different approach for rendering particle-based volume data where kernel smoothing, one of several density estimation methods, is explored to represent and reconstruct the light in-scattering effect. The effectiveness of the presented technique is demonstrated with several examples of volume data.
KSCE Journal of Civil and Environmental Engineering Research
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v.33
no.6
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pp.2293-2302
/
2013
Flood inundation map has been used as a fundamental information in flood risk management. However, there are various sources of uncertainty in flood inundation mapping, which can be another risk in preventing damage from flood. Therefore, it is necessary to remove or reduce uncertainty sources to improve the accuracy of flood inundation maps. However, the entire removal of uncertainty source may be impossible and inefficient due to limitations of knowledge and finance. Sensitivity analysis of uncertainty sources allows an efficient flood risk management by considering various conditions in flood inundation mapping because an uncertainty source under different conditions may propagate in different ways. The objectives of this study are (1) to perform sensitivity analysis of uncertainty sources by different conditions on flood inundation map using the FOA method and (2) to find a major contributor to a propagated uncertainty in the flood inundation map in Flatrock at Columbus, U.S.A. Result of this study illustrates that an uncertainty in a variable is differently propagated to flood inundation map by combination with other uncertainty sources. Moreover, elevation error was found to be the most sensitive to uncertainty in the flood inundation map of the study reach.
For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.
A unified picture for magnetism, superconductivity, quantum optics and other properties of molecule-based materials has been presented on the basis of effective model Hamiltonians, where necessary parameter values have been determined by the first principle calculations of cluster models and/or band models. These properties of the matetials are qualitatively discussed on the basis of the spin and pseudo-spin Hamiltonian models, where several quantum operators are expressed by spin variables under the two level approximation. As an example, ab initio broken-symmetry DFT calculations are performed for cyclic magnetic ring constructed of 34 hydrogen atoms in order to obtain effective exchange integrals in the spin Hamiltonian model. The natural orbital analysis of the DFT solution was performed to obtain symmetry-adapted molecular orbitals and their occupation numbers. Several chemical indices such as information entropy and unpaired electron density were calculated on the basis of the occupation numbers to elucidate the spin and pair correlations, and bonding characteristic (kinetic correlation) of this mesoscopic magnetic ring. Both classical and quantum effects for spin alignments and singlet spin-pair formations are discussed on the basis of the true spin Hamiltonian model in detail. Quantum effects are also discussed in the case of superconductivity, atom optics and quantum optics based on the pseudo spin Hamiltonian models. The coherent and squeezed states of spins, atoms and quantum field are discussed to obtain a unified picture for correlation, coherence and decoherence in future materials. Implications of theoretical results are examined in relation to recent experiments on molecule-based materials and molecular design of future molecular soft materials in the intersection area between molecular and biomolecular materials.
Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.
The Present study analyzed the pressure-flow characteristics of a Korean shunt valve. Changes in the characteristic currie depending on the design parameters were also investigated. The Korean shunt valve used in the present study was constant pressure type and our analyses were validated through experiments. We applied fluid-structure interaction to solve the flow dynamic Problem because the small diaphragm in the valve was made from flexible silicone elastomers. Considering the material nonlinearity of the hyper-elastic material. the Mooney-Rivlin approximation was employed. The results of the numerical analyses were close to the experimental results The major Pressure drop was observed to happen in the small diaphragm. The slope of the pressure-flow characteristic curve was computed to be 0.37mm$H_2O$.hr/cc, which was similar to the average value of commercial shunt valves. 0.40mm$H_2O$.hr/cc. Therefore. our valves analyzed in the Present study showed a Proper Pressure control characteristics of the constant pressure type shunt valves. The opening pressure could be controlled by adjusting the amount of predeflection of the valve diaphragm. In order to obtain opening pressures of 25mm$H_2O$ and 80mm$H_2O$, respectively, and the required predeflection was found to be 10.2$\mu$m and 35.3$\mu$m. The flow orifice size was found to be within 10$\mu$m during valve operation Therefore, Precision design and manufacturing techniques are necessary for successful operations of the shunt valve. The study indicated the amount of predeflection as well as the magnitude of corner rounding of the diaphragm edge are important design parameters to influence the slope of the pressure-flow characteristic curve.
Journal of the Korean Society of Clothing and Textiles
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v.32
no.12
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pp.1825-1836
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2008
Many applications in computer graphics require complex and highly detailed models. However it is often desirable to use approximations in place of excessively detailed models in order to control the processing time. Thus, we aim to develop a notion of optimal matrix to simplify surface which can rapidly obtain the high quality 2D patterns flattening 3D surface as follows. Firstly, two 3D bodies are modeled based on existing Size Korea data. Secondly, each model is divided by shell and block for its pattern draft. Thirdly, each block is flattened by grid and bridge method. Finally, we selected the optimal matrix and demonstrated the efficiency and quality of the proposed method. This proposed approach accommodates surfaces with darts, which are commonly used in the clothing industry to reduce the deformation of surface forming and flattening. The resulting optimal matrix could be an initiation of standardization for pattern flattening. It is expected that this method could facilitate much better approximation in both efficiency and precision.
The purpose of this study was to develop the handy non-contact measurement device of the surface roughness by using the optical fiber sensor. The advantages of fiber optic sensors are high-speed responsibility, non-effect of the magnetic, convenience of the product and high precision. The measurement theory for surface roughness of optical fiber sensor is one to one correspondence between the reflected light intensity based on the surface roughness of the object and the measurement value of previously known for surface roughness. The reflected light intensity was determined using the distance to the surface from the sensor probe and the limit reflection angle based on the surface roughness. Therefore, in this study, the sensor probe was produced for determining the value of surface roughness only using the limit reflection angle based on the surface roughness with the fixed distance from the surface. A prototype measurement system was composed of a transmitting part, a receiving part and a signal processing circuit. The materials of standard measurement which was used in this experiment were SM45C, STS303 and Al60. According to the results of this study, approximation surface roughness formulas which was deduced from the correlation of between the standard surface roughness and the sensing output were verified that they were effect against the surface roughness measurement value of the option sample. And handy optical fiber surface roughness measurement device which was produced by an order was verified that it was effect for measuring of the precision surface roughness.
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