• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.49-60
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• 1995

This paper is a study on the design and implementation of the Korean Text-to-Speech system which is used for a small and simple system. In this paper, a parameter synthesis method is chosen for speech syntheiss method, we use PARCOR(PARtial autoCORrelation) coefficient which is one of the LPC analysis. And we use phoneme for synthesis unit which is the basic unit for speech synthesis. We use PARCOR, pitch, amplitude as synthesis parameter of voice, we use residual signal, PARCOR coefficients as synthesis parameter of unvoice. In this paper, we could obtain the 60% intelligibility by using the residual signal as excitation signal of unvoiced sound. The result of synthesis experiment, synthesis of a word unit is available. The controlling of phoneme duration is necessary for synthesizing of a sentence unit. For setting up the synthesis system, PC 486, a 70[Hz]-4.5[KHz] band pass filter for speech input/output, amplifier, and TMS320C30 DSP board was used.

• Solar Energy
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• v.13 no.2_3
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• pp.120-132
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• 1993

The purpose of this research is to study the characteristics of heat transfer in composite rotary heat pipe as modeled turbine rotating by a finite element analysis and experiment. Nu number, Re number, Pr number and dimensionless condensate layer thickness by thermal input and revolutions per minute were given as analysis factors. The comparison between calculated and experimental data showed similar tendency. Therefore the analysis method may be useful to predict the performance of composite heat pipe. The resistance on heat pipe showed the best effect of heat transfer by film condensation, by decreasing film condensation, the heat transfer rate from condenser was increased rapidly. The dimensionless condensate layer thickness according to Re number at given Pr number showed constant values, the dimensionless condensate layer thickness is proportionate to the square root of inverse of revolution number per minute. In this study Nu=A$({\delta}({\omega}/v)^{-1/2}Re^B)$ is used to the convection heat transfer coefficient and A=0.963, B=0.5025 were obtained as analysis predicts.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• The KIPS Transactions:PartA
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• v.13A no.1 s.98
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• pp.79-86
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• 2006

Recently the tagSNP selection problem has been researched for reducing the cost of association studies between human's diversities and SNPs. General approach for this problem is that all of SNPs are separated into appropriate blocks and then tagSNPs are chosen in each block. Marsel in this paper is the system that involved the concept of linkage disequilibrium for overcoming the problem that the existing block partitioning approaches have short of biological meanings. In most approaches, the contiguous regions, which recombinations have LD coefficient |D'| and then tagSNP selection step is performed. And MarSel guarantees the minimum tagSNP selection using entropy-based optimal selection algorithm when tagSNPs are chosen in each block, and enables chromosome-level association studies using efficient memory management technique when input is very large-scale dataset that is impossible to be processed in the existing systems.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.21 no.3
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• pp.91-96
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• 2015

Fruit and vegetables are subjected to complex dynamic stresses in the transportation environment. During a long journey from the production area to customers using parcel delivery service, there is always some degree of vibration present. Vibration inputs are transmitted from the vehicle through the packaging to the fruit. Inside, these cause sustained bouncing of fruits against each other and container wall. These steady state vibration input may cause serous fruit injury, and this damage is particularly severe whenever the fruit inside the package is free to bounce, and is vibrated at its resonance frequency. The determination of the resonance frequencies of the fruit and vegetables may help the packaging designer to determine the proper packaging system providing adequate protection for the fruit, and to understand the complex interaction between the components of fruit when they relate to expected transportation vibration inputs. Instrumentation and technologies are described for determining the vibration response characteristics of the fruits with frequency range 3 to 150 Hz. The resonance frequency of the pear ranged from 53 to 102 Hz and the amplitude at resonance was between 1.08 and 2.48 G. The resonance frequency and amplitude at resonance decreased with the increase of the sample mass, and they were slightly affected by mechanical properties such as bioyield deformation and rupture deformation. Regression analysis was performed among the relatively high correlated parameters from the results of correlation coefficient analysis.

• Journal of Soil and Groundwater Environment
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• v.21 no.5
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• pp.8-15
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• 2016

Bioremediation, which uses microbes to degrade most organic pollutants in soil and groundwater, can be used in solving environmental issues in various polluted sites. In this research, a wind-driven bioventing system is built to degrade about 20,000 mg/kg of high concentration diesel pollutants in soil-pollution mode. The wind-driven bioventing test was proceeded by the bioaugmentation method, and the indigenous microbes used were Bacillus cereus, Achromobacter xylosoxidans, and Pseudomonas putida. The phenomenon of two-stage diesel degradation of different rates was noted in the test. In order to interpret the results of the mode test, three microbes were used to degrade diesel pollutants of same high concentration in separated aerated batch-mixing vessels. The data derived thereof was input into the Haldane equation and calculated by non-linear regression analysis and trial-and-error methods to establish the kinetic parameters of these three microbes in bioventing diesel degradation. The results show that in the derivation of μ_m (maximum specific growth rate) in biodegradation kinetics parameters, K_s (half-saturation constant) for diesel substance affinity, and K_i (inhibition coefficient) for the adaptability of high concentration diesel degradation. The K_s is the lowest in the trend of the first stage degradation of Bacillus cereus in a high diesel concentration, whereas K_i is the highest, denoting that Bacillus cereus has the best adaptability in a high diesel concentration and is the most efficient in diesel substance affinity. All three microbes have a degradation rate of over 50% with regards to Pristane and Phytane, which are branched alkanes and the most important biological markers.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.11 s.158
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• pp.1589-1597
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• 2006

Nowadays, Korean consumers prefer drum-type washing machines to pulsator-type washers. Washing is a complex process involving the interaction of numerous physical and chemical influences. The main factors in the washing operations are the washing chemistry of the detergent along with the mechanical input, the wash temperature, and the time provided by the washing machine. Heavy-duty detergents that are used in drum-type washing machines contain different components from those used in vertical-axis washing machines. The bath ratio and the mechanical actions to which laundry is subjected are different between the drum-type and the vertical-axis washing machines. In this study we examined the effects of wash temperature, wash time, detergent concentration, and revolution speed on the removal of soils from artificially soiled cloths in a drum-type washing machine with heavy-duty commercial detergent. We used multiple regression analyses to find the relative importance of the factors and the optimum washing conditions. The results of these experiments showed that the washing temperature was the most important factor in the effective removal of most soils. This was followed by the washing time, the detergent concentration, and finally the revolution speed. In this study it was found that superfluous amounts of detergent did not sufficiently increase the soil removal rate. Koreans who are used to washing with cold water should increase the wash time to launder more efficiently.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.4
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• pp.27-43
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• 1990

This study aimed at developing a generalized model on the estimation of the long - term run - off volume for practical purpose. During the research period of last 3 years( 1986-1988), 3 types of estimation model on the long - term run - off volume(Effective rainfall model, unit hydrograph model and barne's model for dry season) had been developed by the author. In this study, through regressional analysis between determinant factors (bi of effective rainfall model, ai of unit hydrograph model and Wi of barne's model) and catchment characteris- tics(catchment area, distance round the catchment area, massing degree coefficient, river - exte- nsion, river - slope, river - density, infiltration of Watershed) of 11 test case areas by multiple regressional method, a new methodology on the derivation of determinant factors from catchment characteristics in the watershed areas having no hydrological station was developed. Therefore, in the resulting step, estimation equations on run - off volume for practical purpose of which input facor is only rainfall were developed. In the next stage, the derived equations were applied on the Kang - and Namgye - river catchment areas for checking of their goodness. The test results were as follows ; 1. In Kang - river area, average relative estimation errors of 72 hydrographs and of continuous daily run - off volume for 245 days( 1/5/1982 - 31/12) were calculated as 6.09%, 9.58% respectively. 2. In Namgye - river area, average relative estimation errors of 65 hydrographs and of conti- nuous daily run - off volume for 2fl days(5/4/1980-31/12) were 5.68%, 10.5% respectively. In both cases, relative estimation error was averaged as 7.96%, and so, the methodology in this study might be hetter organized than Kaziyama's formula when comparing with the relative error of the latter, 24~54%. However, two case studies cannot be the base materials enough for the full generalization of the model. So, in the future studies, many test case studies of this model should he carries out in the various catchment areas for making its generalization.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.53 no.1
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• pp.77-88
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• 2017

The concern on the greenhouse gas emissions is increasing globally. Especially, the greenhouse gas emission from fisheries is an important issue due to Cancun Agreements Mexico in 1992 and the Kyoto protocol in 2005. Furthermore, the Korean government has a plan to reduce the GHG emissions as 5.2% compared to the BAU in fisheries until 2020. However, the investigation on the GHG emissions from Korean fisheries has not been executed much. Therefore, the quantitative analysis of GHG emissions from Korean fishery industry is needed as the first step to find a relevant way to reduce GHG emissions from fisheries. The purpose of this research is to investigate which degree of GHG emitted from the major coastal fisheries such as coastal gillnet fishery, coastal dual purpose fishery, coastal pots fishery and coastal small scale stow net fishery. Here, we calculated the GHG emission from the fisheries using the LCA (Life Cycle Assessment) method. The system boundary and input parameters for each process level are defined for LCA analysis. The fuel use coefficients of the fisheries are also calculated according to the fuel type. The GHG emissions from sea activities by the fisheries will be dealt with. Furthermore, the GHG emissions for the unit weight of fishes are also calculated with consideration to the different consuming areas. The results will be helpful to understand the circumstances of GHG emissions from Korean fisheries.

• Structural Engineering and Mechanics
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• v.53 no.5
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• pp.939-956
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• 2015

In this study, in order to propose an efficient model to predict the torque capacity of steel fiber reinforced concrete (SFRC) beams, the existing experimental data related to torsional response of beams is reviewed. It is observed that existing data neglects the effects of some parameters on the variation of torque capacity. Thus, an experimental research was also conducted to obtain the effects of neglected parameters. In the experimental study, a total of seventeen SFRC beams are tested against torsion. The parameters considered in the experiments are concrete compressive strength, steel fiber aspect ratio, volumetric ratio of steel fibers and longitudinal reinforcement ratio. The effect of each parameter is discussed in terms of torque versus unit angle of twist graphs. The data obtained from this experimental research is also combined with the data got from previous studies and employed in artificial neural network (ANN) analysis to estimate the ultimate torque capacity of SFRC beams. In addition to parameters considered in the experiments, aspect ratio of beam cross-section, yield strengths of both transverse and longitudinal reinforcements, and transverse reinforcement ratio are also defined as parameters in ANN analysis due to their significant effects observed in previous studies. Assessment of the accuracy of ANN analysis in estimating the ultimate torque capacity of SFRC beams is performed by comparing the analytical and experimental results. Comparisons are conducted in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of efficiency ($E_f$). The results of this study revealed that addition of steel fibers increases the ultimate torque capacity of reinforced concrete beams. It is also found that ANN is a powerful method and a feasible tool to estimate ultimate torque capacity of both normal and high strength concrete beams within the range of input parameters considered.