Earticle

In this study, we investigated the adhesive strength by molecular weight, mixture ratio, coating thickness, lamination temperature and aging condition of adhesive in manufacture process of Nylon-Aluminum for secondary aluminum pouch. It found that as the molecular weight of adhesive gets lower, the adhesive strength increases. In the mixture ratio, as the content of hardener get higher and as the content of solvent get lower, the adhesive strength increases. Also, as the coating thickness of adhesive get thicker, the adhesive strength increase. In addition, the adhesive strength is higher at 90 degrees of lamination temperature. So, it found that 90 degrees of lamination temperature is appropriate. In the aging condition when aged for 5 days, it found that the reaction and curing of adhesive is sufficient by measuring the adhesive strength.

Slot-die coating is well known technique to guarantee a uniformly coated layer and is compatible with roll-to-roll process. In actual roll-to-roll slot-die coating process, the lateral difference of coated layer thickness is observed. An experimental study was performed to improve the coating quality. Coating speed and coating gap were selected as the experimental factors. A full factorial, statistical method was conducted to optimize the process conditions. Based on the results of repeated experiment, the lowest deviation of lateral thickness (700 nm, <10%) was achieved at 10 m/min coating speed and $300{\mu}m$ coating gap. This result has significance because such optimized process guideline can be utilized with all process improvement in various coating applications.

전통적인 음성 향상 방법은 잘못된 잡음의 추정에 따라 남아있는 잡음이 발생하여 음성 스펙트럼을 왜곡하거나 음성 프레임을 찾지 못하여 음성 인식 성능을 저하시키는 문제가 발생된다. 본 논문에서는 음성 에너지 분포 처리와 음성 에너지 파라미터를 융합한 음성 검출 방법을 제안하였다. 제안한 방법은 음성 에너지를 최대화시켜 잡음의 영향을 적게 받는 특성을 이용하였다. 또한, 음성 신호의 특징 파라미터 중에서 작은 값을 가지는 로그에너지 특징의 구간에서는 큰 에너지를 가지는 구간에 비해 상대적으로 로그에너지 값을 더 많이 키워서 잡음이 포함한 음성신호의 로그에너지 특징의 크기와 비슷하게 하여 훈련과 인식 환경의 불일치를 융합으로 인해 줄여준다. 인식 실험 결과 기존 방법에 비해 향상된 인식 성능을 확인할 수 있었으며, car 잡음 환경의 음성 구간 적중률은 낮은 SNR구간인 0dB과 5dB에서는 97.1%와 97.3%의 정확도를 보였으며, 높은 SNR구간인 10dB와 15dB에서는 98.3%, 98.6%의 정확도를 보였다.

A traditional speech enhancement methods distort the sound spectrum generated according to estimation of the remaining noise, or invalid noise is a problem of lowering the speech recognition performance. In this paper, we propose a speech detection method that convergence the sound energy distribution process and sound energy parameters. The proposed method was used to receive properties reduce the influence of noise to maximize voice energy. In addition, the smaller value from the feature parameters of the speech signal The log energy features of the interval having a more of the log energy value relative to the region having a large energy similar to the log energy feature of the size of the voice signal containing the noise which reducing the mismatch of the training and the recognition environment recognition experiments Results confirmed that the improved recognition performance are checked compared to the conventional method. Car noise environment of Pause Hit Rate is in the 0dB and 5dB lower SNR region showed an accuracy of 97.1% and 97.3% in the high SNR region 10dB and 15dB 98.3%, showed an accuracy of 98.6%.

A study to analyze and solve problems of a stone surface process experiment has presented in this paper. We have taken Taguchi's parameter design approach, specifically orthogonal array, and determined the optimal levels of the selected variables through analysis of the experimental results using S/N ratio.

Micro-EDM is a non-contact process based on the thermoelectric energy between a tool electrode and a workpiece. In μEDM process, the mechanism of material removal is melting and evaporation. The thermal energy in the discharge plasma helps remove material from the workpiece, at the same time deteriorates the quality and integrity of the workpiece surface. The material removal phenomenon in μEDM of partially conductive and non-conductive materials is very complex. This paper presents a novel approach to model the effects of electrical and non-electrical parameters on the material removal phenomenon and surface integrity for a non-conductive ceramic material. The fuzzy logic modeling system is employed for predicting the μEDM process responses. The trends in the material removal rate and hardness values with the chosen electrical and non-electrical parameter for the model and obtained using AOM approach are compared. The average deviation between the model predictions and the results obtained using AOM plots is less than 10%. The material removal rate (MRR) decreases linearly with voltage, indicating a difference in material removal mechanism in the μEDM of non-conductive materials.

In the present work, the operating parameters were optimized using Box Behnken Design (BBD) in response surface methodology (RSM) to maximize the hydrogen production rate (R₁) and hydrogen production rate per unit watt consumed (R₂) of a proton exchange membrane electrolysis cell (PEMEC), a third response (R₃) which was the sum of the scaled values of R₁ and R₂ were selected to be maximized so that both hydrogen production rate and hydrogen production rate per unit watt consumed could be maximized. The major parameters which were influencing the experiment for enhancing the output responses were oxygen electrode/anode electrocatalyst loading (A), current supplied (B) and water inlet temperature (C). The commercial proton exchange membrane Nafion^® was used as the electrolyte. The acetylene black carbon (C_AB) supported IrO₂ was used as the electrocatalyst for preparing oxygen electrode/anode whereas commercial Pt (40 wt%)/C_HSA was used as the H₂ electrode/cathode electrocatalyst. The quadratic model was developed to predict the output/ responses and their proximity to the experimental output values. The developed model was found to be significant as the P values for both the responses were < 0.0001 and F values were greater than 1. The optimum condition for both the responses were O₂ electrode/anode electrocatalyst loading of 1.78 mg/cm², supplied current of 0.33 A and water inlet temperature of 54℃. The predicted values for hydrogen production rate (R₁) and hydrogen production rate per unit watt consumed (R₂) were 2.921 mL/min and 2.562 mL/(min·W), respectively obtained from the quadratic model. The error % between the predicted response values and experimental values were 1.47% and 3.08% for R₁ and R₂, respectively. This model predicted the optimum conditions reasonably in good agreement with the experimental conditions for the enhancement of the output responses of the developed PEM based electrolyser.

BiCMOS is the newly developed technology that integrates both CMOS and bipolar structures on the same integrated circuit. Improved performance can be obtained from combining the advantages of high density and low power in CMOS with the speed and current capibility advantages by bipolar. However, the major drawbacks to BiCMOS are high cost, long fabrication time and difficulty of merging CMOS with bipolar without degrading of device Performance because CMOS and bipolar share same process step. In this paper, N-Well CMOS oriented BiCMOS process and optimization of device performance are studied when N-Well links CMOS with bipolar process step by 2D process and 3D Device simulation. From the simulation, Constriction of linking process step has been understood and provided to give the method of choosing BiCMOS for various analog design request.

Self Piercing Resistance Element Welding (SPREW) is a dissimilar welding process based on resistance spot welding, using a steel rivet. This method employs a steel rivet to pierce the aluminum sheet under a piercing current, then a welding current is conducted to weld the rivet to the steel sheet. Previous studies have focused on relatively thin aluminum sheets (≤1.5 mm), and research on thicker aluminum sheets has rarely been attempted. In this study, the SPREW process was applied to join a 2.4 mm-thick aluminum alloy (Al6451) and a 1.0 mm-thick galvanized high-strength steel (GA-980). By varying the piercing current, piercing time, cooling time, welding current, and welding force, tensile load and fracture mode were evaluated. The best joint performance-a maximum tensile load of 6.38 kN with button fracture-was achieved at 2 kA, 6 s, 10 s, 10 kA, and 4.4 kN, respectively.

A gap is generated in the weld joint due to dimensional error of welded parts and thermal deformation in aluminum alloy’s gas metal arc welding (GMAW) process of aluminum alloy. The optimum welding conditions corresponding to different gaps in the weld joint are required in the field. In this study, the welding conditions were optimized using Taguchi’s design of experiment method in response to gaps. Al5083-O with a thickness of 4.0 mm was used as the base material, and GMAW was performed on the T-fillet joints. An alternating current (AC) pulse was used for the welding process, and the welding experiment was performed for different gap sizes. Three levels of wire feed rate (WFR), electrode negative ratio (EN ratio), and teaching point (T.P) were selected as welding parameters, and 3 gap sizes (0, 0.5, and 1.0 mm) were selected as noise factors. Other welding conditions were fixed at a welding speed of 40 cm/min, a work angle of 40 degrees, a push of 10 degrees, the contact tip to work distance (CTWD) of 15 mm, and a shielding gas of 100 % Ar. The weld sizes such leg length, penetration depth, and throat thickness were meas- ured using the cross-section. The maximum weld size that satisfies the minimum heat input was selected as the tar- get value. Based on signal-to-noise ratio analysis, WFR, EN ratio, and T.P were selected as optimal levels of 11 m/min, 20 %, and 0 mm, respectively.

Response surface methodology is used to optimize the stir casting process parameters for the aluminium metal matrixcomposite reinforced with boron carbide with several weight percentages. The stir casting process parameters used in thiswork are the weight percentage of the reinforcing element, stir casting mixing time and particle size of the reinforcing elementused in the fabrication of the metal matrix composite. The levels are chosen and central composite design is implemented tooptimize the tensile strength of the metal matrix composite. The analysis of variance is calculated and the estimated regressioncoefficients for the tensile strength are obtained. Also the residual plots and the contour plots are plotted and discussed here.

To improve the breakdown characteristics of vertical power devices, field limiting ring(FLR) is popularly used. In this paper, at vertical power device having $300{\sim}600V$ breakdown voltage, FLR thecnique is considered, by two dimensional computer simulator, with the various of parameters; number of FLR, seperation distance of first FLR from the main juncton and second FLR from the first FLR, doping concentration and thickness of epi-layer, etc.. Below $40{\mu}m$ epi thickness, and for the case of one FLR, the maximum breakdown voltage, 580V is obtained.

In this paper, a two-parameter version of Ikeda-Watanabe's mollifiers approximation of the Brownian motion is considered, and an approximation theorem corresponding to the one parameter case is proved. Using this approximation, we formulate Wong-Zakai type theorem is a Stochastic Differential Equation (SDE) driven by a two-parameter Wiener process.

In this paper, we establish limit theorems containing both the moduli of continuity and the large incremental results for finite dimensional Gaussian processes with N parameters, via estimating upper bounds of large deviation probabilities on suprema of the Gaussian processes.

A modulus of continuity on the increments of a two-parameter Gaussian process is obtained via estimating large deviation probability inequalities on the suprema of the Gaussian process.

Marine clays are soft soil deposits having complicated mineralogy and formation characteristics. Thus, characterization of its geotechnical behavior has been a main issue for geotechnical engineers. Nowadays, the importance and applications of geophysical exploration on marine clays are increasing significantly according to the accuracy, efficiency, and reliability of geophysical survey technology. For marine clays, seismic survey is effective for density and elasticity characterization, while electro-magnetic wave provides the information about the fluid conductivity phenomena inside soil. For practical applications, elastic wave technology can evaluate the consolidation state of natural marine clay layers and estimate important geotechnical engineering parameters of artificially reclaimed marine deposits. Electrical resistivity can provide geophysical characteristics such as particle cementation, pore geometry shape, and pore material phase condition. Furthermore, nondestructive geophysical monitoring is applicable for risk management and efficiency enhancement during natural methane gas extraction from gas hydrate-bearing sediments.

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

In this paper, we study the parameter estimation in Bernoulli distribution and Geometric distribution with parameter . We consider the estimator of and we find two confidence intervals for the parameters in these distributions. One is the result of variance stabilization(V. S.) and the other is that of using central limit theorem(C. L. T.). Simulation results shows that the point estimates are not so different and the length of the confidence intervals obtained by V. S. in case of geometric distribution is smaller than that by C. L. T.

We consider fractional Brownian motion and FARIMA process with Gaussian innovations and show that the suitably scaled distributions of the FARIMA processes converge to fractional Brownian motion in the sense of finite dimensional distributions. We figure out ACF function and estimate the self-similarity parameter H of FARIMA(0, d, 0) by using R/S method. Finally, we display power spectrum density of FARIMA process.

The Ti6Al4V alloy was difficult to cut material because of its inherent properties, however greater application in extremeoperating conditions; hence, the impact of machining parameters on nanoparticle diffused cutting fluids study was essential. The aim of this work was to examine the impact of alumina (Al2O3) nano-diffused cutting fluids under the minimal quantitylubrication (MQL) approach in the milling process on Ti6Al4V Alloy. Nano-based cutting fluid was prepared in unique weightpercentage concentrations of 0%, 1%, and 2% of nanoparticles with base fluid. The experiments were conducted to analysethe effect of spindle speed, cutting feed and axial cutting depth on the concentration of nanoparticles using the face-centredcomposite design (CCD) on cutting tool temperature (Ct) and surface roughness (Ra). The results revealed that Aluminananofluid minimizes the Cutting tool temperature and enhances the surface roughness (Ra). The responses were optimizedusing the desirability function based analysis (DFA) for nano based cutting fluids. The optimal process parameters of theAl2O3 nanofluid weight percentage concentration of nanoparticles were (2%), cutting feed (0.142 mm/rev), spindle speed(104 m/min) and DOC (0.5 mm).

We propose a new instrumental variable estimator of the complex parameter of a class of univariate complex-valued diffusion processes defined by the possibly non-stationary and/or nonlinear stochastic differential equations. On the basis of the exact finite sample distribution of the pivotal quantity, we construct the exact confidence intervals and the exact tests for the parameter. Monte-Carlo simulation suggests that the new estimator seems to provide a viable alternative to the maximum likelihood estimator (MLE) for nonlinear and/or non-stationary processes.