• Fisheries and Aquatic Sciences
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• v.13 no.2
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• pp.102-111
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• 2010

This study was conducted to investigate the optimal conditions of collagen extraction from scales of yellowfin tuna (Thunnus albacares) using surface response methodology. Four independent variables of NaOH concentration and pretreatment fime in alkali pretreatment and enzyme concentration and treatment time in enzyme hydrolysis were used to predict a model equation for the collagen yield. The determinant coefficient ($R^2$) for the equation was 0.906. The values of the independent variables for the maximum yield were 0.32 N NaOH, 16.38 h alkali pretreatment time, 0.18% enzyme concentration, and 31.02 h enzyme treatment time. In the physicochemical properties of tuna scale collagen, sodium dodecyl sulfate-polyacrylamide gel electrophoresis of tuna scale collagen showed the same migration distances as that of calf skin collagen. The amide A, I, II, and III regions of tuna scale collagen in Fourier transform infrared measurements were shown in the peaks of 3,414 $cm^{-1}$, 1,645 $cm^{-1}$, 1,553 $cm^{-1}$, and 1,247 $cm^{-1}$, respectively. The amount of imino acids in tuna scale collagen was 18.97% and the collagen denaturation temperature was $33^{\circ}C$. The collagen solubility as a function of NaCl concentration decreased to 4% NaCl (w/v) and the collagen solubility as a function of pH was high at pH 2-4 and sharply decreased from pH 4 to pH 7. Viscosity of the collagen solution decreased continuously until $30^{\circ}C$ and this decreasing rate slowed in the temperature range of $35-50^{\circ}C$.

• Journal of the Korean Society of Food Science and Nutrition
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• v.31 no.4
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• pp.653-657
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• 2002

The hydrolysis conditions (enzym $e_strate ratio, time and temperature) of the mixture of anchovy sauce residue (ASR) and soy sauce residue (SSR) after fermentation by Flavourzyme 500M $G^{TM}$ were optimized using response surface methodology (RSM) for pretreatment of processing functional enzyme-hydrolyzed sauce. A model equation obtained from RSM was hydrolysis ratio (%) = 28.157+1.929enzym $e_strate ratio+1.818time+2.038temperature-1.093temperatur $e^2$, whose stationary point showed saddle point. From the ridge analysis of the saddle point, the conditions producing the highest hydrolysis ratio was determined as follows: 0.49% enzym $e_strate ratio; 3.55hr hydrolysis time; 62.5$^{\circ}C$ hydrolysis temperature. Adding of SSR to the mixture of water and ASR improved sensory qualities of mixture, so it seemed that SSR has masking effects on off-flavor and taste of ASR.R.of ASR.R.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.473-476
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• 2007

Location determination of mobile user via RSSI approach has received ample attention from researchers lately. However, it remains a challenging issue due to the complexities of RSSI signal propagation characteristics, which are easily exacerbated by the mobility of user. Hence, a segmentation-based linear spline interpolation method is proposed to cater for the dynamic fluctuation pattern of radio signal in complex environment. This optimization algorithm is proposed in addition to the current radiolocation's (CC2431, Chipcon, Norway) algorithm, which runs on IEEE802.15.4 standard. The enhancement algorithm involves four phases. First phase consists of calibration model in which RSSI values at different static locations are collected and processed to obtain the mean and standard deviation value for the predefined distance. RSSI smoothing algorithm is proposed to minimize the dynamic fluctuation of radio signal received from each reference node when the user is moving. Distances are computed using the segmentation formula obtain in the first phase. In situation where RSSI value falls in more than one segment, the ambiguity of distance is solved by probability approach. The distance probability distribution function(pdf) for each distances are computed and distance with the highest pdf at a particular RSSI is the estimated distance. Finally, with the distances obtained from each reference node, an iterative trilateration algorithm is used for position estimation. Experiment results obtained position the proposed algorithm as a viable alternative for location tracking.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.4
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• pp.201-212
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• 2016

A Wind resource assessment and optimal micrositing of wind turbines were implemented for the development of an onshore wind farm of 30 MW capacity on Gadeok Island in Busan, Republic of Korea. The wind data measured by the automatic weather system (AWS) that was installed and operated in the candidate area were used, and a reliability investigation was conducted through a data quality check. The AWS data were measured for one year, and were corrected for the long term of 30 years by using the modern era retrospective analysis for research and application (MERRA) reanalysis data and a measure- correlate-predict (MCP) technique; the corrected data were used for the optimal micrositing of the wind turbines. The micrositing of the 3 MW wind turbines was conducted under 25 conditions, then the best-optimized layout was analyzed with a various wake model. When the optimization was complete, the estimated park efficiency and capacity factor were from 97.6 to 98.7 and from 37.9 to 38.3, respectively. Furthermore, the annual energy production (AEP), including wake losses, was estimated to be from 99,598.4 MWh to 100,732.9 MWh, and the area was confirmed as a highly economical location for development of a wind farm.

• Resources Recycling
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• v.21 no.3
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• pp.39-47
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• 2012

This study was conducted to optimize a mixing design of lightweight aerated concrete with the blast furnace slag(BFS) using Box-Behnken method, one of response surface designs. The lightweight aerated concrete with the BFS was made on the conditions of steam curing method at atmospheric pressure. The experimental factors were unit Water(W)/total powder($P_d$) ratio, BFS replacement percentage and Al powder addition based on the total powder (${P_d}^*$%). From the results of the response surface analysis, regression models for dried specific gravity and compressive strength of the lightweight aerated concrete were derived. When the target values for dried specific gravity and compressive strength of the lightweight aerated concrete were set at 0.72 and 4.42 MPa respectively, its optimized mixing conditions driven from the regression models were 0.62 of $W/P_d$ ratio, 35.5% of BFS replacement and 0.05% of Al powder addition. This experimental design model was found to be credible by measuring the dried specific gravity and compressive strength of the sample made from the above mixing conditions.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.3
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• pp.147-160
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• 2010

The system virtualization shows interest in the consolidation of servers for the efficient utilization of system resources. There are many various researches to utilize a server machine more efficiently through the system virtualization technique, and improve performance of the virtualization software. These researches have studied with the activity to control the resource allocation of virtual machines dynamically focused on CPU, or to manage resources in the cross-machine using the migration. However, the researches of the memory management have been wholly lacking. In this respect, the use of memory is limited to allocate the memory statically to virtual machine in server consolidation. Unfortunately, the static allocation of the memory causes a great quantity of the idle memory and decreases the memory utilization. The underutilization of the memory makes other side effects such as the load of other system resources or the performance degradation of services in virtual machines. In this paper, we suggest the dynamic allocation of the memory in Xen to control the memory allocation of virtual machines for the utilization without the performance degradation. Using AR model for the prediction of the memory usage and ACO (Ant Colony Optimization) algorithm for optimizing the memory utilization, the system operates more virtual machines without the performance degradation of servers. Accordingly, we have obtained 1.4 times better utilization than the static allocation.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.2
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• pp.91-99
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• 2014

The common-rail injectors are the most critical component of the CRDI diesel engines that dominantly affect engine performances through high pressure injection with exact control. Thus, from now on the advanced combustion technologies for common-rail diesel injection engine require high performance fuel injectors. Accordingly, the previous studies on the numerical and experimental analysis of the diesel injector have focused on a optimum geometry to induce proper injection rate. In this study, computational predictions of performance of the diesel injector have been performed to evaluate internal flow characteristics for various needle lift and the spray pattern at the nozzle exit. To our knowledge, three-dimensional computational fluid dynamics (CFD) model of the internal flow passage of an entire injector duct including injection and return routes has never been studied. In this study, major design parameters concerning internal routes in the injector are optimized by using a CFD analysis and Response Surface Method (RSM). The computational prediction of the internal flow characteristics of the common-rail diesel injector was carried out by using STAR-CCM+7.06 code. In this work, computations were carried out under the assumption that the internal flow passage is a steady-state condition at the maximum needle lift. The design parameters are optimized by using the L16 orthogonal array and polynomial regression, local-approximation characteristics of RSM. Meanwhile, the optimum values are confirmed to be valid in 95% confidence and 5% significance level through analysis of variance (ANOVA). In addition, optimal design and prototype design were confirmed by calculating the injection quantities, resulting in the improvement of the injection performance by more than 54%.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.171-178
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• 2014

From now on, in order to meet more stringer diesel emission standard, diesel vehicle should be equipped with emission after-treatment devices as NOx reduction catalyst and particulate filters. Urea-SCR is being developed as the most efficient method of reducing NOx emissions in the after-treatment devices of diesel engines, and recent studies have begun to mount the urea-SCR device for diesel passenger cars and light duty vehicles. That is because their operational characteristics are quite different from heavy duty vehicles, urea solution injection should be changed with other conditions. Therefore, the number and diameter of the nozzle, injection directions, mounting positions in front of the catalytic converter are important design factors. In this study, major design parameters concerning urea solution injection in front of SCR are optimized by using a CFD analysis and Taguchi method. The computational prediction of internal flow and spray characteristics in front of SCR was carried out by using STAR-CCM+7.06 code that used to evaluate $NH_3$ uniformity index($NH_3$ UI). The design parameters are optimized by using the $L_{16}$ orthogonal array and small-the-better characteristics of the Taguchi method. As a result, the optimal values are confirmed to be valid in 95% confidence and 5% significance level through analysis of variance(ANOVA). The compared maximize $NH_3$ UI and activation time($NH_3$ UI 0.82) are numerically confirmed that the optimal model provides better conversion efficiency of $NH_3$. In addition, we propose a method to minimize wall-wetting around the urea injector in order to prevent injector blocks caused by solid urea loading. Consequently, the thickness reduction of fluid film in front of mixer is numerically confirmed through the mounting mixer and correcting injection direction by using the trial and error method.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.365-374
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• 2000

This paper is to present the determination of the optimal Joss rate parameters and urnt bydrographs from the observed single rainfall-runoff event using optimization models coupled with a stochastic technique for the global solution. Two kinds of the linear program models are formulated to derive the optimal unit hydrographs and loss rate parameters for gaged basins; one mimmizes the summation of the absolute residual between predlCted and observed runoff ordinates and the other, the maximum absolute residuaL Multistart algorithm which is one or stochastic techniques for the global optimum is adopted to perturb the parameters of the loss rate equations. Multistart efficiently searches the feasIble region to identify the global optimlUll for loss rate parameters, which yields the optimal loss rate parameters and unit hydrograph for Kostiakov's, Plulip's, and Horton's equation. The unique unit hydrograph ordinates for a gIven rainfall-runoff event iS exclusrvely obtained WIth $\Phi$ index, but unit hydrograph ordinates depend upon the parameters [or each loss rate equations. The parameters of Green-Ampt's are determined through a trial and error method. In this paper the single rainfall-nmoff event observed from a watershed is considered to test the proposed method. The optimal unit hydrograph herein found has smaller deviations than the ones reported previously by other researchers.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.