• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.1085-1093
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• 2017

Plasticizers are materials added to give softness and elasticity to plastics having rigid properties to give soft properties as products, and they are mainly added to high molecular materials to give flexibility to improve workability and to improve cold resistance, resistance to volatility and electrical properties. It is used for the purpose. Most plasticizers are inert liquids, similar in function to solvents but with high molecular weight and no volatility. In addition, when dissolved in petrochemical products, only the plasticizer is separated by the matrix effect with other compounds, and qualitative and quantitative analysis. In this study, qualitative and quantitative analysis of DOA and DOP, which are representative components of petrochemical products, were conducted using MD-GC/MS and developed an optimal plasticizer analysis method.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.367-376
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• 2007

In this paper, we introduce an application-specific and adaptive power management technique for portable systems that support dynamic voltage scaling (DVS). We exploit both the idle time of multitasking systems running soft real-time tasks as well as memory- or CPU-bound code regions. Detailed power and execution time profiles guide an adaptive power manager (APM) that is linked to the operating system. A post-pass optimizer marks candidate regions for DVS by inserting calls to the APM. At runtime, the APM monitors the CPU's performance counters to dynamically determine the affinity of the each marked region. for each region, the APM computes the optimal voltage and frequency setting in terms of energy consumption and switches the CPU to that setting during the execution of the region. Idle time is exploited by monitoring system idle time and switching to the energy-wise most economical setting without prolonging execution. We show that our method is most effective for periodic workloads such as video or audio decoding. We have implemented our method in a multitasking operating system (Microsoft Windows CE) running on an Intel XScale-processor. We achieved up to 9% of total system power savings over the standard power management policy that puts the CPU in a low Power mode during idle periods.

• Korean Journal of Poultry Science
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• v.17 no.3
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• pp.179-191
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• 1990

In order to study the effects of supplementation of live yeast(Saccharomyces cerevisiae) on the performance of laying hens, five experiments were conducted. Two experiment were conducted during summer period, one with 37 wk old Dekalb-Delta strain laying birds(Exp. 1) and the other one with 100 wk old molted Nick Chick Brown laying birds(Esp.2) . In each experiment, 240 birds were divided into 12 groups of 20 birds each and randomly distributed. Each of the two experimental diets(Control 71 and 0.05% live yeast supplemented 72) was fed to 6 groups for 4 wks in Exp.1 and 3 wks in Exp.2. Three experiments were conducted during winter period, Exp.2 with 54 wk old Hy-Line strain laying birds, Exp.4 with 52 wk old Hy-Line strain laying birds, and Exp.5 with 36 wk old broiler breeder(Indian River strain). In each experiment, 540 birds were divided into 18 groups of 30 birds each and randomly distributed. Each of the 3 experimental diets(Control：$T_1$0.05% live yeast supplemented：$T_2$ and 0.1% live yeast supplemented ： T$_3$) was fed to 9 groups for 6 wks in Exp.3, 9 wks in Exp.4 and 4 wks in Exp.5. In Exp.4, Latin Square design was employed to determine the effects of switching feeds at 3 wk intervals. All hens were housed in cages of commercial farm and experimental diets were made with commercial layer feeds. In Experiment 1, egg production was significantly(P＜0.05) higher in $T_2$. Feed intake was significantly (P＜higher in 72 at 1st wk but 4 wk average was not significantly different. Feed efficiency was significantly(P＜0.01) better in 72 at End wk but 4 wk average was not significantly different. Other parameters, such as weight, soft egg production, cracked egg production and mortality were not significantly different. In Experiment 2, egg production was significantly(P＜0.05) higher in $T_2$. Feed efficiency was significantly (P＜0.05 and P＜0.01) better at End wk and 3rd wk but 3 wk average was not significantly different. Soft egg production was significantly(P＜0.05) higher in 72. Other parameters were not significantly different. In Experiment 3, egg productions were significantly(P＜0.05) different among treatments ： $T_3$ was higher than $T_1$ and $T_2$ was higher than $T_1$. Egg weight of $T_1$ and $T_2$was significantly(P＜0.05) heavier than $T_3$. Feed intake of $T_2$ and $T_3$ was significantly(P＜0.05) higher than $T_1$ at 6th wk but overall average was not significantly different. Soft egg production were significantly(P＜0.01) different among treatments：$T_1$ was higher than $T_3$ was higher than $T_2$. Feed efficiency cracked e99 Production and mortality were not signifcantly different. In Experiment 4, egg production tended to increase as the level of live yeast supplementation increased but they were not statistically different. In Experiment 5, egg production of broiler breeders of $T_3$ was significantly(P＜0.01) higher than $T_1$. Feed intake of $T_3$ was significantly(P＜0.05) greater than $T_1$ and $T_2$ at 3rd wk but overall average was not significantly different. Fertility and hatchability tended to be higher in the supplemented groups than in the control.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.141-156
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• 2008

Market structure analysis is a very useful tool to analyze the competition boundary of the brand or the company. But most of the studies in market structure analysis, the concern lies in nondurable goods such as candies, soft drink and etc. because of the their availability of the data. In the field of durable goods, the limitation of the data availability and the repurchase time period constrain the study. In the analysis of the automobile market, those of views might be more persuasive. The purpose of this study is to analyze the structure of automobile market based on some idea suggested by prior studies. Usually the buyers of the automobile tend to buy upper tier when they buy in the next time. That kind of behavior make it impossible to analyze the structure of automobile market under the level of automobile model. For that reason I tried to analyze the market structure in the brand or company level. In this study, consideration data was used for market structure analysis. The reasons why we used the consideration data are summarized as following. Firstly, as the repurchase time cycle is too long, brand switching data which is used for the market analysis of nondurable good is not avaliable. Secondly, as we mentioned, the buyers of the automobile tend to buy upper tier when they buy in the next time. We used survey data collected in the U.S.A. market in the year of 2005 through questionaire. The sample size was 8,291. The number of brand analyzed in this study was 9 among 37 which was being sold in U.S.A. market. Their market share was around 50%. The brands considered were BMW, Chevrolet, Chrysler, Dodge, Ford, Honda, Mercedes, and Toyota. �� ratio was derived from frequency of the consideration set. Actually the frequency is different from the brand switch concept. In this study to compute the �� ratio, the frequency of the consideration set was used like a frequency of brand switch for convenience. The study can be divided into 2 steps. The first step is to build hypothetical market structures. The second step is to choose the best structure based on the hypothetical market structures, Usually logit analysis is used for the choice best structure. In this study we built 3 hypothetical market structure. They are type-cost, cost-type, and unstructured. We classified the automobile into 5 types, sedan, SUV(Sport Utility Vehicle), Pickup, Mini Van, and Full-size Van. As for purchasing cost, we classified it 2 groups based on the median value. The median value was $28,800. To decide best structure among them, maximum likelihood test was used. Resulting from market structure analysis, we find that the automobile market of USA is hierarchically structured in the form of 'automobile type - purchasing cost'. That is, result showed that automobile buyers considered function or usage first and purchasing cost next. This study has some limitations in the analysis level and variable selection. First, in this study only type of the automobile and purchasing cost were as attributes considered for purchase. Considering other attributes is very needful. Because of the attributes considered, only 3 hypothetical structure could be analyzed. Second, due to the data, brand level analysis was tried. But model level analysis would be better because automobile buyers consider model not brand. To conduct model level study more cases should be obtained. That is for acquiring the better practical meaning, brand level analysis should be conducted when we consider the actual competition which occurred in the real market. Third, the variable selection for building nested logit model was very limited to some avaliable data. In spite of those limitations, the importance of this study lies in the trial of market structure analysis of durable good.