• Asian-Australasian Journal of Animal Sciences
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• v.24 no.3
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• pp.386-393
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• 2011

Three trials were conducted to determine the available energy of different wheat screening varieties collected from different locations of Khorasan in Iran. In experiment 1, chemical composition and the nitrogen corrected true metabolisable energy (TMEn) were evaluated. A precision-fed rooster assay was used, in which, each wheat screening sample was tube fed to adult roosters, and the excreta were collected for 48-h. In Exp. 2 and 3, five and two wheat screening verities-based diets with or without xylanase and phytase were fed to 16-day old battery reared chicks respectively, and total feed consumption and excreta were measured during next three days. The variable nature of wheat screening varieties led to significant differences in mean TMEn values (p<0.01). The TMEn values of samples determined with adult roosters varied by ${\pm}5.03%$ of the mean value ($3,097.65{\pm}49.32\;kcal/kg$) and ranged from 2,734.90 to 3,245.12 kcal/kg. There was a significant correlation (p<0.05) between crude fiber (CF), neutral detergent fiber (NDF), and acid detergent fiber (ADF) with TMEn, and the greatest correlation coefficient was observed between NDF and TMEn (r = -0.947; p<0.001). The optimal equation in terms of $R^2$ from using a single chemical analysis was obtained with NDF: TMEn = 4,152.09-27.80 NDF ($R^2$ = 0.90, p<0.0001), and the TME prediction equation was improved by the addition of the crude protein (CP) and ASH content to sequential analysis: TMEn = 3,656.97-28.65 NDF+32.54 CP+38.70 ASH ($R^2$ = 0.98, p<0.0001). The average AMEn values of 5 and 2 wheat screening varieties determined with young broiler chickens were $2,968.41{\pm}25.70\;kcal/kg$ and $2,976.38{\pm}8.34\;kcal/kg$ in Exp. 2 and Exp. 3, respectively. Addition of xylanase and phytase to wheat screenings resulted in significant (p<0.01) improvement in AMEn by 4.21 and 2.92%, respectively.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.133-140
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• 2007

Flash memory has penetrated our life in various forms. For example, flash memory is important storage component of ubiquitous computing or mobile products such as cell phone, MP3 player, PDA, and portable storage kits. Behind of the wide acceptance as memory is many advantages of flash memory: for instances, low power consumption, nonvolatile, stability and portability. In addition to mentioned strengths, the recent development of gigabyte range capacity flash memory makes a careful prediction that the flash memory might replace some of storage area dominated by hard disks. In order to have overwriting function, one block must be erased before overwriting is performed. This difference results in the cost of reading, writing and erasing in flash memory[1][5][6]. Since this difference has not been considered in traditional buffer replacement technologies adopted in system software such as OS and DBMS, a new buffer replacement strategy becomes necessary. In this paper, a new buffer replacement strategy, reflecting difference I/O cost and applicable to flash memory, suggest and compares with other buffer replacement strategies using workloads as Zipfian distribution and real data.

• Journal of Distribution Science
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• v.14 no.6
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• pp.81-87
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• 2016

Purpose - In recent, a research finds that advanced selling can influence a consumer's choice(Kim et al., 2013). Advanced selling is defined as the new product launching strategy which company allows consumers to preorder new product before its release(Chu & Zhang, 2011). Prior researches have focused on the benefits of advanced selling(e.g., information gathering for demand prediction, an advantage for pricing strategy, and so on) for companies using this strategy(Chen, 2001; Chu & Zhang, 2011; Li & Zhang, 2013; Tang et al., 2004; Xie & Shugan, 2009). However, Kim et al.(2013) find it can also influence a consumer's choice. In detail, they suggest that when consumers use advanced selling, they are likely to prefer high-performance options rather than low-price options based on construal level theory(Trope & Liberman, 2003). In this paper, we tried to expand the prior researches for finding the mechanism of the influence of advanced selling on a consumer's choice. The purpose of this research is to test the mediating effect on the influence of advanced selling. Research design, data, and methodology - To find the mechanism of the influence of advanced selling, we designed an experiment for testing mediation effect. we recruited 93 students from a university. We assigned participants into one of two groups using randomization method. The participants with each group were given a scenario describing the sales strategy. Finally, they made a choice between high-performance option and low-price option. Sequentially, they also responded some questions for testing mediation effect. Results - First, we replicated prior research to test the influence of advanced selling. As a result, we could find that consumers prefer the high-performance option when they preorder it to purchase at the time of consumption. Thus, the replication result is the same as prior research. Second, we tested that advanced selling can influence the perception of temporal distance. The results confirmed that consumers perceived longer temporal distance in advanced selling condition(β = 1.575, SE = 0.272, p < 0.001). Third, we predicted that temporal distance can increase the importance of desirable attributes and decrease the importance of feasible attributes. The results suggested that temporal distance decreased significantly the importance of attributes related to feasibility(β = -0.19, SE = 0.07, p < 0.01), however, it had non-significant effect on increasing the importance of desirable attributes. Finally, we used Sobel-test for testing mediation effect, and it confirmed that the importance of feasible attributes had mediating role of the influence of advanced selling(Sobel test statistic = -2.110, SE = 0.111, p < 0.05). Conclusions - In this paper, we tried to find the mechanism of the influence on advanced selling from a consumer's choice. With an experiment, we confirmed that the importance of feasible attributes could mediate the effect on advanced selling. Therefore, we suggested some theoretical and practical contributions from this research. Finally, we discussed research limitations and suggested future research topics.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.2
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• pp.136-143
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• 2018

The rate pressure product (RPP) is expressed as a product of the heart rate and systolic blood pressure as an index indirectly measuring the myocardial oxygen consumption, and it indicates the burden on the myocardium. The aim of this study was to determine the optimal level of RPP for preventing metabolic syndrome in a treadmill exercise test in Korean adults. Metabolic syndrome was the diagnosis of the third executive summary report on the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. According to the criteria, the metabolic syndrome diagnosis group (MetS, N=25), pre-metabolic syndrome group (Pre-MetS, N=106), and non-risk factor group (Non-MetS, N=65) were classified. The exercise stress test was performed based on the Bruce protocol. The RPP was calculated as (heart rate${\times}$systolic blood pressure)${\div}1,000$. The results showed that the maximum systolic blood pressure was high despite the low daily dose reached in the diagnostic group of metabolic syndrome. The optimal threshold of the RPP at the time of the exercise treadmill test for a metabolic syndrome prediction was $12.56mmHg{\times}beats/min{\times}10^{-3}$ in the first stage of the exercise stress test. The second stage of the exercise test was $16.94mmHg{\times}beats/min{\times}10^{-3}$, and at the third stage of the exercise test was $21.11mmHg{\times}beats/min{\times}10^{-3}$.

• Fire Science and Engineering
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• v.24 no.3
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• pp.131-138
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• 2010

Experimental and numerical studies were conducted to investigate the thermal and chemical characteristics of heptane fires in a full-scale ISO 9705 room. Representative fire conditions were considered for over-ventilated fire (OVF) and under-ventilated fire (UVF). Fuel flow rate and doorway width were changed to create OVF and UVF conditions. Detailed comparisons of temperature and species concentrations between experimental and numerical data were presented in order to validate the predictive performance of FDS (Fire Dynamic Simulator). The OVF and UVF were explicitly characterized with distributions of temperature and product formation measured in the upper layer, as well as combustion efficiency and global equivalence ratio. It was shown that the numerical results provided a quantitatively realistic prediction of the experimental results observed in the OVF conditions. For the UVF, the numerically predicted temperature showed reasonable agreement with the measured temperature. The predicted steady-state volume fractions of $O_2$, $CO_2$, CO and THC also agreed quantitatively with the experimental data. Although there were some limitations to predict accurately the transient behavior in terms of CO production/consumption in the UVF condition, it was concluded that the current FDS was very useful tool to predict the fire characteristics inside the compartment for the OVF and UVF.

• The Journal of Industrial Distribution & Business
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• v.10 no.10
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• pp.53-65
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• 2019

Purpose - Current study aimed at investigating the effects of the choice easiness as a thought triggered at the time of making decision and the goal achievement emotion as a prediction of how consumers feel in the state of achieving consumption goal on brand purchase intention. And It also explored moderation role of incidental pride type such as ambient hubris pride and ambient authentic pride felt before the event in the effects of message type such as self-verifying message and self-enhancing message on the choice easiness and the goal achievement emotion. Research design, data, and methodology - Message type was divided into self-verifying message and self-enhancing message. Incidental pride type was divided into hubris and authentic pride. Smart mobile phone was selected for empirical study. And the experiment was performed with 2(pride type: hubristic versus authentic) × 2(message type: self-verifying message versus self-enhancing message) between-subjects design. Questionnaires from 215 undergraduate students were used to test hypotheses by Macro process model 7. The hypotheses were tested at each of self-verifying message group and self-enhancing message group. Results - First, both choice easiness and goal achievement emotion positively influenced on the purchase intention at both self-verifying message group and self-enhancing message group. Second, at self-verifying message group, the positive effects of self verification on both choice easiness and goal achievement emotion were higher to the customers under incidental hubris pride than to those under incidental authentic pride customers. Third, at self-enhancing message group, the positive effects of self enhancement on goal achievement emotion were higher to the customers under incidental authentic pride than to those under incidental hubris pride. However, at self-enhancing message group, the positive effects of self enhancement on choice easiness (goal achievement emotion) were not higher (higher) to the customers under incidental authentic pride than to those under incidental hubris pride. Conclusions - Focusing on the results of this study, to promote their brand purchase intention, brand managers should use self-enhancing message to induce goal achievement emotion from incidental authentic pride customers. And the brand managers should develop and use self-verifying message to induce choice easiness as well as goal achievement emotion from hubris pride customers, which in turn, promote their brand purchase intention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.1-6
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• 2019

The objective of this study was to investigate performance characteristics of thermal management system(TMS) in a fuel cell electric vehicle with 100kW Fuel Cell(FC) system. In order to build up analytic modelling for TMS, each component was installed and tested under various operating conditions, such as water pump, radiator, 3-Way valve, COD heater, and FC stack etc. and as the results of them, correlations reflecting component's characteristics with flow rate, air velocity were developed. Developed analytic modelling was carried out under various operating conditions on the road. To verify modelling's accuracy, after prediction for optimum coolant flow rate was fulfilled under certain operating conditions, such as FC system, water pump speed, opening of 3-way valve, and pipe resistance, analytic and experimental values were compared and good agreement was shown. In order to predict cold-start operating performance for analytic modelling, coolant temperature variation was analyzed with $-20^{\circ}C$ ambient temperature and duration was predicted to rise in optimum temperature for FC. Because there is appropriate temperature difference between inlet and outlet of FC stack to operate FC system properly, related analysis was performed with respect to power consumption for TMS and heat rejection rate and performance map was depicted along with FC operating conditions.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.