• Human Ecology Research
• /
• v.59 no.2
• /
• pp.245-259
• /
• 2021

The purpose of this study was to analyze big data to identify the sub-dimensions of ethical consumption, as well as the consumption value associated with ethical consumption that changes over time. For this study, data were collected from Naver and Daum using the keyword 'ethical consumption' and frequency and matrix data were extracted through Textom, for the period January 1, 2016, to December 31, 2018. In addition, a two-way mode network analysis was conducted using the UCINET 6.0 program and visualized using the NetDraw function. The results of text mining show increasing keyword frequency year-on-year, indicating that interest in ethical consumption has grown. The sub-dimensions derived for 2014 and 2015 are fair trade, ethical consumption, eco-friendly products, and cooperatives and for 2016 are fair trade, ethical consumption, eco-friendly products and animal welfare. The results of deriving consumption value keywords were classified as emotional value, social value, functional value and conditional value. The influence of functional value was found to be growing over time. Through network analysis, the relationship between the sub-dimensions of ethical consumption and consumption values derived each year from 2014 to 2018 showed a significantly strong correlation between eco-friendly product consumption and emotional value, social value, functional value and conditional value.

• The Korean Journal of Food And Nutrition
• /
• v.29 no.4
• /
• pp.547-556
• /
• 2016

The purpose of this study was to investigate the relationship between frequency of coffee consumption, metabolic biomarkers, and nutrition intake in adult participants in the combined 2007~2009 Korean National Health and Nutrition Examination Survey (KNHANES). Subjects (2,095 males and 3,297 females) were classified according to sex and frequency of coffee consumption (${\leq}1$ time/month, ${\geq}2$ times/month and ${\leq}6$ times/week, 1 time/day, 2 times/day, 3 times/day) using food frequency questionnaires. Nutrition intake was analyzed using 24 h recall data. The 3 times/day coffee consumption group had a significantly higher age, and frequency of smokers and drinkers compared to the ${\leq}1$ time/month coffee consumption group in both male and female participants. Males in the 3 times/day coffee consumption group had a significantly lower HDL-cholesterol level, but females had a higher waist circumference compared with the ${\leq}1$ time/month coffee consumption group. Males in the 3 times/day coffee consumption group had a significantly lower nutrient density of fiber, vitamin B2, vitamin C, calcium and phosphorus compared with the ${\leq}1$ time/month coffee intake group. Females in the 3 times/day coffee consumption group had a significantly higher nutrient density of fat and niacin, but lower nutrient density of carbohydrate, calcium, phosphorus, and iron compared with the ${\leq}1$ time/month coffee intake group. In males, the frequency of coffee consumption was not associated with the levels of metabolic biomarkers. In females, the frequency of coffee consumption was positively associated with diastolic blood pressure after adjustments for multiple confounding factors, including age, BMI, smoking status, alcohol consumption, physical activity and energy intake. Coffee consumption was associated with decreased diastolic blood pressure in females. These findings suggest the importance of an awareness of the association between coffee consumption and metabolic risk.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.9
• /
• pp.971-976
• /
• 2014

In this paper, we proposed a low power algorithm using state transition ready method. The proposed algorithm defined a sleep state, a idle state and a run state for the task. A state transition occurring at the time due to the delay time created in order to reduce the power consumption state in the middle of each inserted into the ready state. The ready state considering a power consumption and a delay time in state transition. A scheduling step of performing the steps in excess of the increasing problems have the delay time is long. The power consumption increased for the operation step increase. A state transition from a sleep state with the longest delay time in operating state occurs when the state is switched by the time delay caused by the increase in operating time reduces the overall power consumption reduced. Experiments [6] were compared with the results of the power consumption. The experimental results [6] is reduced power consumption than the efficiency of the algorithm has been demonstrated.

• Journal of Digital Contents Society
• /
• v.15 no.3
• /
• pp.433-438
• /
• 2014

In this paper, we proposed the low power algorithm consider the battery and the task. The proposed algorithm setting the power consumption of unit time consider the capacity of the battery and the target time. Calculate the power consumption of all tasks. Calculate the average power consumption by the task have maximum power consumption and the task have minimum power consumption. Recalculate average power consumption consider the unit time of task. Compare calculated average power consumption and average power consumption of task. Compared results, low power algorithm processing the average power consumption less than or equal calculated power consumption of task. Low-power algorithm is greater than the average power consumption of the task to perform targeted tasks. Low-power processors and the task by dividing the power consumption of the device in large part for the low-power consumption is performed. Experiments [6] were compared with the results of the power consumption. The experimental results [6] is reduced power consumption than the efficiency of the algorithm has been demonstrated.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.6
• /
• pp.185-195
• /
• 2020

The study investigates the impact of globalization on coal consumption in Vietnam. This study employs an autoregressed distributed lag approach on time series data for the period of 1990 to 2017. The study tests the stationary, cointegration of time series data and utilizes autoregressed distributed lag modeling technique to determine the short-run and long-run relationship among coal consumption, globalization, income, population, and CO₂ emissions. The results show that globalization increases coal consumption in Vietnam in the long run. The results also show that rapid economic growth promotes more coal consumption in the short run as well as in the long run. Moreover, higher population reduces coal consumption, and CO₂ emissions decrease coal consumption both in the short run and the long run. The findings of the study suggest that globalization increases coal consumption in Vietnam in the long run. This result suggests that the increase in globalization level in Vietnam increases coal consumption. An interesting finding is that higher population reduces coal consumption, and population is an important factor towards the lessening in coal consumption. The findings confirm that environmental pollution decreases coal consumption in the short run and the long run. This implies that coal consumption may be green consumption in Vietnam.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.10
• /
• pp.4833-4848
• /
• 2019

This paper introduces the structure and effects of Kakao's news curation algorithm, which is created based on the Deep Reading Index (DRI). The DRI examines the extent of deep reading through content reading time, that is, the duration of reader engagement with an article. Current news curation algorithms focus on reader choice, with the click-through rate or pageviews as the gauge for consumption frequency. DRI is a product of the challenge of introducing and adopting a new factor called 'consumption time' instead of 'frequency of consumption', which is the basis of existing curation algorithms. The analysis of DRI-based services proves that the new algorithm can act as a curation system that is more effective in providing in-depth and quality news reports.

• Communications for Statistical Applications and Methods
• /
• v.27 no.6
• /
• pp.589-602
• /
• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• Journal of Computing Science and Engineering
• /
• v.5 no.4
• /
• pp.338-345
• /
• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.28 no.2
• /
• pp.29-35
• /
• 2020

In this paper, we established a prediction model of fuel consumption at the aircraft's taxi operation. To look for countermeasures to reduce fuel consumption and carbon emissions, Airbus A380's actual ground taxi data was used. As a result, the number of stops or turnings during the taxi operation was not related to fuel consumption. It was confirmed that the amount of fuel consumption in the taxi operation was the taxi time and the thrust change. It can be confirmed that ground control optimization, which is the result of close cooperation between the control organization and the airline, is absolutely necessary to reduce taxi time and minimize the occurrence of thrust change events.

• Asian-Australasian Journal of Animal Sciences
• /
• v.17 no.3
• /
• pp.371-373
• /
• 2004

A total of sixty 67 week-old Manina White strain laying hens were individually housed in cages to investigate feed consumption pattern during the day in relation to time of oviposition. Hourly feed intake and time of oviposition were recorded for each bird for seven days. Mean hourly feed intake of all hens showed a smaller peak at 10:00-12:00 and a larger peak at 17:00-19:00. There were no significant differences in amount of daily feed consumption and hourly eating pattern between egg-laying days and non-laying days. However, hens consumed about 10 g more feed (p<0.01) on egg-forming days (the day before oviposition) than on non-eggforming days. Hourly feed intake decreased prior to oviposition, but increased immediately during the time of oviposition. The peak consumption during the evening hours (17:00-19:00) was consistent regardless of the time of oviposition.