• Fashion & Textile Research Journal
• /
• v.23 no.6
• /
• pp.799-809
• /
• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.149-156
• /
• 2023

This research tried to develop an ESS electricity trading system linked with blockchain for energy participation consumers. For the purpose of publishing renewable energy ESS power amount and demand information, we will build a smart contract system on the blockchain DB, utilize the blockchain DB data of energy prosumers and consumers, and expand the power trading market flexibly. to provide realistic solutions. Therefore, the main contents of the development of the ESS power trading system linked to the blockchain are cloud-based web construction for ESS management, coin issuance and exchange registration for activating the blockchain, and to reflect the blockchain technology, building a blockchain database for collecting and supplying ESS-based production demand data, selecting a blockchain-based platform and building a foundation, and creating a smart contract, etc.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.15 no.4
• /
• pp.291-297
• /
• 2013

Automated weather stations were installed at 9 locations with, three different elevations, (i.e., 50m, 100m, and 300m a.s.l.) with different slope and aspect in a small watershed ($50km^2$ area). Air temperature at 1500 LST and solar radiation accumulated for 1100-1500 LST were collected from January to December 2012. Topography of the study area was defined by a $30{\times}30$ m digital elevation model (DEM) grid. Accumulated solar irradiance was calculated for each location with the spatially averaged slope and aspect of surrounding circles with 5, 10, 15, 20, 25, and 30 grid cell radii, respectively. The 1500 LST air temperature from clear sky conditions with zero cloud amount was regressed to the 1100-1500 LST solar irradiance at 9 locations. We found the highest coefficient of determination ($r^2$ = 0.544) at 25 grid cell radius and the temperature variation in this study was explained by Y = 0.8309X + 0.0438, where Y is 1500 LST temperature (in $^{\circ}C$) and X is 1100-1500 LST accumulated solar irradiance (in $MJ/m^2$).

• Current Research on Agriculture and Life Sciences
• /
• v.31 no.1
• /
• pp.38-44
• /
• 2013

To estimate the radiative heat loss from greenhouses, a new equation for calculating the sky temperature is presented. The sky temperature in the Daegu region was measured using a pyrgeometer and calculated using different equations according to the cloudiness. The calculated and measured results were then compared to identify the best equation for calculating the sky temperature. The difference value between the air and sky temperature was dissimilar and increased as the cloud amount decreased. On clear days the difference value was 10~20 times greater than that on cloudy and rainy days. When analyzing the correlationship between the calculated and measured sky temperatures on clear days, Bliss's and Clark & Allen's equations were found to be superior to the other equations. However, on cloudy days, the best correlationship between the calculated and measured sky temperatures was exhibited by Fuentes's equation. Therefore, a new equation is proposed for calculating the sky temperature on a cloudy day.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.6
• /
• pp.1293-1300
• /
• 2014

The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.11
• /
• pp.409-418
• /
• 2014

The amount of data located in storage servers has dramatically increased with the growth in cloud and social networking services. Storage systems with very large capacities may suffer from poor reliability and long latency, problems which can be addressed by the use of a hybrid disk, in which mechanical and flash memory storage are combined. The Linux-based SSD(solid-state disk) uses a caching technique based on the DM-cache utility. We assess the limitations of DM-cache by evaluating its performance in diverse environments, and identify problems with the caching policy that it operates in response to various commands. This policy is effective in reducing latency when Linux is running in native mode; but when Linux is installed as a guest operating systems on a virtual machine, the overhead incurred by caching actually reduces performance.

• Smart Media Journal
• /
• v.12 no.11
• /
• pp.145-153
• /
• 2023

In a P2P-based distributed storage system where peers frequently join and leave, it is essential to guarantee not only data availability but also playback quality comparable to that provided by local storage devices when playing back video files with real-time constraints. In addition, cloud storage services based on distributed storage systems provide each user with the functionality to share their files with other users, so when multiple users request playback of the same video file at the same time, all playback should be supported seamlessly in real time. Therefore, in this paper, we propose a scheme that process multiple simultaneous playback requests for each video file in real time as well as data availability in a P2P-based distributed storage system that supports file sharing. This scheme can support real-time simultaneous playback and efficiently use storage space by adjusting the amount of redundant data encoded through erasure coding according to the number of concurrent playback requests for each video file.

• Atmosphere
• /
• v.26 no.1
• /
• pp.19-33
• /
• 2016

Water vapor in the atmosphere is an important element that generates various meteorological phenomena and modifies a hydrological cycle. In general, the Yeongdong region has a lot of snow compared to the other regions in winter due to the complex topography and an adjacent East Sea. However, the phase change from water vapor to ice cloud and further snowfall has little been examined in detail. Therefore, in this study, we investigated phase change of liquid water in terms of a quantitative budget as well as time lag of water vapor conversion to snowfall in the ESSAY (Experiment on Snow Storms At Yeongdong) campaign that had been carried out from 2012 to 2015. First, we classified 3 distinctive synoptic patterns such as Low Crossing, Low Passing, and Stagnation. In general, the amount of water vapor of Low Crossing is highest, and Low Passing, Stagnation in order. The snowfall intensity of Stagnation is highest, whereas that of Low Crossing is the lowest, when a sharp increase in water vapor and accordingly a following increase in precipitation are shown with the remarkable time lag. Interestingly, the conversion rate of water vapor to snowfall seems to be higher (about 10%) in case of the Stagnation type in comparison with the other types at Bukgangneung, which appears to be attributable to significant cooling caused by cold surge in the lower atmosphere. Although the snowfall is generally preceded by an increase in water vapor, its amount converted into the snowfall is also controlled by the atmosphere condition such as temperature, super-saturation, etc. These results would be a fundamental resource for an improvement of snowfall forecast in the Yeongdong region and the successful experiment of weather modification in the near future.

• Korean Journal of Construction Engineering and Management
• /
• v.20 no.6
• /
• pp.126-131
• /
• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.

• Atmosphere
• /
• v.26 no.2
• /
• pp.227-241
• /
• 2016

This study investigated the cause of the heavy snowfall that occurred in the East Coast of Korea from 6 February to 14 February 2014. The synoptic conditions were analyzed using blocking index, equivalent potential temperature, potential vorticity, maritime temperature difference, temperature advection, and ground convergence. During the case period, a large blocking pattern developed over the Western Pacific causing the flow to be stagnant, and there was a North-South oriented High-to-Low pressure system over the Korean Peninsula because of this arrangement. The case period was divided into three parts based on the synoptic forcing that was responsible for the heavy snowfall; detailed analyses were conducted for the first and last period. In the first period, a heavy snowfall occurred over the entire Korean Peninsula due to strong updrafts from baroclinic instability and a low pressure caused by potential vorticity located at the mid-troposphere. In the lower atmosphere, a North-South oriented High-to-Low pressure system over the Eastern Korea intensified the easterly airflow and created a convergence zone near the ground which strengthened the upslope effect of the Taebaek Mountain range with a cumulative fresh snowfall amount of 41 cm in the East Coast region. In the last period, the cold air nestled in the Maritime Province of Siberia and Manchuria strengthened much more than that in the first half and extended to the East Sea. The temperature difference between the 850 hPa air and the SST was large and convective clouds developed over the sea. The highest cumulative fresh snow amount of 39.7 cm was recorded in the coastal area during this period. During the entire period, vertically oriented equivalent potential temperature showed neutral stability layer that helped the cloud formation and development in the East Coast. The 2014 heavy snowfall case over the East Coast provinces of Korea were due to: 1) stagnation of the system by blocking pattern, 2) the dynamic effect of mid-level potential vorticity of 1.6 PVU, 3) the easterly air flow from North-South oriented High-to-Low pressure system, 4) the existence of vertically oriented neutral stable layer, and 5) the expansion of strong cold air into the East Sea which created a large temperature difference between the air and the ocean.