• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.249-267
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• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.

• Smart Media Journal
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• v.11 no.4
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• pp.9-18
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• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

• The Journal of Pediatrics of Korean Medicine
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• v.23 no.2
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• pp.131-144
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• 2009

Objectives : The purpose of this study is to have better data and to make efficient clinical reviews on pre-school children's growth based on two measurements; Body composition for measuring body volume and bone age for potential growth. Methods : The study was conducted with 221 children(118 of boys and 103 of girls) from three kindergartens. Body compositions(soft lean mass, body fat mass, percent body fat) were measured by bioelectrical impedance analysis, bone age was measured by bone density through ultrasonic image of calcaneus. Results and Conclusions : 1. The higher level on weight or BMI, the more averages of soft lean mass, body fat mass, percent body fat. 2. The average bone ages and bone age-chronological age were lower in under 50 percentile's group, but it was higher in upper 50 percentile's group. Also, children with high BMI had older in bone ages and bone age-chronological age. 3. The higher in height percentile based on the bone age; there were more soft lean mass. 4. The averages of bone age and bone age-chronological age were significantly decreased, the more percentiles of height according to bone age were big, they were higher than total average in under 50 percentile's group of height, lower than total average in over 50 percentile's group of height in both boys and girls. 5. The average of MPH were significantly decreased in top percentiles of children's height distribution. Also, in the upper percentiles of height distribution based on bone age were big in only boys. 6. The body compositions(soft lean mass, body fat mass, percent body fat) were related to body volume growth, which can he measured by weight or BMI. The bone age, bone age-chronological age, and MPH were related in terms of hight. The body volume growth was a little hit related with potential growth.

• Journal of KIISE
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• v.44 no.5
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• pp.553-558
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• 2017

With the rapid growth of Big Data, research on extracting meaningful information is being pursued by both academia and industry. Especially, data characteristics derived from analysis, and researcher intention are key factors for search algorithms to obtain accurate output. Therefore, reflecting both data characteristics and researcher intention properly is the final goal of data analysis research. The data analyzed properly can help users to increase loyalty to the service provided by company, and to utilize information more effectively and efficiently. In this paper, we explore various methods of document-evaluation, so that we can improve the accuracy of searching article one of the most frequently searches used in real life. We also analyze the experiment result, and suggest the proper manners to use various methods.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.593-598
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• 2022

Recently, data production has seen explosive growth, and the storage systems to store these big data safely and quickly is evolving in various ways. A typical configuration of storage systems is the use of SSDs with fast data processing speed as a RAID group that can maintain reliable data. However, since NAND flash memory, which composes SSD, has the feature that deterioration if writes more than a certain number of times are repeated, can increase the likelihood of simultaneous failure on multiple SSDs in a RAID group. And this can result in serious reliability problems that data cannot be recovered. Thus, in order to solve this problem, we propose a method of throttling IOs so that each SSD within a RAID group leads to a different life-end. The technique proposed in this paper utilizes SMART to control the state of each SSD and the number of IOs allocated according to the data pattern used step by step. In addition, this method has the advantage of preventing large amounts of concurrency defects in RAID because it induces differential lifetime finishes of SSDs.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.199-204
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• 2022

The purpose of this study is to broaden the understanding of the Security Services Industry Act, and also to examine the meanings of various phenomena by analyzing the media report big data rather than the researchers' perspective on the Security Services Industry Act. In the research method, this study searched for a keyword 「Security Services Industry Act」 that prescribes the security work as an important subject of crime prevention and maintenance of public order in Korea. The data was searched from 1990 to 2021 the BIG KINDS could provide. Also, for the concrete analysis during the period of data search, it was divided into settlement period(1976~2001), growth period-quantitative(2002~2012), and growth period-qualitative(2013~2021). In the results of this study, the media report perception of the Security Services Industry Act is continuously emphasizing the social roles and importance of private security according to the flow of time. The consequent marketability of private security will play great roles in the protection of people's lives and properties in the combination with various other industries in the future. However, the private security industry that provides public peace service together with the police, could be rising as an element that hinders the development of private security industry because of various social issues caused by legal regulations and illegal problems, so it would be necessary to more strengthen its responsibility and roles accordingly.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.623-634
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• 2016

Most of existing frequent pattern mining methods address time efficiency and greatly rely on the primary memory. However, in the era of big data, the size of real-world databases to mined is exponentially increasing, and hence the primary memory is not sufficient enough to mine for frequent patterns from large real-world data sets. To solve this problem, there are some researches for frequent pattern mining method based on disk, but the processing time compared to the memory based methods took very time consuming. There are some researches to improve scalability of frequent pattern mining, but their processes are very time consuming compare to the memory based methods. In this paper, we present PPFP as a novel disk-based approach for mining frequent itemset from big data; and hence we reduced the main memory size bottleneck. PPFP algorithm is based on FP-growth method which is one of the most popular and efficient frequent pattern mining approaches. The mining with PPFP consists of two setps. (1) Constructing an IFP-tree: After construct FP-tree, we assign index number for each node in FP-tree with novel index numbering method, and then insert the indexed FP-tree (IFP-tree) into disk as IFP-table. (2) Mining frequent patterns with PPFP: Mine frequent patterns by expending patterns using stack based PUSH-POP method (PPFP method). Through this new approach, by using a very small amount of memory for recursive and time consuming operation in mining process, we improved the scalability and time efficiency of the frequent pattern mining. And the reported test results demonstrate them.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Journal of People, Plants, and Environment
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• v.23 no.1
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• pp.115-123
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• 2020

With the aim of accumulating data that can be used to infer the basis for the acculturation of trees called 'Goe,' this study aims to identify the survival status of the pagoda and zelkova trees known as 'Goe' trees by comparing and analyzing the status of pagoda trees (Sophora japonica) and zelkova tree (Zelkova serrata) designated as a protected tree in Korea. The results of this study are as follows; Zelkova serrata designated as a protected tree grows the most, with 2,147 trees (29.4%) in Cheonnam, followed by Chungnam (16.5%) and Gyeongbuk (14.4%). However, Sophora japonica showed a different result from zelkova Serrata as the total number of 210 Sophora japonica (55.7%) in Gyeongbuk and Daegu is much larger than that of zelkova Serrata. As a result, in the Yeongnam region, where the Confucianism of Yeongnam was actively practiced, the existence of Sophora japonica is much larger than that of the Zelkova Serrata, which is not a coincidence, and it is difficult to determine it only based on their flora and planting distribution. Results of comparing protected trees of Sophora japonica and Zelkova Serrata showed that the average age of Zelkova Serrata wass 289 years, while that of Sophora japonica was 302 years, and that the average height of Zelkova Serrata wass 18 m, which is higher than the height of 16 m of Sophora japonica. The average diameter at breast height of Zelkova Serrata was 398 cm and that of Sophora japonica was 314 cm, which indicates that Zelkova Serrata is relatively big. Therefore, it can be assumed that Zelkova Serrata has a larger growth potential than Sophora japonica, and the possibility of growth as a big tree is also high, but it seems that the explanation that "they are relatively long-lived" is not clearly determined.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.209-215
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• 2022

In this study, detailed factors of public sentiment toward the 'post-corona new normal' were examined through social media big data sentiment analysis. Thus, it is to provide basic data to preemptively cope with the post-COVID-19 era. For data collection and analysis, the emotional analysis program of 'Textom', a big data analysis program, was used. The data collection period is one year from October 5, 2020 to October 5, 2021, and the collection channels are set as blogs, cafes, Twitter, and Facebook on Daum and Naver. The original data edited and refined a total of 3,770 collected texts from this channel were used for this study. The conclusion is as follows. First, there is a high level of interest and liking for the 'post-corona new normal'. In other words, it can be seen that optimism such as daily recovery, technological growth, and expectations for a new future took the lead at 77.62%. Second, negative emotions such as sadness and rejection are 22.38% of the total, but the intensity of emotions is 23.91%, which is higher than the ratio, suggesting that these negative emotions are intense. This study has a contribution to the detailed factor analysis of the public's positive and negative emotions through big data analysis on the 'post-corona new normal'.