• Journal of Korean Society of Rural Planning
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• v.26 no.3
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• pp.39-47
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• 2020

While agricultural water has been declared free in Korea to ensure access to these limited resource since the year 2000, farmers have however developed lackadaisical behavior towards water saving leading to its scarcity. To overcome this problem, a variety of experience-oriented education model was developed for farmers to promote the adoption of water conservation, and preventive measure against the drought impacts. In this study, farmers' awareness and their attitudinal behavior of agricultural water savings were investigated. The monitoring of water supply structure and the repair facilities in Pohang and Yeongdeok areas were conducted. The field visits and behavioral surveys showed a high degree of over-use and illegal water withdrawals by the farmers due to poor water-saving and management practices. We found that most of the KRC employees strongly admitted the necessity of water-saving education. On the other hand, the farmers showed good interest in the implementation of water-saving awareness through education. Besides this, most farmers agreed to adopt water-saving practices in the fields. Farmers also acknowledged the recklessness of water use was due to the aging of waterways, poor water management, and illegal water consumption. The majority of the farmers responded against imposing the water-use tariffs. However, there was a low response to paying the water-use charge. Aging agricultural facilities and the lack of institutional penalties or incentives were the major obstacles in achieving efficient water-saving. Considering the current drought frequency, urgent water-saving education of farmers was deemed necessary to prepare the farming community against the water shortages. Based on the results of this study, we have to identify the irrigation practices of farmers and provide water-saving education to enforce more efficient use and management of agricultural water.

• The Journal of Korean Society for School & Community Health Education
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• v.16 no.1
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• pp.93-105
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• 2015

Objectives: This study is for understanding the perceived job training of the public health promotion program officials and analysis the educational needs in order to serve as the basis for the development of the future educational programs. Methods: To this end, we have developed a survey based on references and consultations with experts. We had explained the purpose and the intent of the survey to nationwide public health directors in advance and an online questionnaire was conducted for health promotion program and service providing personnel; the results from a total of 763 survey respondents were used in the final analysis. Collected data were analyzed through SPSS Win 21.0. Results: The results shows that the tasks of public health promotion personnel are 'business practices,' 'planning and evaluation related work' and so on in order of importance while the duties of service providing personnel are 'counseling,' 'education' and so on. One of the factors affecting field applications of education is 'awareness of the connection between education and career development' which accounts for 33% of explanatory power; the selected six other factors constitute 41.7%. Necessary educational contents for public officials are 'public health service planning,' 'report writing skills' and 'project evaluation methods.' On the other hand, for service providers, the contents are 'counseling methods,' 'development of educational materials,' 'monitoring health status' and so on. Conclusions: In order for trained service personnel to accord with the changed health promotion environment and the demand of local residents, Public Health Promotion officials should increase educational opportunities based on the competencies for each job, provide continuous learning opportunities and information even after the training, and finally, create a system that can link to career development.

• Journal of Korean Foot and Ankle Society
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• v.25 no.3
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• pp.126-132
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• 2021

Purpose: This study examined whether there are seasonal variations in the number of plantar fasciitis cases from the database of the Korean Health Insurance Review & Assessment Service and an internet search of the volume data related to plantar fasciitis and whether there are correlations between variations. Materials and Methods: The number of plantar fasciitis cases per month was acquired from the Korean Health Insurance Review & Assessment Service from January 2016 to December 2019. The monthly internet relative search volumes for the keywords "plantar fasciitis" and "heel pain" were collected during the same period from DataLab, an internet search query trend service provided by the Korean portal website, Naver. Cosinor analysis was performed to confirm the seasonality of the monthly number of cases and relative search volumes, and Pearson and Spearman correlation analysis was conducted to assess the correlation between them. Results: The number of cases with plantar fasciitis and the relative search volume for the keywords "plantar fasciitis" and "heel pain" all showed significant seasonality (p<0.001), with the highest in the summer and the lowest in the winter. The number of cases with plantar fasciitis was correlated significantly with the relative search volumes of the keywords "plantar fasciitis" (r=0.632; p<0.001) and "heel pain" (r=0.791; p<0.001), respectively. Conclusion: Both the number of cases with plantar fasciitis and the internet search data for related keywords showed seasonality, which was the highest in summer. The number of cases showed a significant correlation with the internet search data for the seasonality of plantar fasciitis. Internet big data could be a complementary resource for researching and monitoring plantar fasciitis.

• Safety and Health at Work
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• v.12 no.3
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• pp.339-345
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• 2021

Background: Some researchers state that they are not yet able to provide a deep understanding of the underlying causes of unsafe behaviors (UBs). Therefore, the present study was conducted to investigate the attitudes and experiences of Iranian workers of UBs. Methods: This present study was conducted in 35 industries using a semistructured interview based on grounded theory. Forty participants were interviewed, including 13 industrial safety and health experts and 27 workers and supervisors. The analysis of the present study consisted of a three-step coding process including open, axial, and selective coding. Results: The results showed that the factors affecting UBs could be classified into three categories: organizational, individual, and socioeconomic factors. Organizational factors were divided into 6 parts: procedure and environmental conditions, communications, monitoring, organizational safety culture, resource allocation, and human resources. Socioeconomic factors had three subcategories: community safety culture, type of organizational ownership, and economic problems. Finally, the individual factors were classified into two categories of personality traits and individual competence. Conclusion: The results showed that organizational factors were the most categorized, and it is estimated that this factor has a more important role in the UBs. Of course, to better understand the close relationship between these factors and find the weight and importance of each factor, it needs to measure it with multicriteria decision systems.

• Korean Journal of Cognitive Science
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• v.30 no.1
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• pp.1-30
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• 2019

The main goal of this paper is to understand the underlying cognitive mechanisms of collaborative learning and how it can be supported by technology. The paper first overviews changes in conceptions of learning and distinguishes different types of joint works including collaborative learning. Cognitive mechanisms of collaborative learning are examined in terms of resource pooling, facilitation of constructive activities, knowledge co-construction, and supports for monitoring and regulation. Socio-emotional mechanisms of collaborative learning moderate the directions and strengths of these cognitive mechanisms. Technology supports these mechanisms in a number of different ways. Seven major supports technology provides for collaborative learning are discussed. A deeper understanding of the cognitive mechanisms of collaborative learning can help us to harness the benefits of collaborative learning better and also to develop more sophisticated tools supporting collaborative learning.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.145-159
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• 2019

A good number of drought indices have been introduced and applied in different regions for monitoring drought conditions, but some of those are region-specific and have limitations for use under other climatic conditions because of the inherently complex characteristics of drought phenomenon. Standardized Precipitation Index (SPI) indices are widely used all over the world, including Bangladesh. Although newly developed, studies have demonstrated The Effective Drought Index (EDI) to perform better compared to SPIs in some areas. This research examined the performance of EDI to the SPI for detecting drought events throughout 35 years (1981 to 2015) in Bangladesh. Rainfall data from 27 meteorological stations across Bangladesh were used to calculate the EDI and SPI values. Results suggest that the EDI can detect historical records of actual events better than SPIs. Moreover, EDI is more efficient in assessing both short and long-term droughts than SPIs. Results also indicate that SPI3 and the EDI indices have a better capability of detecting drought events in Bangladesh compared to other SPIs; however, SPI1 produced erroneous estimates. Therefore, EDI is found to be more responsive to drought conditions and can capture the real essence of the drought situation in Bangladesh. Outcomes from this study bear policy implications on mitigation measures to minimize the loss of agricultural production in drought-prone areas. Information on severity level and persistence of drought conditions will be instrumental for resource managers to allocate scarce resources optimally.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.75-94
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• 2021

The 4th industrial revolution needs a fusion of artificial intelligence, robotics, the Internet of Things (IoT), edge computing, and other technologies. For the fusion of technologies, cloud computing technology can provide flexible and high-performance computing resources so that cloud computing can be the foundation technology of new emerging services. The emerging services become a global-scale, and require much higher performance, availability, and reliability. Public cloud providers already provide global-scale services. However, their services, costs, performance, and policies are different. Enterprises/ developers to come out with a new inter-operable service are experiencing vendor lock-in problems. Therefore, multi-cloud technology that federatively resolves the limitations of single cloud providers is required. We propose a software platform, denoted as Cloud-Barista. Cloud-Barista is a multi-cloud service common platform for federating multiple clouds. It makes multiple cloud services as a single service. We explain the functional architecture of the proposed platform that consists of several frameworks, and then discuss the main design and implementation issues of each framework. To verify the feasibility of our proposal, we show a demonstration which is to create 18 virtual machines on several cloud providers, combine them as a single resource, and manage it.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.94-103
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• 2022

As a large amount of data generated by sensors or devices at the manufacturing site is transmitted to the server or client, problems arise in network processing time delay and storage resource cost increase. To solve this problem, considering the manufacturing site, where real-time responsiveness and non-disruptive processes are essential, QRC (Quotient Remainder Compression) and BL_beta compression algorithms that enable real-time and lossless compression were applied to actual manufacturing site sensor data for the first time. As a result of the experiment, BL_beta had a higher compression rate than QRC. As a result of experimenting with the same data by slightly adjusting the data size of QRC, the compression rate of the QRC algorithm with the adjusted data size was 35.48% and 20.3% higher than the existing QRC and BL_beta compression algorithms.

• Journal of Platform Technology
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• v.9 no.4
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• pp.79-89
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• 2021

In this paper, the fine dust (PM10) and ultrafine dust (PM2.5) measurement platforms are designed to be mobile and fixed using oneM2M, the international standard for IoT. The fine dust measurement platform is composed and designed with a fine dust measurement device, agent, oneM2M platform, oneM2M IPE, and monitoring system. The main difference between mobile and fixed is that the mobile uses the MQTT protocol for interconnection between devices and services without blind spots based on LTE connection, and the fixed uses the LoRaWAN protocol with low power and wide communication range. Not only fine dust, but also temperature, humidity, atmospheric pressure, volatile organic compounds (VOC), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and noise data related to daily life were collected. The collected sensor values were managed using the common API provided by oneM2M through the agent and oneM2M IPE, and it was designed into four resource types: AE and container. Six functions of operability, flexibility, convenience, safety, reusability, and scalability were analyzed through the fine dust measurement platform design.

• KNOM Review
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• v.24 no.1
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• pp.1-12
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• 2021

VM (Virtual Machine) live migration is a server virtualization technique for deploying a running VM to another server node while minimizing downtime of a service the VM provides. Currently, in cloud data centers, VM live migration is widely used to apply load balancing on CPU workload and network traffic, to reduce electricity consumption by consolidating active VMs into specific location groups of servers, and to provide uninterrupted service during the maintenance of hardware and software update on servers. It is critical to use VMlive migration as a prevention or mitigation measure for possible failure when its indications are detected or predicted. In this paper, we propose two VNF live migration methods; one for predictive load balancing and the other for a proactive measure in failure. Both need machine learning models that learn periodic monitoring data of resource usage and logs from servers and VMs/VNFs. We apply the second method to a vEPC (Virtual Evolved Pakcet Core) failure scenario to provide a detailed case study.