• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Physical Therapy Korea
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• v.14 no.3
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• pp.9-15
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• 2007

The purpose of this study was to investigate electromyographic activities of the flexor digitorum superficialis (FDS) and the flexor carpi ulnaris (FCU) by the shape of the ultrasound head. Twelve healthy subjects participated and performed ultrasound therapy with a round head and a long handled head during each 5-minute application. Electromyographic activities of the FDS and FCU were recorded by surface electrodes and normalized by maximal voluntary isometric contraction (MVIC) values. There was no difference in the muscular fatigue of FDS and FCU as determined by the shape of the ultrasound head (p>.05). Without the shape of head, the mean power frequency decreased with the time. There also was no difference in %MVIC of the FDS and FCU as determined by the shape of the ultrasound head (p>.05), but the force exerted exceeded 20%MVIC. There was however a significant difference in the amount of cumulative workload of the FDS and FCU as determined by the shape of ultrasound head (p<.05). The workload was however not affected by the shape of the ultrasound head. Constant static grasp of ultrasound transducer head during ultrasound therapy is considered a high risk factor of work-related musculoskeletal disease.

• Journal of the Ergonomics Society of Korea
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• v.35 no.2
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• pp.67-74
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• 2016

Objective: The aim of this study is to develop a farming shovel to reduce workload, which helps farmers lower the risk of musculoskeletal disease. Background: Most of work using farming tools including shovels requires repetitive works and awkward postures on body parts, and it could possibly cause work-related musculoskeletal disorders. It is necessary to develop and distribute farm equipment and tools in order to reduce physical workload. Method: To improve the most uncomfortable task perceived by ten farmers during the work with a shovel, the improved shovel was designed and made as a prototype for experiment for the comparison of the existing and improved shovels. Twenty males were recruited for this experiment, and muscle activity (%MVC) of six body parts and subjective discomfort ratings by body parts while working with a shovel were measured. A paired t-test was performed to compare physical workload between the existing shovel and the developed one. Results: A shovel applying foothold tied between shaft and blade was designed, which can help workers reduce repetitive bending of back and pressures for upper limbs while digging soil. According to compared evaluation of the developed shovel and the existing shovel, the developed shovel's %MVCs in all experimental muscles were significantly lower than those of the existing shovel. The developed shovel showed the biggest drop in perceived subjective discomfort rating of back, followed by arm and neck, compared to the existing shovel. Conclusion: It was confirmed that attaching a foothold to a shovel was an effective way of reducing workload in back and upper limbs during digging. Application: In the near future, if we put the prototype of developed shovel to practical use after making up for defects, it will help farm work environment be healthier and safer.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.123-124
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• 2019

In the labor-intensive construction site, potential threats of the musculoskeletal diseases mainly caused by various repetitive physical tasks, vulnerable environment, and the aging of the labor worker exist. However, quantitative measuring method of construction labor worker's work posture has not been improved yet. This study proposed musculoskeletal measuring method by using wearable motion recognition sensor for quantitative evaluation and analysis of working posture of construction workers. This method is expected to be used as a basic data for posture analysis and prevention construction safety accidents, as well as physical workload and labor productivity analysis by labor work type.

• Journal of the Ergonomics Society of Korea
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• v.24 no.3
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• pp.43-52
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• 2005

A two-dimensional posture measurement system was developed to evaluate the risks of work-related musculoskeletal disorders(MSDs) easily on various conditions of work. The posture measurement system is an essential tool to analyze the workload for preventing work-related musculoskeletal disorders. Although several posture measurement systems have been developed for workload assessment, some restrictions in industry still exist because of its difficulty on measuring work postures. In this study, an image recognition algorithm was developed based on a neural network method to measure work posture. Each joint angle of human body was automatically measured from the recognized images through the algorithm, and the measurement system makes it possible to evaluate the risks of work-related musculoskeletal disorders easily on various working conditions. The validation test on upper body postures was carried out to examine the accuracy of the measured joint angle data from the system, and the results showed good measuring performance for each joint angle. The differences between the joint angles measured directly and the angles measured by posture measurement software were not statistically significant. It is expected that the result help to properly estimate physical workload and can be used as a postural analysis system to evaluate the risk of work-related musculoskeletal disorders in industry.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.167-173
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• 1998

Many workers like welders work in squatting postures with the object on the ground during an entire work shift. It is suspected that such prolonged squatting without any supporting stool would gradually cause musculoskeletal injuries to workers. This study is to examine the physical stress caused by the prolonged squatting and to recommend a safe work/rest schedule for a welding task with squatting posture based on the lab experiments. In this study, 8 healthy student subjects participated in the experiment. They maintained a squatting work posture for 16 minutes with 4 different stool height conditions: no stool; 10cm height; 15cm height; and 20cm height. Every 2 minutes, the discomfort was subjectively assessed with the magnitude estimation method for the whole body, lower back, upper leg and lower leg. Based on discomfort ratings, we found that a 10cm height stool relieved the workload most. Discomfort rating results also indicated that a 20cm height stool showed the highest workload, and the there were no difference in workload between a 15cm height stool and no stool. We recommend to use low height stools and to maintain such working postures no longer than 6 minutes for prolonged squatting tasks.

• Journal of the Ergonomics Society of Korea
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• v.28 no.3
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• pp.7-15
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• 2009

The objective of this study is to survey the working environmental conditions and musculoskeletal workload(DMQ) in red pepper farmers. 155 full-time farmers(males=91, females=64) lived in Chungnam and Jeonbuk participated in the study. To offer the fundamental data for agricultural improvement of red pepper farms, information about working farm conditions, health condition, musculoskeletal disorders(MSDs), labor intensity and musculoskeletal workload was obtained by questionnaire and interviews. The results are as follows: 1. The farmers reported sunlights, high temperature and cold/ draught as uncomfortable work environment. 2. Physical and mental fatigue in females is higher than that of males. 3. The prevalence rates of medical diagnosed diseases are higher in order of osteoarthritis, herniated nucleus pulposus(HNP), and chronic gastritis/gastric ulcer. 4. Prevalence rates of musculoskeletal symptoms more than standard 1 among the various pain areas are higher in order of low back, shoulders and knees. These results can be used practically for agricultural improvement of red pepper farms to prevent MSDs.

• Physical Therapy Korea
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• v.19 no.2
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• pp.69-79
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• 2012

Importance of the work-related musculoskeletal disorders (WMSDs) has been increasing in the hospital industry such as health care industry and financial industry. This study investigated in order to identify the factors like general, occupational and ergonomically characteristics of the subjects related to musculoskeletal disorders (MSDs) of physical therapists (PTs). Ergonomic tools of rapid upper limb assessment (RULA) were used for evaluation workload of the tasks. Prevalence of MSDs were 13 PTs (26.0%) for neck, 31 PTs (62.0%) for shoulder, 9 PTs (18.0%) for arm/elbow, 27 PTs (54.0%) for hand/wrist, 28 PTs (56.0%) for back, 14 PTs (28.0%) for leg/foot. The analysis of the rate of the pain intensity showed that 53.5% subjects experience moderate pain and 14.0% subjects experience severe pain. Factors which were general characteristics, for example, height, ergonomically characteristics such as 'Posture Score A' were related musculoskeletal subjective symptoms in logistic analysis (p<.05). Among physical therapists, action level of RULA were action level 2 (6.0%), action level 3 (52.0%), action level 4 (42.0%). Physical therapists were estimated one of the highest risk factor in this study. This study suggested that the need of preventive education and program for PTs (physical therapists). Comprehensive and systematic management plans should be established to include both ergonomic and sociopsychological aspects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.186-194
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• 2018

The aim of this study was to examine the effects of an after-school basketball training program of 12 weeks on physical abilities (grip strength, endurance, and flexibility) and learning-related factors (cognition strength, cognition speed, concentration, and mental workload) in middle school students. Middle school students (Males, N=20) were recruited for use in this study and were randomly divided into either a basketball training group (n = 10, BT) or a non-exercise control group (n = 10, CON). Two-way repeated measures ANOVA with post-hoc testing was used for data analysis. Results found endurance and flexibility in the BT group were significantly increased, but not in the CON group. In addition, cognition strength, speed, and concentration in the BT group increased and mental workload in the BT group slightly decreased. In contrast, the CON group showed a significant increase in mental workload. Our findings show that participation in after-school physical education activities (e.g., basketball training program) positively improves physical abilities and increases brain functions for learning.

• Journal of the Korean Society of Safety
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• v.29 no.1
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• pp.47-53
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• 2014

The application of advanced Main Control Room(MCR) is accompanied with lots of changes and different forms and features through the virtue of new digital technologies. The characteristics of these digital technologies and devices give many opportunities to the interface management, and can be integrated into a compact single workstation in advanced MCR so that workers can operate the plant with minimum physical burden under any operation conditions. However, these devices may introduce new types of human errors and thus a means to evaluate and prevent such errors is needed, especially those related to characteristics of digital devices. This paper reviewed the new type of human error hazards of tasks based on digital devices and surveyed researches on physiological assessment related to human error. An experiment was performed to verify human error hazards by physiological responses such as EEG which was measured to evaluate the cognitive workload of operators. And also, the performances of four tasks which are representative in human error hazard tasks based on digital devices were compared. Response time, ${\beta}$ power spectrum rate of each task by EEG, and mental workload by NASA-TLX were evaluated. In the results of the experiment, the rate of the ${\beta}$ power was increased in the task 1 and task 4 which are searching and navigating task and memory task of hierarchical information, respectively. In case of the mental workload, in most of evaluation items, task 1 and 4 were highly rated comparatively. In this paper, human error hazards might be identified by highly cognitive workload. Conclusively, it was concluded that the predictive method which is utilized in this paper and an experimental verification can be used to ensure the safety when applying the digital devices in Nuclear Power Plants (NPPs).