• Safety and Health at Work
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• v.14 no.1
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• pp.85-92
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• 2023

Background: Presenteeism is closely related to work performance, work quality and quantity, and productivity at work. According to the job demand-control-support model, job demand, job control, and support play important roles in presenteeism. The present study investigated job characteristics profiles based on the job demand-control-support model and identify the association between job characteristics profiles and presenteeism. Methods: This secondary data analysis used the Sixth Korean Working Condition Survey, a nationwide cross-sectional dataset. The study included 25,361 Korean wage workers employed in the workplace with two or more workers. Participants were classified into four job characteristics profiles based on the job demand-control-support model, using latent profile analysis, and logistic regression was performed to examine the association between study variables. Results: Overall, 11.0 % of study participants reported experience of presenteeism in the past 12 months. Age, sex, location, monthly income, shift work, work hours, health problems, and sleep disturbances were significantly associated with presenteeism. The rate of presenteeism was the highest in the passive isolate group. The passive collective, active collective, and low-stain collective groups had a 23.0%, 21.0%, and 29.0% lower likelihood of experiencing presenteeism, respectively, than the passive isolate group. Conclusions: The job demand-control-support profiles and the risk of presenteeism were significantly associated. The most significant group that lowered the experience of presenteeism was the low-strain collective group, which had a low level of demand and high levels of control and support. Therefore, we need a policy to reduce job demand and increase job control and support at the organizational and national levels.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1A
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• pp.91-103
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• 2005

In designing a video-on-demand (VoD) system, the major challenge may be how to reduce the channels concurrently used maintaining the client's waiting time. For this reason, the various architectures which integrate the multicast streams with the unicast streams were suggested in order to improve channel efficiency in recent years. In combining multicast with unicast, the ways to group the unicast channels together are important so that clients can share the multicast transmission channels. This paper proposes a hybrid video-on-demand system which gathers the unicast channels in new ways and shares multicast transmission channels efficiently by using dynamic channel allocation architecture. The numerical results demonstrate that the proposed architecture in some case achieves performance gain of $551{\%}$ compared to existing architecture. This paper presents procedure of channel release and reuse, performance analysis, and simulation results of the dynamic channel allocation architecture.

• 한국전기화학회:학술대회논문집
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• 2000.10a
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• pp.48-48
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• 2000

• Structural Engineering and Mechanics
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• v.48 no.1
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• pp.57-75
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• 2013

It is still inadequate for investigating the highly nonlinear and complex mechanical behaviors of single-layer latticed domes by only performing a force-based demand-capacity analysis. The energy-based balance method has been largely accepted for assessing the seismic performance of a structure in recent years. The various factors, such as span-to-rise ratio, joint rigidity and damping model, have a remarkable effect on the load-carrying capacity of a single-layer latticed dome. Therefore, it is necessary to determine the maximum load-carrying capacity of a dome under extreme loading conditions. In this paper, a mechanical model for members of the semi-rigidly jointed single-layer latticed domes, which combines fiber section model with semi-rigid connections, is proposed. The static load-carrying capacity and seismic performance on the single-layer latticed domes are evaluated by means of the mechanical model. In these analyses, different geometric parameters, joint rigidities and roof loads are discussed. The buckling behaviors of members and damage distribution of the structure are presented in detail. The sensitivity of dynamic demand parameters of the structures subjected to strong earthquakes to the damping is analyzed. The results are helpful to have a better understanding of the seismic performance of the single-layer latticed domes.

• International Journal of Fluid Machinery and Systems
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• v.6 no.1
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• pp.25-32
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• 2013

Data centers have been built with spread of cloud computing. Further, electric power consumption of it is growing rapidly. High power cooling small-sized fans; high pressure and large flow rate small-sized fan, are used for servers in the data centers and there is a strong demand to increase power of it because of increase of quantity of heat from the servers. Contra-rotating rotors have been adopted for some of high power cooling fans to meet the demand for high power. There is a limitation of space for servers and geometrical restriction for cooling fans because spokes to support fan motors, electrical power cables and so on should be installed in the cooling fans. It is important to clarify complicated internal flow condition and influence of a geometric shape of the cooling fans on performance to achieve high performance of the cooling fans. In the present paper, the performance and the flow condition of the high power contra-rotating small-sized axial fan with a 40mm square casing are shown by experimental and numerical results. Furthermore, influence of the geometrical shape of the small-sized cooling fan on the internal flow condition is clarified and design guideline to improve the performance is discussed.

• Computers and Concrete
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• v.25 no.3
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• pp.255-266
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• 2020

This study presents comparation of fixed and viscos boundary condition effects on three-dimensional earthquake response and performance of a RCC dam considering linear and non-linear response. For this purpose, Cine RCC dam constructed in Aydın, Turkey, is selected in applications. The Drucker-Prager material model is considered for concrete and foundation rock in the nonlinear time-history analyses. Besides, hydrodynamic effect was considered in linear and non-linear dynamic analyses for both conditions. The hydrodynamic pressure of the reservoir water is modeled with the fluid finite elements based on the Lagrangian approach. The contact-target element pairs were used to model the dam-foundation-reservoir interaction system. The interface between dam and foundation is modeled with welded contact for both fixed and viscos boundary conditions. The displacements and principle stress components obtained from the linear and non-linear analyses are compared each other for empty and full reservoir cases. Seismic performance analyses considering demand-capacity ratio criteria were also performed for each case. According to numerical analyses, the total displacements and besides seismic performance of the dam increase by the effect of the viscous boundary conditions. Besides, hydrodynamic pressure obviously decreases the performance of the dam.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7A
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• pp.662-668
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• 2011

This paper analyzes the performance of the Ad hoc On-demand Distance Vector (AODV) routing protocol and Optimized Link State Routing (OLSR) for Mobile Ad hoc Networks (MANETs) using node mobility models. Mobility affects the performance of a routing protocol as it causes changes to network topology. Thus, evaluating the performance of a MANET routing protocol requires mobility models that can accurately represent the movements of mobile nodes. Therefore, this paper evaluates the performance of the AODV and OLSR routing protocols using the random way point model and the Levy walk model by the ns-2 simulations.

• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.440-448
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• 2022

Recently, due to the introduction of distributed energy resources, the optimal resource allocation problem of the power network is more and more important, and the distributed resource allocation method is required to process huge amount of data in large-scale power networks. In the optimal resource allocation problem, many studies have been conducted on the case when the supply-demand balance is satisfied due to the limitation of the generation capacity of each generator, but the studies considering the supply-demand imbalance, that total demand exceeds the maximum generation capacity, have rarely been considered. In this paper, we propose the consensus-based distributed algorithm for the optimal resource allocation of power network considering the supply-demand imbalance condition as well as the supply-demand balance condition. The proposed distributed algorithm is designed to allocate the optimal resources when the supply-demand balance condition is satisfied, and to measure the amount of required resources when the supply-demand is imbalanced. Finally, we conduct the simulations to verify the performance of the proposed algorithm.