• Journal of the Korea Society for Simulation
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• v.22 no.1
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• pp.41-51
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• 2013

This tutorial paper presents the design methodology of system-level simulators targeted for code division multiple access (CDMA) cellular standards such as EV-DO (Evolution-Data Only) and broadcast multicast service (BCMCS). The basic structure and simulation flow of system-level simulators are delineated, following the procedure of cell layout, mobile drops, channel modeling, received power calculation, scheduling, packet error prediction, and traffic generation. Packet data transmissions on the forward link of CDMA systems and EV-DO BCMCS systems are considered for modeling simulators. System-level simulators for cellular standards are modeled and developed with high-level languages and utilized to evaluate and predict air interface performance metrics including capacity and coverage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2783-2804
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• 2021

In the network function virtualization environment, dynamic changes in network traffic will lead to the dynamic changes of service function chain resource demand, which entails timely dynamic adjustment of service function chain resource configuration. At present, most researches solve this problem through virtual network function migration and link rerouting, and there exist some problems such as long service interruption time, excessive network operation cost and high penalty. This paper proposes a dynamic adjustment method of service function chain resource configuration for the dynamic changes of network traffic. First, a dynamic adjustment request of service function chain is generated according to the prediction of network traffic. Second, a dynamic adjustment strategy of service function chain resource configuration is determined according to substrate network resources. Finally, the resource configuration of a service function chain is pre-adjusted according to the dynamic adjustment strategy. Virtual network functions combination and virtual machine reusing are fully considered in this process. The experimental results show that this method can reduce the influence of service function chain resource configuration dynamic adjustment on quality of service, reduce network operation cost and improve the revenue of service providers.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.149-165
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• 2020

Purpose - This study investigates how employee personality traits affect psychological contract breach. Also, our study examines how Guanxi, a unique socio-cultural characteristic of China, moderates the relationship between personality traits and psychological contract breach. Design/methodology/approach - To test our ideas, we constructed a survey questionnaire based on the literatures on personality traits, Guanxi, and psychological breach. The questionnaires were distributed to Chinese employees, and then we conducted a regression analysis using 378 questionnaires. Findings - We found that neuroticism is positively related to perceived psychological contract breach. We also identified support for the prediction that the positive link between neuroticism and psychological contract breach becomes weaker when employees' awareness of Guanxi is high. Research implications or Originality - This study provides several theoretical and practical implications. First, this study elaborates the personality traits-psychological contract breach relationship by incorporating Guanxi, a critical contingency factor of China. Second, given that the relationship between neuroticism and psychological contract breach can be affected by employees' perceptions of Guanxi, managers should administer Guanxi to function as a lubricant within the organization.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.49-56
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• 2023

Computer architecture serves as a link between application requirements and underlying technology capabilities such as technical, mathematical, medical, and business applications' computational and storage demands are constantly increasing. Machine learning these days grown and used in many fields and it performed better than traditional computing in applications that need to be implemented by using mathematical algorithms. A mathematical algorithm requires more extensive and quicker calculations, higher computer architecture specification, and takes longer execution time. Therefore, there is a need to improve the use of computer hardware such as CPU, memory, etc. optimization has a main role to reduce the execution time and improve the utilization of computer recourses. And for the importance of execution time in implementing machine learning supervised module linear regression, in this paper we focus on optimizing machine learning algorithms, for this purpose we write a (Diabetes prediction program) and applying on it a Practical Swarm Optimization (PSO) to reduce the execution time and improve the utilization of computer resources. Finally, a massive improvement in execution time were observed.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.1 s.9
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• pp.1-12
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• 2006

In order to estimate a dynamic origin and destination demand between on and off-ramps in the freeways, a traffic flow theory can be used to calculate a link distribution proportion of traffics moving between them. We have developed a dynamic traffic estimation model based on the three-phase traffic theory (Kerner, 2004), which explains the complexity of traffic phenomena based on phase transitions among free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatiotemporal features. The developed model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We have estimated the link, on and off-ramp volumes at every time interval by using traffic data collected from vehicle detection systems in Korea freeway sections. The analyzed results show that the developed model describes traffic flows adequately.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1313-1329
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• 2015

In MANET, frequent route breaks lead to repeated route discovery process and this increases control packet overhead and packet drop. AODV-I improves performance of AODV by using the event driven approach which removes periodic Hello message. Unlike the Hello message, Interrupt message which is sent for each event can detect and predict the link failure because it allows node to know the status of the neighbor node. From this characteristics of Interrupt message, performance of AODV-I can be further improved by adding a processing procedures for each type of Interrupt message and it is also possible to improve AODV-I by adding the Backup path scheme because it originally has problems due to a single path of AODV. In this paper, we propose AODV-IB that combines improved Backup path scheme and Interrupt message approach of AODV-I in order to reduce transmission delay and the number of route discoveries. AODV-IB improves AODV-I by adding proper processing procedures for the link failure prediction and detection for each Interrupt message. We also implement improved Backup path strategy in AODV-IB by minimizing delay without additional Control packet. Simulation results, using the simulator QualNet 5.0, indicate that proposed AODV-IB performs better than AODV-I.

• Journal of Korea Water Resources Association
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• v.42 no.3
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• pp.213-225
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• 2009

A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.220-233
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• 2017

Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.