• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.247-255
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• 2015

When the new highway is opened, the new highway has alternative or linkage relationship with current highway networks. This change will lead to the different traffic patterns which can also lead to the change of toll revenue in other surrounding highways. This study investigates the change of revenue on the current highway networks caused by the new highway opening under the unified revenue system. Moreover, this study calculates the effectiveness of the revenue fluctuation when the highway system is under controlled by the public or private institution and proposes the meaning of the revenue fluctuation to those institutions. This study also proposes an index of revenue change that could be used to reflect the individual project evaluation and set the priority between multiple projects. Some limitations and future research agenda have also been discussed.

• Journal of Internet Computing and Services
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• v.1 no.2
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• pp.29-37
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• 2000

Asynchronous transfer mode is flexible to support multimedia communication services using asynchronous time-sharing and statistical multimedia techniques to the existing data communication area, ATM ABR service controls network traffic using feedback information on the network congestion situation in order to guarantee the demanded service qualities and the available cell rates, In this paper we apply the control method using queue length prediction to the formation of feedback information for more efficient ABR traffic control. If backward node receive the longer delayed feedback information on the impending congestion, the switch can be already congested from the uncontrolled arriving traffic and the fluctuation of queue length can be inefficiently high in the continuing time intervals, The feedback control method proposed in this paper predicts the queue length in the switch using the slope of queue length prediction function and queue length changes in time-series, The predicted congestion information is backward to the node, NLMS and neural network are used as the predictive control functions, and they are compared from performance on the queue length prediction. Simulation results show the efficiency of the proposed method compared to the feedback control method without the prediction, Therefore, we conclude that the efficient congestion and stability of the queue length controls are possible using the prediction scheme that can resolve the problems caused from the longer delays of the feedback information.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.697-708
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• 1996

In this paper, we study reactive congestion control with priority in ATM network. The priority schemes for buffer access, partial buffer sharing have been investigated in order to improve the utilization of ATM network resources the network and to satisfy the most demanding traffic class. We consider in this paper a discrete-time queueing model for partial buffer sharing with two Markov modulated Poisson inputs. This model can be used to analyze the the effects of the partial buffer sharing priority scheme on system performance for realistic cases of bursty services. Explicit formulae are derived for the number of cells in the system and the loss probabilities for the traffic. Congestion may still occur because of unpredictable statistical fluctuation of traffic sources even when preventive control is performed in the network. In this Paper, we study reactive congestion control, in which each source changes its cell emitting rate a daptively to the traffic load at the switching node. Our intention is that,by incorporating such a congcstion control method in ATM network,more efficient congsestion control is established. We develope an analytical model,and carry out an approximateanalysis of reactive congestion con-trol with priority.Numerical results show that several orders of magnitude improvement in the loss probability can be achieved for the high priority class with little impact on the low priority class performance.And the results show that the reactive congestion control with priority are very effective in avoiding congestion and in achieving the statistical gain.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.887-896
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• 2015

This study estimates future AADT using historical AADT and socio-economic factors in isolated area. Multiple linear regression method by socio-economic factors are lower MAPE and higher R-square than using historical AADT. Analysis of socio-economic factors influence AADT in isolated typical areas, varied socio-economic factors influence on AADT. In isolated coastal areas, oil price influence on AADT. AADT forecasting model in isolated area is excellent when analysising $R^2$ and MAPE. It is assume that estimation of AADT in isolated area using multiple linear regression is accurate because of a little passed traffic volume and traffic volume fluctuation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.341-357
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• 2010

Forward Error Correction (FEC) techniques have been adopted to overcome packet losses and to improve the quality of video delivery. The efficiency of the FEC has been significantly compromised, however, due to the characteristics of the wireless channel such as burst packet loss, channel fluctuation and lack of Quality of Service (QoS) support. We propose herein an Adaptive Cross-layer FEC mechanism (ACFEC) to enhance the quality of video streaming over 802.11 WLANs. Under the conventional approaches, FEC functions are implemented on the application layer, and required feedback information to calculate redundancy rates. Our proposed ACFEC mechanism, however, leverages the functionalities of different network layers. The Automatic Repeat reQuest (ARQ) function on the Media Access Control (MAC) layer can detect packet losses. Through cooperation with the User Datagram Protocol (UDP), the redundancy rates are adaptively controlled based on the packet loss information. The experiment results demonstrate that the ACFEC mechanism is able to adaptively adjust and control the redundancy rates and, thereby, to overcome both of temporary and persistent channel fluctuations. Consequently, the proposed mechanism, under various network conditions, performs better in recovery than the conventional methods, while generating a much less volume of redundant traffic.

• Journal of the Korea Society for Simulation
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• v.15 no.4
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• pp.79-86
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• 2006

Data transmission in poor environment like underwater has considerably serious delay rate and ewer rate. Like this environment usually has heavy fluctuation of error rate and limited wireless communication state. Therefore, mechanism using in such environment has to be efficient and simple. This paper suggests a new block ack mechanism, called the Pervasive Block ACK (PBA), which transmits aggregated ACKs. This mechanism takes effect on reducing number of traffic, decreasing overhead and delay rate in poor environment networks like underwater. Additionally, we can expect energy consumption. We verify propriety and efficiency of PBA through describing numerical result based analytical formula in this paper.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Journal of Korea Port Economic Association
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• v.37 no.3
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• pp.55-74
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• 2021

The role of container ports contributes greatly to international trade and national or regional economic development by supporting maritime transportation and occupies a central position in the supply chain connecting sea and land. The performance(traffic volume) of a port generally depends on geographic, economic, and operational factors etc. For the past several decades, container port volumes have grown with fluctuation. This study amis to analyze how global ports have undergone changes in terms of cargo volume by region, size and period. For the analysis, only the volumes of global top 100 ports were used. Shift-share analysis and BCG matrix analysis were employed as methodologies. According to the result of the analysis, the relative volatility of port traffic over the past 16 years as a whole was found to be limited. On the other hand, ports in China and Southeast and Southwest Asia, which are economically growing for the last decades, showed growing trends, while ports in Northeast Asia and Europe appeared to be in a stagnant or declining phase. It also shows that most of the global ports maintain limited changes in cargo volume because they are already positioned as central ports in the region. In addition, it can be seen that the global port volume has a close relationship with the change in the economic capability of the relevant region or country.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.12
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• pp.899-907
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• 2022

According to the Unmanned aircraft system Traffic Management (UTM), drones are permitted to fly up to 150m above ground, which is located in the atmospheric boundary layer where there is considerable wind fluctuation due to turbulence. Although it is difficult to predict when turbulence will occur drone aviation safety could be enhanced by having a better understanding of the characteristics of vertical profile of wind in the flight area. We used wind lidar (WIndMast 350M) to observe vertical profiles of wind at the test site for aviation meteorological observation equipment located near Incheon International Airport in July and September, 2022. In this study, we utilized the observed wind profile data to propose a technique for obtaining information that could help improve the drone aviation safety. The Fourier transform analysis is used to evaluate the temporal characteristics of the horizontal wind speed at various vertical levels up to 350m. We also examined the relative contribution of the variance of wind having scales of less than an hour, a crucial scale for drone flight, to the variance of wind having all scales at each vertical altitude for days with and without precipitation.