• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.33-40
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• 2017

In this Paper, Analyse the Steady State Behavior of TCP and TFRC with Packet Error when both TCP and TFRC Flows Co-exist in the Network. First, Model the Network with TCP and TFRC Connections as a Discrete Time System. Second, Calculate Average Round Trip Time of the Packet Between Source and Destination on Packet Loss Environment. Then Derive the Steady State Performance i.e. Throughput of TCP and TFRC, and Average Buffer Size of RED Router Based on the Analytic Network Model. The Throughput of TCP and TFRC Connection Decrease Rapidly with the Growth of Sending Window Size and Their Transmission Rate but Their Declines become Smoothly when the Number of Sending Window Arrives on Threshold Value. The Average Queue Length of RED Router Increases Slowly on Low Transmission Rate but Increases Rapidly on High Transmission Rate.

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.63-74
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• 2007

Traffic information provision plays an important role in increasing the efficiency of network operation and in providing convenience for roadway users. As a typical device for disseminating real-time traffic information for collective general public, VMS is a prevalent device nowadays and it is being expanded. However, the actual monetary value of traffic information is not quantified up to now. The previous studies regarding VMS traffic information are mainly focused on the behavioral aspects of road users such as departure time and route choices under traffic information provision conditions. This paper tried to estimate the monetary value of VMS traffic information using discrete choice theory and logit model through the stated preference study(SP). The methodological framework adopted in this paper can also be used in evaluating the monetary value of other traffic information providers including PDA, CNS, and mobile phone.

• Journal of the Korean Geographical Society
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• v.46 no.6
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• pp.673-692
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• 2011

Advances in computer technology have enabled us to develop and use numerical landscape evolution models (NLEMs) for exploring the dynamics of geomorphic system from a variety of viewpoints which previously could have not been taken. However, as of yet there have been no trials using or developing NLEMs in Korea. The purpose of this research is to develop a 2 dimensional NLEM on a geological time scale and evaluate its usefulness. The newly developed NLEM (ND-NLEM) treats bedrock weathering as one of the major geomorphic processes and attempts to simulate the thickness of soil. As such it is possible to model the weathering-limited as well as the transport-limited environment on hillslopes. Moreover the ND-NLEM includes not only slow and continuous mass transport like soil creep, but also rapid and discrete mass transport like landslides. Bedrock incision is simulated in the ND-NLEM where fluvial transport capacity is large enough to move all channel bed loads, such that ND-NLEM can model the detachment-limited environment. Furthermore the ND-NLEM adopts the D-infinity algorithm when routing flows in the model domain, so it reduces distortion due to the use of the steepest descent slope flow direction algorithm. In the experiments to evaluate the usefulness of the ND-NLEM, characteristics of the channel network observed from the model results were similar to those of the case study area for comparison, and the hypsometry curve log during the experiment showed rational evidence of landscape evolution. Therefore, the ND-NLEM is shown to be useful for simulating landscape evolution on a geological time scale.

• Tunnel and Underground Space
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• v.13 no.6
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• pp.465-475
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• 2003

Groundwater inflow into the caverns constructed in fractured rock mass was simulated by numerical modeling, NAPSAC (DFN, discrete fracture network model) and NAMMU (CPM, continuous porous media model), a finite-element software package for groundwater flow in 3D fractured media developed by AEA Technology, UK. The input parameters for modeling were determined on surface fracture survey, core logging and single hole hydraulic test data. In order to predict the groundwater inflow more accurately, the anisotropic hydraulic conductivity was considered. The anisotropic hydraulic conductivities were calculated from the fracture network properties. With a minor adjustment during model calibration, the numerical modeling is able to reproduce reasonably groundwater inflows into cavern and the travel length and times to the ground surface along the flow paths in the normal, dry and rainy seasons.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.7-16
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• 2004

In mode choice decision, travelers consider not only travel time but also reliability of its modes. In this paper, reliability was expressed in terms of standard deviation and maximum delay that were measured based on triangular distribution. In order to estimate value of time and value of reliability, the Multinomial and Nested Logit models were used. The analysis results revealed that reliability is an important factor affecting mode choice decisions. Elasticity is used to estimate the impacts of the different policies and system improvements for water transportation mode. Among these policies, decision maker can assess and select the best alternative by doing the benefit and cost analysis based on a new market share, the value of time, and the value of reliability. Finally, a set of promising policies and system improvement of the water transportation were proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

• Korean Journal of Social Welfare
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• v.69 no.2
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• pp.117-141
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• 2017

The purpose of this study is to investigate the differences of the factors affecting the entry of depression by generations and to present a practical strategy for preventing of depression by life-cycle. For this purpose, we analyzed the factors influencing the depression of adults, middle-aged and elderly people through the discrete-time hazard model. The results of this study are as follows: First, the lower the self-esteem, the lower the income satisfaction and the family satisfaction people have, the higher the likelihood of entering the depression they have. In addition, age, educational level, health status, presence of chronic diseases, employment status, regional area, and leisure life satisfaction were variables that showed difference by generation. In the case of adulthood(aged 20 ~ 39), unemployed persons are more likely to enter the depression than younger workers. On the other hand, the middle-aged(40 ~ 64 year olds) are more likely to enter the depression if they are older, have poor health status, have no chronic disease, and have low leisure satisfaction. Finally, older people(aged 65 and over) are more likely to enter the depression when the education level is higher, the health condition is worse, and the leisure satisfaction is lower. If they lived in an urban and rural complex, they are more likely to enter the depression. Based on these results, it is necessary to establish a support plan reflecting the characteristics revealed by generations in order to prevent the entry of depression.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.411-418
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• 1992

The purpose of this study is to develop dynamic-stochastic models that can forecast the inflow into reservoir during low/drought periods and flood periods. For the formulation of the models, the discrete transfer function is utilized to construct the deterministic characteristics, and the ARIMA model is utilized to construct the stochastic characteristics of residuals. The stochastic variations and structures of time series on hydrological data are examined by employing the auto/cross covariance function and auto/cross correlation function. Also, general modeling processes and forecasting method are used the model building methods of Box and Jenkins. For the verifications and applications of the developed models, the Chungju multi-purpose reservoir which is located in the South Han river systems is selected. Input data required are the current and past reservoir inflow and Yungchun water levels. In order to transform the water level at Yungchon into streamflows, the water level-streamflows rating curves at low/drought periods and flood periods are estimated. The models are calibrated with the flood periods of 1988 and 1989 and hourly data for 1990 flood are analyzed. Also, for the low/drought periods, daily data of 1988 and 1989 are calibrated, and daily data for 1989 are analyzed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.5
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• pp.321-328
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• 2017

In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.

• Advances in aircraft and spacecraft science
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• v.10 no.6
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• pp.495-519
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• 2023

The drone serves the customers not served by vans. At the same time, considering the safety, policy and terrain as well as the need to replace the battery, the drone needs to be transported by truck to the identified station along with the parcel. From each such station, the drone serves a subset of customers according to a direct assignment pattern, i.e., every time the drone is launched, it serves one demand node and returns to the station to collect another parcel. Similarly, the truck is used to transport the drone and cargo between stations. This is somewhat different from the research of other scholars. In terms of the joint distribution of the drone and road vehicle, most scholars will choose the combination of two transportation tools, while we use three. The drone and vans are responsible for distribution services, and the trucks are responsible for transporting the goods and drone to the station. The goal is to optimize the total delivery cost which includes the transportation costs for the vans and the delivery cost for the drone. A fixed cost is also considered for each drone parking site corresponding to the cost of positioning the drone and using the drone station. A discrete optimization model is presented for the problem in addition to a two-phase heuristic algorithm. The results of a series of computational tests performed to assess the applicability of the model and the efficiency of the heuristic are reported. The results obtained show that nearly 10% of the cost can be saved by combining the traditional delivery mode with the use of a drone and drone stations.