• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.215-217
• /
• 2005

There are two types of packet loss probabilities used in both the network layer and the physical layer within the wireless transmitter such as a queueing discard probability and transmission loss probability. We analyze these loss performances in order to guarantee Quality of Service (QoS) which is the basic of the future network. The queuing loss probability is caused by a maximum allowable delay time and the transmission loss probability is caused by a wireless channel error. These two types of packet loss probabilities are not easily analyzed due to recursive feedback which, originates as a result at a queueing delay and a number of retransmission attempts. We consider a wireless transmitter to a M/D/1 queueing model. We configurate the model to have a finite-size FIFO buffer in order to analyze the real-time traffic streams. Then we present the approaches used for evaluating the loss probabilities of this M/D/1/K queueing model. To analyze the two types of probabilities which have mutual feedbacks with each other, we drive the solutions recursively. The validity and accuracy of the analysis are confirmed by the computer simulation. From the following solutions, we suggest a minimum of 'a Maximum Allowable Delay Time' for real-time traffic in order to initially guarantee the QoS. Finally, we analyze the required service rate for each type utilizing real-time traffic and we apply our valuable analysis to a N-user's wireless network in order to get the fundamental information (types of supportable real-type traffics, types of supportable QoS, supportable maximum number of users) for network design.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.275-276
• /
• 2022

VTS operators instruct ships to wait for entry and departure to sail in one-way to prevent ship collision accidents in ports with narrow routes. Currently, the instructions are not based on scientific and statistical data. As a result, there is a significant deviation depending on the individual capability of the VTS operators. Accordingly, this study built a 1d-convolutional neural network model by collecting ship and weather data to predict the exact travel time for ship entry/departure waiting for instructions in the port. It was confirmed that the proposed model was improved by more than 4.5% compared to other ensemble machine learning models. Through this study, it is possible to predict the time required to enter and depart a vessel in various situations, so it is expected that the VTS operators will help provide accurate information to the vessel and determine the waiting order.

• Design & Manufacturing
• /
• v.15 no.1
• /
• pp.48-56
• /
• 2021

The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.

• Wind and Structures
• /
• v.2 no.1
• /
• pp.25-40
• /
• 1999

In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.5
• /
• pp.290-294
• /
• 2003

Using a backpropagation neural network (BPNN), a high power semiconductor device was empirically modeled. The device modeled is a n-LDMOSFET and its electrical characteristics were measured with a HP4156A and a Tektronix curve tracer 370A. The drain-source current $(I_{DS})$ was measured over the drain-source voltage $(V_{DS})$ ranging between 1 V to 200 V at each gate-source voltage $(V_{GS}).$ For each $V_{GS},$ the BPNN was trained with 100 training data, and the trained model was tested with another 100 test data not pertaining to the training data. The prediction accuracy of each $V_{GS}$ model was optimized as a function of training factors, including training tolerance, number of hidden neurons, initial weight distribution, and two gradients of activation functions. Predictions from optimized models were highly consistent with actual measurements.

• Tunnel and Underground Space
• /
• v.6 no.1
• /
• pp.30-38
• /
• 1996

A two dimensional analysis program for groundwater flow in fractured network was developed to analyze the influence of discontinuity characteristics on groundwater flow. This program involves the generation of discontinuities and also connectivity analysis. The discontinuities were generated by the probabilistic density function(P.D.F.) reflecting the characteristics of discontinuities. And the fracture network model was completed through the connectivity analysis. This program also involves the analysis of groundwater flow through the discontinuity network. The result of numerical experiment shows that the equivalent hydraulic conductivity increased and became closer to isotropic as the density and trace length increased. And hydraulic head decreased along the fracture zone because of much water-flow. The grouting increased the groundwater head around cavern. An analysis of groundwater flow through discontinuity network was performed around underground oil storage cavern which is now under construction. The probabilistic density functions(P.D.F) were obtained from the investigation of the discontinuity trace map. When the anisotropic hydraulic conductivity is used, the flow rate into the cavern was below the acceptable value to maintain the hydraulic containment. But when the isotropic hydraulic conductivity is used, the flow rate was above the acceptable value.

• Journal of Digital Contents Society
• /
• v.15 no.1
• /
• pp.113-120
• /
• 2014

In recent years, devices using the smart ponwa IT service is activated, to research how the fusion of two or more devices will be able to be interest in the soybeans. One of them in the mobile sector through the development of network and hardware digital geo-spatial map of the rapid advances being made and the computer, how do you map data to efficiently simulate a 3D environment, providing services through a virtual environment focused on whether be. In this study, augmented reality and GIS (Geographic Information System), SLM (Static LOD Model) that combines augmented reality technology on the basis of the basic concepts and approaches in geographic space and how Augmented Reality Based on this interpretation of the relevant content What to do in the development and utilization has a purpose. In this study, the conventional SLM 3DS model data structure of a data format conversion of the proposed possibilities for analyzing and, SLM model generation and format of the existing three-dimensional visualization tools SLM model format for converting a format to a model function, and visualization features. In addition, 3D virtual model to propose a format for efficiently making.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.12
• /
• pp.2243-2251
• /
• 2016

In this paper, we propose a theoretical model for characterization and upper bounds of [1,2]-domination set of network which has tree structure. In detail, we propose a theoretic model for upper bounds on [1,2]-domination set of a tree network which has some typical constrains. To that purpose, we introduce a graph theory to model and analyze the characteristics of tree structure networks. We assume a node subset D of a graph G=(V,E). We define that D is a [1,2]-dominant set if for any node v in set V which is not an element of a set D is adjacent to a node or two nodes of an element in a set D (that is, $1{\leq}{\mid}N({\upsilon}){\bigcap}D{\mid}{\leq}2$ for every node $v{\in}V-D$). The minimum cardinality of a [1,2]-dominating set of G, which is denoted by ${\gamma}_{[1,2]}(G)$, is called the [1,2]-domination number of G. In this paper, we show new upper bounds and characteristics about the [1,2]-domination number of tree.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.1
• /
• pp.69-74
• /
• 2005

In this paper, we suggested user assistant soft computing method for 3D effect optimization. In order to maximize 3D effect of image, intervals among cameras have to be set up properly according to distance between cameras and an object. Two data such as interval and distance was obtained to use in neural network as the data for learning. However, if the data for learning was obtained by only human's subjective views, it could be that the obtained data was not optimal for learning because the data had an accidental ewer To obtain optimal data lot learning, we added candidature data to obtained data through data analysis, and then selected the most proper data between the candidature data and the obtained data for learning in neural network. Usually, 3D effect of image was affected by both distance from an object to cameras and an object size. Therefore, we suggested fuzzy inference model which was able to represent two factors like distance and size. Candidature data was added by fuzzy model. In the simulation result, we verified that the mote the obtained data was affected by human's subjective views, the more effective the suggested system was.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.22-24
• /
• 2008

This paper presents a numerical model of internal flows in a lubricant supplying and scavenging flow path of an aero-engine lubrication system. The numerical model was built in the General Analysis Software of Aero-engine Lubrication System, GASLS, developed by Northwestern Polytechnical University. The lubricant flow flux, pressure and temperature distribution at steady state were calculated. GASLS is a general purpose computer program employed a 1-D steady state network algorithm for analyzing flowrates, pressures and temperatures in a complex flow network. All kinds of aero-engine lubrication systems can be divided into finite correlative typical elements and nodes from which the calculation network be developed in GASLS. Special emphasis is on how to use combined elements which is a type of typical elements to replace some complex components like bearing bores, accessory gearboxes or heat exchangers. This method can reduce network complexity and improve calculation efficiency. Final computational results show good agreement with experimental data.